{"meta":{"query_hash":"e80a59c381ec","filters":{"topic":"Robotic Path Planning Algorithms"},"cohort_total":1570,"direct_labels_cover":1,"predictions_cover":1570,"exported":1570,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/e80a59c381ec","api":"https://metacan.xera.ac/api/v1/cohort?topic=Robotic+Path+Planning+Algorithms"},"results":[{"id":"W1161795","doi":"10.1007/978-3-642-34487-9_31","title":"A Hybrid Approach for Adaptive Car Navigation","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Automaton; Learning automata; Adaptive routing; Fuzzy logic; Routing (electronic design automation); Distributed computing; Real-time computing; Artificial intelligence; Embedded system; Static routing; Routing protocol","score_opus":0.03167977978734778,"score_gpt":0.253879394189371,"score_spread":0.2221996144020232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1161795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009961117,0.0006686167,0.9944127,0.00012528984,0.001754756,0.00085357466,0.000019504834,0.0002149904,0.0019405936],"genre_scores_gemma":[0.01873233,0.0000066868433,0.97966886,0.0003842056,0.0008424505,0.000046179845,0.000034081073,0.00004066277,0.00024453207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960562,0.000036522604,0.0004761414,0.0015837406,0.0009442189,0.0009031612],"domain_scores_gemma":[0.9972975,0.00049622636,0.00035697833,0.0012961518,0.00031771036,0.00023543273],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001209422,0.0005611829,0.0005648429,0.0005592478,0.00029330238,0.0003486631,0.0030627132,0.00027199322,0.0000034818981],"category_scores_gemma":[0.00007301776,0.00052815955,0.00016467912,0.0003423828,0.00048027272,0.0007815553,0.00081819476,0.00070764037,0.000025708943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008041178,0.000044445027,0.000014675475,0.000064634034,0.000021511432,0.000037942267,0.0011226833,0.1621519,0.000054679633,0.021602456,0.000045398792,0.8148316],"study_design_scores_gemma":[0.0002410047,0.00017085188,0.000028317569,0.00021362185,0.000013144371,0.00016385892,1.4673253e-7,0.9432014,0.0005216623,0.054332107,0.00049803726,0.000615819],"about_ca_topic_score_codex":0.00001497743,"about_ca_topic_score_gemma":6.0359235e-7,"teacher_disagreement_score":0.81421584,"about_ca_system_score_codex":0.0004017707,"about_ca_system_score_gemma":0.0004703181,"threshold_uncertainty_score":0.999717},"labels":[],"label_agreement":null},{"id":"W117827416","doi":"","title":"3-Dimensional Ellipse-Based Trajectory Generation Model for 4-Legged Robots.","year":2008,"lang":"en","type":"article","venue":"Automation, Robotics and Control Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ellipse; Trajectory; Robot; Computer science; Artificial intelligence; Computer vision; Geometry; Mathematics; Physics","score_opus":0.03585819606422043,"score_gpt":0.23309573929115776,"score_spread":0.19723754322693732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117827416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003891704,0.0004983436,0.9930836,0.00062245707,0.0008026383,0.00075147726,0.000015812033,0.00030757787,0.000026396356],"genre_scores_gemma":[0.75822955,0.0000041020226,0.24085617,0.00027819487,0.00021210349,0.00012405304,0.00003699277,0.000018489298,0.0002403349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998162,0.000117632444,0.00052084465,0.00046042557,0.0004081951,0.00033090558],"domain_scores_gemma":[0.99866897,0.00022315465,0.00024273641,0.00040645702,0.0003172856,0.00014142114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048684108,0.00022786978,0.00035804976,0.00013788442,0.0005107859,0.00016190803,0.00028931274,0.00013228288,4.790145e-7],"category_scores_gemma":[0.00005978001,0.00021585549,0.000083778126,0.00015724484,0.000049827566,0.0003323774,0.00002152505,0.00008823222,0.000008864372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048568095,0.000041267926,0.00019177617,0.000027076308,0.000028959526,0.000004422833,0.00021167012,0.9910153,0.0010642798,0.0059738415,0.0012326024,0.00020395653],"study_design_scores_gemma":[0.0017566826,0.00007957456,0.00064422877,0.00003146242,0.00002443643,0.000046622303,0.000004543339,0.9969519,0.000057158217,0.00011967232,0.000027791588,0.0002559599],"about_ca_topic_score_codex":0.00002350863,"about_ca_topic_score_gemma":0.0000021097571,"teacher_disagreement_score":0.75433785,"about_ca_system_score_codex":0.00006689943,"about_ca_system_score_gemma":0.00026980983,"threshold_uncertainty_score":0.88023317},"labels":[],"label_agreement":null},{"id":"W117902095","doi":"10.1609/aiide.v4i1.18695","title":"Direction Maps for Cooperative Pathfinding","year":2008,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Task (project management); Computer science; Focus (optics); Artificial intelligence; Robotics; Human–computer interaction; Robot; Theoretical computer science; Shortest path problem; Engineering","score_opus":0.06766157125388271,"score_gpt":0.2867194324037068,"score_spread":0.21905786114982406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117902095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47013012,0.000039538758,0.49945453,0.0036468513,0.0015603714,0.0018886438,0.00008264877,0.00017556008,0.023021758],"genre_scores_gemma":[0.9969935,0.000024776014,0.0022207133,0.00014018276,0.000044156943,0.000070924194,0.0000022917034,0.000009529124,0.000493944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860597,0.000009674731,0.00038249962,0.00044739558,0.00028862577,0.00026584975],"domain_scores_gemma":[0.99892366,0.00015542499,0.00027373244,0.00013735998,0.00043350796,0.00007629936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001508581,0.00021265988,0.00023213185,0.00010854973,0.00027093771,0.00029577236,0.000597834,0.00004928641,0.0000050254926],"category_scores_gemma":[0.00032057057,0.0001554996,0.000105848994,0.0002061569,0.00018775766,0.0010931516,0.0002426601,0.0001722128,0.000014950798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061795366,0.0011792238,0.0022467398,0.00009407704,0.00021985186,0.0000064494902,0.023748783,0.00036774698,0.039102063,0.63746405,0.00096985087,0.2939832],"study_design_scores_gemma":[0.00022526464,0.0023818081,0.0012140684,0.0008952486,0.000023994575,0.000102046724,0.0051725646,0.23074006,0.71407884,0.043724786,0.00073219824,0.00070912787],"about_ca_topic_score_codex":0.000008242168,"about_ca_topic_score_gemma":3.2885126e-7,"teacher_disagreement_score":0.67497677,"about_ca_system_score_codex":0.0000859327,"about_ca_system_score_gemma":0.000041372296,"threshold_uncertainty_score":0.63410896},"labels":[],"label_agreement":null},{"id":"W118337989","doi":"","title":"Generalized semi-infinite optimization and its application in robotics' path planning problem","year":2004,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Motion planning; Robotics; Artificial intelligence; Computer science; Mathematical optimization; Path (computing); Optimization problem; Robot; Mathematics","score_opus":0.08382174438595084,"score_gpt":0.32904897130699107,"score_spread":0.2452272269210402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118337989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061264383,0.00004263124,0.98821676,0.0029587583,0.0003764099,0.0003186444,0.000005268077,0.00013477833,0.001820304],"genre_scores_gemma":[0.782694,0.000053924792,0.21681778,0.00023721164,0.00007423011,0.000055264434,0.000021798603,0.000010936781,0.000034858884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981007,0.000057703877,0.00052220304,0.00059730234,0.00043864048,0.00028344942],"domain_scores_gemma":[0.999119,0.000076481105,0.00019203448,0.00025556036,0.0002550707,0.0001018986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034435076,0.00020847644,0.00018389407,0.0003107193,0.00009617296,0.00027460975,0.0007747442,0.00010492676,0.000015061366],"category_scores_gemma":[0.00010512136,0.00021906692,0.000031848987,0.00037916895,0.00004844379,0.0005099229,0.00015129102,0.00025775115,0.0000884649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009803503,0.000050204075,0.00007857305,0.0000033507697,0.0000048074317,0.000014870804,0.0004952874,0.57490367,0.00085316866,0.41738465,0.0000016789119,0.006199914],"study_design_scores_gemma":[0.00009548797,0.000071993345,0.00012458456,0.00017221627,0.0000022715903,0.00001567024,0.000069739246,0.9178684,0.0047882944,0.07655707,0.000016394752,0.00021792376],"about_ca_topic_score_codex":0.00008632523,"about_ca_topic_score_gemma":0.000010045487,"teacher_disagreement_score":0.7765676,"about_ca_system_score_codex":0.00014633799,"about_ca_system_score_gemma":0.00011914559,"threshold_uncertainty_score":0.89332896},"labels":[],"label_agreement":null},{"id":"W1238155273","doi":"10.14864/softscis.2006.0.1772.0","title":"Knowledge-based Genetic Algorithm for Dynamic Path Planning of Mobile Robots","year":2006,"lang":"en","type":"article","venue":"SCIS & ISIS SCIS & ISIS 2006","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Motion planning; Path (computing); Mobile robot; Artificial intelligence; Genetic algorithm; Robot; Algorithm; Machine learning; Computer network","score_opus":0.011748735741223697,"score_gpt":0.2733776377037923,"score_spread":0.2616289019625686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1238155273","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0095409565,0.0049594208,0.9818277,0.00016699037,0.0011966089,0.0009272618,0.00016285537,0.00041291263,0.00080532994],"genre_scores_gemma":[0.13372073,0.000012686044,0.8646559,0.0001350263,0.00027371864,0.00029254614,0.000079459656,0.00008318641,0.000746757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948716,0.00019291569,0.001239205,0.0014614087,0.00096758123,0.0012672779],"domain_scores_gemma":[0.99625224,0.00065554696,0.00061611185,0.0016514556,0.0005653308,0.00025932316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010961792,0.00063305243,0.0009330978,0.00080078456,0.0004344199,0.00030213568,0.0022653257,0.00029449488,0.00002759793],"category_scores_gemma":[0.00008462653,0.00063138455,0.0004722381,0.001832092,0.00023608602,0.0004702307,0.00033495508,0.00027950524,0.00012707053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021434653,0.000879438,0.0033404808,0.00014938551,0.00010339432,0.0000834481,0.00068962836,0.5505488,0.0019002465,0.00023114518,0.03238835,0.40966427],"study_design_scores_gemma":[0.0013416173,0.00038564642,0.011534652,0.00025773278,0.00007154964,0.000025368305,0.00005624817,0.97897124,0.0025418855,0.0015273428,0.0025157337,0.0007709889],"about_ca_topic_score_codex":0.00033181167,"about_ca_topic_score_gemma":0.000016478185,"teacher_disagreement_score":0.42842248,"about_ca_system_score_codex":0.00026566337,"about_ca_system_score_gemma":0.0004300882,"threshold_uncertainty_score":0.99961376},"labels":[],"label_agreement":null},{"id":"W133483171","doi":"","title":"A Novel Optimization-Based Pruning Strategy for Concave Minimum Distance Problems","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Pruning; Simple (philosophy); Algorithm; Set (abstract data type); Interference (communication); Optimization problem; Mathematical optimization; Computer science; Separation (statistics); Mathematics; Global optimization; Machine learning","score_opus":0.041072410915915875,"score_gpt":0.2690494540243707,"score_spread":0.22797704310845482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W133483171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038518716,0.000030417312,0.99634445,0.00080704875,0.0001955742,0.00048084778,0.000008386578,0.00030354064,0.0017912431],"genre_scores_gemma":[0.10898086,4.3986978e-7,0.8902482,0.00031352998,0.000035363457,0.000075434175,0.000014648233,0.000012955072,0.00031856867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876636,0.000011034335,0.00025123957,0.00041617686,0.00020907728,0.00034608485],"domain_scores_gemma":[0.9991215,0.0001477585,0.00011252459,0.00037191238,0.00014625104,0.00010009151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019548078,0.00015382004,0.00015781251,0.000064336076,0.00014218666,0.00019126337,0.00057202775,0.00006186846,0.0000043814875],"category_scores_gemma":[0.00006265725,0.00014112392,0.000049750135,0.00030963813,0.00004279163,0.00031223596,0.000041059207,0.00007529227,0.0000070683373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002363241,0.00006260463,0.000022755694,0.00001906666,0.0000069096404,0.000002938118,0.00017369239,0.9777494,0.00022851062,0.02126235,0.000057274545,0.0004121392],"study_design_scores_gemma":[0.0014391368,0.00014423984,0.000043544318,0.00006348114,0.0000041582507,0.0000066253033,0.000025999898,0.9965062,0.0005980937,0.00086196297,0.000100027246,0.00020654018],"about_ca_topic_score_codex":0.000032721196,"about_ca_topic_score_gemma":0.000004356744,"teacher_disagreement_score":0.10894234,"about_ca_system_score_codex":0.00008370251,"about_ca_system_score_gemma":0.00033478256,"threshold_uncertainty_score":0.5754866},"labels":[],"label_agreement":null},{"id":"W135180951","doi":"10.1007/978-3-540-74472-6_11","title":"A Geometric Approach to Clearance Based Path Optimization","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Path (computing); Motion planning; Path length; Obstacle; Any-angle path planning; Mathematical optimization; Enhanced Data Rates for GSM Evolution; Fast path; Algorithm; Mathematics; Computer science; Computer vision; Artificial intelligence; Geography","score_opus":0.029940794370653133,"score_gpt":0.25304031164828605,"score_spread":0.2230995172776329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135180951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030322935,0.0002283526,0.98927385,0.0002449407,0.0014679348,0.00060909457,0.0000045452007,0.0003091384,0.007859126],"genre_scores_gemma":[0.0046478645,0.000006458213,0.9920589,0.0026898,0.0003367393,0.000011870379,0.000008858194,0.000045079734,0.00019447131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945074,0.00004161943,0.000611417,0.0021491207,0.0017026502,0.0009878146],"domain_scores_gemma":[0.99670243,0.0004779704,0.0003022684,0.0017935351,0.00035646043,0.00036730783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018233247,0.0006140079,0.00060220616,0.0031455455,0.00026057486,0.0006007338,0.0042794375,0.00040736326,0.000007163673],"category_scores_gemma":[0.00025853742,0.0005974027,0.00012520765,0.0037084636,0.0003136162,0.00048412918,0.00091718696,0.0008753503,0.00007238889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040595073,0.00003218782,0.000013254712,0.000020689935,0.0000032072394,0.000045566543,0.00016194372,0.7792092,0.0000028682032,0.00094410114,0.000019668725,0.21954326],"study_design_scores_gemma":[0.00024183915,0.00016919692,0.00017139598,0.00027695677,0.0000050042636,0.000048337224,6.558753e-8,0.99612796,0.000114784656,0.0018240274,0.00033231554,0.0006881028],"about_ca_topic_score_codex":0.000016575586,"about_ca_topic_score_gemma":0.0000013417456,"teacher_disagreement_score":0.21885517,"about_ca_system_score_codex":0.00047291513,"about_ca_system_score_gemma":0.0005894957,"threshold_uncertainty_score":0.99964774},"labels":[],"label_agreement":null},{"id":"W139740488","doi":"","title":"Can Situated Robots Play Soccer","year":2000,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Testbed; Artificial intelligence; Robotics; Computer science; Human–computer interaction; Task (project management); Situated; Cognitive robotics; Perception; Engineering; Systems engineering; Psychology","score_opus":0.014164327898219975,"score_gpt":0.23406724308315205,"score_spread":0.21990291518493207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W139740488","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020634582,0.000055869994,0.8359609,0.009082335,0.0005603142,0.00020425332,0.0000030320568,0.0014899966,0.13200872],"genre_scores_gemma":[0.29397583,0.00000815168,0.6587801,0.0038082644,0.00012957142,0.000011400348,0.000007634054,0.000017859864,0.04326118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989628,0.0000361685,0.00014413596,0.00031528386,0.00022437597,0.00031720533],"domain_scores_gemma":[0.9993167,0.000043817843,0.000022738686,0.00046983385,0.000027405324,0.00011948624],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011909271,0.000114027855,0.00011692154,0.000049052174,0.000093771676,0.000106904416,0.00068450917,0.000054190197,0.00059485546],"category_scores_gemma":[0.000009953992,0.0000965333,0.000034991324,0.00029985927,0.000023197716,0.00020154807,0.00006198539,0.000102860555,0.0011104911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006916812,0.00022916663,0.0025398606,0.000009835231,0.00008816991,0.0005258608,0.004676859,0.24771647,0.0007946492,0.0142495725,0.08994158,0.6392211],"study_design_scores_gemma":[0.0005238639,0.00007986539,0.023007665,0.000019187053,0.000005884252,0.000104066916,0.0000134479415,0.96723974,0.0014364694,0.0023597647,0.0047674784,0.00044255075],"about_ca_topic_score_codex":0.00018035181,"about_ca_topic_score_gemma":0.0000071098516,"teacher_disagreement_score":0.7195233,"about_ca_system_score_codex":0.00003025645,"about_ca_system_score_gemma":0.000053987926,"threshold_uncertainty_score":0.9996673},"labels":[],"label_agreement":null},{"id":"W1444095272","doi":"","title":"Integration and Assessment of Multiple Mobile Manipulators in a Real-World Industrial Production Facility","year":2014,"lang":"en","type":"article","venue":"VBN Forskningsportal (Aalborg Universitet)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Convergent Manufacturing Technologies (Canada)","funders":"","keywords":"Production (economics); Computer science; Manufacturing engineering; Systems engineering; Engineering; Economics","score_opus":0.03244229961744092,"score_gpt":0.26625440113667853,"score_spread":0.23381210151923762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1444095272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94740224,0.000005885891,0.049975183,0.000051771094,0.0003506625,0.00036128866,0.0000062068107,0.00010320016,0.0017435891],"genre_scores_gemma":[0.9707705,0.0000053990816,0.02876434,0.000007594211,0.000039587307,0.0000050174426,0.000033888606,0.000005938138,0.00036778164],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985812,0.000086151886,0.0003280826,0.00048154546,0.00029183098,0.00023119635],"domain_scores_gemma":[0.9991148,0.00008334571,0.00025812417,0.0003670557,0.000085591,0.00009107453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058536424,0.00016626014,0.00026920886,0.00045045084,0.00008411676,0.00003337829,0.00029854296,0.00009995326,0.0000071161717],"category_scores_gemma":[0.000094236675,0.00017188939,0.000046021803,0.0007819979,0.000078962126,0.0006722688,0.0001427022,0.00027272923,0.0000027161582],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033042244,0.00016868526,0.9464918,0.00003347601,0.000027050175,0.000027084352,0.0021963734,0.008601662,0.0017794027,0.009330549,0.0002625139,0.031048374],"study_design_scores_gemma":[0.0017673285,0.0004119751,0.6006411,0.00018188744,0.000028669838,0.000012720441,0.00092142797,0.3901322,0.0028336418,0.0003560939,0.0022422734,0.00047068798],"about_ca_topic_score_codex":0.0015387245,"about_ca_topic_score_gemma":0.0002760963,"teacher_disagreement_score":0.38153055,"about_ca_system_score_codex":0.00016120196,"about_ca_system_score_gemma":0.0000941817,"threshold_uncertainty_score":0.70094454},"labels":[],"label_agreement":null},{"id":"W147738233","doi":"","title":"Memory-based heuristics for explicit state spaces","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Heuristic; Computation; State (computer science); Class (philosophy); Path (computing); State space; Process (computing); Incremental heuristic search; Theoretical computer science; Mathematical optimization; Algorithm; Search algorithm; Mathematics; Artificial intelligence; Beam search","score_opus":0.022663462497907263,"score_gpt":0.26967181866045037,"score_spread":0.2470083561625431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W147738233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009053437,0.000025989024,0.9933709,0.002886073,0.00026289624,0.00019111365,0.0000041789867,0.0003590864,0.0019944308],"genre_scores_gemma":[0.06560132,9.887145e-7,0.9307956,0.0018380056,0.00005767623,0.0000104723185,0.0000033792257,0.0000061869546,0.0016863344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990179,0.000019630286,0.00015961593,0.00029622242,0.00019713923,0.0003095266],"domain_scores_gemma":[0.99915075,0.00016998591,0.000059365735,0.00044224586,0.00007733093,0.00010029388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021387111,0.000119994096,0.00013899477,0.00007238341,0.000085480126,0.00015566949,0.00058005843,0.000031529074,0.00000424301],"category_scores_gemma":[0.000072612325,0.00010354768,0.000047714348,0.00017761633,0.0000115974735,0.00015146457,0.000028810713,0.000058464782,0.00004200094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006278676,0.00065825507,0.00045934136,0.00008944349,0.000056699453,0.00024590187,0.0026972906,0.2582428,0.0036038095,0.08633319,0.11944058,0.5281099],"study_design_scores_gemma":[0.0004710143,0.00027537614,0.0007010808,0.000013339904,0.0000036795166,0.0000045287798,0.000012300839,0.97976655,0.009440887,0.007162459,0.0019412128,0.0002075897],"about_ca_topic_score_codex":0.0000089140985,"about_ca_topic_score_gemma":5.24407e-7,"teacher_disagreement_score":0.72152376,"about_ca_system_score_codex":0.000021445947,"about_ca_system_score_gemma":0.000066138455,"threshold_uncertainty_score":0.4222552},"labels":[],"label_agreement":null},{"id":"W1484240228","doi":"10.1109/iros.1997.655160","title":"A probability-based approach to model-based path planning","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Maxima and minima; Motion planning; Computer science; Representation (politics); Path (computing); Hill climbing; Mathematical optimization; Work (physics); Algorithm; Topology (electrical circuits); Theoretical computer science; Artificial intelligence; Mathematics; Robot; Engineering","score_opus":0.09211055679961326,"score_gpt":0.25569060804125326,"score_spread":0.16358005124164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484240228","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010342391,0.000023758184,0.97352254,0.00092097354,0.0001030757,0.00040163173,0.0000023676403,0.00072841457,0.023262994],"genre_scores_gemma":[0.21806513,3.566601e-8,0.7794578,0.0020054786,0.000025582247,0.00007498057,0.0000028849013,0.000012705611,0.00035538652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978732,0.000079964724,0.00027801044,0.0007607873,0.00047828248,0.00052974466],"domain_scores_gemma":[0.99842256,0.0001066312,0.000058524703,0.0010474842,0.000081230915,0.00028356072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042969172,0.0002287275,0.00023085222,0.00017715122,0.00014145252,0.00017293642,0.0011344752,0.00008539672,0.000010235331],"category_scores_gemma":[0.00008823294,0.00020056465,0.00007806759,0.0006480954,0.000034016386,0.00020953079,0.000121084915,0.00018387039,0.00014684556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024279689,0.0002331749,0.00034627278,0.000021462896,0.0000035054604,0.000010760216,0.00050823484,0.9903147,0.000040697698,0.0027308138,0.0036020172,0.0021859054],"study_design_scores_gemma":[0.00036401418,0.00009106976,0.0001275095,0.000031445696,0.0000032423848,0.000004330762,0.0000063858106,0.9980369,0.00021844223,0.0005963154,0.00023532128,0.00028502912],"about_ca_topic_score_codex":0.000009099593,"about_ca_topic_score_gemma":6.8554435e-8,"teacher_disagreement_score":0.2170309,"about_ca_system_score_codex":0.000083940446,"about_ca_system_score_gemma":0.000086588545,"threshold_uncertainty_score":0.8178789},"labels":[],"label_agreement":null},{"id":"W148575935","doi":"10.1007/978-3-642-19730-7_16","title":"Exploiting Hierarchical Probabilistic Motion Planning for Robot Reachable Workspace Estimation","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Workspace; Probabilistic logic; Kinematics; Motion planning; Computer science; Artificial intelligence; Robot; Mobile robot; Probabilistic roadmap; Representation (politics); Robotics; Process (computing); Computer vision","score_opus":0.02517959229340821,"score_gpt":0.23664067460035218,"score_spread":0.21146108230694396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W148575935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007382786,0.00080899906,0.9959469,0.00014860443,0.00040829246,0.00067677436,0.0000023857735,0.00056088925,0.0014397859],"genre_scores_gemma":[0.020919455,0.000011245616,0.97769606,0.00006243331,0.00038313775,0.00013735009,0.00005120787,0.0001287916,0.0006102885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997344,0.000025173293,0.0005664177,0.00088681537,0.0003787115,0.0007988717],"domain_scores_gemma":[0.99756855,0.0014623117,0.00020947281,0.00052406115,0.00008229394,0.00015328947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004358233,0.00055674353,0.0006175341,0.00060269324,0.00009951386,0.00012488228,0.0006812949,0.0006851466,0.000004069005],"category_scores_gemma":[0.0018222713,0.00058599847,0.00015048386,0.00031417905,0.000025844374,0.00023633914,0.00012718563,0.0015297023,0.0000092300925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000098805185,0.000015837357,0.000004926966,0.00009935751,0.000021827967,0.000038155173,0.00028807227,0.86346275,0.0000778561,0.038593665,0.000009461076,0.09737822],"study_design_scores_gemma":[0.00023587995,0.00014288598,0.00002905942,0.00068963575,0.000023784203,0.000053792883,1.2391087e-7,0.92241406,0.00018103987,0.07552027,0.00016247584,0.0005469787],"about_ca_topic_score_codex":0.000008464017,"about_ca_topic_score_gemma":6.581567e-7,"teacher_disagreement_score":0.09683124,"about_ca_system_score_codex":0.0004414605,"about_ca_system_score_gemma":0.00009402562,"threshold_uncertainty_score":0.9996591},"labels":[],"label_agreement":null},{"id":"W1486394166","doi":"10.1007/11427469_41","title":"A Neuro-fuzzy Controller for Reactive Navigation of a Behaviour-Based Mobile Robot","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Mobile robot; Neuro-fuzzy; Artificial intelligence; Fuzzy logic; Mobile robot navigation; Controller (irrigation); Robot; Control engineering; Fuzzy control system; Human–computer interaction; Robot control; Engineering","score_opus":0.018527942000960407,"score_gpt":0.2668278709782857,"score_spread":0.24829992897732528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486394166","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012050169,0.00018974766,0.9964501,0.00034356982,0.0010553027,0.0013563443,0.000028145208,0.00013813541,0.0003181256],"genre_scores_gemma":[0.13141522,0.0000028079287,0.86732775,0.0006611893,0.00033067976,0.00009829275,0.0000185801,0.000041338302,0.00010412778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612564,0.00004734179,0.00071622204,0.0014944228,0.0009865619,0.00062979903],"domain_scores_gemma":[0.9960167,0.0014751137,0.0006890028,0.0011091563,0.00055092515,0.00015914507],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009159614,0.00053442694,0.0007781363,0.0007241452,0.00017553887,0.0002118228,0.0024837614,0.0003525025,0.0000036342426],"category_scores_gemma":[0.00015625508,0.00050860003,0.00023785145,0.0004923419,0.00053060317,0.00045328017,0.00038452444,0.00063759997,0.00000914515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026737724,0.00008246507,0.00005557574,0.000041549352,0.000013916625,0.00003667366,0.0005371472,0.65214163,0.0011743504,0.0017726322,0.000018415574,0.3440989],"study_design_scores_gemma":[0.0009653383,0.00062091975,0.00014789533,0.00049341295,0.000024294457,0.000039851653,1.7653355e-7,0.97426015,0.0047860397,0.017970957,0.00016328231,0.0005276984],"about_ca_topic_score_codex":0.000030834082,"about_ca_topic_score_gemma":0.0000048689403,"teacher_disagreement_score":0.34357122,"about_ca_system_score_codex":0.00030002126,"about_ca_system_score_gemma":0.00068100245,"threshold_uncertainty_score":0.99973655},"labels":[],"label_agreement":null},{"id":"W1486612343","doi":"10.1007/978-3-540-24840-8_31","title":"Multi-agent Trail Making for Stigmergic Navigation","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Maxima and minima; Construct (python library); Artificial intelligence; Terrain; Human–computer interaction; Cartography; Mathematics","score_opus":0.041857277822939014,"score_gpt":0.29290166582083305,"score_spread":0.251044387997894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486612343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024459821,0.0003769281,0.9945108,0.00047627097,0.003227892,0.0007777718,0.000012399726,0.00026987077,0.0003235488],"genre_scores_gemma":[0.032278854,0.0000073584847,0.9663901,0.00063185056,0.00042612423,0.000028058745,0.0000140302645,0.000042835003,0.00018079969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576503,0.000026070404,0.0006132138,0.0017918905,0.0009763929,0.00082741596],"domain_scores_gemma":[0.99756485,0.00039501343,0.0003975424,0.0012034139,0.00027320074,0.00016594774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096404,0.0005888035,0.0005409388,0.0006527476,0.0003669501,0.00054142455,0.0032261352,0.00036505322,0.0000076245215],"category_scores_gemma":[0.00010973062,0.00057156704,0.00018336422,0.00052131247,0.00043808424,0.000558363,0.000659968,0.0006871821,0.000037289672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003801484,0.000037048423,0.000016350608,0.0000906637,0.000013831068,0.00012828545,0.0013429694,0.56494665,0.00016793657,0.011273856,0.00000817696,0.42197043],"study_design_scores_gemma":[0.00056438986,0.00016009151,0.00012305056,0.0011780277,0.000010820415,0.00008909338,1.7483276e-7,0.91567326,0.0004751332,0.080733724,0.00031071532,0.00068149995],"about_ca_topic_score_codex":0.000012845391,"about_ca_topic_score_gemma":0.0000068619784,"teacher_disagreement_score":0.42128894,"about_ca_system_score_codex":0.00069691153,"about_ca_system_score_gemma":0.00084050023,"threshold_uncertainty_score":0.9996736},"labels":[],"label_agreement":null},{"id":"W1488505718","doi":"10.1023/a:1008108910932","title":"3D Local Trajectory Planner for UAV","year":2000,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"TRACE (psycholinguistics); Mode (computer interface); Trajectory; Planner; Novelty; Motion planning; Control theory (sociology); Motion (physics); Computer science; Engineering; Simulation; Control engineering; Artificial intelligence; Robot; Control (management); Physics","score_opus":0.02668250541762952,"score_gpt":0.26253132537705465,"score_spread":0.23584881995942514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488505718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011260747,0.0022665453,0.9906118,0.00022913923,0.0046459683,0.00032280915,0.0000024733436,0.0000622835,0.00073291775],"genre_scores_gemma":[0.5581677,0.00021279341,0.4304421,0.00047668203,0.0033039865,0.00004168181,0.0000055895607,0.00009407086,0.0072553908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707305,0.00017064875,0.0012311268,0.00030850977,0.0006971246,0.00051953795],"domain_scores_gemma":[0.9978933,0.0004661683,0.0005305277,0.00047413385,0.00033634328,0.00029957757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013259873,0.00026671533,0.0006334398,0.00026639923,0.000115558854,0.00021717427,0.001314676,0.00014285246,0.000046355704],"category_scores_gemma":[0.00008668134,0.00020944553,0.0002936377,0.0002892517,0.00006438923,0.0004104345,0.000037147896,0.00031134224,0.0001591416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004034718,0.00012427288,0.00010562714,0.000068173445,0.00012943886,0.00016074844,0.00076642015,0.939849,0.000039620303,0.000685524,0.007427084,0.050603744],"study_design_scores_gemma":[0.0005743783,0.0008403233,0.00023153806,0.00057201035,0.000055983142,0.0026588251,0.00030348025,0.97761625,0.00030027903,0.00015161517,0.016351044,0.00034427948],"about_ca_topic_score_codex":0.00003193844,"about_ca_topic_score_gemma":6.4997585e-7,"teacher_disagreement_score":0.5601697,"about_ca_system_score_codex":0.00022238387,"about_ca_system_score_gemma":0.00021341574,"threshold_uncertainty_score":0.8540941},"labels":[],"label_agreement":null},{"id":"W1496056286","doi":"10.1007/978-3-540-85126-4_8","title":"Density-Based Clustering Based on Topological Properties of the Data Set","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voronoi diagram; Shortest path problem; Cluster analysis; Path (computing); K shortest path routing; Computer science; Set (abstract data type); Mathematical optimization; Simple (philosophy); Algorithm; Topology (electrical circuits); Mathematics; Theoretical computer science; Artificial intelligence; Combinatorics; Graph","score_opus":0.3148305721766586,"score_gpt":0.37303231467510567,"score_spread":0.05820174249844706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496056286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029047791,0.0010286947,0.98860955,0.0020215989,0.0010074222,0.00041947904,0.00003910912,0.00008046049,0.0067646634],"genre_scores_gemma":[0.5103793,0.00008457447,0.48025215,0.0034260815,0.00024896144,0.000022247668,0.000061436556,0.00004407598,0.0054811533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759525,0.00009838713,0.000591285,0.0007260347,0.00076062704,0.00022841306],"domain_scores_gemma":[0.997349,0.00087639975,0.00031872586,0.001129595,0.0002919667,0.000034266577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005649147,0.00032383096,0.0004677075,0.00017505485,0.00015295832,0.000037805472,0.00261419,0.00013399257,0.0000042752495],"category_scores_gemma":[0.0003856326,0.0002249692,0.00008965719,0.00014556378,0.00057798193,0.000090753085,0.0012019782,0.0004736274,0.000017901719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015070718,0.000031660897,0.000027968495,0.00007406096,0.00003113296,0.000038023136,0.0002500026,0.9707318,8.3920537e-7,0.020836065,0.00020706994,0.0077562914],"study_design_scores_gemma":[0.000068348825,0.0001236664,0.00013758097,0.0014742588,0.000011102839,0.00000918423,0.000025828484,0.9638987,0.000085139334,0.033641875,0.0002799514,0.0002443371],"about_ca_topic_score_codex":0.000008845531,"about_ca_topic_score_gemma":0.000014656976,"teacher_disagreement_score":0.5103503,"about_ca_system_score_codex":0.00014706956,"about_ca_system_score_gemma":0.00025512942,"threshold_uncertainty_score":0.9173978},"labels":[],"label_agreement":null},{"id":"W1496671656","doi":"","title":"A multi-agent system based on active vision and ultrasounds applied to fuzzy behavior based navigation","year":2004,"lang":"en","type":"article","venue":"World Automation Congress","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Mobile robot; Computer science; Doors; Robot; Fuzzy logic; Artificial intelligence; Fuzzy control system; Modular design; Set (abstract data type); Control engineering; Mobile robot navigation; Actuator; Multi-agent system; Human–computer interaction; Robot control; Engineering","score_opus":0.015107724250439736,"score_gpt":0.2752947904959504,"score_spread":0.2601870662455107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496671656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069250114,0.0000057397183,0.92677706,0.00062590867,0.0008910985,0.0010725263,0.000020014022,0.000896073,0.00046148195],"genre_scores_gemma":[0.757538,5.8038527e-8,0.24185608,0.00028655867,0.000028696388,0.00019965348,0.000037490685,0.000013872039,0.000039541726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982088,0.00008831512,0.0003168749,0.0005781893,0.00053832337,0.0002694479],"domain_scores_gemma":[0.9987928,0.00019045157,0.00020231795,0.0005058531,0.00012189316,0.00018666295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032630638,0.00023784868,0.0002272567,0.00040724344,0.00024558473,0.0002949279,0.0003579723,0.00007006168,0.0000027155763],"category_scores_gemma":[0.000035495937,0.00022460458,0.000043339303,0.00069438136,0.000036491365,0.00028467996,0.000052400734,0.00014574561,0.000092718095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011018909,0.0007703432,0.0010133063,0.00022930827,0.000028710832,0.00014171837,0.0022146653,0.89506495,0.0153121445,0.008644987,0.00019772595,0.076271944],"study_design_scores_gemma":[0.0019252303,0.0001545754,0.06465945,0.00061629055,0.000019400964,0.000008717693,0.000052968044,0.92402977,0.008111934,0.000017897171,0.000087076594,0.00031670337],"about_ca_topic_score_codex":0.000027130109,"about_ca_topic_score_gemma":0.000007725343,"teacher_disagreement_score":0.6882879,"about_ca_system_score_codex":0.00041029384,"about_ca_system_score_gemma":0.00010405764,"threshold_uncertainty_score":0.9159109},"labels":[],"label_agreement":null},{"id":"W1502049634","doi":"10.1109/robot.2003.1241953","title":"Computing C-space entropy for view planning with a generic range sensor model","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Entropy (arrow of time); Computer science; Motion planning; Configuration space; Computation; Computer vision; Robot; Artificial intelligence; Algorithm; Mathematics; Physics","score_opus":0.041071378246839006,"score_gpt":0.27312870360217933,"score_spread":0.23205732535534032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502049634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073277852,0.00019125799,0.9898354,0.0011432384,0.00013480482,0.00035383724,0.0000019724034,0.00045758908,0.0005541443],"genre_scores_gemma":[0.07835945,0.0000023258385,0.9206054,0.00064344367,0.00008680961,0.000016510725,0.0000027204255,0.000020369094,0.00026297572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984522,0.000023691895,0.00021057676,0.0005075313,0.00028339203,0.0005226048],"domain_scores_gemma":[0.99912196,0.00010245269,0.00010927744,0.00043850963,0.0000914839,0.00013629743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023966467,0.0002159595,0.00026744144,0.00009088157,0.00020419093,0.00014784165,0.00055901584,0.000054800163,9.086855e-7],"category_scores_gemma":[0.000023127965,0.00016819945,0.000060865244,0.00032439738,0.00003171416,0.00025419815,0.000121645295,0.000117474054,0.000018425873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000084946305,0.000030015553,0.0001731244,0.000025417563,0.000019878718,0.00004343245,0.0011971588,0.9847986,0.00030473815,0.011343939,0.00019184052,0.0018633663],"study_design_scores_gemma":[0.0012579354,0.00014000801,0.00015194724,0.000114057846,0.000011097252,0.00010716633,0.000044638335,0.9964855,0.0005166449,0.0007439334,0.00015428563,0.00027283575],"about_ca_topic_score_codex":0.0000219932,"about_ca_topic_score_gemma":5.808148e-7,"teacher_disagreement_score":0.07103167,"about_ca_system_score_codex":0.00008178754,"about_ca_system_score_gemma":0.00014007473,"threshold_uncertainty_score":0.68589747},"labels":[],"label_agreement":null},{"id":"W1508449426","doi":"10.1007/978-3-540-85126-4_5","title":"Efficient Swarm Neighborhood Management Using the Layered Delaunay Triangulation","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Delaunay triangulation; Bowyer–Watson algorithm; Swarm behaviour; Swarm intelligence; Constrained Delaunay triangulation; Computer science; Triangulation; Mathematical optimization; Artificial intelligence; Algorithm; Particle swarm optimization; Mathematics; Geometry","score_opus":0.1320410202095326,"score_gpt":0.36624232010717844,"score_spread":0.23420129989764585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508449426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013535832,0.0033970496,0.9619267,0.00049163087,0.0012643168,0.0006277083,0.0000063713996,0.000106048676,0.03216664],"genre_scores_gemma":[0.11254505,0.00050391466,0.861171,0.0010382908,0.00056948856,0.000053910044,0.000038976443,0.00007812623,0.024001218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705106,0.000077900964,0.00081968284,0.00077709963,0.0009096726,0.0003646081],"domain_scores_gemma":[0.9978148,0.0008031904,0.0004213013,0.0005705727,0.00033576682,0.00005440554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006915101,0.00043836248,0.00046141227,0.00036712346,0.00030704404,0.00010396296,0.0011830742,0.0001340216,0.0000070480355],"category_scores_gemma":[0.000067858346,0.00035794938,0.00014728776,0.0003009828,0.00023042335,0.00007676535,0.0005825378,0.00042725407,0.00006794341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004574992,0.00002137413,0.0000022652,0.000020914535,0.00010806457,0.00008354488,0.00083139015,0.69795585,1.1286806e-7,0.26417887,0.000080933394,0.036712103],"study_design_scores_gemma":[0.000096480406,0.00004518429,0.000070631846,0.00040969776,0.0000275819,0.000034212106,0.00011115071,0.7622406,0.0000038391186,0.2363491,0.0003150731,0.00029644533],"about_ca_topic_score_codex":0.0000049174155,"about_ca_topic_score_gemma":0.0000015320686,"teacher_disagreement_score":0.11253151,"about_ca_system_score_codex":0.00048609902,"about_ca_system_score_gemma":0.00009787525,"threshold_uncertainty_score":0.9998872},"labels":[],"label_agreement":null},{"id":"W1509842057","doi":"10.5772/6226","title":"Visual Programming of Subsumption-Based Reactive Behaviour","year":2008,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Domain (mathematical analysis); Representation (politics); Programming language; Robot; Artificial intelligence; Human–computer interaction; Visual modeling; Control (management); Theoretical computer science; Unified Modeling Language; Software","score_opus":0.02200530098097818,"score_gpt":0.29674677092955853,"score_spread":0.27474146994858034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509842057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10235949,0.0002848794,0.8924721,0.00016340219,0.004467683,0.00015406533,0.000002084152,0.000039445997,0.00005688497],"genre_scores_gemma":[0.77405566,0.000009794519,0.22561812,0.00001736234,0.00022744195,0.000005490019,0.0000026215525,0.000011327976,0.000052177245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732816,0.000106508625,0.0009149936,0.0002132689,0.0012197162,0.00021734221],"domain_scores_gemma":[0.99660015,0.00028643094,0.0013354206,0.00021627825,0.0014379821,0.00012375013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044304962,0.00016486071,0.00038981473,0.00041419154,0.000062181614,0.00005693901,0.0011343298,0.00006738577,0.0000022656207],"category_scores_gemma":[0.00021964371,0.00014737056,0.0001731456,0.0002484176,0.000079537036,0.00069571956,0.00006591194,0.0002300908,0.000007156171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069012705,0.00038131056,0.018799547,0.000021054368,0.00017695081,0.0009065111,0.0007560892,0.96663666,0.003438396,0.0007043852,0.000056922432,0.008053136],"study_design_scores_gemma":[0.007560323,0.00230066,0.10629926,0.0031214042,0.00011317468,0.01400355,0.0011188034,0.8467368,0.01664082,0.00023394999,0.00079379976,0.0010774409],"about_ca_topic_score_codex":0.000035125162,"about_ca_topic_score_gemma":3.2984474e-7,"teacher_disagreement_score":0.6716962,"about_ca_system_score_codex":0.00021469401,"about_ca_system_score_gemma":0.00029243453,"threshold_uncertainty_score":0.6009597},"labels":[],"label_agreement":null},{"id":"W1511970413","doi":"10.1109/robot.1995.525615","title":"Numerical solution of differential systems with algebraic inequalities arising in robot programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constraint programming; Executable; Constraint (computer-aided design); Computer science; Mathematical optimization; Constraint logic programming; Trajectory; Robot; Differential algebraic equation; Motion planning; Mathematics; Differential equation; Ordinary differential equation; Programming language; Artificial intelligence","score_opus":0.035143616655984145,"score_gpt":0.23448473826088845,"score_spread":0.1993411216049043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511970413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020718936,0.00012827238,0.9781178,0.0001238807,0.00021619422,0.00016393242,2.740169e-7,0.00014636095,0.00038435115],"genre_scores_gemma":[0.8312504,0.0000012237838,0.16855678,0.000009125314,0.000032768035,0.000013248462,0.0000011310655,0.000006590221,0.00012870209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985844,0.00011063786,0.0003443506,0.00027859316,0.0003549279,0.00032710083],"domain_scores_gemma":[0.9993924,0.00008891075,0.00012763383,0.00028777609,0.000045867215,0.000057437053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017726804,0.00013029344,0.0002577069,0.00015591817,0.00004859994,0.00010810423,0.00034506852,0.00005575669,0.000006671353],"category_scores_gemma":[0.000026191936,0.00010177391,0.000028510402,0.00044035277,0.000044918004,0.0003132804,0.00007197824,0.00012829996,0.000008498811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067860965,0.002609023,0.11568585,0.0010961748,0.0002726805,0.00045836193,0.034581907,0.42419195,0.0032306341,0.10553251,0.00068275246,0.31159028],"study_design_scores_gemma":[0.00033426002,0.00015686106,0.0046558096,0.00014767643,0.000003845578,0.000039728853,0.00012260524,0.9941265,0.00020106774,0.000043875323,0.0000121181765,0.00015567738],"about_ca_topic_score_codex":0.00067734806,"about_ca_topic_score_gemma":0.0000039150696,"teacher_disagreement_score":0.8105315,"about_ca_system_score_codex":0.000048759743,"about_ca_system_score_gemma":0.000017549684,"threshold_uncertainty_score":0.41502196},"labels":[],"label_agreement":null},{"id":"W1512587630","doi":"10.1109/mim.2004.1337913","title":"Robotic sensor agents","year":2004,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Information Technology Association of Canada; Carleton University; University of Ottawa","funders":"","keywords":"Homeland security; Environmental monitoring; Wireless sensor network; Embedded system; Hazardous waste; Computer science; Field (mathematics); Task (project management); Engineering; Real-time computing; Systems engineering; Operating system; Environmental engineering; Waste management; Geography","score_opus":0.06289014600333746,"score_gpt":0.27625990183261023,"score_spread":0.21336975582927276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1512587630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026452465,0.000032472406,0.9650719,0.0028025422,0.0026301274,0.00044447317,0.00000221291,0.00043086085,0.0021329587],"genre_scores_gemma":[0.72717214,0.000009147005,0.2715473,0.00084406824,0.00013768623,0.00003708823,0.000008937432,0.000021516928,0.00022213857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99699104,0.00008967551,0.0004675026,0.0005357391,0.0014656727,0.00045034743],"domain_scores_gemma":[0.9987522,0.00001588096,0.0002021878,0.0005813202,0.00026253297,0.0001858962],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006239625,0.00025882595,0.00022465827,0.00020508906,0.00017520723,0.00020648283,0.00063154974,0.00006437413,0.000027371467],"category_scores_gemma":[0.000062461906,0.00026203383,0.00008040459,0.0005162275,0.000043110307,0.0007545754,0.00006258553,0.00016075805,0.0014243164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006322635,0.0013726248,0.00825412,0.00016793748,0.00034533947,0.0004764218,0.004224392,0.7116242,0.13923302,0.008737274,0.011149253,0.1143522],"study_design_scores_gemma":[0.0315636,0.0021141842,0.44284388,0.0014196307,0.0003404097,0.0009042347,0.000401819,0.27149484,0.2184706,0.015875457,0.009856651,0.004714685],"about_ca_topic_score_codex":0.00002709442,"about_ca_topic_score_gemma":0.0000072407497,"teacher_disagreement_score":0.70071965,"about_ca_system_score_codex":0.00057453313,"about_ca_system_score_gemma":0.0001782151,"threshold_uncertainty_score":0.9999832},"labels":[],"label_agreement":null},{"id":"W1514520309","doi":"10.5555/1838206.1838534","title":"On multi-robot area coverage","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Task (project management); Computer science; Point (geometry); Path (computing); Mobile robot; Actuator; Motion planning; Robot kinematics; Human–computer interaction; Robot control; Task analysis; Artificial intelligence; Real-time computing; Engineering; Computer network; Systems engineering","score_opus":0.07079121496115787,"score_gpt":0.2913392058689059,"score_spread":0.22054799090774801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514520309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081233084,0.0001584605,0.9108025,0.00008421673,0.005271426,0.0009505602,0.000053748387,0.00028918678,0.0011568492],"genre_scores_gemma":[0.93250316,0.000028345085,0.06369387,0.00038088058,0.00011890171,0.00006824553,0.000012542878,0.000036431826,0.00315764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972617,0.00016613281,0.00045324845,0.00094812526,0.00054510735,0.0006256665],"domain_scores_gemma":[0.9981196,0.00017975725,0.00026679353,0.0009015305,0.00014120541,0.00039112606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047429634,0.0004267689,0.00043310778,0.00018723086,0.00035087467,0.00034069107,0.00087916834,0.0002020207,0.000018097766],"category_scores_gemma":[0.00012779416,0.00036175718,0.00009688722,0.00023841261,0.000090845686,0.0004350137,0.00033381404,0.00047884264,0.00029630738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005692892,0.018408172,0.16860227,0.0010746125,0.00491483,0.011187028,0.0518564,0.22318624,0.055644654,0.15143578,0.07816609,0.23495464],"study_design_scores_gemma":[0.0021607683,0.00019786123,0.05746984,0.00013125625,0.000018111254,0.000077910845,0.00008345091,0.9363019,0.00011510291,0.000036406716,0.0028980921,0.00050929555],"about_ca_topic_score_codex":0.00042701705,"about_ca_topic_score_gemma":0.0000148642675,"teacher_disagreement_score":0.8512701,"about_ca_system_score_codex":0.000055578774,"about_ca_system_score_gemma":0.000052721847,"threshold_uncertainty_score":0.9998834},"labels":[],"label_agreement":null},{"id":"W1515746590","doi":"10.1002/9780470400531.eorms0108","title":"Computational Pool: An<scp>OR</scp>—Optimization Point of View","year":2011,"lang":"en","type":"preprint","venue":"Wiley Encyclopedia of Operations Research and Management Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Champion; Focus (optics); Computer science; Point (geometry); State (computer science); Human–computer interaction; Artificial intelligence; Data science; Algorithm; Geography; Mathematics","score_opus":0.06554176704300164,"score_gpt":0.34671829872576704,"score_spread":0.2811765316827654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515746590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034143468,0.00036012466,0.9726003,0.00017832569,0.0004165301,0.0013148566,0.000027799017,0.0000744637,0.021613289],"genre_scores_gemma":[0.023051577,0.007689287,0.9676076,0.0000470188,0.000053416,0.0001791549,0.000064125015,0.000016528365,0.0012912754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954842,0.000249037,0.0006866535,0.0011094968,0.0018856655,0.0005849952],"domain_scores_gemma":[0.9968655,0.00023098812,0.00017109897,0.0011761064,0.0012567716,0.00029952198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035471611,0.00025002487,0.0003693702,0.0013829555,0.00053136196,0.00041502912,0.0027841409,0.0001070843,0.00001914462],"category_scores_gemma":[0.00053800887,0.00021940863,0.000046008365,0.0019239411,0.0012007747,0.0015059058,0.0035663096,0.0004151603,0.00001012575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040606596,0.00032278735,0.00013111993,0.00033429314,0.000031971646,0.000020016661,0.0020721254,0.9375621,0.000017743923,0.049028754,0.00083828263,0.009636757],"study_design_scores_gemma":[0.00023549511,0.00036396555,0.0023300657,0.00042757991,0.000013726151,0.000008973157,0.0002391083,0.99007046,0.000093811715,0.0058028293,0.00028436029,0.00012964936],"about_ca_topic_score_codex":0.00024830154,"about_ca_topic_score_gemma":0.000014503096,"teacher_disagreement_score":0.052508347,"about_ca_system_score_codex":0.00008196266,"about_ca_system_score_gemma":0.00085116806,"threshold_uncertainty_score":0.8947224},"labels":[],"label_agreement":null},{"id":"W1518204237","doi":"10.1023/a:1008992902895","title":"Scene Reconstruction and Robot Navigation Using Dynamic Fields","year":2000,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Maxima and minima; Heuristics; Motion planning; Modular design; Computer vision; Artificial intelligence; Matching (statistics); Field (mathematics); Path (computing); Surface (topology); Robot","score_opus":0.01442707898057277,"score_gpt":0.25142167394425335,"score_spread":0.23699459496368058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518204237","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22671996,0.00024225602,0.7708732,0.0003269903,0.00052709854,0.00014171541,0.000001174931,0.00028489903,0.00088273466],"genre_scores_gemma":[0.47013262,0.000019343328,0.52928114,0.00010454606,0.000047768877,0.0000043775876,0.000005001512,0.000010615452,0.00039462003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.000054825763,0.0002414389,0.00042098638,0.00014019312,0.00028025883],"domain_scores_gemma":[0.9993813,0.000039248298,0.00008095915,0.0003716184,0.000033884295,0.00009296489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016461729,0.00015084665,0.00016657475,0.00008122289,0.00020615457,0.00014999838,0.00027691573,0.00011703273,0.000031265463],"category_scores_gemma":[0.000009748359,0.00016154454,0.000034107565,0.00023350528,0.00005309479,0.0005826451,0.00005991768,0.00016433533,0.000053672404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023228492,0.00001719431,0.0004114294,0.000009175336,0.00001189235,0.000025966798,0.00040985388,0.21984789,0.00075660634,0.00019272069,0.000011197581,0.77830374],"study_design_scores_gemma":[0.00021189847,0.000044546003,0.0062942216,0.00006579299,0.000008499199,0.00067327154,0.0000072923713,0.99108386,0.00029225656,0.0010692417,0.000050406645,0.00019869414],"about_ca_topic_score_codex":0.00012882736,"about_ca_topic_score_gemma":0.0000033164054,"teacher_disagreement_score":0.7781051,"about_ca_system_score_codex":0.00009882703,"about_ca_system_score_gemma":0.00007102626,"threshold_uncertainty_score":0.6587595},"labels":[],"label_agreement":null},{"id":"W1519698372","doi":"10.1007/978-3-642-30779-9_33","title":"Reaching Analysis of Wheelchair Users Using Motion Planning Methods","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Wheelchair; Computer science; Planner; Motion planning; Task (project management); Point (geometry); Human–computer interaction; Tree traversal; Path (computing); Simulation; Robot; Artificial intelligence; Engineering; Algorithm","score_opus":0.06449479351257076,"score_gpt":0.34333604162773856,"score_spread":0.2788412481151678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519698372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024345767,0.00091407803,0.99626225,0.00005787526,0.0016489538,0.00020389276,0.000004819818,0.00014763663,0.0005170139],"genre_scores_gemma":[0.0444719,0.0000074259333,0.9550344,0.00019720334,0.00022289473,0.0000018602342,0.0000076203128,0.000029979414,0.000026754076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99566466,0.00018460196,0.0008325048,0.0013954878,0.001114748,0.0008080146],"domain_scores_gemma":[0.99610776,0.00097322924,0.0008249311,0.001642665,0.00023802221,0.00021340992],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036968507,0.00055652065,0.0010836764,0.0033918023,0.00025653452,0.00026359156,0.0028764307,0.00039397276,0.0000073329575],"category_scores_gemma":[0.00020251241,0.0005397569,0.00030549546,0.002398315,0.00044841584,0.000948293,0.001202925,0.0009183715,0.0000036573117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013916778,0.000012185468,0.00062529446,0.000018773108,0.00010021881,0.000019539313,0.0019291021,0.641885,0.0005041696,0.0010426289,5.388026e-7,0.35386115],"study_design_scores_gemma":[0.0000949865,0.000043597003,0.0011801054,0.00037241387,0.00020570804,0.000036955276,5.084997e-7,0.99038213,0.0009955355,0.006125362,0.000043720123,0.0005189442],"about_ca_topic_score_codex":0.00009062282,"about_ca_topic_score_gemma":0.0000040372274,"teacher_disagreement_score":0.3533422,"about_ca_system_score_codex":0.0004772454,"about_ca_system_score_gemma":0.00028434343,"threshold_uncertainty_score":0.9997054},"labels":[],"label_agreement":null},{"id":"W1519987888","doi":"10.1109/robot.2003.1241566","title":"A neural network based torque controller for collision-free navigation of mobile robots","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor; University of Guelph","funders":"","keywords":"Mobile robot; Robot; Artificial neural network; Torque; Controller (irrigation); Computer science; Control theory (sociology); Nonholonomic system; Robot control; Collision; Lyapunov stability; Control engineering; Lyapunov function; Engineering; Artificial intelligence; Control (management); Nonlinear system","score_opus":0.013695738313210576,"score_gpt":0.2560953095281433,"score_spread":0.24239957121493272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519987888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065474105,0.000073559524,0.9913042,0.00054713915,0.00048850215,0.00070490694,0.000006840662,0.00014220188,0.00018523665],"genre_scores_gemma":[0.30589953,3.9627403e-7,0.6934546,0.00032069758,0.0001013953,0.0001194975,0.000008189743,0.000008349165,0.000087343964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880254,0.000037647307,0.00031872798,0.00029186215,0.0002616074,0.00028761977],"domain_scores_gemma":[0.9987575,0.00032421265,0.00014369753,0.00051821664,0.00017500587,0.000081367805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036373007,0.00012558785,0.00024678043,0.000044853827,0.00009405187,0.000053056097,0.00073035347,0.000072421666,0.0000031197374],"category_scores_gemma":[0.00009130886,0.00010683772,0.000096495256,0.00032441266,0.00003434711,0.00023025501,0.00009245657,0.00006806039,0.000005484716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016868706,0.00004603473,0.00008679098,0.000010215192,0.000007993659,0.000003623929,0.00007666905,0.9911613,0.0002057019,0.005272604,0.0012432566,0.0018689377],"study_design_scores_gemma":[0.002767909,0.00034250086,0.0005652413,0.0000618701,0.0000068595523,0.0000055383684,0.000007796032,0.9889074,0.0016084834,0.005487868,0.000108333115,0.00013018979],"about_ca_topic_score_codex":0.00006386961,"about_ca_topic_score_gemma":0.000002450064,"teacher_disagreement_score":0.29935214,"about_ca_system_score_codex":0.000052663563,"about_ca_system_score_gemma":0.000121199235,"threshold_uncertainty_score":0.43567157},"labels":[],"label_agreement":null},{"id":"W1521296549","doi":"","title":"A brochette of socially interactive robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Human–computer interaction; Mobile robot; Computer science; Multidisciplinary approach; Competition (biology); Human–robot interaction; Order (exchange); Artificial intelligence; Business; Sociology","score_opus":0.010768464621111602,"score_gpt":0.2563716490280286,"score_spread":0.245603184406917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521296549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018244466,0.00002182,0.9718477,0.0025666188,0.00015351087,0.000053505057,4.4731573e-7,0.00012116028,0.023410762],"genre_scores_gemma":[0.21010375,0.000001102723,0.7880661,0.0004649636,0.00007624163,0.0000024754188,4.0959998e-7,0.00000376049,0.0012811925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992901,0.00003031048,0.00016208585,0.000181408,0.00017906519,0.00015704238],"domain_scores_gemma":[0.99946404,0.00008615509,0.00008202061,0.00025278,0.00007023221,0.00004477663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012342696,0.0000721575,0.000121090394,0.00005913026,0.000028603536,0.000028467899,0.00052876753,0.00003433934,0.000021367272],"category_scores_gemma":[0.000040067673,0.0000646498,0.00004553384,0.00016744238,0.00002468136,0.000376536,0.00014474524,0.0000837243,0.00012273804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024328829,0.0006446224,0.0018326823,0.00003194055,0.00021995658,0.000060348113,0.018590376,0.060192227,0.020727102,0.1364966,0.0337714,0.7274084],"study_design_scores_gemma":[0.00075215945,0.0001645981,0.016005563,0.00006446964,0.000010644232,0.000051885036,0.0001079169,0.94047785,0.035453692,0.004433312,0.002061176,0.00041672005],"about_ca_topic_score_codex":0.000022171425,"about_ca_topic_score_gemma":0.0000036116041,"teacher_disagreement_score":0.8802856,"about_ca_system_score_codex":0.00003358545,"about_ca_system_score_gemma":0.000078811405,"threshold_uncertainty_score":0.26363423},"labels":[],"label_agreement":null},{"id":"W1521975135","doi":"10.1155/2015/496258","title":"Motion Constraints and Trajectory Planning of a Planar Active Dynamic Balancing Mechanism","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Trajectory; Constraint (computer-aided design); Control theory (sociology); Mechanism (biology); Motion planning; Collision; Computer science; Collision avoidance; Planar; Mathematical optimization; Control (management); Engineering; Mathematics; Robot; Physics; Artificial intelligence; Mechanical engineering","score_opus":0.020710377173658916,"score_gpt":0.237660661763636,"score_spread":0.2169502845899771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521975135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017288467,0.00006314355,0.9818783,0.000025604199,0.00010325772,0.00017237631,0.0000016973939,0.00011799089,0.00034916686],"genre_scores_gemma":[0.6921711,6.4045076e-7,0.3077946,0.0000041018716,0.000005880113,0.000010962389,7.9042877e-7,0.000008916172,0.0000030004906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989669,0.000025338963,0.00030438596,0.00022753519,0.00022582084,0.00025003732],"domain_scores_gemma":[0.999431,0.00018288053,0.000070731236,0.00018169031,0.00002670955,0.0001069673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005566862,0.0001387219,0.0002628524,0.0001788374,0.000014655757,0.000025298015,0.00021945006,0.00007590563,0.0000012929925],"category_scores_gemma":[0.00022786696,0.00013635751,0.000020589814,0.00018680646,0.000035148452,0.0002129121,0.0000693191,0.00019853069,0.0000028801305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039034267,0.000113789356,0.00020910201,0.0009244854,0.000043284756,0.000090456655,0.01716925,0.905413,0.009603842,0.059577383,0.000006938405,0.006844581],"study_design_scores_gemma":[0.00033472246,0.000045419056,0.00033650215,0.0005785355,0.000003867856,0.000086186024,0.00014540138,0.9750511,0.0005231362,0.022756597,5.270994e-7,0.0001380261],"about_ca_topic_score_codex":0.0000030576111,"about_ca_topic_score_gemma":1.4933823e-7,"teacher_disagreement_score":0.67488265,"about_ca_system_score_codex":0.00008217816,"about_ca_system_score_gemma":0.000027874834,"threshold_uncertainty_score":0.5560498},"labels":[],"label_agreement":null},{"id":"W152231130","doi":"10.3182/20020721-6-es-1901.00829","title":"REAL-TIME PATH PLANNING AND TRACKING CONTROL USING A NEURAL DYNAMICS BASED APPROACH","year":2002,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Tracking (education); Dynamics (music); Control (management); Motion planning; Control theory (sociology); Path (computing); Artificial neural network; Computer science; Control engineering; System dynamics; Artificial intelligence; Engineering; Physics; Robot","score_opus":0.026937840175534616,"score_gpt":0.23646212675431252,"score_spread":0.20952428657877792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W152231130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3471818,0.00030297606,0.64763504,0.00032365829,0.00019524255,0.00041820027,0.000010717334,0.00087033276,0.0030620736],"genre_scores_gemma":[0.667557,0.00000234784,0.33209613,0.00010852953,0.00008742653,0.0000119116685,0.0000024054966,0.00002745658,0.00010682631],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782485,0.0000251013,0.00034764005,0.0007258567,0.00043036827,0.0006461823],"domain_scores_gemma":[0.99910176,0.000098899836,0.00024046302,0.00020875581,0.00014976258,0.00020038181],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040177925,0.00032002796,0.0003968105,0.00021060006,0.00034535513,0.00065448316,0.0006181498,0.00014097302,0.0000047036965],"category_scores_gemma":[0.00012513091,0.0003225129,0.00007077577,0.00044318225,0.0000996165,0.0010510554,0.00012821553,0.00028881096,0.000009545983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012678183,0.0011496039,0.5347621,0.0012366686,0.000435932,0.00067029166,0.031309843,0.22829689,0.039020505,0.0076827025,0.0049596876,0.15034904],"study_design_scores_gemma":[0.0007463072,0.00008692025,0.0042404016,0.000101420475,0.000026037673,0.0001813106,0.00012210879,0.99396694,0.000053140462,0.000085545296,0.000015331636,0.00037451377],"about_ca_topic_score_codex":0.0000401086,"about_ca_topic_score_gemma":4.9645244e-8,"teacher_disagreement_score":0.76567006,"about_ca_system_score_codex":0.00011885916,"about_ca_system_score_gemma":0.000026788455,"threshold_uncertainty_score":0.9999227},"labels":[],"label_agreement":null},{"id":"W1523827670","doi":"10.1109/cdc.2003.1271943","title":"Modifed newton's method applied to potential field navigation","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robot; Computer science; Task (project management); Field (mathematics); Newton's method; Stability (learning theory); Descent (aeronautics); Gradient descent; Collision avoidance; Software; Collision; Control engineering; Simulation; Artificial intelligence; Engineering; Mathematics; Aerospace engineering; Artificial neural network; Nonlinear system","score_opus":0.011745401954985052,"score_gpt":0.28023558876447296,"score_spread":0.2684901868094879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523827670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017516179,0.000003035894,0.9850456,0.0055543506,0.00039713143,0.0001759508,3.855375e-7,0.0003222968,0.0067496155],"genre_scores_gemma":[0.11040495,1.6218435e-7,0.8873549,0.0017722355,0.000086329914,0.000014804978,0.000002580052,0.0000057597126,0.0003582722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893355,0.000022888196,0.00015857114,0.0003612134,0.0002733058,0.00025044914],"domain_scores_gemma":[0.99931234,0.00004992063,0.00003706538,0.00042436618,0.000038835744,0.0001374938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022936166,0.0001095045,0.00012250688,0.00007641064,0.00008670804,0.00011852746,0.0005913483,0.0000673927,0.0000096211115],"category_scores_gemma":[0.000024699697,0.00010115421,0.00003734192,0.00033002626,0.000005822241,0.00017396965,0.00017081582,0.00010838415,0.00026024017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011696033,0.00006750956,0.00001141064,0.000008050268,0.000019385916,0.00007271654,0.0011754365,0.7027199,0.01840653,0.17885783,0.0024918288,0.09615766],"study_design_scores_gemma":[0.0021904074,0.00044422175,0.001520111,0.000066704684,0.000020674917,0.00017850166,0.00014574782,0.72515047,0.19508202,0.07299721,0.0012442932,0.0009596426],"about_ca_topic_score_codex":0.0001493992,"about_ca_topic_score_gemma":7.015793e-7,"teacher_disagreement_score":0.17667548,"about_ca_system_score_codex":0.000044671513,"about_ca_system_score_gemma":0.00005578782,"threshold_uncertainty_score":0.4124949},"labels":[],"label_agreement":null},{"id":"W1524477080","doi":"10.1109/iros.1993.583935","title":"Smooth and acceleration minimizing trajectories for mobile robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Acceleration; Trajectory; Piecewise; Mobile robot; Computer science; Obstacle avoidance; Position (finance); Polynomial; Minification; Robot; Trajectory optimization; Obstacle; Control theory (sociology); Simple (philosophy); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Optimal control; Mathematical analysis","score_opus":0.045152913415814115,"score_gpt":0.2606938045324185,"score_spread":0.2155408911166044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524477080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060537443,0.00024049402,0.9912397,0.0003048076,0.00029322167,0.00023413185,9.947506e-7,0.00019054636,0.001442325],"genre_scores_gemma":[0.17712139,0.000013808049,0.82135415,0.00015362875,0.000085718675,0.00007167713,0.0000014961128,0.0000068256923,0.0011913064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933267,0.000014537109,0.00012337795,0.00024866758,0.00010170663,0.00017902248],"domain_scores_gemma":[0.9995558,0.00013025048,0.000034795434,0.00018878959,0.000037568298,0.000052750125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010489061,0.0000847417,0.00009956321,0.000047143625,0.00012619508,0.0001854933,0.00020249885,0.000038899117,0.000012754771],"category_scores_gemma":[0.000030292007,0.00007505002,0.000021449798,0.00011614073,0.000018399116,0.00043814784,0.000044822216,0.00003862585,0.000009076244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008703146,0.00023264281,0.002905852,0.00009655081,0.00004759988,0.000021730419,0.013739295,0.027760778,0.004798029,0.029449606,0.025142767,0.8957964],"study_design_scores_gemma":[0.0003017609,0.00013454174,0.0014156987,0.000008926027,0.0000034775194,0.000011368655,0.0000545991,0.9940974,0.001553478,0.00039849855,0.0018728437,0.0001474187],"about_ca_topic_score_codex":0.000008208185,"about_ca_topic_score_gemma":0.0000011221595,"teacher_disagreement_score":0.9663366,"about_ca_system_score_codex":0.000013273301,"about_ca_system_score_gemma":0.000007079604,"threshold_uncertainty_score":0.3060451},"labels":[],"label_agreement":null},{"id":"W1525670588","doi":"","title":"Convex hull covering of polygonal scenes for accurate collision detection in games","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Convex hull; Convex set; Polyhedron; Convex polytope; Collision detection; Regular polygon; Subderivative; Convex combination; Computer science; Mathematics; Set (abstract data type); Algorithm; Mathematical optimization; Collision; Convex optimization; Combinatorics; Geometry","score_opus":0.03561981725721963,"score_gpt":0.26740469643213366,"score_spread":0.23178487917491403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1525670588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26614735,0.0000377596,0.73317057,0.0000594763,0.00024371936,0.00012757453,0.0000016792592,0.000052112984,0.00015972948],"genre_scores_gemma":[0.8432209,0.000010739042,0.15653025,0.000043362466,0.00002363917,0.000011359519,8.789955e-7,0.00000434022,0.00015451826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920523,0.000024058992,0.00022033864,0.00021215237,0.0001615283,0.00017671617],"domain_scores_gemma":[0.9994603,0.0001719584,0.00008276116,0.00018196426,0.0000667033,0.00003632082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018537344,0.000080192425,0.00015382153,0.00012401902,0.00005928586,0.000017924029,0.00026769022,0.00004807203,0.000002622721],"category_scores_gemma":[0.000078932,0.00007370304,0.00003710252,0.00027256235,0.000032330478,0.00029412765,0.0000700187,0.000051294784,0.0000049298064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047839744,0.00086110533,0.06869663,0.00042142713,0.00012613885,0.0002611201,0.010065586,0.44349444,0.2599701,0.011445028,0.002605625,0.20157437],"study_design_scores_gemma":[0.0005393151,0.00012566402,0.028240744,0.000034193155,0.0000013167763,0.000042534895,0.00002489067,0.8286098,0.14195196,0.0002013593,0.00011850029,0.000109702894],"about_ca_topic_score_codex":0.00012671082,"about_ca_topic_score_gemma":0.000007266388,"teacher_disagreement_score":0.5770736,"about_ca_system_score_codex":0.00003163429,"about_ca_system_score_gemma":0.00006012231,"threshold_uncertainty_score":0.30055228},"labels":[],"label_agreement":null},{"id":"W1526676349","doi":"10.1007/978-3-540-39899-8_5","title":"Needle Steering and Model-Based Trajectory Planning","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Jacobian matrix and determinant; Computer science; Deflection (physics); Obstacle avoidance; Motion planning; Trajectory; Simulation; Robot; Artificial intelligence; Mobile robot; Physics; Mathematics; Optics; Applied mathematics","score_opus":0.025692984699967444,"score_gpt":0.24671103901393288,"score_spread":0.22101805431396543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526676349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010425433,0.0009234168,0.9948355,0.00023498485,0.0011665992,0.000251892,0.0000030502983,0.00022569399,0.0022546276],"genre_scores_gemma":[0.024241587,0.0000073340198,0.9736492,0.0017075205,0.00014856989,0.000007009417,0.0000023533923,0.00004344966,0.00019294395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963339,0.00003417696,0.00044399625,0.0015660492,0.0008597519,0.000762154],"domain_scores_gemma":[0.99788046,0.00041316773,0.00022065335,0.0011240983,0.000115197276,0.00024643427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008308982,0.00058024074,0.0005482941,0.00089283125,0.0002801711,0.00051584287,0.0020161222,0.00032987937,0.0000025023348],"category_scores_gemma":[0.00007263495,0.000577098,0.00008483723,0.000432995,0.0004996154,0.00048052918,0.0005446424,0.0009083429,0.000008023004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016138403,0.000010110415,0.000051441264,0.00003476806,0.0000051975603,0.000116205,0.0006095825,0.91356426,0.00008277672,0.0012545827,0.000014565088,0.084254906],"study_design_scores_gemma":[0.00025906521,0.00009564391,0.000073764655,0.00056032516,0.0000061011756,0.00007609064,1.579506e-7,0.9834433,0.0003348383,0.014383624,0.00013550416,0.00063158374],"about_ca_topic_score_codex":0.0000064534065,"about_ca_topic_score_gemma":0.0000016376331,"teacher_disagreement_score":0.08362332,"about_ca_system_score_codex":0.00024411391,"about_ca_system_score_gemma":0.0005347769,"threshold_uncertainty_score":0.99966806},"labels":[],"label_agreement":null},{"id":"W1527189623","doi":"","title":"Comparison of different grid abstractions for pathfinding on maps","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Grid; Computer science; Shortest path problem; Grid reference; State (computer science); Artificial intelligence; Path (computing); Grid cell; Theoretical computer science; Robot; Algorithm; Mobile robot; Mathematics; Programming language","score_opus":0.07342906172075295,"score_gpt":0.3467630673083124,"score_spread":0.2733340055875595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527189623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013622521,0.000012031488,0.9745842,0.00015712439,0.001125875,0.00017229149,0.0000063637544,0.000092832255,0.01022671],"genre_scores_gemma":[0.74059236,6.340005e-7,0.25896445,0.00003828031,0.000030307996,0.00001621414,0.0000038969856,0.0000050758417,0.0003487719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991862,0.000027275508,0.00022516314,0.00020515472,0.00016869693,0.00018746892],"domain_scores_gemma":[0.9992196,0.0002888774,0.00010042972,0.0002989258,0.000036924754,0.000055219236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013785693,0.00009136825,0.0001719586,0.000081511476,0.000088061475,0.00003288668,0.0002633438,0.000038720285,0.000010541833],"category_scores_gemma":[0.000062714025,0.000074081094,0.00005844878,0.000088433415,0.0000150275255,0.00008489928,0.00002124772,0.00008425107,0.000025429516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013250831,0.0015990175,0.030454293,0.00008263747,0.0000823384,0.000007575161,0.0017836441,0.028915193,0.004660639,0.8802686,0.026946168,0.025186671],"study_design_scores_gemma":[0.0023650976,0.0017819933,0.081904344,0.00019554805,0.000044248238,0.00003682112,0.0006454424,0.61823297,0.2602116,0.022338763,0.011213501,0.0010297018],"about_ca_topic_score_codex":0.0000040222812,"about_ca_topic_score_gemma":8.0147714e-7,"teacher_disagreement_score":0.8579298,"about_ca_system_score_codex":0.000029155412,"about_ca_system_score_gemma":0.000019628678,"threshold_uncertainty_score":0.30209392},"labels":[],"label_agreement":null},{"id":"W1528624149","doi":"10.1023/a:1026248112092","title":"Selection of Strategies for Collision-free Motion in Multi-manipulator Systems","year":2003,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Motion planning; sort; Path (computing); Robot; Computer science; Line (geometry); Artificial intelligence; Engineering; Mathematics","score_opus":0.06184002086566731,"score_gpt":0.29966779056478127,"score_spread":0.23782776969911396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528624149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086777015,0.001367378,0.9838131,0.00002981729,0.005342126,0.0006710842,0.0000020518128,0.000027878974,0.00006884403],"genre_scores_gemma":[0.8790062,0.0000321778,0.12065156,0.0000063051693,0.00013614593,0.000020390387,8.385893e-7,0.000020576714,0.00012579317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656785,0.0003803389,0.0017569081,0.00027769525,0.00066141406,0.00035580085],"domain_scores_gemma":[0.99675435,0.00041843543,0.0013912746,0.0004292836,0.0008734005,0.000133256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024407774,0.0002327408,0.00072373194,0.0005988543,0.00007077903,0.00024352783,0.0008716589,0.00016150971,0.0000013747589],"category_scores_gemma":[0.0006318655,0.00019694057,0.00018502578,0.00066757406,0.000030704134,0.0006693018,0.000042198855,0.00023550692,0.000004781804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014869101,0.00019946376,0.0021984025,0.00025207613,0.00006511221,0.000018784127,0.000594978,0.9758344,0.00063825905,0.019747008,0.00029865495,0.00013794955],"study_design_scores_gemma":[0.0008671825,0.0005324939,0.00091462844,0.0010385453,0.000030489355,0.00068487326,0.002020181,0.99168766,0.001524857,0.000372193,0.00012158367,0.00020531076],"about_ca_topic_score_codex":0.00014202512,"about_ca_topic_score_gemma":0.00000676049,"teacher_disagreement_score":0.8703285,"about_ca_system_score_codex":0.0003538832,"about_ca_system_score_gemma":0.00035021995,"threshold_uncertainty_score":0.80310035},"labels":[],"label_agreement":null},{"id":"W1530110745","doi":"10.5555/1402383.1402395","title":"Autonomous geocaching: navigation and goal finding in outdoor domains","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Computer science; Task (project management); Global Positioning System; Obstacle avoidance; Robot; Mobile robot navigation; Obstacle; Object (grammar); Artificial intelligence; Navigation system; Computer vision; Motion planning; Human–computer interaction; Mobile robot; Autonomous system (mathematics); Real-time computing; Robot control; Engineering; Geography; Systems engineering","score_opus":0.024181623998189204,"score_gpt":0.2583671541982919,"score_spread":0.23418553020010272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1530110745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49592525,0.000029923147,0.5012768,0.00029934527,0.00015268708,0.000072345756,3.6712308e-7,0.00013924595,0.0021040835],"genre_scores_gemma":[0.7288533,0.0000023699415,0.27068156,0.000095218355,0.000024631634,0.0000037347565,0.0000018215916,0.0000038033913,0.00033357544],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914926,0.000039517872,0.00016511591,0.00027607556,0.0001469375,0.00022309137],"domain_scores_gemma":[0.9995925,0.000081719525,0.000042288695,0.00020635342,0.000012119835,0.00006504429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002457818,0.0000898804,0.0001117094,0.00010302854,0.00012959534,0.000053692016,0.0002571212,0.00004826458,0.0000020332554],"category_scores_gemma":[0.000030702005,0.000084347856,0.000016028238,0.00020248936,0.000035035566,0.00037269652,0.00012277655,0.00013502006,0.00002843467],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017301967,0.0004232321,0.66801983,0.00007185859,0.00005101506,0.0038932876,0.04982228,0.043197263,0.003713742,0.09202289,0.0023428483,0.13642442],"study_design_scores_gemma":[0.00047378766,0.000050692626,0.21197627,0.000030391293,0.0000012389087,0.00042892943,0.000040426024,0.7853187,0.00030361436,0.0009922549,0.00019305131,0.00019067101],"about_ca_topic_score_codex":0.0001272226,"about_ca_topic_score_gemma":0.0000023173593,"teacher_disagreement_score":0.7421214,"about_ca_system_score_codex":0.000053883774,"about_ca_system_score_gemma":0.00004809901,"threshold_uncertainty_score":0.34396058},"labels":[],"label_agreement":null},{"id":"W1530765895","doi":"10.5555/1402821.1402883","title":"A new approach to cooperative pathfinding","year":2008,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Plan (archaeology); Fidelity; Path (computing); Human–computer interaction; Distributed computing; Operations research; Artificial intelligence; Shortest path problem; Theoretical computer science; Engineering; Computer network","score_opus":0.13236742723817527,"score_gpt":0.29515774679958934,"score_spread":0.16279031956141407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1530765895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011494715,0.0002707916,0.98306286,0.00004801481,0.00093141414,0.0009140591,0.000014552175,0.00015561003,0.0031080085],"genre_scores_gemma":[0.62683976,0.00003667596,0.36235756,0.00032476833,0.0001638395,0.00007971437,0.000008374624,0.000029290028,0.01016005],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975634,0.00019159156,0.00037714813,0.00087211584,0.0004634947,0.00053225545],"domain_scores_gemma":[0.99861157,0.00006234862,0.00014159216,0.00051797356,0.00012767375,0.00053882576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030694527,0.00032401478,0.0004074562,0.00018277214,0.00044711353,0.00017923817,0.0006804323,0.00010088472,0.000005064367],"category_scores_gemma":[0.000060479633,0.00028003982,0.0000590499,0.00045701096,0.00004262282,0.00043464033,0.00037578185,0.00017182012,0.00026947638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020671249,0.00363677,0.05771827,0.00036338484,0.002524965,0.0039361347,0.30631348,0.22095668,0.0028663373,0.049599513,0.2837064,0.068171345],"study_design_scores_gemma":[0.0015683045,0.00019104748,0.029005012,0.00013433845,0.000016571252,0.0003317742,0.0007286678,0.9617422,0.000061307546,0.000006281556,0.005627213,0.0005872498],"about_ca_topic_score_codex":0.00075156934,"about_ca_topic_score_gemma":0.0000013553037,"teacher_disagreement_score":0.74078554,"about_ca_system_score_codex":0.000094278075,"about_ca_system_score_gemma":0.0001160442,"threshold_uncertainty_score":0.9999652},"labels":[],"label_agreement":null},{"id":"W1534329287","doi":"10.1007/978-3-540-77120-3_58","title":"An O(n 2logn) Time Algorithm for Computing Shortest Paths Amidst Growing Discs in the Plane","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Shortest path problem; Plane (geometry); Path (computing); Algorithm; Combinatorics; Computer science; Mathematics; Geometry; Graph","score_opus":0.024346348618276583,"score_gpt":0.278317481065577,"score_spread":0.25397113244730046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534329287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000803207,0.00021452816,0.9953705,0.00048411373,0.0017366873,0.0009690491,0.00002504257,0.0002883578,0.00083137053],"genre_scores_gemma":[0.011260271,0.000006006191,0.984896,0.0024490147,0.0011812601,0.000013765382,0.000059029986,0.000060334805,0.00007430614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99377036,0.00011328778,0.0009575381,0.002201333,0.0015026843,0.0014547963],"domain_scores_gemma":[0.99481535,0.0024151343,0.0003963557,0.0019311613,0.00021066141,0.00023134986],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0046296758,0.00082001707,0.000828091,0.0011013241,0.00053708017,0.0010104639,0.006982529,0.00048069863,0.0000039646607],"category_scores_gemma":[0.00013006864,0.0006537791,0.00017312319,0.001050636,0.0006666634,0.0010712598,0.00086940156,0.0014022079,0.000032685624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031677714,0.00006627664,0.0000734201,0.00001897572,0.00000908636,0.00042212382,0.0021195423,0.09018083,0.000061431085,0.002670851,0.000019468696,0.9043548],"study_design_scores_gemma":[0.00035523452,0.00037958362,0.00044122888,0.00046043916,0.000010901836,0.00029740704,0.0000020511488,0.9800077,0.00012502351,0.0168252,0.0002707961,0.000824434],"about_ca_topic_score_codex":0.000052485455,"about_ca_topic_score_gemma":0.000034574932,"teacher_disagreement_score":0.9035304,"about_ca_system_score_codex":0.00040403978,"about_ca_system_score_gemma":0.0005056405,"threshold_uncertainty_score":0.99959135},"labels":[],"label_agreement":null},{"id":"W1535991974","doi":"10.1109/icra.2015.7139953","title":"Multi-robot task planning and sequencing using the SAT-TSP language","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Travelling salesman problem; Computer science; Motion planning; Solver; Set (abstract data type); Path (computing); Robot; Variety (cybernetics); Task (project management); Mathematical optimization; Artificial intelligence; Theoretical computer science; Algorithm; Programming language; Mathematics; Engineering","score_opus":0.13327868047525163,"score_gpt":0.33444735134861087,"score_spread":0.20116867087335924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535991974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06500976,0.00063948095,0.932896,0.00024071772,0.00030693863,0.00010112777,8.249572e-7,0.00021911407,0.00058603316],"genre_scores_gemma":[0.3615737,4.5523416e-7,0.6377716,0.00035769498,0.00006716532,0.000001958327,9.16578e-7,0.000007988817,0.00021848928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988489,0.000091183145,0.00016479053,0.00032150935,0.0002587516,0.00031482734],"domain_scores_gemma":[0.99915797,0.00011756246,0.000069907815,0.0004577591,0.00004788824,0.00014893553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006617264,0.000138808,0.00013751096,0.0000661204,0.00016642727,0.00022981664,0.0005514064,0.00005099166,0.0000010942864],"category_scores_gemma":[0.00012563924,0.00009302569,0.000022668914,0.00023291424,0.00005855528,0.00036267668,0.00033973204,0.00017224048,0.000020379575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014732596,0.00009442469,0.04577141,0.00006998282,0.0001525919,0.0019054891,0.2115902,0.58977914,0.12011409,0.0023455003,0.0022437049,0.025918704],"study_design_scores_gemma":[0.0002966623,0.000023460769,0.0012781085,0.00003614128,0.000006240402,0.00025666915,0.0031384537,0.9938419,0.00083310466,0.000065453874,0.000063615946,0.00016019637],"about_ca_topic_score_codex":0.00037902518,"about_ca_topic_score_gemma":0.0000024536614,"teacher_disagreement_score":0.40406272,"about_ca_system_score_codex":0.00008509874,"about_ca_system_score_gemma":0.0001217548,"threshold_uncertainty_score":0.37934774},"labels":[],"label_agreement":null},{"id":"W1537667663","doi":"","title":"Planification de trajectoires pour une flotte d'UAVs","year":2010,"lang":"fr","type":"article","venue":"PhDT","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Art","score_opus":0.041092388938374726,"score_gpt":0.2917756313086068,"score_spread":0.25068324237023204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537667663","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042948097,0.00081131066,0.89132947,0.048044592,0.007342511,0.00020604319,0.000023721306,0.0003161555,0.008978103],"genre_scores_gemma":[0.4216728,0.00005201896,0.55661595,0.0007652333,0.0018897788,0.000022524559,0.000015523818,0.00003958998,0.018926557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982182,0.00012018252,0.00028231673,0.0004766967,0.00029623596,0.0006063654],"domain_scores_gemma":[0.99856025,0.00023962828,0.00013642295,0.0007336524,0.0000976522,0.0002323876],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056938303,0.00021470195,0.00019427946,0.00009087419,0.00018912414,0.00025063765,0.0009099224,0.00020622084,0.00008339003],"category_scores_gemma":[0.0002144939,0.00023554265,0.00007437177,0.00042946212,0.00013128821,0.00040024295,0.00009976791,0.000595249,0.0012284223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019575114,0.0006792378,0.007430358,0.00016178582,0.00010564743,0.00055328384,0.011926802,0.012289371,0.096411355,0.3012905,0.060255762,0.5088763],"study_design_scores_gemma":[0.000452089,0.00008094286,0.08468465,0.000096402415,0.000035430796,0.00051219773,0.00005378617,0.8383528,0.0075121843,0.013193065,0.054591075,0.0004353952],"about_ca_topic_score_codex":0.0011613088,"about_ca_topic_score_gemma":0.000079723715,"teacher_disagreement_score":0.8260634,"about_ca_system_score_codex":0.00005966978,"about_ca_system_score_gemma":0.00034572114,"threshold_uncertainty_score":0.9995492},"labels":[],"label_agreement":null},{"id":"W1539693648","doi":"10.23919/ecc.2001.7076074","title":"On-line path planning for robot manipulators in dynamic environments","year":2001,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Motion planning; Maxima and minima; Path (computing); Computer science; Any-angle path planning; Robot; Line (geometry); Work (physics); Mathematical optimization; Artificial intelligence; Engineering; Mathematics","score_opus":0.04112556076694191,"score_gpt":0.29781510225107855,"score_spread":0.25668954148413664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539693648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025593743,0.000030057357,0.97261184,0.00030002755,0.00030949584,0.0002306169,0.0000012530782,0.00010353121,0.0008194545],"genre_scores_gemma":[0.65504897,0.0000046960968,0.34255624,0.00043033378,0.000027051568,0.00003358525,0.000010097053,0.000016373206,0.0018726808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866843,0.000026250222,0.00024291365,0.00043605152,0.00022608948,0.0004002667],"domain_scores_gemma":[0.9992817,0.00015075876,0.000061208055,0.00041778598,0.000005239559,0.00008331829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023995533,0.00015966891,0.00016886408,0.00015257347,0.00006556002,0.000048523358,0.00055610586,0.00006711134,0.000008345947],"category_scores_gemma":[0.00003525988,0.00014658467,0.000042605425,0.00020284699,0.000015431908,0.0001958898,0.00010284237,0.00012099682,0.00007134741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017300777,0.00018114994,0.008140463,0.000006759033,0.000013071621,0.00022943824,0.0003602605,0.97111166,0.0002387604,0.0051662237,0.00041631234,0.014118628],"study_design_scores_gemma":[0.00060802116,0.00017759034,0.016988821,0.000043316657,0.0000018385691,0.000022068754,0.000017962437,0.97935164,0.00006846774,0.0021355944,0.0003969948,0.00018767783],"about_ca_topic_score_codex":0.000013717427,"about_ca_topic_score_gemma":0.0000011125625,"teacher_disagreement_score":0.6300556,"about_ca_system_score_codex":0.00010973638,"about_ca_system_score_gemma":0.000016218244,"threshold_uncertainty_score":0.59775496},"labels":[],"label_agreement":null},{"id":"W1539767050","doi":"10.1109/icarcv.2002.1235010","title":"An autonomous mobile robot with fuzzy obstacle avoidance behaviors and a visual landmark recognition system","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Obstacle avoidance; Artificial intelligence; Computer science; Robot; Fuzzy logic; Landmark; Computer vision; Autonomous robot; Human–computer interaction; Robotics; Sensor fusion; Field (mathematics); Behavior-based robotics; Obstacle; Robot control; Action (physics); Software; Fuzzy control system","score_opus":0.010353696449035119,"score_gpt":0.24509534020193552,"score_spread":0.2347416437529004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539767050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49424055,0.0000463782,0.50424063,0.000031107626,0.00013354742,0.00024926386,0.000003465038,0.0005377494,0.0005172789],"genre_scores_gemma":[0.7239224,0.0000013879743,0.2758634,0.000044511075,0.000035542114,0.00006051136,0.0000086308,0.000011898412,0.000051756884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986588,0.00005929093,0.00020157531,0.00052367675,0.00023765618,0.00031901244],"domain_scores_gemma":[0.9992896,0.000035771067,0.00008820098,0.0003253169,0.000074074014,0.00018704425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022688688,0.00017967481,0.000194344,0.00008878427,0.00016876409,0.00022456689,0.00029228404,0.00007263021,0.0000021599828],"category_scores_gemma":[0.0000052275786,0.00014757714,0.000020949466,0.00025910095,0.000049953775,0.00069637684,0.000061024744,0.00013306002,0.000048511516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001798391,0.0026282698,0.030071694,0.0004878097,0.00016621809,0.002948437,0.017198019,0.2537393,0.013842344,0.0062454497,0.00013630895,0.6723563],"study_design_scores_gemma":[0.010064152,0.011863169,0.13123608,0.0016070536,0.0001865445,0.006779925,0.005233179,0.797414,0.03037565,0.0012841198,0.00012350483,0.0038326215],"about_ca_topic_score_codex":0.00030981054,"about_ca_topic_score_gemma":0.000021093865,"teacher_disagreement_score":0.6685237,"about_ca_system_score_codex":0.00012316917,"about_ca_system_score_gemma":0.00010030995,"threshold_uncertainty_score":0.6018021},"labels":[],"label_agreement":null},{"id":"W1541189755","doi":"10.1109/icm.2004.1434227","title":"FPGA implementation of fuzzy wall-following control","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; Embedded system; Interfacing; Computer science; Fuzzy logic; Controller (irrigation); Mechatronics; FPGA prototype; Fuzzy control system; Control system; Robot; Programmable logic controller; Microcontroller; Computer hardware; Interface (matter); Fuzzy electronics; Engineering; Neuro-fuzzy; Artificial intelligence","score_opus":0.014233583251821997,"score_gpt":0.2905971429802971,"score_spread":0.2763635597284751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541189755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018238757,0.000041678235,0.9756322,0.0018219933,0.00019460257,0.00012153803,9.572673e-7,0.00013114438,0.003817118],"genre_scores_gemma":[0.70773584,5.6956947e-7,0.29168937,0.00038925008,0.000032892673,0.000004103963,9.551823e-7,0.000002919647,0.0001441275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991554,0.000032374483,0.000226214,0.00017206409,0.00022891004,0.00018505486],"domain_scores_gemma":[0.9995302,0.000062614,0.00007139015,0.0002568558,0.000031980195,0.00004695744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002751155,0.00007097234,0.000121124176,0.00006362847,0.000038859624,0.00003282601,0.00037179474,0.000023916959,0.000024784296],"category_scores_gemma":[0.0000103936245,0.000063412605,0.000059351503,0.00014820481,0.0000080182435,0.00037021688,0.00004424887,0.000042192572,0.000058896825],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054799625,0.00013763501,0.021316607,0.000017288503,0.000183182,0.000034918037,0.0039038488,0.020241272,0.009422663,0.030431096,0.0058694747,0.90843654],"study_design_scores_gemma":[0.0066956147,0.00037329845,0.10205211,0.000048627317,0.000058942725,0.000035688387,0.00050016784,0.8148757,0.06823776,0.0022470516,0.0041448744,0.0007301661],"about_ca_topic_score_codex":0.000101277146,"about_ca_topic_score_gemma":0.0000072630005,"teacher_disagreement_score":0.9077064,"about_ca_system_score_codex":0.00002930735,"about_ca_system_score_gemma":0.000040902883,"threshold_uncertainty_score":0.2585891},"labels":[],"label_agreement":null},{"id":"W1542367368","doi":"10.1109/icra.2015.7139606","title":"Fractal trajectories for online non-uniform aerial coverage","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion planning; Trajectory; Terrain; Sampling (signal processing); Fractal; Task (project management); Path (computing); Robot; Computer vision; Artificial intelligence; Real-time computing; Mathematics; Geography; Engineering; Cartography","score_opus":0.03875989991736055,"score_gpt":0.28659980822221265,"score_spread":0.24783990830485209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542367368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014514931,0.000010242958,0.9797602,0.00046323662,0.0021617247,0.00020788805,0.000024360555,0.00022586662,0.002631529],"genre_scores_gemma":[0.09146324,0.0000010104402,0.90612537,0.00034264996,0.00068994676,0.000016362497,0.000034657056,0.000011411929,0.001315331],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989655,0.0000133953445,0.00018817671,0.00027090433,0.0002614106,0.00030059135],"domain_scores_gemma":[0.9991707,0.0001106317,0.0000597437,0.00035523943,0.0001274775,0.00017616017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024730453,0.00013215566,0.00017476613,0.00005418121,0.00005632683,0.00013482648,0.00060517655,0.00006969744,0.00000485655],"category_scores_gemma":[0.00014219354,0.000110744404,0.000054503656,0.00016556735,0.000025354666,0.00052345934,0.000119601216,0.00008403023,0.000033995337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061541,0.0026109652,0.0041024787,0.000173521,0.00038060787,0.00045215397,0.022198457,0.08007932,0.0035833963,0.11035861,0.427374,0.34807107],"study_design_scores_gemma":[0.0022073663,0.0005951671,0.0012689414,0.000014160942,0.00000806736,0.00004581686,0.00009844176,0.9698478,0.0016441945,0.0046244524,0.019263431,0.00038218262],"about_ca_topic_score_codex":0.00007731057,"about_ca_topic_score_gemma":0.0000074978584,"teacher_disagreement_score":0.8897685,"about_ca_system_score_codex":0.000053342086,"about_ca_system_score_gemma":0.00020906606,"threshold_uncertainty_score":0.45160258},"labels":[],"label_agreement":null},{"id":"W1546176851","doi":"10.1109/cdc.2002.1184275","title":"Centralized cooperative planning for dynamic multi-agent planar manipulation","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Backstepping; Computer science; Trajectory; Object (grammar); Mathematical optimization; Sequential quadratic programming; Quadratic programming; Quadratic equation; Control theory (sociology); Multi-agent system; Plan (archaeology); Planar; Control (management); Mathematics; Artificial intelligence; Adaptive control","score_opus":0.0617114335342438,"score_gpt":0.32049675794287535,"score_spread":0.25878532440863156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546176851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016366934,0.00008076648,0.99619526,0.00014476603,0.00054453034,0.0004286648,0.0000041658964,0.00022645947,0.0007386758],"genre_scores_gemma":[0.14486338,0.0000023858772,0.85349584,0.00021847435,0.0000125082615,0.00003597216,0.000022097009,0.000011422563,0.0013379211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880373,0.00008124312,0.00022527507,0.00037204602,0.00016436314,0.0003533628],"domain_scores_gemma":[0.99935114,0.00012532619,0.00007253201,0.00028282581,0.00007350863,0.000094664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001973701,0.00015186862,0.00016284696,0.00007633821,0.0001766483,0.00011255207,0.00030776652,0.000060803526,0.000010482959],"category_scores_gemma":[0.0001025462,0.00013493218,0.00004839085,0.0001748382,0.000015791322,0.0002733141,0.000021544842,0.00007584188,0.000037800008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004832079,0.00048077048,0.008584798,0.00007831533,0.00021868595,0.00016786858,0.009257664,0.71986204,0.0059027015,0.24123022,0.0060211383,0.008147446],"study_design_scores_gemma":[0.0010928496,0.00006454881,0.0030803615,0.000021583182,0.0000059864915,0.000021561633,0.00008753893,0.99296135,0.0008643853,0.00034608968,0.0012452689,0.00020847934],"about_ca_topic_score_codex":0.000009507971,"about_ca_topic_score_gemma":0.0000017950997,"teacher_disagreement_score":0.27309927,"about_ca_system_score_codex":0.00008371633,"about_ca_system_score_gemma":0.00005329993,"threshold_uncertainty_score":0.5502375},"labels":[],"label_agreement":null},{"id":"W1553969047","doi":"10.1109/isic.2002.1157743","title":"Real-time tracking control with obstacle avoidance of multiple mobile robots","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Obstacle avoidance; Mobile robot; Trajectory; Obstacle; Controller (irrigation); Computer science; Robot; Control theory (sociology); Tracking (education); Control (management); Path (computing); Vehicle dynamics; Robot control; Control engineering; Artificial intelligence; Engineering","score_opus":0.01117517222736803,"score_gpt":0.22576140887317317,"score_spread":0.21458623664580515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553969047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047490522,0.00006290022,0.94438326,0.000033617296,0.00011589349,0.00031121876,0.0000028081645,0.00025005548,0.0073497235],"genre_scores_gemma":[0.5674036,0.000002207337,0.43181482,0.000035447047,0.000009744188,0.000018658304,5.967558e-7,0.000009822706,0.00070504984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985655,0.000111816546,0.00027017796,0.0003826348,0.0003164791,0.00035341334],"domain_scores_gemma":[0.9986184,0.00037670936,0.00015117417,0.00061857386,0.00013258602,0.000102561804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035408224,0.00015940637,0.00028735143,0.00006526499,0.000078987934,0.00005518519,0.0005051658,0.00005362389,0.000024463261],"category_scores_gemma":[0.00010452684,0.00012687917,0.000046473375,0.0003381006,0.00005844715,0.00038087074,0.000029741252,0.000104276754,0.000059372414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050265797,0.0005613489,0.032820463,0.00006462869,0.00013990271,0.00018894847,0.0025544895,0.8379555,0.0955381,0.012388234,0.0007913469,0.01694678],"study_design_scores_gemma":[0.0027561083,0.00047211506,0.0114098275,0.00009880744,0.000015642836,0.000111002584,0.0000842763,0.9362042,0.04793822,0.00023576368,0.00021814812,0.00045590568],"about_ca_topic_score_codex":0.00007534859,"about_ca_topic_score_gemma":0.000002517706,"teacher_disagreement_score":0.51991314,"about_ca_system_score_codex":0.000031279425,"about_ca_system_score_gemma":0.000089981404,"threshold_uncertainty_score":0.51739824},"labels":[],"label_agreement":null},{"id":"W1558471256","doi":"10.1109/icsmc.2002.1173465","title":"Neural dynamics and computation for navigation of multiple robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Robot; Computation; Mobile robot; Dynamics (music); Artificial intelligence; Artificial neural network; Algorithm; Physics","score_opus":0.02244003610614447,"score_gpt":0.28227718623173753,"score_spread":0.25983715012559305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558471256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05334775,0.000016076707,0.9457294,0.0005479663,0.00009240435,0.00012908492,0.0000029539751,0.000061996616,0.00007239602],"genre_scores_gemma":[0.4886466,2.4475813e-7,0.51126164,0.000029291545,0.000014022418,0.0000025014829,0.000012113711,0.0000018390404,0.00003174158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995376,0.000012693017,0.0001350476,0.00013795993,0.00008577908,0.000090938396],"domain_scores_gemma":[0.9996141,0.00014084125,0.00006240097,0.00009068114,0.00006290792,0.000029060238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010661815,0.000050865412,0.00007531174,0.000035278208,0.000038039285,0.000027267144,0.000116142684,0.00002575264,2.412474e-7],"category_scores_gemma":[0.000025023703,0.000047879184,0.000016434156,0.00008151245,0.000017088372,0.0002622974,0.000037566897,0.00002733831,0.0000011218726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004019899,0.000033966717,0.0030371111,0.000024936766,0.0000062751747,6.4774747e-7,0.00044608966,0.6239884,0.00034629047,0.009189722,0.00013349774,0.36278906],"study_design_scores_gemma":[0.00030371398,0.000046936646,0.0076233163,0.000008421173,0.000001934683,0.00000878095,0.000011858111,0.9910317,0.00050975115,0.0003941232,0.0000055736086,0.000053871263],"about_ca_topic_score_codex":0.000018433588,"about_ca_topic_score_gemma":0.00000565687,"teacher_disagreement_score":0.43529886,"about_ca_system_score_codex":0.000019684865,"about_ca_system_score_gemma":0.000009567856,"threshold_uncertainty_score":0.19524564},"labels":[],"label_agreement":null},{"id":"W1561255266","doi":"10.1007/978-3-540-28633-2_13","title":"An Hierarchical Terrain Representation for Approximately Shortest Paths","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Terrain; Preprocessor; Shortest path problem; Representation (politics); Logarithm; Algorithm; Line segment; Grid; Path (computing); Line (geometry); Graph; Enhanced Data Rates for GSM Evolution; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.03130798972528696,"score_gpt":0.29764935541929666,"score_spread":0.2663413656940097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561255266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000068044676,0.00008632512,0.9952153,0.0007524124,0.0017206378,0.0009127902,0.000017611063,0.0003538308,0.0008730486],"genre_scores_gemma":[0.051531255,0.0000065339327,0.9467773,0.0008048035,0.00066037686,0.000045026314,0.000045074456,0.000049883452,0.000079753365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949232,0.00005374855,0.00066933513,0.002341572,0.0011212594,0.00089088734],"domain_scores_gemma":[0.9965342,0.0006190233,0.0003098883,0.0019698527,0.000253118,0.00031390978],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011988059,0.00060020824,0.00061770494,0.00081954984,0.00034683789,0.00079814886,0.0040482553,0.00042530437,0.0000069741122],"category_scores_gemma":[0.00019729543,0.00057005865,0.0001623327,0.0005648154,0.00067953405,0.00094118813,0.00064182916,0.00081895926,0.000018217243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105639,0.000078414196,0.00006215435,0.00004010551,0.000011367114,0.00015908806,0.0020909791,0.261737,0.000295795,0.032765545,0.0000138080895,0.7027352],"study_design_scores_gemma":[0.00032012517,0.00035853873,0.0002620228,0.00023484004,0.000006715902,0.00009745663,2.3609195e-7,0.7159875,0.0005742989,0.28153706,0.000079649566,0.0005415303],"about_ca_topic_score_codex":0.000033711323,"about_ca_topic_score_gemma":0.000013004987,"teacher_disagreement_score":0.7021937,"about_ca_system_score_codex":0.00040709693,"about_ca_system_score_gemma":0.00087351934,"threshold_uncertainty_score":0.9996751},"labels":[],"label_agreement":null},{"id":"W1563082856","doi":"10.1109/icsmc.1999.812485","title":"A neural network approach to real-time motion planning and control of robot manipulators","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Artificial neural network; Robot; Computer science; Control theory (sociology); Motion control; Robot control; Feed forward; Recurrent neural network; Artificial intelligence; Control engineering; Engineering; Mobile robot; Control (management)","score_opus":0.02268741884196211,"score_gpt":0.23798237836577613,"score_spread":0.21529495952381403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563082856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01139781,0.000053064894,0.9780103,0.0000762675,0.0001501112,0.00020883563,4.1946473e-7,0.00013216981,0.0099710375],"genre_scores_gemma":[0.576918,3.8426967e-7,0.42278573,0.000092589005,0.000027535405,0.0000054967036,8.1957523e-7,0.000006170995,0.00016328397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987921,0.00011480225,0.00023389341,0.00034408356,0.00020072194,0.00031439457],"domain_scores_gemma":[0.99933445,0.000088393004,0.00007985401,0.00032142963,0.000037803125,0.00013809334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043139854,0.00013271766,0.00023534434,0.00007664965,0.00007225088,0.000057253976,0.0002644292,0.000055431825,0.0000024062717],"category_scores_gemma":[0.000042379597,0.00011842216,0.000030404712,0.0003280235,0.00002120498,0.00018115365,0.00005939369,0.000077594166,0.0000085574475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052478126,0.000036031768,0.013405002,0.00001022528,0.000017728671,0.000008155336,0.00041145625,0.9685336,0.0006223432,0.015316239,0.00045878196,0.0011752036],"study_design_scores_gemma":[0.00034987787,0.00008066182,0.025171176,0.000017174694,0.000007290355,0.00005230068,0.00001729985,0.9736518,0.00011504964,0.00037824624,0.000016618844,0.00014249883],"about_ca_topic_score_codex":0.000033306358,"about_ca_topic_score_gemma":7.752729e-8,"teacher_disagreement_score":0.56552017,"about_ca_system_score_codex":0.000016134465,"about_ca_system_score_gemma":0.00001594288,"threshold_uncertainty_score":0.48291156},"labels":[],"label_agreement":null},{"id":"W1568701760","doi":"","title":"Sensor fusion for social robotics","year":2000,"lang":"en","type":"article","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Enterprise Ireland","keywords":"Robotics; Artificial intelligence; Computer science; Sensor fusion; Computer vision; Robot","score_opus":0.025847145071018186,"score_gpt":0.26919319261424746,"score_spread":0.24334604754322928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568701760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07081493,0.00020252589,0.8934117,0.027471893,0.0027015256,0.00082634494,0.000037946444,0.0018219836,0.0027111273],"genre_scores_gemma":[0.13768233,0.000030189256,0.8608243,0.00019628015,0.00022891001,0.00005037816,0.000017556436,0.000023475746,0.0009465837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980278,0.000018725867,0.0005156688,0.00058434345,0.00031759497,0.0005358651],"domain_scores_gemma":[0.99865323,0.000047583857,0.00021433359,0.000808558,0.00020315152,0.000073122304],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031156783,0.00025585335,0.00042021615,0.00050443923,0.00033964982,0.00005739767,0.0017184465,0.00047057404,0.00001798928],"category_scores_gemma":[0.00017955413,0.00025778747,0.00014850842,0.0011593765,0.00040422849,0.00056232105,0.00022619922,0.00031023554,0.00010288578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005575375,0.0004710337,0.00040310755,0.00012673251,0.0001414487,0.00013600958,0.00044838642,0.028595246,0.0076113273,0.43822697,0.017708726,0.50607526],"study_design_scores_gemma":[0.005736573,0.0011718085,0.001142022,0.0003495586,0.00014027207,0.00038689852,0.00013280436,0.4591959,0.033217225,0.08105685,0.41547427,0.0019958017],"about_ca_topic_score_codex":0.00001321518,"about_ca_topic_score_gemma":0.000002751516,"teacher_disagreement_score":0.50407946,"about_ca_system_score_codex":0.00006557714,"about_ca_system_score_gemma":0.00014931163,"threshold_uncertainty_score":0.9999874},"labels":[],"label_agreement":null},{"id":"W157085335","doi":"10.1007/978-3-642-21538-4_2","title":"Small Tree Probabilistic Roadmap Planner for Hyper-Redundant Manipulators","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Planner; Computer science; Probabilistic logic; Path (computing); Probabilistic roadmap; Motion planning; Tree (set theory); Configuration space; Connection (principal bundle); Process (computing); Robot; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.05787342181088051,"score_gpt":0.24626404230854404,"score_spread":0.18839062049766353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W157085335","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000676814,0.000329679,0.99071246,0.000338109,0.004093035,0.0011146445,0.000015349984,0.00034400256,0.002985017],"genre_scores_gemma":[0.012978644,0.000008708258,0.98392,0.0007876072,0.0007439013,0.00006297008,0.000017151331,0.000086431886,0.0013945886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946552,0.000041804054,0.00079702865,0.0024210515,0.0008781942,0.0012067204],"domain_scores_gemma":[0.99607956,0.0007128011,0.00044395533,0.0020909787,0.00033094728,0.0003417243],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011323548,0.0008668506,0.00086060003,0.0009049669,0.00035191036,0.00051818875,0.005219036,0.00051339844,0.000011412178],"category_scores_gemma":[0.0002405638,0.0007649622,0.00024768588,0.00047939055,0.00071626663,0.0004243224,0.0012315203,0.000818762,0.00007405425],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022420167,0.0000969314,0.00011235588,0.00014165347,0.00003830268,0.00021170842,0.0017772061,0.08651319,0.000057133526,0.04530221,0.00012363722,0.86560327],"study_design_scores_gemma":[0.00047967088,0.00049514096,0.0002862284,0.00055258774,0.000027978598,0.00024016185,1.5620954e-7,0.7452464,0.00034949742,0.2486137,0.002485797,0.0012226639],"about_ca_topic_score_codex":0.0000457894,"about_ca_topic_score_gemma":0.00005570281,"teacher_disagreement_score":0.8643806,"about_ca_system_score_codex":0.0003924433,"about_ca_system_score_gemma":0.00078906654,"threshold_uncertainty_score":0.9994801},"labels":[],"label_agreement":null},{"id":"W1576358592","doi":"10.1007/3-540-45118-8_38","title":"View Planning via C-space Entropy for Efficient Exploration with Eye-in-Hand Systems","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Planner; Ignorance; Computer science; Entropy (arrow of time); Motion planning; Artificial intelligence; Configuration space; Robot; Space (punctuation); Information space; Human–computer interaction; Computer vision; Physics","score_opus":0.02763780084747654,"score_gpt":0.2719595042256258,"score_spread":0.24432170337814926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576358592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060084658,0.0016366345,0.99211913,0.00054984505,0.00047407293,0.0009427973,0.000008830954,0.000049524908,0.0041590706],"genre_scores_gemma":[0.8523066,0.00017456686,0.14396739,0.002255622,0.00043784687,0.00025931754,0.00007162885,0.000035576064,0.0004914733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799293,0.000028529084,0.0006150948,0.00036503767,0.00061141787,0.00038697946],"domain_scores_gemma":[0.9984307,0.0006166258,0.00049145805,0.00021784667,0.00016748192,0.00007585458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013994803,0.0003023859,0.00044277802,0.0007333898,0.00025713202,0.0007021545,0.00043833946,0.00021541958,0.0000011678081],"category_scores_gemma":[0.00012810616,0.00021844856,0.00003939432,0.0002772493,0.00019731562,0.0018162932,0.00004569396,0.00026240264,0.000006684964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000491035,0.0000075684634,0.00020675334,0.0001289911,0.000012668114,0.000007777844,0.0032254974,0.92251074,0.000015339536,0.027138848,0.0000094633015,0.04668725],"study_design_scores_gemma":[0.0011119304,0.0003104372,0.00016151638,0.00070310367,0.000009847664,0.000022896691,0.00002791516,0.98988926,0.000026538977,0.001956492,0.005458193,0.00032186828],"about_ca_topic_score_codex":0.000032467193,"about_ca_topic_score_gemma":0.000010422936,"teacher_disagreement_score":0.85224646,"about_ca_system_score_codex":0.00009017359,"about_ca_system_score_gemma":0.00014459685,"threshold_uncertainty_score":0.8908074},"labels":[],"label_agreement":null},{"id":"W1580706940","doi":"","title":"Optimal path planning system in time-varying environment","year":2010,"lang":"en","type":"article","venue":"Common Library Network (Der Gemeinsame Bibliotheksverbund)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Path (computing)","score_opus":0.012616428200995742,"score_gpt":0.22187358256374648,"score_spread":0.20925715436275075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580706940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25689632,0.0061678407,0.6493302,0.0022666128,0.007135161,0.0025086582,0.00004254825,0.0066389632,0.069013715],"genre_scores_gemma":[0.36314097,0.00008715455,0.63204575,0.0012609868,0.0015179185,0.000103881954,0.000067558816,0.00018616619,0.0015896508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99548715,0.00033665434,0.0008697115,0.001134054,0.00082139723,0.0013510551],"domain_scores_gemma":[0.9968039,0.0005117893,0.00034297386,0.0019015516,0.000014510169,0.00042529995],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008586262,0.000604621,0.0007043128,0.0018162223,0.00036180066,0.0011796405,0.0035592522,0.0003653364,0.00030746002],"category_scores_gemma":[0.000016589345,0.0006113695,0.00017372528,0.0043996847,0.00014034778,0.003409325,0.0018461995,0.001290113,0.0005929248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078534526,0.00046517115,0.113978,0.00013210722,0.00011806349,0.0015189509,0.0009915839,0.7945319,0.00034863516,0.009729909,0.07209808,0.0060090898],"study_design_scores_gemma":[0.0010387719,0.00018707036,0.01403398,0.0005642373,0.000016408429,0.00019212886,0.00002771599,0.9530078,0.00019398797,0.0007137828,0.029087847,0.0009362357],"about_ca_topic_score_codex":0.000020818215,"about_ca_topic_score_gemma":1.991102e-7,"teacher_disagreement_score":0.15847595,"about_ca_system_score_codex":0.00006996361,"about_ca_system_score_gemma":0.000121686695,"threshold_uncertainty_score":0.99985725},"labels":[],"label_agreement":null},{"id":"W1581008721","doi":"10.1109/icsmc.2002.1173488","title":"Ultrasonic sensor based fuzzy obstacle avoidance behaviors","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Ultrasonic sensor; Obstacle avoidance; Fuzzy logic; Obstacle; Computer science; Collision avoidance; Acoustics; Artificial intelligence; Mobile robot; Computer security; Geography; Physics","score_opus":0.015892752104497303,"score_gpt":0.25214189546220517,"score_spread":0.23624914335770786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581008721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043532435,0.00008830993,0.9447739,0.002937899,0.0004134936,0.00015867663,0.000003382247,0.00083645165,0.007255441],"genre_scores_gemma":[0.3399335,0.0000012563329,0.6565939,0.0010793965,0.00008409032,0.000009755197,0.0000019211686,0.000010407657,0.0022857161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984572,0.000059422593,0.00022753661,0.00045683546,0.00034842413,0.00045056318],"domain_scores_gemma":[0.9988006,0.00016119893,0.00006710171,0.000753814,0.000058763493,0.00015852992],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024472474,0.00016729192,0.000156018,0.00008445161,0.00012320907,0.00012489196,0.0008325887,0.00006995196,0.000056045636],"category_scores_gemma":[0.00005380785,0.00015385136,0.00006924507,0.000348562,0.0000396846,0.00042099404,0.00006736223,0.0001787352,0.00082308776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024720348,0.0017468737,0.052655317,0.00006583241,0.00007573318,0.0006690759,0.0036609394,0.3454848,0.039405547,0.032229804,0.03873218,0.4852492],"study_design_scores_gemma":[0.0009487443,0.00012807341,0.031853385,0.000043241154,0.000013393402,0.00010437549,0.000054883487,0.93142676,0.02569822,0.00015752955,0.0088668605,0.000704534],"about_ca_topic_score_codex":0.000037037502,"about_ca_topic_score_gemma":0.0000031635361,"teacher_disagreement_score":0.585942,"about_ca_system_score_codex":0.00008244611,"about_ca_system_score_gemma":0.000094257826,"threshold_uncertainty_score":0.9999549},"labels":[],"label_agreement":null},{"id":"W1584874716","doi":"10.1007/978-3-642-13059-5_52","title":"On Multi-robot Area Coverage","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Task (project management); Computer science; Mobile robot; Path (computing); Point (geometry); Actuator; Motion planning; Robot kinematics; Robot control; Human–computer interaction; Artificial intelligence; Real-time computing; Engineering; Computer network","score_opus":0.0200022003704376,"score_gpt":0.26741553969595916,"score_spread":0.24741333932552156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584874716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024131393,0.000014638291,0.9706413,0.00082166295,0.0038849972,0.00017941328,0.0000011942674,0.0002445074,0.00008090756],"genre_scores_gemma":[0.51286304,5.916319e-7,0.48585233,0.0011875458,0.00008383513,0.000004455308,5.3595403e-7,0.0000057447887,0.000001911038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971137,0.000052932744,0.0002729246,0.0011019679,0.00072850194,0.0007299712],"domain_scores_gemma":[0.9975425,0.00066291186,0.00010304076,0.0013727262,0.00011127606,0.00020755157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091219635,0.00027190923,0.0002397804,0.0004490773,0.00027470823,0.0005187131,0.0031696358,0.00012650543,0.000010102595],"category_scores_gemma":[0.0005096006,0.00023190973,0.00005738535,0.0018360963,0.00035507316,0.00061991555,0.0006490923,0.00083345635,0.0000862227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034562424,0.00016425339,0.0022316682,0.000005426821,0.0000029932785,0.00012250457,0.00079972565,0.45702857,0.00671928,0.0016283211,0.000026228709,0.5312676],"study_design_scores_gemma":[0.0003888223,0.00012144815,0.0090542985,0.000039549483,9.13198e-7,0.00008325522,9.57439e-8,0.968088,0.010452977,0.011438403,0.000029432767,0.00030284276],"about_ca_topic_score_codex":0.000041126958,"about_ca_topic_score_gemma":0.00003442346,"teacher_disagreement_score":0.53096473,"about_ca_system_score_codex":0.00011225053,"about_ca_system_score_gemma":0.00024381754,"threshold_uncertainty_score":0.9457004},"labels":[],"label_agreement":null},{"id":"W1585270292","doi":"10.1109/iros.1992.594208","title":"Motion Planning With Acceleration Constraint","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Acceleration; Constraint (computer-aided design); Computer science; Motion (physics); Motion planning; Artificial intelligence; Mathematics; Physics; Classical mechanics; Robot; Geometry","score_opus":0.029117366947156248,"score_gpt":0.26069614726201046,"score_spread":0.2315787803148542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585270292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005088417,0.000012418845,0.9747353,0.0014764586,0.00007190357,0.00007018743,1.9424252e-7,0.00029942102,0.018245708],"genre_scores_gemma":[0.486807,1.8299914e-7,0.51263964,0.00022329258,0.000055729524,0.0000030460678,0.0000014971089,0.000002414195,0.0002671972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999334,0.000018847073,0.00010679442,0.00020620645,0.00017507878,0.00015912484],"domain_scores_gemma":[0.9996268,0.000028815939,0.000040391315,0.00021191959,0.000039571205,0.000052501706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013630954,0.00007536598,0.00006623373,0.00008157967,0.000076257646,0.00013235799,0.0002304445,0.000029391322,0.000017683153],"category_scores_gemma":[0.0000092141545,0.00005783864,0.0000114829,0.0001432261,0.000021036862,0.0005804546,0.000033399065,0.00007445238,0.00008267267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006909124,0.000099379824,0.0057701715,0.000007393347,0.000027569029,0.00007035166,0.0032214262,0.41172817,0.0013393626,0.06430656,0.0025703504,0.51085234],"study_design_scores_gemma":[0.00027425776,0.00006461752,0.0061500003,0.000018808207,0.0000020468005,0.000106928666,0.000038629565,0.9895677,0.002676744,0.00013675196,0.0008279787,0.00013551043],"about_ca_topic_score_codex":0.0000051011534,"about_ca_topic_score_gemma":9.4378595e-7,"teacher_disagreement_score":0.57783955,"about_ca_system_score_codex":0.000031631145,"about_ca_system_score_gemma":0.00003108016,"threshold_uncertainty_score":0.23585911},"labels":[],"label_agreement":null},{"id":"W1585601164","doi":"10.1109/nafips.2005.1548645","title":"Autonomous Parallel Parking of a Car-Like Mobile Robot by a Neuro-Fuzzy Behavior-Based Controller","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mobile robot; Controller (irrigation); Robot; Control theory (sociology); Trajectory; Computer science; Fuzzy control system; Control engineering; Fuzzy logic; Robot control; Process (computing); Position (finance); Control (management); Artificial intelligence; Engineering","score_opus":0.0140343532543686,"score_gpt":0.24742369908351952,"score_spread":0.2333893458291509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585601164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016805554,0.00027445273,0.9791135,0.00052383234,0.00036258443,0.00068447995,0.000010429431,0.0004960298,0.0017290965],"genre_scores_gemma":[0.6341417,0.0000020564441,0.3642855,0.00078669994,0.00004534211,0.0001696523,0.0000054085267,0.00001798629,0.000545674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784833,0.00010617083,0.0005132405,0.00058279897,0.000430521,0.00051896437],"domain_scores_gemma":[0.99856454,0.00024571223,0.00021516028,0.0007018331,0.000094949995,0.00017779434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029838574,0.00026166716,0.00042559928,0.00012498058,0.00009901315,0.00009921292,0.0010154847,0.00010696054,0.00002907001],"category_scores_gemma":[0.000028237497,0.00023885307,0.00014563542,0.00029505068,0.00007157169,0.00028452944,0.0001472372,0.00020084805,0.00006030686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005528552,0.0012508921,0.006409523,0.000025693862,0.00007189976,0.00013415345,0.0010000921,0.870976,0.015835851,0.0013280215,0.015339328,0.087573305],"study_design_scores_gemma":[0.0018728385,0.00034734342,0.0020891384,0.000018386085,0.000027727614,0.000027540207,0.000016830145,0.9885982,0.0013093941,0.000023704313,0.0053200596,0.00034879378],"about_ca_topic_score_codex":0.0001883009,"about_ca_topic_score_gemma":0.00000482489,"teacher_disagreement_score":0.61733615,"about_ca_system_score_codex":0.00007040795,"about_ca_system_score_gemma":0.00013881104,"threshold_uncertainty_score":0.9740146},"labels":[],"label_agreement":null},{"id":"W1585938803","doi":"10.1109/crv.2015.46","title":"Vision-Based Collision Avoidance for Personal Aerial Vehicles Using Dynamic Potential Fields","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Collision avoidance; Obstacle avoidance; Operator (biology); Obstacle; Trajectory; Collision; Cruise control; Computer science; Simulation; Collision avoidance system; Real-time computing; Mobile robot; Control engineering; Control (management); Engineering; Artificial intelligence; Robot","score_opus":0.03282029722396603,"score_gpt":0.3025024354858417,"score_spread":0.2696821382618757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585938803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11318034,0.000038553077,0.8840862,0.00079694233,0.0014871595,0.00019576373,0.0000070852448,0.00014571972,0.00006225724],"genre_scores_gemma":[0.5295904,2.4823464e-7,0.46990454,0.00022001688,0.00012447698,0.0000063029806,0.0000051656157,0.0000075803396,0.00014122817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863845,0.00005815309,0.00020259185,0.00038886716,0.00039689752,0.000315019],"domain_scores_gemma":[0.999205,0.00013741139,0.00008000556,0.00024444255,0.00017350983,0.0001596196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042319167,0.00014119399,0.00016602292,0.00008270865,0.00017116706,0.000188547,0.00047771557,0.000111865986,0.000004073387],"category_scores_gemma":[0.0001100975,0.00012627263,0.0000804433,0.00018644764,0.000040358078,0.0003079275,0.00010951437,0.00009948637,0.00001630566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041069608,0.00045701343,0.0004140784,0.000079852885,0.000054826105,0.00016510296,0.0027307626,0.9174344,0.01818686,0.0011671286,0.014804769,0.044094507],"study_design_scores_gemma":[0.0010851916,0.00021871472,0.00018786413,0.000031176754,0.0000062806002,0.00001635185,0.00003431565,0.9966696,0.00096850714,0.000462686,0.00014165335,0.00017766563],"about_ca_topic_score_codex":0.00006193477,"about_ca_topic_score_gemma":0.0000030613508,"teacher_disagreement_score":0.41641012,"about_ca_system_score_codex":0.000100203455,"about_ca_system_score_gemma":0.00033260757,"threshold_uncertainty_score":0.5149248},"labels":[],"label_agreement":null},{"id":"W1589297171","doi":"10.1109/robot.1999.769998","title":"Kinodynamic motion planning for all-terrain wheeled vehicles","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Terrain; Motion planning; Robot; Computer science; Task (project management); Mobile robot; Motion (physics); Set (abstract data type); State space; Tree (set theory); Artificial intelligence; Robot kinematics; Actuator; Control theory (sociology); Engineering; Control (management); Mathematics; Geography","score_opus":0.03144756726827081,"score_gpt":0.28648349264706224,"score_spread":0.25503592537879144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589297171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012275322,0.000048991114,0.9826232,0.00037283727,0.00048232908,0.00021932302,0.0000012187509,0.00033513017,0.0036416256],"genre_scores_gemma":[0.40964368,6.520668e-7,0.5892584,0.0003524847,0.00003275529,0.000024892472,0.00000584088,0.000010690687,0.0006706241],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988004,0.000071650524,0.00020249773,0.0003726185,0.0001828688,0.00036995442],"domain_scores_gemma":[0.9992864,0.00014810472,0.00006915337,0.0003631166,0.000048429916,0.0000847871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046370065,0.0001372443,0.00014406376,0.00010238692,0.00011615408,0.0001217171,0.00044825146,0.00007249382,0.000006953495],"category_scores_gemma":[0.00011654557,0.0001243542,0.000060087063,0.00016421967,0.000018603669,0.00031376834,0.000045568417,0.00008883587,0.000036462745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033623215,0.0005592865,0.014000322,0.00018419605,0.0002959339,0.00021072505,0.013049242,0.19229129,0.03388577,0.60850644,0.024757998,0.112225145],"study_design_scores_gemma":[0.0005390336,0.000078678,0.0029229315,0.000022898428,0.0000063515427,0.000042471267,0.00006000654,0.9866996,0.0015182461,0.006235902,0.001644159,0.00022971383],"about_ca_topic_score_codex":0.000010343649,"about_ca_topic_score_gemma":6.8902483e-7,"teacher_disagreement_score":0.7944083,"about_ca_system_score_codex":0.000045541627,"about_ca_system_score_gemma":0.000035870085,"threshold_uncertainty_score":0.5071017},"labels":[],"label_agreement":null},{"id":"W1597748831","doi":"10.1109/ccece.1995.526410","title":"Intelligent control by intentional selection of reactions","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Mobile robot; Selection (genetic algorithm); Control (management); Autonomy; Ethology; Robot; Human–computer interaction; Cover (algebra); Intelligent control; Artificial intelligence; Intelligent decision support system; Cognitive science; Engineering; Psychology","score_opus":0.02344611837336558,"score_gpt":0.24360561722309498,"score_spread":0.2201594988497294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597748831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036101355,0.00004213696,0.9929921,0.0006635654,0.00024292553,0.000058979902,0.0000018630634,0.00011066363,0.0055267652],"genre_scores_gemma":[0.8116659,0.000012361585,0.17990057,0.00018193717,0.000035700155,0.000010147832,0.0000021824612,0.0000045547076,0.008186656],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993412,0.000030733896,0.00018985476,0.00015253201,0.0001710696,0.000114594906],"domain_scores_gemma":[0.99958557,0.000070315196,0.00006774903,0.00013908997,0.00009387162,0.000043430555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011876248,0.00005611999,0.00008043514,0.00006912929,0.000044687444,0.000022497059,0.00021669453,0.000030455732,0.00024818102],"category_scores_gemma":[0.00004405856,0.00005093026,0.000040151004,0.00023157564,0.000020931539,0.00018019955,0.000022756065,0.000075376316,0.00011979095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016062939,0.0021111201,0.013405617,0.000052960382,0.00039092437,0.000013360527,0.0012131793,0.025303653,0.07913926,0.19580531,0.46271506,0.2198335],"study_design_scores_gemma":[0.00016543936,0.000070342976,0.0007190941,0.000008935287,0.0000037922232,0.00003132081,0.000010041476,0.98927444,0.0048736706,0.00037368372,0.0043994025,0.00006981848],"about_ca_topic_score_codex":0.00003986412,"about_ca_topic_score_gemma":5.2766006e-7,"teacher_disagreement_score":0.9639708,"about_ca_system_score_codex":0.000030746338,"about_ca_system_score_gemma":0.000007324056,"threshold_uncertainty_score":0.27174082},"labels":[],"label_agreement":null},{"id":"W1598367376","doi":"10.1007/978-3-642-01818-3_34","title":"A Procedural Planning System for Goal Oriented Agents in Games","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Animation; Control (management); Order (exchange); Human–computer interaction; Artificial intelligence; Computer graphics (images)","score_opus":0.027415890295474032,"score_gpt":0.27957412465738435,"score_spread":0.2521582343619103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598367376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016122492,0.00030115104,0.994282,0.00031954388,0.0022925818,0.0009866313,0.000008005314,0.0003202328,0.0013286212],"genre_scores_gemma":[0.08049298,0.0000035255764,0.9175213,0.0009526567,0.0005422578,0.00004927016,0.000014654151,0.000047311998,0.00037605283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99513465,0.000039491548,0.0007743908,0.0019141649,0.0010429063,0.0010943885],"domain_scores_gemma":[0.9976378,0.00044840883,0.0004048883,0.0010715893,0.00023112202,0.00020615758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010717897,0.000655349,0.0007800911,0.0012642533,0.00022418969,0.00045208397,0.0032240415,0.00038952124,0.0000012443336],"category_scores_gemma":[0.00017182388,0.0006167736,0.00014160661,0.00085160247,0.00027141927,0.00059394736,0.0006521622,0.00078290707,0.000015924568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003384905,0.00007158172,0.0005081561,0.000329406,0.00002212868,0.0008759641,0.0033657837,0.4444957,0.00006486575,0.011556158,0.0001398781,0.5385365],"study_design_scores_gemma":[0.0006870174,0.0002877419,0.0007984245,0.0025484387,0.000007416482,0.00021423695,9.861551e-7,0.9855687,0.00025524755,0.008182562,0.00071151095,0.00073769555],"about_ca_topic_score_codex":0.000016866936,"about_ca_topic_score_gemma":0.0000068025356,"teacher_disagreement_score":0.541073,"about_ca_system_score_codex":0.0007749679,"about_ca_system_score_gemma":0.0006834846,"threshold_uncertainty_score":0.99962837},"labels":[],"label_agreement":null},{"id":"W160047510","doi":"","title":"Analogical path planning","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Path (computing); Computer science; Probabilistic logic; Robot; Trajectory; Mathematical optimization; Set (abstract data type); Mobile robot; Probability distribution; Probabilistic roadmap; Markov process; Configuration space; Artificial intelligence; Mathematics","score_opus":0.2076154789963048,"score_gpt":0.37192380708654105,"score_spread":0.16430832809023624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W160047510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002359312,0.00001981147,0.9762454,0.0028315876,0.00043392487,0.000119869124,0.000006341849,0.0002361717,0.017747559],"genre_scores_gemma":[0.8891264,0.000003455253,0.10984914,0.00080511556,0.00013780079,0.000016284057,0.000008125966,0.0000065473137,0.000047086858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763554,0.00006151522,0.00039160936,0.00055827637,0.000996411,0.00035667233],"domain_scores_gemma":[0.99881697,0.00018937812,0.00012014189,0.00028313693,0.00043892296,0.00015147979],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00051986636,0.00019449096,0.00017840603,0.00020549384,0.00022732215,0.00027957288,0.0010049308,0.00011057029,0.000058376314],"category_scores_gemma":[0.00046849012,0.00018032147,0.00006482267,0.0005544969,0.00010696318,0.00034945182,0.000114665054,0.00033793223,0.00094036217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007787605,0.00010079466,0.000022903288,0.0000016434565,0.00000586506,0.000048630096,0.00031256917,0.08604413,0.00039734435,0.89083874,0.000062562554,0.022157008],"study_design_scores_gemma":[0.000042081065,0.00019698474,0.0012573685,0.000078723824,0.0000019124373,0.000024783812,0.00007857259,0.289243,0.005844115,0.7028329,0.0001240835,0.00027547663],"about_ca_topic_score_codex":0.0000184437,"about_ca_topic_score_gemma":0.0000012536551,"teacher_disagreement_score":0.88676715,"about_ca_system_score_codex":0.00016149067,"about_ca_system_score_gemma":0.00040831038,"threshold_uncertainty_score":0.9998375},"labels":[],"label_agreement":null},{"id":"W1602403251","doi":"10.1109/icsmc.1988.712809","title":"Collision-free Traiectory Planning For Robot Manipulators†","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cartesian coordinate system; Trajectory; Kinematics; Inverse kinematics; Robot; Computer science; Constraint (computer-aided design); Feature (linguistics); Cartesian coordinate robot; Control theory (sociology); Space (punctuation); Robot kinematics; Configuration space; Mathematics; Artificial intelligence; Mobile robot; Geometry; Physics","score_opus":0.041729157671402554,"score_gpt":0.2909728678730233,"score_spread":0.24924371020162073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602403251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018354077,0.00016094786,0.9906876,0.0013840562,0.0005791001,0.00025723007,0.0000035831372,0.00050598633,0.0045860657],"genre_scores_gemma":[0.063975185,9.3430526e-7,0.93284905,0.00070003525,0.00033789795,0.0000349925,0.0000036978122,0.000016728778,0.0020814536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855816,0.000025716105,0.00026536846,0.00043540378,0.0002950006,0.00042036964],"domain_scores_gemma":[0.99865884,0.00026112818,0.00007254479,0.0008011733,0.00006243698,0.00014388932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034774715,0.00016754655,0.00019076749,0.00012289753,0.0001758423,0.0001323706,0.0013909115,0.00008539309,0.000014278382],"category_scores_gemma":[0.0001160902,0.00015120461,0.00008475674,0.00022484866,0.000022748705,0.0004816156,0.00021286999,0.00010936326,0.00007481742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023218745,0.00021891594,0.0016205785,0.000042292973,0.000077775236,0.000063616484,0.002750186,0.6804734,0.0012274982,0.07711157,0.17078826,0.06560269],"study_design_scores_gemma":[0.00082707347,0.00010578588,0.004078029,0.00003449133,0.0000066491707,0.00004177782,0.000031652806,0.9750884,0.00198571,0.0016121288,0.015870715,0.00031756505],"about_ca_topic_score_codex":0.000008979232,"about_ca_topic_score_gemma":0.0000016593016,"teacher_disagreement_score":0.294615,"about_ca_system_score_codex":0.00007142977,"about_ca_system_score_gemma":0.000067459594,"threshold_uncertainty_score":0.61659455},"labels":[],"label_agreement":null},{"id":"W1602814827","doi":"10.1109/icsmc.1999.812484","title":"A paradigm for intelligent motion planning of robot manipulators","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Robot; Computer science; Motion planning; Artificial intelligence; Robot manipulator; Robotics; Control engineering; Engineering","score_opus":0.06115458394824316,"score_gpt":0.2968227061535883,"score_spread":0.23566812220534514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602814827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017898247,0.00008304741,0.9956848,0.00009542674,0.0004145094,0.00019184995,5.509616e-7,0.0000996503,0.0016403397],"genre_scores_gemma":[0.43411398,8.6686725e-7,0.5656393,0.000048729,0.000014023299,0.000011614622,0.0000013549233,0.000005890196,0.00016419678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999094,0.00003504168,0.00024635,0.0002461277,0.00015690863,0.00022154854],"domain_scores_gemma":[0.99938077,0.00011092946,0.00009269328,0.0003229671,0.000028926283,0.00006371477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003253976,0.000099432364,0.00014972672,0.00009713586,0.00005023782,0.000032013744,0.00032737642,0.000046902624,0.000005887615],"category_scores_gemma":[0.000071770155,0.000089419285,0.00006063043,0.0001986439,0.00001643975,0.00016429168,0.00003246485,0.00005120215,0.000010623451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045272445,0.00010498382,0.0060713384,0.000044168846,0.00003726088,0.000009714169,0.0012453529,0.23164761,0.00027687897,0.7493792,0.000561675,0.010617264],"study_design_scores_gemma":[0.000438285,0.00023144088,0.004117243,0.00007106277,0.000012523769,0.00005165,0.00009532164,0.93478495,0.030527335,0.028035996,0.0013172698,0.00031693035],"about_ca_topic_score_codex":0.000008644572,"about_ca_topic_score_gemma":1.5993223e-7,"teacher_disagreement_score":0.7213432,"about_ca_system_score_codex":0.000028005255,"about_ca_system_score_gemma":0.000030207131,"threshold_uncertainty_score":0.36464128},"labels":[],"label_agreement":null},{"id":"W1606450834","doi":"10.5555/1838206.1838451","title":"Multi-robot area coverage with limited visibility","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Delaunay triangulation; Robot; Visibility; Computer science; Constrained Delaunay triangulation; Graph; Cover (algebra); Set cover problem; Set (abstract data type); Spanning tree; Artificial intelligence; Euclidean minimum spanning tree; Theoretical computer science; Algorithm; Mathematics; Engineering; Geography; Connected dominating set; Combinatorics","score_opus":0.024403518087620733,"score_gpt":0.2527861148472048,"score_spread":0.22838259675958408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606450834","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06497904,0.0000034816035,0.9305628,0.00039219743,0.0004135607,0.00015560832,0.0000016699178,0.0004973519,0.0029942682],"genre_scores_gemma":[0.4455137,3.4783153e-7,0.553754,0.00025155346,0.000021433361,0.0000051910415,0.0000018600365,0.000005772225,0.00044614772],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986528,0.000036236357,0.00017364752,0.0005282399,0.00028455263,0.00032450628],"domain_scores_gemma":[0.998402,0.00013883416,0.00006466808,0.0011210277,0.00010847521,0.0001649809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031192123,0.00016387823,0.00016494073,0.00006883496,0.00010309457,0.00016886338,0.0008425094,0.00008270479,0.00006413218],"category_scores_gemma":[0.00010834065,0.0001165771,0.000036417765,0.00035107345,0.00006605281,0.0004056578,0.00018011144,0.00033967738,0.00012942967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014004277,0.00407881,0.5095538,0.000118408825,0.00033208626,0.0017213714,0.0063507906,0.05914425,0.168749,0.035439644,0.0073883384,0.20698345],"study_design_scores_gemma":[0.00070422195,0.00011465278,0.18313794,0.000010484565,0.0000040985856,0.00006132555,0.0000070763276,0.8128212,0.0024027764,0.00023558579,0.0002308353,0.00026982385],"about_ca_topic_score_codex":0.00007949141,"about_ca_topic_score_gemma":0.000030933617,"teacher_disagreement_score":0.75367695,"about_ca_system_score_codex":0.000017506032,"about_ca_system_score_gemma":0.00007930486,"threshold_uncertainty_score":0.4753876},"labels":[],"label_agreement":null},{"id":"W1608143487","doi":"10.1109/imtc.2005.1604373","title":"Utilitarian Robots Acting In A Virtual Medical Center Scene","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robot; Computer science; Workspace; OpenGL; Motion planning; Mobile robot; Center (category theory); Frame (networking); Decomposition; Virtual reality; Computer graphics (images); Virtual machine; Human–computer interaction; Path (computing); Artificial intelligence; Computer vision; Visualization; Operating system","score_opus":0.029104126887681322,"score_gpt":0.2514279482160989,"score_spread":0.22232382132841755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608143487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45654178,0.00014703168,0.5123891,0.018364068,0.0016237575,0.0010900322,0.0000064829533,0.0014436849,0.008394064],"genre_scores_gemma":[0.9477499,0.00001622182,0.051741853,0.00019464408,0.000080540645,0.00011595474,0.000005529187,0.000014701965,0.00008061309],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99631804,0.00002249356,0.00067626097,0.0007650075,0.0014384993,0.0007797002],"domain_scores_gemma":[0.9989568,0.000023692619,0.00023895086,0.00023231294,0.00041124233,0.00013699167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011046951,0.0003085395,0.00032394152,0.0007468804,0.0001874934,0.00016757671,0.0011932607,0.00035493248,0.000040535044],"category_scores_gemma":[0.00022308616,0.00031285334,0.000047995047,0.00082584337,0.00019356191,0.00084751484,0.00017658166,0.000524577,0.000045447632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062589985,0.0013749135,0.44476295,0.0000917214,0.000111439156,0.00015651033,0.0016720822,0.00032510108,0.024817187,0.13678436,0.004009208,0.38583192],"study_design_scores_gemma":[0.024577728,0.0012803997,0.12441948,0.0039702468,0.00007042548,0.0012144081,0.0050122184,0.66090024,0.11161761,0.05708639,0.0057436456,0.004107242],"about_ca_topic_score_codex":0.00009971105,"about_ca_topic_score_gemma":0.00010150211,"teacher_disagreement_score":0.6605751,"about_ca_system_score_codex":0.00043000144,"about_ca_system_score_gemma":0.0005008226,"threshold_uncertainty_score":0.99993235},"labels":[],"label_agreement":null},{"id":"W1630437312","doi":"10.1017/cbo9780511780929","title":"Computational Principles of Mobile Robotics","year":2010,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":654,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Artificial intelligence; Variety (cybernetics); Field (mathematics); Mobile robot; Computer science; Robot; Key (lock); Range (aeronautics); State (computer science); Human–computer interaction; Engineering; Mathematics; Computer security; Algorithm","score_opus":0.023729614886663773,"score_gpt":0.2156093713069322,"score_spread":0.19187975642026842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1630437312","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016352617,0.000042685748,0.35176262,0.0000065424842,0.0004428981,0.00027420322,0.00010058051,0.00015968135,0.64719445],"genre_scores_gemma":[0.000050910836,0.00000531997,0.17961822,0.000018421686,0.00008073745,8.458921e-7,0.000063642176,0.00002103186,0.82014084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982617,0.000067672416,0.00030107732,0.00056399,0.0005121898,0.00029340194],"domain_scores_gemma":[0.99787676,0.00023352442,0.00048032313,0.00090026925,0.00034165962,0.00016746097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001721897,0.0003154715,0.0004874979,0.00024270052,0.00013237169,0.00005545312,0.00200013,0.00043298682,6.065979e-7],"category_scores_gemma":[0.000018057823,0.00038157924,0.00018177732,0.000030844803,0.00028461142,0.00014206805,0.0010158629,0.0007047808,0.000010913915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007075949,0.000031785257,0.0000045141805,0.00012117293,0.00009545461,0.0002244223,0.00012643245,0.06431997,0.00001756866,0.91378385,0.020146286,0.0011214527],"study_design_scores_gemma":[0.0006349478,0.00013036943,0.000081148915,0.00025699302,0.000106684995,0.00006644194,0.000010356974,0.22408311,0.00021483611,0.000050029783,0.7736922,0.0006729007],"about_ca_topic_score_codex":0.000027155482,"about_ca_topic_score_gemma":2.1612196e-7,"teacher_disagreement_score":0.91373384,"about_ca_system_score_codex":0.00016268593,"about_ca_system_score_gemma":0.00083517627,"threshold_uncertainty_score":0.9998636},"labels":[],"label_agreement":null},{"id":"W1654774199","doi":"10.1109/robot.1990.126076","title":"View planning for mobile robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Viewpoints; Delaunay triangulation; Heuristic; Computer science; Scheme (mathematics); Polygon (computer graphics); Mobile robot; Rectangle; Merge (version control); Robot; Theoretical computer science; Artificial intelligence; Combinatorics; Mathematics; Algorithm; Information retrieval; Frame (networking); Computer network","score_opus":0.06090310764429689,"score_gpt":0.28991580042715726,"score_spread":0.22901269278286035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1654774199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018629714,0.0007900117,0.988107,0.00031759206,0.00039443484,0.00024728992,8.4203725e-7,0.00036248442,0.009594047],"genre_scores_gemma":[0.019109972,0.000008622711,0.9751711,0.0006274984,0.0001007308,0.00011766345,0.0000014583396,0.000010513728,0.004852453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990875,0.000017505288,0.0001564065,0.0002955852,0.00014584367,0.00029716935],"domain_scores_gemma":[0.9993044,0.00014556224,0.000042354248,0.00038791035,0.000037564965,0.00008219489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015956482,0.0001027734,0.0001356788,0.000055510125,0.000104048944,0.00009730273,0.0006001246,0.000042145213,0.000049785307],"category_scores_gemma":[0.000029404444,0.00008896942,0.00005096863,0.00018651191,0.000013323791,0.0002649378,0.00009011822,0.000061930245,0.00023329424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029639166,0.00025208044,0.0009721598,0.00009488619,0.00006118283,0.00011089144,0.0041850973,0.21983461,0.00041131434,0.028095664,0.16474445,0.5812347],"study_design_scores_gemma":[0.00022066904,0.000106467895,0.00017128402,0.000028389415,0.0000027477902,0.000028533033,0.000014121231,0.9794292,0.0002396567,0.00049087836,0.019109234,0.00015881227],"about_ca_topic_score_codex":0.0000030045574,"about_ca_topic_score_gemma":6.267903e-8,"teacher_disagreement_score":0.7595946,"about_ca_system_score_codex":0.000018002,"about_ca_system_score_gemma":0.0000077015475,"threshold_uncertainty_score":0.36280677},"labels":[],"label_agreement":null},{"id":"W165624473","doi":"10.5220/0002205600600066","title":"HIERARCHICAL PROBABILISTIC ESTIMATION OF ROBOT REACHABLE WORKSPACE","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Workspace; Computer science; Probabilistic logic; Robot; Artificial intelligence; Computer vision; Mobile robot","score_opus":0.017021771827765344,"score_gpt":0.26274600798812736,"score_spread":0.24572423616036201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W165624473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086138345,0.000026258447,0.9901288,0.002297452,0.000099947174,0.00012635234,2.2685396e-7,0.00020591129,0.0062536392],"genre_scores_gemma":[0.22716479,7.3014667e-7,0.7722275,0.00010725109,0.000015942189,0.0000024802073,0.0000014682532,0.0000024206763,0.0004773855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902564,0.000047320627,0.000210836,0.00025399696,0.0002503554,0.00021183578],"domain_scores_gemma":[0.99920875,0.00012323182,0.00007202632,0.00046948966,0.000048641377,0.00007786239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028192907,0.00009246322,0.00015369635,0.00007851335,0.00004333947,0.00004656944,0.0004861793,0.000053585365,0.000006184621],"category_scores_gemma":[0.0001909825,0.000078919686,0.000033326185,0.00037128766,0.000034116914,0.0002523643,0.00005592253,0.000118929405,0.000027801138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045751362,0.00014246444,0.000069123824,0.000012274228,0.000005415117,0.000012300745,0.00043704652,0.62402177,0.00032806117,0.20139919,0.0009783582,0.1725894],"study_design_scores_gemma":[0.00012298988,0.00013954453,0.0046236794,0.000035697434,0.0000028547272,0.000017839655,0.0000031704587,0.9595899,0.0003943299,0.034959167,0.000018067058,0.00009272967],"about_ca_topic_score_codex":0.000012469734,"about_ca_topic_score_gemma":2.5523053e-7,"teacher_disagreement_score":0.33556813,"about_ca_system_score_codex":0.000026189771,"about_ca_system_score_gemma":0.000054289685,"threshold_uncertainty_score":0.32182515},"labels":[],"label_agreement":null},{"id":"W1659382545","doi":"10.1609/socs.v2i1.18205","title":"A Polynomial-Time Algorithm for Non-Optimal Multi-Agent Pathfinding","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Tree (set theory); Computer science; Time complexity; Variety (cybernetics); Constructive; Algorithm; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Shortest path problem; Process (computing); Combinatorics; Graph; Programming language","score_opus":0.019924925181980364,"score_gpt":0.27297429488792707,"score_spread":0.2530493697059467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1659382545","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49417338,0.00011235063,0.3657448,0.039949957,0.0767971,0.0048640906,0.00036420676,0.0008049646,0.017189158],"genre_scores_gemma":[0.44850054,0.000019901167,0.54324913,0.00045105416,0.0022800935,0.0002197212,0.000023504097,0.00009002596,0.0051660263],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734306,0.000020301457,0.00042276143,0.00060873246,0.0011694754,0.00043565748],"domain_scores_gemma":[0.9979525,0.0002604668,0.0002290147,0.00025173184,0.0011789774,0.00012731134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008462198,0.00022240203,0.0002805255,0.00011918128,0.00025220713,0.00036140808,0.0025121171,0.00012114401,0.0000105220515],"category_scores_gemma":[0.00027804362,0.00019253854,0.0002451869,0.00041163425,0.00006843813,0.00035643255,0.001054053,0.0003059406,0.00003512385],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025674526,0.0028396654,0.0023539788,0.00015672653,0.00074530457,0.000040667433,0.0038019437,0.002613235,0.89074814,0.03795072,0.029892292,0.028600585],"study_design_scores_gemma":[0.0022096802,0.00024126213,0.00047575714,0.00016523898,0.000015100938,0.00003594224,0.000050906267,0.5922694,0.40192062,0.00035603537,0.0020033491,0.0002566628],"about_ca_topic_score_codex":0.000027705255,"about_ca_topic_score_gemma":8.403142e-8,"teacher_disagreement_score":0.58965623,"about_ca_system_score_codex":0.00032438812,"about_ca_system_score_gemma":0.00020685606,"threshold_uncertainty_score":0.7851494},"labels":[],"label_agreement":null},{"id":"W1664478978","doi":"","title":"TOWARDS AUTONOMOUS LONG-RANGE NAVIGATION","year":2005,"lang":"en","type":"article","venue":"ESASP","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mars Exploration Program; Terrain; Planetary exploration; Exploration of Mars; Robotics; Mars rover; Computer science; Motion planning; Systems engineering; Mobile robot; Mission control center; Software; Range (aeronautics); Aeronautics; Artificial intelligence; Engineering; Robot; Aerospace engineering; Geography; Astrobiology; Cartography","score_opus":0.017757800713136773,"score_gpt":0.26246394356827435,"score_spread":0.2447061428551376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1664478978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01501667,0.00023409499,0.9688645,0.002263262,0.000636534,0.000111616246,0.0000017254731,0.0005218143,0.012349783],"genre_scores_gemma":[0.41103378,0.000003288414,0.5861304,0.00052189955,0.00043769504,0.0000134497595,0.000007703651,0.000011608174,0.001840212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990561,0.000032907476,0.0001556231,0.00027032115,0.0002404839,0.00024455873],"domain_scores_gemma":[0.99936366,0.000025901547,0.000055579414,0.0004277768,0.00004058717,0.00008649973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021845862,0.00010247917,0.000104396306,0.000054901873,0.00007402718,0.000094819065,0.0005796535,0.00005559294,0.00001476691],"category_scores_gemma":[0.000022547396,0.00009955717,0.00004038364,0.00016468411,0.000021706082,0.0004344405,0.00010879944,0.0001189016,0.00059890567],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029409298,0.00012483775,0.0030674452,0.000018493702,0.000024778812,0.00018162858,0.004032105,0.012695607,0.00015497382,0.019548167,0.005423789,0.9547252],"study_design_scores_gemma":[0.0005644778,0.000108412554,0.08307623,0.00006129909,0.000009214662,0.00019355988,0.000009749805,0.87999773,0.002032798,0.0009376707,0.032554276,0.0004545907],"about_ca_topic_score_codex":0.000046353976,"about_ca_topic_score_gemma":0.0000013987285,"teacher_disagreement_score":0.95427066,"about_ca_system_score_codex":0.00008252999,"about_ca_system_score_gemma":0.00006739273,"threshold_uncertainty_score":0.7697921},"labels":[],"label_agreement":null},{"id":"W1666476","doi":"","title":"Speeding up learning in real-time search via automatic state abstraction","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Abstraction; Heuristic; Path (computing); Beam search; State (computer science); Search algorithm; Bidirectional search; Domain (mathematical analysis); Artificial intelligence; Incremental heuristic search; Machine learning; Algorithm; Programming language; Mathematics","score_opus":0.1356894275948526,"score_gpt":0.37130966447218483,"score_spread":0.23562023687733222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1666476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08302116,0.000008759369,0.896251,0.003032662,0.00052353385,0.0003174663,0.000003779642,0.0005188519,0.01632275],"genre_scores_gemma":[0.9493226,0.000017478256,0.04960012,0.000088474124,0.00014020762,0.000013704621,0.000008611313,0.0000117929285,0.0007970121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720484,0.00015933922,0.0005867786,0.0005305751,0.0010882476,0.00043023351],"domain_scores_gemma":[0.9986137,0.00046508526,0.00016229117,0.00019903512,0.0004431724,0.00011670305],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014508497,0.0001933736,0.0002026783,0.00047997702,0.0001863887,0.00030515133,0.0006512057,0.00009334555,0.00022304694],"category_scores_gemma":[0.0003809694,0.00020891694,0.00004586501,0.0006734386,0.0000694149,0.0007591053,0.00009347584,0.00056874525,0.0029536246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013527175,0.00010875924,0.00008188446,0.0000073745177,0.0000073483748,0.000015195985,0.0014452316,0.34388494,0.010039772,0.04896372,0.00004593795,0.59538627],"study_design_scores_gemma":[0.000033119,0.00007973662,0.002623888,0.000072791045,0.0000011699069,0.000012055361,0.00007047093,0.96232796,0.0150286555,0.019504959,0.00003624996,0.00020896035],"about_ca_topic_score_codex":0.0001236414,"about_ca_topic_score_gemma":0.000023265033,"teacher_disagreement_score":0.8663014,"about_ca_system_score_codex":0.00036899373,"about_ca_system_score_gemma":0.00034242924,"threshold_uncertainty_score":0.9978227},"labels":[],"label_agreement":null},{"id":"W1668716869","doi":"10.1109/ccece.1993.332348","title":"A parallel algorithm for the static interference checking problem","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Parallelism (grammar); Computer science; Interference (communication); Point (geometry); Parallel algorithm; Position (finance); Algorithm; Divide and conquer algorithms; Parallel computing; Theoretical computer science; Mathematics; Computer network","score_opus":0.0551439769003474,"score_gpt":0.2678228529154396,"score_spread":0.21267887601509222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1668716869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009551871,0.00017734931,0.99387014,0.002723075,0.00026258983,0.0004220168,0.0000018602531,0.00023515135,0.0022982783],"genre_scores_gemma":[0.0060879667,0.000015300817,0.9898659,0.00060629373,0.000059009297,0.00014386182,7.3558306e-7,0.000009404475,0.003211543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988277,0.00003348585,0.00022424634,0.0003364776,0.00020183477,0.0003762814],"domain_scores_gemma":[0.998806,0.00046495692,0.000078940415,0.0005128224,0.00007138193,0.000065881024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032164616,0.00013939358,0.00013599788,0.0000465495,0.00018914732,0.00023336033,0.0012178548,0.000035823654,0.000035158297],"category_scores_gemma":[0.000044172502,0.00008774615,0.000058267167,0.00023502097,0.00004277901,0.0002855025,0.0001815392,0.00012601267,0.000108312284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.704218e-7,0.00005459118,0.000030324421,0.000013383157,0.000034470922,0.000009164798,0.0023921155,0.0034954373,0.000015638221,0.008918205,0.009778423,0.97525746],"study_design_scores_gemma":[0.00026903447,0.00008726998,0.00008435779,0.000026477735,0.000006155684,0.000026222431,0.00005611623,0.9948622,0.000051450446,0.0026822065,0.0017090691,0.00013943516],"about_ca_topic_score_codex":0.000023941313,"about_ca_topic_score_gemma":0.0000011144931,"teacher_disagreement_score":0.99136674,"about_ca_system_score_codex":0.000028763428,"about_ca_system_score_gemma":0.000018220228,"threshold_uncertainty_score":0.3578184},"labels":[],"label_agreement":null},{"id":"W16696109","doi":"","title":"Autonomous Rover Navigation in Partially Known Terrain","year":2003,"lang":"en","type":"article","venue":"Harvard Heart Letter : From Harvard Medical School","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mobile robot; Terrain; Planner; Computer science; Motion planning; Software; Path (computing); Artificial intelligence; Robot; Real-time computing; Simulation; Engineering; Computer vision; Geography; Cartography","score_opus":0.013128069120199327,"score_gpt":0.2509311660868707,"score_spread":0.2378030969666714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W16696109","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09664568,0.00007493778,0.8583869,0.03362577,0.004924934,0.0011740407,0.000038657843,0.00112085,0.004008249],"genre_scores_gemma":[0.40928525,0.000021745098,0.47185507,0.11413481,0.002754524,0.00037236355,0.00017570333,0.00013708179,0.0012634246],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945764,0.0007591467,0.000902441,0.0011194912,0.0015670729,0.0010754726],"domain_scores_gemma":[0.99696434,0.00039541867,0.00014881502,0.0011391172,0.000086524706,0.0012657921],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017280605,0.00042284894,0.0006019183,0.00019357755,0.00016313772,0.0003388773,0.0012088518,0.00044585924,0.0051146834],"category_scores_gemma":[0.002029029,0.0004054628,0.00017605937,0.00056804233,0.0001317759,0.0009512365,0.00026745896,0.0013006649,0.014852837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008188726,0.0013092884,0.14166531,0.00010815497,0.0002631658,0.005918593,0.003931159,0.002467282,0.009129112,0.0067399754,0.78932536,0.03906073],"study_design_scores_gemma":[0.0018937264,0.00010359717,0.07025002,0.00038620835,0.000016485104,0.00012341408,0.000015236052,0.056285653,0.0012047716,0.0019551192,0.86706257,0.0007031859],"about_ca_topic_score_codex":0.00047845938,"about_ca_topic_score_gemma":0.00004314688,"teacher_disagreement_score":0.3865318,"about_ca_system_score_codex":0.0003210415,"about_ca_system_score_gemma":0.00087264617,"threshold_uncertainty_score":0.9998397},"labels":[],"label_agreement":null},{"id":"W1672203963","doi":"10.1109/icinfa.2015.7279352","title":"Sensor coverage with a heterogeneous fleet of autonomous surface vessels","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Robot; Kinematics; Computer science; Real-time computing; Decomposition; Variety (cybernetics); Mobile robot; Marine engineering; Aquatic environment; Robot kinematics; Simulation; Distributed computing; Artificial intelligence; Engineering; Ecology","score_opus":0.028580841592180762,"score_gpt":0.24684247511031557,"score_spread":0.2182616335181348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1672203963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19312152,0.00004752522,0.8006987,0.00020661711,0.0001942672,0.00013848254,0.000003994271,0.00024745654,0.0053414367],"genre_scores_gemma":[0.61355054,7.6759187e-7,0.38567308,0.00010446941,0.000014396846,0.0000014976875,0.0000011236731,0.000008203662,0.00064592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880785,0.00006100973,0.00019723925,0.0003191241,0.0003488352,0.00026596806],"domain_scores_gemma":[0.9988836,0.000074690324,0.00010223627,0.0006209334,0.0001398729,0.00017867402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023890594,0.00014109786,0.00022623972,0.000043118886,0.00002870778,0.000060896575,0.0005601277,0.000048108373,0.000007577451],"category_scores_gemma":[0.00002598801,0.00010807229,0.00002975422,0.00024590094,0.000045279456,0.00020957598,0.00013659552,0.000075939126,0.0000659196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031318905,0.00017109733,0.00394047,0.000024966557,0.00008037955,0.0006002298,0.00248027,0.9840307,0.001271194,0.0027789748,0.0014293715,0.003160993],"study_design_scores_gemma":[0.0014942321,0.0009848095,0.0010265334,0.000043608736,0.000015027438,0.00066411134,0.00006573756,0.9609889,0.032628126,0.0007321033,0.000854104,0.0005027543],"about_ca_topic_score_codex":0.000093710776,"about_ca_topic_score_gemma":0.00000133478,"teacher_disagreement_score":0.42042905,"about_ca_system_score_codex":0.00004293875,"about_ca_system_score_gemma":0.00019765731,"threshold_uncertainty_score":0.44070598},"labels":[],"label_agreement":null},{"id":"W1674276221","doi":"10.1109/icinfa.2015.7279550","title":"The obstacle detection and obstacle avoidance algorithm based on 2-D lidar","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Obstacle avoidance; Obstacle; Mobile robot; Computer science; Collision avoidance; Computer vision; Lidar; Artificial intelligence; Cluster analysis; Point cloud; Robot; Algorithm; Collision; Remote sensing; Geography","score_opus":0.022139555293632148,"score_gpt":0.23991376720422314,"score_spread":0.217774211910591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1674276221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002267524,0.00016395337,0.99283314,0.0011146427,0.000799593,0.00014205983,0.0000012673275,0.00029953517,0.0023783047],"genre_scores_gemma":[0.3324723,0.000006151518,0.6646749,0.0009498011,0.00014872685,0.000031316016,9.847497e-7,0.000019079891,0.0016967378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986246,0.00011354309,0.0001654621,0.00037054953,0.00040951223,0.0003163057],"domain_scores_gemma":[0.99866056,0.00036814463,0.00006581133,0.0006139908,0.000101193305,0.00019031666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006570946,0.00014092609,0.00010966692,0.00005439613,0.00031020812,0.0003065367,0.0005145764,0.000056568726,0.0000011444907],"category_scores_gemma":[0.00019747156,0.00009715578,0.000028440521,0.0003001099,0.000068254914,0.00027678965,0.00010933445,0.00017431194,0.000083688385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011498661,0.000051311177,0.00021450502,0.0000034092468,0.0000084161775,0.000032938067,0.00044120557,0.005215758,0.00017962398,0.0012674774,0.00096008665,0.99161375],"study_design_scores_gemma":[0.00043297812,0.00021682415,0.0023653714,0.000011148629,0.0000022834101,0.000026238904,0.00006992019,0.9879572,0.0023409766,0.0010165113,0.0054110517,0.0001495383],"about_ca_topic_score_codex":0.000055763394,"about_ca_topic_score_gemma":0.0000046846035,"teacher_disagreement_score":0.99146426,"about_ca_system_score_codex":0.00007212097,"about_ca_system_score_gemma":0.00008037491,"threshold_uncertainty_score":0.39618978},"labels":[],"label_agreement":null},{"id":"W170118461","doi":"","title":"RRT-plan: a randomized algorithm for STRIPS planning","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"STRIPS; Plan (archaeology); Planner; Motion planning; Computer science; Path (computing); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Robot","score_opus":0.02141073249067149,"score_gpt":0.2597629641109467,"score_spread":0.2383522316202752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W170118461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013372385,0.00020805914,0.989523,0.00035657233,0.00065623946,0.00064197485,0.000009061712,0.00056659826,0.007904779],"genre_scores_gemma":[0.0039553745,0.0000011645722,0.9918213,0.00025245818,0.00031702846,0.0001336349,0.00002786105,0.000015390839,0.0034757904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981864,0.00012988602,0.00042165726,0.0004701151,0.00030205055,0.0004899104],"domain_scores_gemma":[0.9980742,0.0011720401,0.00013811802,0.00043442068,0.00009151322,0.00008974308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011846175,0.00020987293,0.00049359025,0.00014952531,0.00020334254,0.0002188094,0.00074187847,0.00009297745,0.000009730535],"category_scores_gemma":[0.000118162156,0.00016990367,0.00017326296,0.000258614,0.000054534914,0.00031544373,0.000106446,0.00014616377,0.000047077243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030201639,0.00064983533,0.0005630532,0.000099310375,0.0004482948,0.00056499126,0.0023155138,0.07170031,0.0006838722,0.26938668,0.21803267,0.4325353],"study_design_scores_gemma":[0.042596646,0.000041843865,0.00009458082,0.000023354556,0.0000143058805,0.00004539369,0.000017975572,0.94783837,0.00048380205,0.0062455386,0.0023449608,0.00025321028],"about_ca_topic_score_codex":0.000111769914,"about_ca_topic_score_gemma":5.207817e-7,"teacher_disagreement_score":0.8761381,"about_ca_system_score_codex":0.00003462511,"about_ca_system_score_gemma":0.000069197915,"threshold_uncertainty_score":0.692847},"labels":[],"label_agreement":null},{"id":"W1732459976","doi":"10.1109/ccece.2003.1226166","title":"Collision detection algorithm for NURBS surfaces in interactive applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Collision detection; Bounding volume; Minimum bounding box; Computer science; Algorithm; Bounding overwatch; Parametric equation; Surface (topology); Collision; Solver; Parametric statistics; Parametric surface; Function (biology); Computer graphics (images); Point (geometry); Computational geometry; Computer vision; Geometry; Mathematics; Artificial intelligence","score_opus":0.013028570685833712,"score_gpt":0.27649365818154586,"score_spread":0.2634650874957122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1732459976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017999412,0.00002365497,0.9964644,0.00027980978,0.00023395705,0.0005819615,0.000003734065,0.00016384094,0.00044875505],"genre_scores_gemma":[0.12070591,0.0000032109626,0.87878585,0.00006732615,0.000036745427,0.00025971507,0.000003722765,0.0000062797417,0.00013126245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991598,0.000020588364,0.0001776554,0.0003241522,0.00012781541,0.00018998836],"domain_scores_gemma":[0.9994047,0.00015401392,0.00006095697,0.00025258627,0.000080172416,0.00004756217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002025493,0.00009214198,0.000108403845,0.00013439074,0.000090690824,0.00007852843,0.0003533593,0.000053928383,0.0000012575948],"category_scores_gemma":[0.000025537693,0.0000864308,0.00003288632,0.00046299244,0.000017112841,0.00043639215,0.0000619899,0.000090576774,0.00003794177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072049174,0.0002526955,0.0000691449,0.000009380677,0.000014593989,0.000006906219,0.0012242282,0.10786721,0.0023520563,0.0046623275,0.00007703775,0.88345724],"study_design_scores_gemma":[0.0007234389,0.00011744276,0.001424868,0.0000240034,0.0000023701696,0.000015251696,0.00011197051,0.9590703,0.029301407,0.008305187,0.0007461359,0.00015760447],"about_ca_topic_score_codex":0.00020103941,"about_ca_topic_score_gemma":0.000032645275,"teacher_disagreement_score":0.8832996,"about_ca_system_score_codex":0.00016950858,"about_ca_system_score_gemma":0.00005366944,"threshold_uncertainty_score":0.35245457},"labels":[],"label_agreement":null},{"id":"W1740267357","doi":"","title":"Navigation of Mobile Robot Using the PSO Particle Swarm Optimization","year":2012,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Robot; Mobile robot; Computer science; Position (finance); Point (geometry); Function (biology); Artificial intelligence; Mathematical optimization; Mathematics; Algorithm","score_opus":0.06309966101050563,"score_gpt":0.34003948149228247,"score_spread":0.27693982048177684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1740267357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42770827,0.005819223,0.56593114,0.00025242308,0.00019077903,0.00006884252,2.3789556e-7,0.000007367549,0.000021703398],"genre_scores_gemma":[0.89127886,0.00046439364,0.108021975,0.00006353822,0.00016206595,0.000002277518,8.13386e-8,0.000003055874,0.0000037282934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992188,0.000033532513,0.00034262447,0.000054604825,0.00020779674,0.00014263579],"domain_scores_gemma":[0.9992488,0.00013937296,0.0004219442,0.00007711136,0.0000688444,0.000043936878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007032918,0.00006858606,0.00018369367,0.000028113953,0.000118149794,0.000011208857,0.00019423837,0.000043258133,2.478036e-7],"category_scores_gemma":[0.000034334396,0.000042435942,0.000025010419,0.00016067129,0.000084080224,0.00029286783,0.00010230292,0.00021674743,3.256259e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012284701,0.000031917934,0.0023491809,0.000027515682,0.00010569539,7.6094074e-7,0.017645115,0.9537826,0.01373349,0.001954284,0.00017124417,0.010185904],"study_design_scores_gemma":[0.0019257566,0.00033739427,0.011689771,0.00048931607,0.00034367552,0.00054940867,0.023665743,0.85645175,0.10105625,0.0027424977,0.00024927052,0.0004991632],"about_ca_topic_score_codex":5.6345726e-7,"about_ca_topic_score_gemma":4.2025134e-9,"teacher_disagreement_score":0.4635706,"about_ca_system_score_codex":0.00001798853,"about_ca_system_score_gemma":0.000017120778,"threshold_uncertainty_score":0.17304875},"labels":[],"label_agreement":null},{"id":"W1761537199","doi":"10.1109/aerocs.1993.720983","title":"Constraining the Motion of a Robot Manipulator Using the Vector Potential Approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Path (computing); Position (finance); Robot; Velocity vector; A priori and a posteriori; Motion (physics); Computer science; Work (physics); Vector field; Control theory (sociology); Robot manipulator; Motion planning; Manipulator (device); Artificial intelligence; Control engineering; Simulation; Engineering; Mathematics; Control (management); Aerospace engineering; Mechanical engineering","score_opus":0.0467846006094892,"score_gpt":0.2614181637764515,"score_spread":0.2146335631669623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1761537199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0112187965,0.00004514634,0.9861406,0.00076428876,0.00021870849,0.00014856979,6.026667e-7,0.000075046686,0.0013882616],"genre_scores_gemma":[0.61885107,3.003242e-7,0.38090256,0.00009718059,0.000108659515,0.0000020297816,4.6295594e-7,0.0000037838613,0.00003394749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989752,0.0000970913,0.00021384945,0.00020896638,0.00029297237,0.00021187846],"domain_scores_gemma":[0.99927706,0.000066354885,0.00011470342,0.00046009076,0.000048486963,0.000033296194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004810694,0.00009858857,0.00011187202,0.000036843372,0.00018702589,0.000091293026,0.00075478485,0.00004094204,0.000006173209],"category_scores_gemma":[0.000025296706,0.00005313715,0.000060884755,0.00023115656,0.000108690016,0.00021009265,0.0001498619,0.00013558815,0.0000059231606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024958588,0.00007002937,0.00034540793,0.000008357996,0.00004177754,0.000004272743,0.0022338878,0.93822384,0.004918636,0.025219278,0.000098969824,0.02883307],"study_design_scores_gemma":[0.00012741136,0.000012780378,0.003020561,0.000007985306,0.000010280934,0.00012807928,0.00019629873,0.9955177,0.00083323603,0.00005724604,0.00001795737,0.000070512295],"about_ca_topic_score_codex":0.000041250074,"about_ca_topic_score_gemma":4.4195406e-7,"teacher_disagreement_score":0.6076323,"about_ca_system_score_codex":0.000026400887,"about_ca_system_score_gemma":0.00004929393,"threshold_uncertainty_score":0.21668702},"labels":[],"label_agreement":null},{"id":"W1765178018","doi":"10.48550/arxiv.1503.07253","title":"Safe Platooning of Unmanned Aerial Vehicles via Reachability","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona; Multidisciplinary University Research Initiative; University of Pennsylvania; National Aeronautics and Space Administration; National Science Foundation","keywords":"Reachability; Liveness; Platoon; Computer science; Set (abstract data type); Controller (irrigation); Distributed computing; Control (management); Artificial intelligence","score_opus":0.09437999517206433,"score_gpt":0.2008441500944276,"score_spread":0.10646415492236327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1765178018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37239897,0.0000473277,0.6237709,0.000042302647,0.0011492986,0.00024146104,0.000020025453,0.00023845678,0.0020912753],"genre_scores_gemma":[0.9621087,0.000009908033,0.037329562,0.000019766034,0.00012049385,6.4928855e-7,0.000019080631,0.000017800532,0.0003740822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747777,0.00029481028,0.00037625016,0.0012187796,0.00020214441,0.00043023896],"domain_scores_gemma":[0.9968506,0.000198522,0.0005138801,0.0018445507,0.00033909603,0.00025336267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089287484,0.0003600326,0.0006013755,0.00026199283,0.00010555186,0.000061703446,0.002443437,0.00041548445,0.000008043819],"category_scores_gemma":[0.00015195808,0.00042401708,0.00021120282,0.0005313872,0.0002156816,0.0003510313,0.0027501744,0.0006453745,0.00005307004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012595796,0.00023579512,0.008296297,0.00018950325,0.00019027249,0.00046752003,0.0014737906,0.96206385,0.0003582163,0.023369685,0.000588249,0.00264086],"study_design_scores_gemma":[0.0007359809,0.0001149213,0.002400829,0.00016535749,0.00006060878,0.000010289638,0.00005618921,0.946899,0.0005626117,0.048348535,0.00013074785,0.0005149219],"about_ca_topic_score_codex":0.00054511544,"about_ca_topic_score_gemma":0.000009204765,"teacher_disagreement_score":0.5897097,"about_ca_system_score_codex":0.00030384536,"about_ca_system_score_gemma":0.00046357082,"threshold_uncertainty_score":0.9998212},"labels":[],"label_agreement":null},{"id":"W1771553496","doi":"10.1017/cbo9780511780929.019","title":"Bibliography","year":2010,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Artificial intelligence; Variety (cybernetics); Computer science; Field (mathematics); Mobile robot; Key (lock); Robot; State (computer science); Human–computer interaction; Mathematics","score_opus":0.021513769667505095,"score_gpt":0.19863578133076132,"score_spread":0.17712201166325622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1771553496","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003093713,0.000086803935,0.123529986,0.000026464804,0.00088925863,0.00022151475,0.000067946035,0.00051969785,0.87465525],"genre_scores_gemma":[0.000018746214,0.00025106446,0.0445062,0.00009556597,0.00016156392,4.6154932e-7,0.000017687247,0.000037663252,0.95491105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981237,0.000030439765,0.00018840018,0.00081311224,0.0004452293,0.00039913156],"domain_scores_gemma":[0.99770004,0.00009171928,0.00025673374,0.001476345,0.0001883283,0.00028682826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014366038,0.00043273636,0.00040354466,0.0057477253,0.0002491824,0.00017110047,0.0024661964,0.00061020075,0.0000021534067],"category_scores_gemma":[0.0000056398644,0.00051554904,0.00031743216,0.00021146907,0.00025728226,0.0002301151,0.0010001196,0.0010732719,0.000060306887],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059675885,0.0000068739596,0.000002904274,0.000024160423,0.000090861206,0.0008220166,0.000019750205,0.0000044930684,0.00007429991,0.8818923,0.11571598,0.001340386],"study_design_scores_gemma":[0.0003088045,0.000045840367,0.000035966823,0.00009644867,0.000067175926,0.000059858445,0.0000017450258,0.0009543169,0.00029680575,0.000060136896,0.99747825,0.00059464475],"about_ca_topic_score_codex":0.000078514786,"about_ca_topic_score_gemma":2.9999774e-7,"teacher_disagreement_score":0.8818322,"about_ca_system_score_codex":0.000027458265,"about_ca_system_score_gemma":0.0000848472,"threshold_uncertainty_score":0.99972963},"labels":[],"label_agreement":null},{"id":"W1800547807","doi":"10.1109/robot.1999.769911","title":"A neural network approach to real-time collision-free navigation of 3-DOF robots in 2D","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robot; Holonomic; Computer science; Trajectory; Artificial neural network; Workspace; Mobile robot; Cartesian coordinate system; Collision; Artificial intelligence; Control theory (sociology); Simulation; Mathematics","score_opus":0.018109136786023675,"score_gpt":0.24573530431029422,"score_spread":0.22762616752427056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1800547807","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055346195,0.000028140837,0.89804393,0.00031607837,0.00033140634,0.00047332828,0.0000016327447,0.00016176913,0.045297515],"genre_scores_gemma":[0.08535432,0.0000014466692,0.9137022,0.00011019535,0.000034777142,0.000019747504,0.000004975258,0.000009730951,0.00076264405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983312,0.00017082678,0.00036578864,0.00040445957,0.00036585188,0.00036190427],"domain_scores_gemma":[0.9987529,0.00014505646,0.000095801435,0.0008122211,0.0000743857,0.00011962777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007018727,0.0001385743,0.00025147627,0.00010848747,0.000048218415,0.000050138005,0.0008684921,0.00007279107,0.0000042380684],"category_scores_gemma":[0.00013719544,0.00012674947,0.000040588995,0.001301304,0.000022599077,0.0002456432,0.00019454706,0.000109785295,0.000032737807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040809737,0.000083056664,0.0014508755,0.00000838393,0.0000042750694,0.000010680674,0.00047288503,0.97906184,0.00027726786,0.012839635,0.005018211,0.0007688297],"study_design_scores_gemma":[0.0004111277,0.00009573427,0.008902327,0.000055940913,0.0000026426658,0.000029187624,0.000022451095,0.98767143,0.0003528972,0.0022332226,0.000041922554,0.00018113939],"about_ca_topic_score_codex":0.00017871475,"about_ca_topic_score_gemma":0.0000019077363,"teacher_disagreement_score":0.044534873,"about_ca_system_score_codex":0.00005011759,"about_ca_system_score_gemma":0.00006596878,"threshold_uncertainty_score":0.5168693},"labels":[],"label_agreement":null},{"id":"W182025308","doi":"10.3182/20050703-6-cz-1902.01391","title":"PATH TRACKING OF AN AUTONOMOUS LHD ARTICULATED VEHICLE","year":2005,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Path (computing); Tracking (education); Motion control; Fuzzy logic; Control theory (sociology); Fuzzy control system; Trajectory; Control logic; Engineering; Control (management); Motion (physics); Control system; Articulated vehicle; Control engineering; Computer science; Simulation; Automotive engineering; Computer vision; Artificial intelligence; Robot; Physics; Electrical engineering","score_opus":0.014989207325713863,"score_gpt":0.2443917227929993,"score_spread":0.22940251546728546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W182025308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9687974,0.00013365937,0.028203808,0.00055719825,0.0001700564,0.00017681486,0.0000023995171,0.0006324799,0.0013261893],"genre_scores_gemma":[0.7595163,0.000001960676,0.24009955,0.00010051259,0.000109215245,0.000008014163,0.0000013344771,0.000014899835,0.00014819203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982666,0.000012465215,0.00040095294,0.00048887846,0.00037934896,0.00045179998],"domain_scores_gemma":[0.999063,0.000028921535,0.00022490117,0.00026608983,0.00024761478,0.00016946292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039253276,0.00019179306,0.00026767,0.00013358158,0.00012499481,0.00020735561,0.0008432729,0.00010429136,0.000009749331],"category_scores_gemma":[0.0001031419,0.0001902607,0.00006645823,0.00046112973,0.000067980756,0.0016610968,0.00014741613,0.00018104688,0.00005976676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030268637,0.00096272415,0.03815072,0.00020494865,0.00012764652,0.00006949759,0.0256157,0.009364367,0.13034375,0.011434331,0.0017403419,0.7819557],"study_design_scores_gemma":[0.00035365083,0.0001801974,0.031199627,0.00006807199,0.00001193941,0.000055838318,0.000116298164,0.9312894,0.035416193,0.00051140884,0.00052573346,0.000271638],"about_ca_topic_score_codex":0.00003280851,"about_ca_topic_score_gemma":7.9480276e-7,"teacher_disagreement_score":0.921925,"about_ca_system_score_codex":0.00006915253,"about_ca_system_score_gemma":0.00006769634,"threshold_uncertainty_score":0.77586067},"labels":[],"label_agreement":null},{"id":"W182188404","doi":"10.1007/978-3-642-21538-4_4","title":"Toward Opportunistic Collaboration in Target Pursuit Problems","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Observability; Computer science; Robot; Inference; Action (physics); Relation (database); Artificial intelligence; Inference engine; Distributed computing; Data mining; Mathematics","score_opus":0.0459958402866514,"score_gpt":0.26213892721865434,"score_spread":0.21614308693200293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W182188404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009043399,0.00033455767,0.9890953,0.0009112396,0.0026293271,0.00066216715,0.00001106591,0.00020555618,0.006141767],"genre_scores_gemma":[0.027146956,0.00004444657,0.9707449,0.0009257255,0.0003291683,0.00003351375,0.000023506773,0.0000503002,0.00070146885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951682,0.00006389367,0.00078805716,0.001840572,0.0012586121,0.0008806367],"domain_scores_gemma":[0.9972841,0.00028620102,0.00043024143,0.001430477,0.00031643393,0.00025251423],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015646861,0.0006133504,0.0006782558,0.0012875368,0.00015330248,0.00067184906,0.0044541387,0.00044338583,0.000029331104],"category_scores_gemma":[0.0001839549,0.00059852697,0.00008049649,0.0012197504,0.0005324472,0.0008839752,0.0011980666,0.00095256756,0.00008987632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014171715,0.00017567944,0.00057292054,0.00023365777,0.000028789907,0.0018702474,0.011396968,0.30432782,0.00020250477,0.09175504,0.00020376257,0.58921844],"study_design_scores_gemma":[0.00030707024,0.00024001813,0.00021257508,0.00061346556,0.0000057281063,0.000103854916,3.2464558e-7,0.7234214,0.00017567135,0.2726195,0.0014752717,0.0008251451],"about_ca_topic_score_codex":0.00005665683,"about_ca_topic_score_gemma":0.000043205622,"teacher_disagreement_score":0.58839333,"about_ca_system_score_codex":0.0005136359,"about_ca_system_score_gemma":0.0015042626,"threshold_uncertainty_score":0.9996466},"labels":[],"label_agreement":null},{"id":"W1824470003","doi":"10.1109/cca.2003.1223518","title":"A novel intelligent technique for mobile robot navigation","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot navigation; Mobile robot; Computer science; Obstacle avoidance; Navigation system; Robot; Fuzzy logic; Artificial intelligence; Computer vision; Real-time computing; Robot control; Embedded system","score_opus":0.029520811212582168,"score_gpt":0.2940390352779809,"score_spread":0.26451822406539877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824470003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014858347,0.000019450545,0.99787974,0.00028077912,0.00023010255,0.00076143286,0.0000018692402,0.00029623596,0.0003818097],"genre_scores_gemma":[0.04002322,9.584952e-7,0.9590919,0.0001491144,0.000041766405,0.00049109414,0.0000064463243,0.000007685119,0.00018779421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999185,0.000006190092,0.00017396448,0.00028291187,0.00014413067,0.00020777712],"domain_scores_gemma":[0.9994165,0.00004944457,0.00005091159,0.00034369246,0.00007776734,0.00006171544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024445588,0.000096904194,0.00009747188,0.000060930633,0.00007082169,0.000066123575,0.00048860424,0.00005898526,0.0000021169587],"category_scores_gemma":[0.000023273375,0.00008619385,0.000049802602,0.0002219482,0.000018167284,0.00021922684,0.00008843232,0.00007207884,0.000035677964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007105035,0.0004960566,0.00003759568,0.000059588398,0.000029804674,0.000016510488,0.0017294216,0.55300653,0.22819628,0.16092286,0.00042521438,0.05507302],"study_design_scores_gemma":[0.0007265046,0.0005454222,0.00014088134,0.0001451179,0.0000068459144,0.00021006943,0.00006125579,0.24637921,0.72561806,0.024288166,0.0014441124,0.0004343716],"about_ca_topic_score_codex":0.000050844792,"about_ca_topic_score_gemma":6.4002694e-7,"teacher_disagreement_score":0.49742177,"about_ca_system_score_codex":0.0000907381,"about_ca_system_score_gemma":0.0000720567,"threshold_uncertainty_score":0.35148835},"labels":[],"label_agreement":null},{"id":"W1834712474","doi":"10.1109/acc.2005.1470156","title":"An object transportation system with multiple robots and machine learning","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Research Council Canada","keywords":"Reinforcement learning; Computer science; Robot; Task (project management); Artificial intelligence; Object (grammar); Genetic algorithm; Probabilistic logic; Scheme (mathematics); Machine learning; Architecture; Engineering; Systems engineering","score_opus":0.012516521899319132,"score_gpt":0.21945926278863737,"score_spread":0.20694274088931824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834712474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04993322,0.000060259114,0.94893026,0.000109541936,0.00003731237,0.000092141636,0.0000012346608,0.00048417872,0.0003518583],"genre_scores_gemma":[0.61621284,0.0000010098449,0.38363773,0.000025164212,0.000022722827,0.0000035368253,0.000007717907,0.000005297476,0.00008395201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991924,0.000047617392,0.00012734348,0.00028930706,0.00017334164,0.00016994585],"domain_scores_gemma":[0.9995781,0.00004625324,0.000050654602,0.00019994366,0.00003154609,0.00009345907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001723082,0.0001038994,0.00011433013,0.00005858502,0.00011674387,0.00009089063,0.00019567057,0.000032478783,0.00000188363],"category_scores_gemma":[0.0000052510145,0.00007909703,0.000011438912,0.00014320742,0.000015244064,0.00054092566,0.000007018029,0.00010513514,0.000009933138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012498932,0.00004559704,0.08328446,0.00003445765,0.00001708604,0.00005710651,0.002889295,0.8664401,0.0006093257,0.002679483,0.00001314203,0.043917406],"study_design_scores_gemma":[0.0003836366,0.00014678443,0.05515132,0.000027307631,0.0000042372008,0.000047630154,0.000089707566,0.9435321,0.00043549377,0.0000016737296,0.000059431324,0.000120659795],"about_ca_topic_score_codex":0.00028515852,"about_ca_topic_score_gemma":0.00017705877,"teacher_disagreement_score":0.56627965,"about_ca_system_score_codex":0.000023235767,"about_ca_system_score_gemma":0.000018734538,"threshold_uncertainty_score":0.32254833},"labels":[],"label_agreement":null},{"id":"W184259923","doi":"10.3233/978-1-58603-891-5-931","title":"Automatic Animation Generation of a Teleoperated Robot Arm","year":2008,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Université de Sherbrooke","funders":"","keywords":"Teleoperation; Animation; Computer science; Robotic arm; Computer graphics (images); Robot; Computer vision; Artificial intelligence","score_opus":0.07148017689908549,"score_gpt":0.27653081743100943,"score_spread":0.20505064053192396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W184259923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044803615,0.00081732176,0.98986423,0.00014978097,0.00024034533,0.0006153079,0.000012030602,0.00008483617,0.008171352],"genre_scores_gemma":[0.018282492,0.0019675395,0.9704994,0.00010809321,0.00036788342,0.0003058616,0.0001626301,0.00004790411,0.008258229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839413,0.00002287594,0.0006984869,0.00047301297,0.00023480272,0.00017668019],"domain_scores_gemma":[0.9989943,0.000041985844,0.00029965537,0.00046726785,0.0001325413,0.000064244545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017618254,0.00021985323,0.0003518554,0.00036382923,0.00013399366,0.00006575726,0.00043724576,0.0002143525,0.000014372475],"category_scores_gemma":[0.000020732623,0.00023773735,0.0000523909,0.00023473133,0.00016137755,0.00019494664,0.000073546544,0.00022736256,0.000038986134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019158674,0.00008842266,0.000020037025,0.000041400857,0.000028150678,0.0000061598384,0.0007802707,0.00878886,0.0002517051,0.18613668,0.0018258828,0.8020305],"study_design_scores_gemma":[0.000021405567,0.00006217037,0.000027333597,0.00009671153,0.000016217784,0.000014318903,0.000035390265,0.94991136,0.001995068,0.044442464,0.0030988003,0.00027879322],"about_ca_topic_score_codex":0.000016387823,"about_ca_topic_score_gemma":0.0000063879907,"teacher_disagreement_score":0.9411225,"about_ca_system_score_codex":0.00006984857,"about_ca_system_score_gemma":0.00011477244,"threshold_uncertainty_score":0.9694648},"labels":[],"label_agreement":null},{"id":"W1856294922","doi":"10.1109/wcica.2002.1020769","title":"Sensor fusion in mobile robot: some perspectives","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Mobile robot; Computer science; Sensor fusion; Fusion; Robot; Artificial intelligence; Computer vision","score_opus":0.014539043650604372,"score_gpt":0.25483167980105975,"score_spread":0.24029263615045537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1856294922","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056103624,0.0011001151,0.91930294,0.0004933018,0.0006023564,0.00033926085,5.953921e-7,0.00041325242,0.021644564],"genre_scores_gemma":[0.30550823,0.00003346097,0.69159615,0.00017547679,0.000039315233,0.000022607972,4.143009e-7,0.000009021508,0.0026152986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989306,0.00009375914,0.00014345755,0.00036931213,0.00018803822,0.0002748608],"domain_scores_gemma":[0.99940497,0.0000760881,0.00003122339,0.00038826317,0.000032786113,0.00006664729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026831153,0.00010550789,0.00012926964,0.00013372838,0.000053459295,0.000060952403,0.0003297973,0.00004981689,0.000039444945],"category_scores_gemma":[0.000068757385,0.00009167219,0.000033037737,0.0003449913,0.000024799221,0.00037434904,0.00007436144,0.00012068078,0.0001780617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009981942,0.0012940838,0.0143133355,0.00003438684,0.00004020254,0.0008975012,0.042194955,0.19592835,0.016208038,0.68389153,0.0027303954,0.04245723],"study_design_scores_gemma":[0.0030464577,0.0007144725,0.049206067,0.000132763,0.000009384299,0.00042099587,0.011960128,0.88637704,0.01857865,0.020559285,0.0071797525,0.0018149961],"about_ca_topic_score_codex":0.000027067466,"about_ca_topic_score_gemma":0.0000013726216,"teacher_disagreement_score":0.6904487,"about_ca_system_score_codex":0.000065099215,"about_ca_system_score_gemma":0.00004718418,"threshold_uncertainty_score":0.37382832},"labels":[],"label_agreement":null},{"id":"W1856364322","doi":"10.1109/robot.1999.770000","title":"The path planning of mobile manipulator with genetic-fuzzy controller in flexible manufacturing cell","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Computer science; Mobile manipulator; Fuzzy logic; Path (computing); Controller (irrigation); Genetic algorithm; Process (computing); Task (project management); Object (grammar); Mobile robot; Collision; Computation; Control engineering; Control theory (sociology); Control (management); Robot; Artificial intelligence; Engineering; Algorithm","score_opus":0.011648041850308247,"score_gpt":0.2261819374020619,"score_spread":0.21453389555175367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1856364322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27211976,0.0007899871,0.70264095,0.000036610538,0.00023523819,0.0005294799,8.154398e-7,0.00013841079,0.023508715],"genre_scores_gemma":[0.7796776,0.000005809989,0.21947563,0.000050264687,0.000011308217,0.000031377593,2.1382688e-7,0.000010674176,0.0007370682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985815,0.00008945777,0.00031595211,0.0003182799,0.00029762092,0.0003971442],"domain_scores_gemma":[0.9990735,0.0002179959,0.00012809769,0.00047797666,0.000031965203,0.000070468726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039361606,0.00015868344,0.00022187125,0.00008235148,0.000109693865,0.00008496342,0.00058075396,0.000049777507,0.0000047948797],"category_scores_gemma":[0.000013801612,0.00009764435,0.000034876404,0.00018948992,0.00004154502,0.00012816674,0.00007190808,0.00014798008,0.000014193948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046989844,0.00019857952,0.039852086,0.000051676565,0.000050931885,0.00023402555,0.0023380795,0.9379993,0.0014917713,0.009047609,0.000577637,0.008111322],"study_design_scores_gemma":[0.008432952,0.001369961,0.13091065,0.0003558684,0.00003429907,0.00024428227,0.0018578587,0.5566684,0.28658155,0.0063685807,0.005670025,0.0015056051],"about_ca_topic_score_codex":0.00004554984,"about_ca_topic_score_gemma":0.0000014633408,"teacher_disagreement_score":0.50755787,"about_ca_system_score_codex":0.00003151767,"about_ca_system_score_gemma":0.000064322085,"threshold_uncertainty_score":0.3981821},"labels":[],"label_agreement":null},{"id":"W1860476718","doi":"10.1109/robot.1998.677414","title":"A neural network approach to real-time trajectory generation [mobile robots]","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Trajectory; Artificial neural network; Computer science; Mobile robot; Robot; Tracking (education); Activation function; Stability (learning theory); Control theory (sociology); Artificial intelligence; Machine learning; Control (management)","score_opus":0.039954540613604136,"score_gpt":0.23597721733450308,"score_spread":0.19602267672089896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1860476718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056006475,0.00007973559,0.94048184,0.00028991536,0.0005384251,0.00037673186,6.2724087e-7,0.0006251522,0.052006904],"genre_scores_gemma":[0.035013206,0.0000062885256,0.9538298,0.0006516714,0.0007710651,0.00009715556,0.0000074025397,0.000017829538,0.0096055325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837327,0.00010730389,0.00023397674,0.0005337137,0.00029553357,0.0004561949],"domain_scores_gemma":[0.9990433,0.00004763658,0.00004941436,0.00062537176,0.00004354146,0.00019075925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025450735,0.00017071652,0.00018794497,0.00007741453,0.0001515673,0.00017360965,0.0006682841,0.000070757065,0.00006225763],"category_scores_gemma":[0.0000131691895,0.00015477202,0.000057468027,0.0005595843,0.000016962467,0.00031723065,0.00013211124,0.00011153316,0.0006372109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.008284e-7,0.00006550157,0.00004944618,0.0000020421803,0.0000066307784,0.000007844103,0.0005192004,0.9003747,0.0012685467,0.0006924794,0.08830281,0.008710195],"study_design_scores_gemma":[0.00012088093,0.00010821662,0.00043883602,0.0000036056429,0.0000033633676,0.00003560492,0.0000051054963,0.9985701,0.0000730366,0.000021469088,0.0004131275,0.00020667755],"about_ca_topic_score_codex":0.00003551689,"about_ca_topic_score_gemma":7.831632e-7,"teacher_disagreement_score":0.098195374,"about_ca_system_score_codex":0.000049001137,"about_ca_system_score_gemma":0.0000142628205,"threshold_uncertainty_score":0.819027},"labels":[],"label_agreement":null},{"id":"W1864797539","doi":"10.1109/robot.1985.1087248","title":"Algorithmic motion planning in robotics: Coordinated motion of several disks amidst polygonal obstacles","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Bounded function; Robotics; Motion (physics); Artificial intelligence; Set (abstract data type); Algorithm; Computer science; Combinatorics; Computer vision; Computer graphics (images); Mathematics; Robot; Mathematical analysis; Programming language","score_opus":0.02099709154615599,"score_gpt":0.2655979054251975,"score_spread":0.24460081387904153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1864797539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037577424,0.00010176474,0.96014917,0.00068986876,0.00031273178,0.00017933342,0.0000049228356,0.0002788183,0.0007059366],"genre_scores_gemma":[0.65363884,0.0000022060235,0.34598187,0.00005780459,0.000075725155,0.000005055294,0.000015630627,0.000010391206,0.00021246201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802786,0.000112201524,0.00054231205,0.0004675046,0.00040087407,0.00044922187],"domain_scores_gemma":[0.99909776,0.000113035276,0.0001823312,0.00040565315,0.0000971632,0.00010402633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004098734,0.00022060516,0.000301545,0.00036122944,0.00007468258,0.00007604266,0.00063624466,0.00013221851,0.0000142282815],"category_scores_gemma":[0.000060194066,0.00021696136,0.00006836525,0.0006260229,0.000063531865,0.0008543681,0.00017134173,0.00025638664,0.00003638171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008341261,0.00018676261,0.008797611,0.000017240185,0.000018162784,0.000034290402,0.0012576989,0.9376122,0.0007524427,0.004191595,0.00016703815,0.046956617],"study_design_scores_gemma":[0.0006294476,0.000058489713,0.07538927,0.00007741247,0.0000049115342,0.00003321174,0.00006412305,0.92166674,0.0013718812,0.00046591897,0.000016188038,0.00022239253],"about_ca_topic_score_codex":0.00021545037,"about_ca_topic_score_gemma":0.000008649171,"teacher_disagreement_score":0.61606145,"about_ca_system_score_codex":0.00020843127,"about_ca_system_score_gemma":0.000050804607,"threshold_uncertainty_score":0.88474274},"labels":[],"label_agreement":null},{"id":"W186670194","doi":"10.1007/978-1-84800-155-8_11","title":"Getting Mobile Autonomous Robots to Form a Prescribed Geometric Arrangement","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Rendezvous; Task (project management); Mobile robot; Supervisor; Computer science; Polygon (computer graphics); Point (geometry); Antenna (radio); Control engineering; Control theory (sociology); Control (management); Engineering; Artificial intelligence; Mathematics; Telecommunications; Geometry","score_opus":0.021709905039516572,"score_gpt":0.26253179372733987,"score_spread":0.24082188868782328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W186670194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004452773,0.0006970853,0.95180476,0.0007265813,0.00051018107,0.00084897934,0.00001102751,0.0001141582,0.04524271],"genre_scores_gemma":[0.25138533,0.00021523914,0.7209508,0.02390237,0.00068079896,0.0003113613,0.00004523522,0.000043564585,0.0024653152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975567,0.000016894568,0.0007199904,0.00041458,0.00078368216,0.0005081923],"domain_scores_gemma":[0.9984142,0.00056058407,0.00038916848,0.0003258507,0.000156213,0.00015398885],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015826024,0.00032488327,0.00041309762,0.0017209956,0.0002651889,0.00062642404,0.000898672,0.0002330683,0.0000143255165],"category_scores_gemma":[0.00035141417,0.00026456793,0.00006820264,0.0006318673,0.00011533284,0.002119981,0.00021404398,0.00034483208,0.00006148115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008333906,0.000006657626,0.000067874076,0.000035042045,0.0000150534115,0.000005876077,0.002103286,0.12893695,0.00000831139,0.005076566,0.00006680646,0.8636693],"study_design_scores_gemma":[0.0014911252,0.00094560505,0.0015600054,0.00041219158,0.00002710929,0.00006659136,0.000026449345,0.9135005,0.0001053428,0.012409541,0.06830321,0.0011523676],"about_ca_topic_score_codex":0.00005440377,"about_ca_topic_score_gemma":0.000014521301,"teacher_disagreement_score":0.8625169,"about_ca_system_score_codex":0.00013526107,"about_ca_system_score_gemma":0.00015895942,"threshold_uncertainty_score":0.9999806},"labels":[],"label_agreement":null},{"id":"W1879345365","doi":"10.1109/robot.1999.769945","title":"Proportional navigation guidance in robot trajectory planning for intercepting moving objects","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Trajectory; Computer vision; Robot; Artificial intelligence; Scheme (mathematics); Motion planning; Tracking (education); Interception; Object (grammar); Mathematics","score_opus":0.03491938510006079,"score_gpt":0.2977228008135337,"score_spread":0.2628034157134729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879345365","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03200782,0.00008552061,0.96410877,0.000060358656,0.0004118944,0.00027556033,5.7640665e-7,0.00017520114,0.0028743276],"genre_scores_gemma":[0.4721005,1.3497475e-7,0.52757096,0.00007925849,0.00003249321,0.000040260256,0.0000035061232,0.000007190518,0.00016569447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985486,0.000074442134,0.00037117285,0.00041805411,0.00023037144,0.00035730744],"domain_scores_gemma":[0.99932903,0.00017922651,0.00013036908,0.00023083646,0.00007174337,0.000058788344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008558842,0.00013723894,0.00015378762,0.00013701846,0.00010848736,0.0000917625,0.00034257254,0.000064095424,0.000005702443],"category_scores_gemma":[0.0003029633,0.00013641779,0.000046682617,0.00026620328,0.000021216752,0.00050938316,0.000044147993,0.00015082609,0.000009340663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002422692,0.0002781514,0.08335254,0.0002510487,0.000041670213,0.00018446159,0.017304406,0.78484726,0.024302648,0.052485183,0.0010195165,0.03590886],"study_design_scores_gemma":[0.00068904046,0.0001039391,0.032489907,0.00043081597,0.0000029292592,0.00008011977,0.00040890824,0.9568795,0.005572649,0.0028604537,0.00010871512,0.00037302132],"about_ca_topic_score_codex":0.000015110775,"about_ca_topic_score_gemma":0.0000018242487,"teacher_disagreement_score":0.44009268,"about_ca_system_score_codex":0.00012429206,"about_ca_system_score_gemma":0.0001293025,"threshold_uncertainty_score":0.5562956},"labels":[],"label_agreement":null},{"id":"W1884799660","doi":"10.1016/j.ifacol.2015.08.147","title":"Adaptive Charged System Search Approach to Path Planning for Multiple Mobile Robots","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Education and Research, Romania; Autoritatea Natională pentru Cercetare Stiintifică; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service; Michigan Economic Development Corporation","keywords":"Particle swarm optimization; Motion planning; Computer science; Mobile robot; Mathematical optimization; Fitness function; Gravitational search algorithm; Holonomic; Algorithm; Path (computing); Robot; Mathematics; Artificial intelligence; Genetic algorithm","score_opus":0.09886589676885361,"score_gpt":0.30244612045851604,"score_spread":0.20358022368966244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1884799660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008621082,0.00027835392,0.9866088,0.00019371456,0.0007799393,0.0015562193,0.000096078365,0.00061931624,0.0012464818],"genre_scores_gemma":[0.110432856,5.972293e-7,0.88788944,0.0001793337,0.00043955373,0.00040519072,0.000067894835,0.000042065396,0.00054306467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969121,0.00014894757,0.0003961389,0.00095974957,0.0007150774,0.0008679816],"domain_scores_gemma":[0.99783987,0.00028544854,0.00011978755,0.0007709685,0.00034629807,0.0006376004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011134372,0.00035529683,0.00049920793,0.00020822219,0.00023055133,0.00016077889,0.0012551618,0.00015568185,8.002349e-7],"category_scores_gemma":[0.00020047408,0.00032235694,0.000120213816,0.00058845436,0.000038715065,0.00035496606,0.00033336447,0.0002863126,0.000120792625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019008075,0.0004890825,0.0011637677,0.00018815415,0.00012972797,0.00011453625,0.024739329,0.95690244,0.001564889,0.0018003763,0.00057010114,0.012147517],"study_design_scores_gemma":[0.0013607024,0.00057649706,0.0003413167,0.00014427958,0.000012418859,0.00006506982,0.0036517153,0.9927955,0.00032122992,0.000009784201,0.00030626,0.00041517126],"about_ca_topic_score_codex":0.00008325024,"about_ca_topic_score_gemma":7.48788e-7,"teacher_disagreement_score":0.101811774,"about_ca_system_score_codex":0.00029328716,"about_ca_system_score_gemma":0.00023592735,"threshold_uncertainty_score":0.9999229},"labels":[],"label_agreement":null},{"id":"W1886178236","doi":"10.1109/iros.1993.583853","title":"The sequential framework for developing motion planners for many degree of freedom manipulators: Experimental results","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Backtracking; Serial manipulator; Computer science; Degrees of freedom (physics and chemistry); Motion planning; Variety (cybernetics); Motion (physics); Robot; Manipulator (device); Mechanism (biology); Trajectory; Exploit; Planner; Control theory (sociology); Mathematical optimization; Artificial intelligence; Parallel manipulator; Mathematics; Algorithm; Control (management); Physics","score_opus":0.1274882134796472,"score_gpt":0.31628459175781,"score_spread":0.18879637827816279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1886178236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013635327,0.0001351997,0.9953458,0.0008663584,0.0012114751,0.0005278706,0.00001424306,0.00011938322,0.00041617523],"genre_scores_gemma":[0.13584958,0.0000030750602,0.8635705,0.000060057653,0.00017318667,0.000073385796,0.000007329395,0.000012088644,0.00025080034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986162,0.000032280648,0.0003981025,0.00035324227,0.00024594698,0.0003541951],"domain_scores_gemma":[0.998654,0.0005767284,0.00018325039,0.00044884533,0.00008635507,0.000050804065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003957898,0.00014621562,0.00015698718,0.000060158523,0.00029059162,0.000114120056,0.000771896,0.00010622306,0.000002031559],"category_scores_gemma":[0.00021840673,0.00010989355,0.00009540124,0.00015581092,0.00005864882,0.00022307195,0.00011797003,0.00007948801,0.0000071910335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014326218,0.00014845957,0.0001592047,0.000060702103,0.00014473975,0.0000086104155,0.0042291298,0.014418888,0.0011457546,0.93849665,0.017039737,0.02400486],"study_design_scores_gemma":[0.00087066076,0.00018921436,0.00018173325,0.00006920041,0.0000066353373,0.000011698973,0.00021611684,0.9780767,0.014619011,0.0046559395,0.0009017596,0.0002013058],"about_ca_topic_score_codex":0.000019705261,"about_ca_topic_score_gemma":0.000001284337,"teacher_disagreement_score":0.96365786,"about_ca_system_score_codex":0.00007875554,"about_ca_system_score_gemma":0.000022236381,"threshold_uncertainty_score":0.4481329},"labels":[],"label_agreement":null},{"id":"W1889109853","doi":"10.1023/a:1008920117364","title":"Real-time Collision-free Path Planning of Robot Manipulators using Neural Network Approaches","year":2000,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion planning; Artificial neural network; Robot; Trajectory; Path (computing); Mobile robot; State space; Artificial intelligence; Mathematics","score_opus":0.05171565915996137,"score_gpt":0.25414251104472857,"score_spread":0.2024268518847672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1889109853","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61511314,0.0006590518,0.37036937,0.00030445596,0.0012370269,0.0008889239,0.000015676556,0.0012545533,0.010157825],"genre_scores_gemma":[0.2679013,0.000009014833,0.73099506,0.00007468305,0.00028333283,0.000012693445,0.000015623236,0.000056461657,0.0006518457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969264,0.00018533612,0.00072382455,0.0007872218,0.0005157987,0.00086138997],"domain_scores_gemma":[0.9976474,0.00021175767,0.000329774,0.0015307097,0.000056415567,0.00022394951],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055046246,0.00039664953,0.0006198769,0.00014592287,0.00031520112,0.00015332608,0.0019052513,0.00019539386,0.000042898566],"category_scores_gemma":[0.000037581747,0.00039702264,0.00016010256,0.0007920582,0.00010399866,0.0005741663,0.00043701506,0.00026830283,0.00006498597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010769552,0.0000599882,0.001613855,0.00001443987,0.00003621713,0.000107177926,0.00060496136,0.9889811,0.00021097307,0.00052017375,0.0006729563,0.007167349],"study_design_scores_gemma":[0.00042529008,0.00011902446,0.011728181,0.00013133313,0.000026397493,0.00014085218,0.000016574695,0.98616654,0.00017162625,0.00059582904,0.00006118035,0.00041718545],"about_ca_topic_score_codex":0.00023374861,"about_ca_topic_score_gemma":5.192884e-7,"teacher_disagreement_score":0.36062565,"about_ca_system_score_codex":0.00013686238,"about_ca_system_score_gemma":0.00018696263,"threshold_uncertainty_score":0.9998482},"labels":[],"label_agreement":null},{"id":"W1895843046","doi":"10.1109/robot.1994.351079","title":"Integration of a robust trajectory planner with a feedforward neural controller for robotic manipulators","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planner; Trajectory; Controller (irrigation); Robot manipulator; Computer science; Artificial neural network; Artificial intelligence; Control engineering; Robot; Control theory (sociology); Robotics; Robotic arm; Engineering; Control (management)","score_opus":0.053136774090917556,"score_gpt":0.23230290419406094,"score_spread":0.1791661301031434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1895843046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003303735,0.00006207611,0.99463046,0.00036899184,0.00026680832,0.00040630394,0.0000014237728,0.00014671481,0.0008135163],"genre_scores_gemma":[0.6593891,7.22176e-7,0.33962348,0.00012502329,0.000048743026,0.00003306223,0.0000023915156,0.000011381556,0.0007661167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882805,0.000036712925,0.00027521,0.0003197954,0.0002505609,0.000289673],"domain_scores_gemma":[0.9992372,0.0001455347,0.00012823967,0.0002957381,0.000109335335,0.00008398618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014853312,0.0001717929,0.00029254344,0.00014158279,0.000064277425,0.000059531732,0.00040914305,0.00006350349,0.0000129039],"category_scores_gemma":[0.00003729258,0.00011852313,0.00008135484,0.00025014934,0.000042759126,0.00032833504,0.000029179137,0.00009853032,0.000009927091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006560974,0.00018139997,0.00088483136,0.000038760783,0.00009980193,0.000018388728,0.0015231207,0.9688664,0.0007120759,0.008248308,0.006636842,0.012724475],"study_design_scores_gemma":[0.0011532003,0.00041600614,0.0023913106,0.000030990417,0.000019330497,0.00003780981,0.000052097123,0.99536055,0.00026680002,0.00008425193,0.000023383016,0.00016429565],"about_ca_topic_score_codex":0.000029222765,"about_ca_topic_score_gemma":0.000007242868,"teacher_disagreement_score":0.6560854,"about_ca_system_score_codex":0.000032664302,"about_ca_system_score_gemma":0.000015192765,"threshold_uncertainty_score":0.4833233},"labels":[],"label_agreement":null},{"id":"W1896018525","doi":"10.1109/robot.1999.770452","title":"A mobile manipulator","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Desk; Task (project management); Computer science; Manipulator (device); Mobile manipulator; Mobile robot; Variety (cybernetics); Orientation (vector space); Robot; Constant (computer programming); Simulation; Control engineering; Human–computer interaction; Computer vision; Artificial intelligence; Engineering; Mathematics","score_opus":0.01951175078243457,"score_gpt":0.24915099681555458,"score_spread":0.22963924603312003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1896018525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001215311,0.000062223706,0.9270454,0.000021241452,0.00029778254,0.00006272927,8.262382e-8,0.00023490662,0.07106031],"genre_scores_gemma":[0.17724846,0.0000011326131,0.81786996,0.00024805026,0.0000129170785,0.000013340275,1.6424116e-7,0.0000038126504,0.0046021757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994308,0.000026670346,0.00007906194,0.00017977877,0.00011961828,0.00016407791],"domain_scores_gemma":[0.9995191,0.00002075136,0.000016574264,0.00036401046,0.000017202156,0.00006232309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012981206,0.00005660523,0.00005995621,0.000032749726,0.000039103696,0.000051571507,0.00032080908,0.000023457249,0.000042378895],"category_scores_gemma":[0.000022434215,0.00004780116,0.000021628031,0.00016615083,0.000008239466,0.00014658159,0.000037969756,0.00004391938,0.0004786522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.7612733e-7,0.000103956285,0.0030210651,0.000006928571,0.000016407208,0.00011771391,0.0008364133,0.0038616199,0.0005656068,0.9600989,0.0130892955,0.018281609],"study_design_scores_gemma":[0.0010878368,0.0004956878,0.0044214143,0.000026089203,0.000008141957,0.0005784839,0.00013775533,0.73502964,0.018610796,0.028543605,0.21003671,0.0010238637],"about_ca_topic_score_codex":0.000003953257,"about_ca_topic_score_gemma":7.924682e-8,"teacher_disagreement_score":0.93155533,"about_ca_system_score_codex":0.0000128891,"about_ca_system_score_gemma":0.000026280999,"threshold_uncertainty_score":0.6152265},"labels":[],"label_agreement":null},{"id":"W1896795971","doi":"10.1002/rob.21474","title":"Planning using a Network of Reusable Paths: A Physical Embodiment of a Rapidly Exploring Random Tree","year":2013,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mobile robot; Computer science; Planner; Robot; Real-time computing; Motion planning; Tree (set theory); Global Positioning System; Reuse; Distributed computing; Artificial intelligence; Plan (archaeology); Human–computer interaction; Simulation; Engineering; Geography","score_opus":0.06095458054614089,"score_gpt":0.2809059399825026,"score_spread":0.21995135943636168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1896795971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11884293,0.00041253993,0.8792189,0.00033404803,0.0008232115,0.00012569476,2.6163747e-7,0.000013531827,0.00022891146],"genre_scores_gemma":[0.465651,0.000020606027,0.5338893,0.000058430196,0.00035601325,0.0000017916999,1.1157442e-7,0.000008862937,0.0000138841315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981092,0.00010707692,0.00080814626,0.00014825488,0.00050617725,0.00032113763],"domain_scores_gemma":[0.9976095,0.0006435279,0.0009248223,0.00036563165,0.00033270638,0.00012383338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055953907,0.0001572659,0.00063327973,0.00011762458,0.000059583537,0.000049960312,0.0006953113,0.000072756586,0.000005149166],"category_scores_gemma":[0.00022115238,0.00012938418,0.0002172199,0.00032768972,0.000033544464,0.000589241,0.00017242656,0.0003453463,0.0000017362072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039315957,0.00013235783,0.0018643201,0.00004929108,0.00011033533,0.000060527298,0.0022324314,0.9856826,0.0028675762,0.0004467957,0.0007656702,0.005748744],"study_design_scores_gemma":[0.0017032212,0.00084317074,0.0011599897,0.0011115626,0.000078963305,0.00014295646,0.00025016314,0.9854546,0.006271477,0.0027643421,0.000019250345,0.0002003126],"about_ca_topic_score_codex":0.000026850732,"about_ca_topic_score_gemma":1.3372086e-7,"teacher_disagreement_score":0.34680808,"about_ca_system_score_codex":0.000039877512,"about_ca_system_score_gemma":0.00013285615,"threshold_uncertainty_score":0.5276134},"labels":[],"label_agreement":null},{"id":"W1901839089","doi":"10.1109/pacrim.1999.799612","title":"Neural network application in robot motion planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robot; Artificial neural network; Motion planning; Holonomic; Computer science; Mobile robot; Nonholonomic system; Obstacle avoidance; Artificial intelligence; Robot kinematics; Robot control; Motion (physics); Control engineering; Engineering","score_opus":0.02375388967921627,"score_gpt":0.2623012999357578,"score_spread":0.23854741025654153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901839089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034440265,0.00007160716,0.9895054,0.00017489037,0.00028183724,0.00011858501,5.133522e-8,0.00017779185,0.0062257852],"genre_scores_gemma":[0.5961353,5.0548726e-7,0.40350938,0.00018836446,0.000042495405,0.000015390717,0.0000016610561,0.0000043933933,0.00010249745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904627,0.000080034406,0.00017256306,0.00028351037,0.00014181102,0.00027584413],"domain_scores_gemma":[0.9995083,0.000058739675,0.000049852115,0.00031582313,0.000018035686,0.000049232505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037196308,0.00008583602,0.00009421279,0.00006396261,0.000057777394,0.000058416448,0.00030379448,0.000047321148,0.0000027331148],"category_scores_gemma":[0.000030754138,0.000083259416,0.000018474022,0.0004957239,0.000009607779,0.00027315755,0.00003982878,0.00011692057,0.00004518159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.043236e-7,0.000015891645,0.027441021,0.0000016837357,0.0000012746489,0.000009567081,0.00013256187,0.93652755,0.000057674617,0.022607448,0.00024784383,0.012957008],"study_design_scores_gemma":[0.00014453863,0.000014628703,0.044838704,0.000008802276,7.3055617e-7,0.000020232383,0.000009421363,0.9519647,0.00009136909,0.0025777458,0.00022622042,0.00010295899],"about_ca_topic_score_codex":0.00002607583,"about_ca_topic_score_gemma":0.000001698278,"teacher_disagreement_score":0.5926913,"about_ca_system_score_codex":0.00003571507,"about_ca_system_score_gemma":0.000015853591,"threshold_uncertainty_score":0.33952203},"labels":[],"label_agreement":null},{"id":"W1903400936","doi":"10.5555/1999416.1999495","title":"Multi-hop shortest path computation for rotary wing search and rescue","year":2010,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Dijkstra's algorithm; Shortest path problem; Computer science; Measure (data warehouse); Component (thermodynamics); Heuristic; Position (finance); Computation; Asset (computer security); Mathematical optimization; Graph; Computer security; Algorithm; Data mining; Mathematics; Artificial intelligence; Theoretical computer science; Business; Finance","score_opus":0.08654303871478089,"score_gpt":0.33829699668379,"score_spread":0.2517539579690091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1903400936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08859901,0.00001921421,0.9092166,0.00034962376,0.0010213398,0.0005166212,0.000009379369,0.00024730276,0.00002095714],"genre_scores_gemma":[0.5288367,9.4794126e-7,0.4708251,0.0001338935,0.00014102242,0.00001160333,0.000023500454,0.000010587311,0.000016610367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980602,0.000100708865,0.00037747467,0.0007297139,0.00032981686,0.00040206156],"domain_scores_gemma":[0.9982028,0.00057563314,0.00006721709,0.00046146696,0.0005082647,0.00018459403],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000535228,0.00024355139,0.00025602986,0.0001809379,0.0002923622,0.00045452928,0.000560953,0.00013441074,0.000004093323],"category_scores_gemma":[0.000066834255,0.0002496106,0.00005708996,0.00024811958,0.0000854085,0.00073122646,0.00029770532,0.00034357255,0.000015365533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008825432,0.00006663771,0.0058947476,0.000025007837,0.000021085454,0.0000099206945,0.0014189722,0.8200785,0.0012999158,0.002019815,0.00005790681,0.16909866],"study_design_scores_gemma":[0.0007161216,0.00009178952,0.05856374,0.000044256667,0.000007006981,0.00000997275,0.00001352126,0.93974125,0.00010316629,0.0002931439,0.0001321399,0.0002838998],"about_ca_topic_score_codex":0.00007733667,"about_ca_topic_score_gemma":0.000023374843,"teacher_disagreement_score":0.44023773,"about_ca_system_score_codex":0.000020593403,"about_ca_system_score_gemma":0.00012318861,"threshold_uncertainty_score":0.9999956},"labels":[],"label_agreement":null},{"id":"W1904614363","doi":"10.5623/cig2014-401","title":"Small Unmanned Vehicle Systems in Geomatics/Les Systèmes De Petits Véhicules Sans Pilote En Géomatique E 261","year":2014,"lang":"fr","type":"article","venue":"GEOMATICA","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Geomatics; Physics; Geography; Remote sensing","score_opus":0.028910360369688404,"score_gpt":0.23676250219129366,"score_spread":0.20785214182160525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1904614363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16339926,0.0036237529,0.81242895,0.010014218,0.0024380581,0.0011244435,0.000032349028,0.0006009232,0.00633802],"genre_scores_gemma":[0.678882,0.00014855897,0.3168001,0.00033292297,0.00072465674,0.00018003192,0.000020252877,0.00010746669,0.0028039964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928807,0.002371611,0.0014471003,0.00090304174,0.00070404087,0.0016934844],"domain_scores_gemma":[0.99493533,0.002378567,0.0005320455,0.0014547154,0.00021587801,0.00048347024],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003731267,0.00068071275,0.0010783101,0.00039281815,0.000376113,0.0008094326,0.0019106192,0.00057538186,0.000039226466],"category_scores_gemma":[0.0016161222,0.00070716266,0.00017540788,0.00081746513,0.0002817991,0.00051805185,0.00053361326,0.00069681404,0.00086777745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036946047,0.0015508936,0.018874682,0.013943621,0.00047053915,0.0013385657,0.05494837,0.37543252,0.0015642517,0.46695587,0.0047685895,0.060115144],"study_design_scores_gemma":[0.0008087909,0.0002530346,0.03692636,0.0053981915,0.00007272133,0.00039448595,0.0009461233,0.94456285,0.00020408996,0.008019329,0.0016536376,0.0007603809],"about_ca_topic_score_codex":0.0039163013,"about_ca_topic_score_gemma":0.00034157553,"teacher_disagreement_score":0.5691303,"about_ca_system_score_codex":0.00048337202,"about_ca_system_score_gemma":0.0003351437,"threshold_uncertainty_score":0.9999102},"labels":[],"label_agreement":null},{"id":"W190953657","doi":"","title":"Robofoot ´ EPM Team Description - RoboCup2007 MiddleSize League","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Robot; League; Control (management); Robotics; Machine learning","score_opus":0.03289121965489224,"score_gpt":0.2579497693777026,"score_spread":0.22505854972281036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W190953657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026877879,0.00011890218,0.94497454,0.0003563791,0.0011357998,0.00016053976,4.9816794e-7,0.00070515944,0.0498604],"genre_scores_gemma":[0.2098922,0.0000040231166,0.78172266,0.0006648313,0.0002194056,0.0000048849497,0.0000036544752,0.000017110206,0.007471256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784786,0.000052314674,0.00040260283,0.00054352934,0.00046571332,0.0006879913],"domain_scores_gemma":[0.9986238,0.00017007263,0.00010688141,0.00075619406,0.00010396764,0.0002390837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012978246,0.00021765943,0.0002146286,0.00022274852,0.00016684906,0.0001888975,0.0010044427,0.00013862549,0.000030301751],"category_scores_gemma":[0.000107617474,0.00019694999,0.00008506029,0.0005987656,0.000051025003,0.0007314989,0.00020992436,0.00023761876,0.00063038355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082065766,0.0011980163,0.034865152,0.00010538397,0.00019539689,0.0011340749,0.005264892,0.038494647,0.02948134,0.28707144,0.09523674,0.50687087],"study_design_scores_gemma":[0.0024478973,0.00067231857,0.17033443,0.0001950668,0.000040730494,0.0008710852,0.0004636107,0.7608261,0.029299386,0.009635601,0.022974774,0.0022390161],"about_ca_topic_score_codex":0.00012593706,"about_ca_topic_score_gemma":0.000017387363,"teacher_disagreement_score":0.72233146,"about_ca_system_score_codex":0.00011905403,"about_ca_system_score_gemma":0.00006393871,"threshold_uncertainty_score":0.81025153},"labels":[],"label_agreement":null},{"id":"W193081048","doi":"10.1609/aiide.v7i1.12445","title":"Any-Angle Path Planning for Computer Games","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Block (permutation group theory); Motion planning; Computer science; Grid; Representation (politics); Path (computing); Set (abstract data type); Combinatorial game theory; Field (mathematics); Mathematical optimization; Algorithm; Theoretical computer science; Sequential game; Mathematics; Artificial intelligence; Game theory; Geometry","score_opus":0.09004277620528393,"score_gpt":0.29028018002234435,"score_spread":0.2002374038170604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W193081048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14330427,0.000027826105,0.8397351,0.00076021295,0.00090729434,0.00091303186,0.00003216019,0.00010951919,0.0142106],"genre_scores_gemma":[0.9830265,0.0000054324355,0.016457098,0.00023822536,0.00005649546,0.00005213009,0.0000016535569,0.000011968631,0.00015047388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842703,0.0000088366805,0.00043611915,0.00050459645,0.0002810763,0.00034232656],"domain_scores_gemma":[0.99892133,0.0001222672,0.00033132432,0.00019164104,0.00033282983,0.00010061275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018650417,0.0002485552,0.00026512446,0.00011029782,0.00012898279,0.0003861168,0.00095532514,0.00005580225,0.000010516395],"category_scores_gemma":[0.0001246336,0.00018051696,0.00012773939,0.00015159538,0.0001638171,0.0010135666,0.00040987707,0.00017693086,0.000015148251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000588272,0.0012559829,0.0043684603,0.00012878794,0.00022161726,0.0000069105704,0.03576797,0.00014683181,0.0042750556,0.49408084,0.00097288075,0.4581864],"study_design_scores_gemma":[0.0002400523,0.003699043,0.0031654197,0.0017873908,0.00003972078,0.000043809567,0.0065979105,0.54114014,0.33046767,0.11137781,0.0005714933,0.0008695298],"about_ca_topic_score_codex":0.000009943286,"about_ca_topic_score_gemma":1.3670275e-7,"teacher_disagreement_score":0.8397223,"about_ca_system_score_codex":0.0000504532,"about_ca_system_score_gemma":0.00003035802,"threshold_uncertainty_score":0.7361268},"labels":[],"label_agreement":null},{"id":"W1933635577","doi":"10.1109/robot.1996.509213","title":"Constraint programming for Platonic Beast legged robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constraint programming; Computer science; Robot; Constraint (computer-aided design); Software; Constraint satisfaction; Architecture; Software architecture; Programming paradigm; Programming language; Distributed computing; Artificial intelligence; Mathematical optimization; Engineering; Mathematics","score_opus":0.04702502690199143,"score_gpt":0.2537726460207469,"score_spread":0.20674761911875547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1933635577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033652352,0.000100996294,0.9924553,0.0011785309,0.00037407732,0.00038329986,0.0000017178048,0.00049278623,0.004676736],"genre_scores_gemma":[0.102341644,0.0000014256317,0.89594287,0.00021503259,0.00006665146,0.00005665354,0.0000020044304,0.000009777175,0.0013639524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871963,0.000020700561,0.00021381822,0.0003835861,0.00019865899,0.00046362664],"domain_scores_gemma":[0.9992161,0.00013251822,0.00006386465,0.00039983043,0.00006108043,0.00012659765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022844489,0.0001391177,0.00016129436,0.00006810358,0.0001532154,0.00018172966,0.00060346193,0.000060860493,0.000031765387],"category_scores_gemma":[0.00006089021,0.00012400952,0.00007530553,0.00021465884,0.000056365046,0.00026404386,0.00009732786,0.000102873026,0.00012228523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029274713,0.00023362941,0.00055464247,0.000037190544,0.00006554177,0.00006783989,0.001453397,0.010055313,0.00027050587,0.093555585,0.007574573,0.88612884],"study_design_scores_gemma":[0.0006036817,0.00013289698,0.00031303795,0.000026208312,0.0000060003485,0.00012041365,0.000046738354,0.9920464,0.00035751055,0.00040397176,0.005684786,0.00025834536],"about_ca_topic_score_codex":0.000007321677,"about_ca_topic_score_gemma":9.973958e-7,"teacher_disagreement_score":0.9819911,"about_ca_system_score_codex":0.00003548186,"about_ca_system_score_gemma":0.000026295695,"threshold_uncertainty_score":0.5056962},"labels":[],"label_agreement":null},{"id":"W1937852272","doi":"10.1109/sicici.1992.637637","title":"An Autonomous Mobile Robot","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Mobile robot; Computer science; Robot; Human–computer interaction; Artificial intelligence","score_opus":0.01284192649302339,"score_gpt":0.27005828612693633,"score_spread":0.2572163596339129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1937852272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029700585,0.000045644254,0.9836277,0.00044226792,0.00020855456,0.0000876202,3.5619755e-7,0.0007293389,0.01188848],"genre_scores_gemma":[0.25256863,8.238114e-7,0.7452791,0.00050582585,0.00012436624,0.000013366312,0.0000011287486,0.0000051626507,0.0015016168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991304,0.000029399422,0.00013374363,0.00030224884,0.00015361702,0.0002505418],"domain_scores_gemma":[0.99913293,0.000027575014,0.000029615037,0.00065280095,0.000027511014,0.00012954546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016969538,0.00009055432,0.00009244197,0.00005570357,0.00006889741,0.00012612877,0.00086655,0.000040258496,0.000059737424],"category_scores_gemma":[0.000005711072,0.000079677426,0.00002616358,0.00015562285,0.000016850656,0.00064742356,0.00008400006,0.000077367426,0.0007539049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.820944e-7,0.0001965372,0.00040049478,0.0000021040114,0.000008405911,0.000031082694,0.0011733919,0.393456,0.0013697706,0.013074505,0.0030769636,0.58720994],"study_design_scores_gemma":[0.00010867962,0.000106739295,0.0015384429,0.0000021650412,0.0000011533663,0.000036221732,0.000013481649,0.9878478,0.0018720414,0.00015766901,0.0081742,0.00014137235],"about_ca_topic_score_codex":0.000025363228,"about_ca_topic_score_gemma":0.0000018960707,"teacher_disagreement_score":0.5943918,"about_ca_system_score_codex":0.000038840048,"about_ca_system_score_gemma":0.00004763801,"threshold_uncertainty_score":0.9690174},"labels":[],"label_agreement":null},{"id":"W1943349172","doi":"10.1109/robot.1992.220235","title":"Experimental results with the dynamic urgency algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Australian Government; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Sorting; Algorithm; Computer science; Function (biology); Sorting algorithm; Collision; Scheme (mathematics); Artificial intelligence; Mathematics; Computer security","score_opus":0.01178216347414694,"score_gpt":0.24785963160358473,"score_spread":0.2360774681294378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943349172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089054863,0.0001958119,0.97070247,0.0010396447,0.00032327115,0.00015278393,0.0000017322292,0.00021197934,0.026481785],"genre_scores_gemma":[0.17536351,0.0000018233544,0.8201842,0.0003506066,0.000018242143,0.000021278283,0.00000195045,0.000010229252,0.0040482054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875706,0.000091074304,0.00015360046,0.00037154026,0.00031989222,0.0003068575],"domain_scores_gemma":[0.99904376,0.00008449426,0.00005840558,0.0006934567,0.00003595631,0.0000839229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003250109,0.00014058527,0.000101256446,0.00004077206,0.000181638,0.00010559401,0.00068628835,0.0000331671,0.000017648345],"category_scores_gemma":[0.000022370783,0.000077918674,0.000031379022,0.00034524722,0.0000580214,0.0002274002,0.00007292416,0.00011777976,0.00011750628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011280128,0.0025600283,0.0005808734,0.000026447631,0.0005757536,0.002262208,0.039570108,0.024577087,0.011235132,0.32192346,0.12381685,0.47275925],"study_design_scores_gemma":[0.0016873577,0.00053551694,0.0011552763,0.000022853003,0.000009077087,0.0005498131,0.00094161305,0.95964974,0.018823763,0.00049713696,0.015486494,0.000641337],"about_ca_topic_score_codex":0.000021298963,"about_ca_topic_score_gemma":0.0000010615738,"teacher_disagreement_score":0.93507266,"about_ca_system_score_codex":0.000041293788,"about_ca_system_score_gemma":0.00007145341,"threshold_uncertainty_score":0.31774312},"labels":[],"label_agreement":null},{"id":"W1949211579","doi":"10.1115/1.4030775","title":"A Human-Inspired Method for Point-to-Point and Path-Following Navigation of Mobile Robots","year":2015,"lang":"en","type":"article","venue":"Journal of Mechanisms and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Obstacle avoidance; Motion planning; Computer vision; Robot; Computer science; Artificial intelligence; Heading (navigation); Mobile robot navigation; Real Time Kinematic; Terrain; Obstacle; Path (computing); Kinematics; Point (geometry); Differential (mechanical device); Simulation; Global Positioning System; Robot control; Engineering; Mathematics; Geography; GNSS applications","score_opus":0.03202361778416367,"score_gpt":0.31503026055254646,"score_spread":0.2830066427683828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1949211579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005152362,0.00026644403,0.99312514,0.00046625626,0.00070728816,0.00024991247,0.0000023691446,0.000017965096,0.000012257704],"genre_scores_gemma":[0.0377829,0.0000062183863,0.9619724,0.000109047214,0.00008046506,0.0000047091517,8.829728e-7,0.000013934832,0.000029403023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849623,0.00009004519,0.00059016695,0.00020716582,0.0003973367,0.00021903393],"domain_scores_gemma":[0.9985408,0.00013009459,0.00049593084,0.00020236665,0.0003356917,0.00029509372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022193235,0.00015266582,0.0004516603,0.00016908713,0.00010514769,0.00008939739,0.0003092317,0.000095291776,2.92921e-7],"category_scores_gemma":[0.00014455385,0.00012601996,0.00010830869,0.00017482805,0.000015642576,0.00036559775,0.00014498764,0.00014866867,4.3781031e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034985365,0.00015988079,0.0000848653,0.000111041845,0.00013855091,0.00013217363,0.0056248102,0.89827114,0.04254082,0.031896885,0.00035600577,0.02064885],"study_design_scores_gemma":[0.002472012,0.0038802738,0.00026157885,0.0006153948,0.00014025929,0.00063985155,0.0009344681,0.9275956,0.0062726126,0.056809664,0.000042244348,0.00033604953],"about_ca_topic_score_codex":0.000008692413,"about_ca_topic_score_gemma":3.4323227e-7,"teacher_disagreement_score":0.036268204,"about_ca_system_score_codex":0.000042672447,"about_ca_system_score_gemma":0.000092592636,"threshold_uncertainty_score":0.5138945},"labels":[],"label_agreement":null},{"id":"W1955308247","doi":"10.1109/isic.1993.397640","title":"Integration of the artificial potential field approach with simulated annealing for robot path planning","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Maxima and minima; Simulated annealing; Motion planning; Robustness (evolution); Computer science; Robot; Adaptive simulated annealing; Annealing (glass); Mathematical optimization; Heuristic; Artificial intelligence; Algorithm; Mathematics; Materials science; Chemistry","score_opus":0.04739951244795645,"score_gpt":0.25281568254302184,"score_spread":0.20541617009506538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955308247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0125249885,0.000021118307,0.9857619,0.00035371498,0.0002181906,0.0002643879,0.000001385642,0.00008899091,0.00076534314],"genre_scores_gemma":[0.64863914,1.7094236e-7,0.35108373,0.00009263922,0.000049852086,0.000004823527,0.0000022430006,0.000005134247,0.0001222486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990531,0.00003978324,0.00022478325,0.00025225812,0.0002288162,0.00020123596],"domain_scores_gemma":[0.9992891,0.000119609394,0.00012380318,0.00034178217,0.000092357164,0.00003335375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018162298,0.00010899568,0.00013658445,0.000050900217,0.00014598454,0.00007508219,0.00047202053,0.00006675197,0.0000043818754],"category_scores_gemma":[0.00006690287,0.00006560645,0.000055963334,0.000283782,0.000025320618,0.0001823798,0.000062022584,0.00011993326,0.0000013899988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015816648,0.00006206048,0.00015709062,0.00001192105,0.000018595962,0.0000032287392,0.0014014005,0.9813019,0.0038411103,0.0020525465,0.00049107097,0.010643225],"study_design_scores_gemma":[0.00020002529,0.00014533526,0.00035709725,0.000041378265,0.000009497589,0.000011560044,0.00009667352,0.99208486,0.006706507,0.00023878324,0.000010327515,0.00009792775],"about_ca_topic_score_codex":0.000028124852,"about_ca_topic_score_gemma":3.9934957e-7,"teacher_disagreement_score":0.6361142,"about_ca_system_score_codex":0.000012298766,"about_ca_system_score_gemma":0.00001599757,"threshold_uncertainty_score":0.26753533},"labels":[],"label_agreement":null},{"id":"W1961676567","doi":"10.48550/arxiv.1211.2365","title":"Discrete Dubins Paths","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Bounded function; Path (computing); Shortest path problem; Motion planning; Arc length; Arc (geometry); Combinatorics; Geometry; Mathematical analysis; Computer science; Artificial intelligence; Robot","score_opus":0.08190046594147332,"score_gpt":0.19406073517747668,"score_spread":0.11216026923600336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961676567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02330342,0.00013289254,0.9664577,0.00012510571,0.0021309895,0.00023877644,0.000024305407,0.00057568034,0.0070110927],"genre_scores_gemma":[0.967017,0.000059658574,0.02979783,0.00009424698,0.00021972296,0.0000010546743,0.000023856008,0.000026054235,0.0027605651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976184,0.00017697581,0.00021447706,0.0011793067,0.00014646347,0.00066439895],"domain_scores_gemma":[0.99713683,0.000111921094,0.00028016476,0.0020141082,0.000103490456,0.00035346954],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003365431,0.00040833754,0.00039664828,0.00023627904,0.00017406711,0.00015268923,0.0029446383,0.0003727404,0.00002738],"category_scores_gemma":[0.000040964682,0.00045681885,0.00024321412,0.0004954992,0.0001080317,0.0005613517,0.0035218853,0.00081982213,0.0005699625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021892401,0.00027513478,0.01717609,0.0001868923,0.00032040852,0.0025016211,0.0020037522,0.61872506,0.00006299643,0.35277995,0.0025816192,0.0033645895],"study_design_scores_gemma":[0.000411356,0.000039964707,0.007129019,0.00014801571,0.00009478611,0.000024020022,0.000045730078,0.9682553,0.0000734206,0.02156976,0.0012707746,0.00093789445],"about_ca_topic_score_codex":0.000120018856,"about_ca_topic_score_gemma":0.0000029615335,"teacher_disagreement_score":0.9437136,"about_ca_system_score_codex":0.00022301335,"about_ca_system_score_gemma":0.0001993216,"threshold_uncertainty_score":0.99978834},"labels":[],"label_agreement":null},{"id":"W1964808430","doi":"10.1177/0278364904046632","title":"Sun-Synchronous Robotic Exploration: Technical Description and Field Experimentation","year":2005,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terrain; Computer science; Software; Process (computing); Field (mathematics); Plan (archaeology); Mars Exploration Program; Exploration of Mars; Systems engineering; Simulation; Engineering; Geography; Astrobiology; Physics","score_opus":0.12665639610523438,"score_gpt":0.40210436698391655,"score_spread":0.27544797087868217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964808430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004056032,0.0004453214,0.9448771,0.049360257,0.00071536895,0.00011326543,2.61252e-7,0.000022846581,0.00040956773],"genre_scores_gemma":[0.7186535,0.00020337504,0.27996492,0.0003068936,0.000678443,0.0000054279594,0.0000011064951,0.0000078369885,0.00017848713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974963,0.00020976762,0.00042516174,0.00016381711,0.0014787948,0.00022617426],"domain_scores_gemma":[0.9979176,0.0006198501,0.00018032674,0.00023579762,0.000947939,0.00009850267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019897479,0.000092613845,0.00013055584,0.00032066295,0.00016909215,0.0004954417,0.0015386346,0.00006114282,0.000013416316],"category_scores_gemma":[0.00041333504,0.00006825756,0.00005105975,0.00024296135,0.00010679626,0.0012126861,0.00035235693,0.00053617125,0.000028814435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001608584,0.0005017831,0.0005970043,0.000012201265,0.00019484361,0.00025639136,0.005182044,0.752677,0.030574122,0.05912203,0.018015182,0.13270652],"study_design_scores_gemma":[0.0013970099,0.0012209715,0.0014660622,0.00021979326,0.000020603902,0.0025310707,0.00087587326,0.95534825,0.020779802,0.01403367,0.0018154285,0.0002914679],"about_ca_topic_score_codex":0.000016766051,"about_ca_topic_score_gemma":0.000006070842,"teacher_disagreement_score":0.71459746,"about_ca_system_score_codex":0.0002620984,"about_ca_system_score_gemma":0.00016791436,"threshold_uncertainty_score":0.4777556},"labels":[],"label_agreement":null},{"id":"W1964918420","doi":"10.1109/robot.2010.5509530","title":"The search for survivors: Cooperative human-robot interaction in search and rescue environments using semi-autonomous robots","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Urban search and rescue; Search and rescue; Rescue robot; Mobile robot; Computer science; Situation awareness; Robot control; Mobile robot navigation; Human–computer interaction; Social robot; Artificial intelligence; Human–robot interaction; Engineering","score_opus":0.05808891158535614,"score_gpt":0.3399015266935167,"score_spread":0.2818126151081606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964918420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39114478,0.000019937275,0.60746527,0.00033304727,0.00040917587,0.00042111857,0.0000016749253,0.000030711042,0.00017432048],"genre_scores_gemma":[0.91136664,0.00000799176,0.08756447,0.000056379544,0.0000806022,0.000030141622,0.0000042208335,0.000015867132,0.00087371137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836415,0.00018473098,0.00026610165,0.0004749329,0.0002642747,0.00044579981],"domain_scores_gemma":[0.9989239,0.00044633436,0.000027894977,0.00045782677,0.000048188038,0.00009588211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013076816,0.00015462142,0.00015994647,0.00011941303,0.00052172295,0.00036182208,0.0005591728,0.00009249733,0.0000053274644],"category_scores_gemma":[0.0000548823,0.00011632936,0.000028382477,0.00019497734,0.00013672252,0.000502892,0.00035439542,0.0004946024,0.000011131306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032919084,0.0001805038,0.0071539544,0.000022367569,0.000047963356,0.000029789322,0.005654319,0.7503438,0.21294892,0.007045283,0.000053284548,0.016486902],"study_design_scores_gemma":[0.00037214253,0.000097000404,0.009604289,0.000021338075,0.0000023404982,0.00003119475,0.0002616835,0.97162575,0.017655566,0.000060429426,0.000108701504,0.00015957982],"about_ca_topic_score_codex":0.0017065831,"about_ca_topic_score_gemma":0.0008721881,"teacher_disagreement_score":0.5202218,"about_ca_system_score_codex":0.000097858116,"about_ca_system_score_gemma":0.000073182375,"threshold_uncertainty_score":0.47437736},"labels":[],"label_agreement":null},{"id":"W1968848824","doi":"10.1002/cav.173","title":"Robust continuous collision detection for interactive deformable surfaces","year":2007,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut Périmètre de physique théorique; Hong Kong Polytechnic University","keywords":"Computer science; Collision detection; Collision; Process (computing); Artificial intelligence; Computer graphics; Computer vision; Motion (physics); Sampling (signal processing); Computer graphics (images); Algorithm","score_opus":0.02235171064290967,"score_gpt":0.26140214767406794,"score_spread":0.23905043703115827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968848824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06723598,0.00003509072,0.93099517,0.000094643074,0.0008987957,0.00027028582,0.0000025431339,0.00018741979,0.00028010478],"genre_scores_gemma":[0.71594185,0.0000037082216,0.28341562,0.0001907201,0.00013479023,0.000007965895,0.000007856998,0.000008261667,0.00028921317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989256,0.00003665759,0.00028333382,0.00032238176,0.00017164863,0.00026037847],"domain_scores_gemma":[0.9991149,0.00032289617,0.00013628382,0.00017121309,0.00015712182,0.00009760436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065294775,0.00014366239,0.00017703623,0.00017890843,0.00024833655,0.00026592804,0.00023448195,0.00006475232,0.0000021839458],"category_scores_gemma":[0.000029628822,0.00013403098,0.00004359444,0.00029308058,0.000029871811,0.0008100391,0.00013059133,0.00010825194,0.0000147567025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022378951,0.00019163055,0.00069216604,0.00005676297,0.00007430728,0.000017979619,0.005368138,0.1075138,0.0045565595,0.012425529,0.0032437039,0.86563563],"study_design_scores_gemma":[0.0005841948,0.00047965345,0.008194228,0.000040810068,0.0000057044326,0.00003294658,0.000091792586,0.9836606,0.0050088363,0.00019442305,0.001533651,0.00017318228],"about_ca_topic_score_codex":0.000013163327,"about_ca_topic_score_gemma":0.000010842348,"teacher_disagreement_score":0.8761468,"about_ca_system_score_codex":0.000060847116,"about_ca_system_score_gemma":0.000017840835,"threshold_uncertainty_score":0.5465625},"labels":[],"label_agreement":null},{"id":"W1969098212","doi":"10.1017/s0263574714000939","title":"Simulation-based fast collision detection for scaled polyhedral objects in motion by exploiting analytical contact equations","year":2014,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Council","keywords":"Scaling; Collision detection; Collision; Regular polygon; Algorithm; Polyhedron; Motion (physics); Rotation (mathematics); Computer science; Translation (biology); Feature (linguistics); Mathematics; Geometry; Artificial intelligence","score_opus":0.02458500469895364,"score_gpt":0.2833142536868941,"score_spread":0.2587292489879405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969098212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061281985,0.0000075323996,0.9923465,0.00053674483,0.00028098418,0.0004157038,0.0000045788265,0.00018669655,0.00009306025],"genre_scores_gemma":[0.8866529,1.2796616e-7,0.113005444,0.00014875003,0.00008180339,0.000041626205,0.00002866959,0.00001511814,0.000025578043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818736,0.00018849678,0.0004099117,0.0004760795,0.00033603885,0.0004021035],"domain_scores_gemma":[0.9959107,0.0033171896,0.00013709239,0.0003567612,0.00014818975,0.00013005588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006848673,0.00017210176,0.00024758105,0.00024165754,0.00022940514,0.00016241844,0.00030270574,0.00013296778,0.0000031609009],"category_scores_gemma":[0.0025164008,0.00018048772,0.00008137155,0.00057151867,0.00002637598,0.00039169597,0.000043610573,0.00016492914,0.000021917245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012297226,0.000075923694,0.00043302667,0.000012143834,0.0000046752243,0.0000011395844,0.00010697703,0.9879208,0.0013001574,0.0017181546,0.000011631933,0.00840306],"study_design_scores_gemma":[0.0010476974,0.00019320249,0.0023695312,0.00006626587,0.000012669888,6.9989284e-7,0.000020844742,0.994046,0.001610939,0.0004234307,0.000011748256,0.00019701055],"about_ca_topic_score_codex":0.000039805684,"about_ca_topic_score_gemma":0.000011743602,"teacher_disagreement_score":0.8805247,"about_ca_system_score_codex":0.00017715365,"about_ca_system_score_gemma":0.00006390103,"threshold_uncertainty_score":0.7360076},"labels":[],"label_agreement":null},{"id":"W1969839206","doi":"10.1109/tcyb.2014.2360368","title":"A Multirobot Path-Planning Strategy for Autonomous Wilderness Search and Rescue","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Search and rescue; Path (computing); Robot; Computer science; Software deployment; Piecewise; Set (abstract data type); Mathematical optimization; Rescue robot; Operations research; Artificial intelligence; Mobile robot; Engineering; Mathematics; Computer network","score_opus":0.04285276070436285,"score_gpt":0.29332574121334404,"score_spread":0.2504729805089812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969839206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018822197,0.000047108144,0.97960645,0.00022194244,0.00052415626,0.00033362783,0.000014846519,0.0002484577,0.00018120655],"genre_scores_gemma":[0.7678994,0.000009741379,0.23124464,0.00010345707,0.000060268285,0.00004932797,0.0000018717126,0.000026104697,0.0006052082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982951,0.00010882945,0.00028111765,0.0005618745,0.0002828266,0.00047021234],"domain_scores_gemma":[0.99875045,0.00038636453,0.000036402427,0.00053497695,0.00009765698,0.00019415298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004101132,0.00023210858,0.00024235595,0.00015467466,0.00029511045,0.00021377865,0.00048800287,0.00014612876,0.0000026396438],"category_scores_gemma":[0.000013119013,0.00023490476,0.00007049239,0.00020754075,0.000091705384,0.00018644928,0.000005543519,0.00032403306,0.000018986646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012892011,0.00009670706,0.000020121704,0.000030619743,0.000023535884,0.000009244553,0.0009943772,0.86962104,0.00040979253,0.0009922043,0.00005118613,0.12773831],"study_design_scores_gemma":[0.00078459835,0.00046120712,0.0008566314,0.00007027106,0.00002036217,0.000047641333,0.00005215773,0.99312043,0.0036873145,0.00030813328,0.00030498617,0.00028626662],"about_ca_topic_score_codex":0.000116807976,"about_ca_topic_score_gemma":0.000008888965,"teacher_disagreement_score":0.7490772,"about_ca_system_score_codex":0.00005108144,"about_ca_system_score_gemma":0.00007811672,"threshold_uncertainty_score":0.9579138},"labels":[],"label_agreement":null},{"id":"W1970024404","doi":"10.1109/icsmc.2011.6084034","title":"Agile unmanned vehicle navigation in highly confined environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Obstacle avoidance; Computer science; Obstacle; Agile software development; Planner; Mobile robot; Vehicle dynamics; Trajectory; Navigation system; Task (project management); Motion planning; Control engineering; Artificial intelligence; Simulation; Robot; Aerospace engineering; Engineering; Systems engineering","score_opus":0.02785574291825822,"score_gpt":0.2238647920306629,"score_spread":0.1960090491124047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970024404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31749663,0.000026179207,0.6586628,0.0003418634,0.00046325254,0.00024201903,0.000001317307,0.0002472673,0.022518694],"genre_scores_gemma":[0.84226716,0.0000011605706,0.15660755,0.00018133511,0.000012205219,0.000009964275,0.0000041532194,0.0000053870126,0.00091106805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908936,0.00004977742,0.00018473495,0.00027556575,0.00017708373,0.0002234738],"domain_scores_gemma":[0.9994861,0.000030388848,0.000050942905,0.00036064492,0.000008494943,0.00006346778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019276264,0.00009082177,0.000098788194,0.00006693793,0.00003566776,0.000025584217,0.00047187845,0.000056828278,0.000041782194],"category_scores_gemma":[0.000013641318,0.0000860094,0.000019470946,0.00018648882,0.000026530184,0.0003236301,0.000111250054,0.00009503853,0.00044750533],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013230467,0.003517935,0.24925955,0.00009234866,0.00017868303,0.0024270178,0.05674783,0.010778618,0.14878969,0.3004985,0.0065842206,0.22099333],"study_design_scores_gemma":[0.0014760223,0.00023229107,0.3866252,0.000056339602,0.0000046352748,0.000019730676,0.000086563035,0.5452097,0.059225515,0.0056066015,0.0009956236,0.00046180675],"about_ca_topic_score_codex":0.00017979935,"about_ca_topic_score_gemma":0.0000018900195,"teacher_disagreement_score":0.53443104,"about_ca_system_score_codex":0.00003795356,"about_ca_system_score_gemma":0.000016062193,"threshold_uncertainty_score":0.5751925},"labels":[],"label_agreement":null},{"id":"W1970422188","doi":"10.1163/15685530360663409","title":"Sensor-based navigation for car-like mobile robots based on a generalized Voronoi graph","year":2003,"lang":"en","type":"article","venue":"Advanced Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Mobile robot; Motion planning; Robot; Voronoi diagram; Computer science; Path (computing); Graph; Mobile robot navigation; Artificial intelligence; Computer vision; Mathematics; Robot control; Theoretical computer science","score_opus":0.017185999229078572,"score_gpt":0.27106996742043654,"score_spread":0.25388396819135794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970422188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014624659,0.000107359825,0.9949202,0.00030515256,0.0015847005,0.001015517,0.000016779495,0.00041557456,0.00017228577],"genre_scores_gemma":[0.04876816,0.0000042570914,0.94932276,0.0012251968,0.00005700377,0.0002537258,0.00008151385,0.000054656735,0.00023272826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972976,0.00020004887,0.0004635943,0.00083251717,0.0004977286,0.0007085154],"domain_scores_gemma":[0.99754137,0.000543344,0.00025908745,0.001167957,0.00026428633,0.00022393106],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003978907,0.0004010151,0.00041477967,0.0002003259,0.0003087985,0.00011115601,0.0006094252,0.00016523589,0.0000038039916],"category_scores_gemma":[0.00017396218,0.0004033457,0.00021113869,0.00065773475,0.00006899497,0.0002954149,0.000032080465,0.00023287501,0.000027701211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025802776,0.00013870471,0.00013351422,0.000034773264,0.000013539616,0.000022091714,0.000059028476,0.9906677,0.0010512205,0.0050041373,0.00018044299,0.002669047],"study_design_scores_gemma":[0.002704869,0.0005128549,0.00009867005,0.00010368557,0.000027626236,0.000009159868,0.000018306217,0.98751926,0.0053581893,0.0014322458,0.0016881364,0.0005270103],"about_ca_topic_score_codex":0.000007713886,"about_ca_topic_score_gemma":8.6332034e-7,"teacher_disagreement_score":0.047305696,"about_ca_system_score_codex":0.00016446834,"about_ca_system_score_gemma":0.00021389707,"threshold_uncertainty_score":0.99984187},"labels":[],"label_agreement":null},{"id":"W1971062211","doi":"10.2316/journal.206.2009.4.206-3160","title":"A SINGULARITY-BASED APPROACH TO IMPROVE MOBILE ROBOT MOTION PLANNING","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Motion planning; Mobile robot; Singularity; Motion (physics); Robot; Artificial intelligence; Mathematics","score_opus":0.01524755725142514,"score_gpt":0.27908425723278507,"score_spread":0.26383669998135995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971062211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058031953,0.00006751793,0.99129707,0.0016615145,0.0008089762,0.000107586544,0.0000014563932,0.000051957486,0.00020075674],"genre_scores_gemma":[0.47486943,0.0000013584878,0.524655,0.0003228412,0.00013421773,0.0000012260464,0.00000356016,0.0000033275776,0.000009031324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866116,0.000043744672,0.0004090134,0.00017915909,0.0005644371,0.00014250477],"domain_scores_gemma":[0.9989189,0.0000549685,0.0003501198,0.00013425962,0.0004293248,0.00011242768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046940363,0.00011570797,0.00016419773,0.00035005307,0.00006473595,0.00033175366,0.0005260643,0.00005900236,5.823706e-7],"category_scores_gemma":[0.00009812614,0.00010699307,0.00006013553,0.00016668421,0.000013060679,0.0005212216,0.000047206053,0.00015867715,0.0000026992006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008991204,0.00013032429,0.00016279165,0.0000043610016,0.000020693455,0.000025325957,0.0004440525,0.9262501,0.0018640414,0.0041218614,0.00007117409,0.06689628],"study_design_scores_gemma":[0.00049424724,0.00029603418,0.0096042305,0.00009909709,0.000009877289,0.0001526303,0.000024765031,0.98609656,0.0007006791,0.0023297418,0.00007048865,0.00012166898],"about_ca_topic_score_codex":0.0000023307716,"about_ca_topic_score_gemma":2.7913678e-8,"teacher_disagreement_score":0.46906623,"about_ca_system_score_codex":0.00009150883,"about_ca_system_score_gemma":0.00006644672,"threshold_uncertainty_score":0.43630508},"labels":[],"label_agreement":null},{"id":"W1971168253","doi":"10.1109/iros.2010.5652621","title":"Path planning with variable-fidelity terrain assessment","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Terrain; Fidelity; Motion planning; Computer science; Path (computing); Modular design; Chassis; Simulation; Real-time computing; Artificial intelligence; Robot; Engineering; Geography; Aerospace engineering; Telecommunications; Cartography","score_opus":0.015210714714964251,"score_gpt":0.281824399659582,"score_spread":0.26661368494461773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971168253","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009659608,0.000004212019,0.9532229,0.0004267158,0.00051736005,0.0001172581,0.0000014608519,0.00042100323,0.03562954],"genre_scores_gemma":[0.20641617,1.15069895e-7,0.79272294,0.00042098307,0.00007332847,0.000013023335,0.0000031447776,0.000008184424,0.00034208954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984936,0.000050782255,0.00020746728,0.0004678227,0.00039829663,0.0003820376],"domain_scores_gemma":[0.998722,0.0001310636,0.0000859433,0.0008226233,0.00008020406,0.0001581533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062958704,0.00016669268,0.00016663886,0.000068798894,0.00014693364,0.00022654957,0.00085426227,0.000076520315,0.00004497865],"category_scores_gemma":[0.000038756145,0.00012093551,0.000022811284,0.00030453925,0.00004269273,0.00043250195,0.00020368372,0.00046357917,0.000027868218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027837561,0.00079061935,0.23762487,0.00011270303,0.00022926339,0.0011648173,0.0049308506,0.034384172,0.03421316,0.6102173,0.02326573,0.05303872],"study_design_scores_gemma":[0.0005748354,0.00019338455,0.06285341,0.000056390512,0.000008094545,0.00019987539,0.00010011639,0.92889583,0.0002895401,0.0031270566,0.0032614572,0.00044003784],"about_ca_topic_score_codex":0.00012495935,"about_ca_topic_score_gemma":0.0000027325339,"teacher_disagreement_score":0.89451164,"about_ca_system_score_codex":0.000026711026,"about_ca_system_score_gemma":0.00022400657,"threshold_uncertainty_score":0.4931607},"labels":[],"label_agreement":null},{"id":"W1971387266","doi":"10.1115/imece2003-42032","title":"Simultaneous Manipulator/Controller Design Optimization Using Multi-Objective Evolutionary Algorithms","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control theory (sociology); Quintic function; Nonlinear system; Controller (irrigation); Computer science; Point (geometry); Polynomial; Control engineering; Mathematics; Engineering; Artificial intelligence; Control (management)","score_opus":0.04942630974726129,"score_gpt":0.2744551413779602,"score_spread":0.2250288316306989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971387266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007110063,0.00020277577,0.9976473,0.00003132099,0.0005756808,0.00046014707,0.0000014281711,0.00040521537,0.0006050557],"genre_scores_gemma":[0.066861145,0.0000040121795,0.932341,0.00015232166,0.000047662867,0.000013988754,0.0000019547026,0.000023375289,0.00055452954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977955,0.00034750285,0.0003376453,0.0006368583,0.00039255826,0.0004899209],"domain_scores_gemma":[0.9985742,0.00044574487,0.00013667681,0.00043602785,0.00025134117,0.00015598755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043886577,0.00026712415,0.00026647092,0.0001858482,0.0003086475,0.00013190015,0.00048348904,0.00014187806,0.000029096747],"category_scores_gemma":[0.0004305741,0.00025212887,0.00007871921,0.000561547,0.00004644598,0.0005893566,0.00008457327,0.00016920512,0.00006332019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004602755,0.000089982095,0.000144424,0.0000021498768,0.000031608837,0.00007595264,0.0001838186,0.99699205,0.000095975505,0.0012931911,0.000048226364,0.0010380254],"study_design_scores_gemma":[0.00096600567,0.0000682372,0.00014003179,0.000014778762,0.000014167438,0.00021936545,0.000044316792,0.9975571,0.000278049,0.00033364713,0.00003506122,0.00032922943],"about_ca_topic_score_codex":0.000048033133,"about_ca_topic_score_gemma":2.441701e-7,"teacher_disagreement_score":0.066790044,"about_ca_system_score_codex":0.0002661905,"about_ca_system_score_gemma":0.00018262702,"threshold_uncertainty_score":0.9999931},"labels":[],"label_agreement":null},{"id":"W1971809461","doi":"10.1109/isic.2014.6967643","title":"Multi-robot charged system search-based optimal path planning in static environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Motion planning; Mobile robot; Mathematical optimization; Robot; Path (computing); Computer science; Set (abstract data type); Any-angle path planning; Population; Spare part; Holonomic; Artificial intelligence; Mathematics; Engineering","score_opus":0.033079132894321406,"score_gpt":0.2640973620801707,"score_spread":0.2310182291858493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971809461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021103948,0.000018400857,0.977766,0.00013605069,0.00021867653,0.00023512532,0.000002098873,0.00023251762,0.00028720262],"genre_scores_gemma":[0.48871034,2.0193177e-7,0.51097393,0.00012290939,0.000017217282,0.000016803562,0.0000050143913,0.0000113436545,0.00014224186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976731,0.0003080585,0.00035662792,0.0005644827,0.0005005368,0.0005972081],"domain_scores_gemma":[0.9988868,0.00021082694,0.00008579527,0.0006209322,0.000012733471,0.0001828789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010009292,0.00021397494,0.00027865492,0.00020691732,0.00010209897,0.00011402092,0.0008488841,0.00007542596,0.0000068610398],"category_scores_gemma":[0.000051762116,0.00019529584,0.00004359361,0.00025031422,0.00004517681,0.00025891067,0.00017306823,0.00024046339,0.0002690246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006965446,0.00014525237,0.009719504,0.00006906367,0.000011426013,0.00016340362,0.001154826,0.9834699,0.0022221527,0.00061595393,0.00010302715,0.0023185431],"study_design_scores_gemma":[0.0010982887,0.00009581437,0.026762268,0.00016434437,0.0000024479953,0.000009220031,0.00010951036,0.9704414,0.0010233286,0.0000028260697,0.00006409836,0.0002264801],"about_ca_topic_score_codex":0.000068891335,"about_ca_topic_score_gemma":2.4613166e-7,"teacher_disagreement_score":0.4676064,"about_ca_system_score_codex":0.00016866332,"about_ca_system_score_gemma":0.000055547065,"threshold_uncertainty_score":0.79639333},"labels":[],"label_agreement":null},{"id":"W1972446581","doi":"10.2495/dne-v5-n3-230-241","title":"Natural flow systems: Acquiring their constructal morphology","year":2010,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Constructal law; Natural (archaeology); Morphology (biology); Computer science; Mechanics; Geography; Zoology; Biology; Physics; Heat transfer","score_opus":0.00924227053949507,"score_gpt":0.24365363810641807,"score_spread":0.234411367566923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972446581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1377901,0.000898271,0.8438265,0.0008297364,0.016413976,0.00007246425,0.000009972343,0.000034246685,0.00012476007],"genre_scores_gemma":[0.7385421,0.00003811213,0.26074404,0.00017741491,0.00046223286,7.2046856e-7,0.000002636693,0.000007313446,0.000025432371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987959,0.000066177796,0.00038789448,0.00018962348,0.00036502964,0.0001953644],"domain_scores_gemma":[0.9985767,0.00030654937,0.00036669662,0.00016681004,0.00048254282,0.00010069487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005727102,0.00015356556,0.00022765798,0.00028246932,0.000063960266,0.00026724263,0.0011756286,0.00022940394,0.0000032303992],"category_scores_gemma":[0.0001723606,0.00011977077,0.00007821914,0.00010570939,0.000085393345,0.0005928961,0.00015254576,0.00122673,0.0000032899436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000720278,0.0005739971,0.017928777,0.00009953552,0.0036457982,0.012728299,0.005943819,0.1824737,0.23474626,0.20549604,0.0074104713,0.328233],"study_design_scores_gemma":[0.00056632824,0.00008217329,0.0018054129,0.000059352606,0.000010952967,0.016343407,0.000052584754,0.97759813,0.0003980151,0.0025937965,0.00032444744,0.00016541855],"about_ca_topic_score_codex":0.0000028169325,"about_ca_topic_score_gemma":0.0000012663425,"teacher_disagreement_score":0.7951244,"about_ca_system_score_codex":0.000046769186,"about_ca_system_score_gemma":0.000121248195,"threshold_uncertainty_score":0.53295994},"labels":[],"label_agreement":null},{"id":"W1972741546","doi":"10.1115/1.1846053","title":"A Linear Programming Solution for Exact Collision Detection","year":2005,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Collision detection; Collision; Computer science; Robustness (evolution); Linear programming; Inter frame; Algorithm; Regular polygon; Mathematical optimization; Frame (networking); Mathematics; Geometry","score_opus":0.011119744377998,"score_gpt":0.2619502733762366,"score_spread":0.2508305289982386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972741546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14134468,0.000019985167,0.8579756,0.00013762651,0.00040328238,0.00007503295,9.3205315e-8,0.000029541396,0.000014181909],"genre_scores_gemma":[0.56487584,0.000003505521,0.4350367,0.00001652196,0.000065381195,5.5620677e-7,8.152723e-8,0.0000010641992,3.7909163e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989632,0.000007226333,0.00045535815,0.000069566486,0.0003052744,0.00019939087],"domain_scores_gemma":[0.99924505,0.00010320604,0.00026524143,0.00007231989,0.0002471298,0.000067025976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022396948,0.00006471326,0.00010584003,0.0006453056,0.0001265201,0.00021634341,0.0002740645,0.000029474693,6.326812e-8],"category_scores_gemma":[0.0004604387,0.00005892244,0.000025551377,0.0007067152,0.000022103774,0.0062546236,0.000055295033,0.00013347655,7.300415e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020187802,0.000005072462,0.00005850215,0.000014019334,8.135958e-7,2.8788492e-7,0.001213265,0.68388677,0.00078722404,0.00013366753,0.0000026822959,0.31389567],"study_design_scores_gemma":[0.00027729952,0.00009502497,0.0026385123,0.00010955014,0.0000011376756,0.00011139267,0.0000405187,0.9932171,0.0021722177,0.000006875438,0.0012647557,0.000065607266],"about_ca_topic_score_codex":0.0000018331008,"about_ca_topic_score_gemma":9.6453505e-8,"teacher_disagreement_score":0.42353114,"about_ca_system_score_codex":0.00011980551,"about_ca_system_score_gemma":0.0000758593,"threshold_uncertainty_score":0.4534451},"labels":[],"label_agreement":null},{"id":"W1972768104","doi":"10.1142/s1469026811003148","title":"VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS","year":2011,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Artificial neural network; Instrumentation (computer programming); Motion planning; Robot; Path (computing); Software; Mobile robot; Artificial intelligence; Trajectory; Controller (irrigation); Real-time computing; Computer network","score_opus":0.05692463903864865,"score_gpt":0.32222581622429425,"score_spread":0.2653011771856456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972768104","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021971995,0.00016925066,0.9770185,0.000040687595,0.00039564967,0.00031773947,0.000020922882,0.000025029545,0.00004023323],"genre_scores_gemma":[0.73089135,0.000020027146,0.2688088,0.00004131839,0.00016007808,0.00003890004,0.000025758833,0.000008713204,0.000005073519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983556,0.000052295643,0.0008074746,0.0002078497,0.0004329652,0.00014381319],"domain_scores_gemma":[0.99747384,0.0004116783,0.00086748716,0.0001247306,0.0010306638,0.00009162161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037725666,0.00013683384,0.00022486826,0.00029816615,0.000100706275,0.00009756452,0.0006898997,0.000060062186,0.000004457365],"category_scores_gemma":[0.000027010634,0.00013385512,0.00009297256,0.00023334373,0.0000874441,0.00048194942,0.00006401927,0.00010497691,0.0000014417102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000326924,0.00010442349,0.00059152616,0.000007678219,0.00006548126,0.0000047098147,0.0003761243,0.9725938,0.00024326067,0.009044764,0.00001964685,0.016915929],"study_design_scores_gemma":[0.00023592447,0.00024496778,0.0010278055,0.00010809217,0.000021231024,0.00010575943,0.00016377802,0.994163,0.00057996524,0.0032035795,0.00002636118,0.00011955114],"about_ca_topic_score_codex":0.000031136973,"about_ca_topic_score_gemma":6.9081466e-8,"teacher_disagreement_score":0.70891935,"about_ca_system_score_codex":0.00006406219,"about_ca_system_score_gemma":0.00014239673,"threshold_uncertainty_score":0.5458453},"labels":[],"label_agreement":null},{"id":"W1973546417","doi":"10.1177/0278364910388315","title":"Bounding on rough terrain with the LittleDog robot","year":2010,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Motion planning; Bounding overwatch; Computer science; Kinematics; Control theory (sociology); Terrain; Underactuation; Robot; Configuration space; Collision detection; Mathematics; Artificial intelligence; Collision","score_opus":0.0637981599763835,"score_gpt":0.3800991571025951,"score_spread":0.3163009971262116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973546417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029637873,0.000083804785,0.7470246,0.21429202,0.0039698756,0.00025497723,0.0000022826064,0.00003806979,0.0046965047],"genre_scores_gemma":[0.8776926,0.000020184014,0.1190457,0.00064718426,0.001444714,0.0000047692038,7.549644e-7,0.000020257652,0.0011238743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99577844,0.00030820366,0.00032148688,0.00018982867,0.0030108604,0.00039120443],"domain_scores_gemma":[0.99582994,0.0018630467,0.0002516571,0.0006065979,0.0013322057,0.00011655894],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0056630797,0.00012825285,0.00014783438,0.00031213195,0.00038290952,0.0008968863,0.005942091,0.000056253735,0.000016326867],"category_scores_gemma":[0.00070002145,0.000060708022,0.000082978135,0.00044170817,0.00033762451,0.00034852538,0.00049455557,0.0021656211,0.000061897845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003107566,0.00039993055,0.0010914605,0.000008224024,0.0005760074,0.0011845472,0.008175249,0.71135294,0.010832526,0.20900592,0.023977945,0.033084482],"study_design_scores_gemma":[0.0032487053,0.0028288483,0.014645832,0.0007133396,0.000048913498,0.009040639,0.0014534425,0.88684475,0.008226557,0.04017702,0.03199648,0.00077549263],"about_ca_topic_score_codex":0.00003070597,"about_ca_topic_score_gemma":0.000016153203,"teacher_disagreement_score":0.8480547,"about_ca_system_score_codex":0.0001280509,"about_ca_system_score_gemma":0.0003848835,"threshold_uncertainty_score":0.99943626},"labels":[],"label_agreement":null},{"id":"W1973850740","doi":"10.2316/journal.206.2015.1.206-3971","title":"DGPS-BASED LOCALIZATION AND PATH FOLLOWING APPROACH FOR OUTDOOR WHEELED MOBILE ROBOTS","year":2014,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mobile robot; Computer science; Path (computing); Robot; Global Positioning System; Artificial intelligence; Real-time computing; Computer vision; Computer network; Telecommunications","score_opus":0.010747829197849168,"score_gpt":0.2602641367266767,"score_spread":0.24951630752882753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973850740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005710562,0.00010022057,0.99277896,0.00047896846,0.00072140456,0.00013530285,0.0000016294795,0.00003460141,0.00003834044],"genre_scores_gemma":[0.48151252,0.0000067079104,0.5182073,0.00013271102,0.00011046714,0.0000046539617,0.000010047023,0.000006138901,0.000009443015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988999,0.00005359667,0.00037906895,0.0001620107,0.00038944246,0.000115978284],"domain_scores_gemma":[0.9989478,0.00016495945,0.00037563522,0.00009627708,0.0003419029,0.00007341518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006546326,0.00010641089,0.00017754002,0.00018778448,0.000083135,0.00026831415,0.0003038618,0.0000580307,4.1405366e-7],"category_scores_gemma":[0.000170082,0.00009421366,0.00006920855,0.00008276083,0.00001996943,0.00046537086,0.00004626788,0.0000761603,4.5613663e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009242615,0.00006111425,0.0017114017,0.000021803387,0.000050767285,0.000004373171,0.00025141882,0.9594523,0.00023113324,0.004600958,0.00008663639,0.033518903],"study_design_scores_gemma":[0.0009978105,0.0001790314,0.0018713097,0.000083687235,0.000020766209,0.000045760495,0.000019651292,0.99503404,0.00016624777,0.001358775,0.000121155805,0.00010178184],"about_ca_topic_score_codex":0.00000235095,"about_ca_topic_score_gemma":1.1611475e-7,"teacher_disagreement_score":0.47580194,"about_ca_system_score_codex":0.00004226546,"about_ca_system_score_gemma":0.000048047805,"threshold_uncertainty_score":0.38419217},"labels":[],"label_agreement":null},{"id":"W1974042596","doi":"10.1016/s0921-8890(99)00113-x","title":"An efficient neural network method for real-time motion planning with safety consideration","year":2000,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Computer science; Artificial neural network; Motion (physics); Artificial intelligence; Real-time computing","score_opus":0.015913213684904597,"score_gpt":0.26442723013447317,"score_spread":0.24851401644956858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974042596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005137649,0.00013617724,0.9928319,0.00017950112,0.00035287716,0.0006051902,0.000006290337,0.00028478508,0.00046562194],"genre_scores_gemma":[0.20458715,0.000004182014,0.7946096,0.000060972307,0.00031520234,0.000031551677,0.0000369699,0.000026588192,0.000327803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826,0.00020019471,0.00038892077,0.0005248264,0.00019929977,0.00042672624],"domain_scores_gemma":[0.9989564,0.00024800067,0.00016192027,0.00041201056,0.00006731308,0.00015437229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081806397,0.00021566116,0.0003432416,0.000056589906,0.00045209646,0.0004542107,0.00022853457,0.00009501377,0.0000022191346],"category_scores_gemma":[0.000008828857,0.00018386784,0.00003415554,0.00016528662,0.000029279752,0.000210521,0.000024508672,0.00009985507,0.0000071842946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016302956,0.000028615355,0.00012683592,0.000020262163,0.00001779296,0.000015224956,0.00048245455,0.98740244,0.00007542946,0.0039201397,0.000071197195,0.007823322],"study_design_scores_gemma":[0.0004615544,0.00032462165,0.00096257473,0.00008962204,0.000021724405,0.00020364886,0.000027879907,0.9974404,0.00001172334,0.00007359444,0.00013550997,0.0002471446],"about_ca_topic_score_codex":0.00009074002,"about_ca_topic_score_gemma":8.379791e-7,"teacher_disagreement_score":0.19944951,"about_ca_system_score_codex":0.00006436075,"about_ca_system_score_gemma":0.00006679229,"threshold_uncertainty_score":0.7497913},"labels":[],"label_agreement":null},{"id":"W1974835798","doi":"10.1117/12.606483","title":"Control and learning for intelligent mobility of unmanned ground vehicles in complex terrains","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"McGill University","keywords":"Terrain; Robotics; Computer science; Limiting; Robot; Artificial intelligence; Control (management); Perception; Human–computer interaction; Systems engineering; Control engineering; Engineering","score_opus":0.019221749016220382,"score_gpt":0.2492201508702262,"score_spread":0.22999840185400583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974835798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9884652,0.00007599879,0.007976891,0.0024684882,0.000085636464,0.00066038716,0.000017341454,0.00006417149,0.0001859076],"genre_scores_gemma":[0.65290004,0.000017081351,0.34677792,0.00006151693,0.00011015561,0.00008247668,0.0000028842508,0.000018672765,0.000029279836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979597,5.2187342e-8,0.0007701702,0.00042343294,0.00047044802,0.00037624827],"domain_scores_gemma":[0.9981048,0.00041648993,0.00039326565,0.000064963184,0.0009222878,0.00009819082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012827787,0.00024980854,0.00046996787,0.00012827989,0.00007658804,0.00009531443,0.0010344158,0.00012143006,0.0000014534089],"category_scores_gemma":[0.0008032446,0.00022087598,0.00032210158,0.0002694184,0.00023627494,0.0005852126,0.00019972304,0.00030690109,3.3419747e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016631543,0.00041636513,0.003756784,0.0009364988,0.0004164666,1.5996272e-7,0.002152636,0.009950248,0.4062672,0.57045054,0.0005597804,0.004927006],"study_design_scores_gemma":[0.0016790248,0.00047279027,0.01079182,0.00023949845,0.000048458456,0.0000132769155,0.0009144705,0.96483135,0.018171696,0.001629505,0.0009246237,0.00028348202],"about_ca_topic_score_codex":0.000020229369,"about_ca_topic_score_gemma":2.4823692e-7,"teacher_disagreement_score":0.95488113,"about_ca_system_score_codex":0.00014664163,"about_ca_system_score_gemma":0.000029069863,"threshold_uncertainty_score":0.9007061},"labels":[],"label_agreement":null},{"id":"W1976572614","doi":"10.1117/12.497283","title":"Azimut: a multimodal locomotion robotic platform","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Mobile robot; Terrain; Computer science; Robot locomotion; Artificial intelligence; Orientation (vector space); Computer vision; Simulation; Engineering; Human–computer interaction; Robot control","score_opus":0.015444323156331513,"score_gpt":0.23339214383283524,"score_spread":0.21794782067650373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976572614","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97735167,0.00008161131,0.016383225,0.0015400196,0.0006585002,0.00061320857,0.0000106390235,0.00021490395,0.0031462417],"genre_scores_gemma":[0.22290935,0.000025473124,0.77636373,0.00010242605,0.00021840377,0.00010688963,0.0000042712645,0.00004893796,0.00022052541],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706495,2.1418336e-8,0.00078316865,0.0005822818,0.00097036187,0.00059919845],"domain_scores_gemma":[0.9976202,0.00020068679,0.00045974037,0.00012322153,0.0014054095,0.00019069265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009988755,0.00039256268,0.00047958567,0.0001570771,0.00013605374,0.00023118636,0.0018892906,0.00023550668,0.0000071410295],"category_scores_gemma":[0.0008896956,0.00033753706,0.00060959544,0.00057893584,0.00018177935,0.0012080515,0.00023527213,0.0004202261,0.000006558925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019844014,0.00019289494,0.0003873103,0.00026805702,0.0002942471,3.9867632e-7,0.0004363625,0.004016361,0.11459454,0.8773125,0.0018181371,0.00065937865],"study_design_scores_gemma":[0.0021104463,0.0005541146,0.001703145,0.00047963334,0.0001357124,0.00012333885,0.00074226334,0.8875215,0.09533385,0.008554628,0.0019474842,0.0007938511],"about_ca_topic_score_codex":0.000008295541,"about_ca_topic_score_gemma":4.8676508e-8,"teacher_disagreement_score":0.88350517,"about_ca_system_score_codex":0.00022830402,"about_ca_system_score_gemma":0.00006671137,"threshold_uncertainty_score":0.9999077},"labels":[],"label_agreement":null},{"id":"W1977826709","doi":"10.1109/ssrr.2010.5981561","title":"DEX - A design for Canine-Delivered Marsupial Robot","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robot; Rubble; Urban search and rescue; Search and rescue; Rescue robot; Traverse; Computer science; Simulation; Mobile robot; Engineering; Artificial intelligence; Geography","score_opus":0.029484117933075687,"score_gpt":0.26277099522304254,"score_spread":0.23328687728996686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977826709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093181984,0.0000070505603,0.9954299,0.00062225387,0.0012811476,0.0003254044,0.0000027278793,0.0003270698,0.001072648],"genre_scores_gemma":[0.052234493,4.4772602e-7,0.94591665,0.0002947127,0.00013843714,0.00004333697,0.0000023853684,0.000011724872,0.0013577965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887556,0.00003104744,0.00017135704,0.00036823057,0.0001891276,0.0003646838],"domain_scores_gemma":[0.99893373,0.00026442687,0.000051853065,0.0005130451,0.000101263424,0.00013565322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003812636,0.00013476946,0.00014406654,0.00006725923,0.00012213357,0.00013467288,0.00093351083,0.00008916442,0.000036962938],"category_scores_gemma":[0.0001378544,0.000117800315,0.00005670526,0.0001691901,0.000030367615,0.00025585605,0.00012842841,0.0001523762,0.00009322664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017705251,0.0006305792,0.0019298919,0.00007654503,0.00025064946,0.00030345822,0.003544882,0.13894634,0.1802247,0.21886545,0.19764228,0.25740817],"study_design_scores_gemma":[0.0005958446,0.00013569156,0.00117883,0.0000046692494,0.0000060774764,0.000050939394,0.0000062454783,0.99187773,0.0027497592,0.0021041012,0.0010696836,0.00022041937],"about_ca_topic_score_codex":0.00011639898,"about_ca_topic_score_gemma":0.000011722633,"teacher_disagreement_score":0.8529314,"about_ca_system_score_codex":0.000017910465,"about_ca_system_score_gemma":0.00013994194,"threshold_uncertainty_score":0.48037574},"labels":[],"label_agreement":null},{"id":"W1978239298","doi":"10.1109/humanoids.2014.7041336","title":"A hierarchical and adaptive mobile manipulator planner","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"HAMP; Mobile manipulator; Collision; Path (computing); Computer science; Motion planning; Parallel manipulator; Planner; Base (topology); Enhanced Data Rates for GSM Evolution; Node (physics); Workspace; Mobile robot; Simulation; Control theory (sociology); Real-time computing; Engineering; Robot; Artificial intelligence; Mathematics; Computer network; Structural engineering","score_opus":0.015655841953795723,"score_gpt":0.23369584863599865,"score_spread":0.21804000668220294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978239298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010598029,0.00003118287,0.9811556,0.00021985722,0.00015852414,0.00008804565,5.522142e-7,0.00019830954,0.007549915],"genre_scores_gemma":[0.5037653,9.294265e-7,0.4950942,0.0003778678,0.00006793308,0.000014299515,6.5855255e-7,0.000005202655,0.0006736144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919885,0.00005242116,0.00010066979,0.00029438466,0.00015353654,0.000200143],"domain_scores_gemma":[0.99941045,0.00013638903,0.000022838307,0.00028774186,0.00001809708,0.00012448416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001856668,0.00008970008,0.000111954265,0.00004919833,0.000063184394,0.00006799543,0.00031009378,0.000044236047,0.0000081408425],"category_scores_gemma":[0.00002360466,0.000070304275,0.000018001074,0.00009281964,0.000040590134,0.00015515502,0.00018431463,0.00010588267,0.00008191807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018574043,0.00013434309,0.0041741193,0.000017702278,0.00004680352,0.00008831771,0.0024090633,0.0052473233,0.00032830203,0.75215155,0.0076997275,0.2276842],"study_design_scores_gemma":[0.00021226017,0.00021834607,0.0067157107,0.0000088376155,0.0000020061284,0.000060309503,0.000013790089,0.9852968,0.00010046313,0.004579311,0.002650521,0.00014168274],"about_ca_topic_score_codex":0.000015776955,"about_ca_topic_score_gemma":5.382978e-7,"teacher_disagreement_score":0.98004943,"about_ca_system_score_codex":0.00000888803,"about_ca_system_score_gemma":0.0000133791455,"threshold_uncertainty_score":0.2866925},"labels":[],"label_agreement":null},{"id":"W1978330498","doi":"10.2316/journal.206.2006.2.206-2791","title":"PHYSICAL ROBOT AGENTS: COORDINATED INTELLIGENT AND RATIONAL AGENTS FOR COLLABORATIVE ROBOTS","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robot; Computer science; Human–computer interaction; Artificial intelligence","score_opus":0.021139501539043974,"score_gpt":0.2969094722501553,"score_spread":0.2757699707111113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978330498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020317076,0.00011391216,0.97477585,0.0036023126,0.00090817595,0.00015983879,0.000012474424,0.000028316344,0.00008206162],"genre_scores_gemma":[0.6886605,0.000032832868,0.31059113,0.00013530687,0.00039391135,0.000005670658,0.000030318866,0.000009605587,0.00014068131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873155,0.000050821767,0.00044266993,0.00018119722,0.00045857456,0.00013517537],"domain_scores_gemma":[0.9981206,0.0001942172,0.00048157186,0.00007881168,0.0010534743,0.00007131957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002955172,0.00013574172,0.0001992524,0.00021104989,0.00009522311,0.00031657264,0.00032831111,0.000049749247,0.0000020381058],"category_scores_gemma":[0.00011325599,0.00012203898,0.000056733683,0.0001536934,0.000048448725,0.0006423535,0.00008619301,0.00009918315,0.0000019537101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004008806,0.00030764967,0.0013455924,0.000017835791,0.00020392165,0.00004903838,0.00080880953,0.87940174,0.0014140543,0.08901136,0.0037462604,0.023653666],"study_design_scores_gemma":[0.00088326103,0.00017351404,0.016741684,0.000078940335,0.00002345129,0.00012986407,0.000044332115,0.9691968,0.00092677993,0.0112391915,0.0004253528,0.0001368165],"about_ca_topic_score_codex":0.000007297111,"about_ca_topic_score_gemma":0.000001086987,"teacher_disagreement_score":0.6683435,"about_ca_system_score_codex":0.00008707689,"about_ca_system_score_gemma":0.00008894142,"threshold_uncertainty_score":0.49766052},"labels":[],"label_agreement":null},{"id":"W1978580081","doi":"10.3166/jesa.37.451-476","title":"Planification locale de trajectoires dans l'espace des vitesses pour des robots mobiles non holonomes","year":2003,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science; Sociology; Computer science","score_opus":0.034082555368419934,"score_gpt":0.2698639128558062,"score_spread":0.23578135748738627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978580081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26285762,0.01915727,0.71362525,0.00047837078,0.0013960688,0.00032706605,0.000024080931,0.00034162894,0.0017926411],"genre_scores_gemma":[0.40411025,0.0015733893,0.58508605,0.000112405374,0.0004262318,0.00002927828,0.000004763699,0.00012297808,0.008534656],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99314713,0.0020861335,0.0013224827,0.0008585409,0.0008346206,0.0017510728],"domain_scores_gemma":[0.99577576,0.00090682606,0.001076178,0.00086639886,0.00063299463,0.0007418248],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0022934342,0.0008226266,0.00089047203,0.00051002234,0.001707205,0.002124999,0.0015759306,0.00035600175,0.00009995677],"category_scores_gemma":[0.0014415197,0.00080701616,0.00034587792,0.0013015277,0.0012714257,0.002323506,0.00017569921,0.0009010226,0.00038002414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043785687,0.0011427775,0.052479375,0.0016726658,0.00055346236,0.0036595098,0.044339012,0.37152833,0.013291866,0.007233285,0.006679142,0.4973768],"study_design_scores_gemma":[0.000974256,0.0006824176,0.5865988,0.0035072763,0.00020883132,0.025687698,0.002374818,0.35760936,0.0072087506,0.013168699,0.0009298105,0.0010492647],"about_ca_topic_score_codex":0.00035830552,"about_ca_topic_score_gemma":0.000081833154,"teacher_disagreement_score":0.5341194,"about_ca_system_score_codex":0.0014232915,"about_ca_system_score_gemma":0.0013313197,"threshold_uncertainty_score":0.9995924},"labels":[],"label_agreement":null},{"id":"W197970659","doi":"","title":"Strategies for safety in human robot interaction","year":2003,"lang":"en","type":"article","venue":"International Conference on Advanced Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Human–robot interaction; Task (project management); Computer science; Controller (irrigation); Motion planning; Process (computing); Control (management); Human interaction; Work (physics); Maxima and minima; Engineering; Human–computer interaction; Artificial intelligence; Systems engineering; Mathematics","score_opus":0.06582333661235336,"score_gpt":0.35735635640167046,"score_spread":0.2915330197893171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W197970659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004104709,0.000009336839,0.9626848,0.0010772349,0.0017405476,0.00025324407,0.000006403008,0.00010768581,0.03371028],"genre_scores_gemma":[0.5927967,0.000012484483,0.40642303,0.0001833901,0.000041037747,0.000037357437,0.000021295722,0.000012323025,0.00047236946],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984331,0.00006916559,0.00040972716,0.00046760624,0.00031911928,0.00030129115],"domain_scores_gemma":[0.9989734,0.00017712642,0.00017570257,0.00034152248,0.00026326202,0.0000689497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026032652,0.00020217187,0.0002032019,0.00024061755,0.00010376154,0.00023702497,0.0007688981,0.00007826662,0.00002714414],"category_scores_gemma":[0.00015316416,0.00020963029,0.00006322899,0.0001831253,0.000038558104,0.0008423024,0.00005729135,0.0002625769,0.000024094657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013743227,0.000060548,0.00005380394,0.0000035201156,0.000008302029,0.0000074137683,0.00011503574,0.410635,0.001209999,0.5852378,0.00002380911,0.002631048],"study_design_scores_gemma":[0.0017457628,0.00032758724,0.00092165207,0.0002243397,0.0000047025965,0.000030135787,0.0006568804,0.8340858,0.0017381432,0.15722212,0.002560022,0.00048282626],"about_ca_topic_score_codex":0.000007926079,"about_ca_topic_score_gemma":0.000015553547,"teacher_disagreement_score":0.59238625,"about_ca_system_score_codex":0.00022350115,"about_ca_system_score_gemma":0.00014976706,"threshold_uncertainty_score":0.8548475},"labels":[],"label_agreement":null},{"id":"W1979953055","doi":"10.2316/journal.206.2007.4.206-3059","title":"AN ART GALLERY-BASED APPROACH: ROADMAP CONSTRUCTION AND PATH PLANNING IN GLOBAL ENVIRONMENTS","year":2007,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Plan (archaeology); Computer science; Path (computing); Robot; Artificial intelligence; Computer vision; Mathematical optimization; Geography; Mathematics; Programming language; Archaeology","score_opus":0.013694953221485902,"score_gpt":0.271157602140425,"score_spread":0.2574626489189391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979953055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07826128,0.00004386236,0.92083555,0.00024696314,0.00041815528,0.00004024392,0.0000014352662,0.00001366045,0.00013881655],"genre_scores_gemma":[0.5482858,0.000006467418,0.45158663,0.000057194888,0.000054975902,2.5219393e-7,0.0000040309947,0.0000022234033,0.00000244067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989276,0.000039694954,0.00036902603,0.00013947814,0.00040848932,0.00011574089],"domain_scores_gemma":[0.99943,0.00005243298,0.00031046043,0.00007567363,0.000060185586,0.00007127027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006346234,0.00008735049,0.000115992734,0.00018646501,0.000034922054,0.00014484827,0.0002370346,0.00005481046,7.522274e-7],"category_scores_gemma":[0.000038625487,0.00008349317,0.000020809697,0.00008608162,0.000040220573,0.00056775776,0.00003471659,0.00011058333,9.1451875e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003466305,0.00017719087,0.10012599,0.000008411086,0.00003979575,0.00017533173,0.00049213174,0.8262566,0.0006274053,0.0055159125,0.00004814262,0.066498436],"study_design_scores_gemma":[0.00055782776,0.000090684574,0.18684493,0.00008906864,0.0000044844573,0.00043783255,0.00007957553,0.8106226,0.00005716129,0.0010801984,0.000053369225,0.00008227705],"about_ca_topic_score_codex":0.0000028033992,"about_ca_topic_score_gemma":2.879737e-7,"teacher_disagreement_score":0.4700245,"about_ca_system_score_codex":0.000101637386,"about_ca_system_score_gemma":0.0000379623,"threshold_uncertainty_score":0.34047526},"labels":[],"label_agreement":null},{"id":"W1980221990","doi":"10.2316/journal.206.2010.2.206-3281","title":"A NOVEL AUTONOMOUS LOCALIZATION TECHNIQUE OF SUBSEA IN-PIPE ROBOT","year":2010,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subsea; Robot; Marine engineering; Computer science; Artificial intelligence; Engineering","score_opus":0.011149726839187026,"score_gpt":0.2632213375001877,"score_spread":0.2520716106610007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980221990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011478943,0.000024330624,0.9865071,0.0008256604,0.0009859145,0.00007712354,0.0000017051535,0.00002175919,0.00007743596],"genre_scores_gemma":[0.5733044,0.000009143545,0.42658785,0.000031257245,0.00005512378,0.0000011710538,0.0000018449293,0.0000038112742,0.000005357113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882376,0.000023475905,0.0005429436,0.00011252568,0.00040227463,0.000094993135],"domain_scores_gemma":[0.9986925,0.00008227462,0.0005489463,0.000121517456,0.0005061364,0.000048601836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005524518,0.00008580316,0.00016014479,0.0004121055,0.00002226117,0.00008558067,0.0005196064,0.00007904398,0.000002290323],"category_scores_gemma":[0.00017062038,0.00008019441,0.00004205838,0.00019188249,0.00003831274,0.00052342087,0.00008049315,0.0002130572,8.630111e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018272323,0.00038548472,0.01063569,0.000030758976,0.00006944503,0.0000750742,0.0010209532,0.68752486,0.20177887,0.052763455,0.000047304944,0.045649808],"study_design_scores_gemma":[0.0005191151,0.000077622724,0.032404613,0.00012511692,0.000006145045,0.0005644822,0.000012065872,0.9537073,0.009703196,0.0027366304,0.00004919785,0.00009456389],"about_ca_topic_score_codex":0.00001989382,"about_ca_topic_score_gemma":0.000004704067,"teacher_disagreement_score":0.5618255,"about_ca_system_score_codex":0.000042504507,"about_ca_system_score_gemma":0.00011829099,"threshold_uncertainty_score":0.32702333},"labels":[],"label_agreement":null},{"id":"W1981105365","doi":"10.1109/itst.2006.288799","title":"A Specific Genetic Algorithm for Optimum Path Planning in Intelligent Transportation System","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Crossover; Genetic algorithm; Path (computing); Motion planning; Encoding (memory); Computer science; Mathematical optimization; Shortest path problem; Mutation; Algorithm; Artificial intelligence; Mathematics; Theoretical computer science; Machine learning; Computer network","score_opus":0.020458793360899848,"score_gpt":0.24299150572707232,"score_spread":0.22253271236617247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981105365","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026818537,0.00035147948,0.9950287,0.00004544774,0.000503083,0.00044909088,0.000009065282,0.0002760779,0.00065522484],"genre_scores_gemma":[0.08072604,0.000002917638,0.9188407,0.00002283691,0.00013077263,0.00007737397,0.000027121361,0.000014862637,0.00015734711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983743,0.000033579294,0.00048040802,0.0004745382,0.00024874482,0.00038845895],"domain_scores_gemma":[0.99936694,0.00009958056,0.000096687254,0.0003193883,0.00005665883,0.000060737853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026838604,0.00017334649,0.00021541434,0.00019527046,0.00006725328,0.00010753052,0.00046212398,0.00007624594,0.0000022629672],"category_scores_gemma":[0.00000264912,0.00016636903,0.000066604014,0.00035473565,0.000016158838,0.0001818394,0.000015100584,0.000092846276,0.000021758256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010722385,0.00020765838,0.003891914,0.00012425987,0.000019246367,0.0006095339,0.0019140824,0.7526063,0.00033217284,0.017766623,0.002663518,0.21985401],"study_design_scores_gemma":[0.00039831814,0.000074987176,0.0156041365,0.00011221401,0.0000035766432,0.000022880744,0.00014429317,0.9818363,0.00067140826,0.00037482288,0.00053783995,0.0002192133],"about_ca_topic_score_codex":0.00013141702,"about_ca_topic_score_gemma":0.0000019610488,"teacher_disagreement_score":0.22923006,"about_ca_system_score_codex":0.00012517092,"about_ca_system_score_gemma":0.000040198374,"threshold_uncertainty_score":0.67843324},"labels":[],"label_agreement":null},{"id":"W1982682728","doi":"10.1109/ccece.2010.5575184","title":"Parking control of an active-joint center-articulated mobile robot based on feedback from beacons","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Beacon; Mobile robot; Robot; Computer science; Orientation (vector space); Robot kinematics; Kinematics; Position (finance); Control theory (sociology); Robot control; Control (management); Control engineering; Artificial intelligence; Engineering; Real-time computing; Mathematics","score_opus":0.02141232756259606,"score_gpt":0.25711965165242745,"score_spread":0.2357073240898314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982682728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13440372,0.000017108956,0.86131287,0.00028320414,0.0017931258,0.00084147044,0.000177813,0.0004285371,0.0007421318],"genre_scores_gemma":[0.70796305,8.9937873e-7,0.291405,0.00027538286,0.00010260567,0.00007564387,0.00012483694,0.00002687755,0.000025725642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965266,0.00032130198,0.00070384337,0.0012777334,0.0006406779,0.000529847],"domain_scores_gemma":[0.9961296,0.00037764283,0.0006069844,0.0023544165,0.00022971194,0.00030167127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043921155,0.00050948036,0.0008524729,0.00024341744,0.00009450015,0.0002141746,0.0016545823,0.00054318155,0.00009502772],"category_scores_gemma":[0.00009366484,0.0004597902,0.0002612231,0.00017784035,0.00010642246,0.00021400778,0.00062529603,0.0013374996,0.00004691642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006261862,0.0010074326,0.00076518196,0.000037027752,0.00015476605,0.00008661133,0.00061089575,0.9566935,0.020843238,0.00010036254,0.000078194345,0.019560203],"study_design_scores_gemma":[0.0014734269,0.00023867429,0.011199212,0.00030988382,0.000039640123,0.000003899054,0.000019038192,0.9551499,0.030635105,0.000447361,0.000032492648,0.00045134994],"about_ca_topic_score_codex":0.00062678463,"about_ca_topic_score_gemma":0.000019376415,"teacher_disagreement_score":0.5735593,"about_ca_system_score_codex":0.0001004082,"about_ca_system_score_gemma":0.0002916304,"threshold_uncertainty_score":0.99978536},"labels":[],"label_agreement":null},{"id":"W1982770663","doi":"10.1007/s00453-001-0045-3","title":"Searching for Mobile Intruders in a Polygonal Region by a Group of Mobile Searchers","year":2001,"lang":"en","type":"article","venue":"Algorithmica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Combinatorics; Simple polygon; Polygon (computer graphics); Mathematics; Binary logarithm; Upper and lower bounds; Theory of computation; Discrete mathematics; Regular polygon; Computer science; Algorithm; Geometry","score_opus":0.020223396290688023,"score_gpt":0.28532774633730645,"score_spread":0.2651043500466184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982770663","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05513712,0.00045274725,0.9424844,0.0003821474,0.00021503647,0.00090366305,0.000012664377,0.0001259407,0.0002863175],"genre_scores_gemma":[0.46267438,0.00007018954,0.53565055,0.00027772484,0.00014635146,0.0006856985,0.00004243018,0.000047663707,0.00040503588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783754,0.00013867195,0.0004033874,0.00055640104,0.00041886567,0.00064513745],"domain_scores_gemma":[0.99868685,0.00044267267,0.00012965573,0.00050387444,0.00007829924,0.00015866467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000718634,0.00019208278,0.00031684482,0.00028335996,0.00008809837,0.000065190936,0.0009823057,0.000112172886,0.0000048033776],"category_scores_gemma":[0.00007669485,0.00019720379,0.000102689344,0.00077026273,0.000121722784,0.00039332628,0.00021691632,0.0002900225,0.000009001803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099574514,0.00070363213,0.0018471127,0.000100660654,0.00008818536,0.00020223418,0.0061047045,0.009492262,0.006169867,0.0025369532,0.0065244725,0.9661303],"study_design_scores_gemma":[0.0019213827,0.0010313191,0.0007059121,0.00013454708,0.000008863787,0.00016295869,0.0005819759,0.9802374,0.0022277187,0.002912685,0.009609706,0.00046548893],"about_ca_topic_score_codex":0.0003691828,"about_ca_topic_score_gemma":0.0000046523255,"teacher_disagreement_score":0.9707452,"about_ca_system_score_codex":0.00012842778,"about_ca_system_score_gemma":0.00011244521,"threshold_uncertainty_score":0.8041737},"labels":[],"label_agreement":null},{"id":"W1986210666","doi":"10.1109/iros.2010.5650614","title":"Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Extension (predicate logic); State (computer science); Asymptotically optimal algorithm; Mathematical optimization; Computer science; Action (physics); Motion planning; Tree (set theory); Algorithm; Mathematics; Artificial intelligence; Robot; Combinatorics","score_opus":0.04447851612308683,"score_gpt":0.2830100876012684,"score_spread":0.23853157147818155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986210666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03568518,0.000026539956,0.95969105,0.00041936568,0.0016985313,0.00031485193,0.0000018638127,0.00063798774,0.0015246457],"genre_scores_gemma":[0.2743957,0.0000014811143,0.7248401,0.000076825796,0.00031830615,0.00007914931,0.000005300727,0.000019319432,0.00026379988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980686,0.000045715726,0.00036262473,0.00059381477,0.00033258565,0.00059670873],"domain_scores_gemma":[0.9984012,0.00065679755,0.00010363299,0.0005330959,0.000105129286,0.00020012567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016777,0.00023136666,0.00029266812,0.00020854,0.00030393482,0.00032573956,0.0008509711,0.000113595466,0.000022295324],"category_scores_gemma":[0.00042007325,0.00020700572,0.0001278261,0.00025954196,0.000050292467,0.0011245277,0.00018231507,0.00033018438,0.00004743692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028461363,0.0004668703,0.0073164357,0.0001038312,0.00026020358,0.0002172944,0.0060836542,0.28534573,0.112312086,0.1503037,0.0025180061,0.43478757],"study_design_scores_gemma":[0.0017948273,0.00012589335,0.0054206303,0.000041726835,0.000013135571,0.00006199236,0.000045484816,0.9854872,0.0054863654,0.00064995384,0.00054732803,0.00032544695],"about_ca_topic_score_codex":0.0000069083176,"about_ca_topic_score_gemma":6.460604e-7,"teacher_disagreement_score":0.7001415,"about_ca_system_score_codex":0.000027192284,"about_ca_system_score_gemma":0.000049776754,"threshold_uncertainty_score":0.8441448},"labels":[],"label_agreement":null},{"id":"W1986503980","doi":"10.1109/icra.2014.6907753","title":"Optimizing online occupancy grid mapping to capture the residual uncertainty","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Occupancy grid mapping; Occupancy; Residual; Computer science; Grid; Representation (politics); Function (biology); Data mining; Mobile robot; Artificial intelligence; Algorithm; Mathematics; Robot; Engineering","score_opus":0.031609036472453785,"score_gpt":0.26236089759829384,"score_spread":0.23075186112584006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986503980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003515196,0.000049424125,0.9797205,0.011658505,0.0008990735,0.0001696981,0.0000046705695,0.00034364293,0.00363932],"genre_scores_gemma":[0.039865635,0.0000016598655,0.95241207,0.00591,0.00062111416,0.000010359078,0.00000820222,0.0000118572125,0.0011591209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839276,0.00012870287,0.00023920381,0.0004478863,0.00035523303,0.0004362389],"domain_scores_gemma":[0.99852353,0.0002848907,0.000066154935,0.00089893106,0.00008331722,0.00014319285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006422648,0.00017281377,0.00017986353,0.000089964575,0.0002335153,0.00018875695,0.0014888573,0.00006222057,0.00000909073],"category_scores_gemma":[0.00023352483,0.0001096747,0.000047832436,0.00047700622,0.000030632254,0.00017946823,0.00045290767,0.00025310204,0.00010780644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012455766,0.00012536948,0.0012638015,0.000029230068,0.00006752715,0.00006597885,0.016879039,0.7827835,0.00081036135,0.028448507,0.09372172,0.07579249],"study_design_scores_gemma":[0.00026180808,0.000072169154,0.0061617033,0.00007179124,0.00000573704,0.000043477106,0.00042133755,0.97066045,0.00013587461,0.0005140871,0.021311324,0.00034025274],"about_ca_topic_score_codex":0.00022767937,"about_ca_topic_score_gemma":0.000021665837,"teacher_disagreement_score":0.18787692,"about_ca_system_score_codex":0.00004103801,"about_ca_system_score_gemma":0.00005648241,"threshold_uncertainty_score":0.44724044},"labels":[],"label_agreement":null},{"id":"W1987474585","doi":"10.1109/icra.2013.6631066","title":"A graph-based approach to multi-robot rendezvous for recharging in persistent tasks","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rendezvous; Robot; Computer science; Mathematical optimization; Travelling salesman problem; Graph; Heuristic; Vertex (graph theory); Robot kinematics; Mobile robot; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Engineering","score_opus":0.06543835337402218,"score_gpt":0.2727956505544964,"score_spread":0.20735729718047421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987474585","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010930386,0.000028948094,0.994612,0.0013828517,0.00021599575,0.000986984,0.0000017473511,0.00023803333,0.0014404043],"genre_scores_gemma":[0.13264732,2.76795e-7,0.8654924,0.00082745915,0.0000213317,0.0004317442,0.0000049805144,0.000012913282,0.0005615723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829704,0.000057946145,0.0002713052,0.0006176019,0.00022937928,0.0005267528],"domain_scores_gemma":[0.99895746,0.00012143359,0.000056516055,0.0005845316,0.000092292845,0.00018776902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042469456,0.0001799754,0.0002193515,0.00034004461,0.00008783287,0.00016328691,0.00085102423,0.000077535005,0.0000047660646],"category_scores_gemma":[0.00009991605,0.00015992454,0.00012641764,0.0005920797,0.000019185105,0.00023844077,0.00012151562,0.00013136226,0.00007052302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021837257,0.0013432272,0.0026927677,0.0001549205,0.00007220618,0.000022739267,0.0072328635,0.9036323,0.004369854,0.007293282,0.010290701,0.06287334],"study_design_scores_gemma":[0.00073971774,0.00013484893,0.0018612474,0.000032719334,0.0000033021481,0.000007660929,0.0002005985,0.99623424,0.00031736944,0.00013166721,0.000091934475,0.00024470137],"about_ca_topic_score_codex":0.00032859674,"about_ca_topic_score_gemma":0.000004251789,"teacher_disagreement_score":0.13155429,"about_ca_system_score_codex":0.00009317591,"about_ca_system_score_gemma":0.000060144455,"threshold_uncertainty_score":0.6521534},"labels":[],"label_agreement":null},{"id":"W1987855954","doi":"10.1109/sysose.2007.4304217","title":"A Two-Stage Formation Flying Strategy to Reduce the Mission Time","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Control reconfiguration; Computer science; Task (project management); Set (abstract data type); Real-time computing; Phase (matter); Interoperability; Process (computing); Engineering; Embedded system; Systems engineering; Operating system","score_opus":0.04484924657927885,"score_gpt":0.3208038592071751,"score_spread":0.2759546126278962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987855954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012258254,0.000016089594,0.963964,0.0009377943,0.00014780258,0.00017923366,3.5788528e-7,0.0002048418,0.022291666],"genre_scores_gemma":[0.43563476,5.936187e-7,0.552932,0.0010019657,0.00012408635,0.000003606564,0.0000028298173,0.0000091122165,0.010290984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990188,0.000037694466,0.00019498375,0.00019307356,0.00026739482,0.00028803662],"domain_scores_gemma":[0.99926436,0.0001124982,0.000053770953,0.0004085776,0.000046941936,0.000113854694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009757297,0.000087889326,0.00007191927,0.00007702555,0.00021778978,0.00016106803,0.00062631303,0.000029640474,0.000023062898],"category_scores_gemma":[0.000045252622,0.000056729365,0.00002486727,0.00037313113,0.000009866188,0.00045821883,0.00013890736,0.000096950746,0.0005233817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024952898,0.000083695406,0.00007464286,0.000020219219,0.000016888062,0.00012216844,0.009322476,0.08602461,0.09729067,0.030251503,0.014958329,0.7618098],"study_design_scores_gemma":[0.00017744498,0.00008503897,0.0009233432,0.000026714668,0.0000017778249,0.000043218257,0.00023326027,0.97753376,0.019309202,0.00029395535,0.0012287352,0.00014356255],"about_ca_topic_score_codex":0.000045169283,"about_ca_topic_score_gemma":0.0000014472237,"teacher_disagreement_score":0.8915091,"about_ca_system_score_codex":0.000044672077,"about_ca_system_score_gemma":0.000033490167,"threshold_uncertainty_score":0.67271876},"labels":[],"label_agreement":null},{"id":"W1988196484","doi":"10.1109/tnn.2011.2169808","title":"Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Computer science; Artificial neural network; Artificial intelligence; Robotics; Motion planning; Alliance; Path (computing); Collision avoidance; Order (exchange); Collision; Computer security","score_opus":0.026455500938740276,"score_gpt":0.238045988728049,"score_spread":0.21159048778930872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988196484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017586544,0.00005632865,0.97962904,0.000119126744,0.00147298,0.0007309214,0.000016629798,0.00024770742,0.00014070151],"genre_scores_gemma":[0.8989496,0.00005044016,0.099396594,0.00041795196,0.00016914034,0.00021779317,0.000018736308,0.00006168726,0.0007180464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973484,0.00021120509,0.00052439235,0.00078790914,0.00023734251,0.0008907488],"domain_scores_gemma":[0.9987414,0.0003381164,0.00016422314,0.00054108957,0.000028347076,0.00018682223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002773732,0.00039027276,0.00039156998,0.00012055814,0.00038330563,0.00009015294,0.0007179283,0.00020833079,0.000017097651],"category_scores_gemma":[0.0000058886467,0.00038852004,0.00014784525,0.00058114016,0.00009868728,0.00055122917,0.000009605931,0.0004923598,0.000025095327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005957021,0.00019271972,0.00012718006,0.000003484827,0.000024804358,0.000022103315,0.00032253825,0.97258735,0.0008658709,0.000017081617,0.000462738,0.025314573],"study_design_scores_gemma":[0.0009669833,0.00035811638,0.00090768904,0.000045583824,0.00001834707,0.000013243318,0.00000799388,0.9959362,0.001278228,0.00001715849,0.000060553924,0.0003898647],"about_ca_topic_score_codex":0.00013101312,"about_ca_topic_score_gemma":0.000022976179,"teacher_disagreement_score":0.8813631,"about_ca_system_score_codex":0.00009308428,"about_ca_system_score_gemma":0.000018732919,"threshold_uncertainty_score":0.99985665},"labels":[],"label_agreement":null},{"id":"W1989052211","doi":"10.1007/s10846-005-9010-8","title":"A Novel Approach for Mobile Robot Navigation with Dynamic Obstacles Avoidance","year":2005,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Obstacle avoidance; Mobile robot; Obstacle; Computer science; Trajectory; Robot; Path (computing); Motion planning; Representation (politics); Mobile robot navigation; Real-time computing; Artificial intelligence; Control engineering; Robot control; Engineering","score_opus":0.023201343688182444,"score_gpt":0.2689790018510827,"score_spread":0.24577765816290026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989052211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022757861,0.0024575423,0.99307024,0.00014841207,0.0011327842,0.0007679497,0.0000033051915,0.000067200745,0.00007679614],"genre_scores_gemma":[0.3513334,0.000021101107,0.6478913,0.000034203233,0.00033844815,0.000054570497,0.0000050212825,0.000026139816,0.00029583028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971672,0.00009180654,0.0011240504,0.00038907686,0.0007674883,0.00046034926],"domain_scores_gemma":[0.99712294,0.0003140169,0.0011686806,0.0005134012,0.0006694277,0.00021152735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011802373,0.0003002059,0.00061207195,0.0002543752,0.00014529812,0.0003098965,0.0011876776,0.00011502747,0.0000010659909],"category_scores_gemma":[0.00007391755,0.0002240806,0.0001910386,0.00042276096,0.000059485577,0.00074883946,0.00006426817,0.00032303028,0.000013284036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027994914,0.00026389453,0.00010880776,0.0001295317,0.00011116024,0.00001446373,0.0009771286,0.98854536,0.0013800829,0.0006902168,0.00013902367,0.007612335],"study_design_scores_gemma":[0.0005934504,0.00067689974,0.00016511838,0.0006546929,0.000047193287,0.0024087199,0.0006145949,0.9933926,0.00073829654,0.000027127153,0.00040047764,0.00028085534],"about_ca_topic_score_codex":0.000019478854,"about_ca_topic_score_gemma":0.0000010742936,"teacher_disagreement_score":0.34905764,"about_ca_system_score_codex":0.00040701803,"about_ca_system_score_gemma":0.00020735608,"threshold_uncertainty_score":0.9137742},"labels":[],"label_agreement":null},{"id":"W1989117619","doi":"10.1002/rnc.578","title":"Linear time computation of feasible regions for robust compensators","year":2001,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Univerza v Mariboru; McGill University","keywords":"Minkowski addition; Quantitative feedback theory; Transfer function; Computation; Convolution (computer science); Interval (graph theory); Quotient; Inefficiency; Mathematics; Set (abstract data type); Computer science; Function (biology); Minkowski space; Control theory (sociology); Robust control; Algorithm; Mathematical optimization; Control system; Control (management); Pure mathematics; Artificial intelligence","score_opus":0.03111647698201668,"score_gpt":0.28351553165285936,"score_spread":0.25239905467084267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989117619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010712836,0.00026063714,0.98509645,0.0029541817,0.0007054818,0.00013168306,0.000018507166,0.000019746356,0.00010044679],"genre_scores_gemma":[0.20877817,0.00006780821,0.79002726,0.00021562209,0.00074514194,0.0000022837037,0.000008747858,0.000013634146,0.0001413452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987204,0.000054026114,0.0005271175,0.00014647377,0.000404604,0.00014734587],"domain_scores_gemma":[0.99784106,0.00032915582,0.00055253535,0.0001046994,0.0010732978,0.00009925749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005062518,0.000116082905,0.00029243916,0.0002605922,0.00005498422,0.000065991764,0.0005558198,0.00005892141,0.0000045097117],"category_scores_gemma":[0.00017560815,0.00010198934,0.00013201936,0.00013117857,0.00005307855,0.0003622175,0.000048438556,0.00013354915,0.0000044359626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067305256,0.0005005026,0.0056758877,0.000032282445,0.0007339027,0.00025363665,0.00049944106,0.95392674,0.0020123182,0.003119048,0.002816912,0.029756289],"study_design_scores_gemma":[0.0028487097,0.00025876376,0.0012993006,0.00012397624,0.000030503534,0.0004397913,0.000016201295,0.99330914,0.00012947069,0.0004762291,0.00096611906,0.00010180184],"about_ca_topic_score_codex":0.000005970083,"about_ca_topic_score_gemma":5.8675005e-7,"teacher_disagreement_score":0.19806534,"about_ca_system_score_codex":0.00003603631,"about_ca_system_score_gemma":0.00011599608,"threshold_uncertainty_score":0.41590044},"labels":[],"label_agreement":null},{"id":"W1990299461","doi":"10.1007/s11633-011-0602-4","title":"Application of fuzzy logic for autonomous bay parking of automobiles","year":2011,"lang":"en","type":"article","venue":"International Journal of Automation and Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Victoria; University of British Columbia","funders":"","keywords":"Fuzzy logic; Controller (irrigation); Set (abstract data type); Control engineering; Fuzzy control system; Computer science; Control (management); Engineering; Automotive engineering; Simulation; Control theory (sociology); Artificial intelligence","score_opus":0.029129105213858277,"score_gpt":0.2864331687700922,"score_spread":0.2573040635562339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990299461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056822434,0.00007611954,0.94199467,0.00013526366,0.00045632318,0.00008256409,0.0000017605978,0.00002998885,0.00040090107],"genre_scores_gemma":[0.6436394,0.0000029892783,0.3562701,0.0000269988,0.000053422988,9.090809e-7,0.0000010256828,0.0000026537489,0.0000024832227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988686,0.000034671786,0.0006307422,0.000109804976,0.00027250237,0.000083690684],"domain_scores_gemma":[0.99794155,0.00016872305,0.0011592924,0.00009660177,0.00059912796,0.00003467247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006220806,0.00007272373,0.00017877173,0.00022061872,0.000033121614,0.000028340968,0.0005265611,0.00003801239,0.0000016451049],"category_scores_gemma":[0.00009070343,0.00006622575,0.00007119842,0.00009708928,0.00003516855,0.000251679,0.00008492099,0.000063002706,5.408212e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004993188,0.00029331993,0.013872944,0.00009798684,0.00026448027,0.000012690743,0.009065561,0.02056531,0.006824891,0.1558726,0.0000704374,0.7930099],"study_design_scores_gemma":[0.00045812,0.00015215858,0.039290234,0.00012636013,0.000010977902,0.00013127284,0.00010101212,0.9453404,0.0045908852,0.009665829,0.000059857273,0.00007287476],"about_ca_topic_score_codex":0.000008313148,"about_ca_topic_score_gemma":1.897071e-7,"teacher_disagreement_score":0.9247751,"about_ca_system_score_codex":0.000030424111,"about_ca_system_score_gemma":0.00006271655,"threshold_uncertainty_score":0.27006078},"labels":[],"label_agreement":null},{"id":"W1991446466","doi":"10.1007/s10846-006-9055-3","title":"A Fuzzy–Braitenberg Navigation Strategy for Differential Drive Mobile Robots","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Obstacle avoidance; Robot; Mobile robot navigation; Fuzzy logic; Scheme (mathematics); Engineering; Computer science; Differential (mechanical device); Artificial intelligence; Robot control; Control engineering; Real-time computing; Mathematics","score_opus":0.02482876956848496,"score_gpt":0.27698763648240016,"score_spread":0.2521588669139152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991446466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013119524,0.001307892,0.97924566,0.00009396775,0.005193027,0.00069330726,0.0000046356704,0.00007155664,0.00027043273],"genre_scores_gemma":[0.9293251,0.0000185296,0.068072304,0.000021083239,0.0016929934,0.000055048564,0.000017230705,0.00003651604,0.00076120003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965244,0.0001886391,0.0015664774,0.0003814171,0.0008125645,0.0005265314],"domain_scores_gemma":[0.99683464,0.00040186782,0.0013280918,0.0004535926,0.00078112667,0.00020067517],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007577919,0.00032863964,0.0006984222,0.00032653092,0.0001565507,0.00048573763,0.0011189487,0.00017122927,0.0000045175702],"category_scores_gemma":[0.00006186674,0.00026908563,0.00036321953,0.0003794969,0.00005202122,0.0005869173,0.00008602938,0.0003025467,0.000033346016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001979729,0.00021051358,0.00031385577,0.00009584011,0.0001036561,0.00008552209,0.00029082227,0.9829649,0.0012380101,0.009040945,0.0021504418,0.0034857437],"study_design_scores_gemma":[0.0010788754,0.0016494732,0.00151532,0.0010522801,0.0001267187,0.0018613692,0.00048837636,0.98446757,0.0025648922,0.004114149,0.00049767114,0.0005833119],"about_ca_topic_score_codex":0.000114631264,"about_ca_topic_score_gemma":0.0000017434903,"teacher_disagreement_score":0.9162056,"about_ca_system_score_codex":0.00025996965,"about_ca_system_score_gemma":0.0001990522,"threshold_uncertainty_score":0.99997616},"labels":[],"label_agreement":null},{"id":"W1991765360","doi":"10.1109/icra.2014.6907420","title":"Feature-rich path planning for robust navigation of MAVs with Mono-SLAM","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature (linguistics); Visibility; Motion planning; Planner; Computer science; Artificial intelligence; Computer vision; Simultaneous localization and mapping; Monocular; Path (computing); Robot; Mobile robot; Geography","score_opus":0.019938957610202396,"score_gpt":0.24522108437773255,"score_spread":0.22528212676753015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991765360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010771443,0.000026023454,0.9859524,0.0004512456,0.00017258964,0.00020862857,0.0000019182824,0.00015736133,0.0022584172],"genre_scores_gemma":[0.15684855,2.680869e-7,0.8423641,0.000083998,0.00006304395,0.000019750178,0.000011497911,0.000010532114,0.00059828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989981,0.00003517007,0.00014694703,0.000321636,0.00025030941,0.0002478472],"domain_scores_gemma":[0.99905676,0.00018589618,0.00014341091,0.00041581542,0.00013558759,0.00006254122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036644135,0.0001319395,0.00019031575,0.00006889514,0.00008032766,0.000061509694,0.00047449558,0.00006905558,8.8988685e-7],"category_scores_gemma":[0.000044029603,0.00009946579,0.00003213598,0.00025312766,0.000026382333,0.00026436662,0.00006166288,0.00009853154,0.0000051699303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009477786,0.00022732811,0.023598325,0.0003459451,0.00013834523,0.000034600096,0.0053670206,0.78608215,0.002540226,0.071101144,0.025215205,0.08525492],"study_design_scores_gemma":[0.0005313152,0.00037253278,0.006155941,0.00013320483,0.000009484929,0.000025195652,0.00004895112,0.98882204,0.00272521,0.0005789727,0.00041351924,0.00018363576],"about_ca_topic_score_codex":0.000009595551,"about_ca_topic_score_gemma":2.5597697e-7,"teacher_disagreement_score":0.20273988,"about_ca_system_score_codex":0.000019330353,"about_ca_system_score_gemma":0.000038122947,"threshold_uncertainty_score":0.40560973},"labels":[],"label_agreement":null},{"id":"W1992009003","doi":"10.2316/journal.201.2007.4.201-1788","title":"FIRA MIDDLE-LEAGUE ROBOT SOCCER GAME STRATEGY","year":2007,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Computer science; Robot; Identifier; Mobile robot; League; Ball (mathematics); Task (project management); Engineering; Control engineering; Control (management); Control theory (sociology); Artificial intelligence; Simulation; Mathematics; Computer network","score_opus":0.03899332563583701,"score_gpt":0.25792364062087586,"score_spread":0.21893031498503884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992009003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029748273,0.004845662,0.98695785,0.00016782191,0.0017584673,0.00039117018,0.000004102866,0.00020313048,0.0026969868],"genre_scores_gemma":[0.9948262,0.000039439063,0.0028331869,0.00024477494,0.0004049497,0.000018050721,0.000002677845,0.00001517329,0.0016155322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979253,0.00009929407,0.00056909415,0.00049890444,0.0003360917,0.0005713544],"domain_scores_gemma":[0.9986666,0.0002724696,0.00017423178,0.0004965496,0.00012480798,0.00026533232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010747416,0.00024360426,0.00038554546,0.00013933567,0.00011281148,0.00032674472,0.00059928116,0.00013996084,0.000007110409],"category_scores_gemma":[0.000053514406,0.00020272814,0.000082895225,0.00019993102,0.00006605151,0.00028719756,0.00007788325,0.00020638535,0.00018982212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025642355,0.0008050842,0.02339493,0.00088835333,0.0011119184,0.0017086036,0.009901854,0.19116646,0.009963442,0.25381744,0.008548704,0.49843678],"study_design_scores_gemma":[0.0008731051,0.00031388726,0.007059142,0.0002152203,0.000024835499,0.00022850331,0.00039868223,0.9818426,0.00047292956,0.00040812098,0.007657975,0.0005050512],"about_ca_topic_score_codex":0.00018693123,"about_ca_topic_score_gemma":0.0000037396849,"teacher_disagreement_score":0.9918514,"about_ca_system_score_codex":0.000057404242,"about_ca_system_score_gemma":0.000046179888,"threshold_uncertainty_score":0.8267014},"labels":[],"label_agreement":null},{"id":"W1992604904","doi":"10.1007/s10846-009-9323-0","title":"On Multiple Secondary Task Execution of Redundant Nonholonomic Mobile Manipulators","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nonholonomic system; Control theory (sociology); Computer science; Jacobian matrix and determinant; Motion planning; Workspace; Obstacle avoidance; Mobile robot; Mobile manipulator; Redundancy (engineering); Control engineering; Artificial intelligence; Robot; Engineering; Mathematics; Control (management)","score_opus":0.017773937900241937,"score_gpt":0.25364385712027654,"score_spread":0.2358699192200346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992604904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10878775,0.0014824985,0.8841725,0.00010240427,0.0047301,0.00038988603,0.0000027934377,0.000048982012,0.00028306883],"genre_scores_gemma":[0.9784609,0.000043467637,0.021060081,0.00006718696,0.0002513071,0.000003658367,0.000001893261,0.000014905532,0.0000965784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970018,0.0001949159,0.001504621,0.00030601618,0.000637404,0.00035523606],"domain_scores_gemma":[0.9971083,0.000383088,0.0014071262,0.00059785694,0.00029680392,0.00020684987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010817313,0.0002593504,0.0006934348,0.00047010643,0.00007959756,0.000114249226,0.0010693296,0.00012878407,0.0000072489074],"category_scores_gemma":[0.00017876347,0.00021500864,0.0002441855,0.00028874248,0.000045660225,0.000371772,0.000059257894,0.00040754798,0.000059028185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004703067,0.0003042021,0.00050360075,0.000051026094,0.00008876311,0.00016319819,0.00091944716,0.984403,0.0024029163,0.0018581088,0.0014060057,0.007852683],"study_design_scores_gemma":[0.0013453893,0.0058561577,0.009983032,0.0020860513,0.00007629635,0.003135989,0.00090771524,0.96230936,0.011982591,0.000793132,0.00084483664,0.00067943794],"about_ca_topic_score_codex":0.00003525743,"about_ca_topic_score_gemma":3.1737417e-7,"teacher_disagreement_score":0.8696732,"about_ca_system_score_codex":0.00030989636,"about_ca_system_score_gemma":0.00020782372,"threshold_uncertainty_score":0.87677974},"labels":[],"label_agreement":null},{"id":"W1993673048","doi":"10.1109/ssrr.2009.5424169","title":"Control of an unconventional VTOL UAV for search and rescue operations within confined spaces based on the MARC control architecture","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Modular design; Architecture; Obstacle avoidance; Obstacle; Robotics; Computer science; Search and rescue; Mobile robot; Control (management); Remotely operated underwater vehicle; Collision avoidance; Control engineering; Artificial intelligence; Engineering; Robot; Collision","score_opus":0.017832400263110493,"score_gpt":0.2647377784400909,"score_spread":0.24690537817698038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993673048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019603314,0.000013271136,0.95631015,0.022936292,0.00007319123,0.0007760665,0.00004357072,0.000057959438,0.0001861751],"genre_scores_gemma":[0.8037594,1.4372087e-7,0.19389573,0.0021068396,0.00004135453,0.00003093302,0.000007597588,0.000004379431,0.00015361691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868226,0.00023023215,0.00023166265,0.00030840468,0.00033343412,0.0002139826],"domain_scores_gemma":[0.9987401,0.0005555562,0.000028363524,0.0004124059,0.00016868043,0.00009493944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092699984,0.00013219268,0.0002011655,0.000099476405,0.00019987975,0.00015940898,0.00045712016,0.000054479922,0.000017555343],"category_scores_gemma":[0.00016711376,0.000082537204,0.00005407981,0.00013626576,0.00009502244,0.000114605944,0.000015121877,0.00015215261,0.000001944719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012645846,0.000119994285,0.00008409123,0.00000883451,0.00002733838,0.000004375517,0.00030953638,0.84243023,0.003715964,0.15066046,0.00009428488,0.0024184224],"study_design_scores_gemma":[0.0025146352,0.0008389034,0.0030122108,0.000027040449,0.000010507326,0.000008885373,0.000034621546,0.99121267,0.0011183665,0.0010946621,0.00002434977,0.000103127844],"about_ca_topic_score_codex":0.000106910615,"about_ca_topic_score_gemma":0.000050544102,"teacher_disagreement_score":0.7841561,"about_ca_system_score_codex":0.000012213177,"about_ca_system_score_gemma":0.00020039945,"threshold_uncertainty_score":0.33657694},"labels":[],"label_agreement":null},{"id":"W1993709127","doi":"10.4236/wjet.2014.24034","title":"Model Helicopter Control Using Body-Mounted Vibro-Tactile Transducers","year":2014,"lang":"en","type":"article","venue":"World Journal of Engineering and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Joystick; Sight; Computer science; Computer vision; Engineering; Simulation; Artificial intelligence","score_opus":0.00815366017150981,"score_gpt":0.2219136514859385,"score_spread":0.2137599913144287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993709127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04390098,0.0003108566,0.9543716,0.0008841768,0.00030498725,0.00003426329,5.7619854e-7,0.00013199898,0.00006056808],"genre_scores_gemma":[0.7364833,0.000006667058,0.26338693,0.000044416858,0.000048409394,7.6993814e-7,6.354679e-8,0.00000916507,0.000020254445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991662,0.000014244352,0.00028526242,0.00014805056,0.00013356117,0.00025268592],"domain_scores_gemma":[0.9994206,0.00006939042,0.00013364581,0.00021069225,0.00008595555,0.00007973176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026708818,0.00013249223,0.0002829248,0.0007753525,0.00004709877,0.00004220211,0.00039800126,0.00008622208,0.0000010425649],"category_scores_gemma":[0.00006887031,0.00012104697,0.000042228574,0.00043385074,0.000034505545,0.0002000938,0.000035384943,0.00037536415,0.0000011512147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008078064,0.000021617452,0.0002787117,0.000021160837,0.00007855962,0.00004881016,0.00009933715,0.90503055,0.072362535,0.010128763,0.00006853853,0.011853353],"study_design_scores_gemma":[0.00060862023,0.00008021327,0.00007698051,0.00008427613,0.000018459916,0.00044597284,0.000003985123,0.99506444,0.0022668187,0.00066087523,0.00056763686,0.00012170807],"about_ca_topic_score_codex":0.0000019637778,"about_ca_topic_score_gemma":2.5833205e-7,"teacher_disagreement_score":0.6925823,"about_ca_system_score_codex":0.000035802514,"about_ca_system_score_gemma":0.000031667067,"threshold_uncertainty_score":0.4936152},"labels":[],"label_agreement":null},{"id":"W1993730936","doi":"10.1177/0278364914558017","title":"Motion primitives and 3D path planning for fast flight through a forest","year":2015,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Path (computing); Waypoint; Motion planning; Computer science; Control theory (sociology); Trajectory; Motion (physics); Constant (computer programming); Motion control; Obstacle; Obstacle avoidance; Configuration space; State (computer science); Sequence (biology); Robot; Control (management); Mobile robot; Artificial intelligence; Algorithm; Real-time computing","score_opus":0.2084760234536936,"score_gpt":0.42492986621057866,"score_spread":0.21645384275688506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993730936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061214264,0.0004146802,0.9841638,0.0076458375,0.00087020977,0.0001566171,0.0000034954533,0.000013781643,0.0006101402],"genre_scores_gemma":[0.40591237,0.000058456477,0.5927903,0.00015182361,0.0007848524,0.0000072690395,0.0000031297297,0.000015062735,0.00027675254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751496,0.00017908891,0.00033405976,0.0001683509,0.0015213317,0.00028218352],"domain_scores_gemma":[0.9965204,0.00088593,0.00021372557,0.00020000985,0.0020544962,0.0001254461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032530942,0.0000968269,0.00015167653,0.00021684339,0.0001478616,0.00040059356,0.0016662928,0.000048844748,7.410448e-7],"category_scores_gemma":[0.0010494544,0.000065175685,0.00005321427,0.00018550524,0.00014087999,0.00062348234,0.0004265299,0.00041695533,0.0000052198934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033489545,0.00028142697,0.008055103,0.000030243817,0.00041031858,0.00046565334,0.023122003,0.82036126,0.0006632123,0.09954322,0.014574062,0.03215862],"study_design_scores_gemma":[0.0015537888,0.0006748271,0.004378161,0.00029221587,0.000013512672,0.0010465361,0.0011235058,0.9365213,0.00045110463,0.051249977,0.002533079,0.00016199588],"about_ca_topic_score_codex":0.000013463745,"about_ca_topic_score_gemma":7.66659e-7,"teacher_disagreement_score":0.39979094,"about_ca_system_score_codex":0.00015297232,"about_ca_system_score_gemma":0.00027312269,"threshold_uncertainty_score":0.3862933},"labels":[],"label_agreement":null},{"id":"W1994043892","doi":"10.2316/journal.206.2012.1.206-3589","title":"MOTION PLANNING OF A WALKING ROBOT USING ATTITUDE GUIDANCE","year":2012,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Motion (physics); Computer vision; Motion planning; Physical medicine and rehabilitation; Artificial intelligence; Robot; Psychology; Medicine","score_opus":0.03653006357864191,"score_gpt":0.31809773772399824,"score_spread":0.28156767414535633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994043892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10595395,0.0003699205,0.89200854,0.00023382886,0.0013388357,0.000029076446,7.3754455e-7,0.000016075244,0.000049020415],"genre_scores_gemma":[0.5878046,0.000007768046,0.411987,0.000022686301,0.00017053053,1.5585634e-7,7.030917e-7,0.0000033325375,0.0000032018506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987691,0.000052745414,0.00047407043,0.00008034239,0.0004892322,0.00013448787],"domain_scores_gemma":[0.9986673,0.00007876967,0.0007201171,0.0000915221,0.00037735663,0.00006494855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069666497,0.00008429717,0.00015273961,0.00023825216,0.000043177562,0.000083114835,0.00037015733,0.000043631393,0.0000015250001],"category_scores_gemma":[0.00012793555,0.00007807609,0.000050103212,0.000113550566,0.000022908767,0.0010971564,0.00009319101,0.00010431768,9.60137e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000849777,0.00009988028,0.041912876,0.000021710917,0.00010627634,0.000020901736,0.0019442247,0.90386796,0.010965774,0.007562024,0.000032408283,0.033457454],"study_design_scores_gemma":[0.00029940566,0.000036821002,0.14870305,0.00033352256,0.00001604256,0.00043369635,0.00003157133,0.84791595,0.0015167573,0.0006117028,0.000015613543,0.00008588047],"about_ca_topic_score_codex":0.000005063128,"about_ca_topic_score_gemma":4.9344557e-8,"teacher_disagreement_score":0.48185065,"about_ca_system_score_codex":0.00007262829,"about_ca_system_score_gemma":0.000037607853,"threshold_uncertainty_score":0.31838506},"labels":[],"label_agreement":null},{"id":"W1994593272","doi":"10.1007/s10846-008-9276-8","title":"FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Field-programmable gate array; Motion planning; Path (computing); Payload (computing); Computer science; Genetic algorithm; Real-time computing; Process (computing); Embedded system; Robot; Artificial intelligence; Operating system","score_opus":0.03929995506321054,"score_gpt":0.3061820211934899,"score_spread":0.26688206613027937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994593272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007933041,0.0012226625,0.98805356,0.000052247895,0.0021533147,0.000477985,0.000006618506,0.000039622802,0.000060950435],"genre_scores_gemma":[0.119602546,0.00022249184,0.8789009,0.00002895788,0.00080392865,0.000021983527,0.0000061476603,0.000042453303,0.00037062351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964137,0.00017470206,0.0018425565,0.00029566,0.0008320797,0.00044131055],"domain_scores_gemma":[0.9964628,0.0003997491,0.0017801032,0.00042724994,0.00072400656,0.00020608304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010064096,0.00025941434,0.00073906337,0.00039688637,0.0001409363,0.0001022173,0.00096641073,0.00010173772,0.0000060989887],"category_scores_gemma":[0.000073648924,0.00022279106,0.00027508277,0.00035942238,0.000054405376,0.0003879471,0.000085170395,0.00017014224,0.0000143916195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031483363,0.00029397462,0.006259352,0.00027089787,0.0005860973,0.0007378721,0.0076468005,0.8756104,0.0028196834,0.0003905267,0.006567965,0.09878495],"study_design_scores_gemma":[0.0010502547,0.0016754305,0.008474328,0.00077607844,0.000101950965,0.004108702,0.0015620441,0.97541976,0.005865514,0.00017451501,0.00036885936,0.0004225791],"about_ca_topic_score_codex":0.000125669,"about_ca_topic_score_gemma":1.3261787e-7,"teacher_disagreement_score":0.1116695,"about_ca_system_score_codex":0.00018757704,"about_ca_system_score_gemma":0.00032289998,"threshold_uncertainty_score":0.9085156},"labels":[],"label_agreement":null},{"id":"W1995225993","doi":"10.2316/journal.201.2008.1.201-1775","title":"GENETIC ALGORITHMS FOR A SINGLE-TRACK VEHICLE AUTONOMOUS PILOT","year":2008,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Genetic algorithm; Computer science; Simulation; Artificial intelligence; Real-time computing; Machine learning; Operating system","score_opus":0.05567750388701655,"score_gpt":0.2542999932897938,"score_spread":0.19862248940277727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995225993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011187568,0.004502141,0.98103625,0.00020846879,0.001538531,0.00089018757,0.000013438469,0.0002587945,0.00036462274],"genre_scores_gemma":[0.96891975,0.000051757106,0.029463207,0.00020254015,0.00041254278,0.000173437,0.0000024236554,0.000022628214,0.0007516934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981354,0.00009028558,0.00049797544,0.0005429823,0.00024696934,0.00048641878],"domain_scores_gemma":[0.99872357,0.00027996802,0.00016117384,0.000466301,0.00015401938,0.00021499782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003070109,0.00023585539,0.00040219876,0.00010634031,0.00026291033,0.00017657387,0.0005334438,0.00007928499,0.000001533069],"category_scores_gemma":[0.000071045,0.00020851684,0.000083192965,0.00014375964,0.000087261724,0.0002011031,0.00006225675,0.00010736479,0.00006621184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006419136,0.004453284,0.029216567,0.0016502569,0.0019184501,0.0022871317,0.018347746,0.16590366,0.11908782,0.038987447,0.025306365,0.5921993],"study_design_scores_gemma":[0.0010027605,0.000987666,0.0019779531,0.00005521049,0.00001734529,0.00057660404,0.00003320135,0.9885701,0.00046617328,0.00012931788,0.005884608,0.00029908845],"about_ca_topic_score_codex":0.00009621302,"about_ca_topic_score_gemma":5.906791e-7,"teacher_disagreement_score":0.9577322,"about_ca_system_score_codex":0.000069268965,"about_ca_system_score_gemma":0.000068889596,"threshold_uncertainty_score":0.850307},"labels":[],"label_agreement":null},{"id":"W1996562341","doi":"10.1108/00022661211194951","title":"Formulas for the fuel of climbing propeller driven airplanes","year":2012,"lang":"en","type":"article","venue":"Aircraft Engineering and Aerospace Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Climb; Constant (computer programming); Descent (aeronautics); Propeller; Mach number; Differential equation; Equations of motion; Flight dynamics; Mathematics; Control theory (sociology); Aerospace engineering; Mathematical analysis; Aerodynamics; Computer science; Engineering; Classical mechanics; Physics; Marine engineering","score_opus":0.010803068460149244,"score_gpt":0.21943075916373117,"score_spread":0.20862769070358192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996562341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028374152,0.0025652999,0.96537167,0.0024575477,0.00042516954,0.0002776859,0.0000021353035,0.0004964293,0.000029933324],"genre_scores_gemma":[0.7726251,0.000050083006,0.22711107,0.000019614508,0.00004532494,0.00004407912,6.328402e-7,0.000012178617,0.00009193479],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992028,0.0000052262576,0.00014508638,0.00017219374,0.00008676028,0.00038797403],"domain_scores_gemma":[0.99928117,0.00019397146,0.000060805298,0.00038203778,0.00003611294,0.000045879744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021234297,0.00013063305,0.0001792708,0.00011991821,0.000072957315,0.000018948798,0.00045378972,0.0001220035,4.5077607e-7],"category_scores_gemma":[0.00011022767,0.00009514161,0.000034554203,0.0002599703,0.00006425351,0.00016383472,0.00018224005,0.00015046667,0.000004662241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003433737,0.0004701348,0.0761996,0.00157505,0.00075371406,0.000026743133,0.011345855,0.27015686,0.066995524,0.41079178,0.004290522,0.15735987],"study_design_scores_gemma":[0.00066858245,0.00027479365,0.0037435286,0.00013711162,0.000037274414,0.00016567709,0.00021808305,0.9699352,0.010994858,0.00048557346,0.012939684,0.00039966975],"about_ca_topic_score_codex":0.0000056345284,"about_ca_topic_score_gemma":4.426219e-7,"teacher_disagreement_score":0.74425095,"about_ca_system_score_codex":0.000012116373,"about_ca_system_score_gemma":0.000011618584,"threshold_uncertainty_score":0.3879762},"labels":[],"label_agreement":null},{"id":"W1996955458","doi":"10.1002/rob.20073","title":"Safe planning for human-robot interaction","year":2004,"lang":"en","type":"article","venue":"Journal of Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Task (project management); Focus (optics); Human–robot interaction; Inertia; Control (management); Engineering; Control engineering; Computer science; Artificial intelligence; Control theory (sociology); Simulation; Systems engineering","score_opus":0.06388367947157501,"score_gpt":0.3357488728651633,"score_spread":0.2718651933935883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996955458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033776371,0.0004934823,0.988581,0.0004347981,0.0064998763,0.00022051179,5.1733304e-7,0.000051470935,0.0003406796],"genre_scores_gemma":[0.708798,0.000002696487,0.28998747,0.00006001305,0.00093325396,0.000007909642,0.000001130009,0.000018525994,0.00019101205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980518,0.00007657795,0.00086982135,0.00021501687,0.00046294817,0.00032382837],"domain_scores_gemma":[0.99800247,0.00022862079,0.00096115906,0.00032157853,0.0003282802,0.00015786754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009941413,0.00017593503,0.00045520454,0.00031731508,0.00017663531,0.00027430363,0.000811461,0.00009212683,0.0000010902116],"category_scores_gemma":[0.00016193194,0.00014955048,0.00017877863,0.00023631875,0.000022313567,0.00075542217,0.00005843447,0.00026855723,0.000017665328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000092040755,0.000054270648,0.0003199514,0.00005296263,0.000066517794,0.00011622304,0.0008318018,0.9909567,0.0014273867,0.0049711457,0.00061094563,0.00058284],"study_design_scores_gemma":[0.011804884,0.005085704,0.010582291,0.009436579,0.00029413722,0.023837568,0.0027666586,0.91445553,0.002832868,0.011352259,0.00579143,0.0017600717],"about_ca_topic_score_codex":0.000034431014,"about_ca_topic_score_gemma":3.2837963e-7,"teacher_disagreement_score":0.7054204,"about_ca_system_score_codex":0.00029491438,"about_ca_system_score_gemma":0.00015201276,"threshold_uncertainty_score":0.60984915},"labels":[],"label_agreement":null},{"id":"W1997885432","doi":"10.3166/jesa.40.497-534","title":"Un cadre pour un système multirobot de service","year":2006,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Robot; User interface; Human–computer interaction; Programming language; Artificial intelligence","score_opus":0.025513136914963274,"score_gpt":0.25621728267450733,"score_spread":0.23070414575954407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997885432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04326028,0.03263286,0.8975658,0.013635745,0.006323081,0.0005305656,0.000067767214,0.0010199086,0.0049639866],"genre_scores_gemma":[0.18190578,0.00034058205,0.7941029,0.001248981,0.0035376346,0.00001973211,0.000012684737,0.00023700784,0.018594692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909828,0.0022478807,0.0020027661,0.0009758094,0.0014688737,0.002321873],"domain_scores_gemma":[0.99458635,0.0006688525,0.0015490503,0.00115697,0.001111201,0.00092758506],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002542158,0.0009918563,0.0011205642,0.0005002507,0.001424847,0.0023660255,0.002752296,0.00052983686,0.0003321066],"category_scores_gemma":[0.0004368847,0.0010014827,0.00046108937,0.0018867397,0.00028065528,0.0018083154,0.0006598417,0.0016116867,0.0022583264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002839294,0.0010778935,0.010505603,0.0014393466,0.00065473747,0.019907117,0.0053909123,0.2189238,0.0022181435,0.024961252,0.02871102,0.6861818],"study_design_scores_gemma":[0.0011999479,0.00016315714,0.36573693,0.0022039602,0.0001740931,0.036394168,0.000090887625,0.5740618,0.00047588735,0.017072894,0.0016518107,0.00077442866],"about_ca_topic_score_codex":0.0024339634,"about_ca_topic_score_gemma":0.000040184616,"teacher_disagreement_score":0.68540734,"about_ca_system_score_codex":0.0018091198,"about_ca_system_score_gemma":0.0014153288,"threshold_uncertainty_score":0.9998752},"labels":[],"label_agreement":null},{"id":"W1997980548","doi":"10.5539/mas.v4n5p196","title":"Study for solving the path on the three-dimensional surface based on Cellular Automata method","year":2010,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cellular automaton; Shortest path problem; Asynchronous cellular automaton; Path (computing); Computer science; Dimension (graph theory); Surface (topology); Interval (graph theory); Mobile automaton; Discretization; Simple (philosophy); Ant colony optimization algorithms; Algorithm; Mathematics; Mathematical optimization; Automaton; Theoretical computer science; Combinatorics; Automata theory","score_opus":0.03490982040020035,"score_gpt":0.28546867137509535,"score_spread":0.250558850974895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997980548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061269518,0.0000031530622,0.93396556,0.0019339051,0.000628726,0.0012450777,0.000004569031,0.00020611484,0.0007433713],"genre_scores_gemma":[0.68148303,1.9993177e-8,0.31750998,0.0008442302,0.000041416904,0.00008083835,5.647107e-7,0.000011922411,0.000028000539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643934,0.00007477904,0.00024729286,0.0010520581,0.0015444865,0.0006420328],"domain_scores_gemma":[0.9954497,0.0019867765,0.00015288022,0.0021726782,0.00011159273,0.00012632698],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0071814186,0.00025669177,0.000197434,0.00008565213,0.0017161983,0.00049701723,0.0041644145,0.00005728031,0.000004097718],"category_scores_gemma":[0.00023505544,0.00014017099,0.00006321957,0.00072010857,0.00034007934,0.00016394985,0.00048883696,0.0005294405,0.000048876296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040281448,0.00074319425,0.00019341319,0.0000083552,0.000015588523,0.000022188698,0.003028814,0.47530958,0.4467436,0.053639654,0.00058578904,0.019669523],"study_design_scores_gemma":[0.00035167055,0.0001526233,0.0011387221,0.000010526789,0.0000062690447,0.000002796047,0.000039402632,0.98082787,0.011656329,0.0055865697,0.00003150642,0.00019569081],"about_ca_topic_score_codex":0.000019199024,"about_ca_topic_score_gemma":0.000006058202,"teacher_disagreement_score":0.6202135,"about_ca_system_score_codex":0.000049400685,"about_ca_system_score_gemma":0.0003508267,"threshold_uncertainty_score":0.9995834},"labels":[],"label_agreement":null},{"id":"W1998089632","doi":"10.1115/detc2013-13523","title":"A Human-Inspired Method for Mobile Robot Navigation","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Mobile robot; Computer science; Mobile robot navigation; Robot; Artificial intelligence; Computer vision; Terrain; Obstacle avoidance; Deadlock; Real-time computing; Fuzzy logic; Robot control; Human–computer interaction; Distributed computing","score_opus":0.02857129328697893,"score_gpt":0.3281945195022155,"score_spread":0.29962322621523657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998089632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015347085,0.000016958838,0.9953412,0.00033958713,0.00023606038,0.0006917091,8.466381e-7,0.00038448023,0.0014544667],"genre_scores_gemma":[0.02132291,1.4039507e-7,0.9755338,0.00027899377,0.00006656273,0.00053870527,0.000007599735,0.0000090635995,0.0022422175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990067,0.000047430163,0.00019857188,0.00032994486,0.00016178165,0.00025558454],"domain_scores_gemma":[0.99917465,0.00011879954,0.00007004876,0.00043177663,0.00012408833,0.00008065907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031389928,0.00010624841,0.00013570779,0.00005565046,0.00013959184,0.00017150363,0.0005554087,0.000058058264,0.00003010688],"category_scores_gemma":[0.000022897257,0.00009159535,0.00005756306,0.0001833603,0.000013278923,0.0004951701,0.00009344606,0.00006214408,0.00020555493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000262468,0.00032481918,0.00044913706,0.000109987435,0.00009469925,0.000014132387,0.003267601,0.086760975,0.10363822,0.09010805,0.045560125,0.6696696],"study_design_scores_gemma":[0.00029380133,0.00014660787,0.0013226898,0.000015166104,0.0000037663349,0.000011095701,0.00002916757,0.9842292,0.0052184444,0.007967711,0.000599537,0.00016281486],"about_ca_topic_score_codex":0.00019748614,"about_ca_topic_score_gemma":5.638897e-7,"teacher_disagreement_score":0.8974682,"about_ca_system_score_codex":0.000030065445,"about_ca_system_score_gemma":0.000024044622,"threshold_uncertainty_score":0.37351498},"labels":[],"label_agreement":null},{"id":"W1998319395","doi":"10.1109/ccece.2006.277523","title":"A Wall Climbing Robotic System for Non Destructive Inspection of Above Ground Tanks","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Climbing; Robot; Computer science; Unmanned ground vehicle; Artificial intelligence; Engineering; Structural engineering","score_opus":0.012822217267473008,"score_gpt":0.2313129711355323,"score_spread":0.2184907538680593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998319395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037042335,0.000020919138,0.95802075,0.000052748164,0.0009073951,0.00033844865,0.0000013407982,0.00031348682,0.0033026019],"genre_scores_gemma":[0.66918665,2.4004132e-7,0.33049548,0.00001151613,0.000074935444,0.000018734832,0.0000023719454,0.000008416307,0.00020165315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875885,0.00003304436,0.00034541672,0.00036120033,0.00022311715,0.00027839205],"domain_scores_gemma":[0.99913234,0.00014832697,0.00020017935,0.00031975875,0.00015671854,0.000042683747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026301446,0.00013775354,0.00024270559,0.00013589543,0.00011741747,0.00007964511,0.00038212966,0.000080881684,5.0994964e-7],"category_scores_gemma":[0.000019827094,0.0001280151,0.00007779278,0.0003278475,0.000035279427,0.00038051384,0.00006562185,0.000081126534,0.000009579534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008011479,0.0004972653,0.008981496,0.0010149743,0.00019836385,0.000089240966,0.0032441465,0.34248647,0.026375072,0.60239804,0.0015706018,0.013064211],"study_design_scores_gemma":[0.0005634811,0.00021030604,0.01813398,0.000115958595,0.00001783587,0.000080168014,0.00016513589,0.9732942,0.0048326296,0.0023905332,0.000007646886,0.00018813155],"about_ca_topic_score_codex":0.00078043755,"about_ca_topic_score_gemma":0.000013771278,"teacher_disagreement_score":0.63214433,"about_ca_system_score_codex":0.00020469369,"about_ca_system_score_gemma":0.00005051149,"threshold_uncertainty_score":0.5220305},"labels":[],"label_agreement":null},{"id":"W1998474857","doi":"10.1109/iecon.2013.6699821","title":"Teleoperation of a mobile robot with model-predictive obstacle avoidance control","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Teleoperation; Obstacle avoidance; Mobile robot; Model predictive control; Obstacle; Sonar; Robot; Robot control; Computer science; Collision avoidance; Autonomous robot; Control engineering; Control (management); Engineering; Artificial intelligence","score_opus":0.0069511291020045935,"score_gpt":0.20450448587849854,"score_spread":0.19755335677649394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998474857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020682083,0.000039759925,0.97697055,0.0001963775,0.000056302648,0.00048440674,0.0000027366905,0.00015506965,0.0014127259],"genre_scores_gemma":[0.6405946,9.2595957e-7,0.3588088,0.00012210484,0.000011778286,0.00011441364,8.567992e-7,0.000004989944,0.00034155784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989587,0.000041032185,0.00021085746,0.00029310156,0.0002782649,0.00021801406],"domain_scores_gemma":[0.99906045,0.00008109554,0.000093721545,0.00044038193,0.00024666399,0.000077666664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012136582,0.00011925606,0.00019440743,0.000054390573,0.00005935593,0.0000693332,0.00043770793,0.000043818156,0.000012976647],"category_scores_gemma":[0.000020135902,0.00008656849,0.000025808484,0.00021012702,0.000052139552,0.0007884218,0.00005077108,0.00009370448,0.00006110087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004937864,0.000049212482,0.0007064935,0.0000047967796,0.000016982365,0.0000020249859,0.00069146894,0.98999906,0.0037802935,0.0013784099,0.0002773574,0.0030889828],"study_design_scores_gemma":[0.00053860014,0.0003117114,0.0025584742,0.00001921173,0.000004748418,0.000009737999,0.000066535205,0.99266446,0.0033516802,0.0003566751,0.000003954058,0.00011419996],"about_ca_topic_score_codex":0.00011056726,"about_ca_topic_score_gemma":0.0000023619405,"teacher_disagreement_score":0.6199125,"about_ca_system_score_codex":0.000030900283,"about_ca_system_score_gemma":0.00010107116,"threshold_uncertainty_score":0.35301608},"labels":[],"label_agreement":null},{"id":"W1998944623","doi":"10.2316/journal.206.2014.1.206-3788","title":"BEHAVIOUR TREE BASED CONTROL FOR EFFICIENT NAVIGATION OF HOLONOMIC ROBOTS","year":2014,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Holonomic; Robot; Tree (set theory); Computer science; Control (management); Holonomic constraints; Artificial intelligence; Mathematics; Physics","score_opus":0.010756191554105359,"score_gpt":0.2611460735523263,"score_spread":0.25038988199822093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998944623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07573156,0.000024125009,0.9220124,0.0012519008,0.0008518197,0.00008757804,0.000005164245,0.00001530015,0.000020147083],"genre_scores_gemma":[0.7314346,0.0000010836505,0.2684118,0.000051827665,0.00008612314,0.0000015459864,0.000005218053,0.000004064363,0.000003747842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989649,0.000048374943,0.000460819,0.00010647874,0.00033114193,0.00008830216],"domain_scores_gemma":[0.99836093,0.00024527698,0.0006827463,0.00009895758,0.00056313764,0.000048945578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006591153,0.000079432735,0.00016963799,0.00019197508,0.00003421472,0.00007992672,0.00037449252,0.00004430678,5.997035e-7],"category_scores_gemma":[0.00013817503,0.00007209988,0.00007776734,0.00005706013,0.000025330428,0.00018283352,0.000018690955,0.00006617833,8.4234625e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017651164,0.00007018504,0.0017837512,0.000009855289,0.000029914594,0.0000025978704,0.00010274941,0.9686485,0.0014659258,0.006751373,0.00003254474,0.021084981],"study_design_scores_gemma":[0.0014232098,0.00019804956,0.034142304,0.00011290738,0.000022126276,0.000035171248,0.000005530755,0.96175075,0.0013193097,0.0009127461,0.00001244433,0.000065474764],"about_ca_topic_score_codex":0.0000030814242,"about_ca_topic_score_gemma":1.7195181e-7,"teacher_disagreement_score":0.65570307,"about_ca_system_score_codex":0.00004973854,"about_ca_system_score_gemma":0.000058621685,"threshold_uncertainty_score":0.29401478},"labels":[],"label_agreement":null},{"id":"W1999152182","doi":"10.2495/safe-v2-n4-351-367","title":"Vehicle/people access control system for security management in ports","year":2012,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer security; Access control; Control (management); Security management; Computer science; Risk analysis (engineering); Business; Transport engineering; Engineering","score_opus":0.007902966615066549,"score_gpt":0.24696342320837317,"score_spread":0.23906045659330663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999152182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04649147,0.0005347121,0.9493006,0.00065180805,0.0026449075,0.00016075844,0.000011035628,0.000035933717,0.00016877342],"genre_scores_gemma":[0.98519003,0.000058347505,0.014410331,0.000047675443,0.00027881353,0.0000046279656,0.0000015124184,0.000006190771,0.0000024668832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891406,0.000020538075,0.0004194513,0.00010783084,0.0003208198,0.00021729803],"domain_scores_gemma":[0.99936205,0.00013705179,0.00017309116,0.00009203473,0.00012619347,0.00010956446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008364741,0.000103479375,0.0002050336,0.00023178612,0.000027400789,0.000100939425,0.0006031495,0.000044933233,0.0000010691756],"category_scores_gemma":[0.00004086539,0.00010103792,0.00006233412,0.00010972223,0.000006647736,0.0009809453,0.00012700594,0.00016339908,6.619133e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080687285,0.00088034244,0.087045886,0.0012798953,0.001954403,0.0013716895,0.023200791,0.48783883,0.00041099006,0.35250643,0.00044198707,0.042261872],"study_design_scores_gemma":[0.0024227,0.000044398315,0.06160082,0.00037319187,0.000022657894,0.0005790637,0.00013594293,0.93194604,0.00015536982,0.0006277056,0.0018755625,0.00021652246],"about_ca_topic_score_codex":0.000010038237,"about_ca_topic_score_gemma":0.0000011034529,"teacher_disagreement_score":0.9386986,"about_ca_system_score_codex":0.00013563207,"about_ca_system_score_gemma":0.000014974063,"threshold_uncertainty_score":0.41202068},"labels":[],"label_agreement":null},{"id":"W1999755059","doi":"10.2316/journal.206.2004.2.206-2562","title":"Building Environment Maps using Neural Networks","year":2004,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sonar; Representation (politics); Workspace; Mobile robot; Computer vision; Robot; Artificial intelligence; Grid; Artificial neural network; Grid reference; Motion planning; Geography","score_opus":0.017048969076474244,"score_gpt":0.26294515987016603,"score_spread":0.2458961907936918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999755059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040360503,0.00013702354,0.957087,0.0012361428,0.0011235557,0.000027762411,5.400859e-7,0.000017351944,0.000010165497],"genre_scores_gemma":[0.4821397,0.000019714396,0.5176126,0.00006853813,0.00015380846,1.08151355e-7,6.5854135e-7,0.000003218587,0.0000016418213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907744,0.000023187737,0.00031154617,0.0000982942,0.00038417306,0.00010537639],"domain_scores_gemma":[0.99940604,0.00003654637,0.00032970533,0.00007691477,0.0000910068,0.000059800674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025224418,0.00007798544,0.00010049779,0.00014188401,0.00005174832,0.00018136729,0.0003751173,0.000036328405,0.0000010019855],"category_scores_gemma":[0.000024938101,0.000071346934,0.00004293858,0.000056400193,0.00002105845,0.0005341798,0.000098987046,0.0001210891,9.0394803e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018034098,0.000020582813,0.00021671012,0.0000013127504,0.000025935617,0.00006088694,0.00010081033,0.97672284,0.0003498058,0.010193964,0.0000064504884,0.012298884],"study_design_scores_gemma":[0.000393098,0.00004600256,0.0028066237,0.000054204298,0.000008803715,0.000717061,0.00000542715,0.98780614,0.00009203325,0.007976446,0.000022973794,0.000071210074],"about_ca_topic_score_codex":0.0000050004724,"about_ca_topic_score_gemma":5.792447e-8,"teacher_disagreement_score":0.4417792,"about_ca_system_score_codex":0.00012974486,"about_ca_system_score_gemma":0.000029347237,"threshold_uncertainty_score":0.29094437},"labels":[],"label_agreement":null},{"id":"W2000182702","doi":"10.2316/journal.201.2011.4.201-2312","title":"A NOVEL APPROACH TO PATH PLANNING FOR AUTONOMOUS MOBILE ROBOTS","year":2011,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mobile robot; Computer science; Motion planning; Path (computing); Human–computer interaction; Robot; Artificial intelligence; Computer network","score_opus":0.06236130168732711,"score_gpt":0.2596233367345855,"score_spread":0.19726203504725837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000182702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054719084,0.0012648493,0.9930643,0.000018499431,0.0010237395,0.0014480571,0.000014584568,0.00018630525,0.0024324872],"genre_scores_gemma":[0.7556271,0.0000032533092,0.24231897,0.00021845702,0.0002086755,0.0010204293,0.000004510497,0.000022391698,0.00057621655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984201,0.000040686333,0.00039390245,0.0005439998,0.00017010084,0.00043116833],"domain_scores_gemma":[0.9989856,0.00011545039,0.00012595118,0.00042783978,0.0001034629,0.00024170894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005307764,0.00021391636,0.00037850218,0.00012584402,0.0001354622,0.00016516744,0.0005966761,0.00008872034,6.5184287e-7],"category_scores_gemma":[0.00004539498,0.00017945089,0.000070769325,0.00013736772,0.00002446709,0.00018109215,0.00009196944,0.00009297419,0.000025047275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034393152,0.0019961859,0.004461089,0.0010139082,0.0008291148,0.000077709316,0.08215033,0.6345083,0.007838184,0.15764,0.010421247,0.09872001],"study_design_scores_gemma":[0.0005548164,0.0004061229,0.00053224125,0.00009957515,0.000015754218,0.00008927153,0.00034822692,0.9933864,0.00012600757,0.0000686175,0.004092943,0.00028001913],"about_ca_topic_score_codex":0.00011848942,"about_ca_topic_score_gemma":9.107658e-8,"teacher_disagreement_score":0.7550799,"about_ca_system_score_codex":0.00004382166,"about_ca_system_score_gemma":0.000042751923,"threshold_uncertainty_score":0.7317795},"labels":[],"label_agreement":null},{"id":"W2000458501","doi":"10.1109/robot.2010.5509179","title":"Prediction-based reactive control strategy for human-robot interactions","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robot; Computer science; Control (management); Artificial neural network; Artificial intelligence; Motion (physics); Motion control; Control engineering; Robot control; Mobile robot; Engineering","score_opus":0.04948534131582762,"score_gpt":0.3169656025772524,"score_spread":0.2674802612614248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000458501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017654042,0.0000018205617,0.98957,0.00072705885,0.0013116773,0.00035726745,0.000025333726,0.00041471777,0.0058266846],"genre_scores_gemma":[0.77090496,3.8130874e-8,0.22767441,0.00024458295,0.00018574919,0.00011454834,0.00001301423,0.000008758098,0.0008539081],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989929,0.000028828608,0.0002150557,0.0003535596,0.00015626154,0.000253372],"domain_scores_gemma":[0.99867535,0.00045500527,0.00009459005,0.00048040948,0.00018745824,0.000107198284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023007755,0.00012811366,0.00014075021,0.00011160388,0.00024003345,0.00015080645,0.00047748213,0.00006857013,0.000041935145],"category_scores_gemma":[0.00009326128,0.00011516278,0.00007829522,0.00014157829,0.000042667587,0.00042351405,0.00002387145,0.00028912618,0.000039870807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009296985,0.0014260627,0.005805061,0.00005223248,0.0003726301,0.00006501268,0.0009443631,0.20532969,0.38244316,0.32833835,0.02405188,0.05107859],"study_design_scores_gemma":[0.0011009347,0.00020092161,0.006449913,0.0000093923045,0.000014318454,0.000020413272,0.000023741573,0.9839098,0.004123747,0.00264339,0.0013435822,0.00015986101],"about_ca_topic_score_codex":0.0000876773,"about_ca_topic_score_gemma":0.000036363148,"teacher_disagreement_score":0.7785801,"about_ca_system_score_codex":0.00003042884,"about_ca_system_score_gemma":0.00011997318,"threshold_uncertainty_score":0.4696202},"labels":[],"label_agreement":null},{"id":"W2001157267","doi":"10.2316/journal.206.2014.4.206-4048","title":"FUZZY REACTIVE NAVIGATION OF MOBILE ROBOT IN UNKNOWN ENVIRONMENTS","year":2014,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mobile robot; Fuzzy logic; Artificial intelligence; Mobile robot navigation; Robot; Robot control","score_opus":0.007807915682928216,"score_gpt":0.2525341690265569,"score_spread":0.24472625334362869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001157267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13695525,0.000053777283,0.8617153,0.00038433116,0.0006136403,0.00005864154,9.860397e-7,0.000008137771,0.0002099461],"genre_scores_gemma":[0.846857,0.000022298134,0.15301201,0.000023857465,0.00006129214,0.0000011579201,0.0000032380265,0.0000035219844,0.000015637217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988687,0.00006544602,0.00045479822,0.000102774335,0.00043023904,0.0000780348],"domain_scores_gemma":[0.9990423,0.00013887079,0.0005648824,0.000092620525,0.00012916727,0.00003215927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050301466,0.00007247917,0.00014981306,0.00021333995,0.000017017128,0.000047474274,0.00033796942,0.000043854103,8.969911e-7],"category_scores_gemma":[0.00009653462,0.000067246874,0.000035033903,0.00009302019,0.000027878305,0.0005236776,0.0000637422,0.00011109321,0.000002237067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000123873915,0.00016976072,0.0039157374,0.000011278346,0.000053013548,0.000021567006,0.0012203107,0.87720364,0.009620614,0.021621814,0.0000230913,0.08612675],"study_design_scores_gemma":[0.000732246,0.00024356268,0.06270921,0.00034264592,0.00000988078,0.00014852479,0.000044983266,0.9198988,0.0034124372,0.012205527,0.00013879334,0.00011337108],"about_ca_topic_score_codex":0.000008232782,"about_ca_topic_score_gemma":3.64687e-7,"teacher_disagreement_score":0.70990175,"about_ca_system_score_codex":0.000073366085,"about_ca_system_score_gemma":0.000027909791,"threshold_uncertainty_score":0.2742248},"labels":[],"label_agreement":null},{"id":"W2003349726","doi":"10.1016/s0165-0114(03)00205-7","title":"Fuzzy dynamic localization for mobile robots","year":2003,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sonar; Fuzzy logic; Mobile robot; Matching (statistics); Fuzzy set; Artificial intelligence; Robot; Computer vision; Set (abstract data type); Computer science; Data mining; Mathematics; Pattern recognition (psychology); Statistics","score_opus":0.01872270319494533,"score_gpt":0.2660677480531035,"score_spread":0.24734504485815817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003349726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023166707,0.0015541676,0.99086446,0.000040483817,0.0016985817,0.00066476373,0.000011967283,0.00014489672,0.0027039947],"genre_scores_gemma":[0.9292547,0.000031443622,0.06896339,0.00011067068,0.00007477035,0.00024390826,0.000029161833,0.000025734835,0.0012662263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882454,0.000096989366,0.00024854566,0.0003622703,0.00018515262,0.00028251228],"domain_scores_gemma":[0.999269,0.0000948355,0.00009820687,0.00034856884,0.00008363867,0.000105735344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045439025,0.00013718408,0.00020816055,0.000073776355,0.00017687559,0.00022314687,0.00022880168,0.000084737956,5.399238e-7],"category_scores_gemma":[0.000046748795,0.0001195512,0.000034813645,0.00018483742,0.00002196748,0.00021214878,0.000033149037,0.00005388404,0.000018005052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012529753,0.00021039667,0.0046323244,0.0008255424,0.00012622094,0.000069767935,0.004321516,0.71597564,0.0005095745,0.22148435,0.014960371,0.036871776],"study_design_scores_gemma":[0.000415843,0.0001412559,0.00033876908,0.000073401134,0.000007519756,0.0000956211,0.00012461164,0.9883411,0.00001763782,0.0021346165,0.008089696,0.00021990977],"about_ca_topic_score_codex":0.00002526431,"about_ca_topic_score_gemma":0.0000014394449,"teacher_disagreement_score":0.926938,"about_ca_system_score_codex":0.000038208393,"about_ca_system_score_gemma":0.00004288338,"threshold_uncertainty_score":0.48751566},"labels":[],"label_agreement":null},{"id":"W2003642268","doi":"10.1007/s12239-013-0028-9","title":"Effect of number of wheels on high speed UGV traversability: Online terrain assessment approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Automotive Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Traverse; Terrain; Unmanned ground vehicle; Simulation; Computer science; Process (computing); Range (aeronautics); Motion planning; Automotive engineering; Real-time computing; Engineering; Marine engineering; Artificial intelligence; Aerospace engineering; Robot; Geography; Geodesy","score_opus":0.008520181802196644,"score_gpt":0.3138847599106928,"score_spread":0.30536457810849615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003642268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7983293,0.000010333954,0.19725151,0.0028083334,0.0007034093,0.00019274546,0.000015499221,0.000055195287,0.0006336765],"genre_scores_gemma":[0.8035384,0.0000032637745,0.19631107,0.000045259032,0.00005648097,0.0000027019828,0.000003473319,0.000007792547,0.00003158186],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978234,0.00021540985,0.00074836804,0.0002489468,0.00077046035,0.00019339766],"domain_scores_gemma":[0.9971771,0.00044677584,0.0009577184,0.00034704592,0.0010160527,0.000055258333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085018174,0.00018049596,0.00052045885,0.0006357834,0.000017671402,0.000025117828,0.0018615868,0.0001671134,0.000051448067],"category_scores_gemma":[0.0004818433,0.00014021962,0.0001555049,0.00034049654,0.0002523251,0.00029432747,0.00024537486,0.000490505,0.000012965359],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044291568,0.008926474,0.16379541,0.00026714007,0.004600745,0.00062290375,0.0031461797,0.035326544,0.053268824,0.08510581,0.0022897301,0.6422073],"study_design_scores_gemma":[0.010682133,0.015345947,0.3725411,0.0011797375,0.00016200388,0.0030560242,0.0006102948,0.311472,0.2300079,0.053913027,0.00014772073,0.0008821172],"about_ca_topic_score_codex":0.000049625953,"about_ca_topic_score_gemma":2.2707296e-7,"teacher_disagreement_score":0.6413252,"about_ca_system_score_codex":0.00021567834,"about_ca_system_score_gemma":0.000116795214,"threshold_uncertainty_score":0.57179904},"labels":[],"label_agreement":null},{"id":"W2004727989","doi":"10.1145/1247069.1247080","title":"Finding curvature-constrained paths that avoid polygonal obstacles","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Path (computing); Curvature; Fast path; Bounded function; Mathematics; Motion planning; Domain (mathematical analysis); Any-angle path planning; Algorithm; Longest path problem; Computer science; Mathematical optimization; Topology (electrical circuits); Combinatorics; Geometry; Mathematical analysis; Shortest path problem; Graph; Robot; Artificial intelligence","score_opus":0.03392480443478551,"score_gpt":0.2720898942229681,"score_spread":0.23816508978818257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004727989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012231568,0.00017456779,0.9611665,0.00063368375,0.0009845076,0.00011076094,0.0000033289082,0.00051877904,0.02417629],"genre_scores_gemma":[0.5658222,0.0000019862389,0.43233123,0.00065751583,0.00011941785,0.0000018144325,0.0000044064086,0.000009268475,0.0010521645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981891,0.00003787146,0.00024566407,0.0004445772,0.00045758704,0.0006252111],"domain_scores_gemma":[0.9987887,0.00038880142,0.00009428573,0.00048670487,0.00004415905,0.00019733547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008489076,0.00019563112,0.00017834824,0.0001701937,0.0001753318,0.00017304759,0.00089259027,0.00011935178,0.000042008916],"category_scores_gemma":[0.00009146836,0.00016918755,0.000077900724,0.00038416075,0.00006893136,0.00043077694,0.00019335616,0.0002732121,0.0001348331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045499495,0.00046943736,0.07507338,0.000081601334,0.00026102373,0.0029026926,0.010874459,0.0022817103,0.01761042,0.42048508,0.01984881,0.4500659],"study_design_scores_gemma":[0.0035702887,0.00049010845,0.38510203,0.0003830154,0.00005043661,0.0017155232,0.0014023443,0.5017109,0.06992907,0.0146901375,0.01743943,0.0035166934],"about_ca_topic_score_codex":0.000029886623,"about_ca_topic_score_gemma":0.000004384285,"teacher_disagreement_score":0.5535906,"about_ca_system_score_codex":0.000051836127,"about_ca_system_score_gemma":0.000076748176,"threshold_uncertainty_score":0.68992686},"labels":[],"label_agreement":null},{"id":"W2005068613","doi":"10.22215/etd/2013-09919","title":"Multi-Robot Exploration Using Potential Games","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Action (physics); Set (abstract data type); Potential game; Obstacle; Function (biology); Space (punctuation); Sequential game; Robot; Artificial intelligence; Bounded function; Mathematical optimization; Game theory; Theoretical computer science; Nash equilibrium; Mathematics; Mathematical economics; Geography","score_opus":0.05638980100693653,"score_gpt":0.30657537278131386,"score_spread":0.25018557177437734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005068613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037908722,0.00013128288,0.99065435,0.000051486462,0.003918083,0.00029688762,0.0000012485568,0.00037072846,0.0007850848],"genre_scores_gemma":[0.007945954,0.000014509129,0.97021407,0.000061465435,0.0002099995,0.000034094624,0.00025442094,0.000033889228,0.021231573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981818,0.00006890547,0.00036911617,0.0006094362,0.00043976557,0.00033092816],"domain_scores_gemma":[0.99878633,0.000024634872,0.0002758781,0.00058314094,0.00022849828,0.000101491045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013586845,0.0003059815,0.00028632573,0.00024991325,0.00014992258,0.0004636876,0.0008329197,0.00029407316,0.00004633573],"category_scores_gemma":[0.000040150488,0.00028886035,0.00010181157,0.00025460264,0.000013279398,0.0012847073,0.00007374487,0.00025971528,0.00041753604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035221543,0.0008559654,0.00017303444,0.0005337109,0.00050803425,0.00046795412,0.019776735,0.47869518,0.18382877,0.004836529,0.014667915,0.29562095],"study_design_scores_gemma":[0.00020684542,0.000024724704,0.0013879344,0.00010033225,0.000020407999,0.0000136591725,0.0002584771,0.99466527,0.0027091112,0.00019746316,0.00003524195,0.00038056666],"about_ca_topic_score_codex":0.000267618,"about_ca_topic_score_gemma":0.0000088055285,"teacher_disagreement_score":0.51597005,"about_ca_system_score_codex":0.00006958182,"about_ca_system_score_gemma":0.00016616518,"threshold_uncertainty_score":0.99995637},"labels":[],"label_agreement":null},{"id":"W2005888545","doi":"10.1109/ssrr.2013.6719365","title":"Trajectory planning for surface following with a manipulator under RGB-D visual guidance","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Computer science; Motion planning; Artificial intelligence; Automotive industry; Trajectory; Orientation (vector space); RGB color model; Robot; Simulation; Engineering","score_opus":0.028178105143606507,"score_gpt":0.2825237533358516,"score_spread":0.2543456481922451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005888545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16057324,0.0001161659,0.8367894,0.0003146279,0.0003586788,0.00041689962,5.1486967e-7,0.000401619,0.0010288191],"genre_scores_gemma":[0.4714175,1.39808e-7,0.5269876,0.0003582453,0.00004748444,0.000038926053,0.0000016369996,0.000019469902,0.00112901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982718,0.000039148526,0.00024815535,0.0005435987,0.00034574576,0.0005515193],"domain_scores_gemma":[0.9990117,0.00024384173,0.00008780555,0.00042264,0.00008015921,0.00015387646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002691688,0.00022908888,0.00024847747,0.00006454553,0.00018577938,0.0002622016,0.00065257045,0.00007644672,0.0000122520405],"category_scores_gemma":[0.000021839927,0.00018149226,0.000089227986,0.000264315,0.000026443544,0.0007892103,0.00009693218,0.00012057974,0.00008335676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059828704,0.0004677503,0.06299101,0.00019652625,0.00071319233,0.00028863558,0.005662863,0.85013455,0.021023273,0.020789802,0.025256054,0.012416514],"study_design_scores_gemma":[0.0009285512,0.00029516924,0.023802398,0.00011955254,0.000014349477,0.000034553326,0.00024280504,0.97170234,0.0015847044,0.0005629943,0.00021458215,0.00049801706],"about_ca_topic_score_codex":0.0000927805,"about_ca_topic_score_gemma":0.0000016717153,"teacher_disagreement_score":0.31084424,"about_ca_system_score_codex":0.00006477633,"about_ca_system_score_gemma":0.00009822539,"threshold_uncertainty_score":0.74010396},"labels":[],"label_agreement":null},{"id":"W2006710186","doi":"10.1016/j.ins.2014.04.045","title":"Function-segment artificial moment method for sensor-based path planning of single robot in complex environments","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"State Key Laboratory of Robotics and System; Harbin Institute of Technology; National Natural Science Foundation of China","keywords":"Moment (physics); Motion planning; Obstacle; Robot; Controller (irrigation); Computer science; Path (computing); Position (finance); Point (geometry); Function (biology); Control theory (sociology); Zero moment point; Key (lock); Motion controller; Artificial intelligence; Motion control; Mathematics; Control (management); Physics; Geometry; Humanoid robot","score_opus":0.0826167854986922,"score_gpt":0.3142378479436513,"score_spread":0.23162106244495909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006710186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042703054,0.000003843477,0.99431604,0.00035517107,0.00026703888,0.00024062877,0.000004020808,0.000033352084,0.0005095919],"genre_scores_gemma":[0.2989867,1.3366021e-7,0.70053613,0.0004114945,0.000019295298,0.000025782381,0.00001232309,0.0000017958757,0.000006313915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838907,0.000083286846,0.0005312755,0.00018712619,0.00055617886,0.000253072],"domain_scores_gemma":[0.999099,0.00028070254,0.00032826327,0.00020357526,0.000036394,0.00005205476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018295983,0.000105214174,0.00016358105,0.00029784426,0.00016387993,0.00013765453,0.0004440899,0.000038231923,0.000004780166],"category_scores_gemma":[0.00008956585,0.00009373145,0.000039105194,0.00040389798,0.00008294956,0.0010206896,0.000060620932,0.000047491587,0.000015871516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009695093,0.00005519504,0.0011000078,0.000016294516,0.0000033270094,1.7095368e-7,0.001185538,0.9344343,0.0050821626,0.0030972082,0.00012852141,0.05488758],"study_design_scores_gemma":[0.00031571125,0.00032449444,0.0074804993,0.000028301156,0.0000025212908,0.000001150933,0.00025774355,0.9830499,0.0046960018,0.00070160365,0.0030285313,0.00011352728],"about_ca_topic_score_codex":0.000023100998,"about_ca_topic_score_gemma":3.748577e-7,"teacher_disagreement_score":0.29471642,"about_ca_system_score_codex":0.000065001805,"about_ca_system_score_gemma":0.0000517881,"threshold_uncertainty_score":0.38222572},"labels":[],"label_agreement":null},{"id":"W2010173946","doi":"10.1007/s10846-006-9067-z","title":"A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University","funders":"","keywords":"Load balancing (electrical power); Computer science; Obstacle; Decomposition; Parallel computing; Motion planning; Mathematical optimization; Idle; Domain decomposition methods; Exploit; Robot; Algorithm; Distributed computing; Mathematics; Engineering; Artificial intelligence","score_opus":0.028151060956485348,"score_gpt":0.29428538494381357,"score_spread":0.2661343239873282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010173946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024212433,0.004841676,0.988255,0.0003264268,0.0032993506,0.00055956305,0.0000031650884,0.000073106115,0.00022045041],"genre_scores_gemma":[0.59953636,0.000025082483,0.39860365,0.000054759304,0.00067317963,0.000026213,0.000005168252,0.000043079133,0.0010325151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99572057,0.00027485637,0.0015889325,0.0004489124,0.0012084963,0.0007582358],"domain_scores_gemma":[0.996468,0.0007387081,0.0010224571,0.0004933104,0.0010389447,0.00023854579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022910002,0.0003713726,0.00084179157,0.0003685608,0.00023807153,0.00064738415,0.0013071336,0.00016214975,0.0000018850219],"category_scores_gemma":[0.00017987711,0.00030564738,0.00032980216,0.0004451919,0.000055800774,0.00061618985,0.00012516393,0.0005025501,0.00002447466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032116088,0.000112953676,0.0011617878,0.000107977496,0.000121018624,0.00027008285,0.0014301065,0.99120265,0.00076381944,0.0027690516,0.0009908924,0.001037517],"study_design_scores_gemma":[0.00065849605,0.000580701,0.0010374936,0.0011140654,0.000043972956,0.0013983911,0.00087560934,0.9924937,0.0005605125,0.0004538069,0.00041696572,0.00036629528],"about_ca_topic_score_codex":0.00016011333,"about_ca_topic_score_gemma":0.0000017406422,"teacher_disagreement_score":0.5971151,"about_ca_system_score_codex":0.0007604137,"about_ca_system_score_gemma":0.00052845024,"threshold_uncertainty_score":0.99993956},"labels":[],"label_agreement":null},{"id":"W2010185804","doi":"10.2478/s13230-012-0014-3","title":"Fuzzy Control of a Log Carrying Robot on Tree-Filled Steep-Sloping Terrains","year":2012,"lang":"en","type":"article","venue":"Paladyn Journal of Behavioral Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robot; Terrain; Controller (irrigation); Fuzzy logic; Control theory (sociology); Computer science; Position (finance); Modular design; Fuzzy control system; Artificial intelligence; Simulation; Control engineering; Engineering; Control (management); Geography","score_opus":0.048898277049740424,"score_gpt":0.30518509444161107,"score_spread":0.25628681739187065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010185804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062279593,0.0006076655,0.9327373,0.0005384933,0.0030039747,0.0002605576,0.000008146652,0.00006522982,0.0004990715],"genre_scores_gemma":[0.771081,0.0000133290605,0.22841185,0.000099144745,0.00032222524,0.0000024204248,0.000001350811,0.000027027141,0.000041652904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9964138,0.00027611322,0.0012434616,0.00026105103,0.0010040132,0.00080154615],"domain_scores_gemma":[0.9969316,0.0002903398,0.0012860184,0.0006182167,0.00040511676,0.00046867138],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012048995,0.00036045082,0.00087506423,0.00045838798,0.00014129803,0.00009731072,0.0012179544,0.00017710897,0.000006636983],"category_scores_gemma":[0.00012726831,0.00031244333,0.00036086465,0.0004511933,0.00011222642,0.0007591028,0.000146895,0.0006533771,0.000016271335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002169148,0.002707112,0.1825987,0.00012247352,0.00034090315,0.00069023855,0.011110033,0.70074886,0.021389987,0.0066284016,0.0004930622,0.07295332],"study_design_scores_gemma":[0.037303675,0.018905107,0.525721,0.007237564,0.0034840396,0.0077468758,0.0046187076,0.36637178,0.018815622,0.002507336,0.00063504913,0.0066531915],"about_ca_topic_score_codex":0.000016177732,"about_ca_topic_score_gemma":0.0000017554512,"teacher_disagreement_score":0.7088014,"about_ca_system_score_codex":0.0001864722,"about_ca_system_score_gemma":0.00018235098,"threshold_uncertainty_score":0.99993277},"labels":[],"label_agreement":null},{"id":"W2010249150","doi":"10.1109/oceans.2010.5664082","title":"Solutions for practice-oriented requirements for optimal path planning for the AUV &amp;#x201C;SLOCUM Glider&amp;#x201D;","year":2010,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; Deutsche Forschungsgemeinschaft","keywords":"Glider; Path (computing); Computer science; Engineering; Marine engineering; Computer network","score_opus":0.14681419447019475,"score_gpt":0.3866917717169447,"score_spread":0.23987757724674993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010249150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001727318,0.00042365256,0.97296387,0.007670086,0.010220987,0.006847131,0.00053346297,0.0007066598,0.00046140328],"genre_scores_gemma":[0.0005499939,0.000019758612,0.9863224,0.000887606,0.0013338166,0.0051112496,0.0006605576,0.00011514821,0.0049994504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938558,0.0001568989,0.0012058443,0.0019882338,0.0008742497,0.0019189839],"domain_scores_gemma":[0.9877304,0.0059936205,0.001257078,0.00315646,0.0015305091,0.00033196664],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003892777,0.00086338917,0.00082344294,0.0002867469,0.0019182484,0.0009824602,0.0036191645,0.00085698976,0.000014235817],"category_scores_gemma":[0.005385552,0.0006856661,0.0006796505,0.00028002082,0.00018415667,0.0009356916,0.002683703,0.001274453,0.00004404268],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001055221,0.0016654609,0.00014280141,0.0014619444,0.0030449275,0.00001037234,0.012729701,0.3957702,0.0041217757,0.12240401,0.4329854,0.02460818],"study_design_scores_gemma":[0.0013350649,0.00021652499,0.00005966349,0.00024327525,0.00030419175,0.000044465825,0.00019596488,0.48329943,0.000071418894,0.004328403,0.50907254,0.0008290593],"about_ca_topic_score_codex":0.0001604597,"about_ca_topic_score_gemma":0.000015807245,"teacher_disagreement_score":0.1180756,"about_ca_system_score_codex":0.00025636735,"about_ca_system_score_gemma":0.0009784198,"threshold_uncertainty_score":0.99955946},"labels":[],"label_agreement":null},{"id":"W2010416489","doi":"10.1007/s10514-006-9014-7","title":"Spartacus attending the 2005 AAAI conference","year":2006,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robot; Computer science; Mobile robot; Human–computer interaction; Software; Modalities; Artificial intelligence; Key (lock); Computer security","score_opus":0.019716102943929985,"score_gpt":0.23624657111205843,"score_spread":0.21653046816812846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010416489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029932307,0.00029920912,0.9676701,0.0059923027,0.001118229,0.0002455446,0.000003170763,0.0005952859,0.021082917],"genre_scores_gemma":[0.80711323,0.000006838631,0.18360816,0.00069764256,0.0004351031,0.000041020303,0.000009543842,0.000026621612,0.008061875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808836,0.00009031807,0.0003219734,0.0005028354,0.00031792716,0.0006785686],"domain_scores_gemma":[0.9986138,0.00016630322,0.00015357924,0.0009102062,0.000060940085,0.00009518177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004101389,0.00022598215,0.00021763575,0.00008254764,0.0003545484,0.0003790946,0.001534599,0.0000817296,0.000034924997],"category_scores_gemma":[0.000026875185,0.0001687329,0.00007732087,0.00028213309,0.000096710224,0.00038893093,0.00029417817,0.00024897463,0.0006573684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060360303,0.00029044595,0.007272133,0.000025466123,0.00009166418,0.0005779837,0.0016154837,0.45726022,0.0027878128,0.37870207,0.08740014,0.06397053],"study_design_scores_gemma":[0.00045272257,0.00006258125,0.04436583,0.00005168634,0.000020807889,0.00027309297,0.000032460426,0.9210141,0.0014119745,0.0073095,0.0243772,0.000628044],"about_ca_topic_score_codex":0.00031733135,"about_ca_topic_score_gemma":0.000027270395,"teacher_disagreement_score":0.80411994,"about_ca_system_score_codex":0.00009702678,"about_ca_system_score_gemma":0.00016691773,"threshold_uncertainty_score":0.84493595},"labels":[],"label_agreement":null},{"id":"W2010464579","doi":"10.1002/mcda.376","title":"Multi‐criteria optimization of ball passing in simulated soccer","year":2005,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ball (mathematics); Heuristics; Mathematical optimization; Computer science; Heuristic; Set (abstract data type); Mathematics","score_opus":0.042362986144107,"score_gpt":0.3556786528163569,"score_spread":0.3133156666722499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010464579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08271577,0.00029610697,0.9161092,0.00034948837,0.00040202352,0.00008465386,0.000005846404,0.00002676191,0.000010114556],"genre_scores_gemma":[0.4255417,0.000040885152,0.5742254,0.00009183182,0.000057250003,4.261325e-7,0.0000035664991,0.000011085593,0.000027841706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959122,0.0003895852,0.002049963,0.00042302086,0.00086532184,0.0003599209],"domain_scores_gemma":[0.99662805,0.00047001298,0.0012218939,0.00064098387,0.0008231146,0.0002159727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020410751,0.00027588263,0.0009923413,0.0023421878,0.000083145474,0.0002822865,0.0011861273,0.00018574367,0.00017794316],"category_scores_gemma":[0.0008402155,0.00023783017,0.00052317505,0.0028248017,0.000051063533,0.0012499174,0.00022030977,0.0003160066,0.000011877072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056756766,0.00044102393,0.0035181309,0.0000060903067,0.00026022812,0.000091938724,0.00082585035,0.963275,0.004512387,0.000002050713,0.0001257002,0.026884831],"study_design_scores_gemma":[0.0024151339,0.00006745313,0.028011259,0.0001690354,0.00022807853,0.000037124053,0.00005472086,0.9681327,0.0005563689,0.000015491776,0.0000894259,0.0002232594],"about_ca_topic_score_codex":0.000039360526,"about_ca_topic_score_gemma":0.000016865635,"teacher_disagreement_score":0.34282595,"about_ca_system_score_codex":0.00020316447,"about_ca_system_score_gemma":0.00010599153,"threshold_uncertainty_score":0.96984327},"labels":[],"label_agreement":null},{"id":"W2010686798","doi":"10.1109/wac.2014.6936163","title":"Autonomous vehicle navigation and mapping with local minima avoidance paradigm in unknown environments","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Heuristics; Computer science; Motion planning; Maxima and minima; Artificial intelligence; Path (computing); Computer vision; Robot; Obstacle; Software deployment; Obstacle avoidance; Planner; Field (mathematics); Mobile robot; Mathematics; Geography","score_opus":0.008866351947437824,"score_gpt":0.20210370910230535,"score_spread":0.19323735715486753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010686798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17461678,0.0000298662,0.8241289,0.00036945238,0.000044290766,0.00007975973,1.6750066e-7,0.000054605203,0.0006761379],"genre_scores_gemma":[0.8218111,0.0000015619388,0.17780235,0.00014684067,0.000014752721,0.000008480255,0.0000014209768,0.00000532418,0.00020818868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903864,0.000057871574,0.0001546215,0.00035335287,0.00016538963,0.00023014577],"domain_scores_gemma":[0.9995213,0.00007586726,0.000053895677,0.00027211275,0.0000037079737,0.00007309518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024629413,0.00010777542,0.00012327182,0.000052480165,0.00005811144,0.000061945284,0.00024309881,0.000041870506,0.0000013765766],"category_scores_gemma":[0.000007367133,0.00009262551,0.000008602507,0.00015260394,0.00007455356,0.0003093728,0.000084955915,0.000107048625,0.00003269261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027486776,0.00032533487,0.12263223,0.000088296234,0.000041270367,0.00023332614,0.0075705596,0.12896746,0.0036602456,0.05018121,0.00021172549,0.68606085],"study_design_scores_gemma":[0.0004461723,0.00008484579,0.10203489,0.00005783756,0.000001141746,0.000030558378,0.000021496082,0.8948207,0.0005696023,0.00062252587,0.0011653674,0.00014488603],"about_ca_topic_score_codex":0.000046537203,"about_ca_topic_score_gemma":0.0000027951417,"teacher_disagreement_score":0.7658532,"about_ca_system_score_codex":0.000054563963,"about_ca_system_score_gemma":0.000017834813,"threshold_uncertainty_score":0.37771586},"labels":[],"label_agreement":null},{"id":"W2011193826","doi":"10.1109/tro.2014.2380593","title":"Multirobot Rendezvous Planning for Recharging in Persistent Tasks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Time horizon; Robot; Motion planning; Computer science; Scheduling (production processes); Schedule; Robustness (evolution); Travelling salesman problem; Real-time computing; Mathematical optimization; Operations research; Engineering; Artificial intelligence; Mathematics; Aerospace engineering; Algorithm","score_opus":0.1063186945706296,"score_gpt":0.3101755045786667,"score_spread":0.20385681000803713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011193826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044949327,0.00007688478,0.9956579,0.00087064155,0.00204113,0.0003643312,0.000009261199,0.00027993257,0.00025037845],"genre_scores_gemma":[0.25674087,0.0000046700134,0.74245274,0.00017282713,0.0000521638,0.00006379686,0.0000032346202,0.000030239351,0.00047943316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810725,0.00008296245,0.00037797182,0.00052679155,0.00036934004,0.00053566496],"domain_scores_gemma":[0.9987089,0.0002737487,0.00009909619,0.0005664562,0.00011598257,0.00023579663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005247148,0.00024169982,0.00028267308,0.0003646781,0.0001711034,0.00011030854,0.0006188525,0.00014628138,0.000001289903],"category_scores_gemma":[0.00003629148,0.00025669273,0.00015700662,0.0004831324,0.000037448706,0.00030624415,0.0000049984556,0.00039556413,0.000033234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022613196,0.00014673977,0.000029684727,0.000015598924,0.000024160183,0.000038081158,0.0021782557,0.99113464,0.000083257386,0.00018003257,0.00024286227,0.0059040785],"study_design_scores_gemma":[0.0012463167,0.0003821584,0.000038911196,0.00013569769,0.000023335971,0.000056562323,0.00044674962,0.9962028,0.00085720595,0.00021758657,0.00007793367,0.00031472635],"about_ca_topic_score_codex":0.000032841297,"about_ca_topic_score_gemma":0.0000041724543,"teacher_disagreement_score":0.2562914,"about_ca_system_score_codex":0.00030854382,"about_ca_system_score_gemma":0.00014600619,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W2011432612","doi":"10.1109/iros.2014.6942790","title":"Recursive non-uniform coverage of unknown terrains for UAVs","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traverse; Computer science; Terrain; Cover (algebra); Tree (set theory); Motion planning; Path (computing); Sample (material); Artificial intelligence; Robot; Mathematics; Geography; Computer network; Cartography; Engineering","score_opus":0.013644013163802787,"score_gpt":0.2513674048185488,"score_spread":0.237723391654746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011432612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014408776,0.0000044341955,0.9697576,0.00048108777,0.00042489488,0.0002031264,0.000003947727,0.00007806145,0.027605949],"genre_scores_gemma":[0.16849388,0.0000030289264,0.8291941,0.00040427552,0.00008568734,0.000016746933,0.0000041976923,0.0000091324155,0.0017889221],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999079,0.000021540201,0.00020927026,0.00026111898,0.00017462527,0.0002544442],"domain_scores_gemma":[0.99894786,0.00024781606,0.00011076029,0.00050772313,0.00010834388,0.00007750784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002715432,0.00010898509,0.00019247342,0.000077584766,0.000060502116,0.000035526078,0.00069250393,0.00005747431,0.0000062060676],"category_scores_gemma":[0.00013135331,0.000091664726,0.000071066286,0.00016303806,0.00003712952,0.00022279724,0.00011807908,0.00006242107,0.000026707647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040234845,0.00042156325,0.0010060597,0.00023977784,0.00014361182,0.000015105057,0.006856737,0.02251001,0.003902647,0.5111821,0.038475964,0.41520616],"study_design_scores_gemma":[0.00084665796,0.00053788174,0.001918657,0.000053307373,0.00000787075,0.000009197928,0.000021103417,0.9687775,0.006527142,0.012434012,0.008617361,0.00024935283],"about_ca_topic_score_codex":0.000019273815,"about_ca_topic_score_gemma":0.0000015042623,"teacher_disagreement_score":0.9462674,"about_ca_system_score_codex":0.000024054214,"about_ca_system_score_gemma":0.0000482687,"threshold_uncertainty_score":0.37379792},"labels":[],"label_agreement":null},{"id":"W2012191252","doi":"10.4018/ijimr.2011010102","title":"Optimal Robot Path Planning with Cellular Neural Network","year":2011,"lang":"en","type":"article","venue":"International Journal of Intelligent Mechatronics and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Artificial neural network; Computer science; Robot; Path (computing); Analogy; Mobile robot; Cellular neural network; State space; Artificial intelligence; Topology (electrical circuits); Mathematics","score_opus":0.030826229673249276,"score_gpt":0.2482994689580393,"score_spread":0.21747323928479004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012191252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009047256,0.0012409091,0.986944,0.00035202666,0.0021539978,0.00006741945,0.0000014574011,0.000029813484,0.0001631137],"genre_scores_gemma":[0.34020382,0.00014805695,0.65912884,0.00012917058,0.00033551085,8.129905e-7,0.000002027789,0.00001524381,0.000036530277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980905,0.00005684184,0.0005796763,0.0002393834,0.0006768932,0.00035667355],"domain_scores_gemma":[0.9984312,0.000087062144,0.0005494877,0.00022216364,0.00051589153,0.00019419203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005250076,0.00021873765,0.00027766536,0.00017680606,0.00009200955,0.00017335899,0.0012567888,0.00008138317,0.000009910448],"category_scores_gemma":[0.000031055482,0.00016555576,0.00010590371,0.00014457697,0.000056103843,0.0004325053,0.00027344545,0.00043463468,0.0000054202796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006825406,0.000083477426,0.0014563183,0.000004415069,0.00022909953,0.0007170715,0.0011548643,0.9620582,0.00006686767,0.029454907,0.00015308896,0.004553476],"study_design_scores_gemma":[0.0005354985,0.0011740153,0.00048548714,0.00028566987,0.000052100575,0.0014772144,0.00026257389,0.9910314,0.0012414104,0.0026763347,0.00044681862,0.00033148978],"about_ca_topic_score_codex":0.0000072966154,"about_ca_topic_score_gemma":2.6967567e-7,"teacher_disagreement_score":0.33115658,"about_ca_system_score_codex":0.00008042918,"about_ca_system_score_gemma":0.00012866952,"threshold_uncertainty_score":0.6751168},"labels":[],"label_agreement":null},{"id":"W2012270409","doi":"10.1016/j.aei.2012.01.003","title":"Improving lifting motion planning and re-planning of cranes with consideration for safety and efficiency","year":2012,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Smoothness; Path (computing); Computer science; Motion (physics); Dual (grammatical number); Random tree; Industrial engineering; Real-time computing; Operations research; Mathematical optimization; Engineering; Simulation; Artificial intelligence; Mathematics; Robot","score_opus":0.010113707891316293,"score_gpt":0.22762239898295558,"score_spread":0.21750869109163928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012270409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065734506,0.00033544502,0.9334463,0.000011546808,0.0001283173,0.00017736615,0.0000018178245,0.00011521497,0.000049485243],"genre_scores_gemma":[0.4480794,0.0000025766692,0.5518744,0.000011444925,0.000016627755,0.000005025762,0.000002768014,0.000006209635,0.0000015476461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991581,0.0000055632568,0.00034961937,0.00008691716,0.0001280508,0.00027173638],"domain_scores_gemma":[0.9992461,0.00028203608,0.00019642236,0.00014283656,0.000060256545,0.00007229991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035797764,0.00013170608,0.00017639148,0.000109096545,0.000115066854,0.000054851665,0.00008700302,0.000043815657,8.176577e-8],"category_scores_gemma":[0.00018831699,0.000121124984,0.000011724246,0.00013023896,0.000021481786,0.0013431536,0.000053338863,0.0000936544,1.53609e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075025546,0.0000052730106,0.002261964,0.00037176374,0.000009071047,5.770323e-7,0.009327552,0.96941113,0.00076053117,0.0012508777,8.956693e-7,0.01659288],"study_design_scores_gemma":[0.0004250807,0.00008580159,0.0030844638,0.00025460994,0.000009047809,0.000047333662,0.00038511335,0.99392134,0.0015696565,0.000011598976,0.00005053996,0.00015543161],"about_ca_topic_score_codex":0.0000012976874,"about_ca_topic_score_gemma":3.0302783e-8,"teacher_disagreement_score":0.3823449,"about_ca_system_score_codex":0.00001988257,"about_ca_system_score_gemma":0.000013523406,"threshold_uncertainty_score":0.49393335},"labels":[],"label_agreement":null},{"id":"W2012320692","doi":"10.1007/s10514-015-9427-2","title":"A hierarchical and adaptive mobile manipulator planner with base pose uncertainty","year":2015,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Planner; Mobile manipulator; Base (topology); Artificial intelligence; Manipulator (device); Mobile robot; Computer vision; Robot","score_opus":0.03189700956716965,"score_gpt":0.24575105014463464,"score_spread":0.213854040577465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012320692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09355716,0.00055219553,0.8989126,0.0010311948,0.00073005474,0.0008475358,0.000018675762,0.00085054955,0.0035000283],"genre_scores_gemma":[0.6229235,0.000001910788,0.37595037,0.00040261674,0.00011963859,0.00007103442,0.000009577325,0.000024932273,0.00049644645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981969,0.00010837439,0.00022188357,0.000646301,0.00035294128,0.00047360302],"domain_scores_gemma":[0.9985902,0.00012419578,0.00009408435,0.0006007495,0.000100552825,0.0004902448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003171104,0.00025737495,0.00029077654,0.00011540055,0.000117777745,0.00014538677,0.00054138683,0.00009792962,0.0000036145673],"category_scores_gemma":[0.000035668334,0.00020304839,0.000032636544,0.00025928917,0.00012801959,0.0003814459,0.0002878878,0.0002663472,0.000073526004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022463867,0.00039326856,0.003959787,0.000024601353,0.00015618892,0.0021883096,0.008867994,0.9097078,0.0000946353,0.020309947,0.0061700963,0.047902748],"study_design_scores_gemma":[0.0013534556,0.0013068658,0.0037293786,0.00005178869,0.000018845629,0.0006297181,0.0001587297,0.9895546,0.00009480228,0.0015927325,0.001012663,0.00049646164],"about_ca_topic_score_codex":0.00018158446,"about_ca_topic_score_gemma":0.000009598999,"teacher_disagreement_score":0.5293663,"about_ca_system_score_codex":0.00012503359,"about_ca_system_score_gemma":0.00033906676,"threshold_uncertainty_score":0.8280073},"labels":[],"label_agreement":null},{"id":"W2014864176","doi":"10.1117/12.910074","title":"Way-point navigation for a skid-steer vehicle in unknown environments","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tree traversal; Traverse; Motion planning; Computer science; Real-time computing; Skid (aerodynamics); Global Positioning System; Planner; Unmanned ground vehicle; Mobile robot; Robot; Path (computing); Compass; Point (geometry); Simulation; Artificial intelligence; Engineering; Algorithm","score_opus":0.013251181974495043,"score_gpt":0.2335686827156998,"score_spread":0.22031750074120476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014864176","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99044657,0.000102354,0.005941947,0.0017420348,0.00042498653,0.000706053,0.000019175803,0.00007332467,0.0005435631],"genre_scores_gemma":[0.42408353,0.000021449878,0.5748313,0.00011632837,0.0003889655,0.0003066925,0.000010672589,0.00004644915,0.00019463596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997657,2.499247e-8,0.0006745738,0.00040698596,0.00066565414,0.0005957866],"domain_scores_gemma":[0.9987967,0.00020196065,0.00035105963,0.000088702545,0.00040483326,0.00015673641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011271716,0.00028817402,0.0003577836,0.00011010118,0.000077350276,0.00010740902,0.0013080799,0.00018549353,0.000002456831],"category_scores_gemma":[0.00042134197,0.00025383488,0.00039686213,0.0003326365,0.00012090435,0.0013437509,0.00026468738,0.00027339184,0.000003417328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044085886,0.00031511765,0.001962745,0.00034276213,0.00020312426,1.2334797e-7,0.0011487585,0.0008751288,0.3731618,0.61920696,0.0014810715,0.0012583529],"study_design_scores_gemma":[0.003282567,0.0005885399,0.012114394,0.0007897749,0.0001243258,0.000040297302,0.0009680375,0.7559269,0.21230984,0.005485561,0.007445,0.0009247757],"about_ca_topic_score_codex":0.000007186577,"about_ca_topic_score_gemma":3.0478468e-8,"teacher_disagreement_score":0.75505173,"about_ca_system_score_codex":0.00026824727,"about_ca_system_score_gemma":0.000022512151,"threshold_uncertainty_score":0.9999914},"labels":[],"label_agreement":null},{"id":"W2014914032","doi":"10.1007/s10846-013-9933-4","title":"Control-Oriented Physical Input Modelling for a Helicopter UAV","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Thrust; Flapping; Rotor (electric); Simple (philosophy); Control theory (sociology); Engineering; Control engineering; Pitch control; Aerodynamics; Servomotor; Control (management); Computer science; Aerospace engineering; Mechanical engineering; Artificial intelligence","score_opus":0.027581947519843823,"score_gpt":0.2607779740000287,"score_spread":0.2331960264801849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014914032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006644275,0.00055995357,0.98757046,0.00054847304,0.003769972,0.000740751,0.0000016872846,0.00006436322,0.000100039055],"genre_scores_gemma":[0.8227414,0.000013205468,0.17537537,0.00018180672,0.0012701907,0.00005574531,0.0000010067029,0.000032143696,0.00032912113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969961,0.00016446025,0.0012002484,0.00033978157,0.0007403194,0.00055910926],"domain_scores_gemma":[0.9964813,0.0006402472,0.00090894755,0.0005089545,0.0011349999,0.00032557093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008835108,0.00030004367,0.0008076958,0.00029670808,0.00011578328,0.0003821288,0.0010942298,0.00010782912,0.0000052608343],"category_scores_gemma":[0.00015546384,0.00022769284,0.00038245073,0.00030443654,0.000045582838,0.000755147,0.000080303755,0.00032180795,0.0001331206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016909502,0.00017407173,0.00013116923,0.00006161768,0.00016496755,0.000021081376,0.0008562345,0.989888,0.0003445467,0.0047779684,0.0018400926,0.001723319],"study_design_scores_gemma":[0.00067527895,0.00053226843,0.000036802165,0.0003915378,0.00004949398,0.0003671176,0.00015854475,0.9957449,0.00042965406,0.0006922948,0.00068523793,0.00023685864],"about_ca_topic_score_codex":0.000079421996,"about_ca_topic_score_gemma":1.2794509e-7,"teacher_disagreement_score":0.81609714,"about_ca_system_score_codex":0.00017861434,"about_ca_system_score_gemma":0.00013565147,"threshold_uncertainty_score":0.92850447},"labels":[],"label_agreement":null},{"id":"W2015136933","doi":"10.1109/robot.2004.1302402","title":"A knowledge based genetic algorithm for path planning of a mobile robot","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":297,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Genetic algorithm; Computer science; Mobile robot; Robot; Path (computing); Genetic representation; Cultural algorithm; Domain knowledge; Representation (politics); Any-angle path planning; Artificial intelligence; Domain (mathematical analysis); Mathematical optimization; Population-based incremental learning; Algorithm; Machine learning; Mathematics","score_opus":0.02681135199092444,"score_gpt":0.28979951992997527,"score_spread":0.26298816793905083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015136933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008690636,0.00043053136,0.99707127,0.000049539663,0.00032243243,0.00044859547,0.000007890839,0.00021806383,0.00058260397],"genre_scores_gemma":[0.03990594,0.0000010142264,0.9596307,0.00009234747,0.00006598788,0.00014247911,0.0000044911617,0.000016046299,0.0001409886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860895,0.000030934407,0.00034511334,0.00042537934,0.0002061956,0.00038344605],"domain_scores_gemma":[0.9989017,0.0001863213,0.00012013602,0.00053135765,0.00014036613,0.00012006656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002630401,0.00017740588,0.00027600522,0.00015698533,0.0000851045,0.000048836035,0.00075268454,0.00007851514,0.000004940526],"category_scores_gemma":[0.00003313599,0.00016103528,0.00011031121,0.00038631598,0.000043843636,0.00014153155,0.00011219947,0.00008498509,0.00002211076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005081493,0.0004395459,0.000204192,0.000081134414,0.000037129772,0.000051454997,0.0020742465,0.7211581,0.001062155,0.0013952785,0.0005348415,0.27295685],"study_design_scores_gemma":[0.0013002006,0.00048085532,0.0009510857,0.00011165389,0.000009707706,0.000019694764,0.00004121278,0.9899076,0.005652114,0.0009572284,0.00034646995,0.00022216458],"about_ca_topic_score_codex":0.000030427147,"about_ca_topic_score_gemma":3.0618105e-7,"teacher_disagreement_score":0.27273467,"about_ca_system_score_codex":0.000059878297,"about_ca_system_score_gemma":0.0003026249,"threshold_uncertainty_score":0.6566828},"labels":[],"label_agreement":null},{"id":"W2018682346","doi":"10.1002/rob.20004","title":"A New Navigation Method for an Automatic Guided Vehicle","year":2004,"lang":"en","type":"article","venue":"Journal of Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Process (computing); Scheme (mathematics); Automated guided vehicle; Computer science; Artificial neural network; Control engineering; Fuzzy logic; Artificial intelligence; Measure (data warehouse); Control (management); Navigation system; Automatic control; Engineering; Real-time computing; Simulation; Data mining","score_opus":0.049665501229992255,"score_gpt":0.33929690907001514,"score_spread":0.2896314078400229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018682346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026913364,0.00025386637,0.9932016,0.00067302014,0.0027327149,0.0003149991,4.8428524e-7,0.000085956584,0.00004602201],"genre_scores_gemma":[0.051333334,0.0000010533493,0.94789314,0.0000624715,0.0005737706,0.0000055540963,0.0000010555932,0.000016478245,0.00011312584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789387,0.00017765962,0.0008820892,0.00021236372,0.000547159,0.00028688338],"domain_scores_gemma":[0.9978856,0.0002269636,0.0008381804,0.0004191734,0.00033785144,0.0002922204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018229856,0.00015628291,0.00044833458,0.00018585974,0.00009902632,0.0002799589,0.00090097927,0.00008956931,8.28976e-7],"category_scores_gemma":[0.00019428217,0.0001299014,0.00013896413,0.00034147978,0.000010219548,0.00088580715,0.000038586746,0.00015139444,0.0000134319935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035136954,0.000049537262,0.00007495763,0.00007573865,0.00005366076,0.000048345668,0.001239765,0.97930044,0.00162675,0.00461123,0.0013911297,0.011524942],"study_design_scores_gemma":[0.0015132884,0.00063032826,0.0005714532,0.0006112995,0.000042349224,0.0026552628,0.00011334246,0.98930097,0.00035625751,0.0039448305,0.000097653596,0.00016294734],"about_ca_topic_score_codex":0.00016444146,"about_ca_topic_score_gemma":5.935962e-7,"teacher_disagreement_score":0.048642,"about_ca_system_score_codex":0.00022545128,"about_ca_system_score_gemma":0.0004945246,"threshold_uncertainty_score":0.5297225},"labels":[],"label_agreement":null},{"id":"W2018693296","doi":"10.1007/s10514-012-9319-7","title":"Multi-robot repeated area coverage","year":2013,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Computer science; Robot; Metric (unit); Benchmark (surveying); Range (aeronautics); Workload; Set (abstract data type); Representation (politics); Performance metric; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02637132649266924,"score_gpt":0.24039371595820408,"score_spread":0.21402238946553484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018693296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007363803,0.00012947609,0.9856058,0.0010005854,0.0008888931,0.00045936563,0.0000025194943,0.0011365499,0.0034130036],"genre_scores_gemma":[0.37457365,0.000008687913,0.619379,0.0008883808,0.000071023445,0.000089378824,0.000014639418,0.000032976026,0.004942257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770385,0.00009453399,0.0004350128,0.00076563144,0.00030809647,0.0006928873],"domain_scores_gemma":[0.99796146,0.00016717386,0.00018204459,0.0012757978,0.00014679022,0.0002667119],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022510932,0.00031083237,0.00033502787,0.00015689372,0.00020762793,0.00039070504,0.0013513035,0.00014867977,0.00011062743],"category_scores_gemma":[0.00014675688,0.0002938706,0.00010650321,0.00043895366,0.000057616493,0.00089282135,0.00039070373,0.00027691596,0.0025557482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007716305,0.0009775698,0.0137361055,0.000058154925,0.00027015933,0.0009696769,0.004044391,0.6959911,0.026418343,0.0028042123,0.028297663,0.22642493],"study_design_scores_gemma":[0.0006654835,0.00008788215,0.07766596,0.00003568053,0.000007872746,0.00012618775,0.000010371996,0.91865313,0.0010808975,0.00045848626,0.00072248274,0.00048559363],"about_ca_topic_score_codex":0.0004140863,"about_ca_topic_score_gemma":0.00000295164,"teacher_disagreement_score":0.36720985,"about_ca_system_score_codex":0.00013796642,"about_ca_system_score_gemma":0.00013842163,"threshold_uncertainty_score":0.99995136},"labels":[],"label_agreement":null},{"id":"W2019340319","doi":"10.1007/s10489-008-0122-2","title":"EKEMAS, an agent-based geo-simulation framework to support continual planning in the real-word","year":2008,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Firefighting; Crowd simulation; Space (punctuation); Software; Virtual machine; Virtual reality; Human–computer interaction; Simulation; Artificial intelligence; Computer security","score_opus":0.07944703094191889,"score_gpt":0.3445610663383779,"score_spread":0.265114035396459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019340319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06390682,0.000012491858,0.93309504,0.00019640442,0.0002565602,0.0004641942,0.0000021019998,0.00019843798,0.0018679561],"genre_scores_gemma":[0.754838,0.0000028491168,0.24341673,0.0015333144,0.00009418716,0.0000635477,0.000011174508,0.000013454205,0.000026756934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759346,0.000115052826,0.00047864713,0.0006619453,0.0006012553,0.00054961786],"domain_scores_gemma":[0.9978321,0.0007820551,0.00013466213,0.0010304566,0.000065451764,0.00015524215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008888039,0.00024235646,0.00024845675,0.00019802892,0.00023609177,0.00012564484,0.0018066679,0.0001443265,0.00001790953],"category_scores_gemma":[0.00013679374,0.00020711894,0.00004490679,0.0009927636,0.00008271896,0.00024481423,0.00013097824,0.0004242494,0.00027311686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002867085,0.000118832744,0.0038421103,0.0000066934053,0.000005845476,0.00023122446,0.016936496,0.9215062,0.00011819049,0.014806115,0.00031281434,0.042086788],"study_design_scores_gemma":[0.00019728385,0.0004139852,0.044963,0.0001066353,0.000008016886,0.000037428985,0.00091279007,0.9435298,0.002843808,0.0048300806,0.0014675291,0.0006896562],"about_ca_topic_score_codex":0.00010095159,"about_ca_topic_score_gemma":0.000004475885,"teacher_disagreement_score":0.6909312,"about_ca_system_score_codex":0.00007016303,"about_ca_system_score_gemma":0.00012529138,"threshold_uncertainty_score":0.8446065},"labels":[],"label_agreement":null},{"id":"W2020710154","doi":"10.1115/detc2008-49266","title":"The Conceptual Design of a Perfusion Reactor Using a Complexity Measure","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Measure (data warehouse); Throughput; Ceramic; Conceptual design; Computer science; Process (computing); Conceptual model; Porosity; Flow (mathematics); Process engineering; Simple (philosophy); Simulation; Biological system; Materials science; Mechanics; Engineering; Physics; Composite material; Data mining; Operating system","score_opus":0.22020888936612065,"score_gpt":0.28899199204551573,"score_spread":0.06878310267939508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020710154","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021789052,0.0001691196,0.97712123,0.0001239621,0.00017455751,0.00013871037,6.69743e-7,0.00008773817,0.00039492888],"genre_scores_gemma":[0.41285333,0.000008860004,0.5869667,0.00007105303,0.000029102292,0.0000016732808,2.5720743e-7,0.000004430815,0.00006460926],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988584,0.00019481954,0.00019921582,0.000199013,0.0003360803,0.00021245956],"domain_scores_gemma":[0.9989266,0.00031982077,0.00010259067,0.00045300357,0.00013830807,0.000059668386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047665028,0.00009436279,0.00014017952,0.000029658951,0.0002992232,0.000025187552,0.0006857162,0.000041388448,0.000005176255],"category_scores_gemma":[0.00013465933,0.000059763697,0.000040760155,0.00022836335,0.00036086456,0.00015707525,0.00015359618,0.00010426363,0.000010316893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032153656,0.001179255,0.0130934175,0.000055901717,0.0003008774,0.00057134236,0.093130834,0.109570265,0.45164418,0.21619917,0.03665407,0.07727915],"study_design_scores_gemma":[0.0002598514,0.000093357754,0.0018508728,0.00002711162,0.000003546281,0.00017695101,0.00013059705,0.9927664,0.003990294,0.00039238608,0.00018588775,0.0001227761],"about_ca_topic_score_codex":0.00013431748,"about_ca_topic_score_gemma":8.692786e-7,"teacher_disagreement_score":0.8831961,"about_ca_system_score_codex":0.000032689357,"about_ca_system_score_gemma":0.00016841976,"threshold_uncertainty_score":0.2437093},"labels":[],"label_agreement":null},{"id":"W2020957543","doi":"10.1109/robot.2010.5509860","title":"Optimal coverage of a known arbitrary environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft Research","keywords":"Computer science","score_opus":0.008563783794535495,"score_gpt":0.21352559483875694,"score_spread":0.20496181104422145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020957543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06687652,0.000015428684,0.91228735,0.00021911434,0.00031340343,0.000065863875,0.0000015486484,0.00007949097,0.020141285],"genre_scores_gemma":[0.21694994,0.0000036383628,0.78220713,0.00010266122,0.000034635308,0.0000032597775,9.02995e-7,0.000003958173,0.0006938665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992612,0.000014991046,0.00014763595,0.00021177556,0.00020213512,0.0001622419],"domain_scores_gemma":[0.9993129,0.000056465306,0.00005050097,0.00049980846,0.000008591385,0.00007175727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015185596,0.00007932259,0.00010640324,0.000043209162,0.000027935905,0.000024350056,0.0005521721,0.000047268157,0.00011597264],"category_scores_gemma":[0.000012716327,0.00006786511,0.00003860615,0.00007014007,0.000043438173,0.00017743422,0.00018490429,0.00016362837,0.00014005331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030047822,0.0018199144,0.015270738,0.000087865505,0.0001934499,0.0005681421,0.0045533916,0.08852092,0.2160667,0.34872103,0.0099559175,0.31421188],"study_design_scores_gemma":[0.0007111897,0.00026975307,0.03021178,0.000015612532,0.000008459644,0.00012262142,0.000013149789,0.9131636,0.041463,0.0022090506,0.011374993,0.00043681182],"about_ca_topic_score_codex":0.000012610809,"about_ca_topic_score_gemma":1.7663515e-7,"teacher_disagreement_score":0.82464266,"about_ca_system_score_codex":0.000006387719,"about_ca_system_score_gemma":0.000032863474,"threshold_uncertainty_score":0.2767459},"labels":[],"label_agreement":null},{"id":"W2021238165","doi":"10.1109/icinfa.2006.374113","title":"Investigating the Performance of Corridor and Door Detection Algorithms in Different Environments","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Robustness (evolution); Computer vision; Robot; Artificial intelligence; Mobile robot; Real-time computing","score_opus":0.011133701925992659,"score_gpt":0.2049329305372605,"score_spread":0.19379922861126783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021238165","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8057676,0.00003092926,0.19376108,0.00009619754,0.00010234332,0.00009221317,3.30706e-7,0.000020778296,0.00012851776],"genre_scores_gemma":[0.9589459,0.0000045589895,0.040816955,0.00003954163,0.00002964621,0.0000095699625,5.765951e-7,0.0000035222095,0.00014974603],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923235,0.000042201507,0.00020294813,0.00018640282,0.00018323139,0.00015285805],"domain_scores_gemma":[0.99960166,0.0000765307,0.00008188455,0.00020888723,0.0000052284727,0.00002580033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018635883,0.0000899519,0.00010365169,0.00005151834,0.00006681466,0.000029236247,0.0002413199,0.00003014778,0.0000011916497],"category_scores_gemma":[0.000012294164,0.00005644748,0.0000133181275,0.00013814597,0.000073485724,0.00016213108,0.00012241994,0.00009791407,0.0000031293418],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005105995,0.0002313882,0.596423,0.00007097832,0.000019452067,0.000009883919,0.0018603741,0.04727735,0.080221355,0.00097361516,0.00010036359,0.27280718],"study_design_scores_gemma":[0.00013632132,0.000041522053,0.35513163,0.000014937692,0.0000012229203,0.000007568033,0.000012132445,0.6188804,0.025607169,0.00010908083,0.000008887246,0.000049146947],"about_ca_topic_score_codex":0.00018510583,"about_ca_topic_score_gemma":0.000007844018,"teacher_disagreement_score":0.57160306,"about_ca_system_score_codex":0.000026860174,"about_ca_system_score_gemma":0.0000067107826,"threshold_uncertainty_score":0.23018615},"labels":[],"label_agreement":null},{"id":"W2021253355","doi":"10.1142/s0219878910002142","title":"TERRAIN ROUGHNESS ASSESSMENT FOR HIGH SPEED UGV NAVIGATION IN UNKNOWN HETEROGENEOUS TERRAINS","year":2010,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Terrain; Computer science; Traverse; Unmanned ground vehicle; Range (aeronautics); Navigation system; Standard deviation; Simulation; Real-time computing; Computer vision; Artificial intelligence; Aerospace engineering; Geodesy; Geology; Engineering; Mathematics","score_opus":0.008731781000479569,"score_gpt":0.29465356279135774,"score_spread":0.2859217817908782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021253355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20976073,0.000003618028,0.7836927,0.002234862,0.0037883583,0.00019144332,0.000018546483,0.000033045646,0.0002766747],"genre_scores_gemma":[0.78577596,0.0000023796385,0.21312292,0.00061099726,0.0003342096,0.000008583404,0.00012902122,0.00000535501,0.000010549486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979623,0.00004936785,0.0009530874,0.000113303235,0.00074118725,0.00018074535],"domain_scores_gemma":[0.9976268,0.00011778733,0.00090683694,0.00017886932,0.0010921203,0.00007762044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010283163,0.00013730973,0.0001771576,0.0005995098,0.00006085463,0.00038220955,0.00090518035,0.000106646476,0.000027007103],"category_scores_gemma":[0.0001374402,0.0001306934,0.00009763687,0.00019237923,0.000029060311,0.004033818,0.00007912602,0.00029955845,0.000014998087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049343857,0.0009378209,0.0050027356,0.00012988276,0.00043757103,0.000280391,0.013897751,0.28522032,0.041309487,0.16916966,0.0029330824,0.48018786],"study_design_scores_gemma":[0.005447708,0.0005374896,0.05120783,0.00035877942,0.00001801693,0.0018427534,0.0001976747,0.90555876,0.009458414,0.021125857,0.0037527883,0.000493924],"about_ca_topic_score_codex":0.000021327269,"about_ca_topic_score_gemma":0.0000031190232,"teacher_disagreement_score":0.62033844,"about_ca_system_score_codex":0.0002534856,"about_ca_system_score_gemma":0.00016330447,"threshold_uncertainty_score":0.53295225},"labels":[],"label_agreement":null},{"id":"W2021265848","doi":"10.1109/tvcg.2004.44","title":"Image-based collision detection for deformable cloth models","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong University of Science and Technology; University of Waterloo; Hong Kong Polytechnic University","keywords":"Collision detection; Computer science; Bounding volume; Computer vision; Computer graphics (images); Collision; Bounding overwatch; Collision response; Artificial intelligence; Intersection (aeronautics); Object detection; Voxel; Computer graphics; Rendering (computer graphics); Computation; Graphics hardware; Segmentation; Algorithm","score_opus":0.024341554606875083,"score_gpt":0.2702500829006082,"score_spread":0.2459085282937331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021265848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018613439,0.000018298677,0.99642456,0.00006970673,0.00079388655,0.00040653185,0.000011237538,0.00040105538,0.00001338522],"genre_scores_gemma":[0.7307088,0.000048863596,0.26802704,0.0010123567,0.000056328754,0.00009145681,0.000010913756,0.000029748546,0.000014531173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874294,0.00004896569,0.0002743722,0.0004171698,0.0002674509,0.00024913167],"domain_scores_gemma":[0.99920815,0.00008359887,0.000094172676,0.0002901701,0.00020556162,0.00011834079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021593428,0.00019586865,0.0001690578,0.00039582988,0.00051038974,0.00024263658,0.00023272606,0.00012228308,6.724455e-7],"category_scores_gemma":[0.0000019562028,0.00019717221,0.00009822576,0.0007134827,0.0000528102,0.00071657286,0.0000028725801,0.00012613059,0.000003758269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037053018,0.00026160327,0.0000017244697,0.000051724954,0.000025016063,0.0000026804053,0.00047845964,0.87800646,0.00010287097,0.1128945,0.000037221558,0.008100704],"study_design_scores_gemma":[0.0014628989,0.00047513554,0.000021537398,0.00005634505,0.000017046,0.000015353266,0.000007640363,0.9802746,0.01225718,0.005091157,0.000097026415,0.00022408934],"about_ca_topic_score_codex":0.000035400542,"about_ca_topic_score_gemma":0.00000822571,"teacher_disagreement_score":0.72884744,"about_ca_system_score_codex":0.00006045579,"about_ca_system_score_gemma":0.000081000755,"threshold_uncertainty_score":0.8040449},"labels":[],"label_agreement":null},{"id":"W2024285643","doi":"10.1108/17563781211255853","title":"Terrain based co‐operative UAV mapping of complex obstacles using 2‐D splinegon","year":2012,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Terrain; Extended Kalman filter; Obstacle; Motion planning; Position (finance); Path (computing); Kalman filter; Filter (signal processing); Algorithm; Artificial intelligence; Computer vision; Robot; Geography","score_opus":0.06695151673179797,"score_gpt":0.3507884536154031,"score_spread":0.2838369368836051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024285643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24826822,0.00038468334,0.7500085,0.00018964703,0.00091013976,0.00004552197,0.0000036428357,0.000013797914,0.0001758145],"genre_scores_gemma":[0.6679426,0.000017229399,0.33161035,0.00013615805,0.00027450323,1.0615848e-7,0.0000022450395,0.000007103904,0.000009707123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980887,0.0001250468,0.00078494777,0.00014657376,0.0006074822,0.0002472601],"domain_scores_gemma":[0.99798316,0.00037871572,0.0007176273,0.00014476788,0.0006342791,0.0001414368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008306096,0.00016467314,0.00028613856,0.00030815444,0.000064689935,0.00011477181,0.0007638717,0.00005625049,0.000009679511],"category_scores_gemma":[0.00016820084,0.00014753647,0.00010473957,0.00014499306,0.00009662899,0.0002204989,0.00017944041,0.00023087005,0.0000028262314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113226335,0.0013471745,0.13386194,0.00012628037,0.0011403222,0.00021784543,0.038710672,0.4881781,0.037009493,0.015414422,0.0010311877,0.2828493],"study_design_scores_gemma":[0.00038706002,0.00012188179,0.01122385,0.00048316686,0.000016246122,0.00039387983,0.0004965297,0.9740475,0.011615831,0.00022529393,0.00080045545,0.00018829573],"about_ca_topic_score_codex":0.000025784206,"about_ca_topic_score_gemma":2.443878e-7,"teacher_disagreement_score":0.48586938,"about_ca_system_score_codex":0.00009519956,"about_ca_system_score_gemma":0.00008660229,"threshold_uncertainty_score":0.6016363},"labels":[],"label_agreement":null},{"id":"W2025873700","doi":"10.1115/detc2002/dac-34105","title":"Using Genetic Algorithms With Niche Formation to Solve the Minimum Distance Problem Among Concave Objects","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of New Brunswick","funders":"","keywords":"Maxima and minima; Mathematical optimization; Algorithm; Optimization problem; Regular polygon; Genetic algorithm; Set (abstract data type); Point (geometry); Computer science; Computational geometry; Mathematics; Geometry","score_opus":0.03937237066759954,"score_gpt":0.2457192473787921,"score_spread":0.20634687671119256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025873700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076106843,0.00010939616,0.98699105,0.00054253015,0.0001669706,0.0005995775,0.0000016205166,0.0001983748,0.0037798025],"genre_scores_gemma":[0.1998899,0.0000031237762,0.7990528,0.00047121398,0.0000651774,0.000036847046,9.4807996e-7,0.000014606421,0.0004653973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824697,0.00005496822,0.00029888755,0.00042757616,0.00048128478,0.0004903256],"domain_scores_gemma":[0.9987474,0.0001139567,0.0001485929,0.00068445405,0.00015384429,0.00015176443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022274573,0.00021569376,0.00018121244,0.00007723115,0.00028137176,0.00031061828,0.0008615563,0.00005769652,0.000011052465],"category_scores_gemma":[0.000028469636,0.00013626722,0.000035146288,0.00078945555,0.00008250722,0.00062702317,0.00017637163,0.00015978738,0.00007462445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034838937,0.00044204548,0.008582345,0.00019359174,0.00024506933,0.000581233,0.09193185,0.724925,0.0022186008,0.007716513,0.01727601,0.14585291],"study_design_scores_gemma":[0.00029787517,0.00013921101,0.0024853987,0.00006753889,0.000011665893,0.000104645165,0.0002337618,0.9952887,0.00080018636,0.00015920604,0.00015655252,0.00025520727],"about_ca_topic_score_codex":0.00005911492,"about_ca_topic_score_gemma":0.000021649814,"teacher_disagreement_score":0.27036375,"about_ca_system_score_codex":0.000101927675,"about_ca_system_score_gemma":0.00003777021,"threshold_uncertainty_score":0.55568165},"labels":[],"label_agreement":null},{"id":"W2026435861","doi":"10.2316/journal.206.2013.3.206-3818","title":"AN IMMUNE SMALL WORLD ALGORITHM FOR MOTION PLANNING OF THE MOBILE MANIPULATOR","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mobile manipulator; Motion planning; Motion (physics); Manipulator (device); Artificial intelligence; Algorithm; Mobile robot; Robot","score_opus":0.019171233038172364,"score_gpt":0.27398062117937094,"score_spread":0.2548093881411986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026435861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034923084,0.00010518903,0.9630355,0.00049695023,0.0012345404,0.0001664531,0.000002678075,0.000016539214,0.000019039018],"genre_scores_gemma":[0.4864089,0.0000037831876,0.51336503,0.00004185174,0.00013225037,0.0000052358064,0.000002948442,0.0000052504406,0.000034771685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990011,0.000040065726,0.00044943084,0.00009863778,0.00031585232,0.00009493757],"domain_scores_gemma":[0.99853116,0.00009194234,0.00060670095,0.00014172947,0.0005837547,0.000044699205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000331829,0.00008037603,0.00013364012,0.00017783607,0.00005778517,0.00016111232,0.00064371084,0.000034435998,0.0000026765092],"category_scores_gemma":[0.00003671469,0.000059212613,0.00007201393,0.00010879015,0.000024844676,0.00060058566,0.00006665091,0.00009338136,9.912966e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041665744,0.00013272658,0.0022889704,0.000014818748,0.000097343924,0.000004646859,0.0007910813,0.49649426,0.0037135517,0.0047454266,0.000110969144,0.49160203],"study_design_scores_gemma":[0.00034057716,0.00010043041,0.04273589,0.0001121307,0.000010195305,0.00007003318,0.00003966168,0.95220256,0.0014954298,0.0027878792,0.00004336776,0.00006186351],"about_ca_topic_score_codex":0.000013601394,"about_ca_topic_score_gemma":3.527277e-7,"teacher_disagreement_score":0.49154016,"about_ca_system_score_codex":0.000041620173,"about_ca_system_score_gemma":0.000036785183,"threshold_uncertainty_score":0.24146204},"labels":[],"label_agreement":null},{"id":"W2026624999","doi":"10.1109/grc.2009.5255067","title":"A GA-based fuzzy logic approach to mobile robot navigation in unknown dynamic environments with moving obstacles","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Computer science; Fuzzy logic; Fuzzy control system; Robot; Genetic algorithm; Maxima and minima; Path (computing); Artificial intelligence; Fuzzy inference; Mobile robot navigation; Adaptive neuro fuzzy inference system; Control engineering; Robot control; Engineering; Mathematics; Machine learning","score_opus":0.012467618900337766,"score_gpt":0.24061166719847166,"score_spread":0.2281440482981339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026624999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036800705,0.000036013258,0.96012974,0.00023945274,0.000046183613,0.00057183026,8.3167765e-7,0.000160462,0.0020147727],"genre_scores_gemma":[0.50066507,5.283234e-7,0.49872872,0.0003399632,0.000006105686,0.00006114517,0.000011023863,0.0000060713833,0.00018135973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983063,0.00006971129,0.00024154811,0.00059977896,0.00038107001,0.00040162064],"domain_scores_gemma":[0.9992068,0.00004691354,0.00007368989,0.0005322759,0.000014653853,0.00012565684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002457453,0.00020340574,0.00019804687,0.00016928068,0.00007354702,0.00010695551,0.0006210542,0.00006783687,0.0000018058429],"category_scores_gemma":[0.000012408863,0.00016728506,0.000028591534,0.00056348153,0.000026086534,0.00031483275,0.00006770584,0.00015657012,0.00004533253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000827475,0.00026327034,0.00054008584,0.0000063630014,0.0000033531287,0.000032870244,0.00040125792,0.9726605,0.0016911061,0.0010616858,0.000009650222,0.023321588],"study_design_scores_gemma":[0.00049302995,0.00041419448,0.024056463,0.00006620521,0.0000032514272,0.000016141708,0.000058476555,0.9735294,0.00042712793,0.0006081927,0.000045798737,0.00028170785],"about_ca_topic_score_codex":0.000031005868,"about_ca_topic_score_gemma":0.0000025781335,"teacher_disagreement_score":0.4638644,"about_ca_system_score_codex":0.00017872042,"about_ca_system_score_gemma":0.000046124285,"threshold_uncertainty_score":0.68216866},"labels":[],"label_agreement":null},{"id":"W2026920996","doi":"10.1016/s1045-2354(03)00054-6","title":"A ghostly CAR ride","year":2003,"lang":"en","type":"article","venue":"Critical Perspectives on Accounting","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Business","score_opus":0.01913559188906061,"score_gpt":0.2981750513752438,"score_spread":0.2790394594861832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026920996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027088124,0.0007259984,0.87003154,0.0029806986,0.0012261914,0.00020734711,0.0000034420373,0.00077698415,0.09695968],"genre_scores_gemma":[0.8163542,0.0000035138773,0.1827215,0.00064322585,0.00014769792,0.000010356149,2.7031615e-7,0.000017888824,0.000101366255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789613,0.00012927089,0.00022329896,0.00071353436,0.00044114338,0.0005965995],"domain_scores_gemma":[0.9982169,0.00077479315,0.000042146003,0.00058834016,0.00022485909,0.00015292426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005747756,0.00020670397,0.00021701342,0.00014925555,0.0002992171,0.00038002364,0.0006209198,0.00008364173,0.00003313844],"category_scores_gemma":[0.0051220586,0.00020192802,0.00009527856,0.00042759313,0.00016737802,0.0006151309,0.00009401088,0.00039095964,0.00037444517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029299176,0.00014492332,0.0006517658,0.000013222495,0.000014217917,0.00012489728,0.004122155,0.00019908941,0.0005681758,0.99255246,0.000453019,0.0011531607],"study_design_scores_gemma":[0.0057570697,0.0020670462,0.07805566,0.0014982648,0.00019733574,0.0016933999,0.071734875,0.49461228,0.02206169,0.29165128,0.022791164,0.007879945],"about_ca_topic_score_codex":0.000013095996,"about_ca_topic_score_gemma":2.7993673e-7,"teacher_disagreement_score":0.78926605,"about_ca_system_score_codex":0.00014934188,"about_ca_system_score_gemma":0.00008315511,"threshold_uncertainty_score":0.8234386},"labels":[],"label_agreement":null},{"id":"W2028680473","doi":"10.1145/1836845.1836997","title":"HCCMeshes","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Bounding volume; Collision detection; Tree traversal; Computer science; Ray tracing (physics); Bounding overwatch; Tracing; Object (grammar); Collision; Computer graphics (images); Minimum bounding box; Artificial intelligence; Computer vision; Algorithm; Image (mathematics); Programming language","score_opus":0.006992566990724689,"score_gpt":0.22555210391557162,"score_spread":0.21855953692484692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028680473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005568031,0.000003871592,0.9230025,0.0010703434,0.00096525886,0.000024820489,9.353773e-8,0.0003493442,0.06901573],"genre_scores_gemma":[0.14724901,1.9001406e-7,0.8502573,0.0003027301,0.000059653154,0.0000017808044,1.7234825e-7,0.0000020877767,0.0021270723],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995791,0.000005940426,0.000055264583,0.00013737727,0.00009918943,0.00012316932],"domain_scores_gemma":[0.99951065,0.000036774625,0.000012525992,0.00036928445,0.000018100565,0.000052691234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110076515,0.000041607385,0.00004140102,0.000027187774,0.000036586192,0.000066705725,0.0005435821,0.000026983547,0.00004117827],"category_scores_gemma":[0.000032590626,0.00003285086,0.000015409418,0.00010523392,0.000015245092,0.00017731808,0.00008990481,0.00010775027,0.00046139417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.0847297e-7,0.000059536644,0.006134469,0.000003462196,0.0000098735245,0.00006903283,0.00054204365,0.0001564061,0.019066691,0.76439345,0.025770057,0.1837945],"study_design_scores_gemma":[0.00033045927,0.000063834384,0.049995564,0.000005684587,0.0000024857102,0.00020469891,0.000015230223,0.8594948,0.018709466,0.02015634,0.050579336,0.00044210406],"about_ca_topic_score_codex":0.000010849512,"about_ca_topic_score_gemma":0.0000013084771,"teacher_disagreement_score":0.8593384,"about_ca_system_score_codex":0.000001888087,"about_ca_system_score_gemma":0.00001931184,"threshold_uncertainty_score":0.5930443},"labels":[],"label_agreement":null},{"id":"W2028936060","doi":"10.1016/j.robot.2013.07.014","title":"An integrated robust probing motion planning and control scheme: A tube-based MPC approach","year":2013,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Trajectory; Control theory (sociology); Controller (irrigation); Model predictive control; Scheme (mathematics); Motion planning; Robot; Motion control; Control engineering; Control (management); Artificial intelligence; Mathematics; Engineering","score_opus":0.020717025043501123,"score_gpt":0.22226368933880813,"score_spread":0.20154666429530702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028936060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01109756,0.00062309665,0.98644656,0.00017932514,0.00033211574,0.0007986099,0.0000044407157,0.0003632648,0.00015503951],"genre_scores_gemma":[0.70523477,0.0000011360138,0.29447365,0.00006169176,0.00007243713,0.00006981529,0.000017657061,0.00002066461,0.00004821808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979977,0.00018200163,0.0004405245,0.0006649705,0.00023684117,0.0004779384],"domain_scores_gemma":[0.9987867,0.00009386914,0.00020849312,0.00048696194,0.0001570574,0.0002669356],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005604535,0.00030371838,0.0004305177,0.00018156887,0.00029159486,0.0011488572,0.00040035174,0.0001667588,8.6601636e-7],"category_scores_gemma":[0.000029850817,0.00026337177,0.000034076704,0.00024882276,0.00007435903,0.0006321366,0.00006719587,0.0002655035,0.00000876664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021129035,0.000077435136,0.0035661426,0.00011225274,0.000027369106,0.00001250977,0.0005475889,0.9900309,0.00081612216,0.002276926,0.000040084702,0.0024905843],"study_design_scores_gemma":[0.00076799386,0.00014400709,0.0021986556,0.00017358617,0.000014889224,0.00009990543,0.00024086998,0.9959403,0.000033891898,0.000033233864,0.000030001323,0.0003226642],"about_ca_topic_score_codex":0.00030399588,"about_ca_topic_score_gemma":3.5218318e-7,"teacher_disagreement_score":0.69413716,"about_ca_system_score_codex":0.0000793455,"about_ca_system_score_gemma":0.00010521201,"threshold_uncertainty_score":0.9999818},"labels":[],"label_agreement":null},{"id":"W2030227900","doi":"10.2316/journal.206.2006.2.206-2797","title":"A SENSOR-BASED NAVIGATION ALGORITHM FOR A MOBILE ROBOT USING FUZZY LOGIC","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Fuzzy logic; Computer science; Mobile robot navigation; Robot; Position (finance); Obstacle; Obstacle avoidance; Navigation system; Algorithm; Computer vision; Fuzzy control system; Real-time computing; Artificial intelligence; Robot control","score_opus":0.0205579265631916,"score_gpt":0.2929484168507384,"score_spread":0.2723904902875468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030227900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007847129,0.00010329285,0.99016756,0.0006617576,0.0010048782,0.00014259965,0.000008100197,0.000041665913,0.000023043465],"genre_scores_gemma":[0.12879728,0.0000035288426,0.87071776,0.00009610899,0.00033902278,0.0000036729905,0.000018656314,0.000008058445,0.000015936344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987177,0.000041303232,0.00048659887,0.0001530702,0.00046048625,0.00014083646],"domain_scores_gemma":[0.9983969,0.00013591324,0.00057922176,0.000098107856,0.00074109755,0.00004881468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041140075,0.000116746654,0.00016537236,0.00024402949,0.00008190168,0.00025447516,0.00034201267,0.00006469242,9.116772e-7],"category_scores_gemma":[0.00003543628,0.00010740065,0.000087790904,0.00012892841,0.000028273847,0.00048765072,0.000038378308,0.00009574217,0.0000013709234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005507093,0.00007563073,0.00018043965,0.000008557727,0.000028488072,0.00003537907,0.00005570781,0.9407489,0.0014262422,0.0032732917,0.000054120108,0.054107767],"study_design_scores_gemma":[0.00078294287,0.00013819562,0.0012726664,0.000116687355,0.000018696192,0.00033021998,0.000018565586,0.98838073,0.00058010087,0.008164749,0.00008148452,0.00011498123],"about_ca_topic_score_codex":0.000018495472,"about_ca_topic_score_gemma":1.7744438e-7,"teacher_disagreement_score":0.12095015,"about_ca_system_score_codex":0.000117263204,"about_ca_system_score_gemma":0.00010208301,"threshold_uncertainty_score":0.43796715},"labels":[],"label_agreement":null},{"id":"W2030670936","doi":"10.1109/ccip.2015.7100710","title":"Simulation for path planning of autonomous underwater vehicle using Flower Pollination Algorithm, Genetic Algorithm and Q-Learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Underwater; Algorithm; Motion planning; Genetic algorithm; Computer science; Function (biology); Path (computing); Artificial intelligence; Machine learning; Robot; Geology; Oceanography","score_opus":0.05059184010633227,"score_gpt":0.30150113080604946,"score_spread":0.2509092906997172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030670936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026104402,0.00013996645,0.9729086,0.000056001354,0.0002755124,0.00029582798,0.000002841519,0.00015512508,0.00006171925],"genre_scores_gemma":[0.19982229,6.504543e-7,0.79993,0.00004628252,0.00007832032,0.0000071484023,0.0000073319284,0.000016947415,0.00009102806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849325,0.00010479669,0.00037424848,0.00040023413,0.0003110869,0.00031635386],"domain_scores_gemma":[0.9988601,0.00030880474,0.00018877014,0.00022355626,0.00027701544,0.00014175543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006117506,0.00017447777,0.00023067753,0.00017406217,0.00015873201,0.000119587676,0.00023843718,0.00014364586,0.0000011703831],"category_scores_gemma":[0.00012858748,0.00016620985,0.000044925502,0.0002246419,0.000039636114,0.00046271706,0.00013658412,0.00017576427,0.0000021860317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041028306,0.000022910619,0.00046703394,0.000010377929,0.000014915055,0.0000067022856,0.0014407038,0.8884225,0.0001824342,0.00005852056,0.000009493543,0.10936029],"study_design_scores_gemma":[0.0011258244,0.00019447732,0.0013801795,0.000035308905,0.000016316719,0.000015329102,0.00018345618,0.99544483,0.0006375609,0.00063497113,0.00012271888,0.0002090382],"about_ca_topic_score_codex":0.000059659185,"about_ca_topic_score_gemma":1.2849812e-7,"teacher_disagreement_score":0.17371789,"about_ca_system_score_codex":0.00009239386,"about_ca_system_score_gemma":0.000106569016,"threshold_uncertainty_score":0.6777841},"labels":[],"label_agreement":null},{"id":"W2031115500","doi":"10.1109/nafips.2006.365497","title":"Vision-Based Fuzzy 2D Motion Control of a Model Helicopter","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Position (finance); Computer science; Fuzzy logic; OpenGL; Orientation (vector space); Base (topology); Control theory (sociology); Controller (irrigation); Nonlinear system; Domain (mathematical analysis); Nonlinear model; Fuzzy control system; Motion (physics); Motion control; Artificial intelligence; Control engineering; Control (management); Engineering; Mathematics; Robot","score_opus":0.012309352602657107,"score_gpt":0.24397005015200363,"score_spread":0.2316606975493465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031115500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030336338,0.000024813808,0.98978865,0.00064278627,0.00010438547,0.00011256128,0.0000033011179,0.00015939756,0.006130479],"genre_scores_gemma":[0.65302426,1.0042809e-7,0.346439,0.00026332005,0.000021017064,0.0000045113093,0.0000019158038,0.0000038648473,0.0002420395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989879,0.000037027603,0.00024093396,0.00025114662,0.00028892228,0.00019407712],"domain_scores_gemma":[0.99926686,0.00008612257,0.000085344276,0.00042191832,0.000096876116,0.00004286331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021670113,0.0001010288,0.00015492844,0.00010209792,0.000038234153,0.000041774885,0.0003901444,0.00005420301,0.00000621097],"category_scores_gemma":[0.00001533589,0.00008443114,0.00005549646,0.00017340876,0.000028052948,0.00019697701,0.000035874124,0.000058864298,0.000042767933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042115366,0.00010931059,0.00057399686,0.0000075310113,0.0000037573122,0.000004927916,0.000027283264,0.97773963,0.0031188247,0.011453955,0.0008528792,0.006103699],"study_design_scores_gemma":[0.0006608082,0.00005470601,0.0021533517,0.00001550992,0.0000037981167,0.0000020188131,0.0000010862952,0.990716,0.0027800016,0.0035069147,0.00001075539,0.00009506824],"about_ca_topic_score_codex":0.000102914484,"about_ca_topic_score_gemma":0.0000013832125,"teacher_disagreement_score":0.6499906,"about_ca_system_score_codex":0.000022461445,"about_ca_system_score_gemma":0.00005214149,"threshold_uncertainty_score":0.3443002},"labels":[],"label_agreement":null},{"id":"W2031310371","doi":"10.2316/journal.206.2006.3.206-2804","title":"RANDOMIZED MOTION PLANNING: A TUTORIAL","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion (physics); Computer science; Artificial intelligence","score_opus":0.013129193613313182,"score_gpt":0.2688556370359988,"score_spread":0.25572644342268563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031310371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01035657,0.0001472816,0.9838263,0.0017967464,0.0033835745,0.00006291503,7.5162785e-7,0.000040587052,0.0003852667],"genre_scores_gemma":[0.70087767,0.000011806745,0.29801387,0.00005245363,0.0009773608,0.0000011557631,0.000004814116,0.0000048679562,0.00005601781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869627,0.00008452012,0.00050223985,0.000101353064,0.00052296853,0.0000926415],"domain_scores_gemma":[0.9987641,0.00019085634,0.00053364446,0.00007564974,0.0003966491,0.000039113245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007470515,0.000085885535,0.00021203999,0.00023561984,0.000042260133,0.00026072786,0.00036231065,0.000048383037,0.0000017736235],"category_scores_gemma":[0.00016917859,0.00007114472,0.0000822934,0.00008290101,0.00003480833,0.00057448173,0.00005352866,0.00010438305,0.0000030597105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009973969,0.00013996533,0.00094389945,0.000009004895,0.00016078049,0.00019210979,0.0006328523,0.788573,0.0006241768,0.18609814,0.0013455275,0.020283092],"study_design_scores_gemma":[0.024836365,0.000039201364,0.0034440537,0.00007991992,0.000019593928,0.00038210937,0.000008250009,0.9445631,0.00020618443,0.026126975,0.00019627364,0.00009797929],"about_ca_topic_score_codex":0.000012731172,"about_ca_topic_score_gemma":1.08135005e-7,"teacher_disagreement_score":0.69052106,"about_ca_system_score_codex":0.00005095113,"about_ca_system_score_gemma":0.000044365708,"threshold_uncertainty_score":0.29011974},"labels":[],"label_agreement":null},{"id":"W2032166099","doi":"10.1109/rose.2007.4373960","title":"Vision-based Exploration Algorithms for Rough Terrain Modeling Using Triangular Mesh Maps","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brampton Civic Hospital; University of Saskatchewan","funders":"","keywords":"Terrain; Computer science; Boundary (topology); Computer vision; Algorithm; Artificial intelligence; Mobile robot; Motion planning; Elevation (ballistics); Triangle mesh; Function (biology); Stage (stratigraphy); Robot; Computer graphics (images); Mathematics; Geology; Geometry; Polygon mesh; Geography","score_opus":0.09405712812020996,"score_gpt":0.3461265472528519,"score_spread":0.25206941913264197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032166099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000976831,0.000045052744,0.99647385,0.00064486975,0.0007323894,0.00049925206,0.0000045885754,0.00037842433,0.00024474895],"genre_scores_gemma":[0.04514274,6.129826e-7,0.95396096,0.00046265966,0.00024111876,0.00001863739,0.000026842776,0.00002405001,0.00012241093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786824,0.000059520316,0.0005037621,0.00057555165,0.00044786223,0.00054506987],"domain_scores_gemma":[0.9986963,0.0002604232,0.00012646915,0.00058080634,0.00018757189,0.00014839173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019722204,0.00021895693,0.00024390493,0.0002610197,0.00025848023,0.00022502641,0.0006141726,0.00012883652,0.0000041144813],"category_scores_gemma":[0.00014051037,0.00020433145,0.00012326223,0.00047047122,0.000021709619,0.0010562413,0.000083999184,0.00011831597,0.00001289386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002327562,0.00008312363,0.000012025712,0.000016857846,0.000016258844,0.000030152414,0.00084771455,0.940459,0.0016654696,0.002704869,0.00035043934,0.053790793],"study_design_scores_gemma":[0.00095747807,0.000119844684,0.0000051899447,0.00004560344,0.000009759415,0.000008425447,0.00010960202,0.9918012,0.0026899262,0.0037914147,0.00018432691,0.0002772421],"about_ca_topic_score_codex":0.000081687554,"about_ca_topic_score_gemma":0.0000033062663,"teacher_disagreement_score":0.05351355,"about_ca_system_score_codex":0.00013102325,"about_ca_system_score_gemma":0.00013415518,"threshold_uncertainty_score":0.83323944},"labels":[],"label_agreement":null},{"id":"W2032587419","doi":"10.1109/icra.2014.6906924","title":"SMT-based synthesis of integrated task and motion plans from plan outlines","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Satisfiability modulo theories; Computer science; Solver; Plan (archaeology); Satisfiability; Motion planning; Graph; Abstraction; Set (abstract data type); Theoretical computer science; Generalization; Task (project management); Boolean satisfiability problem; Robot; Algorithm; Programming language; Artificial intelligence; Mathematics; Systems engineering; Engineering","score_opus":0.01590318316072727,"score_gpt":0.2157386580454878,"score_spread":0.19983547488476053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032587419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061394412,0.000010413234,0.93732095,0.00034176515,0.00015615493,0.000051965126,0.000036823974,0.0001557739,0.00053174014],"genre_scores_gemma":[0.7182891,7.3885656e-7,0.28152093,0.000104889274,0.00002598167,0.0000038206604,0.000015622772,0.000004822374,0.000034116056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991506,0.000095311785,0.00019390257,0.0002680319,0.00015985926,0.00013233833],"domain_scores_gemma":[0.99891084,0.0005552549,0.0000827255,0.00033909435,0.000048510352,0.00006355468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024266618,0.00010950868,0.00018833614,0.00009115704,0.000038519822,0.000046351845,0.00034195336,0.000067889225,0.000007883712],"category_scores_gemma":[0.0002492427,0.00008469821,0.000024098725,0.00013601515,0.000046252346,0.00011404122,0.000034523197,0.00007070148,0.000014611843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087087144,0.0005771681,0.20948319,0.000145118,0.00021067802,0.000046683515,0.0027924264,0.028364796,0.036927655,0.0152652515,0.0037114092,0.7023885],"study_design_scores_gemma":[0.00021211001,0.00005065528,0.02682823,0.000059004262,0.000009937964,0.000002077403,0.000029802612,0.94997275,0.022122025,0.00046349867,0.00012978907,0.00012010834],"about_ca_topic_score_codex":0.0005422783,"about_ca_topic_score_gemma":0.000013451544,"teacher_disagreement_score":0.921608,"about_ca_system_score_codex":0.000010780922,"about_ca_system_score_gemma":0.000023939909,"threshold_uncertainty_score":0.34538928},"labels":[],"label_agreement":null},{"id":"W2033828630","doi":"10.1109/tro.2011.2142470","title":"Distributed Roadmaps for Robot Navigation in Sensor Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Computer science; Node (physics); Path (computing); Robot; Wireless sensor network; Distributed computing; Overhead (engineering); Representation (politics); Sensor node; Real-time computing; Distributed algorithm; Mobile robot; Key distribution in wireless sensor networks; Artificial intelligence; Computer network; Engineering; Telecommunications","score_opus":0.043163750101913166,"score_gpt":0.2554466009345716,"score_spread":0.21228285083265844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033828630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003013494,0.000015554735,0.99712217,0.000244458,0.0015408556,0.00042716335,0.000027835767,0.00026495138,0.000055673492],"genre_scores_gemma":[0.30819988,0.0000071359805,0.69144696,0.00009554731,0.000044153017,0.00006356139,0.000019172498,0.00002221561,0.00010139096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983998,0.000077151846,0.0003947367,0.00044956984,0.00021005877,0.00046869012],"domain_scores_gemma":[0.9989006,0.00019369705,0.00010611644,0.0005526083,0.00011706089,0.00012989831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002658092,0.00022300248,0.00024338994,0.00017335599,0.00017329608,0.000062667066,0.00050923857,0.00017485378,0.0000048063216],"category_scores_gemma":[0.000010690518,0.00023486123,0.00011412275,0.00062561803,0.000045976118,0.00030333392,0.0000029778241,0.00033902942,0.000027384787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020236976,0.00020746002,0.000051307896,0.000009792769,0.000018432333,0.00002104147,0.0003421336,0.99274033,0.000043094682,0.0003781111,0.00006805426,0.006100013],"study_design_scores_gemma":[0.0006380872,0.00018608212,0.0013005927,0.0000719855,0.000021420652,0.000027654649,0.000032205688,0.9951599,0.0018617611,0.00041528218,0.000012051983,0.00027297993],"about_ca_topic_score_codex":0.00004589855,"about_ca_topic_score_gemma":0.000010189949,"teacher_disagreement_score":0.30789855,"about_ca_system_score_codex":0.00012252582,"about_ca_system_score_gemma":0.000053505264,"threshold_uncertainty_score":0.9577363},"labels":[],"label_agreement":null},{"id":"W2033954340","doi":"10.1109/robot.2004.1307265","title":"A Delaunay triangulation based node connection strategy for probabilistic roadmap planners","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Delaunay triangulation; Probabilistic roadmap; Node (physics); Probabilistic logic; Constrained Delaunay triangulation; Computer science; Bowyer–Watson algorithm; Set (abstract data type); Connection (principal bundle); Motion planning; Point (geometry); Mathematical optimization; Topology (electrical circuits); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Robot; Engineering","score_opus":0.045319693628301405,"score_gpt":0.2862175437736189,"score_spread":0.2408978501453175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033954340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004249674,0.00001022385,0.9926112,0.0010160245,0.0003964595,0.0005644564,0.00000450346,0.00038953006,0.0007579026],"genre_scores_gemma":[0.54337335,1.331993e-7,0.45608562,0.00022940399,0.000066606735,0.0000759462,0.0000282381,0.000009317601,0.00013138402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988377,0.00003797754,0.00024752456,0.0003941231,0.00020119523,0.00028148465],"domain_scores_gemma":[0.999186,0.00018681074,0.00009387192,0.0003343426,0.00010732067,0.00009168469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034784467,0.00013894522,0.00014828221,0.00011706701,0.00012675076,0.0001310678,0.00030926478,0.00008523384,0.000006348313],"category_scores_gemma":[0.00018654023,0.00012490091,0.00006491941,0.00027911004,0.000023048215,0.0003154665,0.000020068197,0.00007086846,0.00002743754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019521802,0.000057393612,0.00004777595,0.000016870841,0.000008480603,0.0000054063494,0.000109762266,0.96584827,0.00026316673,0.030429102,0.00015329807,0.0030409659],"study_design_scores_gemma":[0.0018836034,0.0002601066,0.0011962521,0.000026416641,0.000009546904,0.0000134464835,0.00002417013,0.9817807,0.0015596924,0.012949047,0.00010351143,0.00019347321],"about_ca_topic_score_codex":0.00009466356,"about_ca_topic_score_gemma":0.000011756293,"teacher_disagreement_score":0.5391237,"about_ca_system_score_codex":0.00014487271,"about_ca_system_score_gemma":0.00023447804,"threshold_uncertainty_score":0.5093311},"labels":[],"label_agreement":null},{"id":"W2034104715","doi":"10.1145/1957656.1957736","title":"Exploring sketching for robot collaboration","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Affordance; Robot; Human–computer interaction; Task (project management); Computer science; Human–robot interaction; Task analysis; Artificial intelligence; Engineering; Systems engineering","score_opus":0.3447951611985202,"score_gpt":0.28920486896177017,"score_spread":0.05559029223675005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034104715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026461843,0.000014728347,0.99111485,0.00017207714,0.0007533044,0.00014759794,4.4788e-7,0.00029403667,0.0048567723],"genre_scores_gemma":[0.113162294,0.0000013419436,0.8863824,0.00011275928,0.00006192672,0.00006811181,9.2851263e-7,0.0000057921197,0.00020446414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993495,0.00001724136,0.00012745046,0.00021768502,0.00010500805,0.00018315842],"domain_scores_gemma":[0.99949723,0.000057700254,0.00004164608,0.00027556086,0.000075956836,0.000051901934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002300467,0.0000707778,0.00007762666,0.00006401173,0.000099851306,0.00007800678,0.00037448207,0.00001979908,0.000006688696],"category_scores_gemma":[0.000055024277,0.00006492912,0.000023351846,0.00023033869,0.0000069354387,0.0011450625,0.00006458732,0.000038339273,0.00003340881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027733266,0.00023199787,0.0013223444,0.000064497035,0.00007522089,0.000060625294,0.050845742,0.0118620405,0.0056662047,0.6880009,0.0032323033,0.23861037],"study_design_scores_gemma":[0.00053503306,0.00022327837,0.0059596756,0.00003448196,0.000007875166,0.00001863372,0.0005103899,0.95101184,0.031118726,0.009315,0.000867351,0.00039769866],"about_ca_topic_score_codex":0.000022723993,"about_ca_topic_score_gemma":0.0000010966475,"teacher_disagreement_score":0.9391498,"about_ca_system_score_codex":0.00002163065,"about_ca_system_score_gemma":0.000035688794,"threshold_uncertainty_score":0.26477325},"labels":[],"label_agreement":null},{"id":"W2035440851","doi":"10.1007/s10846-006-9080-2","title":"Guidance-Based On-Line Robot Motion Planning for the Interception of Mobile Targets in Dynamic Environments","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interception; Acceleration; Rendezvous; Kinematics; Mobile robot; Computer science; Obstacle avoidance; Obstacle; Robot; Control theory (sociology); Engineering; Simulation; Motion (physics); Trajectory; Matching (statistics); Control engineering; Artificial intelligence; Control (management); Aerospace engineering; Mathematics; Spacecraft","score_opus":0.02910899721530944,"score_gpt":0.2890310009155599,"score_spread":0.2599220037002504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035440851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002862101,0.0021394328,0.99194074,0.00016538416,0.0022492397,0.00059851364,0.0000024932126,0.000014477936,0.000027621127],"genre_scores_gemma":[0.9541023,0.00002393539,0.04543094,0.000027795122,0.00020607596,0.00003945385,0.0000046053337,0.00001914886,0.00014579357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997159,0.00019096541,0.0014680966,0.00025584616,0.0006131075,0.00031296676],"domain_scores_gemma":[0.99753404,0.0006219401,0.0012368915,0.00042184425,0.00012519998,0.00006006167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017061611,0.0002212557,0.00049161224,0.000388398,0.00007232581,0.000077057324,0.00086532603,0.00009806151,0.000002751378],"category_scores_gemma":[0.00010538131,0.00015744682,0.00020328505,0.00027148685,0.000052910047,0.00021438664,0.000049560615,0.00025368523,0.000008633161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040071325,0.00024037751,0.001269432,0.00006525042,0.0000276568,0.000019489538,0.00030077223,0.99315596,0.0010290421,0.00008826319,0.00015452302,0.0036091756],"study_design_scores_gemma":[0.00053692865,0.0007842508,0.0050018574,0.0011542675,0.000023447807,0.000059906026,0.00023256632,0.9907021,0.0011480917,0.00008863918,0.00013056575,0.00013737784],"about_ca_topic_score_codex":0.000055553435,"about_ca_topic_score_gemma":0.0000014316773,"teacher_disagreement_score":0.9512401,"about_ca_system_score_codex":0.0004118047,"about_ca_system_score_gemma":0.00007193006,"threshold_uncertainty_score":0.6420495},"labels":[],"label_agreement":null},{"id":"W2036203342","doi":"10.1109/iros.2014.6942977","title":"Integrating multiple soft constraints for planning practical paths","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bidding; Flexibility (engineering); Motion planning; Range (aeronautics); Mathematical optimization; Sampling (signal processing); Domain (mathematical analysis); Adaptive sampling; Resource allocation; Artificial intelligence; Robot; Engineering","score_opus":0.04679518960887721,"score_gpt":0.3216184003474906,"score_spread":0.2748232107386134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036203342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075903756,0.0000061806054,0.99196565,0.00080182723,0.0004848001,0.00019413853,0.0000018676786,0.00037210147,0.0054144175],"genre_scores_gemma":[0.30276406,6.906251e-8,0.6965621,0.0004317221,0.000096281015,0.000020369731,0.0000030517378,0.000007630815,0.00011469435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986777,0.00007411662,0.00025389149,0.00039938098,0.00020546715,0.00038946085],"domain_scores_gemma":[0.9969823,0.0023305446,0.00010413139,0.000356099,0.00009672677,0.00013024152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008062215,0.00014517085,0.00018289141,0.0000624179,0.00016656899,0.00018488441,0.00042792785,0.000078139536,0.000007588183],"category_scores_gemma":[0.0032352053,0.00012083434,0.000058358823,0.0001191891,0.00007097385,0.00037077497,0.00012664232,0.00018119227,0.00004805488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026060281,0.0002493542,0.023696924,0.00007904228,0.000082848426,0.000094344156,0.0055594854,0.008152804,0.0032586916,0.3878535,0.016740462,0.5542065],"study_design_scores_gemma":[0.00044637598,0.00011285107,0.0005949148,0.000043973694,0.0000038613475,0.000074046475,0.0001486479,0.9937917,0.0006162363,0.0018413512,0.0021442582,0.00018177647],"about_ca_topic_score_codex":0.000008704185,"about_ca_topic_score_gemma":6.474103e-7,"teacher_disagreement_score":0.9856389,"about_ca_system_score_codex":0.0000235444,"about_ca_system_score_gemma":0.00008076324,"threshold_uncertainty_score":0.49274814},"labels":[],"label_agreement":null},{"id":"W2037071615","doi":"10.1016/j.robot.2005.10.005","title":"Real-time safety for human–robot interaction","year":2005,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robot; Trajectory; Human–robot interaction; Collision avoidance; Motion (physics); Measure (data warehouse); Index (typography); Simulation; Artificial intelligence; Collision; Data mining; Computer security","score_opus":0.02071753133433007,"score_gpt":0.26975942435356065,"score_spread":0.24904189301923058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037071615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009961944,0.0001578077,0.9930831,0.0008480446,0.0009778338,0.00051492354,0.000009450274,0.00036789096,0.0030447063],"genre_scores_gemma":[0.39978078,0.000041584397,0.5913454,0.000096767966,0.0010820772,0.00006941011,0.000040228726,0.000051275216,0.0074924678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984551,0.000059303362,0.0004790068,0.000459499,0.00016700564,0.00038010662],"domain_scores_gemma":[0.99893713,0.00016592372,0.00021470286,0.0004555062,0.000090066766,0.00013668882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004727394,0.00020586787,0.00033716927,0.00011093286,0.0003278026,0.00034752057,0.00039812594,0.000106901556,0.0000025032077],"category_scores_gemma":[0.0000202489,0.00019779053,0.000069010224,0.00011721423,0.000029647497,0.0003696025,0.00012120585,0.00011405868,0.00007333221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065618515,0.00008785622,0.000086483225,0.00008567876,0.00005612553,0.000009189121,0.000710322,0.9060646,0.0045755166,0.07376271,0.0012530035,0.013301919],"study_design_scores_gemma":[0.00040112966,0.00012590102,0.00033134525,0.00008261222,0.000014702637,0.000092254966,0.000027023449,0.9911444,0.00011561677,0.00016045028,0.0072456286,0.00025888925],"about_ca_topic_score_codex":0.00010997243,"about_ca_topic_score_gemma":0.0000022052939,"teacher_disagreement_score":0.40173775,"about_ca_system_score_codex":0.00016674111,"about_ca_system_score_gemma":0.000057167686,"threshold_uncertainty_score":0.8065664},"labels":[],"label_agreement":null},{"id":"W2037737568","doi":"10.1016/j.matcom.2006.02.010","title":"Modeling and adaptive control of redundant robots","year":2006,"lang":"en","type":"article","venue":"Mathematics and Computers in Simulation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Robot; Trajectory; Computer science; Control theory (sociology); Redundancy (engineering); Kinematics; Inverse kinematics; Robot control; Robot kinematics; Control engineering; Mobile robot; Artificial intelligence; Control (management); Engineering","score_opus":0.019713642007316578,"score_gpt":0.24363137815681107,"score_spread":0.2239177361494945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037737568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08286327,0.000094260235,0.9167321,0.00003684326,0.00005220108,0.000112607195,6.3040085e-7,0.000024350213,0.00008376618],"genre_scores_gemma":[0.5699195,0.0000011419783,0.4300561,0.000008281332,0.0000095095365,0.0000010684233,3.9161125e-7,0.000002471577,0.0000015390872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993161,0.000020050034,0.00027047863,0.00016208916,0.0001271358,0.0001041344],"domain_scores_gemma":[0.99951184,0.00020583117,0.00008325861,0.00013784152,0.00003981455,0.00002143684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021566787,0.00008237576,0.00017802796,0.00009227836,0.000033354903,0.0000444875,0.00010649905,0.00003547663,1.215924e-7],"category_scores_gemma":[0.000010949955,0.00007777542,0.000014667464,0.0001056408,0.000023190009,0.00013069774,0.00006077366,0.000048174894,2.7795846e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018359385,0.000029973939,0.00016574352,0.000020455105,0.000003611087,0.0000025218583,0.0005800628,0.9694257,0.000041880317,0.026191525,9.402276e-7,0.0035357615],"study_design_scores_gemma":[0.0004273972,0.000035817,0.0007215837,0.00009855902,0.000003825296,0.0000048712836,0.000020811243,0.9595516,0.000010524138,0.03904894,4.0041937e-7,0.00007570411],"about_ca_topic_score_codex":0.00003008644,"about_ca_topic_score_gemma":8.0111835e-7,"teacher_disagreement_score":0.48705623,"about_ca_system_score_codex":0.000012569227,"about_ca_system_score_gemma":0.000008742216,"threshold_uncertainty_score":0.31715894},"labels":[],"label_agreement":null},{"id":"W2038641877","doi":"10.1007/s10665-009-9356-1","title":"Optimal-control approach to trajectory planning for a class of mobile robotic manipulations","year":2009,"lang":"en","type":"article","venue":"Journal of Engineering Mathematics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University","funders":"","keywords":"Controllability; Trajectory; Turning radius; Control theory (sociology); Mobile robot; Robot; Optimal control; Computer science; Object (grammar); Motion planning; Constant (computer programming); Control (management); Mathematics; Mathematical optimization; Artificial intelligence; Engineering","score_opus":0.026994158149910182,"score_gpt":0.2606163069189926,"score_spread":0.23362214876908244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038641877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007832595,0.00012421043,0.9913672,0.00005593226,0.00020926385,0.00026147126,0.0000013465637,0.000042770585,0.000105167106],"genre_scores_gemma":[0.29062676,5.681099e-7,0.70924956,0.000018207586,0.0000729191,0.000006931749,2.7347005e-7,0.000010044447,0.0000147371375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987308,0.000010616072,0.00064057205,0.00011213459,0.00028097144,0.00022494979],"domain_scores_gemma":[0.9988489,0.00027447875,0.00033321252,0.00025935462,0.00016216178,0.00012188566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053792796,0.0001414122,0.00042600447,0.00030073637,0.000030836673,0.000044214106,0.0005389319,0.00005626362,3.5982586e-7],"category_scores_gemma":[0.00021867927,0.00012775262,0.00014319994,0.00025308935,0.000006626901,0.00019966741,0.000018524035,0.00015439739,8.6680575e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029142689,0.0001336494,0.000005259257,0.000089081914,0.00003797315,0.000005423628,0.0016965894,0.9929116,0.0019529831,0.0027961847,0.00012547504,0.00024289155],"study_design_scores_gemma":[0.0004089346,0.00037197617,0.00038406232,0.00022523134,0.000037086505,0.00016599282,0.00006409826,0.99761856,0.00030235437,0.0002608387,0.000035946316,0.00012491715],"about_ca_topic_score_codex":1.3313013e-7,"about_ca_topic_score_gemma":3.1736178e-9,"teacher_disagreement_score":0.28279415,"about_ca_system_score_codex":0.00005307645,"about_ca_system_score_gemma":0.00004724576,"threshold_uncertainty_score":0.52096003},"labels":[],"label_agreement":null},{"id":"W2039200457","doi":"10.1109/icmech.2007.4280039","title":"Cooperation Random Mobile Robots Based on Chaos Synchronization","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Synchronizing; Robot; CHAOS (operating system); Mobile robot; Synchronization (alternating current); Computer science; Motion planning; Chaotic; Path (computing); Control theory (sociology); Simulation; Artificial intelligence; Transmission (telecommunications); Computer network; Telecommunications","score_opus":0.009643295028598514,"score_gpt":0.24973865587091718,"score_spread":0.24009536084231867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039200457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007149543,0.00001618774,0.991973,0.0001925801,0.00046588705,0.00030338217,3.9602344e-7,0.00033345926,0.0060001616],"genre_scores_gemma":[0.69125634,0.0000011897891,0.30718344,0.00086290785,0.00010470823,0.000018727293,0.000014887654,0.000009068244,0.0005487254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892914,0.000047826197,0.00020138378,0.00029786577,0.00028485962,0.0002388998],"domain_scores_gemma":[0.9991785,0.0001954655,0.00005330057,0.00039240494,0.00010005061,0.000080227386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066005037,0.000111423535,0.00011319291,0.0001315052,0.00012136254,0.00010767857,0.0003135374,0.00006298634,0.00003603351],"category_scores_gemma":[0.00007084135,0.0000955462,0.00002828228,0.0004124222,0.00001586051,0.0002581562,0.00003125267,0.000081629245,0.00018910156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001610697,0.00007937116,0.00023382515,0.0000044794024,0.0000035037333,0.000021419599,0.00013409731,0.95331895,0.00035750112,0.0026130206,0.00071325956,0.04250446],"study_design_scores_gemma":[0.0010296784,0.00015608987,0.0007557295,0.000017709568,0.0000020085477,0.0000048121788,0.00000496795,0.99380106,0.0038521457,0.000025597355,0.00022422819,0.00012596438],"about_ca_topic_score_codex":0.0000125047645,"about_ca_topic_score_gemma":0.0000020529098,"teacher_disagreement_score":0.6905414,"about_ca_system_score_codex":0.00007248316,"about_ca_system_score_gemma":0.00006275412,"threshold_uncertainty_score":0.3896261},"labels":[],"label_agreement":null},{"id":"W2041075843","doi":"10.1109/rose.2012.6402624","title":"Rigorous movement of convex polygons on a path using multiple robots","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Path (computing); Robot; Object (grammar); Orientation (vector space); Computer science; Motion planning; Task (project management); Boundary (topology); Computer vision; Regular polygon; Trajectory; Point (geometry); Artificial intelligence; Mathematics; Geometry; Engineering; Physics","score_opus":0.04465167941110011,"score_gpt":0.27299800495129495,"score_spread":0.22834632554019485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041075843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054205727,0.00007433177,0.9427627,0.000095836156,0.0006446547,0.00014859381,0.0000036950541,0.00012142622,0.0019430616],"genre_scores_gemma":[0.60213464,7.7639476e-7,0.39731973,0.00036644598,0.000047368743,0.0000028996187,9.212328e-7,0.0000062165886,0.000121007295],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874705,0.000054005435,0.00023591891,0.0002193547,0.00033056142,0.00041311534],"domain_scores_gemma":[0.9990328,0.00013213011,0.00011268687,0.0005305471,0.00004338789,0.0001484777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029153613,0.00013449552,0.00019020573,0.0000919833,0.00006760292,0.000026505317,0.00043802775,0.000050114744,0.000015192066],"category_scores_gemma":[0.00004978507,0.0001128918,0.000053074,0.0002054303,0.000030946747,0.0002730535,0.000174812,0.00008929138,0.00004716173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065184235,0.005053108,0.31292692,0.00017289804,0.00042033955,0.00014765767,0.017731756,0.30876818,0.072010085,0.17235924,0.0052775587,0.10506708],"study_design_scores_gemma":[0.00045998668,0.00012486399,0.01669491,0.000047994065,0.0000074608015,0.000013749047,0.000059625425,0.96906346,0.013041081,0.00020048393,0.00007109838,0.0002152853],"about_ca_topic_score_codex":0.0003395924,"about_ca_topic_score_gemma":0.0000010364781,"teacher_disagreement_score":0.6602953,"about_ca_system_score_codex":0.000056889254,"about_ca_system_score_gemma":0.00004151201,"threshold_uncertainty_score":0.4603594},"labels":[],"label_agreement":null},{"id":"W2041333329","doi":"10.1111/j.1468-0394.2006.00409.x","title":"Distributed fuzzy discrete event system for robotic sensory information processing","year":2006,"lang":"en","type":"article","venue":"Expert Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Controllability; Observability; Computer science; Fuzzy logic; Supervisory control; Fuzzy control system; Mobile robot; Event (particle physics); Artificial intelligence; Robot; Neuro-fuzzy; Control engineering; Control theory (sociology); Control (management); Mathematics; Engineering","score_opus":0.015347208508435062,"score_gpt":0.25524541394716177,"score_spread":0.2398982054387267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041333329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031540173,0.0007616989,0.9946625,0.00020726275,0.002029733,0.0007622248,0.000022143911,0.0006654413,0.00057358464],"genre_scores_gemma":[0.9552909,6.918115e-7,0.043439705,0.000033067696,0.000506569,0.00027779536,0.00019105506,0.00001708879,0.00024308625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980155,0.000093069306,0.00066819816,0.00031756723,0.00044497562,0.00046064076],"domain_scores_gemma":[0.9987787,0.000077685814,0.00035354725,0.0004942445,0.00019979157,0.00009605307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041656688,0.00022883876,0.0003282426,0.00013802596,0.0003007399,0.0005451901,0.00054124865,0.000109230954,2.0661011e-7],"category_scores_gemma":[0.000033308173,0.0001978348,0.000093009294,0.0003079102,0.000023474584,0.0012457649,0.00007970973,0.00008457312,0.000053244505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022955659,0.00008106083,0.00036381706,0.0014036816,0.000055170764,0.00004481949,0.003625832,0.93800616,0.0012435109,0.027660672,0.020690275,0.0068020364],"study_design_scores_gemma":[0.0004327017,0.000043521723,0.00034794398,0.00039673992,0.0000060299344,0.00012753146,0.00056354405,0.994304,0.0003161376,0.00003673141,0.003139758,0.00028537097],"about_ca_topic_score_codex":0.00034095492,"about_ca_topic_score_gemma":7.717208e-7,"teacher_disagreement_score":0.95497555,"about_ca_system_score_codex":0.00031112024,"about_ca_system_score_gemma":0.000072005976,"threshold_uncertainty_score":0.8067469},"labels":[],"label_agreement":null},{"id":"W2041655521","doi":"10.1145/1341012.1341064","title":"Geometric algorithms for clearance based optimal path computation","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computational geometry; Shortest path problem; Path (computing); Algorithm; Disjoint sets; Computer science; Computation; Motion planning; Path length; Any-angle path planning; Mathematical optimization; Obstacle; Mathematics; Combinatorics; Theoretical computer science; Graph; Artificial intelligence","score_opus":0.035064574654623974,"score_gpt":0.30232408739407146,"score_spread":0.2672595127394475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041655521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015095561,0.00005582642,0.9959991,0.000268614,0.0006114288,0.00030740534,0.000003504677,0.00040860462,0.00083595247],"genre_scores_gemma":[0.10822317,6.314827e-7,0.89098775,0.00045100757,0.00010542367,0.000011284061,0.000009607948,0.000012310596,0.0001988419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983674,0.000024333007,0.00030082342,0.00044939658,0.00036765804,0.0004903724],"domain_scores_gemma":[0.99872917,0.00053728296,0.0001074998,0.00031011575,0.00017691658,0.00013904218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011362473,0.00015210883,0.00017410025,0.00037734324,0.00014489435,0.00012198649,0.0005695597,0.00007830282,0.000004474936],"category_scores_gemma":[0.00011505586,0.00014399856,0.00007986494,0.0012073502,0.000029188217,0.0003133858,0.00006356709,0.000104257095,0.000055606186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039393843,0.00021822519,0.00060906267,0.000041565527,0.000024250092,0.00006828992,0.00021718217,0.30208257,0.00018626671,0.003870604,0.0055569643,0.6870856],"study_design_scores_gemma":[0.0007087335,0.00020092676,0.011057109,0.000012754563,0.0000036828153,0.000011872505,0.000013228897,0.9852163,0.0015775871,0.00018463023,0.0008095962,0.00020355165],"about_ca_topic_score_codex":0.000016337535,"about_ca_topic_score_gemma":2.1615259e-7,"teacher_disagreement_score":0.6868821,"about_ca_system_score_codex":0.00006334223,"about_ca_system_score_gemma":0.000067122,"threshold_uncertainty_score":0.5872091},"labels":[],"label_agreement":null},{"id":"W2042909816","doi":"10.1145/2614217.2614285","title":"VisionGL","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.006938667599601935,"score_gpt":0.21632338424195002,"score_spread":0.2093847166423481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042909816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029087704,0.000004024307,0.9289778,0.0007559292,0.00026041255,0.000015729423,2.5053264e-8,0.00024837497,0.06944682],"genre_scores_gemma":[0.15160607,2.3900262e-7,0.8459751,0.0006449011,0.00004794853,0.000001127413,1.8257428e-7,0.0000020948844,0.0017223404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954957,0.000022281623,0.00005932569,0.00014117596,0.00011506674,0.00011257167],"domain_scores_gemma":[0.99952143,0.00006172956,0.00001344631,0.00033810677,0.000016916225,0.00004836869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001809204,0.00003853009,0.000046217196,0.000028246324,0.000035510828,0.00005205543,0.00045244282,0.000016740316,0.000014118053],"category_scores_gemma":[0.000047110763,0.00002980232,0.000014470573,0.00010779764,0.0000073982387,0.00013326341,0.00009742032,0.000034167148,0.0006535782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.4667679e-7,0.000028855384,0.0009954085,0.0000026737785,0.0000043389095,0.00000867559,0.0002207941,0.0016918837,0.00030965047,0.5998507,0.018058611,0.37882802],"study_design_scores_gemma":[0.000082254315,0.00003178926,0.004472095,0.0000033221365,3.9285317e-7,0.00001037262,0.0000014740481,0.9767677,0.00042735485,0.0056651817,0.01246654,0.00007150798],"about_ca_topic_score_codex":0.000005877095,"about_ca_topic_score_gemma":8.385816e-8,"teacher_disagreement_score":0.97507584,"about_ca_system_score_codex":0.000004542745,"about_ca_system_score_gemma":0.000007127743,"threshold_uncertainty_score":0.8400644},"labels":[],"label_agreement":null},{"id":"W2043110744","doi":"10.1109/iros.2005.1544995","title":"Time-optimal rendezvous with moving objects in dynamic cluttered environments using a guidance based technique","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendezvous; Obstacle; Interception; Computer science; Path (computing); Trajectory; Motion planning; Obstacle avoidance; Real-time computing; Simulation; Computer vision; Artificial intelligence; Robot; Mobile robot; Engineering; Aerospace engineering","score_opus":0.009300300359813852,"score_gpt":0.23250512492799105,"score_spread":0.2232048245681772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043110744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026446162,0.00003285926,0.97218233,0.00019359797,0.000030398842,0.00031168785,0.0000013180742,0.0001769042,0.00062472495],"genre_scores_gemma":[0.3617571,4.2041623e-7,0.6376704,0.00022148606,0.000012395122,0.000021597529,0.0000015130164,0.00001469897,0.00030040098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981572,0.00009517511,0.00029551797,0.0005884329,0.00036625192,0.0004973962],"domain_scores_gemma":[0.9990449,0.00006455876,0.00012303228,0.00066375424,0.000011831838,0.00009191165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003768052,0.00023027624,0.00022344595,0.00022430027,0.00008050152,0.0000730142,0.00069578213,0.00009173701,0.000016475566],"category_scores_gemma":[0.000021172935,0.0002088581,0.000032655138,0.00039903785,0.00006200849,0.0004902792,0.00017536065,0.00020786007,0.000056961515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000130358485,0.0001188022,0.0008239246,0.000008554104,0.00000901444,0.00019099312,0.0003231061,0.95182884,0.042334873,0.000012577089,0.000029522385,0.004306756],"study_design_scores_gemma":[0.0005025678,0.00008540112,0.0019467885,0.00013063027,0.0000035806836,0.00009163727,0.000009755026,0.98686105,0.010040051,0.000010173263,0.000035321144,0.00028306415],"about_ca_topic_score_codex":0.000047865702,"about_ca_topic_score_gemma":0.000010962888,"teacher_disagreement_score":0.33531094,"about_ca_system_score_codex":0.00038760173,"about_ca_system_score_gemma":0.0001135435,"threshold_uncertainty_score":0.8516986},"labels":[],"label_agreement":null},{"id":"W2044526330","doi":"10.1016/j.robot.2003.11.004","title":"Motion planning for formations of mobile robots","year":2004,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":204,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Computer science; Motion planning; Kinematics; Testbed; Robot; Holonomic; Motion (physics); Class (philosophy); Work (physics); Simulation; Artificial intelligence; Classical mechanics; Computer network; Mechanical engineering; Physics; Engineering","score_opus":0.023052338612472082,"score_gpt":0.26056327771998583,"score_spread":0.23751093910751375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044526330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026282677,0.0005688114,0.9952767,0.000106949316,0.0005336591,0.0004957957,0.000009748455,0.00011170374,0.00026839584],"genre_scores_gemma":[0.72650003,0.000004332648,0.27329394,0.000013176189,0.00004879993,0.00005862186,0.000009460574,0.000009365475,0.00006229265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989721,0.000019155945,0.00039936186,0.00022629001,0.00014142225,0.00024166702],"domain_scores_gemma":[0.9992334,0.00007961056,0.00022123232,0.0002905164,0.00009765023,0.000077583296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002914425,0.00012504357,0.00025507034,0.00011424349,0.0001587159,0.000119305536,0.00026755934,0.00007371125,1.4269737e-7],"category_scores_gemma":[0.000021259915,0.00011944761,0.0000488327,0.00014845576,0.000027769445,0.00029661343,0.000068051784,0.00006350535,0.0000034698928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.9150923e-7,0.000030183894,0.00010203596,0.00009155434,0.000013092162,0.0000019707454,0.000907337,0.94492257,0.00019112857,0.052605174,0.000015316844,0.0011189489],"study_design_scores_gemma":[0.0005542592,0.00019333084,0.0004659391,0.0001755596,0.000013080415,0.000063250416,0.00017611336,0.99620867,0.0003754788,0.001422888,0.00018247015,0.00016895587],"about_ca_topic_score_codex":0.000044322733,"about_ca_topic_score_gemma":3.1882692e-7,"teacher_disagreement_score":0.72387177,"about_ca_system_score_codex":0.000060704926,"about_ca_system_score_gemma":0.000077066914,"threshold_uncertainty_score":0.4870932},"labels":[],"label_agreement":null},{"id":"W2046635640","doi":"10.1002/rob.20213","title":"Autonomous system for navigation and surveying in underground mines","year":2007,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supervisor; Navigation system; Context (archaeology); Planner; Mode (computer interface); Robotics; Artificial intelligence; Engineering; Robot; Real-time computing; Computer science; Human–computer interaction; Geography","score_opus":0.02514269861892292,"score_gpt":0.2857765880156399,"score_spread":0.26063388939671694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046635640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015894318,0.0002878595,0.98212975,0.0006082665,0.0009057278,0.00009021383,1.9429534e-7,0.000013737047,0.00006992244],"genre_scores_gemma":[0.5460898,0.0000063014254,0.45368573,0.000061434555,0.000119412056,4.2180912e-7,2.814029e-7,0.000004177225,0.000032389045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990995,0.000036493046,0.00044957772,0.000094945535,0.00015047325,0.00016898941],"domain_scores_gemma":[0.99844736,0.0009632302,0.0002810349,0.00011427758,0.00013087128,0.00006322087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015678438,0.0000729335,0.00020393026,0.00014532088,0.000047216792,0.00007893689,0.0002443841,0.00007732934,8.5219064e-8],"category_scores_gemma":[0.00020314376,0.0000638208,0.000039293616,0.00014821523,0.000010033778,0.000264598,0.000035552075,0.00013318549,2.4534248e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027007947,0.0005003479,0.11035815,0.009110689,0.000339145,0.0032091057,0.00985855,0.6983992,0.0041803936,0.084663264,0.002013689,0.07709741],"study_design_scores_gemma":[0.0026844651,0.0012196798,0.03653999,0.004738478,0.00007135537,0.0030735172,0.0011338238,0.94301146,0.0033375747,0.003543669,0.0000903917,0.0005555884],"about_ca_topic_score_codex":0.000013157547,"about_ca_topic_score_gemma":0.00000721033,"teacher_disagreement_score":0.53019553,"about_ca_system_score_codex":0.0000732578,"about_ca_system_score_gemma":0.000052507192,"threshold_uncertainty_score":0.2602537},"labels":[],"label_agreement":null},{"id":"W2047599849","doi":"10.1016/j.cor.2014.06.016","title":"An innovative multi-agent search-and-rescue path planning approach","year":2014,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Mathematical optimization; Motion planning; Time horizon; Constraint (computer-aided design); Path (computing); Integer programming; Variety (cybernetics); Representation (politics); Relaxation (psychology); Computational complexity theory; Computation; Bellman equation; Algorithm; Mathematics; Artificial intelligence; Robot","score_opus":0.14433389054318788,"score_gpt":0.4048317940207116,"score_spread":0.2604979034775238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047599849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051870212,0.000074255695,0.94637626,0.00048430983,0.00021817822,0.00045802313,0.0000037996851,0.00021339805,0.00030157212],"genre_scores_gemma":[0.4234217,0.000003587061,0.57610565,0.0001394025,0.00012607842,0.00004957953,0.00002884754,0.000015056705,0.000110070876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99573123,0.0013095628,0.00034144026,0.00092904025,0.00091369904,0.00077504176],"domain_scores_gemma":[0.9974376,0.00026232813,0.000013736493,0.0010868835,0.00084752846,0.0003519289],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0034565083,0.0002184579,0.00025543614,0.0006406276,0.0011180546,0.0012121387,0.00170535,0.00010684641,0.0000024716244],"category_scores_gemma":[0.00020997746,0.000201061,0.0000305521,0.0016858927,0.00028552857,0.00088066707,0.0009010466,0.0008234517,0.00006322655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050886924,0.00032219934,0.0007128255,0.00002606307,0.000033509,0.00003138958,0.014054587,0.9101384,0.0013998976,0.027337343,0.0005786743,0.04536001],"study_design_scores_gemma":[0.00048043142,0.00029127434,0.0077236975,0.000046875255,0.0000013200289,0.000033449352,0.00026491826,0.9903188,0.00026111465,0.000037388836,0.00031057853,0.00023019908],"about_ca_topic_score_codex":0.00029103956,"about_ca_topic_score_gemma":0.0000038625403,"teacher_disagreement_score":0.37155148,"about_ca_system_score_codex":0.00011395479,"about_ca_system_score_gemma":0.00025365723,"threshold_uncertainty_score":0.9998247},"labels":[],"label_agreement":null},{"id":"W2050466529","doi":"10.1109/ssrr.2012.6523872","title":"The effects of communication and visual range on multi-robot repeated boundary coverage","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Boundary (topology); Event (particle physics); Computer science; Heuristic; Range (aeronautics); Travelling salesman problem; Mobile robot; Artificial intelligence; Path (computing); Algorithm; Mathematics; Engineering","score_opus":0.016185421922316295,"score_gpt":0.2808748439578225,"score_spread":0.26468942203550616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050466529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1439714,0.0027629104,0.8496348,0.00045913013,0.0006370673,0.0004287819,7.7781635e-7,0.00020031216,0.0019048222],"genre_scores_gemma":[0.95138013,0.000101690246,0.047972344,0.00016560203,0.000017168277,0.000009611669,0.0000017621884,0.0000056472654,0.00034604484],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99917066,0.00020613497,0.00014572206,0.00012164218,0.00016495111,0.00019088527],"domain_scores_gemma":[0.9980733,0.0012515745,0.000084950494,0.00049379625,0.000034718098,0.000061663246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051881943,0.00008414464,0.00010533568,0.000033442342,0.00022422004,0.000074851385,0.000372971,0.00004007283,7.988848e-7],"category_scores_gemma":[0.00025233146,0.000055383276,0.000021709413,0.00012474883,0.00008726478,0.0002496942,0.00020037229,0.0001100899,0.000012686594],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014942953,0.0022082825,0.121197335,0.00031229955,0.00032001696,0.000036147474,0.01771833,0.0009596058,0.018979106,0.031903535,0.0060574207,0.8001585],"study_design_scores_gemma":[0.0018634567,0.00045977667,0.65927136,0.00017539754,0.000017915712,0.00003215709,0.00004220243,0.32423508,0.012209727,0.000185458,0.0012075412,0.00029992434],"about_ca_topic_score_codex":0.000047623485,"about_ca_topic_score_gemma":0.0000013605495,"teacher_disagreement_score":0.80740875,"about_ca_system_score_codex":0.000018791812,"about_ca_system_score_gemma":0.000015862846,"threshold_uncertainty_score":0.22584644},"labels":[],"label_agreement":null},{"id":"W2050805266","doi":"10.2316/journal.206.2010.2.206-3308","title":"HEAVY OBJECT MANIPULATION BY A HYBRID SERIAL-PARALLEL MOBILE ROBOT","year":2010,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Computer hardware; Parallel computing; Embedded system; Artificial intelligence","score_opus":0.0100511505903455,"score_gpt":0.26092761506907597,"score_spread":0.25087646447873047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050805266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06652304,0.000088296496,0.9284943,0.0014471251,0.0032097592,0.000090607755,0.0000043827963,0.000050677103,0.00009183864],"genre_scores_gemma":[0.6264633,0.000031773332,0.37302852,0.000099647106,0.00029512303,0.0000024100848,0.00001262892,0.0000076940805,0.00005892484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986398,0.00004030261,0.00047631038,0.00015906834,0.0005485908,0.00013591384],"domain_scores_gemma":[0.99876636,0.000084104395,0.00049258315,0.00014696422,0.00041681214,0.0000931917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043755048,0.000120498364,0.00016494504,0.00019126154,0.000069906055,0.00035035695,0.0005583669,0.00005859499,0.000009105832],"category_scores_gemma":[0.00008859011,0.00011068065,0.00006154896,0.000087774,0.0000294392,0.000864341,0.00009079915,0.00024118714,0.000009359749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004992034,0.0003064318,0.0020568857,0.000019314492,0.00020364061,0.00015647463,0.00087474205,0.83824974,0.04316508,0.014417554,0.0045335945,0.095966645],"study_design_scores_gemma":[0.0007274822,0.00016023203,0.0067186537,0.000051407627,0.000013388889,0.0009993195,0.000015789918,0.9847573,0.0023535306,0.0033486823,0.0006900897,0.00016409096],"about_ca_topic_score_codex":0.0000125277165,"about_ca_topic_score_gemma":0.000001247466,"teacher_disagreement_score":0.5599402,"about_ca_system_score_codex":0.000044345765,"about_ca_system_score_gemma":0.00007177895,"threshold_uncertainty_score":0.4513426},"labels":[],"label_agreement":null},{"id":"W2051259596","doi":"10.1109/rose.2007.4373975","title":"Intelligent Parallel Parking of a Car-like Mobile Robot Using RFID Technology","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Mobile robot; Robot; Embedded system; Artificial intelligence","score_opus":0.032411289409396124,"score_gpt":0.2972152857298974,"score_spread":0.2648039963205013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051259596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035701785,0.00020986432,0.9622851,0.000056394514,0.0004893164,0.0001794886,3.6611365e-7,0.00027186808,0.0008058228],"genre_scores_gemma":[0.3926499,0.0000044498165,0.6071705,0.000054253596,0.000022894175,0.0000043256728,4.0921398e-7,0.0000071483814,0.00008611956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984173,0.000025221232,0.00042841354,0.00038606543,0.00027235653,0.00047063237],"domain_scores_gemma":[0.9988899,0.00010545017,0.00015925783,0.0006554534,0.00010565529,0.00008424336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006025461,0.00015444109,0.0002549426,0.0003641628,0.00007469218,0.000029721075,0.00090541714,0.0001346273,0.000009319655],"category_scores_gemma":[0.000033877426,0.00014164325,0.00006100357,0.0008633679,0.000095150914,0.00015258718,0.00038152045,0.0001754998,0.000019686186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030811,0.00047456598,0.03016531,0.00008604188,0.0001778646,0.0005435251,0.004217405,0.49685928,0.040558487,0.036126956,0.00031201597,0.39044774],"study_design_scores_gemma":[0.00035584765,0.00026259825,0.0016540262,0.000107728425,0.000016026795,0.0002846306,0.00066025567,0.9442949,0.048406467,0.0020670455,0.0014308746,0.0004595628],"about_ca_topic_score_codex":0.000104103914,"about_ca_topic_score_gemma":0.000007144717,"teacher_disagreement_score":0.44743565,"about_ca_system_score_codex":0.000074756106,"about_ca_system_score_gemma":0.000069305235,"threshold_uncertainty_score":0.5776044},"labels":[],"label_agreement":null},{"id":"W2051378666","doi":"10.1177/095965180221600307","title":"A modification of cooperative collision avoidance for multiple mobile robots using the avoidance circle","year":2002,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Collision avoidance; Mobile robot; Computer science; Robot; Collision; Simulation; Collision avoidance system; Artificial intelligence; Computer security","score_opus":0.022645330981549732,"score_gpt":0.22492028200223432,"score_spread":0.2022749510206846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051378666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08704707,0.0015126994,0.91023487,0.00008716609,0.0006108713,0.00048182084,0.000008524786,0.000012715566,0.000004285616],"genre_scores_gemma":[0.9801781,0.000050570583,0.019644821,0.000005293475,0.00008503521,0.000022991846,8.845853e-8,0.000007690087,0.0000053621256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862,0.000013865963,0.0007290687,0.00013514722,0.00034547533,0.00015647057],"domain_scores_gemma":[0.9981536,0.00021794379,0.00075302087,0.0001244674,0.00069187366,0.000059073933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091857533,0.00013179681,0.00042382083,0.00009121744,0.00009043708,0.000034207864,0.0004945443,0.00007420421,1.5424366e-7],"category_scores_gemma":[0.00056801876,0.000089836576,0.00012137168,0.0002838818,0.000050892126,0.00039350108,0.00004274348,0.00017654453,6.685328e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016267024,0.000031047344,0.000012877231,0.00017659695,0.000046926096,2.1373216e-7,0.0002697218,0.8704731,0.12114302,0.007569217,0.000011613666,0.00024942387],"study_design_scores_gemma":[0.0009531113,0.00017357973,0.0000485137,0.00085518206,0.000038339273,0.000050256862,0.00011933919,0.9719926,0.025596015,0.000019787502,0.00007045893,0.000082829945],"about_ca_topic_score_codex":0.000006930354,"about_ca_topic_score_gemma":5.2623708e-8,"teacher_disagreement_score":0.8931311,"about_ca_system_score_codex":0.00005286169,"about_ca_system_score_gemma":0.000031273958,"threshold_uncertainty_score":0.36634293},"labels":[],"label_agreement":null},{"id":"W2051742739","doi":"10.1017/cbo9780511780929.007","title":"Visual Sensors and Algorithms","year":2010,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Artificial intelligence; Variety (cybernetics); Field (mathematics); Computer science; Robot; Mobile robot; Key (lock); State (computer science); Graduate students; Human–computer interaction; Range (aeronautics); Algorithm; Multimedia; Engineering; Mathematics; Psychology; Aerospace engineering","score_opus":0.01852128648634288,"score_gpt":0.2120295718864429,"score_spread":0.1935082854001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051742739","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010456404,0.00008694359,0.079505965,0.000040594852,0.0007421778,0.00028852286,0.000053578537,0.0004152948,0.9187624],"genre_scores_gemma":[0.00009478332,0.000043377528,0.044586953,0.00006257614,0.00018497015,3.917241e-7,0.000014695643,0.000040208695,0.954972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805,0.00004524184,0.0001992223,0.0009090903,0.00039862696,0.00039786144],"domain_scores_gemma":[0.9983276,0.00013822108,0.0002394824,0.0008145692,0.00015002729,0.00033008042],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017526893,0.00046462842,0.00047872277,0.0002622433,0.000281097,0.00015566993,0.0010736731,0.0006094366,0.0000012681567],"category_scores_gemma":[0.000014862754,0.00054816046,0.00013799159,0.000012840629,0.0003357654,0.00019026197,0.0011120917,0.0010031724,0.000027766553],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015550164,0.000014228875,0.000003000549,0.00006577773,0.00015013356,0.0023641442,0.00020421016,0.000012854329,0.00018432854,0.97651994,0.0054976144,0.014968207],"study_design_scores_gemma":[0.0006869064,0.00012095265,0.000056243825,0.0001606531,0.000114465394,0.0003117265,0.000013605402,0.023361064,0.0006251561,0.00004025416,0.9734869,0.0010220514],"about_ca_topic_score_codex":0.00006712241,"about_ca_topic_score_gemma":2.5814018e-7,"teacher_disagreement_score":0.9764797,"about_ca_system_score_codex":0.00008343394,"about_ca_system_score_gemma":0.00012395898,"threshold_uncertainty_score":0.99969697},"labels":[],"label_agreement":null},{"id":"W2051885751","doi":"10.1115/detc2009-87761","title":"Object Pose Estimation for Multi-Robot Cooperative Object Transportation","year":2009,"lang":"en","type":"article","venue":"Volume 3: ASME/IEEE 2009 International Conference on Mechatronic and Embedded Systems and Applications; 20th Reliability, Stress Analysis, and Failure Prevention Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Pose; Computer science; Robot; Robustness (evolution); Mobile robot; Object (grammar); Orientation (vector space); Object detection; Segmentation; Mathematics","score_opus":0.03249856682659577,"score_gpt":0.3181806913427998,"score_spread":0.285682124516204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051885751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0084456485,0.00034319362,0.98824555,0.0009308228,0.00018258349,0.0013285865,0.00025987794,0.00011870009,0.00014503213],"genre_scores_gemma":[0.92848945,0.00042929742,0.06916567,0.000050859162,0.00009895749,0.00048459292,0.000444738,0.000010908894,0.00082554313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970259,0.00021436153,0.00080422626,0.0011434612,0.00045995886,0.00035205652],"domain_scores_gemma":[0.99787116,0.0001276103,0.00049101876,0.0005071094,0.0007961564,0.000206959],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007834005,0.00039938444,0.00059482525,0.0003771643,0.00041446902,0.00075153226,0.00048540806,0.0002194193,0.000022890694],"category_scores_gemma":[0.000058576974,0.00037508688,0.00015961286,0.00045592454,0.00012194346,0.0006801128,0.000035432513,0.00026872376,0.0000062559784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021136609,0.001381971,0.007072583,0.0004924193,0.0014429167,0.0000065119016,0.0032808513,0.10357009,0.0020612886,0.71852946,0.00040662766,0.16154389],"study_design_scores_gemma":[0.0008822132,0.0003145777,0.010654645,0.00017933678,0.00027432747,0.0000073624037,0.00072800217,0.97951424,0.00013919844,0.006698586,0.00019210402,0.00041543492],"about_ca_topic_score_codex":0.00019338996,"about_ca_topic_score_gemma":0.0001487668,"teacher_disagreement_score":0.92004377,"about_ca_system_score_codex":0.00006785918,"about_ca_system_score_gemma":0.00018640126,"threshold_uncertainty_score":0.9998701},"labels":[],"label_agreement":null},{"id":"W2053870186","doi":"10.2316/journal.206.2006.2.206-2794","title":"A NOVEL HYBRID NAVIGATION SCHEME FOR RECONFIGURABLE MULTI-AGENT TEAMS","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Control reconfiguration; Computer science; Robustness (evolution); Scheme (mathematics); Task (project management); Distributed computing; Imperfect; Human–computer interaction; Real-time computing; Embedded system; Engineering; Systems engineering","score_opus":0.01997880030245743,"score_gpt":0.27687740529053956,"score_spread":0.25689860498808215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053870186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013988716,0.00008070036,0.9827526,0.0016909445,0.001282795,0.000097947355,0.000008303858,0.000033310604,0.000064650274],"genre_scores_gemma":[0.23637572,0.0000079753845,0.76318103,0.0000624577,0.00023588665,0.0000028380562,0.000019616517,0.000006210119,0.0001082909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989293,0.000015347005,0.00045357543,0.00013183922,0.0003527695,0.00011715998],"domain_scores_gemma":[0.9986659,0.00009004105,0.0004998046,0.00008717486,0.00061182963,0.000045231718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041048482,0.000092270646,0.00012838209,0.00015923427,0.00006519795,0.00023899382,0.00036736854,0.000035112847,0.0000012795327],"category_scores_gemma":[0.00007670621,0.00008532145,0.00006542346,0.00006581156,0.000017876137,0.0005915488,0.000029012444,0.000088913446,0.0000029952694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035999405,0.00067335524,0.0019626992,0.000056767472,0.00023473988,0.000079573976,0.00043567087,0.7975136,0.06229867,0.04826178,0.0032074181,0.08523971],"study_design_scores_gemma":[0.001037818,0.00007014746,0.0046061114,0.00012701376,0.0000090996855,0.00049636525,0.000011347071,0.9872162,0.0032438815,0.002584476,0.0004968703,0.00010070918],"about_ca_topic_score_codex":0.00002031374,"about_ca_topic_score_gemma":5.295997e-7,"teacher_disagreement_score":0.222387,"about_ca_system_score_codex":0.00009690787,"about_ca_system_score_gemma":0.00007147015,"threshold_uncertainty_score":0.3479308},"labels":[],"label_agreement":null},{"id":"W2054720697","doi":"10.1007/s10462-011-9233-y","title":"Automated camera planning to film robot operations","year":2011,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Université de Sherbrooke","funders":"","keywords":"Computer science; Computer vision; Robot; Artificial intelligence; Computer graphics (images); Trajectory; Computer graphics; Animation; Zoom; Computer animation; Task (project management); Frame (networking)","score_opus":0.17469800194250049,"score_gpt":0.3688946238463915,"score_spread":0.194196621903891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054720697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013263781,0.007016607,0.9878025,0.0012215456,0.0005764555,0.0005402786,0.0000026161729,0.0008958686,0.0018114639],"genre_scores_gemma":[0.044384204,0.0017857219,0.94785535,0.0054609356,0.0001268102,0.0001767932,0.000009856834,0.0000307328,0.00016956973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978831,0.00014419375,0.00068320084,0.0005412915,0.00028810074,0.00046011273],"domain_scores_gemma":[0.9986014,0.00007897424,0.00008314518,0.0008106839,0.0001737007,0.00025213233],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006677821,0.00022235897,0.00036514553,0.00013013964,0.00022000734,0.0001271553,0.0012404246,0.000057828653,0.00015015846],"category_scores_gemma":[0.00039223864,0.0002024344,0.000093374176,0.0011366622,0.00004352578,0.0004292014,0.00023049084,0.00018193331,0.0031290555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006566709,0.0003339618,0.0001356524,0.00048142372,0.00007646323,0.00032864267,0.009632806,0.1004438,0.0008188859,0.099430665,0.009311214,0.7789999],"study_design_scores_gemma":[0.000013133883,0.00020123938,0.00034114698,0.0034517285,0.000036179405,0.000087185006,0.000097309545,0.9830884,0.00714696,0.0017992748,0.0031231483,0.0006143261],"about_ca_topic_score_codex":0.00016503833,"about_ca_topic_score_gemma":0.000007181668,"teacher_disagreement_score":0.8826446,"about_ca_system_score_codex":0.00004550938,"about_ca_system_score_gemma":0.0001115773,"threshold_uncertainty_score":0.9976471},"labels":[],"label_agreement":null},{"id":"W2055548166","doi":"10.1007/s10846-013-9841-7","title":"Real-Time Path Planning Algorithm for Autonomous Border Patrol: Design, Simulation, and Experimentation","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pursuer; Motion planning; Solver; Path (computing); Pursuit-evasion; Computer science; Random tree; Mathematical optimization; Nonlinear system; Algorithm; Simulation; Control engineering; Engineering; Artificial intelligence; Mathematics; Robot","score_opus":0.038975586233691135,"score_gpt":0.32237293413636214,"score_spread":0.283397347902671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055548166","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013734918,0.0010636181,0.99500656,0.00011077188,0.001483393,0.00084937754,0.0000012336855,0.00007230672,0.000039253242],"genre_scores_gemma":[0.07833421,0.00003050099,0.9206788,0.000050702332,0.00046793013,0.00005815197,0.0000037124428,0.000035156056,0.0003408322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744594,0.00022604836,0.0011503397,0.0003142196,0.0004830915,0.00038033657],"domain_scores_gemma":[0.9968952,0.0009056453,0.00096160476,0.00029679443,0.0007083587,0.0002324232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010853345,0.00026064957,0.00052734034,0.00027939913,0.00016110578,0.00049406226,0.00050117914,0.000114569026,0.0000115934645],"category_scores_gemma":[0.00012885433,0.00021796732,0.00011588752,0.00020295651,0.000034524983,0.00085109327,0.00006495766,0.00014929513,0.000034825418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069185503,0.000056023036,0.00013604182,0.000026925421,0.00009195055,0.000024101564,0.0020640001,0.9742668,0.0008730109,0.00008296572,0.001026619,0.021344636],"study_design_scores_gemma":[0.0004357707,0.00057776325,0.00041042827,0.00027073093,0.00003239439,0.00026478287,0.00054742565,0.9964485,0.00047957562,0.00019372115,0.000109897665,0.00022897453],"about_ca_topic_score_codex":0.000076982826,"about_ca_topic_score_gemma":1.8586725e-8,"teacher_disagreement_score":0.07696072,"about_ca_system_score_codex":0.00018284032,"about_ca_system_score_gemma":0.00014526414,"threshold_uncertainty_score":0.8888449},"labels":[],"label_agreement":null},{"id":"W2055936046","doi":"10.2316/journal.206.2011.4.206-3561","title":"NON-OSCILLATORY MULTI-ROBOT MOTION FOR STABLE TARGET CAPTURE","year":2011,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Motion (physics); Robot; Motion capture; Artificial intelligence; Computer vision","score_opus":0.03232351263454092,"score_gpt":0.2673620389499561,"score_spread":0.23503852631541516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055936046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002301169,0.000099597586,0.9949265,0.00047748807,0.0019385288,0.000098061384,0.0000049987184,0.000030321584,0.00012330548],"genre_scores_gemma":[0.2993852,0.00001294532,0.70030475,0.000087166976,0.00012312616,0.0000017897004,0.0000039938736,0.0000065144345,0.00007453018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898046,0.000021285066,0.00038317707,0.00013935454,0.00034274638,0.00013294554],"domain_scores_gemma":[0.99867535,0.000047436184,0.00044610558,0.000107777385,0.0006455658,0.000077780875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043600108,0.0001038799,0.00014926959,0.0002085198,0.000062803156,0.00011643595,0.00046586612,0.00006729206,0.000005344488],"category_scores_gemma":[0.00008089714,0.000092155686,0.00006768331,0.00007567851,0.000023452205,0.0007327003,0.00006430837,0.00011015915,0.0000033328058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011397684,0.0009780652,0.021577096,0.00012267151,0.0007339275,0.00022331097,0.014614375,0.81892914,0.0071779364,0.040474813,0.003500781,0.091553934],"study_design_scores_gemma":[0.00076000224,0.00009337782,0.027940148,0.00006915272,0.000012808629,0.00014790127,0.0000480711,0.96680665,0.001072386,0.0027835846,0.0001512642,0.000114636794],"about_ca_topic_score_codex":0.000011628343,"about_ca_topic_score_gemma":5.7150413e-7,"teacher_disagreement_score":0.29708403,"about_ca_system_score_codex":0.00006578063,"about_ca_system_score_gemma":0.000070109636,"threshold_uncertainty_score":0.37579998},"labels":[],"label_agreement":null},{"id":"W2056288820","doi":"10.1109/robot.2004.1307285","title":"Optimized motion strategies for cooperative localization of mobile robots","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Beacon; Mobile robot; Equilateral triangle; Robot; Computer science; Motion planning; Motion (physics); Measure (data warehouse); Artificial intelligence; Work (physics); Function (biology); Computer vision; Compass; Real-time computing; Mathematics; Engineering; Geography; Geometry; Data mining","score_opus":0.021229738301698487,"score_gpt":0.2781454968098142,"score_spread":0.2569157585081157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056288820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033035674,0.000028288905,0.9981715,0.00007883681,0.00015936454,0.0003737172,0.0000021058552,0.00012154543,0.00073430536],"genre_scores_gemma":[0.1908337,0.0000026820683,0.80894613,0.000053554526,0.000016642009,0.00005333906,0.000009948743,0.0000048116576,0.000079185316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931407,0.000023637356,0.00019957787,0.00020154561,0.00012971283,0.0001314696],"domain_scores_gemma":[0.99942565,0.000049557257,0.000077945515,0.0002033668,0.0002106269,0.000032871998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015211405,0.00008307874,0.00014472147,0.000057555168,0.0000618327,0.00007009588,0.00024476938,0.000045052657,0.000004672904],"category_scores_gemma":[0.000027671105,0.00007080492,0.00003381323,0.00022736847,0.000032480064,0.00052980584,0.000036331854,0.000030679254,0.000005770381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037263987,0.000042252457,0.000003757227,0.000008885325,0.0000067827564,7.0995293e-7,0.0006944285,0.94629973,0.00025044818,0.051200688,0.00005187368,0.0014366893],"study_design_scores_gemma":[0.0009541877,0.00022376489,0.000026845086,0.000021829177,0.0000039884526,0.0000034787947,0.00025555992,0.98194957,0.010423718,0.0060301186,0.000014911779,0.0000920376],"about_ca_topic_score_codex":0.00003809486,"about_ca_topic_score_gemma":9.639189e-7,"teacher_disagreement_score":0.19050334,"about_ca_system_score_codex":0.00003394558,"about_ca_system_score_gemma":0.0001307509,"threshold_uncertainty_score":0.28873408},"labels":[],"label_agreement":null},{"id":"W2056716388","doi":"10.1142/s0219878905000477","title":"GA BASED SENSOR PLACEMENT AND CONTROL DESIGN FOR REACTIVE NAVIGATION OF MOBILE ROBOTS","year":2005,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Hong Kong Government; University of Alberta","keywords":"Computer science; Mobile robot; Obstacle avoidance; Genetic algorithm; Robot; Controller (irrigation); Mobile robot navigation; Obstacle; Robot control; Control (management); Real-time computing; Artificial intelligence","score_opus":0.011409704698537573,"score_gpt":0.27145289641652615,"score_spread":0.26004319171798856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056716388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009722751,0.000026305386,0.988719,0.00080491585,0.00035794274,0.0002678662,0.00002148445,0.000015129615,0.000064625776],"genre_scores_gemma":[0.5557352,0.0000037984173,0.44367853,0.00043423375,0.00010651336,0.000013520143,0.00002186932,0.000002377652,0.000004001887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986537,0.00005553576,0.0006419497,0.00006308386,0.00049784704,0.00008785806],"domain_scores_gemma":[0.9972989,0.00028077452,0.001002673,0.000080450605,0.001286743,0.00005045243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082136237,0.00008261255,0.00013814213,0.00029613823,0.00003767774,0.00010030758,0.00026469879,0.000046516354,0.000008765072],"category_scores_gemma":[0.00009681998,0.00007657536,0.000059504197,0.000074776435,0.000020947613,0.0025202222,0.000018514695,0.00007082917,0.000005226963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032921552,0.000071418384,0.00008190038,0.00001684836,0.00008231909,0.0000024885564,0.0013159344,0.96100664,0.004464248,0.0009000791,0.00040469086,0.03132419],"study_design_scores_gemma":[0.0028638127,0.00041439183,0.0010895447,0.00014795942,0.000016042268,0.00012280815,0.00014112444,0.9705964,0.023801662,0.00027235842,0.00044450903,0.00008939017],"about_ca_topic_score_codex":0.0000015421274,"about_ca_topic_score_gemma":2.9456318e-8,"teacher_disagreement_score":0.5460124,"about_ca_system_score_codex":0.00012908377,"about_ca_system_score_gemma":0.00009395379,"threshold_uncertainty_score":0.31226525},"labels":[],"label_agreement":null},{"id":"W2056964743","doi":"10.1155/2014/492965","title":"Rotor‐Flying Manipulator: Modeling, Analysis, and Control","year":2014,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Manipulator (device); Rotor (electric); Control theory (sociology); Control (management); Computer science; Control engineering; Engineering; Mechanical engineering; Robot; Artificial intelligence","score_opus":0.0120626360923726,"score_gpt":0.21301550628534366,"score_spread":0.20095287019297106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056964743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004239843,0.00006927708,0.9949566,0.000060028196,0.000056504894,0.00019021904,2.8538926e-7,0.00022087566,0.00020633097],"genre_scores_gemma":[0.75540656,0.0000011714102,0.24449883,0.000021506346,0.000020365647,0.000033070763,4.1997995e-7,0.000010952569,0.000007126319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998695,0.000030921314,0.0003723507,0.0003295832,0.00022542724,0.0003467263],"domain_scores_gemma":[0.99924123,0.00019321579,0.000041349213,0.00039229987,0.00001952238,0.00011239133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008017964,0.00016823494,0.00036556457,0.00028930712,0.00004131226,0.00011426748,0.00036050507,0.0000732741,0.0000027140604],"category_scores_gemma":[0.00018642635,0.00015687052,0.00005496158,0.00046875476,0.000011790235,0.00020244149,0.00011401582,0.00020058715,0.00001197632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.140165e-7,0.000017899622,0.00085550355,0.000115547686,0.00004004628,0.0000030363526,0.00029086464,0.9781245,0.00018836983,0.019711131,9.827326e-7,0.0006518795],"study_design_scores_gemma":[0.0002670623,0.000015822587,0.0003138039,0.00010373898,0.00002965651,0.000010744534,0.0000029465286,0.9895208,0.00001902007,0.009530399,0.000006648448,0.00017938756],"about_ca_topic_score_codex":0.000010605559,"about_ca_topic_score_gemma":6.823128e-7,"teacher_disagreement_score":0.7511667,"about_ca_system_score_codex":0.00003482734,"about_ca_system_score_gemma":0.000006041221,"threshold_uncertainty_score":0.63969946},"labels":[],"label_agreement":null},{"id":"W2056983835","doi":"10.1109/coase.2010.5584243","title":"Collision avoidance for nonholonomic mobile robots among unpredictable dynamic obstacles including humans","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Collision avoidance; Obstacle avoidance; Mobile robot; Computer science; Robot; Obstacle; Collision; Motion planning; Path (computing); Simulation; Control theory (sociology); Artificial intelligence; Control (management); Computer network; Computer security","score_opus":0.014886171802859529,"score_gpt":0.28069984370840567,"score_spread":0.26581367190554617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056983835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32339588,0.000041044856,0.6737762,0.00007618119,0.001270563,0.00047741373,0.000009467609,0.00038181982,0.0005713941],"genre_scores_gemma":[0.5418189,0.00000456329,0.4569172,0.000060988685,0.00007223398,0.00014102932,0.00001072215,0.000019858382,0.00095450267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815655,0.000032490254,0.000354346,0.0006677145,0.00023403116,0.00055486895],"domain_scores_gemma":[0.9983941,0.00035115462,0.00015968697,0.000835057,0.00010421716,0.00015583052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000578863,0.00022542974,0.0002716258,0.00014616553,0.0004737996,0.00025553408,0.0013163068,0.0001532956,0.000012840988],"category_scores_gemma":[0.000099666846,0.00021323007,0.00008000037,0.00024676893,0.00009926995,0.00083296915,0.0003949564,0.000316457,0.00006505566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040123847,0.00054423034,0.040380392,0.0001670516,0.00012024927,0.00007971779,0.00507029,0.7722092,0.11231651,0.01634682,0.00878812,0.04393726],"study_design_scores_gemma":[0.00051944767,0.00011873134,0.012955987,0.000036590234,0.000006365761,0.000010821622,0.000063001615,0.98076415,0.0035070642,0.0007905455,0.0009166658,0.000310604],"about_ca_topic_score_codex":0.00008480495,"about_ca_topic_score_gemma":0.00008387492,"teacher_disagreement_score":0.21842301,"about_ca_system_score_codex":0.00009074878,"about_ca_system_score_gemma":0.00009894121,"threshold_uncertainty_score":0.869527},"labels":[],"label_agreement":null},{"id":"W2057598075","doi":"10.1109/cisda.2012.6291538","title":"Toward optimizing static target search path planning","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Defence Research and Development Canada; Université TÉLUQ; Institut National d'Optique","funders":"","keywords":"Mathematical optimization; Heuristics; Computer science; Motion planning; Heuristic; Integer programming; Path (computing); Relaxation (psychology); Linear programming; Mathematics; Artificial intelligence","score_opus":0.07820885651380662,"score_gpt":0.30753167877378434,"score_spread":0.22932282225997774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057598075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031242652,0.00023909388,0.98663324,0.00043685132,0.0005776956,0.00010298328,0.0000011525876,0.0003844287,0.008500274],"genre_scores_gemma":[0.24770507,0.0000015498204,0.75151753,0.0003150035,0.00010667552,0.000006216385,0.0000027887886,0.000010247243,0.00033490892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820703,0.00009597604,0.00020814354,0.00027819225,0.0004298412,0.0007808158],"domain_scores_gemma":[0.9990305,0.00017299484,0.00004528529,0.00042877655,0.000045856184,0.00027660443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007850503,0.00015134466,0.00017256987,0.000116548224,0.00012339486,0.0001524059,0.0007358075,0.00005219812,0.00004042461],"category_scores_gemma":[0.00005072439,0.00013052754,0.0000419223,0.00030949397,0.000026552734,0.0009063015,0.00032000188,0.00021321049,0.00028021826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020912692,0.00071324065,0.11622742,0.00026569518,0.00023579672,0.0007560816,0.12307652,0.5890483,0.002047973,0.07704202,0.047422707,0.043143332],"study_design_scores_gemma":[0.00024362182,0.00006207633,0.004978264,0.000046978854,0.000003896605,0.00008289955,0.0005018719,0.9911668,0.0014290994,0.0003222685,0.0008494607,0.00031281347],"about_ca_topic_score_codex":0.000025055393,"about_ca_topic_score_gemma":7.032721e-9,"teacher_disagreement_score":0.40211844,"about_ca_system_score_codex":0.000053335494,"about_ca_system_score_gemma":0.00006357765,"threshold_uncertainty_score":0.53227586},"labels":[],"label_agreement":null},{"id":"W2057933286","doi":"10.1016/j.robot.2005.09.002","title":"Probabilistic Road Map sampling strategies for multi-robot motion planning","year":2005,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic roadmap; Robot; Mobile robot; Motion planning; Probabilistic logic; Odometry; Artificial intelligence; Distributed computing; Configuration space; Real-time computing","score_opus":0.0776086684401754,"score_gpt":0.30774734354801664,"score_spread":0.23013867510784125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057933286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008593841,0.001287646,0.99509084,0.00043055252,0.0011351287,0.00071637303,0.00000916856,0.0003642565,0.00010663922],"genre_scores_gemma":[0.34828222,0.0000034074706,0.6508555,0.00005297894,0.0003623214,0.00007443959,0.000014089932,0.000026435911,0.00032860503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980385,0.000060932576,0.00053378346,0.0006110327,0.00020991165,0.00054586073],"domain_scores_gemma":[0.99889636,0.00014169436,0.00023903394,0.0004400858,0.00012627023,0.00015653713],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055754336,0.00028496888,0.00038763732,0.00013132146,0.00034900327,0.0008735994,0.00046561586,0.00014060945,4.5973192e-7],"category_scores_gemma":[0.000047516754,0.0002718459,0.0000680932,0.00013172711,0.00004880123,0.00054920366,0.00012393377,0.00016253308,0.000017958935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019094114,0.000052025665,0.00009351572,0.00017031185,0.000022424847,0.0000052711343,0.00095336593,0.9617298,0.00013074487,0.029859476,0.000068619665,0.0069125374],"study_design_scores_gemma":[0.00048927724,0.000092391856,0.00076938816,0.00016388114,0.000018487792,0.0000637367,0.00021861964,0.9964772,0.000013311104,0.00041129615,0.00094772404,0.0003346718],"about_ca_topic_score_codex":0.00006402992,"about_ca_topic_score_gemma":0.0000028775914,"teacher_disagreement_score":0.34742284,"about_ca_system_score_codex":0.00013294643,"about_ca_system_score_gemma":0.00013881779,"threshold_uncertainty_score":0.99997336},"labels":[],"label_agreement":null},{"id":"W2059370927","doi":"10.1117/12.474436","title":"&lt;title&gt;Robotic concepts for urban operations&lt;/title&gt;","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terrain; Robot; Morphing; Computer science; Function (biology); Robotics; Mobile robot; Perception; Human–computer interaction; Artificial intelligence; Simulation; Computer security; Geography","score_opus":0.01783184261201777,"score_gpt":0.2414458893119999,"score_spread":0.22361404669998214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059370927","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7706154,0.0018816584,0.062352277,0.016285477,0.004951806,0.0029778616,0.0001517609,0.0010348344,0.13974896],"genre_scores_gemma":[0.055863887,0.00007542013,0.93652713,0.0002326234,0.0008495424,0.00015891319,0.000008962261,0.00006197691,0.0062215137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862844,1.692211e-8,0.00036392035,0.00030370362,0.00041655896,0.0002873379],"domain_scores_gemma":[0.998866,0.00009826777,0.00012729806,0.00007287637,0.0007472366,0.00008827197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002797084,0.00019085464,0.00023972106,0.00007133888,0.0000791779,0.00013373549,0.0009838599,0.00012447173,0.000049601753],"category_scores_gemma":[0.0003421548,0.00016625402,0.00029905088,0.00023164437,0.00010766698,0.0003578386,0.00011835278,0.0001561588,0.000042378797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041920307,0.000064517306,0.000024055124,0.00012332966,0.00013959047,1.3808116e-7,0.0002033438,0.00067708874,0.03749829,0.8696806,0.09057498,0.0010098672],"study_design_scores_gemma":[0.00059181015,0.00019100592,0.00010399819,0.00016450074,0.00006217449,0.000019747265,0.000063932246,0.94002306,0.0072865793,0.0011365403,0.0500205,0.00033614977],"about_ca_topic_score_codex":8.716531e-7,"about_ca_topic_score_gemma":1.8144917e-8,"teacher_disagreement_score":0.93934596,"about_ca_system_score_codex":0.00009087625,"about_ca_system_score_gemma":0.000022695756,"threshold_uncertainty_score":0.6779642},"labels":[],"label_agreement":null},{"id":"W2059829531","doi":"10.1016/j.robot.2013.08.010","title":"Optimal routing strategies for autonomous underwater vehicles in time-varying environment","year":2013,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Community Sector Council Newfoundland and Labrador","funders":"","keywords":"Computer science; Motion planning; Underwater; Underwater glider; Position (finance); Task (project management); Real-time computing; Path (computing); Graph; Routing (electronic design automation); Glider; Mobile robot; Distributed computing; Artificial intelligence; Robot; Algorithm; Systems engineering; Embedded system; Computer network; Theoretical computer science","score_opus":0.018440226661486075,"score_gpt":0.2233465563130811,"score_spread":0.20490632965159503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059829531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036382284,0.0003801225,0.9609962,0.00046370612,0.00033380318,0.00090270856,0.0000043987698,0.00018966617,0.00034709892],"genre_scores_gemma":[0.77122176,0.000007046851,0.22787274,0.000046852314,0.00009393299,0.00011865402,0.0000075204634,0.000026776215,0.0006047436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979761,0.000074493866,0.00054180523,0.00056718034,0.00021583804,0.000624566],"domain_scores_gemma":[0.9990855,0.00018022017,0.00017417944,0.0003930799,0.00003172634,0.00013529322],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047124582,0.0002735114,0.00039727858,0.000131872,0.0002007374,0.0011406508,0.0005382137,0.0001320512,0.0000035202652],"category_scores_gemma":[0.000009240148,0.0002529388,0.00005598234,0.000093466915,0.000053563952,0.00060125144,0.00025473675,0.00015659728,0.000099140714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012590322,0.000045940425,0.00057923875,0.000054594184,0.000021783328,0.000013934704,0.00094616326,0.98922145,0.00089697074,0.0062637688,0.000058627655,0.0018962828],"study_design_scores_gemma":[0.00043842258,0.000106059524,0.00094983587,0.000094784016,0.000008006883,0.0000462945,0.0002321897,0.9969863,0.00008136083,0.000583673,0.00014680147,0.00032623604],"about_ca_topic_score_codex":0.0003065442,"about_ca_topic_score_gemma":5.797267e-7,"teacher_disagreement_score":0.73483944,"about_ca_system_score_codex":0.000165838,"about_ca_system_score_gemma":0.00010810013,"threshold_uncertainty_score":0.9999923},"labels":[],"label_agreement":null},{"id":"W2060188826","doi":"10.3182/20060906-3-it-2910.00015","title":"AN OPTIMAL CONTROL APPROACH TO STABLE-PUSHING PLANNING","year":2006,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Control theory (sociology); Trajectory; Object (grammar); Constant (computer programming); Optimal control; Robot; Control (management); RADIUS; Motion planning; Turning radius; Computer science; Mathematics; Mathematical optimization; Engineering; Artificial intelligence; Physics","score_opus":0.013232489078067513,"score_gpt":0.2416194817586733,"score_spread":0.22838699268060578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060188826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09907029,0.00014082823,0.88620526,0.00028508558,0.00025605218,0.0003658383,0.0000061405176,0.00095759315,0.012712931],"genre_scores_gemma":[0.52300036,2.0815926e-7,0.47619373,0.00023188694,0.00026783737,0.00004558805,0.000004434929,0.00002282983,0.00023312203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719626,0.0000193922,0.00039356158,0.00091645384,0.000602896,0.00087143935],"domain_scores_gemma":[0.99893844,0.000048634076,0.00016488314,0.00033011846,0.00022899931,0.00028895118],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00066911627,0.00032487803,0.00036978294,0.00027102928,0.0003464213,0.0011239279,0.0015719082,0.00013047148,0.0000027050219],"category_scores_gemma":[0.000082776365,0.0003284776,0.00006588719,0.00065154966,0.000046329722,0.0019356561,0.00018100267,0.0002932685,0.000055129294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013245211,0.0010993804,0.11049932,0.00021231246,0.00014181051,0.00011909771,0.01717425,0.73550695,0.026404694,0.044952087,0.039992563,0.023765095],"study_design_scores_gemma":[0.00061548356,0.00019537949,0.016309949,0.000063132844,0.000014236495,0.000085465785,0.0003471167,0.97832566,0.0011361819,0.0004263428,0.0019279602,0.00055306125],"about_ca_topic_score_codex":0.00012979865,"about_ca_topic_score_gemma":2.3550459e-7,"teacher_disagreement_score":0.42393008,"about_ca_system_score_codex":0.00009956877,"about_ca_system_score_gemma":0.000067531284,"threshold_uncertainty_score":0.99991673},"labels":[],"label_agreement":null},{"id":"W2061045858","doi":"10.1109/ssrr.2012.6523902","title":"Learning based semi-autonomous control for robots in urban search and rescue","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Urban search and rescue; Reinforcement learning; Robot; Rescue robot; Computer science; Robustness (evolution); Artificial intelligence; Search and rescue; Mobile robot","score_opus":0.023766951527172456,"score_gpt":0.26916379865509804,"score_spread":0.2453968471279256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061045858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021422207,0.0001604007,0.97687364,0.00057340815,0.00015142767,0.00028805228,9.490788e-7,0.00014091638,0.0003889831],"genre_scores_gemma":[0.8019876,7.096113e-7,0.19713028,0.00023897072,0.000069767564,0.000027570313,0.0000016861328,0.000008595231,0.00053481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987462,0.00012432008,0.00017271716,0.0002694448,0.00015635989,0.0005309131],"domain_scores_gemma":[0.99916965,0.00038347326,0.000019106019,0.00023465946,0.00003951813,0.0001535999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011072595,0.000111441,0.00017520976,0.00013447822,0.00009269445,0.000086556174,0.00028904088,0.00006723303,0.0000041928847],"category_scores_gemma":[0.00013164479,0.00010111513,0.000028431212,0.0001746174,0.000027209864,0.0003366756,0.0000732711,0.0001893889,0.00001411298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027191205,0.00013504515,0.1663162,0.000051159775,0.00001901042,0.000017487942,0.0029734247,0.8022748,0.0008775694,0.005025031,0.0004998989,0.021783179],"study_design_scores_gemma":[0.0010448007,0.000102788305,0.031057473,0.000017981214,0.000002503154,0.000008057275,0.000033105396,0.9669593,0.0003628117,0.00001967058,0.00025733365,0.00013420051],"about_ca_topic_score_codex":0.000121290424,"about_ca_topic_score_gemma":0.000004647704,"teacher_disagreement_score":0.7805654,"about_ca_system_score_codex":0.00004959078,"about_ca_system_score_gemma":0.00006699532,"threshold_uncertainty_score":0.41233554},"labels":[],"label_agreement":null},{"id":"W2061778301","doi":"10.1117/12.669556","title":"Intelligent mobility research for robotic locomotion in complex terrain","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"McGill University","keywords":"Computer science; Robotics; Mobile robot; Terrain; Intelligent decision support system; Robot; Artificial intelligence; Human–computer interaction; Intelligent transportation system; Active perception; Engineering; Transport engineering","score_opus":0.04343404558250471,"score_gpt":0.29906453855936216,"score_spread":0.25563049297685747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061778301","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9657223,0.000061268474,0.027038718,0.004576521,0.00026254117,0.0013162206,0.000016298702,0.00011479814,0.0008912899],"genre_scores_gemma":[0.38699123,0.000012183069,0.6121047,0.000047385573,0.00031808557,0.000338574,0.000014478444,0.00003807991,0.00013528517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967375,6.9499315e-8,0.0009343322,0.0006399887,0.001007356,0.00068078743],"domain_scores_gemma":[0.99689984,0.00047051653,0.00030322527,0.00012330095,0.0020944714,0.00010863395],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028180375,0.00029728733,0.0004559886,0.00026237854,0.00012465882,0.0002107491,0.0020687694,0.0001984122,0.00000358759],"category_scores_gemma":[0.000853332,0.00026473656,0.0004652656,0.000767358,0.00028919338,0.00065393304,0.00036643827,0.00044804925,0.000002083005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053236137,0.00048437258,0.00087385223,0.0005843392,0.0001134514,2.419021e-7,0.00043634506,0.0115423715,0.1907679,0.78796196,0.006321542,0.0008603676],"study_design_scores_gemma":[0.0010230881,0.00044404223,0.00697391,0.00031312281,0.000027627984,0.000015303498,0.0005759193,0.93671453,0.032734934,0.019636553,0.001177473,0.00036351458],"about_ca_topic_score_codex":0.00006956112,"about_ca_topic_score_gemma":7.3621607e-7,"teacher_disagreement_score":0.92517215,"about_ca_system_score_codex":0.00042917344,"about_ca_system_score_gemma":0.000058027883,"threshold_uncertainty_score":0.9999805},"labels":[],"label_agreement":null},{"id":"W2062105084","doi":"10.1007/s00454-003-2952-3","title":"Pseudo Approximation Algorithms with Applications to Optimal Motion Planning","year":2003,"lang":"en","type":"article","venue":"Discrete & Computational Geometry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Mathematics; Approximation algorithm; Algorithm; Combinatorics; Euclidean geometry; Path (computing); Geometry; Computer science","score_opus":0.0213936465075181,"score_gpt":0.28247218594386614,"score_spread":0.26107853943634807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062105084","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008298949,0.00005824429,0.98860407,0.00031837565,0.00015123474,0.00055170944,0.000015179799,0.0003173242,0.0016849047],"genre_scores_gemma":[0.25693128,3.5934477e-7,0.7424083,0.0002372971,0.00006624812,0.0001473774,0.00010366551,0.000019593106,0.00008590208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762607,0.00009771144,0.00036228276,0.0007136411,0.0007717464,0.0004285504],"domain_scores_gemma":[0.99867016,0.00020433636,0.00016908013,0.0004505728,0.00025784035,0.00024800617],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043121225,0.00026318867,0.00023052542,0.00050367985,0.00033109818,0.00027668523,0.00057889766,0.00007915613,0.000013019896],"category_scores_gemma":[0.00006813382,0.0002496397,0.00006030251,0.0019093498,0.00004992712,0.00060446543,0.000101015896,0.000208862,0.00016937648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005152493,0.00005777166,0.0010346898,0.0000135548435,0.00002995034,0.000011324994,0.0005764054,0.94878805,0.00002755604,0.04126287,0.00013605751,0.00805662],"study_design_scores_gemma":[0.0005537469,0.00016922862,0.015794432,0.000054031538,0.000015697358,0.00016752644,0.00021523001,0.97649276,0.00019454402,0.0049833665,0.0008706404,0.00048877805],"about_ca_topic_score_codex":0.0000051553434,"about_ca_topic_score_gemma":8.869498e-8,"teacher_disagreement_score":0.24863233,"about_ca_system_score_codex":0.000106302185,"about_ca_system_score_gemma":0.00012148604,"threshold_uncertainty_score":0.9999956},"labels":[],"label_agreement":null},{"id":"W2062303874","doi":"10.1007/s10846-010-9401-3","title":"Predictive Guidance-Based Navigation for Mobile Robots: A Novel Strategy for Target Interception on Realistic Terrains","year":2010,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendezvous; Traverse; Terrain; Computer science; Interception; Heading (navigation); Mobile robot; Obstacle avoidance; Collision avoidance; Trajectory; Motion planning; Proportional navigation; Robot; Obstacle; Position (finance); Model predictive control; Real-time computing; Simulation; Engineering; Artificial intelligence; Collision; Control (management); Aerospace engineering","score_opus":0.04748226254325559,"score_gpt":0.3191646789978063,"score_spread":0.2716824164545507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062303874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020047484,0.000106471074,0.98884124,0.00018194133,0.007097925,0.0015793425,0.000047960657,0.00007198039,0.000068397996],"genre_scores_gemma":[0.66627127,0.0000029187445,0.33198708,0.000080939804,0.0011785058,0.00026388504,0.000038878698,0.00003879137,0.00013776583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970121,0.000107167805,0.0013821045,0.00043436902,0.00061474787,0.0004495349],"domain_scores_gemma":[0.9958155,0.00085988076,0.0013885336,0.00052690733,0.001176146,0.00023302622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018829814,0.0003228204,0.0005977119,0.00035770558,0.00016260853,0.00030596773,0.001020869,0.00021092725,0.000003942685],"category_scores_gemma":[0.0005461147,0.0002672139,0.00033081305,0.00026387977,0.00007237299,0.0004247562,0.000037960937,0.00044291982,0.000009330541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016341737,0.00030355924,0.000067181536,0.00016845959,0.00008706534,0.000009007864,0.0006621726,0.9888715,0.003871891,0.0023753399,0.001226441,0.002193976],"study_design_scores_gemma":[0.00093436014,0.003549076,0.0002781329,0.0011413989,0.00005424659,0.00021141348,0.0003884988,0.9909532,0.0012684541,0.0005478745,0.00041312372,0.00026022684],"about_ca_topic_score_codex":0.000032752425,"about_ca_topic_score_gemma":0.000002420712,"teacher_disagreement_score":0.66426647,"about_ca_system_score_codex":0.0003305899,"about_ca_system_score_gemma":0.00034569588,"threshold_uncertainty_score":0.999978},"labels":[],"label_agreement":null},{"id":"W2062644434","doi":"10.1109/tcyb.2014.2314294","title":"A Learning-Based Semi-Autonomous Controller for Robotic Exploration of Unknown Disaster Scenes While Searching for Victims","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Urban search and rescue; Teleoperation; Search and rescue; Robot; Rescue robot; Computer science; Robustness (evolution); Rubble; Artificial intelligence; Controller (irrigation); Reinforcement learning; Robotics; Disaster area; Mobile robot; Engineering","score_opus":0.03400015539494928,"score_gpt":0.26833568465154184,"score_spread":0.23433552925659257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062644434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010800639,0.000013280511,0.99670327,0.0004963844,0.0006541718,0.0007922916,0.000010582221,0.00016861073,0.00008136093],"genre_scores_gemma":[0.8237193,0.0000030320818,0.17534481,0.00010181587,0.000068619294,0.00023001802,0.0000070729207,0.00003287213,0.0004924324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998372,0.00014342682,0.00040252405,0.0004260006,0.00028156696,0.0003744683],"domain_scores_gemma":[0.99790615,0.0011930274,0.00017821699,0.00037633986,0.00023686279,0.00010942788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046753188,0.0002182223,0.00034120365,0.00019644795,0.00025619048,0.00011256556,0.00040336125,0.000113793154,0.0000034009179],"category_scores_gemma":[0.000053830492,0.00021422452,0.00018693325,0.00022043493,0.00006711546,0.00030286825,0.0000028479642,0.00021397759,0.000011168787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000528114,0.0001529849,0.0000069242888,0.000055111537,0.00003270755,2.7402447e-7,0.0010949848,0.96538895,0.00088028493,0.00037997894,0.000038381353,0.031916622],"study_design_scores_gemma":[0.0017898084,0.0010554808,0.000028116956,0.000088831504,0.000050803643,0.0000021759736,0.0000457432,0.9850081,0.010382008,0.00055282447,0.00077526004,0.00022082386],"about_ca_topic_score_codex":0.000015162767,"about_ca_topic_score_gemma":0.000010814507,"teacher_disagreement_score":0.8226393,"about_ca_system_score_codex":0.00006297356,"about_ca_system_score_gemma":0.00009144041,"threshold_uncertainty_score":0.87358224},"labels":[],"label_agreement":null},{"id":"W2062784856","doi":"10.1109/iros.2010.5651799","title":"A goal-oriented fuzzy reactive control for mobile robots with automatic rule optimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Fuzzy rule; Fuzzy logic; Computer science; Fuzzy control system; Robot; Neuro-fuzzy; Control engineering; Artificial intelligence; Engineering","score_opus":0.0055403465793610095,"score_gpt":0.23426245542804927,"score_spread":0.22872210884868827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062784856","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047473796,0.0000052406654,0.9912821,0.00024256941,0.0004366258,0.00094673515,0.000005618774,0.0005401517,0.0017935453],"genre_scores_gemma":[0.10290926,2.1313306e-7,0.89612377,0.00016081375,0.000056227684,0.0003542361,0.000010503038,0.000015555912,0.00036940363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878156,0.00003422646,0.00020765132,0.00040509438,0.0002452602,0.00032617955],"domain_scores_gemma":[0.9987161,0.0002562816,0.0001408499,0.00053436676,0.0002305181,0.00012186972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000269454,0.0001654735,0.00021441365,0.000095024225,0.00013800246,0.00011541452,0.0004220998,0.00008223448,0.000014372609],"category_scores_gemma":[0.000100100726,0.00012567708,0.00004294045,0.00030521242,0.000045755634,0.00052408624,0.00004629163,0.00015399141,0.00002560397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003326901,0.0002351582,0.0005466808,0.00002371915,0.00008035947,0.000017138314,0.0009403403,0.9705654,0.0025569797,0.010179952,0.0006292918,0.014191711],"study_design_scores_gemma":[0.0014241097,0.00028248032,0.0006895143,0.00001758968,0.000017038356,0.000037594156,0.000036516856,0.9961535,0.00076144555,0.00030849627,0.00008141545,0.00019030899],"about_ca_topic_score_codex":0.000025340345,"about_ca_topic_score_gemma":0.0000030151562,"teacher_disagreement_score":0.098161876,"about_ca_system_score_codex":0.000031561896,"about_ca_system_score_gemma":0.00010838614,"threshold_uncertainty_score":0.51249623},"labels":[],"label_agreement":null},{"id":"W2065385563","doi":"10.1007/s11633-009-0335-9","title":"Effective virtual reality based building navigation using dynamic loading and path optimization","year":2009,"lang":"en","type":"article","venue":"International Journal of Automation and Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Virtual reality; Interactivity; Immersion (mathematics); Visualization; Computation; Computer science; Human–computer interaction; Path (computing); Motion planning; Simulation; Artificial intelligence; Multimedia","score_opus":0.01070186274121541,"score_gpt":0.3028290128755693,"score_spread":0.29212715013435386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065385563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27096602,0.000037884536,0.72807527,0.00045082349,0.00035778698,0.00005399382,6.3378997e-7,0.000041947995,0.000015646365],"genre_scores_gemma":[0.64717966,0.0000021066048,0.352645,0.00010028358,0.000067400615,1.2140582e-7,0.0000021756796,0.0000026586306,5.520269e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988229,0.00011856179,0.00039920726,0.00016405432,0.00038770784,0.00010757023],"domain_scores_gemma":[0.9987713,0.00018690428,0.00055869215,0.000067766865,0.00035154907,0.00006375562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007631079,0.00010234154,0.00014487015,0.00024467797,0.0001268659,0.00029834517,0.00023630312,0.000046056746,6.7796674e-7],"category_scores_gemma":[0.00012947101,0.00010132755,0.000036070214,0.0001528418,0.000021417145,0.0007241852,0.0000543256,0.00014111618,2.0313884e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000806878,0.000020545063,0.00020331294,0.0000040482937,0.000018326431,0.000022859289,0.00043633117,0.8438145,0.001796398,0.0012253089,0.000002915127,0.1524474],"study_design_scores_gemma":[0.00052766566,0.000115776056,0.014908409,0.0003789367,0.000008766522,0.00037067005,0.000032229877,0.98261106,0.00030934732,0.000640487,0.0000026338107,0.00009403295],"about_ca_topic_score_codex":0.0000070674064,"about_ca_topic_score_gemma":4.8321237e-8,"teacher_disagreement_score":0.37621364,"about_ca_system_score_codex":0.00014856234,"about_ca_system_score_gemma":0.000051335075,"threshold_uncertainty_score":0.41320175},"labels":[],"label_agreement":null},{"id":"W2065499315","doi":"10.2316/journal.206.2013.3.206-3598","title":"ROBOT PATH PLANNING IN NARROW PASSAGES BASED ON PROBABILISTIC ROADMAPS","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probabilistic roadmap; Workspace; Probabilistic logic; Path (computing); Motion planning; Robot; Computer science; Artificial intelligence; Space (punctuation); Computer network","score_opus":0.014591589384702841,"score_gpt":0.26023319484782276,"score_spread":0.24564160546311992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065499315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04178069,0.00008894136,0.94980663,0.006706347,0.0011559036,0.0001527311,0.0000013788139,0.00004630293,0.00026108185],"genre_scores_gemma":[0.80264825,0.0000031107054,0.19700167,0.00022567209,0.00009077235,0.0000036787537,0.000002801608,0.0000058464893,0.000018201279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985812,0.000076069475,0.00046522426,0.00015583182,0.0005750819,0.00014660675],"domain_scores_gemma":[0.99887645,0.00021968452,0.0003798297,0.00012479007,0.00032360293,0.000075619566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043611732,0.000118147,0.00016520159,0.0003757664,0.000038185713,0.00029447497,0.0005020409,0.000055629433,0.0000062349673],"category_scores_gemma":[0.00024619492,0.00010058126,0.000043906562,0.00014118238,0.000023675775,0.0006031017,0.000050202467,0.00019670528,0.000009537459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049769847,0.000082872284,0.004280583,0.000007800067,0.0000122124375,0.00008333623,0.00028999854,0.97973675,0.000237221,0.0017185629,0.00032560813,0.013220111],"study_design_scores_gemma":[0.000484542,0.00013512115,0.11633833,0.0002814142,0.0000032729213,0.000072094335,0.000019780618,0.8793559,0.000064960994,0.003128164,0.000022074713,0.00009433144],"about_ca_topic_score_codex":0.000012692574,"about_ca_topic_score_gemma":2.7432898e-7,"teacher_disagreement_score":0.76086754,"about_ca_system_score_codex":0.000103813516,"about_ca_system_score_gemma":0.00008040502,"threshold_uncertainty_score":0.41015846},"labels":[],"label_agreement":null},{"id":"W2065602781","doi":"10.1163/016918610x512604","title":"Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments","year":2010,"lang":"en","type":"article","venue":"Advanced Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mobile robot; Computer science; Grid; Grid reference; Mobile robot navigation; Robot; Artificial intelligence; Sonar; Interface (matter); Occupancy grid mapping; Real-time computing; Human–computer interaction; Computer vision; Robot control","score_opus":0.012041306144856096,"score_gpt":0.2809508086817597,"score_spread":0.2689095025369036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065602781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08510082,0.000079398844,0.9131889,0.00015006862,0.0011113748,0.00030071166,9.4965696e-7,0.00005910191,0.000008696386],"genre_scores_gemma":[0.40623468,0.000007945483,0.5935604,0.000017568227,0.000034601584,0.000040238265,0.000009070347,0.000010805163,0.00008470399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895495,0.000024803165,0.0002166187,0.00035815206,0.00015310368,0.00029235627],"domain_scores_gemma":[0.9994196,0.00015412354,0.000107815504,0.00021573863,0.00002038774,0.00008229721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020161075,0.000134764,0.00014823387,0.00006619967,0.00011980008,0.00005571983,0.00026400443,0.00007593994,5.9319825e-7],"category_scores_gemma":[0.000086044514,0.00014438102,0.000024063373,0.0001032395,0.000049055685,0.00035403232,0.00012721986,0.0003561206,0.0000047576596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025097472,0.000124826,0.012503059,0.00003973875,0.000006159968,0.000026673133,0.0004995982,0.6792995,0.048498116,0.014382979,0.000006779201,0.24461012],"study_design_scores_gemma":[0.0012375034,0.00014760057,0.019500682,0.00009633857,0.000018493047,0.000029232786,0.000026861879,0.9712832,0.003828799,0.0011290383,0.0023433582,0.00035887267],"about_ca_topic_score_codex":0.0000023154923,"about_ca_topic_score_gemma":0.0000012771569,"teacher_disagreement_score":0.32113385,"about_ca_system_score_codex":0.000037913775,"about_ca_system_score_gemma":0.000016436206,"threshold_uncertainty_score":0.5887687},"labels":[],"label_agreement":null},{"id":"W2067071761","doi":"10.1155/2010/578406","title":"Design and Lyapunov Stability Analysis of a Fuzzy Logic Controller for Autonomous Road Following","year":2010,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Nonholonomic system; Control theory (sociology); Controller (irrigation); Fuzzy logic; Control engineering; Fuzzy control system; Lyapunov function; Stability (learning theory); Lyapunov stability; Convergence (economics); Computer science; Focus (optics); Engineering; Control (management); Mobile robot; Robot; Artificial intelligence; Nonlinear system","score_opus":0.02600243796180825,"score_gpt":0.2504396496857319,"score_spread":0.22443721172392364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067071761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015634488,0.000048707567,0.9834294,0.00006155201,0.000094082134,0.0005595097,0.0000012837318,0.00011001755,0.000061000148],"genre_scores_gemma":[0.5015287,2.7976702e-7,0.49838558,0.0000048467978,0.0000048910683,0.000065337284,4.3826145e-7,0.0000063926077,0.0000035295905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869317,0.00002704721,0.0004847164,0.0003220843,0.00016749262,0.00030550532],"domain_scores_gemma":[0.9987471,0.0007183723,0.000078972545,0.0003430615,0.00003552845,0.00007693553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013502303,0.00015860998,0.000551614,0.0002599667,0.000027899734,0.000052178773,0.00034250077,0.00009788921,0.0000026028304],"category_scores_gemma":[0.00058685674,0.00013835484,0.00012260348,0.00055605796,0.000027592107,0.00015277085,0.00009637958,0.00019128324,9.754187e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029059372,0.00011010785,0.00023632006,0.0002617,0.00023285096,0.000004295866,0.0013217081,0.95847523,0.014619321,0.021951687,0.0000016578944,0.0027822352],"study_design_scores_gemma":[0.00039278046,0.000041107694,0.0005922465,0.000043313525,0.000078834164,0.0000030829033,0.0000058375167,0.97963345,0.00029936392,0.018766016,0.0000020037978,0.00014198918],"about_ca_topic_score_codex":0.0000054027687,"about_ca_topic_score_gemma":5.2788835e-7,"teacher_disagreement_score":0.4858942,"about_ca_system_score_codex":0.00002745538,"about_ca_system_score_gemma":0.000021395635,"threshold_uncertainty_score":0.5641947},"labels":[],"label_agreement":null},{"id":"W2067418688","doi":"10.1007/s11044-014-9438-3","title":"Modeling of spherical robots rolling on generic surfaces","year":2014,"lang":"en","type":"article","venue":"Multibody System Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Nonholonomic system; Holonomic; Quaternion; Equations of motion; Slipping; Classical mechanics; Holonomic constraints; Computer science; Robot; Mathematics; Physics; Mobile robot; Geometry; Artificial intelligence","score_opus":0.020075842903873518,"score_gpt":0.24098171317082565,"score_spread":0.22090587026695213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067418688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107055664,0.000036449343,0.8908466,0.000045484994,0.00080469064,0.00015516482,0.0000039423167,0.00029472538,0.00075725245],"genre_scores_gemma":[0.6528488,0.0000012901257,0.34701473,0.000015958629,0.000057895042,0.0000049632463,0.000005707299,0.000017359063,0.000033285683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978338,0.00019724508,0.00053842,0.00052042556,0.0005153697,0.0003947656],"domain_scores_gemma":[0.9985387,0.00019091078,0.00020087243,0.00081296795,0.00013213162,0.00012445645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068668247,0.00024037596,0.00045532413,0.000102979015,0.00012229905,0.00008492496,0.0009662091,0.000128452,4.5960377e-7],"category_scores_gemma":[0.00008008463,0.00021835601,0.00010097126,0.0003777931,0.000029080855,0.00016937683,0.00016773405,0.000175116,0.000039382605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035817052,0.000036832073,0.0010386539,0.00008079934,0.000016526512,0.000006097388,0.00019582348,0.9916479,0.00033198658,0.0043675206,0.0000027992378,0.0022715079],"study_design_scores_gemma":[0.00029417282,0.0000996223,0.00040811146,0.00021220934,0.000008375733,0.000017984368,0.00008295624,0.9985587,0.000059114052,0.000029725761,0.0000027290978,0.00022630909],"about_ca_topic_score_codex":0.00021731465,"about_ca_topic_score_gemma":0.0000074396585,"teacher_disagreement_score":0.5457932,"about_ca_system_score_codex":0.00020540487,"about_ca_system_score_gemma":0.00004374932,"threshold_uncertainty_score":0.89043},"labels":[],"label_agreement":null},{"id":"W2067611664","doi":"10.4236/ojop.2013.23010","title":"Human-Robot Collaborative Planning for Navigation Based on Optimal Control Theory","year":2013,"lang":"en","type":"article","venue":"Open Journal of Optimization","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Architecture; Computer science; Control (management); Obstacle avoidance; Plan (archaeology); Obstacle; Process (computing); Motion planning; Control engineering; Robot; Set (abstract data type); Mode (computer interface); Human–computer interaction; Telerobotics; Trajectory; Human–robot interaction; Artificial intelligence; Mobile robot; Engineering","score_opus":0.02390240390056353,"score_gpt":0.3129645144843042,"score_spread":0.28906211058374065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067611664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060050294,0.000028659302,0.9969019,0.0006494146,0.00030630216,0.0007797734,0.0000034782108,0.000019455229,0.0007105104],"genre_scores_gemma":[0.17290325,5.463489e-7,0.8264921,0.00035029618,0.00009050337,0.00004163797,0.000012372077,0.000013906428,0.00009541607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985915,0.00024830538,0.00046654293,0.0001982454,0.00030318237,0.00019226436],"domain_scores_gemma":[0.9976224,0.00035301395,0.0007501105,0.00022416779,0.000948101,0.00010222998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001243591,0.00014261625,0.0002682334,0.0001599586,0.00025664302,0.00071825983,0.00095234485,0.000069274145,0.000031600317],"category_scores_gemma":[0.00020439905,0.00012182506,0.00005767259,0.00030704035,0.000029434113,0.0017105466,0.00004882902,0.00015359372,0.000006691935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005719642,0.00006022305,0.0000552605,0.000004740532,0.000026018095,0.0000112046055,0.00035591668,0.99541336,0.00020663516,0.0015203287,0.0008445493,0.0014445618],"study_design_scores_gemma":[0.0024707597,0.0008330826,0.00036549024,0.00021244971,0.000019041234,0.000021040376,0.00011046449,0.99465746,0.0004153214,0.0007227728,0.000029370887,0.00014273133],"about_ca_topic_score_codex":0.000002504032,"about_ca_topic_score_gemma":1.291938e-8,"teacher_disagreement_score":0.17230275,"about_ca_system_score_codex":0.00008087571,"about_ca_system_score_gemma":0.00019363711,"threshold_uncertainty_score":0.6926196},"labels":[],"label_agreement":null},{"id":"W2068708887","doi":"10.1115/imece2014-38639","title":"Redundancy Resolution for Singularity Avoidance of Wheeled Mobile Manipulators","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Obstacle avoidance; Redundancy (engineering); Mobile manipulator; Mobile robot; Computer science; Singularity; Control theory (sociology); Kinematics; Workspace; Collision avoidance; Motion planning; Control engineering; Robot; Artificial intelligence; Mathematics; Engineering; Collision","score_opus":0.014021442689826965,"score_gpt":0.25295641232203775,"score_spread":0.23893496963221078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068708887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01729877,0.00005879264,0.9804395,0.00010436537,0.00042252746,0.00026772567,0.0000012045276,0.00017093269,0.0012362007],"genre_scores_gemma":[0.41440576,7.653898e-7,0.5851921,0.000045772802,0.000048575817,0.000023060988,0.0000020561354,0.0000058226847,0.00027611258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883366,0.00006679427,0.00026892964,0.0003250281,0.000205373,0.00030024303],"domain_scores_gemma":[0.99892855,0.00015767562,0.00013429114,0.00058301806,0.000121883735,0.0000746055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007723974,0.00010438366,0.00020110066,0.00006927543,0.000098755816,0.000038346177,0.0005427007,0.00006912949,0.0000029853722],"category_scores_gemma":[0.00021487719,0.000098349425,0.000071347,0.00020478907,0.000041077696,0.00022594703,0.000098110606,0.0000721743,0.000010983291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000390544,0.00040503737,0.006242275,0.0003058535,0.00006162807,0.000004781912,0.0017590342,0.046449978,0.009751329,0.8452888,0.0052161864,0.08447605],"study_design_scores_gemma":[0.00039456665,0.00023022872,0.005185975,0.00004182372,0.0000057771526,0.0000065382983,0.000009176123,0.97344035,0.0068497667,0.011202605,0.002470674,0.00016252883],"about_ca_topic_score_codex":0.000055651373,"about_ca_topic_score_gemma":0.0000024183253,"teacher_disagreement_score":0.9269904,"about_ca_system_score_codex":0.000058268746,"about_ca_system_score_gemma":0.000042148822,"threshold_uncertainty_score":0.40105733},"labels":[],"label_agreement":null},{"id":"W2068716166","doi":"10.1017/s0263574701003356","title":"On dynamic stability of manipulators mounted on mobile platforms","year":2001,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Excavator; Loader; Base (topology); Stability (learning theory); Computer science; Manipulator (device); Mobile manipulator; Control theory (sociology); Control engineering; Engineering; Robot; Artificial intelligence; Mechanical engineering; Mobile robot; Mathematics","score_opus":0.020826450652606302,"score_gpt":0.27658157544073314,"score_spread":0.25575512478812684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068716166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54755247,0.000022700171,0.4486911,0.00012702367,0.0004989743,0.0003338877,0.000004361299,0.000260367,0.0025091448],"genre_scores_gemma":[0.9628742,0.000004456433,0.03685132,0.00011654417,0.00001662935,0.000014183635,0.000005450517,0.000014903635,0.00010230959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822265,0.000037580237,0.00035150233,0.00049186265,0.0005027674,0.00039365367],"domain_scores_gemma":[0.9982962,0.00030399163,0.00012606489,0.0010720917,0.00007111647,0.00013050916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003312837,0.00020401007,0.00028943626,0.00014534392,0.00008460537,0.0000376685,0.000782999,0.00010799451,0.00004207415],"category_scores_gemma":[0.000115273506,0.00017109793,0.00008745091,0.00047717578,0.000071036586,0.00018887233,0.00012665105,0.000250941,0.00018918597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085703665,0.0009014352,0.005478947,0.00005243384,0.000052535717,0.00013633913,0.0008189604,0.91901714,0.0011084351,0.063035764,0.00019411102,0.009118209],"study_design_scores_gemma":[0.00039613762,0.0007427542,0.015327049,0.00008288877,0.000007408322,0.000024501234,0.000031320902,0.9774003,0.0010863873,0.004621388,0.000033404875,0.00024645543],"about_ca_topic_score_codex":0.000018443963,"about_ca_topic_score_gemma":0.0000020739978,"teacher_disagreement_score":0.41532177,"about_ca_system_score_codex":0.00015087174,"about_ca_system_score_gemma":0.00005471502,"threshold_uncertainty_score":0.69771713},"labels":[],"label_agreement":null},{"id":"W2069528667","doi":"10.1109/iros.2012.6386271","title":"An autonomous 9-DOF mobile-manipulator system for in situ 3D object modeling","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mobile manipulator; Workspace; Mobile robot; Computer science; Computer vision; Object (grammar); Artificial intelligence; Point cloud; Task (project management); Real-time computing; Simulation; Robot; Engineering","score_opus":0.03610534138313473,"score_gpt":0.2845896714234783,"score_spread":0.24848433004034357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069528667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07116878,0.00013545697,0.92582476,0.000019468891,0.0007113923,0.00045286227,0.0000013971593,0.0004900019,0.0011958524],"genre_scores_gemma":[0.6036024,2.6607387e-7,0.39604086,0.00004003391,0.00017095733,0.000084345156,0.0000023706655,0.000011077623,0.000047693487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998429,0.000060392078,0.0003417168,0.00038049996,0.00019657939,0.00059183734],"domain_scores_gemma":[0.9989765,0.00007857651,0.00006516632,0.0006407147,0.000052032676,0.00018699864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007711244,0.00017063493,0.00024245425,0.00014974439,0.000082708066,0.00010746796,0.00071440625,0.00009273031,0.0000016412199],"category_scores_gemma":[0.00001680104,0.00015559403,0.00005239069,0.0002021372,0.000008797194,0.0010291409,0.00010141396,0.0001045756,0.00005189969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004746756,0.00015853005,0.0013178322,0.00006234199,0.000012144667,0.000007329666,0.0026462732,0.9698717,0.0014839503,0.009007179,0.000029381732,0.015398582],"study_design_scores_gemma":[0.00027983257,0.00007979432,0.00029444005,0.000031950578,0.000004648838,0.000030714407,0.0001996419,0.9979212,0.0008676145,0.000023594741,0.00005132793,0.00021521785],"about_ca_topic_score_codex":0.00009544509,"about_ca_topic_score_gemma":0.0000072837456,"teacher_disagreement_score":0.5324336,"about_ca_system_score_codex":0.00020346906,"about_ca_system_score_gemma":0.000070010894,"threshold_uncertainty_score":0.634494},"labels":[],"label_agreement":null},{"id":"W2069731879","doi":"10.1177/027836402320556377","title":"On Path Planning and Obstacle Avoidance for Nonholonomic Platforms with Manipulators: A Polynomial Approach","year":2002,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Nonholonomic system; Motion planning; Obstacle; Obstacle avoidance; Cartesian coordinate system; Constraint (computer-aided design); Configuration space; Control theory (sociology); Polynomial; Path (computing); Computer science; Orientation (vector space); Space (punctuation); Mobile robot; Mathematics; Mathematical optimization; Robot; Control (management); Artificial intelligence; Geometry; Mathematical analysis; Physics","score_opus":0.1329448556421738,"score_gpt":0.3472689054151658,"score_spread":0.21432404977299202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069731879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12443094,0.0003864789,0.8707898,0.0029624745,0.000535584,0.00026575802,0.0000050188696,0.00001999325,0.00060389616],"genre_scores_gemma":[0.8068344,0.000018881194,0.19240233,0.0001257115,0.00036029104,0.0000070337805,0.0000010144763,0.000015628384,0.00023468235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979656,0.000054426364,0.0003109358,0.00021480408,0.0011201564,0.0003340962],"domain_scores_gemma":[0.9979935,0.0009359904,0.00021771136,0.00026343632,0.0004754722,0.000113878246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016383026,0.000121432044,0.00017432786,0.0003014025,0.0002124531,0.0003667843,0.0018414819,0.000050024464,0.0000020996397],"category_scores_gemma":[0.00023355777,0.00007476814,0.000053018022,0.00016933196,0.00013276847,0.00035867834,0.0002247404,0.0006012542,0.0000075731464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054822216,0.00040638307,0.0017534177,0.000024311925,0.00033635515,0.00031376042,0.0047479444,0.9397082,0.00027727816,0.03129814,0.010443281,0.010142722],"study_design_scores_gemma":[0.0014765643,0.0007883682,0.0013098978,0.00016782149,0.000006640383,0.0010549369,0.00019327275,0.9916943,0.00018813905,0.0027921584,0.00019740303,0.0001305373],"about_ca_topic_score_codex":0.0000063127254,"about_ca_topic_score_gemma":2.2869293e-7,"teacher_disagreement_score":0.6824035,"about_ca_system_score_codex":0.0001672547,"about_ca_system_score_gemma":0.000080099926,"threshold_uncertainty_score":0.35369095},"labels":[],"label_agreement":null},{"id":"W2070482544","doi":"10.1109/crv.2011.11","title":"Conformative Filter: A Probabilistic Framework for Localization in Reduced Space","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reduction (mathematics); Computer science; Probabilistic logic; Filter (signal processing); Path (computing); Context (archaeology); Motion planning; Mathematical optimization; Probabilistic roadmap; Artificial intelligence; Algorithm; Robot; Mathematics; Computer vision","score_opus":0.05769304892091529,"score_gpt":0.2796837628432725,"score_spread":0.22199071392235722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070482544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039377433,0.000007484569,0.993235,0.00019793007,0.0002494565,0.0004609788,0.0000014795852,0.00014599599,0.0053079333],"genre_scores_gemma":[0.11217912,6.904675e-7,0.8873015,0.0002596447,0.000017578703,0.00007497045,0.000003021979,0.0000057287266,0.00015771345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916154,0.00003714489,0.00021547903,0.00023133776,0.00011689085,0.00023759375],"domain_scores_gemma":[0.9992793,0.00018026517,0.00008058459,0.0003214329,0.000081930164,0.000056475958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025434498,0.00009986129,0.00013139118,0.00009289073,0.000044709468,0.00004462632,0.00041596126,0.00007564186,0.000015486996],"category_scores_gemma":[0.00038077036,0.000087184126,0.000025910576,0.00032594538,0.000031508283,0.00035231462,0.000073346084,0.00008923098,0.00003427862],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001670396,0.00009693186,0.00037974922,0.000046890935,0.000009470612,0.000007780283,0.017320823,0.0053493534,0.000017889071,0.97170997,0.0008545701,0.0041898713],"study_design_scores_gemma":[0.00022243391,0.00011327832,0.0008557157,0.000074930314,0.000002114947,0.0000055204296,0.00010584468,0.87659055,0.0010684794,0.12070083,0.00012208596,0.00013821569],"about_ca_topic_score_codex":0.00004232065,"about_ca_topic_score_gemma":0.000002344223,"teacher_disagreement_score":0.8712412,"about_ca_system_score_codex":0.000045157823,"about_ca_system_score_gemma":0.00006125405,"threshold_uncertainty_score":0.35552654},"labels":[],"label_agreement":null},{"id":"W2071067499","doi":"10.1109/crv.2010.32","title":"Rough Terrain Reconstruction for Rover Motion Planning","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec; Canadian Space Agency","funders":"","keywords":"Terrain; Computer science; Computer vision; Motion planning; Boundary (topology); Artificial intelligence; Hausdorff distance; Computer graphics (images); Point (geometry); Robot; Triangle mesh; Remote sensing; Geology; Geography; Geometry; Mathematics; Cartography","score_opus":0.02036550976060138,"score_gpt":0.27286056737846137,"score_spread":0.25249505761786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071067499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01547176,0.0000040565224,0.9764845,0.000528519,0.0024970297,0.00014878759,0.0000013078933,0.00028653335,0.004577532],"genre_scores_gemma":[0.14517134,1.3840382e-7,0.85369134,0.00016220797,0.00022840544,0.000018536079,0.000003708072,0.000006670745,0.0007176673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992179,0.000015202087,0.00015063767,0.00028750196,0.00011729095,0.00021143757],"domain_scores_gemma":[0.99942327,0.00008831073,0.00006497088,0.00031655806,0.00005003839,0.000056842116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000295775,0.00008914952,0.00008929364,0.00007272053,0.00011444162,0.000112621965,0.00032889794,0.00008228399,0.000014099263],"category_scores_gemma":[0.00009976755,0.000081122096,0.000041188654,0.00011864794,0.00002170305,0.0005701551,0.00005055834,0.00014868563,0.000027994347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009739925,0.000059910966,0.005735574,0.000021234964,0.00002798587,0.000010735528,0.0019406152,0.0075657787,0.01927287,0.04965238,0.007201625,0.90850157],"study_design_scores_gemma":[0.00033312262,0.000050384297,0.0053196386,0.000013801671,0.000003181014,0.00014815928,0.000028869164,0.9821906,0.0032579796,0.0065207523,0.0019617628,0.0001717458],"about_ca_topic_score_codex":0.000018305454,"about_ca_topic_score_gemma":0.000001710907,"teacher_disagreement_score":0.9746248,"about_ca_system_score_codex":0.00001596024,"about_ca_system_score_gemma":0.00002472811,"threshold_uncertainty_score":0.33080631},"labels":[],"label_agreement":null},{"id":"W2071118292","doi":"10.1115/detc2011-47114","title":"Analysis and Synthesis of Multi-Rotor Aerial Vehicles","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Acceleration; Rotor (electric); Orientation (vector space); Position (finance); Work (physics); Computer science; Set (abstract data type); Control theory (sociology); Engineering; Mechanical engineering; Mathematics; Physics; Artificial intelligence; Geometry","score_opus":0.041855834547600376,"score_gpt":0.24953494507473956,"score_spread":0.20767911052713917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071118292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0761002,0.000023882423,0.92248404,0.000027574953,0.000092240494,0.000066984045,0.000002211388,0.00008281741,0.0011200387],"genre_scores_gemma":[0.48395193,0.000001853745,0.51595944,0.000012335411,0.0000083848745,0.0000048190163,1.10655904e-7,0.0000017946664,0.00005935628],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936086,0.00004246224,0.00015324425,0.00020707396,0.00011481443,0.000121553036],"domain_scores_gemma":[0.9994227,0.00009948542,0.00006381856,0.00031846497,0.000036049714,0.00005947475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018872292,0.000069164176,0.00018421348,0.00015635852,0.000029454235,0.000019754647,0.00034853385,0.000035839505,0.000020040201],"category_scores_gemma":[0.00007260901,0.000056159835,0.00005214567,0.0003743872,0.00004677852,0.00013756713,0.00012163416,0.000029155226,0.000009300879],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055273278,0.0011741447,0.5916997,0.00008971647,0.0025001261,0.00011777277,0.0124151725,0.0005932064,0.011996344,0.011626629,0.0003077327,0.3674242],"study_design_scores_gemma":[0.00026372494,0.00005226769,0.40665615,0.000010827358,0.00015055487,0.00000486301,0.000043981574,0.53594327,0.056569926,0.000117231124,0.000013304359,0.00017391148],"about_ca_topic_score_codex":0.00040035212,"about_ca_topic_score_gemma":0.000006196219,"teacher_disagreement_score":0.5353501,"about_ca_system_score_codex":0.0000047877993,"about_ca_system_score_gemma":0.000014282106,"threshold_uncertainty_score":0.22901316},"labels":[],"label_agreement":null},{"id":"W2071880409","doi":"10.1109/jbhi.2014.2315798","title":"A Chance-Constrained Programming Approach to Preoperative Planning of Robotic Cardiac Surgery Under Task-Level Uncertainty","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University; SickKids Foundation; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Maximization; Computer science; Artificial intelligence; Entropy (arrow of time); Robotics; Robustness (evolution); Mathematical optimization; Robot; Machine learning; Mathematics","score_opus":0.07788039145171523,"score_gpt":0.3150091377617082,"score_spread":0.23712874630999298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071880409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008730512,0.00015448984,0.98812205,0.0019010023,0.0007817834,0.00022235543,0.0000059491185,0.000021294923,0.000060566883],"genre_scores_gemma":[0.3241713,0.0000376842,0.6742927,0.0012109398,0.00026258442,0.0000059466292,0.000004402451,0.0000069494463,0.000007501304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697983,0.00013835105,0.0015882113,0.00011451909,0.00071744574,0.0004616349],"domain_scores_gemma":[0.99751323,0.00039121258,0.0010327485,0.00019030357,0.00022498127,0.00064749154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004001144,0.00017386545,0.0008028965,0.0003794754,0.00014219624,0.00007685428,0.00038563914,0.00010120575,4.0116075e-7],"category_scores_gemma":[0.0001993465,0.0001265932,0.000101084064,0.00049859716,0.00013969478,0.00036570636,0.00007705206,0.0003538807,0.00000152157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006334158,0.00055308844,0.0023079154,0.0029327187,0.00037445378,0.000015334866,0.0849888,0.094396345,0.000080650294,0.002697554,0.009175051,0.8024148],"study_design_scores_gemma":[0.00080467464,0.001558832,0.007882084,0.0017811062,0.000031818912,0.0004238271,0.0028822054,0.9820689,0.000058216647,0.00035834932,0.0017620164,0.00038797135],"about_ca_topic_score_codex":0.000021243502,"about_ca_topic_score_gemma":2.1106281e-7,"teacher_disagreement_score":0.88767254,"about_ca_system_score_codex":0.00007897004,"about_ca_system_score_gemma":0.0007188388,"threshold_uncertainty_score":0.516232},"labels":[],"label_agreement":null},{"id":"W2072771680","doi":"10.1115/imece2010-40055","title":"Multi-Robot Cooperative Transportation of Objects Using Modified Q-Learning","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Robustness (evolution); Mobile robot; Computer science; Artificial intelligence; Object (grammar); Robot control; Social robot; Computer vision; Task (project management); Human–computer interaction; Engineering; Systems engineering","score_opus":0.03733732766053533,"score_gpt":0.2878889955004935,"score_spread":0.2505516678399582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072771680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16930442,0.000005249703,0.8297621,0.000022898656,0.00029339344,0.000090177506,8.6552467e-7,0.00011345893,0.00040742615],"genre_scores_gemma":[0.5220794,3.162221e-7,0.4777837,0.000015843425,0.000011662123,0.000001430199,0.0000021193935,0.000004045508,0.00010149667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991102,0.000045409128,0.0002234981,0.00025302524,0.00018860765,0.00017926401],"domain_scores_gemma":[0.99937046,0.00007748446,0.00010796546,0.00023066481,0.00015068638,0.00006272884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021103364,0.000105765524,0.00015628526,0.000086629574,0.00009455385,0.000039769762,0.00032603974,0.000071521245,0.000012277422],"category_scores_gemma":[0.00007117051,0.000095196745,0.00003657552,0.00028923643,0.00004292414,0.00033955532,0.00001848192,0.00026184486,0.0000076198767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031492827,0.00006394633,0.0018846247,0.000010720261,0.000015251422,0.000014831071,0.0048004678,0.57698876,0.4110813,0.0030717144,0.0000065861095,0.0020586392],"study_design_scores_gemma":[0.00032803515,0.000040568524,0.010893024,0.000014020538,0.000004836644,0.000005773549,0.000104502404,0.9549296,0.033534445,0.000025389683,0.0000034419886,0.00011638685],"about_ca_topic_score_codex":0.00017277154,"about_ca_topic_score_gemma":0.000029324072,"teacher_disagreement_score":0.3779408,"about_ca_system_score_codex":0.0000075742696,"about_ca_system_score_gemma":0.00008239445,"threshold_uncertainty_score":0.38820106},"labels":[],"label_agreement":null},{"id":"W2073442429","doi":"10.1177/0278364909105053","title":"Robotic Routers: Algorithms and Implementation","year":2009,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Software deployment; Computer network; Base station; Distributed computing; Motion (physics); Wireless network; Motion planning; Robot; Adversarial system; Wireless; Artificial intelligence; Telecommunications","score_opus":0.08267368106074037,"score_gpt":0.4249953283357911,"score_spread":0.34232164727505077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073442429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004435642,0.00042241282,0.93892825,0.054590218,0.0011166786,0.0001462417,0.0000011422688,0.000021958234,0.00033744334],"genre_scores_gemma":[0.6201527,0.00029393143,0.37792712,0.0006855934,0.00072107906,0.000001751819,0.0000021270616,0.0000100838615,0.00020555759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996997,0.00022282598,0.00043558792,0.00017087202,0.0018607486,0.0003129603],"domain_scores_gemma":[0.99774003,0.0004607526,0.00021763466,0.00026996917,0.0011859619,0.0001256451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030585453,0.00010195227,0.00015369618,0.00040684085,0.00015875405,0.00057778525,0.0023809057,0.000039774808,0.0000072846547],"category_scores_gemma":[0.00020508708,0.00007057198,0.0000612424,0.00032665965,0.000093506926,0.00061116926,0.00030000322,0.000534614,0.000015074536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082431034,0.00030276013,0.0014799822,0.000009326425,0.0003965646,0.0009702982,0.0064219795,0.21414658,0.0036501612,0.117736235,0.012536868,0.6422668],"study_design_scores_gemma":[0.0026730564,0.0018424959,0.041009713,0.00024272029,0.00003702017,0.0042726486,0.0015468261,0.85021865,0.0027878382,0.093470894,0.0014692575,0.0004288941],"about_ca_topic_score_codex":0.000022709632,"about_ca_topic_score_gemma":0.0000014561006,"teacher_disagreement_score":0.6418379,"about_ca_system_score_codex":0.00015980087,"about_ca_system_score_gemma":0.00019570462,"threshold_uncertainty_score":0.55715966},"labels":[],"label_agreement":null},{"id":"W2073912270","doi":"10.1142/s1469026814500084","title":"PARALLEL HYBRID METAHEURISTIC ON SHARED MEMORY SYSTEM FOR REAL-TIME UAV PATH PLANNING","year":2014,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Parallel computing; Particle swarm optimization; Metaheuristic; Multi-core processor; Parallel algorithm; Genetic algorithm; Speedup; Path (computing); Shared memory; Mathematical optimization; Algorithm","score_opus":0.031873828696133574,"score_gpt":0.3112131873067623,"score_spread":0.2793393586106287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073912270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071710185,0.000071237766,0.9967227,0.0009102572,0.0003616483,0.00023196598,0.000032543976,0.000062018014,0.0008905342],"genre_scores_gemma":[0.53639877,0.000016221904,0.46240798,0.00028526288,0.00067533914,0.0000720922,0.000044865305,0.000014160321,0.00008534599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982256,0.00006479574,0.0006572719,0.00028486742,0.0005999901,0.00016742315],"domain_scores_gemma":[0.99689066,0.0013246742,0.0005614402,0.00018295647,0.000906462,0.00013379831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006217584,0.00016078632,0.00024664457,0.0002706192,0.00015354947,0.00023011929,0.0010362965,0.000038934573,0.000004128552],"category_scores_gemma":[0.00012644238,0.00014784507,0.00011409021,0.00013105929,0.000055749955,0.0003167157,0.000081066195,0.00014668524,0.000045641467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035434394,0.00008956249,0.000037110214,0.000017962813,0.00011617093,0.000015811484,0.00018468649,0.8339019,0.000059022375,0.12623441,0.0013643312,0.03794363],"study_design_scores_gemma":[0.0003005247,0.00019337957,0.0005111257,0.00019743398,0.000023911765,0.00035738156,0.00005728295,0.94076467,0.00031962385,0.054893702,0.0022008806,0.00018009436],"about_ca_topic_score_codex":0.0000041605854,"about_ca_topic_score_gemma":2.9711293e-8,"teacher_disagreement_score":0.53568166,"about_ca_system_score_codex":0.000085705484,"about_ca_system_score_gemma":0.00010431961,"threshold_uncertainty_score":0.6028947},"labels":[],"label_agreement":null},{"id":"W2075119906","doi":"10.2316/journal.206.2014.4.206-3759","title":"ORION: PARROT VIRTUAL MACHINE EXTENSION FOR MOBILE ROBOT PROGRAMMING","year":2014,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Extension (predicate logic); Computer science; Mobile robot; Robot; Embedded system; Operating system; Artificial intelligence; Programming language","score_opus":0.01372379579855083,"score_gpt":0.2771710887953172,"score_spread":0.2634472929967664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075119906","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005274033,0.00010979422,0.9912334,0.0014349094,0.0017528903,0.00013092806,0.0000016203679,0.00004410539,0.000018323284],"genre_scores_gemma":[0.44075647,0.00001770547,0.55876964,0.00010071686,0.00030551737,0.0000050638964,0.000006415855,0.000006689746,0.000031772946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987985,0.000045704746,0.00042715797,0.00015500847,0.0004422667,0.00013135445],"domain_scores_gemma":[0.99857885,0.00019623095,0.00044954722,0.00012104399,0.0005749552,0.000079401834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007105573,0.00010540692,0.00016815362,0.00017573415,0.00008003741,0.0002384674,0.00044231024,0.00005091328,0.00000151245],"category_scores_gemma":[0.00020448053,0.000089221336,0.00007065587,0.0000806871,0.000023393404,0.00049625075,0.000083694176,0.00010816355,0.000002637467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019593535,0.00009320869,0.00034870126,0.000010820011,0.00005709538,0.000014594769,0.00035751698,0.37817562,0.0010746501,0.019561682,0.00019024646,0.6000963],"study_design_scores_gemma":[0.0006983925,0.0004976991,0.0021567396,0.00010337753,0.0000131015,0.00028668728,0.000019934836,0.99163944,0.00029340354,0.0020398148,0.0021449947,0.000106416206],"about_ca_topic_score_codex":0.000002888789,"about_ca_topic_score_gemma":3.7656628e-7,"teacher_disagreement_score":0.6134638,"about_ca_system_score_codex":0.00004499474,"about_ca_system_score_gemma":0.000041119743,"threshold_uncertainty_score":0.36383405},"labels":[],"label_agreement":null},{"id":"W2076286736","doi":"10.1109/ieeegcc.2009.5734288","title":"Near-optimal energy fuzzy parking of mobile robots","year":2009,"lang":"en","type":"article","venue":"exhibition","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Trajectory; Mobile robot; Controller (irrigation); Robot; Fuzzy logic; Adaptive neuro fuzzy inference system; Kinematics; Fuzzy control system; Control theory (sociology); Sonar; Range (aeronautics); Energy (signal processing); Control engineering; Simulation; Artificial intelligence; Engineering; Control (management); Mathematics","score_opus":0.011088160218486836,"score_gpt":0.23867416342462872,"score_spread":0.2275860032061419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076286736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020792885,0.00013628279,0.9741458,0.00019862276,0.00023874584,0.000061508916,0.0000012359559,0.00017334199,0.0042515756],"genre_scores_gemma":[0.7344834,0.000004931925,0.26518002,0.00014713698,0.00008852247,0.000006093001,0.0000070505516,0.000004071223,0.000078750236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990142,0.000047590944,0.00021869928,0.00025653254,0.00024597353,0.00021698924],"domain_scores_gemma":[0.9994018,0.000037554542,0.00011764822,0.00033026416,0.000053928667,0.000058767022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017329145,0.00009959119,0.00014374586,0.000066161396,0.00008284124,0.00006482183,0.0002908217,0.00005921541,0.000005632218],"category_scores_gemma":[0.000015624459,0.000102119215,0.000051343355,0.0002698515,0.000028237288,0.00033556836,0.00004823701,0.00007234541,0.000019796984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012999023,0.00018867616,0.0001652649,0.000012755171,0.000013777704,0.0000639997,0.0015439194,0.6248701,0.019319734,0.0297093,0.0039377534,0.32016173],"study_design_scores_gemma":[0.00064716395,0.00076924573,0.009524253,0.00016907089,0.000009598031,0.00007280896,0.000033287815,0.9439891,0.033989556,0.008116006,0.0022737964,0.00040612856],"about_ca_topic_score_codex":0.0000216565,"about_ca_topic_score_gemma":2.6785577e-7,"teacher_disagreement_score":0.7136905,"about_ca_system_score_codex":0.000029600165,"about_ca_system_score_gemma":0.000039194158,"threshold_uncertainty_score":0.4164301},"labels":[],"label_agreement":null},{"id":"W2076406387","doi":"10.1007/s10514-013-9364-x","title":"Efficient complete coverage of a known arbitrary environment with applications to aerial operations","year":2013,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Traverse; Eulerian path; Path (computing); Motion planning; Trajectory; Holonomic; Decomposition; Field (mathematics); Algorithm; Real-time computing; Artificial intelligence; Robot; Lagrangian; Mathematics","score_opus":0.014776636404783881,"score_gpt":0.22200812520562244,"score_spread":0.20723148880083855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076406387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017679013,0.000030799936,0.9785192,0.0008028556,0.00010051809,0.0011182496,0.00001619784,0.00012211058,0.0016110825],"genre_scores_gemma":[0.3650085,0.0000016424818,0.6338419,0.00031482172,0.00005880162,0.00052874227,0.000013491603,0.000014553303,0.00021755874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986067,0.000045656576,0.00031684706,0.0004497447,0.00026946721,0.0003116227],"domain_scores_gemma":[0.99881595,0.00006192504,0.00007669527,0.0008057778,0.0000539612,0.00018569252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009911142,0.00017608,0.00023268581,0.000109646586,0.00016414878,0.000107659725,0.00067694264,0.000045303965,0.000053005042],"category_scores_gemma":[0.000006669461,0.00015525856,0.00004232381,0.00026465917,0.00006247787,0.00012236158,0.00022514483,0.00010504126,0.00076114386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019768847,0.0001567773,0.000058765687,0.0000056245685,0.000018858127,0.0000035570788,0.0004898356,0.9840518,0.00268942,0.0061317557,0.00013879496,0.0062528104],"study_design_scores_gemma":[0.000547797,0.00032354816,0.01163918,0.00003046172,0.00001451145,0.00003775922,0.00001808414,0.9833356,0.0008950949,0.00022980852,0.0025610644,0.00036707654],"about_ca_topic_score_codex":0.0001821716,"about_ca_topic_score_gemma":0.0000025310967,"teacher_disagreement_score":0.3473295,"about_ca_system_score_codex":0.00008911412,"about_ca_system_score_gemma":0.00013234343,"threshold_uncertainty_score":0.97832185},"labels":[],"label_agreement":null},{"id":"W2076450475","doi":"10.1017/s0263574708004438","title":"Real-time obstacle avoidance for multiple mobile robots","year":2008,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Villanova University","keywords":"Robot; Mobile robot; Obstacle; Obstacle avoidance; Motion planning; Position (finance); Computer science; Path (computing); Scheme (mathematics); Simulation; Robotics; Artificial intelligence; Real-time computing; Control theory (sociology); Control engineering; Engineering; Mathematics; Control (management)","score_opus":0.023760488180864254,"score_gpt":0.25112627912319596,"score_spread":0.2273657909423317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076450475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006457415,0.00011723612,0.98951876,0.00037130475,0.0005733067,0.0006638737,0.000010379728,0.0007214224,0.0015663228],"genre_scores_gemma":[0.08036921,0.000030901454,0.9151059,0.00013392359,0.00017997948,0.00017839488,0.000013532551,0.00003614074,0.0039520185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787056,0.00007358205,0.00034868863,0.0006722322,0.00035524156,0.00067968573],"domain_scores_gemma":[0.9977933,0.0007291296,0.00012432625,0.0009772488,0.00015193831,0.00022401268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028452708,0.00024504025,0.0003501465,0.00009347217,0.00040880038,0.00007177269,0.0010843624,0.00012302482,0.000014600516],"category_scores_gemma":[0.00026962013,0.0002446586,0.00013375295,0.0003706811,0.00010234575,0.00041365047,0.00020369912,0.00016500271,0.0005638456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032375272,0.00043355205,0.0021781274,0.00006550136,0.000072820534,0.00025803136,0.0021968638,0.94453335,0.017929008,0.004579568,0.019691586,0.008029214],"study_design_scores_gemma":[0.0007634351,0.00022684062,0.0050563617,0.00003782375,0.000009219315,0.00015867423,0.000009669035,0.9894307,0.002234599,0.00044335134,0.0012289302,0.00040035823],"about_ca_topic_score_codex":0.000035777604,"about_ca_topic_score_gemma":7.5203855e-7,"teacher_disagreement_score":0.07441284,"about_ca_system_score_codex":0.000082181585,"about_ca_system_score_gemma":0.00014754623,"threshold_uncertainty_score":0.9976887},"labels":[],"label_agreement":null},{"id":"W2077245982","doi":"10.1177/0278364913487931","title":"Optimality and Robustness in Multi-Robot Path Planning with Temporal Logic Constraints","year":2013,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robot; Correctness; Linear temporal logic; Robustness (evolution); Motion planning; Computer science; Software deployment; Set (abstract data type); Function (biology); Trajectory; Mathematical optimization; Temporal logic; Path (computing); Mathematics; Algorithm; Artificial intelligence; Theoretical computer science","score_opus":0.16655694677959967,"score_gpt":0.40041469808930774,"score_spread":0.23385775130970807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077245982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062227897,0.0001736563,0.9292221,0.007715986,0.00027888018,0.00019287327,0.0000012641353,0.000013202337,0.00017415744],"genre_scores_gemma":[0.5899574,0.000015758917,0.40984476,0.00005980715,0.00005989909,0.0000036380652,6.596859e-7,0.000006293617,0.000051780662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971762,0.00034265543,0.00044555846,0.00021505069,0.0014482131,0.00037232644],"domain_scores_gemma":[0.99737626,0.0007085739,0.0002346563,0.0002513545,0.0012871209,0.00014204653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032207442,0.00012794761,0.00021361891,0.00038449356,0.000110611545,0.00050224253,0.0020802992,0.000059248636,0.000012477432],"category_scores_gemma":[0.00036291676,0.000078571305,0.000034987374,0.00033216045,0.00044165133,0.00058999256,0.00048128722,0.00091031176,0.000009309619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000388713,0.00014661484,0.032506153,0.000008299615,0.00007643117,0.00088014884,0.0009296862,0.95772177,0.00023918072,0.0025361099,0.00027787074,0.004638866],"study_design_scores_gemma":[0.0011247832,0.00022730035,0.06720333,0.00030058567,0.0000033642386,0.0014402408,0.0005585964,0.92735916,0.00007477441,0.0015648954,0.000010365705,0.00013262231],"about_ca_topic_score_codex":0.00012326695,"about_ca_topic_score_gemma":0.000003594471,"teacher_disagreement_score":0.5277295,"about_ca_system_score_codex":0.00014273355,"about_ca_system_score_gemma":0.00029641239,"threshold_uncertainty_score":0.48431367},"labels":[],"label_agreement":null},{"id":"W2077539472","doi":"10.1109/ais.2010.5547019","title":"Environment mapping using probabilistic quadtree for the guidance and control of autonomous mobile robots","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Quadtree; Mobile robot; Computer science; Probabilistic logic; Robot; Motion planning; Mobile robot navigation; Obstacle avoidance; Artificial intelligence; Computer vision; Obstacle; Real-time computing; Robot control; Geography","score_opus":0.021641138949622742,"score_gpt":0.24352544914547736,"score_spread":0.2218843101958546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077539472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012721425,0.00017235886,0.9858496,0.00021919531,0.00022884161,0.00069756317,0.0000036873655,0.0000451756,0.000062115614],"genre_scores_gemma":[0.46379384,0.0000012953773,0.53594434,0.00006200979,0.000035675555,0.00006823116,2.8301383e-7,0.0000053299955,0.0000890056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991019,0.000024885654,0.00023911841,0.0002797295,0.00013925729,0.00021512354],"domain_scores_gemma":[0.99877167,0.0005121801,0.00012459859,0.00051115546,0.000030477502,0.000049907972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026429,0.00010819876,0.00016345545,0.000032331413,0.00012321935,0.00005238344,0.00047463187,0.00004493643,0.000004236344],"category_scores_gemma":[0.00007173255,0.00007454811,0.00003788877,0.00006276187,0.000117517004,0.00011723167,0.000115333256,0.0000956886,0.0000028713152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011181484,0.00016121968,0.0020463453,0.00011381021,0.00008853179,0.0000065881236,0.0018767376,0.75897336,0.14481364,0.019072447,0.000113180606,0.07272297],"study_design_scores_gemma":[0.0003499943,0.0000581446,0.0026042187,0.000011967016,0.000011238221,0.000018953382,0.000022725955,0.9946137,0.00079940504,0.0009689555,0.00044121593,0.000099484256],"about_ca_topic_score_codex":0.00004434724,"about_ca_topic_score_gemma":0.0000020154837,"teacher_disagreement_score":0.45107242,"about_ca_system_score_codex":0.000020376445,"about_ca_system_score_gemma":0.000043895507,"threshold_uncertainty_score":0.30399835},"labels":[],"label_agreement":null},{"id":"W2077643920","doi":"10.1504/ijma.2013.058347","title":"Energy efficient complete coverage of mapped areas by single and multiple robots","year":2013,"lang":"en","type":"article","venue":"International Journal of Mechatronics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Terrain; Robot; Computer science; Energy consumption; Motion planning; Energy (signal processing); Point (geometry); Function (biology); Path (computing); Real-time computing; Power consumption; Work (physics); Power (physics); Simulation; Artificial intelligence; Engineering; Electrical engineering; Geography; Mathematics; Cartography; Computer network; Mechanical engineering","score_opus":0.009034909910062809,"score_gpt":0.20979916015908906,"score_spread":0.20076425024902625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077643920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23025958,0.00033335006,0.7682097,0.00077063084,0.00033467868,0.000037197984,0.00000908361,0.000011874579,0.00003390739],"genre_scores_gemma":[0.95611864,0.00006263602,0.0436867,0.0000686122,0.000035095713,8.9144197e-7,0.000007746423,0.0000041001713,0.00001558614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901205,0.000037939506,0.00034755748,0.000107612046,0.00039821755,0.000096615484],"domain_scores_gemma":[0.99900293,0.00011036066,0.00044337884,0.000078782396,0.0003047205,0.000059811864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020331738,0.00007903365,0.00013889481,0.00012906513,0.00003188931,0.00012459501,0.00028804713,0.00003579925,0.0000047607077],"category_scores_gemma":[0.00004404711,0.00006947643,0.000035098506,0.000053501673,0.000021852537,0.00031641003,0.00009520494,0.000064872365,0.0000013471395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042359574,0.00068782177,0.0023347524,0.000046615947,0.00041723598,0.000035631354,0.001552846,0.2362498,0.17577259,0.052328188,0.0028422324,0.52768993],"study_design_scores_gemma":[0.0006577486,0.00013947705,0.0022562982,0.00007207891,0.000004842646,0.00010194096,0.000019076388,0.99128395,0.0026272368,0.002462785,0.00030353188,0.000071013776],"about_ca_topic_score_codex":0.000052606654,"about_ca_topic_score_gemma":5.5253946e-7,"teacher_disagreement_score":0.75503415,"about_ca_system_score_codex":0.000051161813,"about_ca_system_score_gemma":0.000030837175,"threshold_uncertainty_score":0.28331667},"labels":[],"label_agreement":null},{"id":"W2077754916","doi":"10.1177/0278364904046631","title":"C-space Entropy: A Measure for View Planning and Exploration for General Robot-Sensor Systems in Unknown Environments","year":2004,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Robot; Kinematics; Entropy (arrow of time); Computer science; Configuration space; Measure (data warehouse); Ignorance; Mathematics; Mathematical optimization; Artificial intelligence; Physics; Data mining; Classical mechanics","score_opus":0.143072137371111,"score_gpt":0.3845045442694378,"score_spread":0.24143240689832682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077754916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003651731,0.0013726163,0.98177224,0.0118853925,0.00080794614,0.00048506915,0.0000040275277,0.000006947178,0.000014004186],"genre_scores_gemma":[0.35643733,0.00030443113,0.6414743,0.00012490383,0.0010995169,0.000088144116,0.000008230889,0.00003533557,0.0004277691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975587,0.00018066009,0.00045899968,0.00020851393,0.0012578315,0.00033525788],"domain_scores_gemma":[0.99828035,0.00065141654,0.00024515146,0.0001962271,0.0005298066,0.00009703421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033161289,0.000118134674,0.00021222404,0.00035481006,0.00014752375,0.00037248275,0.001226869,0.0000619929,3.3550512e-7],"category_scores_gemma":[0.0005193937,0.00008737926,0.000066718894,0.00017588404,0.000074872565,0.00049910264,0.00018345343,0.0003516047,0.0000025299676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009047534,0.00006925362,0.00014732308,0.000017284188,0.00007661642,0.000047010377,0.001155418,0.96016455,0.0061980933,0.030369561,0.00014295171,0.0015214938],"study_design_scores_gemma":[0.0036403993,0.0006158878,0.0008403866,0.0007586342,0.000017495824,0.00041588573,0.00044033982,0.97135115,0.0017034172,0.017631361,0.0023757184,0.00020933409],"about_ca_topic_score_codex":0.000025882991,"about_ca_topic_score_gemma":0.0000025079364,"teacher_disagreement_score":0.35278562,"about_ca_system_score_codex":0.0003864743,"about_ca_system_score_gemma":0.00020861466,"threshold_uncertainty_score":0.359186},"labels":[],"label_agreement":null},{"id":"W2077831477","doi":"10.1109/tii.2012.2198665","title":"Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1034,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Particle swarm optimization; Motion planning; Multi-core processor; Path (computing); Genetic algorithm; Speedup; Mathematical optimization; Algorithm; Dynamic programming; Swarm behaviour; Parallel computing; Artificial intelligence; Mathematics","score_opus":0.07969009193666098,"score_gpt":0.31740670975289936,"score_spread":0.23771661781623837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077831477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01231579,0.000023079941,0.98613,0.000041068615,0.0008131798,0.00044978163,0.000033947526,0.00011935008,0.00007382507],"genre_scores_gemma":[0.13209224,0.0000130859125,0.86765254,0.000031043768,0.00010644082,0.00004788945,0.0000073158417,0.000013169094,0.00003629596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983948,0.000053249703,0.00075725355,0.00011854483,0.00028593434,0.00039020999],"domain_scores_gemma":[0.99877846,0.00032144506,0.00032527195,0.00030523748,0.0000863433,0.00018321957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039331632,0.0001802722,0.00032160885,0.00012466946,0.00020488842,0.00008303079,0.00026448257,0.00019304876,0.0000054980696],"category_scores_gemma":[0.000020659763,0.00017782598,0.00006427177,0.00030008564,0.000049212482,0.0007648023,0.0000056628,0.00021852295,0.000010440824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020159834,0.00014113908,0.0001552185,0.000018429731,0.000034687444,2.7290537e-7,0.0043434133,0.94872016,0.000022431052,0.000040394352,0.00029827392,0.04620545],"study_design_scores_gemma":[0.0011357313,0.00032685793,0.00006817202,0.0000644871,0.000045197754,0.000008429912,0.000337903,0.9930986,0.004631695,0.000015435733,0.000077785095,0.00018970182],"about_ca_topic_score_codex":0.000014713813,"about_ca_topic_score_gemma":3.636957e-8,"teacher_disagreement_score":0.11977645,"about_ca_system_score_codex":0.00004853997,"about_ca_system_score_gemma":0.00006807509,"threshold_uncertainty_score":0.72515327},"labels":[],"label_agreement":null},{"id":"W2079846333","doi":"10.1115/detc2008-49630","title":"Design and Control of an Omnibot Autonomous Vehicle","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Obstacle; Controller (irrigation); Obstacle avoidance; Computer science; Mobile robot; Collision avoidance; Path (computing); Control system; Collision; Routing (electronic design automation); Control engineering; Robot; Real-time computing; Engineering; Embedded system; Artificial intelligence","score_opus":0.02836167015394085,"score_gpt":0.23617241194272878,"score_spread":0.20781074178878794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079846333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038158335,0.000055129705,0.96097714,0.00014258974,0.0000612681,0.00009815181,3.5216078e-7,0.000123522,0.00038352216],"genre_scores_gemma":[0.51888317,0.0000013803124,0.48090172,0.00010526655,0.000008547048,0.0000020015812,1.1994659e-7,0.000002340609,0.00009544722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931884,0.00007142754,0.00013382437,0.00020058491,0.00012545146,0.00014989755],"domain_scores_gemma":[0.99941725,0.00011698104,0.000044757086,0.00029795663,0.00003648309,0.000086576314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021686274,0.000068648274,0.00013106552,0.000043285025,0.00006449619,0.000018101722,0.00031438694,0.000032711057,0.0000039156357],"category_scores_gemma":[0.000017689432,0.000059203052,0.000013195499,0.00009863595,0.000059609098,0.00026861715,0.000041581145,0.00004691786,0.0000108041395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016374254,0.0017186627,0.029685969,0.00008310005,0.00028484655,0.002233519,0.022500224,0.37540942,0.14342585,0.044203646,0.0024066926,0.37788433],"study_design_scores_gemma":[0.00050965376,0.0002077834,0.011418955,0.0000029543676,0.0000021732421,0.00012016036,0.000005093467,0.98093,0.006414863,0.00028776773,0.000019865542,0.00008074565],"about_ca_topic_score_codex":0.00003646577,"about_ca_topic_score_gemma":1.290483e-7,"teacher_disagreement_score":0.60552055,"about_ca_system_score_codex":0.0000082711595,"about_ca_system_score_gemma":0.000058271195,"threshold_uncertainty_score":0.24142304},"labels":[],"label_agreement":null},{"id":"W2080950762","doi":"10.1109/icra.2012.6225171","title":"Trust-driven interactive visual navigation for autonomous robots","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robot; Human–computer interaction; Computer vision; Artificial intelligence; Mobile robot; Computer graphics (images)","score_opus":0.02357885252639647,"score_gpt":0.3138797648981534,"score_spread":0.2903009123717569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080950762","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011018959,0.000019499912,0.9850943,0.00028059675,0.0011297484,0.00024832878,0.000001678664,0.00027334833,0.0019335581],"genre_scores_gemma":[0.46039563,2.2929571e-7,0.5385697,0.00014535649,0.00018646607,0.000039033555,0.000010183206,0.000007733426,0.0006457122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905854,0.000035680405,0.00017115583,0.00023104709,0.0001436745,0.00035989063],"domain_scores_gemma":[0.9993146,0.00017506759,0.000086866,0.00022665822,0.000073001145,0.00012379035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021011275,0.000116223135,0.00012820297,0.00006479251,0.00009287938,0.00008462353,0.00036928206,0.000057249894,0.000009912844],"category_scores_gemma":[0.000048729475,0.00010427383,0.000056182147,0.00014092421,0.000017696715,0.0011003984,0.00011633887,0.00009069808,0.00014541879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009483189,0.0019145457,0.031038791,0.0001286131,0.00037994224,0.00003772056,0.034393772,0.07369639,0.01010497,0.17824714,0.01771355,0.65224975],"study_design_scores_gemma":[0.00030239113,0.00012751177,0.006997379,0.000019260677,0.000006960862,0.000032729487,0.00006381475,0.9871362,0.0037709216,0.0003885055,0.0009677929,0.00018654563],"about_ca_topic_score_codex":0.000016493535,"about_ca_topic_score_gemma":1.9937026e-7,"teacher_disagreement_score":0.9134398,"about_ca_system_score_codex":0.0000935769,"about_ca_system_score_gemma":0.000040550764,"threshold_uncertainty_score":0.42521635},"labels":[],"label_agreement":null},{"id":"W2081505722","doi":"10.1109/mesa.2010.5551989","title":"Motion planning for multi-link robots using Artificial Potential Fields and modified Simulated Annealing","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Maxima and minima; Motion planning; Simulated annealing; Robot; Computer science; Path (computing); Motion control; Adaptive simulated annealing; Convergence (economics); Artificial intelligence; Mathematical optimization; Algorithm; Mathematics","score_opus":0.10074240608734546,"score_gpt":0.33713448650684286,"score_spread":0.23639208041949739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081505722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17407091,0.000014099566,0.8241377,0.00020697957,0.0011096987,0.00021602686,0.0000018163743,0.0002115362,0.00003124963],"genre_scores_gemma":[0.51807076,1.7022164e-7,0.48165604,0.000056833025,0.00017940757,0.0000018494255,0.0000046683335,0.000007921773,0.000022331142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987073,0.000030465402,0.00028834032,0.0004479012,0.0001657439,0.0003602496],"domain_scores_gemma":[0.99927104,0.000106046486,0.00010144717,0.00029459092,0.00011056332,0.000116303796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036854504,0.00016249783,0.00018113984,0.0001265572,0.0003126099,0.00025538326,0.00029980834,0.00021263366,0.0000025259658],"category_scores_gemma":[0.00012436757,0.00015793952,0.000051102994,0.00015367781,0.000034665805,0.00038545308,0.00012160143,0.00026612563,0.00000232678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000929524,0.00002151574,0.0001460513,0.00000936367,0.000011502176,0.000015780057,0.00032234928,0.9669317,0.015360898,0.0018777619,0.00000768383,0.015286068],"study_design_scores_gemma":[0.0004744751,0.00003923758,0.0008394243,0.000017210335,0.00001181674,0.000031315394,0.000016986542,0.9938507,0.0032155649,0.0012992235,0.0000029030393,0.0002011479],"about_ca_topic_score_codex":0.000079878155,"about_ca_topic_score_gemma":0.0000030751055,"teacher_disagreement_score":0.34399986,"about_ca_system_score_codex":0.000012241102,"about_ca_system_score_gemma":0.00003466396,"threshold_uncertainty_score":0.64405864},"labels":[],"label_agreement":null},{"id":"W2083841567","doi":"10.1109/iros.2006.282068","title":"Real-time 3D Collision Avoidance Method for Safe Human and Robot Coexistence","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Robot; Collision avoidance; Computer science; Collision; Workspace; Collision detection; Simulation; Monte Carlo localization; Robot kinematics; Artificial intelligence; Computer vision; Algorithm; Mobile robot","score_opus":0.0196356233383397,"score_gpt":0.3013723164955982,"score_spread":0.2817366931572585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083841567","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015380167,0.000046451263,0.98500216,0.00041331444,0.000132405,0.00030502767,0.000004821113,0.000258492,0.012299338],"genre_scores_gemma":[0.0029167472,0.0000042643746,0.98730266,0.0001095998,0.00008737469,0.000026925076,0.0000074511436,0.0000117654345,0.009533183],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848336,0.00008711791,0.00028628286,0.00056027557,0.00023810136,0.0003448648],"domain_scores_gemma":[0.99876636,0.0004394171,0.00011430354,0.0004933759,0.000101297344,0.00008523917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071407214,0.00016694189,0.00024734944,0.00007571432,0.00029956806,0.00016727128,0.0005469263,0.000077923025,0.0000062612157],"category_scores_gemma":[0.000048150345,0.00014878873,0.000042092008,0.00023497704,0.000050010993,0.00030239605,0.00017228628,0.00007848143,0.000031748343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000608834,0.00053440453,0.0016607837,0.00024913432,0.00007573099,0.00017205074,0.0017189347,0.13972095,0.38317743,0.33443767,0.073404334,0.06478772],"study_design_scores_gemma":[0.00051717245,0.00016825768,0.0053507937,0.00004210173,0.000008101623,0.000041099112,0.000007865843,0.9824204,0.005306839,0.004745385,0.0011091698,0.0002828335],"about_ca_topic_score_codex":0.00046222092,"about_ca_topic_score_gemma":0.000006932345,"teacher_disagreement_score":0.8426994,"about_ca_system_score_codex":0.000041340198,"about_ca_system_score_gemma":0.000035474888,"threshold_uncertainty_score":0.6067428},"labels":[],"label_agreement":null},{"id":"W2083862901","doi":"10.2316/journal.206.2013.3.206-3756","title":"A NOVEL PATH PLANNING APPROACH FOR MULTI-ROBOT BASED TRANSPORTATION","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Motion planning; Path (computing); Object (grammar); Computer science; Simulation; Mobile robot; Artificial intelligence","score_opus":0.04439924878649739,"score_gpt":0.29346257659803504,"score_spread":0.24906332781153764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083862901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002667133,0.000049006998,0.99571,0.00082286005,0.00053340127,0.0001594883,0.0000062522745,0.000034853365,0.000016976743],"genre_scores_gemma":[0.25736567,0.0000021577493,0.74237615,0.000124355,0.000085860695,0.0000075632474,0.000020354215,0.0000060957163,0.000011800392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893355,0.000017138367,0.0004219136,0.00013912642,0.00036808988,0.000120162],"domain_scores_gemma":[0.9987026,0.000096183714,0.00043873492,0.000081112994,0.00061105844,0.0000703342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002883442,0.00010259332,0.00014319915,0.00020313617,0.000055816698,0.00023721321,0.0003958604,0.00005419768,0.0000014518475],"category_scores_gemma":[0.000069527574,0.000090382215,0.00006917111,0.00008223659,0.000018072336,0.0007289781,0.000012674497,0.00009735395,0.0000011315476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007616084,0.00015953778,0.0010532164,0.00002038699,0.000057202462,0.000006275627,0.0005732651,0.9773177,0.0033845243,0.0025253403,0.00018440616,0.014710514],"study_design_scores_gemma":[0.0011188446,0.00007956252,0.03883471,0.00008084179,0.0000116451365,0.000050004484,0.000039340433,0.9591718,0.00021236157,0.00027944465,0.000025597968,0.00009586733],"about_ca_topic_score_codex":0.000010056803,"about_ca_topic_score_gemma":1.02491065e-7,"teacher_disagreement_score":0.25469854,"about_ca_system_score_codex":0.000042368098,"about_ca_system_score_gemma":0.00006597533,"threshold_uncertainty_score":0.36856797},"labels":[],"label_agreement":null},{"id":"W2084153816","doi":"10.3182/20100929-3-ro-4017.00005","title":"Robot Navigation Controller: a Non-Parametric Regression Approach","year":2010,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"University of British Columbia","keywords":"Robot; Mobile robot navigation; Controller (irrigation); Parametric statistics; Obstacle; Motion planning; Computer science; Obstacle avoidance; Robot control; Control theory (sociology); Kernel (algebra); Control engineering; Engineering; Artificial intelligence; Mobile robot; Control (management); Mathematics; Geography","score_opus":0.009532551257860062,"score_gpt":0.24159386431119445,"score_spread":0.23206131305333438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084153816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2994841,0.0001338453,0.6876161,0.0006327341,0.0014966582,0.000801771,0.0000030643675,0.001036314,0.008795377],"genre_scores_gemma":[0.5782883,0.0000027305555,0.42086715,0.00006528486,0.00018021307,0.000055357305,0.000004176795,0.000016321246,0.0005204497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765587,0.000011659161,0.0003739785,0.00077283924,0.00064042484,0.0005452332],"domain_scores_gemma":[0.998731,0.00007830739,0.00030807496,0.00031816767,0.00034199748,0.00022245599],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006757725,0.00029586194,0.00036844172,0.00034432157,0.000296137,0.000534651,0.0012818814,0.00024213146,0.000004177043],"category_scores_gemma":[0.0003718667,0.0002495507,0.000106463325,0.0013218847,0.000093490664,0.0011280173,0.00027082706,0.00067074684,0.0001222931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001294162,0.0014912338,0.09665097,0.0006124011,0.00027371506,0.000080988364,0.011705626,0.0029108138,0.31361064,0.038798407,0.03443091,0.49930486],"study_design_scores_gemma":[0.0008546539,0.000108694265,0.015756426,0.00007898569,0.000014748566,0.00015332269,0.00007724946,0.9759992,0.0043011634,0.001557543,0.0007013851,0.0003966221],"about_ca_topic_score_codex":0.00003587984,"about_ca_topic_score_gemma":1.3154099e-7,"teacher_disagreement_score":0.9730884,"about_ca_system_score_codex":0.000046589033,"about_ca_system_score_gemma":0.00007162109,"threshold_uncertainty_score":0.99999565},"labels":[],"label_agreement":null},{"id":"W2087118732","doi":"10.2316/journal.206.2013.4.206-3716","title":"TRACKING CONTROL OF A MOBILE ROBOT WITH STABILITY ANALYSIS1","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Tracking (education); Computer science; Stability (learning theory); Mobile robot; Control (management); Control theory (sociology); Robot; Artificial intelligence; Psychology; Machine learning","score_opus":0.011314777495964873,"score_gpt":0.24488289857611492,"score_spread":0.23356812108015004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087118732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13550906,0.000082739796,0.8633957,0.0006937234,0.00019107827,0.00007973914,0.000001406303,0.000013748742,0.000032803],"genre_scores_gemma":[0.7851775,0.000008658189,0.21472806,0.00003357465,0.000042228785,0.000002052388,9.266614e-7,0.0000030672343,0.000003914925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874514,0.000054562654,0.00048305313,0.00010822228,0.0005175966,0.000091437134],"domain_scores_gemma":[0.99794966,0.0001459424,0.0006399868,0.00012262854,0.0010823632,0.000059428745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039530758,0.000082403836,0.00021157014,0.00018658898,0.00002766556,0.00015726066,0.0004021961,0.000032200096,0.000010960707],"category_scores_gemma":[0.00007204846,0.00006147641,0.000056247547,0.00013561924,0.00004095244,0.00081208244,0.000035811754,0.00009957701,0.0000019060765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015859192,0.00016290775,0.01955717,0.000016542781,0.00030196903,0.000023201736,0.0009520809,0.9132872,0.004969059,0.0018966321,0.00002764626,0.05878973],"study_design_scores_gemma":[0.00069240236,0.00021385704,0.06146966,0.00008793107,0.000027460002,0.00013506405,0.00007846797,0.93519354,0.001141732,0.00087749655,0.000007234904,0.00007516987],"about_ca_topic_score_codex":0.000020993297,"about_ca_topic_score_gemma":7.640064e-7,"teacher_disagreement_score":0.64966846,"about_ca_system_score_codex":0.000041785064,"about_ca_system_score_gemma":0.00006606287,"threshold_uncertainty_score":0.2506935},"labels":[],"label_agreement":null},{"id":"W2088122177","doi":"10.1006/jvlc.2000.0175","title":"Building Environments for Visual Programming of Robots by Demonstration","year":2000,"lang":"en","type":"article","venue":"Journal of Visual Languages & Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Programming language; Robot; Representation (politics); Visual programming language; Inductive programming; Artificial intelligence; Programming paradigm; Procedural programming; Human–computer interaction; Programming domain; Reactive programming","score_opus":0.010623791022891686,"score_gpt":0.32197019863782567,"score_spread":0.311346407614934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088122177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33360496,0.0003989549,0.66566277,0.00004342543,0.00012833437,0.000111191475,9.449582e-7,0.000022081515,0.000027323784],"genre_scores_gemma":[0.5329912,0.0000045218103,0.46677247,0.000033029413,0.00016349218,7.077612e-7,0.0000017684092,0.000009929211,0.000022873632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816805,0.00009384598,0.00072368135,0.00021833043,0.00045620036,0.00033991633],"domain_scores_gemma":[0.99873745,0.00028064818,0.00068246946,0.00012789185,0.0000615044,0.000110012064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086532184,0.00016432838,0.00033475418,0.0001299722,0.00012760305,0.00010735294,0.00050890626,0.00007473797,0.000005486503],"category_scores_gemma":[0.00008773368,0.000153079,0.00014010514,0.00020593921,0.00003927771,0.00039492673,0.00006934871,0.0002004485,0.0000022486058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022828313,0.00028730422,0.0009412476,0.0000348388,0.000070687915,0.000039444563,0.0008960169,0.030279621,0.03901983,0.00013282277,0.00013036099,0.928145],"study_design_scores_gemma":[0.0014808082,0.0018489792,0.003131233,0.0005281214,0.000061312225,0.00039278166,0.00029636023,0.95545346,0.03535931,0.000102285776,0.00095304043,0.0003923359],"about_ca_topic_score_codex":0.000008629171,"about_ca_topic_score_gemma":1.1373385e-7,"teacher_disagreement_score":0.9277527,"about_ca_system_score_codex":0.00006150719,"about_ca_system_score_gemma":0.00005057751,"threshold_uncertainty_score":0.6242381},"labels":[],"label_agreement":null},{"id":"W2088261091","doi":"10.1109/tro.2014.2354933","title":"Safety Hierarchy for Planning With Time Constraints in Unknown Dynamic Environments","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hierarchy; Interleaving; Planner; Computer science; Time horizon; Baseline (sea); Robot; Motion planning; Mobile robot; Real-time computing; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.011305092750655282,"score_gpt":0.23650681293924994,"score_spread":0.22520172018859466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088261091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048595792,0.0000063602993,0.99783933,0.0004636711,0.00034766772,0.0003699032,0.000015804088,0.00012498494,0.00034629295],"genre_scores_gemma":[0.29525024,0.0000043479026,0.7034494,0.00020396852,0.00001940345,0.000040038085,0.000007809766,0.00003270093,0.0009920947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834996,0.00009237669,0.0003149149,0.00048754338,0.00029121127,0.0004639996],"domain_scores_gemma":[0.9988308,0.0004281533,0.000088703724,0.00050225866,0.000019391418,0.00013070348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003283887,0.00023912698,0.0002761831,0.00023281814,0.00019742701,0.00006102255,0.00049595366,0.000110207046,0.000007978221],"category_scores_gemma":[0.000008677213,0.00022750239,0.000063284984,0.00027983467,0.0001487838,0.00022451801,0.000003357904,0.0003137257,0.00007287572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032120464,0.00012819731,0.000025645302,0.000009504288,0.000026022963,0.000012585053,0.0002575314,0.975466,0.00023222105,0.00031970427,0.000012869234,0.023477599],"study_design_scores_gemma":[0.0013608037,0.00036227482,0.00058041804,0.00012476089,0.000019525412,0.00003873562,0.00001188825,0.9962962,0.00046972578,0.00024820014,0.00019545743,0.00029197702],"about_ca_topic_score_codex":0.0000029354424,"about_ca_topic_score_gemma":0.0000026416274,"teacher_disagreement_score":0.29476428,"about_ca_system_score_codex":0.00016036126,"about_ca_system_score_gemma":0.000061738756,"threshold_uncertainty_score":0.9277278},"labels":[],"label_agreement":null},{"id":"W2089588892","doi":"10.2316/journal.206.2006.2.206-2792","title":"A MULTI-AGENT ARCHITECTURE FOR ROBOTIC SYSTEMS IN REAL-TIME ENVIRONMENTS","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Architecture; Computer science; Multi-agent system; Distributed computing; Real-time computing; Artificial intelligence; Embedded system; Human–computer interaction; Computer architecture","score_opus":0.014607182024321447,"score_gpt":0.2583543300796431,"score_spread":0.24374714805532163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089588892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00929465,0.00013607163,0.9887328,0.00082865305,0.0008294809,0.00013684787,0.000002390993,0.00001706203,0.000022038663],"genre_scores_gemma":[0.32601506,0.000034333756,0.67352295,0.000030324214,0.0002051893,0.00000490875,0.000009205214,0.000009289938,0.0001687662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988472,0.000045745343,0.0004750665,0.00013459817,0.00036765484,0.00012973764],"domain_scores_gemma":[0.99926025,0.00013279948,0.0003894911,0.0000912549,0.00009088868,0.000035334393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035010892,0.0000987698,0.00016861908,0.0002652954,0.00003114928,0.00016098245,0.000375988,0.000049820686,6.832814e-7],"category_scores_gemma":[0.000052982858,0.00008798498,0.000050326656,0.00006859187,0.000017632692,0.0002624032,0.00005255788,0.000088010915,0.0000037044922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052510345,0.0000872349,0.0005789272,0.000007960752,0.000024297513,0.00003680171,0.00012907713,0.99078465,0.003960876,0.0018432192,0.00011060519,0.0024311238],"study_design_scores_gemma":[0.00068589934,0.00006480301,0.026636321,0.00014531432,0.000008051377,0.00018787557,0.00000856927,0.971014,0.0000716687,0.0009915293,0.000099350495,0.000086601896],"about_ca_topic_score_codex":0.000037269812,"about_ca_topic_score_gemma":0.0000013285376,"teacher_disagreement_score":0.3167204,"about_ca_system_score_codex":0.0001200181,"about_ca_system_score_gemma":0.000034159908,"threshold_uncertainty_score":0.35879233},"labels":[],"label_agreement":null},{"id":"W2090400788","doi":"10.4028/www.scientific.net/amm.229-231.2225","title":"Quick Motion Path Plan for Joint-Robot to Pick Tomato under Free-Obstacle","year":2012,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Kinematics; Inverse kinematics; Path (computing); Point (geometry); Motion planning; Motion (physics); Obstacle; Linear motion; Joint (building); Computer science; Robotic arm; Control theory (sociology); Engineering; Simulation; Artificial intelligence; Mathematics; Physics; Structural engineering; Geometry","score_opus":0.03570213773754976,"score_gpt":0.24363019111604234,"score_spread":0.20792805337849257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090400788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020472687,0.00002607261,0.976439,0.00029679536,0.0017814296,0.0006617533,0.000048382328,0.00017273874,0.000101164725],"genre_scores_gemma":[0.44435123,0.000004091332,0.5544554,0.000661737,0.00024183482,0.00017289257,0.000022055054,0.000025092513,0.00006563163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864036,0.000026934002,0.00029062238,0.00035620236,0.0001833551,0.0005025427],"domain_scores_gemma":[0.9990565,0.000049660106,0.00011314133,0.00051676284,0.00003471265,0.0002291761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006964703,0.0001891472,0.00028768842,0.00007344021,0.00013748468,0.00018497337,0.00037190394,0.00010716046,0.00001078268],"category_scores_gemma":[0.000028550068,0.00017561892,0.00002612372,0.00009974723,0.0000052815776,0.00019067315,0.00033881568,0.000040309762,0.000059011752],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000135593455,0.000049938782,0.0000013840789,0.00004687953,0.000015198423,7.53116e-7,0.00058220705,0.000082824045,0.18327571,0.8120141,0.0013480991,0.0025693595],"study_design_scores_gemma":[0.002861416,0.0004679413,0.0011915884,0.00012706452,0.000074011055,0.000102525686,0.00046153754,0.015465302,0.56404716,0.4108645,0.0028850175,0.0014519088],"about_ca_topic_score_codex":0.000020700312,"about_ca_topic_score_gemma":8.812351e-7,"teacher_disagreement_score":0.42387855,"about_ca_system_score_codex":0.000034386212,"about_ca_system_score_gemma":0.000020213716,"threshold_uncertainty_score":0.7161532},"labels":[],"label_agreement":null},{"id":"W2090947154","doi":"10.1109/icar.2013.6766547","title":"Human Motion Behaviour Aware Planner (HMBAP) for path planning in dynamic human environments","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Planner; Robot; Motion planning; Obstacle; Obstacle avoidance; Computer science; Motion (physics); Trajectory; Path (computing); Social robot; Feature (linguistics); Artificial intelligence; Human–robot interaction; Work (physics); Human–computer interaction; Mobile robot; Human motion; Robot control; Engineering; Geography","score_opus":0.022487319473055965,"score_gpt":0.28385497716826874,"score_spread":0.26136765769521275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090947154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30677682,0.000014462129,0.69186264,0.000129139,0.00019002118,0.00054366264,0.0000058584505,0.00015729558,0.00032011306],"genre_scores_gemma":[0.9296796,3.0395503e-7,0.067980446,0.000119699114,0.000035587364,0.00017333704,0.00007389981,0.000022166969,0.001914985],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99822235,0.000054549168,0.0003670668,0.00056796893,0.00028123742,0.0005068303],"domain_scores_gemma":[0.9992002,0.00003870382,0.00012068762,0.00050882366,0.000017686727,0.000113879265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025610926,0.00022214942,0.00021772383,0.00019719453,0.00023387463,0.0001567768,0.00070829026,0.00012637804,0.00003491627],"category_scores_gemma":[0.000010079093,0.00021184758,0.000058875226,0.00013014437,0.000031407315,0.0006203951,0.00018249694,0.00018889569,0.0001041065],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000120958875,0.0023693615,0.72498155,0.00019514785,0.00014078653,0.00051233295,0.013633177,0.13217999,0.04568782,0.011165736,0.023133598,0.04598839],"study_design_scores_gemma":[0.0008520085,0.00017175378,0.5130407,0.00007030913,0.0000061595483,0.000012762663,0.00016333468,0.48348805,0.00020980953,0.0015661649,0.00004729513,0.00037162684],"about_ca_topic_score_codex":0.00021779438,"about_ca_topic_score_gemma":0.0000049034725,"teacher_disagreement_score":0.6238822,"about_ca_system_score_codex":0.00014924075,"about_ca_system_score_gemma":0.000011766035,"threshold_uncertainty_score":0.86388934},"labels":[],"label_agreement":null},{"id":"W2091564622","doi":"10.1007/s10846-013-9895-6","title":"3D Path Planning for Multiple UAVs for Maximum Information Collection","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Apollo Microwaves (Canada)","funders":"","keywords":"Travelling salesman problem; Autopilot; Motion planning; Path (computing); Genetic algorithm; Mathematical optimization; Population; Computer science; MATLAB; Algorithm; Mathematics; Engineering; Artificial intelligence; Control engineering; Robot","score_opus":0.031584192388562,"score_gpt":0.26408275740550435,"score_spread":0.23249856501694235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091564622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010639383,0.00036429008,0.9906198,0.0002494577,0.0061862264,0.0013640422,0.0000032474106,0.00005907548,0.000089939254],"genre_scores_gemma":[0.25760818,0.000019596697,0.74071157,0.00014222962,0.00084921194,0.00024594855,0.000012698297,0.000029024703,0.00038153352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997557,0.00009328492,0.0013029595,0.00017398452,0.00046942726,0.0004033325],"domain_scores_gemma":[0.99620783,0.00089871406,0.0012329734,0.0002829214,0.0012005523,0.0001770023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014128054,0.00021164544,0.00046257753,0.00041379122,0.00020906114,0.000534951,0.00067919295,0.00012838816,0.0000028980119],"category_scores_gemma":[0.00076662743,0.00017412633,0.00021177015,0.0003015685,0.00001907826,0.0016768274,0.00005212062,0.00017473553,0.00004327872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004485262,0.00007883308,0.0013030692,0.00026837434,0.00013701999,0.0000052970004,0.0026079433,0.9560875,0.00017975371,0.0005868404,0.023604192,0.015096266],"study_design_scores_gemma":[0.0006788571,0.0006533194,0.00040488897,0.00045704428,0.000027809965,0.00034581812,0.0009123123,0.9918703,0.00031067771,0.00041704878,0.0037222102,0.00019971549],"about_ca_topic_score_codex":0.00005734876,"about_ca_topic_score_gemma":2.507679e-7,"teacher_disagreement_score":0.25654423,"about_ca_system_score_codex":0.00026457905,"about_ca_system_score_gemma":0.00015912707,"threshold_uncertainty_score":0.71006656},"labels":[],"label_agreement":null},{"id":"W2092305811","doi":"10.1109/icuas.2014.6842250","title":"Optimal flight path planning for UAVs in 3-D threat environment","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Northwestern Polytechnical University; Northwestern University","keywords":"Dijkstra's algorithm; Motion planning; Path (computing); Computer science; Shortest path problem; Battlefield; Node (physics); Real-time computing; A* search algorithm; Field (mathematics); Mathematical optimization; Artificial intelligence; Algorithm; Computer network; Engineering; Graph; Mathematics; Theoretical computer science; Robot","score_opus":0.019116375989820723,"score_gpt":0.245084396015424,"score_spread":0.2259680200256033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092305811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063201943,0.000055102515,0.9894878,0.00046568285,0.00020037478,0.00018382381,9.756858e-7,0.000115212424,0.003170793],"genre_scores_gemma":[0.096029595,0.0000025910424,0.90293413,0.00023990784,0.00007953544,0.000045498542,0.000003910691,0.000010092024,0.0006547489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880385,0.00003764583,0.00020071848,0.0004029429,0.00019237465,0.00036247037],"domain_scores_gemma":[0.9992552,0.00018543632,0.000048013582,0.0004220249,0.000008110378,0.00008123335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033847167,0.00013503291,0.0001685486,0.00008276423,0.00006695874,0.000066715154,0.00055623695,0.000059720605,0.000009208157],"category_scores_gemma":[0.000030337476,0.000115263916,0.000041738545,0.00008440495,0.000020143463,0.00020665037,0.00015495344,0.000087907705,0.00007460796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002467716,0.00026096046,0.017594246,0.00004208576,0.00003141698,0.00008846692,0.004170135,0.8452626,0.0010273033,0.054032423,0.007213403,0.07025229],"study_design_scores_gemma":[0.0005157055,0.00015106253,0.006743151,0.000027835087,0.000002244037,0.000010705534,0.00002202796,0.9854815,0.00072954124,0.0008596911,0.0052641453,0.00019238653],"about_ca_topic_score_codex":0.000011403412,"about_ca_topic_score_gemma":1.5434838e-7,"teacher_disagreement_score":0.14021891,"about_ca_system_score_codex":0.00004911827,"about_ca_system_score_gemma":0.000015855327,"threshold_uncertainty_score":0.47003263},"labels":[],"label_agreement":null},{"id":"W2093641157","doi":"10.1109/aero.2011.5747270","title":"Path planning on a network of paths","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrain; Motion planning; Computer science; Mobile robot; Path (computing); Global Positioning System; Mobile robot navigation; Robot; Real-time computing; Artificial intelligence; Work (physics); Computer vision; Human–computer interaction; Engineering; Computer network; Robot control; Telecommunications; Geography","score_opus":0.05973637401649006,"score_gpt":0.25130656500394405,"score_spread":0.19157019098745398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093641157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059262286,0.000049349117,0.90304893,0.00002574749,0.00043412883,0.0000650985,4.7107847e-7,0.0001830603,0.09026696],"genre_scores_gemma":[0.42493746,8.930242e-7,0.5745764,0.00021251586,0.000051294424,0.0000032166163,4.161099e-7,0.000005121021,0.00021267915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991208,0.000037452326,0.00017666203,0.00022445981,0.00019134615,0.00024929998],"domain_scores_gemma":[0.99928135,0.00007733872,0.00008366955,0.0004572011,0.00003538622,0.000065065055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002537987,0.00009285346,0.00013679914,0.00003443125,0.000041479092,0.0000138673895,0.0005868796,0.000042296066,0.00001907749],"category_scores_gemma":[0.000026989304,0.00007510125,0.000037426944,0.00022657134,0.000020564741,0.000115766765,0.00012014712,0.00009047178,0.00006172219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006167697,0.0006156214,0.09160824,0.00006985693,0.00012819696,0.0008820199,0.025973426,0.038828325,0.00029144928,0.7082735,0.04918309,0.08408465],"study_design_scores_gemma":[0.0011701413,0.002097407,0.25764,0.00074488326,0.000019076084,0.00009372944,0.00023677974,0.70136523,0.0050808783,0.029055625,0.0014661655,0.001030086],"about_ca_topic_score_codex":0.000023249337,"about_ca_topic_score_gemma":6.4202325e-8,"teacher_disagreement_score":0.6792178,"about_ca_system_score_codex":0.000010115596,"about_ca_system_score_gemma":0.000039441325,"threshold_uncertainty_score":0.306254},"labels":[],"label_agreement":null},{"id":"W2094079887","doi":"10.2514/1.50923","title":"Reactive Collision Avoidance of Using Nonlinear Geometric and Differential Geometric Guidance","year":2010,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Air Force Research Laboratory; Air Force Office of Scientific Research; Indian Institute of Science","keywords":"Collision avoidance; Collision; Computer science; Motion planning; Path (computing); Obstacle avoidance; Obstacle; Potential field; Field (mathematics); Trajectory; Function (biology); Simulation; Control theory (sociology); Artificial intelligence; Mobile robot; Robot; Mathematics; Physics; Computer security","score_opus":0.008918959153723283,"score_gpt":0.24655317504750007,"score_spread":0.23763421589377678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094079887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46361765,0.0010018313,0.53455734,0.000106846695,0.000593985,0.00007407086,0.000015386062,0.000008420938,0.000024454646],"genre_scores_gemma":[0.7548995,0.000115433875,0.244741,0.000034129513,0.00017952378,8.270779e-7,5.386079e-7,0.000010216253,0.000018862424],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800557,0.00008156155,0.0007906593,0.00028914085,0.00052970374,0.00030338528],"domain_scores_gemma":[0.9969303,0.00066052674,0.0012211939,0.00032989588,0.00066440046,0.00019366018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009189085,0.00021508777,0.0006185134,0.0007879864,0.0001520357,0.00011996083,0.00057862786,0.00014293604,9.84738e-7],"category_scores_gemma":[0.0011713156,0.00017918773,0.00010056186,0.0011668429,0.00015815881,0.0006419329,0.00014855513,0.0005289355,4.544877e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011916007,0.0014391267,0.4491132,0.00062085694,0.00080726907,0.00072004274,0.0012483229,0.046681993,0.22942272,0.0074847722,0.00023005938,0.26104006],"study_design_scores_gemma":[0.0018861665,0.0002688193,0.115235984,0.00014577409,0.000048156788,0.00045016,0.000017740174,0.88100183,0.0004505633,0.00026756164,0.000043100437,0.0001841336],"about_ca_topic_score_codex":0.000034273202,"about_ca_topic_score_gemma":0.000008479312,"teacher_disagreement_score":0.83431983,"about_ca_system_score_codex":0.0000675117,"about_ca_system_score_gemma":0.00014795657,"threshold_uncertainty_score":0.73070633},"labels":[],"label_agreement":null},{"id":"W2095540866","doi":"10.1109/cec.2010.5586311","title":"A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence; Defence Research and Development Canada","funders":"","keywords":"Mathematical optimization; Survivability; Heuristic; Genetic algorithm; Path (computing); Computer science; Motion planning; Population; Mathematics; Artificial intelligence","score_opus":0.016100304981535238,"score_gpt":0.24870608674554223,"score_spread":0.23260578176400698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095540866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009786047,0.000035579986,0.98805594,0.00023233869,0.0010265117,0.0004181371,0.000010076032,0.00012504902,0.00031034232],"genre_scores_gemma":[0.022497147,0.0000025013114,0.9766934,0.0001789516,0.00024631908,0.000088438086,0.00000920451,0.000016076992,0.00026798426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819374,0.000048358077,0.00032436286,0.000602931,0.0003048749,0.00052574347],"domain_scores_gemma":[0.9989797,0.00018276808,0.00006446361,0.00061557273,0.000018318056,0.0001392052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048792182,0.00019718673,0.00021446872,0.00014414142,0.000098128556,0.00009116902,0.0008348415,0.0000858847,0.00001878843],"category_scores_gemma":[0.000059760696,0.00018530594,0.000066027256,0.00011110181,0.00004548021,0.00019931965,0.00021594443,0.0002928041,0.000044660108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028434595,0.0003538672,0.002736457,0.000026728825,0.00004995419,0.0009826496,0.0021650803,0.17643794,0.003808904,0.0030578098,0.0041046184,0.80624753],"study_design_scores_gemma":[0.0010129105,0.00012316254,0.0043840613,0.00001725135,0.000004240613,0.000061389,0.000023017348,0.9890462,0.0007186284,0.0014942327,0.0028501062,0.0002647956],"about_ca_topic_score_codex":0.00013789548,"about_ca_topic_score_gemma":0.0000014944316,"teacher_disagreement_score":0.81260824,"about_ca_system_score_codex":0.00006259754,"about_ca_system_score_gemma":0.00009312556,"threshold_uncertainty_score":0.7556557},"labels":[],"label_agreement":null},{"id":"W2095668486","doi":"10.1109/icca.2010.5524083","title":"Multi-thread technology based autonomous underwater vehicle","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Sonar; Thread (computing); Computer science; Obstacle avoidance; Underwater; Real-time computing; Remotely operated underwater vehicle; Obstacle; Mobile robot; Control engineering; Simulation; Robot; Engineering; Artificial intelligence","score_opus":0.018443197324203183,"score_gpt":0.2565085214056498,"score_spread":0.2380653240814466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095668486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021041224,0.0000080975415,0.97164756,0.0042207753,0.0006515964,0.000102987164,6.900369e-7,0.0011719248,0.0011551593],"genre_scores_gemma":[0.2930232,1.05034644e-7,0.7058007,0.00041049236,0.000022712602,0.000010915963,9.1409015e-7,0.000007728468,0.00072321424],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989088,0.00001605034,0.00016347504,0.0004034686,0.0001419562,0.0003662336],"domain_scores_gemma":[0.9989199,0.00004937467,0.00004371429,0.0008384482,0.00005710099,0.000091434056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018141294,0.0001325535,0.00013190863,0.00020542451,0.00009844567,0.00009227364,0.0010381958,0.00017088825,0.000048815502],"category_scores_gemma":[0.00004033443,0.000110085195,0.000037919144,0.0003525019,0.00008809605,0.00019241184,0.0001995684,0.00033087455,0.00063480577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009808417,0.0012436995,0.044454914,0.000037413593,0.00008391586,0.0007230804,0.0007899765,0.010669511,0.48759028,0.1108298,0.0034854193,0.3400822],"study_design_scores_gemma":[0.00047760658,0.000048993767,0.0037433386,0.000004350915,0.0000022556042,0.00003383813,0.000006584167,0.953783,0.037707195,0.001542881,0.0024530531,0.0001969108],"about_ca_topic_score_codex":0.00003818458,"about_ca_topic_score_gemma":0.000015130483,"teacher_disagreement_score":0.9431135,"about_ca_system_score_codex":0.000020334382,"about_ca_system_score_gemma":0.0000924372,"threshold_uncertainty_score":0.81593555},"labels":[],"label_agreement":null},{"id":"W2095846163","doi":"10.1109/ijcnn.2009.5178773","title":"Speed control of a mobile robot using neural networks and fuzzy logic","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mobile robot; Artificial neural network; Computer science; Fuzzy logic; Motion planning; Fuzzy control system; Artificial intelligence; Mobile robot navigation; Robot; Control system; Control engineering; Computational intelligence; Path (computing); Robot control; Engineering","score_opus":0.02421154372734251,"score_gpt":0.2666118251362942,"score_spread":0.2424002814089517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095846163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026661724,0.00030610524,0.9718898,0.00018131957,0.00015526591,0.0001521079,3.6451965e-7,0.00009478202,0.00055854843],"genre_scores_gemma":[0.8195372,0.0000022559482,0.17990592,0.00047973596,0.000044737684,5.668625e-7,3.0844595e-7,0.0000026510734,0.000026625565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991148,0.000048604383,0.00020721671,0.0002431934,0.00013846952,0.0002477184],"domain_scores_gemma":[0.9994403,0.000072340474,0.00008835267,0.00027650796,0.000044177898,0.0000782934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019667142,0.00010896455,0.00022135155,0.000056077286,0.000049471728,0.00005850432,0.000309957,0.000053151332,0.0000028637444],"category_scores_gemma":[0.000015796677,0.0000892871,0.00003472124,0.00020655915,0.000037844653,0.00019742448,0.000055263532,0.0000932148,0.0000013412568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000449945,0.000028320888,0.0008555247,0.0000019358486,0.000005940451,0.000023636276,0.000073068564,0.9787201,0.0008629386,0.0016676757,0.000024875877,0.017731478],"study_design_scores_gemma":[0.00043525832,0.00018152139,0.0066905334,0.0000094861,0.000006567912,0.00006730701,0.000009620197,0.99169874,0.000059022692,0.00073752424,0.0000025015368,0.0001019048],"about_ca_topic_score_codex":0.00002521552,"about_ca_topic_score_gemma":2.1945432e-7,"teacher_disagreement_score":0.79287547,"about_ca_system_score_codex":0.000011550338,"about_ca_system_score_gemma":0.000014584524,"threshold_uncertainty_score":0.36410224},"labels":[],"label_agreement":null},{"id":"W2095888684","doi":"10.1109/iros.1991.174507","title":"Avoidance of unknown obstacles using proximity fields","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Obstacle avoidance; Computer science; Scheme (mathematics); Collision avoidance; Computer vision; Artificial intelligence; Object (grammar); Point (geometry); Simple (philosophy); Obstacle; Manipulator (device); Robot; Real-time computing; Mobile robot; Mathematics; Computer security; Geography; Collision","score_opus":0.06199547389133598,"score_gpt":0.25468694404979186,"score_spread":0.1926914701584559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095888684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034888737,0.00017033584,0.95723623,0.00026262054,0.00023869936,0.00007128143,4.9662754e-7,0.000121583216,0.0070100175],"genre_scores_gemma":[0.4664912,0.000004166342,0.5325688,0.00009328464,0.000023574088,0.0000011207474,9.973682e-8,0.0000030357494,0.00081470516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918216,0.000035315203,0.00018126376,0.00021481921,0.00019551598,0.00019094763],"domain_scores_gemma":[0.9993114,0.000078674675,0.00007493696,0.00043070593,0.000054787324,0.000049517857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012826773,0.00008203454,0.00013049037,0.00004842457,0.00005450278,0.000032193835,0.00053160486,0.000052221127,0.00003468474],"category_scores_gemma":[0.000057983325,0.000071443006,0.0000372887,0.00025252075,0.000036980044,0.00033938314,0.00012411817,0.00009330479,0.000022669195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012024896,0.0016620562,0.03149242,0.00046013927,0.00020860991,0.0003792879,0.019898592,0.1706042,0.022083268,0.2801115,0.0149535,0.4581344],"study_design_scores_gemma":[0.000108870605,0.00003091827,0.00095010566,0.00002631105,0.0000022923482,0.000016693302,0.000009285482,0.99219555,0.0054915287,0.00083426293,0.00022868867,0.000105469895],"about_ca_topic_score_codex":0.000042247342,"about_ca_topic_score_gemma":0.0000016760958,"teacher_disagreement_score":0.8215914,"about_ca_system_score_codex":0.000016714384,"about_ca_system_score_gemma":0.000014565313,"threshold_uncertainty_score":0.29133612},"labels":[],"label_agreement":null},{"id":"W2095899858","doi":"","title":"Search and Surveillance in emergency situations - A GIS-based approach to construct optimal visibility graphs","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Mitacs","keywords":"Visibility; Raster graphics; Construct (python library); Terrain; Computer science; Geographic information system; Integer programming; Theoretical computer science; Data mining; Algorithm; Geography; Artificial intelligence; Cartography","score_opus":0.027330472676148326,"score_gpt":0.27691217656309375,"score_spread":0.24958170388694542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095899858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28703654,0.000018393264,0.7099291,0.00074660924,0.000078877354,0.00038583,0.0000026237446,0.00008665359,0.0017153448],"genre_scores_gemma":[0.5246618,0.0000015089196,0.47518757,0.000055124307,0.0000053254435,0.00003631698,0.000002442056,0.000003313107,0.00004660962],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838084,0.00014947599,0.00028696813,0.0005591963,0.0002705656,0.00035297155],"domain_scores_gemma":[0.99901223,0.00010829084,0.000026480677,0.0005436421,0.0001244958,0.0001848606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006321717,0.00013068019,0.00017614785,0.00021019441,0.00008443779,0.00010903445,0.00048662763,0.000049919327,0.00003510724],"category_scores_gemma":[0.000100823105,0.00011792357,0.000029517756,0.0008996379,0.000064870845,0.00028928116,0.0001708639,0.00013587829,0.000050413386],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083084615,0.00044695177,0.73849857,0.00006958831,0.000022119822,0.000009259361,0.0024542843,0.19973026,0.0008486459,0.017437432,0.0014380487,0.039036557],"study_design_scores_gemma":[0.00017665245,0.000035310222,0.30025804,0.0000032158614,3.538551e-7,0.0000046086584,0.000043718774,0.6985183,0.00005284242,0.00078204536,0.0000027705212,0.00012217228],"about_ca_topic_score_codex":0.00083222555,"about_ca_topic_score_gemma":0.000019622057,"teacher_disagreement_score":0.498788,"about_ca_system_score_codex":0.000028552924,"about_ca_system_score_gemma":0.000085975764,"threshold_uncertainty_score":0.48087835},"labels":[],"label_agreement":null},{"id":"W2096411676","doi":"10.1109/iros.1998.724783","title":"Complete real-time path planning during sensor-based discovery","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Occupancy grid mapping; Motion planning; Computation; Computer science; Grid; Path (computing); Representation (politics); Algorithm; Gradient descent; Mathematical optimization; Mobile robot; Mathematics; Robot; Artificial intelligence","score_opus":0.03328112268599148,"score_gpt":0.23706074037863573,"score_spread":0.20377961769264424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096411676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09116341,0.000050266204,0.8796557,0.00081698975,0.0003607355,0.00018750963,0.000013545641,0.001279973,0.02647186],"genre_scores_gemma":[0.30812,0.0000051662414,0.68174756,0.0004878756,0.00018328185,0.000012167003,0.0000120034365,0.00003525941,0.009396697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981554,0.000085653526,0.00028155954,0.00053637614,0.00040823,0.0005327658],"domain_scores_gemma":[0.9987527,0.00020074702,0.00010148859,0.00076485437,0.000036107016,0.0001441145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001700644,0.0002199247,0.0002481658,0.00013952347,0.00022450111,0.0003127596,0.0007360203,0.000063479165,0.00007507765],"category_scores_gemma":[0.000036194553,0.00019869728,0.00008106891,0.00033324835,0.000043219447,0.00064084044,0.00017692862,0.00016091457,0.0007279528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054004355,0.00094205193,0.046756733,0.00029165257,0.0002468088,0.008268515,0.0065711304,0.6424613,0.23241803,0.009394856,0.045664452,0.006930486],"study_design_scores_gemma":[0.00050073647,0.00005544137,0.010744673,0.00008066677,0.0000045477154,0.000064203814,0.000021075512,0.98697656,0.0009088019,0.000049966467,0.000257444,0.0003358575],"about_ca_topic_score_codex":0.000049172068,"about_ca_topic_score_gemma":5.6880967e-8,"teacher_disagreement_score":0.3445153,"about_ca_system_score_codex":0.000062361076,"about_ca_system_score_gemma":0.00001956373,"threshold_uncertainty_score":0.93566036},"labels":[],"label_agreement":null},{"id":"W2097100778","doi":"10.1007/978-3-642-33515-0_40","title":"Modeling and Autonomous Control of Multiple Mobile Manipulators Handling Rigid Objects","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Nonholonomic system; MATLAB; Mobile robot; Terrain; Control engineering; Mobile manipulator; Unmanned ground vehicle; Simulation; Artificial intelligence; Robot; Engineering","score_opus":0.0189640485867582,"score_gpt":0.23666944338876683,"score_spread":0.21770539480200862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097100778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012266869,0.00266847,0.9938739,0.000031560816,0.0013342577,0.0004924431,0.000006280075,0.00014743989,0.00021899072],"genre_scores_gemma":[0.615593,0.000021017757,0.38403752,0.00011643267,0.00017923993,0.0000077410905,0.0000015148288,0.000022231066,0.000021285436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966009,0.000037615955,0.0006460078,0.0012042376,0.00075923407,0.00075200544],"domain_scores_gemma":[0.99760383,0.0005783729,0.00031670663,0.001062567,0.00020045742,0.00023806482],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010075718,0.0005165652,0.00075548835,0.00068810786,0.00021342952,0.00023843828,0.0018113113,0.00033024116,0.000003056629],"category_scores_gemma":[0.00009949786,0.00048432057,0.00010698755,0.00030723962,0.00042547056,0.00059860357,0.0008149011,0.0006452309,0.000009139967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034956724,0.000018574065,0.00031930246,0.000044591972,0.000013783442,0.000027379414,0.0011489693,0.83869046,0.00015974745,0.0009223832,3.4551982e-7,0.15865096],"study_design_scores_gemma":[0.00044998628,0.00012481592,0.000059897844,0.00037533825,0.0000151050535,0.000088508416,3.7925565e-7,0.9908291,0.000596876,0.0069474634,0.000023275492,0.0004892193],"about_ca_topic_score_codex":0.00007025304,"about_ca_topic_score_gemma":0.0000121579615,"teacher_disagreement_score":0.6143663,"about_ca_system_score_codex":0.00017859641,"about_ca_system_score_gemma":0.00034729563,"threshold_uncertainty_score":0.99976087},"labels":[],"label_agreement":null},{"id":"W2097112965","doi":"10.1109/crv.2012.46","title":"Multi-Robot Repeated Area Coverage: Performance Optimization Under Various Visual Ranges","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robot; Computer science; Metric (unit); Range (aeronautics); Workload; Set (abstract data type); Benchmark (surveying); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Engineering","score_opus":0.03337386896277643,"score_gpt":0.27036406782499084,"score_spread":0.2369901988622144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097112965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007429963,0.000104183346,0.98940706,0.00023931832,0.0007225051,0.00015498094,6.84857e-7,0.0005184285,0.0014228541],"genre_scores_gemma":[0.51019806,0.00001908718,0.48839167,0.00030152837,0.000053647513,0.000008038573,0.000009323006,0.0000106198295,0.0010080198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985838,0.000073145486,0.00023429215,0.00032408602,0.00029878048,0.00048588557],"domain_scores_gemma":[0.9991087,0.00010371969,0.000099025594,0.00043407074,0.00009323964,0.00016128947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035995652,0.00018555403,0.00017317734,0.000108001615,0.00016820416,0.00013029596,0.00044250424,0.00009995612,0.000039702907],"category_scores_gemma":[0.00005938186,0.00016090117,0.000038203678,0.00038062996,0.00003039635,0.0011120089,0.00017391595,0.0001507832,0.00012867538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044107396,0.00014994976,0.007149389,0.0000078736075,0.000019133084,0.0000052129003,0.0004452298,0.98793435,0.00024596057,0.00015635001,0.00020450895,0.0036776478],"study_design_scores_gemma":[0.00053777674,0.000058274894,0.03491026,0.000016467788,0.0000075974826,0.00005502704,0.000011289838,0.9633092,0.00081279,0.000005471316,0.00004557562,0.00023021699],"about_ca_topic_score_codex":0.0000326563,"about_ca_topic_score_gemma":5.130599e-7,"teacher_disagreement_score":0.5027681,"about_ca_system_score_codex":0.000075840464,"about_ca_system_score_gemma":0.00004773589,"threshold_uncertainty_score":0.6561359},"labels":[],"label_agreement":null},{"id":"W2097476511","doi":"10.1109/3477.931512","title":"Neural network approaches to dynamic collision-free trajectory generation","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Guelph","funders":"","keywords":"Trajectory; Artificial neural network; Computer science; Workspace; Robot; Control theory (sociology); Collision; State space; Artificial intelligence; Mathematics; Control (management)","score_opus":0.07123373920932854,"score_gpt":0.24040196012512427,"score_spread":0.16916822091579573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097476511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06627691,0.0006692034,0.9255345,0.00075440004,0.004015843,0.00094745413,0.000030432084,0.00036514178,0.0014061187],"genre_scores_gemma":[0.96817976,0.00021442026,0.024413949,0.0003331302,0.0005217184,0.00020416683,0.0000117120835,0.00007202756,0.0060491078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962821,0.00032256916,0.0007501896,0.0010772836,0.00075229106,0.00081554207],"domain_scores_gemma":[0.99755585,0.00019773473,0.00018819464,0.0014401915,0.00010193143,0.00051611464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005526612,0.0005208188,0.00052125455,0.00027483643,0.00047057835,0.0005697492,0.0010156938,0.0002661236,0.000010412249],"category_scores_gemma":[0.000011717464,0.0005272184,0.00013808701,0.00076124683,0.000113034585,0.00024407508,0.000026076794,0.00044096567,0.00013479828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022871538,0.00017882194,0.00007527992,0.000028993625,0.000070698894,0.00005493996,0.00095555035,0.9811056,0.00017868321,0.0012625143,0.004051809,0.012014186],"study_design_scores_gemma":[0.0007522827,0.00046213486,0.0007044647,0.00013052532,0.00006064764,0.00026097224,0.000097194155,0.990911,0.00024242594,0.00013459826,0.0055864677,0.0006572649],"about_ca_topic_score_codex":0.00011200218,"about_ca_topic_score_gemma":0.0001445069,"teacher_disagreement_score":0.90190285,"about_ca_system_score_codex":0.00014866336,"about_ca_system_score_gemma":0.00006933779,"threshold_uncertainty_score":0.99971795},"labels":[],"label_agreement":null},{"id":"W2097891227","doi":"10.1109/robot.2004.1302517","title":"A fuzzy logic approach to reactive navigation of behavior-based mobile robots","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Fuzzy logic; Robot; Obstacle avoidance; Computer science; Mobile robot navigation; State (computer science); Control engineering; Fuzzy control system; Artificial intelligence; Robot control; Control theory (sociology); Engineering; Control (management); Algorithm","score_opus":0.029655773797642896,"score_gpt":0.2814987240465215,"score_spread":0.2518429502488786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097891227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04388146,0.000011235997,0.94745505,0.00009346503,0.00011834082,0.00047899628,0.0000022178913,0.00017734247,0.0077819154],"genre_scores_gemma":[0.46170455,7.6172746e-8,0.538049,0.00009773551,0.000011011587,0.000094740164,0.000006997125,0.0000044567128,0.000031449075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988111,0.00003450961,0.00021768267,0.0003878733,0.000325234,0.00022359464],"domain_scores_gemma":[0.9991356,0.0000352742,0.00009417277,0.0005095453,0.00011681843,0.00010856427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020938033,0.00012631332,0.00017376791,0.00011454137,0.000048295202,0.00003663813,0.0005547236,0.00006805128,0.0000012244532],"category_scores_gemma":[0.000020669473,0.00011130652,0.000052138515,0.000552746,0.000032161915,0.00022070919,0.00009015865,0.000103061466,0.00004619286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008008556,0.00071372156,0.00036610296,0.000016064312,0.000008158547,0.00002300478,0.0012345164,0.96031153,0.00754156,0.022778062,0.00003958504,0.006959662],"study_design_scores_gemma":[0.0072511514,0.0071537998,0.08040235,0.00059808785,0.00014301512,0.0003607096,0.0012132315,0.5449682,0.31878427,0.03601882,0.00012769281,0.0029786453],"about_ca_topic_score_codex":0.00016433971,"about_ca_topic_score_gemma":3.4384132e-7,"teacher_disagreement_score":0.4178231,"about_ca_system_score_codex":0.00010277516,"about_ca_system_score_gemma":0.00011340093,"threshold_uncertainty_score":0.45389482},"labels":[],"label_agreement":null},{"id":"W2098052437","doi":"10.1109/crv.2012.62","title":"Socially-Driven Collective Path Planning for Robot Missions","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Planner; Robot; Parameterized complexity; Path (computing); Computer science; Mathematical optimization; Control (management); Selection (genetic algorithm); Operations research; Simulation; Artificial intelligence; Engineering; Computer network; Mathematics; Algorithm","score_opus":0.061869340417446524,"score_gpt":0.31818254589908745,"score_spread":0.25631320548164094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098052437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040578877,0.000094883995,0.9866823,0.00072974287,0.0007696569,0.00028984432,0.000004346461,0.0002951783,0.01072825],"genre_scores_gemma":[0.07993812,7.2272803e-7,0.9156874,0.00035835456,0.000287459,0.00006008251,0.000004472988,0.000013401754,0.0036499859],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875695,0.000052165586,0.00017213327,0.0002600114,0.00020424699,0.0005544719],"domain_scores_gemma":[0.99898326,0.000358046,0.000079287376,0.00027645708,0.00008237935,0.00022058922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029713495,0.00013849181,0.00017780556,0.00008086647,0.0003766391,0.00008073098,0.0005292014,0.0000846648,0.000012106174],"category_scores_gemma":[0.00014542208,0.0001201532,0.00007888657,0.00028137266,0.000025067235,0.00042490714,0.00015641481,0.000108964334,0.000046564335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008718413,0.0014096682,0.06706853,0.000102788865,0.0005056751,0.00007204234,0.093564756,0.06463304,0.008313586,0.26383668,0.45401847,0.046387576],"study_design_scores_gemma":[0.001391685,0.00040410095,0.04043461,0.00011256657,0.00003633489,0.00005444855,0.0006413741,0.9353593,0.0019758923,0.0076408475,0.011070438,0.00087843183],"about_ca_topic_score_codex":0.0000090423,"about_ca_topic_score_gemma":1.9881278e-7,"teacher_disagreement_score":0.8707262,"about_ca_system_score_codex":0.00010913326,"about_ca_system_score_gemma":0.00027487054,"threshold_uncertainty_score":0.48997054},"labels":[],"label_agreement":null},{"id":"W2098089944","doi":"10.1109/aim.2010.5695875","title":"A smoothness-preserving waypoints follower for mobile platforms","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Tree traversal; Smoothness; Motion planning; Mobile robot; Path (computing); Computer science; Motion control; Motion (physics); Robot; Control (management); Control theory (sociology); Simulation; Artificial intelligence; Algorithm; Mathematics","score_opus":0.011641310050381232,"score_gpt":0.2691817327322487,"score_spread":0.2575404226818675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098089944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05780888,0.000012965088,0.93200475,0.00021121443,0.0020402456,0.00043035613,0.0000029985358,0.00042630418,0.007062264],"genre_scores_gemma":[0.16348761,5.6123633e-7,0.83226025,0.00029242464,0.00015390725,0.00019642938,0.0000037751533,0.000017511424,0.0035875353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867636,0.000004970463,0.00021537708,0.00041921195,0.00024650304,0.0004375691],"domain_scores_gemma":[0.9986015,0.00021680746,0.000064119595,0.0008852554,0.000092635775,0.00013969005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004304394,0.00015161866,0.0001726009,0.0000906065,0.00015720296,0.00017394574,0.001405051,0.000107635824,0.000075466836],"category_scores_gemma":[0.00014964351,0.000118274664,0.00009678332,0.00021730088,0.00002904827,0.00068931776,0.00036745548,0.0001989403,0.000121999015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088574125,0.0012015618,0.019742947,0.00029725212,0.00032762895,0.0002522177,0.009562574,0.009056176,0.08109369,0.37849715,0.12840939,0.37147084],"study_design_scores_gemma":[0.0010407186,0.00018996,0.002477629,0.000023512217,0.000007749301,0.000052992298,0.00005996243,0.94350976,0.008755136,0.027541574,0.015839547,0.00050147093],"about_ca_topic_score_codex":0.000030135776,"about_ca_topic_score_gemma":0.0000110329975,"teacher_disagreement_score":0.93445355,"about_ca_system_score_codex":0.000013580759,"about_ca_system_score_gemma":0.00005940264,"threshold_uncertainty_score":0.4823101},"labels":[],"label_agreement":null},{"id":"W2098125719","doi":"10.1007/s10846-010-9394-y","title":"Path Planning for Autonomous Mobile Robots Using the Boundary Integral Equation Method","year":2010,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University; National Research Council Canada","funders":"","keywords":"Mobile robot; Motion planning; Boundary (topology); Computer science; Path (computing); Robot; Control engineering; Mathematics; Mathematical analysis; Engineering; Artificial intelligence; Computer network","score_opus":0.06582130135411542,"score_gpt":0.34831304113457895,"score_spread":0.28249173978046355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098125719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037004268,0.0013262411,0.98167914,0.00018617368,0.012304908,0.00068533485,0.00000196691,0.00006047422,0.00005535999],"genre_scores_gemma":[0.16481268,0.000006840875,0.8336049,0.000085672975,0.0012887164,0.000033334567,0.0000022593492,0.00003549001,0.00013015534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670184,0.00034256763,0.0013863586,0.00034382744,0.00071183156,0.0005136048],"domain_scores_gemma":[0.9956519,0.0012797696,0.0014582111,0.00064324867,0.0007631441,0.00020372482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045062616,0.00030565585,0.0006188408,0.0003173294,0.0003833298,0.0007324319,0.0016535643,0.00017795007,0.000004272845],"category_scores_gemma":[0.00053238316,0.00020240976,0.00032795776,0.00038612835,0.00007534752,0.00062247325,0.00014440529,0.00081842474,0.000009360178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015010236,0.00006484604,0.00024513662,0.000053205433,0.00010716677,0.000046741123,0.00247904,0.9834246,0.0020958763,0.002596881,0.000455883,0.008415615],"study_design_scores_gemma":[0.00025071047,0.00038435071,0.000096718744,0.00040004015,0.00007083873,0.0022774828,0.0010328472,0.9924368,0.0009848,0.00068925146,0.0011417872,0.00023432128],"about_ca_topic_score_codex":0.00008184338,"about_ca_topic_score_gemma":6.7128576e-7,"teacher_disagreement_score":0.16111225,"about_ca_system_score_codex":0.00024060078,"about_ca_system_score_gemma":0.00053027057,"threshold_uncertainty_score":0.8254031},"labels":[],"label_agreement":null},{"id":"W2098297052","doi":"10.1109/icsmc.1988.754343","title":"An Intelligent Control of Roving Robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blackboard (design pattern); Robot; Computer science; Blackboard system; Control engineering; Mobile robot; Control system; Control (management); Knowledge-based systems; Artificial intelligence; Engineering; Software engineering","score_opus":0.015566922145628364,"score_gpt":0.2654154180914756,"score_spread":0.24984849594584727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098297052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022242537,0.00006440381,0.9947174,0.0004930676,0.00014163945,0.0000677036,4.4430388e-7,0.00014913687,0.002141928],"genre_scores_gemma":[0.5644593,9.2504314e-7,0.43515575,0.00021859418,0.000044889315,0.0000013704234,2.6865112e-7,0.000002659403,0.00011623917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991716,0.00004136335,0.00020695635,0.00020784677,0.0001870923,0.00018512626],"domain_scores_gemma":[0.9992571,0.00006777263,0.00006422309,0.0004680682,0.00005400468,0.00008885979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000260579,0.00007702066,0.00013092438,0.00006677214,0.000030510751,0.00004111596,0.0006914759,0.000031990356,0.000022113156],"category_scores_gemma":[0.000025826657,0.00006574336,0.000033050554,0.00013023644,0.00001954261,0.00037501106,0.000051208655,0.00006167648,0.00006733941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034621225,0.00017805667,0.0023985202,0.0000065024187,0.00002108231,0.000011187122,0.0009898177,0.5686473,0.0044176597,0.03567967,0.0003434293,0.38730332],"study_design_scores_gemma":[0.00016102043,0.00008102658,0.0018916386,0.000008871106,0.0000024591964,0.000009704377,0.000014919881,0.9896387,0.007684644,0.00018487575,0.00023587917,0.00008627196],"about_ca_topic_score_codex":0.000033203596,"about_ca_topic_score_gemma":0.0000023931507,"teacher_disagreement_score":0.56223506,"about_ca_system_score_codex":0.000023207578,"about_ca_system_score_gemma":0.000034148572,"threshold_uncertainty_score":0.26809365},"labels":[],"label_agreement":null},{"id":"W2098519927","doi":"10.1109/tro.2006.870668","title":"Modified Newton's method applied to potential field-based navigation for mobile robots","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Gradient descent; Obstacle; Newton's method; Oscillation (cell signaling); Descent (aeronautics); Potential field; Mobile robot; Computer science; Control theory (sociology); Field (mathematics); Obstacle avoidance; Robot; Mathematical optimization; Control engineering; Artificial intelligence; Mathematics; Engineering; Aerospace engineering; Control (management); Physics; Artificial neural network","score_opus":0.015179673504134034,"score_gpt":0.2785286964162237,"score_spread":0.26334902291208967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098519927","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022883834,0.000005748478,0.995387,0.0013473862,0.0014333624,0.0010089404,0.000020514904,0.00041345923,0.0001547357],"genre_scores_gemma":[0.17278314,3.708501e-7,0.8258122,0.00052966736,0.00011696326,0.00030357298,0.000014630257,0.000029096978,0.00041033005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806184,0.00006421687,0.00039381266,0.00061352854,0.00039480338,0.00047178514],"domain_scores_gemma":[0.99854076,0.00038810368,0.000105502666,0.0006722881,0.00013796591,0.00015537607],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002803941,0.0002736531,0.0002873894,0.0002511261,0.00033370525,0.00016119136,0.0006026178,0.0001874192,0.0000049583246],"category_scores_gemma":[0.00000516083,0.00029423952,0.00017207552,0.00054159673,0.000019732743,0.00016186458,0.0000037121968,0.00024845422,0.000045903114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004070661,0.00020353193,5.011136e-7,0.000019036335,0.000016198908,0.0000070589135,0.00005800069,0.9748513,0.0030358084,0.0013008465,0.0005896855,0.019877324],"study_design_scores_gemma":[0.0008250925,0.00032930373,0.000020127585,0.000028898801,0.00004380363,0.000009616777,0.000009698313,0.9473703,0.04982987,0.0011393667,0.000070114846,0.0003238054],"about_ca_topic_score_codex":0.00007592883,"about_ca_topic_score_gemma":0.000004762435,"teacher_disagreement_score":0.1725543,"about_ca_system_score_codex":0.0001077758,"about_ca_system_score_gemma":0.00010785125,"threshold_uncertainty_score":0.99995095},"labels":[],"label_agreement":null},{"id":"W2098596292","doi":"10.1109/robot.2002.1013701","title":"Real-time collision-free motion planning of nonholonomic robots using a neural dynamics based approach","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Kinematics; Nonholonomic system; Computer science; Heading (navigation); Motion planning; Control theory (sociology); Robot; Robot kinematics; Robot control; Obstacle avoidance; Obstacle; Collision; Control engineering; Artificial intelligence; Engineering; Control (management); Physics; Classical mechanics","score_opus":0.02986413434693316,"score_gpt":0.25870951141596893,"score_spread":0.22884537706903577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098596292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118484125,0.000010394019,0.8761524,0.000040103474,0.0002141908,0.00018140598,0.000004981546,0.00016797631,0.0047444333],"genre_scores_gemma":[0.20988648,3.2457876e-7,0.78993845,0.00003145498,0.000015541316,0.0000037179777,0.000010946014,0.000015273887,0.00009780279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998281,0.00017595445,0.00038982416,0.0004765297,0.00031523156,0.00036146285],"domain_scores_gemma":[0.99849486,0.00016217446,0.00022131417,0.0009285752,0.00008487953,0.00010817789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057701126,0.00019815142,0.0003020094,0.0002194982,0.00012204397,0.0000839214,0.00086749264,0.000109363405,0.0000052118075],"category_scores_gemma":[0.00014148453,0.0001920307,0.0000795447,0.00051776145,0.00005159207,0.00036431194,0.00014906125,0.00013769773,0.000008424597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044439425,0.00006895475,0.0015359902,0.000013421498,0.000008189368,0.000013699178,0.00011213755,0.99468726,0.0010542313,0.0021341902,0.000076313394,0.00029117512],"study_design_scores_gemma":[0.00053263124,0.00005991366,0.0018875657,0.00003216573,0.0000099945755,0.000054399945,0.000027401318,0.99626744,0.0007375179,0.00019105928,0.0000010407102,0.00019886826],"about_ca_topic_score_codex":0.000104944644,"about_ca_topic_score_gemma":3.4311836e-7,"teacher_disagreement_score":0.09140235,"about_ca_system_score_codex":0.00016707445,"about_ca_system_score_gemma":0.00013619065,"threshold_uncertainty_score":0.78307843},"labels":[],"label_agreement":null},{"id":"W2098789596","doi":"10.1109/cca.2005.1507124","title":"A fuzzy logic-based reactive navigation algorithm for mobile robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Fuzzy logic; Robot; Mobile robot navigation; Computer science; Obstacle; Obstacle avoidance; Computer vision; Navigation system; Algorithm; Feature (linguistics); Artificial intelligence; Real-time computing; Robot control","score_opus":0.022328252931447534,"score_gpt":0.2889832436219081,"score_spread":0.26665499069046056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098789596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028258486,0.00005659825,0.9946038,0.00079571386,0.00027798256,0.0005673957,0.0000079035035,0.00044883045,0.0029591615],"genre_scores_gemma":[0.0097471215,7.137729e-7,0.9883664,0.0007475223,0.00020312834,0.00025233152,0.000030527855,0.000012320629,0.00063989346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998624,0.000040997314,0.0002323519,0.00047947667,0.0002653002,0.0003578583],"domain_scores_gemma":[0.9989285,0.00022016047,0.00011030336,0.00046611566,0.00016504644,0.00010986022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033456626,0.00016714395,0.00017633203,0.00008937913,0.00013223726,0.00010931478,0.00055695494,0.00009323829,0.0000062772374],"category_scores_gemma":[0.000030948824,0.00014796726,0.00008532223,0.0002816894,0.000031212086,0.0005175962,0.00006523647,0.00011265073,0.00012745716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004575798,0.00017347852,0.000022552447,0.000007979898,0.00001748688,0.000012236981,0.00033294404,0.116790056,0.00062999735,0.0057910965,0.0019831748,0.87423444],"study_design_scores_gemma":[0.0005877723,0.0002178152,0.00013999046,0.00002100759,0.000006164205,0.000016708806,0.000023032248,0.98695636,0.0068378057,0.0028249733,0.0021496676,0.00021872109],"about_ca_topic_score_codex":0.000021499349,"about_ca_topic_score_gemma":6.8789325e-7,"teacher_disagreement_score":0.8740157,"about_ca_system_score_codex":0.000119812765,"about_ca_system_score_gemma":0.000097880125,"threshold_uncertainty_score":0.603393},"labels":[],"label_agreement":null},{"id":"W2098874884","doi":"10.1109/pacrim.1991.160729","title":"A practical motion planner for PUMA 560","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Planner; Trajectory; Puma; Computer science; Link (geometry); Motion (physics); Base (topology); Robot; Artificial intelligence; Motion planning; Mathematical optimization; Mathematics; Computer network","score_opus":0.10010189711145588,"score_gpt":0.31462319167171604,"score_spread":0.21452129456026015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098874884","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012407605,0.00001210742,0.9844956,0.00675464,0.00038107642,0.0001423754,0.000001143743,0.00024763227,0.007841397],"genre_scores_gemma":[0.032573394,0.0000012357759,0.96265006,0.00055930373,0.000116919735,0.000025220059,0.000001876946,0.00000603032,0.004065981],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919444,0.000024353656,0.00012253429,0.0002574712,0.00016843427,0.00023277699],"domain_scores_gemma":[0.99934554,0.0001946895,0.00003802826,0.00029889512,0.000045353954,0.000077511606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001672143,0.000076931465,0.00008456317,0.000048359623,0.00007433928,0.00009975511,0.00026175697,0.00005227729,0.000055804903],"category_scores_gemma":[0.00016635876,0.00006390801,0.000036979913,0.00012427868,0.000014242157,0.00040979407,0.00005725416,0.00007267571,0.00035886205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009920051,0.00050686835,0.00047599777,0.0000295512,0.00004738872,0.00013480248,0.0013019992,0.002563883,0.00021812005,0.23960711,0.6006308,0.15447356],"study_design_scores_gemma":[0.00026804034,0.000079986996,0.00031364989,0.000004526817,0.0000030068234,0.000082862956,0.000008129342,0.99103427,0.00020012553,0.0009801121,0.0069233463,0.00010195699],"about_ca_topic_score_codex":0.0000044462818,"about_ca_topic_score_gemma":2.3001853e-7,"teacher_disagreement_score":0.9884704,"about_ca_system_score_codex":0.000020264015,"about_ca_system_score_gemma":0.000008864312,"threshold_uncertainty_score":0.4612565},"labels":[],"label_agreement":null},{"id":"W2099605854","doi":"10.1109/iros.2006.282504","title":"Vision based trajectory tracking controller for a B21R mobile robot","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Trajectory; Mobile robot; Computer science; Tracking (education); Sonar; Computer vision; Obstacle; Artificial intelligence; Controller (irrigation); Monocular vision; Robot; Obstacle avoidance; Robot control; Tracking system; Control theory (sociology); Control (management); Kalman filter; Geography","score_opus":0.013791285433855235,"score_gpt":0.26544989282677517,"score_spread":0.25165860739291995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099605854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011485761,0.00010764621,0.99570674,0.00023744158,0.00033703525,0.00052911264,0.0000029178386,0.00040220335,0.0015283116],"genre_scores_gemma":[0.42412156,2.1482236e-7,0.5745113,0.00031620066,0.00012523094,0.00011564255,0.0000051200877,0.000012642014,0.00079212215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986362,0.00004804244,0.0002714159,0.00042511633,0.00025122098,0.0003680109],"domain_scores_gemma":[0.99902076,0.00039727017,0.00008109483,0.000341699,0.00009339126,0.000065816435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003956786,0.00015704322,0.00022209597,0.00011476996,0.00013813544,0.00016785828,0.0005006133,0.00007683823,0.0000145284575],"category_scores_gemma":[0.000029639617,0.00013249744,0.00012671092,0.00020022118,0.000025281872,0.00030670667,0.00003461108,0.00008370889,0.00003179445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045280816,0.0005147006,0.00071512366,0.000042210224,0.00002494475,0.000034043354,0.00020623051,0.8415361,0.019750059,0.0055288626,0.015355296,0.116247155],"study_design_scores_gemma":[0.0015354641,0.00022575392,0.0037332978,0.000021349415,0.0000061682103,0.0000052175833,0.000007729236,0.9890359,0.003130932,0.0005083755,0.001593318,0.00019647267],"about_ca_topic_score_codex":0.00006847056,"about_ca_topic_score_gemma":0.0000046010528,"teacher_disagreement_score":0.42297298,"about_ca_system_score_codex":0.00004665136,"about_ca_system_score_gemma":0.00006313442,"threshold_uncertainty_score":0.5403089},"labels":[],"label_agreement":null},{"id":"W2100539185","doi":"10.1002/rob.1035","title":"MPK: An open extensible motion planning kernel","year":2001,"lang":"en","type":"article","venue":"Journal of Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Simon Fraser University","funders":"","keywords":"Extensibility; Kernel (algebra); Motion planning; Computer science; Motion (physics); Path (computing); Software; Artificial intelligence; Software engineering; Programming language; Robot; Mathematics","score_opus":0.07316928883701897,"score_gpt":0.32401891619111584,"score_spread":0.2508496273540969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100539185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013467695,0.00072819984,0.97945505,0.00033558646,0.0044332813,0.00022890339,3.4213843e-7,0.000067213725,0.001283739],"genre_scores_gemma":[0.7870701,0.000018704946,0.21064128,0.00013666185,0.0009951275,0.0000048335346,0.0000015320476,0.000029698496,0.0011020716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742925,0.000299862,0.000864187,0.000311181,0.00069336174,0.00040213304],"domain_scores_gemma":[0.9976016,0.00013618259,0.0009028863,0.0006449908,0.00040067002,0.00031362663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020112528,0.00019975747,0.0005523567,0.0002984807,0.00016687949,0.00092885806,0.0024695818,0.000105006606,0.000004828864],"category_scores_gemma":[0.00014698153,0.00016658673,0.00007764311,0.00050238287,0.000025804999,0.0022798018,0.00026122577,0.00030438107,0.000044258984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016845546,0.00013950483,0.01084976,0.00002130168,0.000058401172,0.0013918992,0.001089318,0.9780965,0.0004730477,0.0014856379,0.002257735,0.0041200817],"study_design_scores_gemma":[0.00097009534,0.00065585325,0.02207971,0.0006990691,0.000031540905,0.012973396,0.00057946274,0.9604268,0.000030688163,0.00046612503,0.00078770047,0.00029955115],"about_ca_topic_score_codex":0.00012021607,"about_ca_topic_score_gemma":5.300029e-7,"teacher_disagreement_score":0.77360237,"about_ca_system_score_codex":0.00013939742,"about_ca_system_score_gemma":0.0001667163,"threshold_uncertainty_score":0.8957},"labels":[],"label_agreement":null},{"id":"W2101344580","doi":"10.1109/tsmcb.2008.921002","title":"Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance","year":2008,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Offset (computer science); Motion planning; Robot; Obstacle; Computer science; Path (computing); Queue; Grid; Algorithm; Mathematical optimization; Real-time computing; Mathematics; Artificial intelligence; Geography","score_opus":0.014841814092926507,"score_gpt":0.21906228964306457,"score_spread":0.20422047555013806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101344580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012043322,0.00086663984,0.9869268,0.002841202,0.0026678971,0.0015615592,0.00017798698,0.0012717297,0.0024818648],"genre_scores_gemma":[0.8162851,0.0015268051,0.10903342,0.012543966,0.0037646843,0.001580303,0.0003215658,0.0015385696,0.053405605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99222463,0.0006234355,0.0015028758,0.0022622764,0.0017896222,0.0015971268],"domain_scores_gemma":[0.9955084,0.0004932205,0.0010612685,0.0021960621,0.00030819362,0.00043286415],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0004644083,0.0014450514,0.0016669852,0.00046932127,0.0008384286,0.0008050536,0.001543301,0.0011176075,0.000007137582],"category_scores_gemma":[0.000006522835,0.0013707615,0.00027291372,0.0007375877,0.0004178853,0.0003322891,0.000028533817,0.002683087,0.0002920008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028131384,0.0006114811,0.0001275411,0.0056567555,0.0017851098,0.016022863,0.008482287,0.83631235,0.0007199402,0.00038789288,0.1147465,0.014865981],"study_design_scores_gemma":[0.0014999656,0.00096993224,0.00018233171,0.007355903,0.00027408174,0.003698911,0.00022073713,0.9629568,0.00014955745,0.000015656677,0.0202067,0.002469431],"about_ca_topic_score_codex":0.00048846076,"about_ca_topic_score_gemma":0.000010623076,"teacher_disagreement_score":0.8778934,"about_ca_system_score_codex":0.0005813372,"about_ca_system_score_gemma":0.00022617435,"threshold_uncertainty_score":0.99982995},"labels":[],"label_agreement":null},{"id":"W2101350081","doi":"10.1109/iros.2006.281873","title":"Where to Build a Door","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Lethbridge","funders":"","keywords":"Polygon (computer graphics); Visibility polygon; Boundary (topology); Simple polygon; Visibility; Computer science; Point (geometry); Combinatorics; Algorithm; Computer graphics (images); Mathematics; Monotone polygon; Computer network; Geometry; Physics; Optics","score_opus":0.009510739770851545,"score_gpt":0.23864388516186494,"score_spread":0.2291331453910134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101350081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015587003,0.000039637944,0.95709276,0.0033839669,0.00024949602,0.000071405026,4.3146747e-7,0.00033705737,0.03726655],"genre_scores_gemma":[0.029416751,3.6507794e-7,0.95855033,0.00071228755,0.000115157614,0.000007420873,4.3083216e-7,0.0000048962875,0.01119235],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992516,0.0000137139705,0.00010485027,0.00024034391,0.00017081988,0.00021867485],"domain_scores_gemma":[0.99946827,0.000029942272,0.000016679665,0.0003845579,0.000029048864,0.00007150779],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009832767,0.000071923765,0.00007488775,0.000055558237,0.00004481808,0.00010458054,0.0005745252,0.000026655709,0.000021833788],"category_scores_gemma":[0.000010067677,0.000060485636,0.000022998816,0.00028906934,0.000006949024,0.00014293243,0.00014107632,0.000046873633,0.0008732452],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003428123,0.00016929641,0.011982637,0.000019673627,0.000013974255,0.00025564383,0.00045785028,0.029069224,0.0032153667,0.25983712,0.6360302,0.0589456],"study_design_scores_gemma":[0.00092620915,0.00033772958,0.093511485,0.00011637143,0.000007843348,0.00019192862,0.000036731763,0.6623027,0.007177835,0.013348428,0.22085541,0.0011873497],"about_ca_topic_score_codex":0.00025895637,"about_ca_topic_score_gemma":0.000019400952,"teacher_disagreement_score":0.6332335,"about_ca_system_score_codex":0.00001918534,"about_ca_system_score_gemma":0.000022012162,"threshold_uncertainty_score":0.9999047},"labels":[],"label_agreement":null},{"id":"W2101355691","doi":"10.1109/icinfa.2011.5949001","title":"Fuzzy logic-based multi-robot cooperation for object-pushing","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Fuzzy logic; Object (grammar); Computer science; Artificial intelligence; Scheme (mathematics); Robotics; Control engineering; Fuzzy control system; Mobile robot; Engineering; Mathematics","score_opus":0.12898077326266605,"score_gpt":0.294442243403227,"score_spread":0.16546147014056095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101355691","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023190469,0.000030348254,0.994469,0.00031468255,0.0005281903,0.0003105273,0.0000016996521,0.00041679258,0.0036968393],"genre_scores_gemma":[0.124377765,4.32359e-7,0.87389934,0.0012745204,0.00004213915,0.000044406454,0.0000063193293,0.000009089061,0.00034596559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989252,0.000045195553,0.00021281623,0.00037970103,0.00013784519,0.0002992198],"domain_scores_gemma":[0.9991482,0.00010748702,0.00006928863,0.00042033507,0.00014297714,0.00011171665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003627409,0.00013636694,0.0001436181,0.00008374675,0.00016372849,0.000111757974,0.00060575176,0.00007461147,0.000013159502],"category_scores_gemma":[0.00021101936,0.00011463202,0.000055069395,0.00019612168,0.000024217845,0.00039393143,0.00006849516,0.00008221149,0.0000923064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014234964,0.0021350791,0.0059684007,0.00020718925,0.00019870386,0.0001708705,0.014493591,0.2779462,0.025651373,0.55742145,0.017559577,0.09810521],"study_design_scores_gemma":[0.0007888992,0.0001657705,0.0026838784,0.000013713187,0.000005565327,0.000004665235,0.000023763921,0.9889128,0.0061847107,0.0008766252,0.00011427708,0.00022529125],"about_ca_topic_score_codex":0.00009499221,"about_ca_topic_score_gemma":0.000007895855,"teacher_disagreement_score":0.71096665,"about_ca_system_score_codex":0.00002859706,"about_ca_system_score_gemma":0.0001141339,"threshold_uncertainty_score":0.4674558},"labels":[],"label_agreement":null},{"id":"W2101664094","doi":"10.1109/ijcnn.2008.4634196","title":"Neural control system of a mobile robot","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mobile robot; Artificial neural network; Computer science; Artificial intelligence; Robot; Motion planning; Mobile robot navigation; Process (computing); Set (abstract data type); Path (computing); Robot control","score_opus":0.015923017277221604,"score_gpt":0.22120376340592884,"score_spread":0.20528074612870723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101664094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016912991,0.000092525275,0.9790344,0.00005915681,0.00033043328,0.00015417895,0.0000012108538,0.00028379876,0.003131309],"genre_scores_gemma":[0.84611326,5.8930334e-7,0.15347117,0.000060800612,0.000027425851,0.0000143323,2.6596328e-7,0.0000036503204,0.00030852604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991262,0.00004850842,0.00021951682,0.00019592381,0.00022094257,0.00018888652],"domain_scores_gemma":[0.9992884,0.00008649416,0.000078094745,0.00041693702,0.000063400934,0.00006666613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012968162,0.000081946375,0.0002013427,0.000056646262,0.000056189627,0.000012763788,0.00055050576,0.00003290507,0.000003945535],"category_scores_gemma":[0.000013031521,0.000066092296,0.00005185815,0.00019233343,0.000038401122,0.00015982617,0.00006340367,0.00005860092,0.000049604416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029113697,0.00040217242,0.034125295,0.00021770259,0.00015760485,0.00145832,0.00473271,0.8839238,0.0067450893,0.03946254,0.006770611,0.021975039],"study_design_scores_gemma":[0.00043481024,0.00011231147,0.0043443753,0.0000137338575,0.0000027315446,0.0003405964,0.00003263247,0.9938327,0.000755767,0.0000072320604,0.000038435286,0.000084641935],"about_ca_topic_score_codex":0.00005771067,"about_ca_topic_score_gemma":1.6398602e-7,"teacher_disagreement_score":0.82920027,"about_ca_system_score_codex":0.000020027224,"about_ca_system_score_gemma":0.000039937215,"threshold_uncertainty_score":0.26951656},"labels":[],"label_agreement":null},{"id":"W2101798901","doi":"10.1115/detc2010-28227","title":"Trajectory Control for an Innovative Rapid Freeze Prototyping System","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Trajectory; Computer science; Trajectory optimization; Scheme (mathematics); Robot; SCARA; Rapid prototyping; Control system; Control engineering; Programming by demonstration; Control logic; Control (management); Simulation; Engineering; Programming language; Artificial intelligence","score_opus":0.0237092678921008,"score_gpt":0.26640001538847596,"score_spread":0.24269074749637518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101798901","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005731123,0.000009500355,0.9860881,0.00015539974,0.0010738631,0.001577376,0.0000064187743,0.0006525816,0.0047056247],"genre_scores_gemma":[0.428072,4.6421754e-8,0.5710632,0.00019953532,0.00019353998,0.00036801948,0.0000024937535,0.000011046168,0.000090094894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987616,0.00005135498,0.0002586249,0.00041298883,0.00018440155,0.00033100753],"domain_scores_gemma":[0.99870515,0.00018341497,0.00010902423,0.00059290405,0.00030689483,0.00010259659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079491036,0.00014776347,0.00021028175,0.00011636484,0.00014810385,0.00013393007,0.0008207417,0.00009607205,0.0000045499296],"category_scores_gemma":[0.0000990008,0.00012340084,0.000039779257,0.00034902748,0.000040217426,0.0005121259,0.000041020954,0.00022143658,0.00001990413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093077746,0.0003421775,0.001970473,0.00035156103,0.0001923585,0.00004196002,0.0056132963,0.0011359685,0.12424048,0.58372253,0.0027468281,0.2795493],"study_design_scores_gemma":[0.0014658605,0.00044012943,0.0028136608,0.000038403472,0.0000055555192,0.0000469618,0.00011628931,0.9856907,0.006495299,0.00022569008,0.0023435184,0.0003179155],"about_ca_topic_score_codex":0.000032927866,"about_ca_topic_score_gemma":0.0000055186383,"teacher_disagreement_score":0.98455477,"about_ca_system_score_codex":0.000028315015,"about_ca_system_score_gemma":0.00013498472,"threshold_uncertainty_score":0.50321406},"labels":[],"label_agreement":null},{"id":"W2101901276","doi":"10.1007/s11633-014-0777-6","title":"Biologically Inspired Node Generation Algorithm for Path Planning of Hyper-redundant Manipulators Using Probabilistic Roadmap","year":2014,"lang":"en","type":"article","venue":"International Journal of Automation and Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; University of Ottawa","funders":"","keywords":"Motion planning; Probabilistic logic; Probabilistic roadmap; Generator (circuit theory); Path (computing); Node (physics); Planner; Tree (set theory); Computer science; Random tree; Configuration space; Robot; Algorithm; Engineering; Artificial intelligence; Mathematics","score_opus":0.04920527365929557,"score_gpt":0.30809869250821004,"score_spread":0.2588934188489145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101901276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28071028,0.00003580255,0.7182049,0.00014593687,0.00078953546,0.00006784334,0.0000016961453,0.000027517919,0.000016494138],"genre_scores_gemma":[0.5324414,9.0694294e-7,0.46723664,0.00007249537,0.00023925405,4.803181e-7,0.0000036385838,0.0000038431403,0.0000013423702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856365,0.00009165311,0.00067806727,0.00017236263,0.00036352387,0.00013072905],"domain_scores_gemma":[0.9981837,0.00020128979,0.00086057483,0.000093522,0.00060200604,0.000058884594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087627646,0.000112797665,0.00022289471,0.00021352911,0.00009685894,0.00013905809,0.0004248483,0.000057480807,8.9652093e-7],"category_scores_gemma":[0.00028085394,0.00009698179,0.00007291596,0.00010094719,0.000033022745,0.00029167486,0.00009661324,0.00009294512,3.6065475e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009406025,0.00010091393,0.003087184,0.000027051528,0.00010394689,0.0000118529715,0.00120215,0.44888437,0.010481332,0.0045617595,0.000058264253,0.5314718],"study_design_scores_gemma":[0.00055049895,0.00014014426,0.0085531855,0.00018927144,0.00001099008,0.00022236454,0.000026812468,0.98871815,0.0006385986,0.0007815992,0.00006786505,0.00010053842],"about_ca_topic_score_codex":0.000007616045,"about_ca_topic_score_gemma":6.395474e-8,"teacher_disagreement_score":0.5398338,"about_ca_system_score_codex":0.00006688167,"about_ca_system_score_gemma":0.00007160083,"threshold_uncertainty_score":0.39548028},"labels":[],"label_agreement":null},{"id":"W2102029687","doi":"10.1109/aim.2003.1225067","title":"A neuro-fuzzy approach to obstacle avoidance of a nonholonomic mobile robot","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Obstacle avoidance; Fuzzy logic; Computer science; Obstacle; Fuzzy control system; Nonholonomic system; Controller (irrigation); Control theory (sociology); Control engineering; Artificial intelligence; Robot; Robot control; Neuro-fuzzy; Computer vision; Engineering; Control (management)","score_opus":0.01730580528875815,"score_gpt":0.23883748063562163,"score_spread":0.22153167534686347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102029687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027597446,0.000045235596,0.9642297,0.00022703742,0.00023743055,0.0003253744,0.0000021088797,0.00021152366,0.007124141],"genre_scores_gemma":[0.38462007,8.0457255e-7,0.6147986,0.00035941918,0.000023368424,0.00004134914,8.328358e-7,0.000008762181,0.00014681168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986135,0.00003324837,0.00027684853,0.00050928706,0.00022343775,0.00034369144],"domain_scores_gemma":[0.99883795,0.0000660025,0.00008260136,0.0008081247,0.00005198004,0.00015334184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001975162,0.00015081374,0.00023338309,0.00011772802,0.00005661084,0.00005677984,0.0011458178,0.00004873735,0.0000023559526],"category_scores_gemma":[0.000046628786,0.00014162221,0.000055551707,0.00044803155,0.000037855716,0.00025447173,0.00028870543,0.00012497723,0.0001924718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000471837,0.00025198358,0.00020951453,0.000016720462,0.00001000373,0.00001510408,0.0017312276,0.97884667,0.0040480006,0.009340937,0.00037123298,0.005153883],"study_design_scores_gemma":[0.005848666,0.0028246562,0.04520259,0.00022977921,0.000032448468,0.0007770856,0.0006443121,0.85888964,0.07116327,0.009037588,0.0026944564,0.0026554845],"about_ca_topic_score_codex":0.00013995857,"about_ca_topic_score_gemma":8.98379e-7,"teacher_disagreement_score":0.3570226,"about_ca_system_score_codex":0.000061576975,"about_ca_system_score_gemma":0.00010861155,"threshold_uncertainty_score":0.5775186},"labels":[],"label_agreement":null},{"id":"W2102145388","doi":"10.1109/tnn.2006.875994","title":"A Neural Network Approach to Dynamic Task Assignment of Multirobots","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Artificial neural network; Task (project management); Artificial intelligence; Time delay neural network","score_opus":0.013378527268674747,"score_gpt":0.23105934192514752,"score_spread":0.21768081465647277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102145388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003761131,0.00008712895,0.9927705,0.00028260806,0.0019766488,0.00050287344,0.00000809921,0.00030815726,0.00030288924],"genre_scores_gemma":[0.86866444,0.0000038393523,0.13050252,0.0003062997,0.00015198419,0.00007500025,0.0000053535887,0.000029491932,0.00026110085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973384,0.0001748921,0.0005416747,0.00071372115,0.00047828743,0.00075301423],"domain_scores_gemma":[0.9985791,0.0001992183,0.00015939031,0.0008005782,0.00006243089,0.00019930155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022651703,0.00035087037,0.00039541442,0.00016786376,0.00024951805,0.000097115946,0.0008904646,0.00015633793,0.0000035871687],"category_scores_gemma":[0.0000021452452,0.00033473264,0.00019883174,0.0010374461,0.00006826426,0.00025789806,0.000011029319,0.00051177724,0.000012778003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002306521,0.00026544897,0.000029781204,0.000007113856,0.00002087593,0.000014193585,0.0000646,0.9720879,0.00016697065,0.00006124936,0.00065495516,0.026603818],"study_design_scores_gemma":[0.0004161981,0.00022861075,0.0015178291,0.00003354488,0.000023015798,0.000036449106,0.000005885298,0.9972051,0.00013044612,0.00005429007,0.00003202682,0.00031665454],"about_ca_topic_score_codex":0.00009123396,"about_ca_topic_score_gemma":0.000010600268,"teacher_disagreement_score":0.8649033,"about_ca_system_score_codex":0.000100670666,"about_ca_system_score_gemma":0.000022817247,"threshold_uncertainty_score":0.9999105},"labels":[],"label_agreement":null},{"id":"W2102499212","doi":"10.1109/tmech.2003.816818","title":"A behavior-based mobile robot with a visual landmark-recognition system","year":2003,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Landmark; Mobile robot; Artificial intelligence; Obstacle avoidance; Computer vision; Computer science; Mobile robot navigation; Robot; Fuzzy logic; Robot control","score_opus":0.015237317741624727,"score_gpt":0.2398786505395749,"score_spread":0.22464133279795018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102499212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020451715,0.000047668218,0.9769365,0.000037141665,0.0008486735,0.00068103214,0.000033626828,0.0007150745,0.00024855582],"genre_scores_gemma":[0.76804554,0.000004609751,0.23116073,0.00006695464,0.00002399283,0.0005649169,0.000011604495,0.000039488757,0.000082192804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975683,0.00023329526,0.0003472131,0.0006714372,0.0005892482,0.0005905018],"domain_scores_gemma":[0.99869347,0.00013615766,0.00014276234,0.0006680289,0.00014442769,0.0002151422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004164864,0.00034926782,0.00031954126,0.00029845804,0.00036031986,0.00016759177,0.00043381986,0.00017870149,0.00003161522],"category_scores_gemma":[0.0000045305,0.00031993288,0.0001471874,0.0006760143,0.00004426595,0.00037209684,0.0000025491302,0.00047559376,0.00025903445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000132138,0.0017408576,0.00007065529,0.00013605501,0.00014355921,0.00023854866,0.00045329722,0.9255078,0.001665654,0.0013924389,0.00004173764,0.06847724],"study_design_scores_gemma":[0.004695568,0.004688925,0.00008200757,0.0005096989,0.00034874128,0.0005736707,0.0005662914,0.92030746,0.0658846,0.000103054444,0.00075427303,0.001485696],"about_ca_topic_score_codex":0.000032805852,"about_ca_topic_score_gemma":0.00001301177,"teacher_disagreement_score":0.7475938,"about_ca_system_score_codex":0.00035793922,"about_ca_system_score_gemma":0.0003673985,"threshold_uncertainty_score":0.99992526},"labels":[],"label_agreement":null},{"id":"W2102859372","doi":"10.1109/ccece.2011.6030684","title":"Computer-aided system design for educational purposes: An autonomous Self-balancing two-wheeled inverted pendulum robot","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Inverted pendulum; Toolbox; Computer science; Robot; Motion planning; Control engineering; Mobile robot; MATLAB; Robot kinematics; Software; Motion control; Double inverted pendulum; Simulation; Artificial intelligence; Engineering; Nonlinear system","score_opus":0.053979503274668945,"score_gpt":0.2643832800728118,"score_spread":0.21040377679814287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102859372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020263721,0.000024123496,0.99220735,0.00016068689,0.0018781378,0.0010379073,0.0000035649925,0.0014539202,0.0012079445],"genre_scores_gemma":[0.18951842,3.7056404e-7,0.809515,0.00026643765,0.00028789116,0.00016425361,0.000018413593,0.00002816912,0.0002010384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726653,0.00027769833,0.00057810097,0.000877075,0.00034403527,0.0006565557],"domain_scores_gemma":[0.9978047,0.00034859226,0.00026035128,0.00094157615,0.00030469365,0.00034010987],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092485745,0.00033805298,0.00041161155,0.00022992582,0.0003156488,0.0002626223,0.0016473584,0.000117218704,0.000020259133],"category_scores_gemma":[0.000040004314,0.00032135274,0.000103209204,0.0003774749,0.00003524517,0.00096041855,0.00019523267,0.00015697884,0.00012855047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019143774,0.0056134276,0.01182903,0.001212497,0.0014121187,0.00036347922,0.0536852,0.19436227,0.005163133,0.63476366,0.0245518,0.06685195],"study_design_scores_gemma":[0.00092720066,0.000396673,0.003841216,0.00006564699,0.00002641295,0.00019293009,0.00010729183,0.9920285,0.0011094168,0.00083830894,0.000035433623,0.00043101457],"about_ca_topic_score_codex":0.00027686823,"about_ca_topic_score_gemma":0.000008152202,"teacher_disagreement_score":0.7976662,"about_ca_system_score_codex":0.00030551807,"about_ca_system_score_gemma":0.00069146755,"threshold_uncertainty_score":0.9999238},"labels":[],"label_agreement":null},{"id":"W2102967110","doi":"10.1109/robot.1996.509267","title":"Comparative study of velocity and computed torque control schemes for a differentially driven automated vehicle","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Torque; Kinematics; Control theory (sociology); Acceleration; Vehicle dynamics; Angular velocity; Payload (computing); Angular acceleration; Computer science; Inverse dynamics; Curvature; Tracking (education); Trajectory; Engineering; Simulation; Control (management); Automotive engineering; Physics; Mathematics; Artificial intelligence","score_opus":0.039522001556062746,"score_gpt":0.282880390640391,"score_spread":0.24335838908432825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102967110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4607646,0.00002031704,0.5380931,0.00008986901,0.00007836741,0.0005486223,0.000004741297,0.00033457708,0.00006582057],"genre_scores_gemma":[0.8780752,4.1965544e-7,0.12178779,0.000041879903,0.000015581129,0.000027396429,0.0000013359773,0.0000048235515,0.00004557122],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884135,0.00010844476,0.00028853136,0.00034110158,0.00020389464,0.00021667492],"domain_scores_gemma":[0.9990244,0.00030263833,0.00013318575,0.00028734148,0.0001686828,0.000083762614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011473741,0.00015009001,0.00041649482,0.000073774536,0.000111609326,0.000073895986,0.00041741153,0.000047184232,0.0000059764843],"category_scores_gemma":[0.000024795143,0.00012784882,0.00003475241,0.00019230829,0.000056003624,0.0001766048,0.00012483251,0.000072604256,0.0000044323506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076260924,0.043371078,0.28279823,0.000932724,0.008885561,0.00029906275,0.2866,0.13226128,0.066005796,0.04986612,0.042602107,0.085615434],"study_design_scores_gemma":[0.0032027888,0.00062082073,0.051301297,0.000014510296,0.000017529057,0.000003004433,0.00013469758,0.9439184,0.0005937859,0.000041722364,0.00001555852,0.00013588447],"about_ca_topic_score_codex":0.000037247068,"about_ca_topic_score_gemma":0.0000070868014,"teacher_disagreement_score":0.81165713,"about_ca_system_score_codex":0.000018538727,"about_ca_system_score_gemma":0.000014802755,"threshold_uncertainty_score":0.52135235},"labels":[],"label_agreement":null},{"id":"W2103700244","doi":"10.1109/isic.1996.556170","title":"A hybrid, self-organizing controller for multi-agent motion planning in a stationary environment","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Workspace; Controller (irrigation); Computer science; Control engineering; Decentralised system; Function (biology); Decentralization; Self-organization; Distributed computing; Multi-agent system; Control theory (sociology); Field (mathematics); Action (physics); Motion planning; Control (management); Artificial intelligence; Engineering; Robot; Mathematics","score_opus":0.054290708021037935,"score_gpt":0.25664567143738054,"score_spread":0.2023549634163426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103700244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052251555,0.00016184463,0.9933136,0.00036386115,0.00013682252,0.0004546436,0.0000032767898,0.0001934737,0.00014729158],"genre_scores_gemma":[0.25992852,0.00000636367,0.739331,0.00024375023,0.000029769188,0.00006687365,0.000004372356,0.000010604985,0.00037873234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987764,0.00005093821,0.00027191726,0.00038086102,0.00021379552,0.0003061031],"domain_scores_gemma":[0.9994893,0.00013725203,0.00007857965,0.00020458308,0.000019213201,0.00007106108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027627792,0.00013271067,0.00015685378,0.00013295366,0.000093852825,0.00006210825,0.00028460627,0.000034346493,0.000022992574],"category_scores_gemma":[0.000039257204,0.00012853312,0.000038538376,0.00009551264,0.000009953577,0.0002973751,0.00008100741,0.00008779863,0.00010697316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003389695,0.0027556813,0.024339924,0.00011222819,0.00021098468,0.00054573145,0.027790979,0.7874832,0.0036178976,0.00606895,0.008577548,0.13846298],"study_design_scores_gemma":[0.0016475938,0.00005617178,0.007171347,0.000016755499,0.000003991882,0.00001939844,0.000052262712,0.9900848,0.00021616316,0.0001394502,0.00043848218,0.00015358487],"about_ca_topic_score_codex":0.000009113118,"about_ca_topic_score_gemma":1.4122898e-7,"teacher_disagreement_score":0.25470337,"about_ca_system_score_codex":0.0001527993,"about_ca_system_score_gemma":0.000009097772,"threshold_uncertainty_score":0.52414286},"labels":[],"label_agreement":null},{"id":"W2104240257","doi":"10.1109/robot.2004.1307419","title":"Motion planning for mobile robotics using the generalized sigmoid function","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Sigmoid function; Mobile robot; Robotics; Motion planning; Computer science; Artificial intelligence; Task (project management); Computer vision; Motion (physics); Robot; Function (biology); Engineering; Artificial neural network","score_opus":0.053196168437825035,"score_gpt":0.2990908627813334,"score_spread":0.24589469434350836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104240257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00691927,0.000108331544,0.9908371,0.00034364298,0.0009753786,0.00041257002,8.8844496e-7,0.00025863128,0.00014418497],"genre_scores_gemma":[0.12728702,0.0000012016617,0.8718882,0.00039600563,0.00021196523,0.000041464107,0.000006033876,0.000013031892,0.00015505817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894357,0.00003787452,0.00020632034,0.000304128,0.00021454917,0.0002935603],"domain_scores_gemma":[0.99927,0.000088448796,0.000093731294,0.00040986438,0.00008495383,0.00005299892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035770208,0.00013122603,0.00012464642,0.00006633763,0.0003429573,0.0001678167,0.00042902958,0.000065602755,0.0000022981296],"category_scores_gemma":[0.000032197022,0.00009333799,0.00006974722,0.00026407515,0.000026879157,0.00035298427,0.00008566583,0.00009685356,0.000009960022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041192575,0.00001988929,0.00006005646,0.0000043958166,0.000011346181,0.0000022041932,0.00033983804,0.98161244,0.0016352212,0.013787638,0.000090795744,0.0024320225],"study_design_scores_gemma":[0.00062530884,0.000120577766,0.00022104465,0.000019640996,0.000017121205,0.000029884417,0.00007236725,0.99047446,0.0015685997,0.006469285,0.00023317662,0.00014853975],"about_ca_topic_score_codex":0.000041324984,"about_ca_topic_score_gemma":5.543166e-7,"teacher_disagreement_score":0.12036774,"about_ca_system_score_codex":0.00009582212,"about_ca_system_score_gemma":0.00006170927,"threshold_uncertainty_score":0.38062128},"labels":[],"label_agreement":null},{"id":"W2104706738","doi":"10.1109/romoco.2004.240737","title":"Stratified motion planning concept in stair-like environment","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Magyar Tudományos Akadémia; Hungarian Scientific Research Fund","keywords":"Nonholonomic system; Holonomic; Motion planning; Computer science; Motion (physics); Trajectory; Stair climbing; Robot; Tracking (education); Artificial intelligence; Mobile robot; Control engineering; Engineering","score_opus":0.022157274936378875,"score_gpt":0.2450100434150435,"score_spread":0.22285276847866464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104706738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013476527,0.000056685112,0.9839542,0.0004408355,0.00021078663,0.00012798252,7.7244994e-7,0.00014125589,0.0015909708],"genre_scores_gemma":[0.68619084,0.0000013034633,0.3133864,0.0001843119,0.000022276401,0.00000787251,0.000004497761,0.000005002484,0.00019746907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988929,0.000033904016,0.00020908259,0.00033787734,0.00025529735,0.0002709212],"domain_scores_gemma":[0.9995068,0.00003108153,0.000049749775,0.0003348614,0.000006849119,0.000070716094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017484797,0.00011348069,0.000115737115,0.000081144135,0.000050044433,0.00006835411,0.00037840125,0.000054623975,0.000016100781],"category_scores_gemma":[0.0000086515865,0.00010793019,0.0000222171,0.00014368424,0.000030651398,0.00034603014,0.000091601025,0.00013430389,0.000091262365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017135767,0.00008848918,0.0013537823,0.000003005944,0.0000054849183,0.00015290036,0.002861607,0.97134817,0.00043595204,0.01390125,0.000114409966,0.009733241],"study_design_scores_gemma":[0.004706139,0.00050730054,0.13936657,0.0001776478,0.000008186869,0.000097830096,0.0010725363,0.82200444,0.011614976,0.018556837,0.0007495887,0.0011379245],"about_ca_topic_score_codex":0.00008486036,"about_ca_topic_score_gemma":0.0000011855508,"teacher_disagreement_score":0.67271435,"about_ca_system_score_codex":0.00013794976,"about_ca_system_score_gemma":0.000042393593,"threshold_uncertainty_score":0.4401265},"labels":[],"label_agreement":null},{"id":"W2104733322","doi":"10.1109/robot.2002.1013426","title":"A solution to vicinity problem of obstacles in complete coverage path planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Workspace; Path (computing); Robot; Cover (algebra); Artificial neural network; Computer science; Obstacle; Planner; Mobile robot; Artificial intelligence; Engineering; Geography; Computer network","score_opus":0.04176722436237148,"score_gpt":0.2712663492207149,"score_spread":0.22949912485834342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104733322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040791012,0.000035078985,0.9492799,0.00012345432,0.00009792535,0.00020793405,0.00000269139,0.000083531784,0.009378472],"genre_scores_gemma":[0.5397631,6.093264e-7,0.46001995,0.00014603576,0.000004642763,0.0000062109366,0.0000010572788,0.000003673497,0.000054744396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875176,0.00011935356,0.00029978846,0.00028885205,0.0002413808,0.00029884366],"domain_scores_gemma":[0.9993502,0.00012630041,0.00008020123,0.00031079762,0.0000495662,0.00008292135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005159815,0.0001107374,0.00020037421,0.0001528104,0.00004901196,0.000035928013,0.0003657093,0.00004244584,0.0000057209895],"category_scores_gemma":[0.00007814479,0.00010435393,0.000027404698,0.0005119207,0.000017011955,0.00021052644,0.00009928952,0.00010892235,0.000024536894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000442967,0.0008304343,0.20481026,0.00023403445,0.00005759527,0.00040306506,0.025404226,0.4920512,0.03429182,0.20597668,0.004951522,0.030944848],"study_design_scores_gemma":[0.0020278515,0.00068882364,0.1868863,0.0006246008,0.0000078211615,0.00008306086,0.00028405,0.7824048,0.006902521,0.015328616,0.0038537635,0.00090783276],"about_ca_topic_score_codex":0.00018255408,"about_ca_topic_score_gemma":0.0000061545165,"teacher_disagreement_score":0.49897206,"about_ca_system_score_codex":0.000056610406,"about_ca_system_score_gemma":0.00006813027,"threshold_uncertainty_score":0.42554298},"labels":[],"label_agreement":null},{"id":"W2104944680","doi":"10.1017/s0373463307004444","title":"Particle Swarm Guidance System for Autonomous Unmanned Aerial Vehicles in an Air Defence Role","year":2007,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tornado Spectral Systems (Canada)","funders":"","keywords":"Particle swarm optimization; Computer science; Swarm behaviour; Scheme (mathematics); Artificial intelligence","score_opus":0.01892959902012068,"score_gpt":0.2863342681117865,"score_spread":0.2674046690916658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104944680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59774995,0.000096223186,0.4014039,0.00013221883,0.00048090532,0.00009267851,8.4282885e-7,0.000029955203,0.0000133235335],"genre_scores_gemma":[0.85675323,5.384904e-7,0.14292097,0.000034013527,0.00027530818,0.0000032088576,0.0000011385036,0.000006219856,0.0000053550066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985965,0.000072656854,0.0006133139,0.00016209845,0.00029209873,0.0002633667],"domain_scores_gemma":[0.9988211,0.0001429964,0.00047790143,0.00019709341,0.00024432718,0.000116598116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019988688,0.000090914175,0.00018243557,0.00009045607,0.000076358265,0.000071908944,0.00048800395,0.000064323045,1.5117034e-7],"category_scores_gemma":[0.00008226224,0.0000856468,0.000054686694,0.0002896561,0.000020242107,0.0009198721,0.000029499959,0.0001310262,0.0000041196404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007859409,0.00077028875,0.055461396,0.0002528375,0.0000625606,0.0007936052,0.014756495,0.5170166,0.21088283,0.016060937,0.00017262233,0.18298388],"study_design_scores_gemma":[0.0019445489,0.00082767365,0.07665035,0.00050319976,0.000014484868,0.0003463608,0.0005559632,0.78778094,0.1293878,0.001635771,0.000120215576,0.00023267762],"about_ca_topic_score_codex":0.000014456355,"about_ca_topic_score_gemma":0.0000034749958,"teacher_disagreement_score":0.27076435,"about_ca_system_score_codex":0.00022526014,"about_ca_system_score_gemma":0.00010682434,"threshold_uncertainty_score":0.3492575},"labels":[],"label_agreement":null},{"id":"W2105043361","doi":"10.1109/robot.1990.126290","title":"A new path planning algorithm for moving a point object amidst unknown obstacles in a plane","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Path (computing); Motion planning; Point (geometry); Computer science; Computer vision; Plane (geometry); Object (grammar); Artificial intelligence; Algorithm; Mathematics; Geometry; Robot","score_opus":0.0307211124662398,"score_gpt":0.25532480696266047,"score_spread":0.22460369449642067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105043361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075322075,0.00054850645,0.9938994,0.00048152346,0.00044715873,0.0004114726,0.0000072199036,0.0005667878,0.0028847216],"genre_scores_gemma":[0.011134726,0.000009061882,0.9849843,0.0003867322,0.00015329442,0.000044672564,0.000007730305,0.000026828866,0.0032526588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976366,0.000060536106,0.00046175538,0.0007071497,0.0003628039,0.00077115576],"domain_scores_gemma":[0.99857926,0.00045706483,0.00012571605,0.00056221377,0.000045590008,0.00023014027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004111696,0.0002955167,0.00037329952,0.00031221585,0.00013116655,0.0002585359,0.0009158641,0.000121049154,0.00003520746],"category_scores_gemma":[0.00012645805,0.00027372097,0.00009554075,0.0005850661,0.000023981573,0.0005649573,0.00021990042,0.00024706306,0.00006337353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009218932,0.00019302517,0.0013443904,0.00004391564,0.00005134421,0.000743991,0.009665914,0.008962195,0.0003249683,0.004861523,0.025687415,0.9481121],"study_design_scores_gemma":[0.001173148,0.00019017397,0.0013269295,0.00018178765,0.000005418141,0.00011652372,0.00015921037,0.99319196,0.00027889173,0.0011220701,0.0018527065,0.0004011967],"about_ca_topic_score_codex":0.00042947553,"about_ca_topic_score_gemma":0.000011941199,"teacher_disagreement_score":0.98422974,"about_ca_system_score_codex":0.00015252127,"about_ca_system_score_gemma":0.00008188228,"threshold_uncertainty_score":0.9999715},"labels":[],"label_agreement":null},{"id":"W2105798359","doi":"10.1109/icar.2005.1507488","title":"Real-time safety for human - robot interaction","year":2006,"lang":"en","type":"article","venue":"ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–robot interaction; Human–computer interaction; Robot; Artificial intelligence","score_opus":0.033204242391639806,"score_gpt":0.3124402767305434,"score_spread":0.2792360343389036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105798359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002240215,0.000022778248,0.9017165,0.0072037242,0.0043708473,0.0012509711,0.00009463371,0.0012095721,0.081890814],"genre_scores_gemma":[0.23181143,0.0000620269,0.7498515,0.00046139414,0.0013748324,0.00017516942,0.00031823476,0.000106657884,0.015838735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598783,0.000025831332,0.0009645123,0.001239525,0.0009307828,0.00085152395],"domain_scores_gemma":[0.9972982,0.00013358644,0.0007135737,0.00044323623,0.0011779077,0.00023351132],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004974118,0.0005621402,0.0005388225,0.00043963714,0.0004284798,0.0005891736,0.0018947775,0.00022541411,0.00014844286],"category_scores_gemma":[0.00021003655,0.0005796071,0.0002150589,0.00026675704,0.00013409532,0.0014990829,0.00028731613,0.0004945883,0.00022260194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018857326,0.0004343385,0.00020932639,0.00004844948,0.00011287439,0.000025862566,0.00022058995,0.1330379,0.04943517,0.80032736,0.010185365,0.005774227],"study_design_scores_gemma":[0.0026585637,0.00075901864,0.0021359767,0.000580981,0.000046258836,0.00009777135,0.00014065459,0.9389825,0.009401611,0.033626094,0.010275196,0.0012953753],"about_ca_topic_score_codex":0.00006502545,"about_ca_topic_score_gemma":0.000009303904,"teacher_disagreement_score":0.8059446,"about_ca_system_score_codex":0.00063476735,"about_ca_system_score_gemma":0.00013994914,"threshold_uncertainty_score":0.99966556},"labels":[],"label_agreement":null},{"id":"W2105848344","doi":"10.1007/s10472-009-9120-2","title":"Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach","year":2008,"lang":"en","type":"article","venue":"Annals of Mathematics and Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robot; Computer science; Mobile robot; Set (abstract data type); Task (project management); Distributed computing; Cover (algebra); Greedy algorithm; Algorithm; Real-time computing; Artificial intelligence; Engineering","score_opus":0.1974527357382306,"score_gpt":0.3430350180906082,"score_spread":0.1455822823523776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105848344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08697973,0.00016583035,0.9121322,0.00013050291,0.00012515234,0.00019804777,0.000005793604,0.0000803721,0.00018238346],"genre_scores_gemma":[0.42273515,0.00007108655,0.57704055,0.000062499705,0.000040785268,0.00000755535,0.0000019384515,0.000010846556,0.000029561572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981334,0.000052085084,0.00060094445,0.00043877153,0.0004011738,0.00037362095],"domain_scores_gemma":[0.99864036,0.00012904363,0.00024790087,0.0005968599,0.00020512457,0.00018073499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005834601,0.00021624306,0.00034867148,0.00013061285,0.00022556861,0.00008465778,0.00068500114,0.000086913205,0.0000048760476],"category_scores_gemma":[0.00012539992,0.00019464012,0.00007732079,0.00031939984,0.00023172828,0.0001543379,0.00017947434,0.00016429841,0.000024982188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019811572,0.0065880357,0.000106693355,0.00037867803,0.00010424257,0.00019522176,0.02697745,0.5434901,0.0072433036,0.19878541,0.00014536321,0.21596566],"study_design_scores_gemma":[0.000029311204,0.00017517181,0.00011897725,0.00004546029,0.0000047690346,0.000100486875,0.00027161167,0.97670424,0.016035207,0.0062902346,0.00000876352,0.00021577545],"about_ca_topic_score_codex":0.000040611583,"about_ca_topic_score_gemma":5.2128246e-7,"teacher_disagreement_score":0.4332141,"about_ca_system_score_codex":0.0000070403703,"about_ca_system_score_gemma":0.000056004057,"threshold_uncertainty_score":0.7937194},"labels":[],"label_agreement":null},{"id":"W2106599774","doi":"10.1109/robot.2007.363760","title":"The Frugal Feeding Problem: Energy-efficient, multi-robot, multi-place rendezvous","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE International Conference on Robotics and Automation/Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Travelling salesman problem; Robot; Heuristics; Mathematical optimization; Computer science; Set (abstract data type); Mobile robot; Set cover problem; Robot kinematics; Energy (signal processing); Euclidean geometry; Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.04697058972676788,"score_gpt":0.29726930689094855,"score_spread":0.2502987171641807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106599774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024228659,0.00013081938,0.9388097,0.009362005,0.0030177245,0.0007517362,0.00001516549,0.0014417502,0.022242444],"genre_scores_gemma":[0.6787671,0.00010435314,0.31827632,0.00042933153,0.00037858463,0.00006336643,0.000007768791,0.00005421746,0.0019189916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952976,0.000008729893,0.0010561481,0.0012108836,0.0013993015,0.001027323],"domain_scores_gemma":[0.9962843,0.00021132067,0.00088182976,0.000188698,0.0020420998,0.0003917684],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018260514,0.0006327618,0.00042011956,0.00056205026,0.0011564656,0.002715745,0.0021068403,0.00027646907,0.000007985539],"category_scores_gemma":[0.00033102412,0.00053006277,0.00013615162,0.0007696477,0.00027266447,0.0011811602,0.0004464414,0.0006629283,0.000041260337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006311568,0.0004314978,0.002221478,0.000086370754,0.00017234162,0.000018044271,0.0033751482,0.008670892,0.013236166,0.95444286,0.0027740416,0.014508045],"study_design_scores_gemma":[0.0010635036,0.00015528713,0.004617584,0.00032694332,0.000026122136,0.0001561348,0.0008112412,0.98649067,0.0028187446,0.0021246637,0.00074105966,0.0006680324],"about_ca_topic_score_codex":0.000043006825,"about_ca_topic_score_gemma":0.0000061537944,"teacher_disagreement_score":0.9778198,"about_ca_system_score_codex":0.00031613186,"about_ca_system_score_gemma":0.00012933322,"threshold_uncertainty_score":0.9997151},"labels":[],"label_agreement":null},{"id":"W2106690144","doi":"10.1109/ssrr.2005.1501252","title":"Navigation of unmanned vehicles using a swarm of intelligent dynamic landmarks","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Artificial intelligence; Task (project management); Computer vision; Position (finance); Swarm behaviour; Process (computing); Mobile robot; Human–computer interaction; Engineering","score_opus":0.02129996106255206,"score_gpt":0.28820929482587965,"score_spread":0.2669093337633276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106690144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34586352,0.00005517319,0.6535694,0.000099966994,0.00006728773,0.000052212472,8.4757545e-7,0.000034343237,0.00025723188],"genre_scores_gemma":[0.6374027,0.0000025398883,0.36251652,0.000016873495,0.000009727463,5.724079e-7,0.0000018532567,0.0000028622626,0.000046352525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991911,0.000033600783,0.00028383717,0.0001590764,0.00020079267,0.0001316074],"domain_scores_gemma":[0.9994049,0.000062024366,0.00014169623,0.00027571534,0.00007984109,0.000035790712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023053886,0.00007373069,0.00014281439,0.00008657945,0.000023081211,0.000013144435,0.0003551178,0.00004178396,0.000003940342],"category_scores_gemma":[0.00001815283,0.00006517659,0.000040782663,0.00023399194,0.000033702563,0.00018300615,0.00009548308,0.00005842564,0.000007257609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022269662,0.00038284206,0.0047114943,0.00014906406,0.0000877924,0.000015313746,0.005459278,0.66754913,0.067316875,0.011833502,0.00010766612,0.24236476],"study_design_scores_gemma":[0.00011273205,0.000041574807,0.0011820135,0.00006471767,0.0000046484993,0.0000110762685,0.000039276223,0.9403269,0.057676136,0.00045449985,0.00001893947,0.00006743429],"about_ca_topic_score_codex":0.000092899296,"about_ca_topic_score_gemma":0.0000030692954,"teacher_disagreement_score":0.2915392,"about_ca_system_score_codex":0.00004286832,"about_ca_system_score_gemma":0.00004607996,"threshold_uncertainty_score":0.26578242},"labels":[],"label_agreement":null},{"id":"W2106878815","doi":"10.1109/robio.2013.6739472","title":"Collision-free single-step motion planning of biped pole-climbing robots in spatial trusses","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Robot; Computer science; Climbing; Path (computing); Climb; Trajectory; Truss; Control theory (sociology); Robot kinematics; Convergence (economics); Collision detection; Engineering; Collision; Artificial intelligence; Mobile robot; Structural engineering","score_opus":0.027934640352041535,"score_gpt":0.2443996249649836,"score_spread":0.21646498461294206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106878815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11632685,0.000066742505,0.8791047,0.00048322842,0.00050902215,0.00027721192,0.0000015477061,0.00017705897,0.003053636],"genre_scores_gemma":[0.6300676,0.0000013798904,0.36962572,0.00007742615,0.000048971153,0.000008235104,0.000003260955,0.00001096952,0.00015644245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807906,0.00010764659,0.0005254911,0.00042977932,0.00044921483,0.00040882683],"domain_scores_gemma":[0.99852556,0.00032568662,0.00020469658,0.00072632433,0.000112165,0.000105566505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033429693,0.00018763542,0.00030314588,0.00035535498,0.00007126313,0.00013946288,0.001041636,0.00011343811,0.00003322808],"category_scores_gemma":[0.00034749004,0.00017104529,0.000054556123,0.0006157283,0.000044983924,0.00074373203,0.00038919938,0.00016008703,0.000056124743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031640368,0.0010268803,0.0900815,0.00016083596,0.00005017351,0.00017050195,0.005610638,0.6538092,0.029575406,0.0044295224,0.0046668933,0.21038683],"study_design_scores_gemma":[0.00089461805,0.00018796213,0.05966193,0.00021705189,0.000004114212,0.000018893501,0.00013688048,0.93027747,0.007313766,0.0010113961,0.00001743024,0.00025847185],"about_ca_topic_score_codex":0.0021452596,"about_ca_topic_score_gemma":0.000016924047,"teacher_disagreement_score":0.5137407,"about_ca_system_score_codex":0.00007513333,"about_ca_system_score_gemma":0.000054720127,"threshold_uncertainty_score":0.6975025},"labels":[],"label_agreement":null},{"id":"W2106962227","doi":"10.1109/5254.956078","title":"Experiences with an autonomous robot attending AAAI","year":2001,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Computer science; Presentation (obstetrics); Human–computer interaction; Robotics; Autonomous robot; Artificial intelligence; Human–robot interaction; Mobile robot; Multimedia","score_opus":0.04482143113377924,"score_gpt":0.28408121137113934,"score_spread":0.2392597802373601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106962227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12018981,0.0002497082,0.8739462,0.00008453576,0.0034939882,0.00030472732,7.544385e-7,0.00052988343,0.0012003811],"genre_scores_gemma":[0.96569043,0.000008851713,0.032378767,0.00007525216,0.00041194606,0.00013605921,0.00000241262,0.000025137935,0.0012711249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747914,0.000151468,0.00046682582,0.0007231061,0.0005736556,0.00060583226],"domain_scores_gemma":[0.998379,0.000098872704,0.00020836333,0.0009298496,0.000106267486,0.00027768206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048583123,0.00028793295,0.0003375367,0.00019627599,0.00022632543,0.0005358165,0.0014487351,0.000092708186,0.000012847724],"category_scores_gemma":[0.000015765072,0.00022650167,0.000054973305,0.0005133093,0.00008240052,0.00080722274,0.00006585509,0.00017738537,0.00027810334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007839692,0.0007355055,0.017141411,0.00014010922,0.00027128126,0.0038274615,0.16681026,0.75224006,0.0018550192,0.007495992,0.0021823798,0.047222137],"study_design_scores_gemma":[0.00018198472,0.00067810994,0.00026078874,0.00025322105,0.00001047079,0.001790538,0.010714217,0.9787425,0.003325675,0.00003099518,0.0033591972,0.00065235025],"about_ca_topic_score_codex":0.0004135837,"about_ca_topic_score_gemma":0.000006989329,"teacher_disagreement_score":0.84550065,"about_ca_system_score_codex":0.00016496787,"about_ca_system_score_gemma":0.00009248673,"threshold_uncertainty_score":0.923647},"labels":[],"label_agreement":null},{"id":"W2107210112","doi":"10.1109/pacrim.1995.520440","title":"A potential maze solving algorithm for a micromouse robot","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mobile robot; Microprocessor; Computer science; Robot; Path (computing); Power (physics); Motion planning; Algorithm; Simulation; Artificial intelligence; Embedded system","score_opus":0.022053626622271102,"score_gpt":0.23227829762754526,"score_spread":0.21022467100527414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107210112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012837847,0.00015153535,0.99640125,0.00096197,0.0007217811,0.00025670227,0.0000049997348,0.0004892847,0.000884111],"genre_scores_gemma":[0.0056136623,0.000006252546,0.9876856,0.00046410566,0.000199384,0.000034212968,0.0000024956128,0.000018267552,0.0059759906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984652,0.00003133076,0.00024379442,0.0004948036,0.00023667567,0.0005282369],"domain_scores_gemma":[0.9990853,0.000088419816,0.00007258197,0.0005339825,0.0000830905,0.0001366226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020983715,0.0001769493,0.00019162438,0.00011442058,0.00013964152,0.00025488925,0.0008969249,0.00008165468,0.000062831525],"category_scores_gemma":[0.000035077464,0.00016510338,0.0001183604,0.00023323663,0.00002950215,0.0003939009,0.00020375404,0.00011136534,0.00024374954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020459413,0.0002866636,0.000030387666,0.000021827722,0.000071850125,0.00023495684,0.0015986159,0.004275905,0.006385244,0.0018352157,0.034289915,0.9509674],"study_design_scores_gemma":[0.0005242565,0.00008867431,0.000079578844,0.000012514104,0.00000809064,0.00017240224,0.000013280362,0.99540657,0.0013420042,0.00036613535,0.0017478908,0.00023858422],"about_ca_topic_score_codex":0.000024760771,"about_ca_topic_score_gemma":5.475558e-7,"teacher_disagreement_score":0.9911307,"about_ca_system_score_codex":0.000039216695,"about_ca_system_score_gemma":0.000018906627,"threshold_uncertainty_score":0.6732721},"labels":[],"label_agreement":null},{"id":"W2107318723","doi":"10.1109/iros.1998.724832","title":"Dynamical trajectory generation with collision free using neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Trajectory; Artificial neural network; Computer science; Sensitivity (control systems); Obstacle avoidance; Control theory (sociology); Robot; Tracking (education); Collision avoidance; Collision; Obstacle; Artificial intelligence; Mobile robot; Control (management); Engineering; Physics","score_opus":0.04394869488277607,"score_gpt":0.2345789182586402,"score_spread":0.1906302233758641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107318723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09057066,0.00006424524,0.9080082,0.00018189145,0.00036524129,0.00008960156,4.1693124e-7,0.00018306551,0.0005366798],"genre_scores_gemma":[0.537255,0.0000013865462,0.46227092,0.0001683637,0.00015603719,0.0000022007873,0.0000018354654,0.0000075709254,0.00013672041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989613,0.000061972925,0.00014652479,0.00031563253,0.00026395448,0.00025057475],"domain_scores_gemma":[0.9992985,0.00004253953,0.000047770136,0.00048201703,0.000043144813,0.000086016094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010469649,0.00011878457,0.0001130191,0.000058586847,0.00013889238,0.00014317017,0.00047964996,0.00006526841,0.000015760506],"category_scores_gemma":[0.000013825423,0.00009147562,0.00002570079,0.00030654034,0.000030537965,0.00038199217,0.000095955,0.0001334463,0.0000072945604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001111253,0.000025619744,0.00035453637,8.9075434e-7,0.0000046056443,0.000044955195,0.000083937426,0.99476737,0.00025745627,0.00038499964,0.0008111927,0.0032633499],"study_design_scores_gemma":[0.00026806645,0.00008647794,0.0005659554,0.000007100348,0.000004330578,0.00013490424,0.000003839973,0.9987079,0.000063766165,0.00000918628,0.000008422719,0.00014004685],"about_ca_topic_score_codex":0.000026307998,"about_ca_topic_score_gemma":0.000008037259,"teacher_disagreement_score":0.4466843,"about_ca_system_score_codex":0.000057010766,"about_ca_system_score_gemma":0.000010776568,"threshold_uncertainty_score":0.37302676},"labels":[],"label_agreement":null},{"id":"W2107358286","doi":"10.1109/icsmc.1993.390771","title":"Efficient bitmap-based implementation of sequential framework for motion planning for manipulators with many degrees of freedom","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Backtracking; Bitmap; Computer science; Motion planning; Degrees of freedom (physics and chemistry); Implementation; Path (computing); Intersection (aeronautics); Graph; Robot; Artificial intelligence; Algorithm; Theoretical computer science; Engineering","score_opus":0.062206904960796214,"score_gpt":0.3144294703885661,"score_spread":0.2522225654277699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107358286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065471195,0.000028909473,0.9334257,0.00008891257,0.00022579402,0.000643435,0.000021190794,0.00007006286,0.000024767349],"genre_scores_gemma":[0.46671715,8.81002e-8,0.5331913,0.000010942155,0.00002517332,0.000033083666,0.000007177182,0.0000074006,0.0000076215942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876505,0.000024820696,0.00036254968,0.0002945013,0.00029866022,0.00025440493],"domain_scores_gemma":[0.9989426,0.0002445172,0.00030199546,0.000305837,0.00015675393,0.000048295573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026153642,0.00013610364,0.00021103311,0.00017651384,0.0000785411,0.000029543713,0.00032667408,0.00006797603,0.0000147632],"category_scores_gemma":[0.00003104972,0.00011441552,0.0000696184,0.00025347003,0.00004179069,0.00008264446,0.000032712356,0.000051804203,0.0000010285947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019464454,0.00008000875,0.0040518586,0.000099960474,0.00003280781,0.0000013466604,0.00064118946,0.96612597,0.0004908512,0.024736887,0.00011754734,0.0036020954],"study_design_scores_gemma":[0.0010117342,0.00059360015,0.0044576945,0.000103763996,0.000026035334,0.0000025758204,0.00015074147,0.98433167,0.00894255,0.00023939575,0.000008792769,0.00013144719],"about_ca_topic_score_codex":0.000059758953,"about_ca_topic_score_gemma":0.0000025213383,"teacher_disagreement_score":0.40124598,"about_ca_system_score_codex":0.000037323243,"about_ca_system_score_gemma":0.000029035025,"threshold_uncertainty_score":0.46657294},"labels":[],"label_agreement":null},{"id":"W2108189350","doi":"10.1109/iscas.2009.5117713","title":"Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Planner; Ant colony optimization algorithms; Computer science; Drone; Path (computing); Motion planning; Gaussian; Real-time computing; Robot; Artificial intelligence; Computer network","score_opus":0.021306153581854354,"score_gpt":0.26395477436852577,"score_spread":0.2426486207866714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108189350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006122791,0.000024708432,0.99689907,0.0008994593,0.00027402135,0.00033424332,0.00009089272,0.0002673516,0.0005979626],"genre_scores_gemma":[0.048475366,0.0000013218586,0.9496583,0.00039338134,0.00017856367,0.000023571101,0.00018537114,0.00001018229,0.0010739772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870443,0.00003933166,0.00019050657,0.00044360274,0.00023060279,0.0003915249],"domain_scores_gemma":[0.9991869,0.00006676466,0.000075106575,0.0003600596,0.00017905638,0.00013212653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013110424,0.00019225618,0.00018591512,0.000057268466,0.00037844875,0.0001937342,0.00031971102,0.00007308838,0.000005390569],"category_scores_gemma":[0.000024766734,0.00014023608,0.000044855762,0.00042400177,0.000037127546,0.0005536581,0.000035672765,0.00012306463,0.00001977643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014290745,0.0011275546,0.0006594298,0.000029966808,0.0002782494,0.00028361336,0.002774676,0.19016632,0.0018938553,0.072714195,0.037320048,0.6926092],"study_design_scores_gemma":[0.0006095288,0.00066553697,0.0016471666,0.00003711185,0.000018338273,0.00010179734,0.00009036895,0.9944919,0.00041549405,0.0003860978,0.0012610047,0.00027570018],"about_ca_topic_score_codex":0.000022636132,"about_ca_topic_score_gemma":0.0000010498904,"teacher_disagreement_score":0.8043255,"about_ca_system_score_codex":0.00010057433,"about_ca_system_score_gemma":0.00014455075,"threshold_uncertainty_score":0.57186615},"labels":[],"label_agreement":null},{"id":"W2108592250","doi":"10.1109/icsmc.1993.384884","title":"Robot path planning by integrating the artificial potential field approach with simulated annealing","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Simulated annealing; Motion planning; Robot; Computer science; Annealing (glass); Path (computing); Potential field; Artificial intelligence; Field (mathematics); Machine learning; Materials science; Mathematics; Physics","score_opus":0.024622301797013073,"score_gpt":0.23053364293494738,"score_spread":0.2059113411379343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108592250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065614786,0.00012695951,0.98741806,0.0009910081,0.00020970954,0.00017557737,0.0000012026504,0.00036159914,0.0041544125],"genre_scores_gemma":[0.7314343,9.0140156e-7,0.26751357,0.00055923726,0.00011826635,0.000005754206,0.000005804481,0.0000135411265,0.00034860594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982432,0.00010363845,0.00029717173,0.00048284043,0.0004109049,0.00046225172],"domain_scores_gemma":[0.99897605,0.00022945879,0.00012301315,0.0005146555,0.00006493731,0.00009187388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033645245,0.00021883707,0.00019223774,0.000059841725,0.00043006468,0.00042165536,0.00088376005,0.00009596836,0.00002289186],"category_scores_gemma":[0.00007248185,0.00013039692,0.00005032075,0.00045708683,0.00004503773,0.00032676608,0.00014299483,0.0004211433,0.000022639939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000119200695,0.00010977423,0.0004266858,0.0000091362335,0.00005307594,0.00010895753,0.0038999193,0.9568176,0.0016733406,0.0013897341,0.009468316,0.026031528],"study_design_scores_gemma":[0.0001688181,0.00013698719,0.00006255755,0.00003482319,0.0000088839,0.00006602553,0.00037694068,0.99791497,0.00084719574,0.00008301057,0.00008340699,0.00021636237],"about_ca_topic_score_codex":0.00010347744,"about_ca_topic_score_gemma":4.020424e-7,"teacher_disagreement_score":0.7248728,"about_ca_system_score_codex":0.000021955424,"about_ca_system_score_gemma":0.000017119632,"threshold_uncertainty_score":0.5317432},"labels":[],"label_agreement":null},{"id":"W2109010302","doi":"10.1109/tsmcc.2010.2049262","title":"Hardware/Software Codesign for a Fuzzy Autonomous Road-Following System","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Software portability; Flexibility (engineering); Computer science; Fuzzy logic; Software; Embedded system; Controller (irrigation); Fuzzy control system; Field (mathematics); Programmable logic controller; Genetic algorithm; Control engineering; Computer hardware; Computer architecture; Artificial intelligence; Engineering; Operating system","score_opus":0.024599577440127036,"score_gpt":0.2609709071260643,"score_spread":0.23637132968593727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109010302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004167029,0.003206881,0.99211824,0.00011866361,0.0011621471,0.002252549,0.00005005081,0.00028853148,0.00038620955],"genre_scores_gemma":[0.6914974,0.002496695,0.28915605,0.0002497895,0.00052943785,0.010259305,0.000025145599,0.00010798014,0.0056782262],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981276,0.00009889181,0.0006130116,0.0006513833,0.00018891884,0.00032022182],"domain_scores_gemma":[0.9984636,0.00016719151,0.00021375628,0.00081921066,0.00008820225,0.00024807156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070172077,0.0002866915,0.00051059993,0.00012909544,0.00060555286,0.0003243995,0.0003968734,0.00016348051,0.000001323975],"category_scores_gemma":[0.0000074666623,0.00024816082,0.00015558703,0.0002624973,0.00006913969,0.00015165289,0.000006228717,0.00027059735,0.00005099716],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001806952,0.00057895586,0.00012624793,0.0034236698,0.00038291598,0.000021288224,0.0017844511,0.0062588044,0.0023864014,0.06398842,0.0039622188,0.91706854],"study_design_scores_gemma":[0.0015095089,0.0003553239,0.0002259249,0.0014645546,0.00041608288,0.0005727675,0.00024621692,0.24535762,0.00055116724,0.00027519974,0.7476614,0.0013642415],"about_ca_topic_score_codex":0.000035989447,"about_ca_topic_score_gemma":0.0000064549845,"teacher_disagreement_score":0.9157043,"about_ca_system_score_codex":0.00003607104,"about_ca_system_score_gemma":0.000054655568,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W2110030783","doi":"10.1109/robot.2001.932893","title":"An information theoretical approach to view planning with kinematic and geometric constraints","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kinematics; Robot; Motion planning; Configuration space; Computer science; Entropy (arrow of time); Ignorance; Robot kinematics; Artificial intelligence; Computer vision; Mobile robot; Physics","score_opus":0.0207672388079474,"score_gpt":0.23763791834006712,"score_spread":0.21687067953211972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110030783","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029124508,0.000031822143,0.96363795,0.0002214559,0.00003044697,0.0002064238,6.9277434e-7,0.00016356031,0.03279519],"genre_scores_gemma":[0.48317033,0.000001092135,0.516269,0.00052827306,0.000008266272,0.000008857981,0.0000019174863,0.0000025794584,0.000009660333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990468,0.000041631076,0.00019190795,0.00020069977,0.00028376334,0.00023520118],"domain_scores_gemma":[0.9992975,0.00009106972,0.000045882785,0.00031703603,0.00004684593,0.00020166648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030210908,0.00011795722,0.00015313787,0.000295244,0.00007610612,0.00027820448,0.00036040717,0.00003883642,0.000026541913],"category_scores_gemma":[0.000069747635,0.00008253739,0.00000972623,0.00080571434,0.00011242746,0.00089653884,0.00006631538,0.00009328519,0.00007489175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007001102,0.00026511098,0.0019418198,0.00020484797,0.000043317952,0.000034434128,0.013229185,0.041749462,0.00001355487,0.55357075,0.0017821856,0.38715833],"study_design_scores_gemma":[0.0002451817,0.0002063766,0.0025868905,0.000048181737,0.0000049977466,0.0002513237,0.00014515022,0.9959529,0.000012322822,0.00030453937,0.00007355703,0.00016856793],"about_ca_topic_score_codex":0.0000020410514,"about_ca_topic_score_gemma":1.3210239e-8,"teacher_disagreement_score":0.9542034,"about_ca_system_score_codex":0.000013097093,"about_ca_system_score_gemma":0.000008757202,"threshold_uncertainty_score":0.3365777},"labels":[],"label_agreement":null},{"id":"W2110305242","doi":"10.1109/robot.2008.4543488","title":"Modeling multi-robot interaction using generalized occupancy grids, with application to reducing spatial interference","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Interference (communication); Generalization; Occupancy grid mapping; Computer science; Grid; Reduction (mathematics); Occupancy; Artificial intelligence; Distributed computing; Mobile robot; Simulation; Engineering; Mathematics; Telecommunications","score_opus":0.08076205691977634,"score_gpt":0.3138306927193671,"score_spread":0.23306863579959075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110305242","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15244769,0.000011514819,0.8464878,0.00009934867,0.00033809908,0.0002637756,6.62435e-7,0.00027856592,0.00007251923],"genre_scores_gemma":[0.4984631,0.0000016557085,0.5013097,0.00008595853,0.00007144935,0.000017347042,0.0000028295317,0.00000924119,0.000038731097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835545,0.000059680795,0.00033517065,0.0006522752,0.00026660357,0.00033083506],"domain_scores_gemma":[0.9989657,0.00002745259,0.000098477016,0.00059103343,0.00017004092,0.00014734406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015553524,0.00020781562,0.00021493068,0.00019670928,0.00024058644,0.00009788134,0.00056530145,0.000058005517,0.000004106487],"category_scores_gemma":[0.000032915133,0.00017983049,0.00003663365,0.00042271416,0.00002147705,0.00063900516,0.00019575836,0.00018059496,0.000044839293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022036578,0.00004492899,0.00060838734,0.0000048701504,0.000010598249,0.0000127191415,0.0012842433,0.975404,0.013728666,0.000082611354,0.000016662434,0.008780231],"study_design_scores_gemma":[0.000359565,0.000078051184,0.0005189266,0.000085129155,0.0000060126913,0.00022224308,0.000032659253,0.9943458,0.004073537,0.000013340766,0.0000068251284,0.00025795534],"about_ca_topic_score_codex":0.0040386803,"about_ca_topic_score_gemma":0.00004134598,"teacher_disagreement_score":0.3460154,"about_ca_system_score_codex":0.000150798,"about_ca_system_score_gemma":0.000107612075,"threshold_uncertainty_score":0.73332745},"labels":[],"label_agreement":null},{"id":"W2111451384","doi":"10.1177/0278364909348805","title":"On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries","year":2009,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Division of Information and Intelligent Systems; Natural Sciences and Engineering Research Council of Canada; Air Force Office of Scientific Research; Boeing; National Science Foundation","keywords":"Payload (computing); Micro air vehicle; Track (disk drive); Computer science; Drone; Real-time computing; Artificial intelligence; Simulation; Aerospace engineering; Engineering; Computer security; Aerodynamics","score_opus":0.12743892453567485,"score_gpt":0.36352251697974003,"score_spread":0.23608359244406518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111451384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19189629,0.00017941845,0.71648407,0.09093491,0.00022375525,0.00021200487,0.000002204444,0.00000792248,0.000059419646],"genre_scores_gemma":[0.80363446,0.00007679326,0.19514152,0.00091827963,0.00007461158,7.6748205e-7,1.05875635e-7,0.0000046419063,0.00014878983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811137,0.00031795475,0.00024262758,0.00011309222,0.0010421079,0.00017284957],"domain_scores_gemma":[0.99583024,0.0030400574,0.0001141787,0.00020958728,0.00072184065,0.000084095074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026076126,0.00007031706,0.00011418052,0.00021476934,0.00012439355,0.00023344977,0.0012678034,0.000027352118,0.0000017077753],"category_scores_gemma":[0.0011111255,0.00003940248,0.00003071475,0.00020576865,0.00015159756,0.00023831984,0.00021748879,0.00040559,0.000004188055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048262737,0.00021893138,0.0004129139,0.0000068219374,0.00018359872,0.00022458783,0.0068034753,0.8687408,0.0220188,0.064721204,0.012450914,0.023735313],"study_design_scores_gemma":[0.0013988663,0.0031793383,0.041294225,0.0006509047,0.00002285396,0.0010968191,0.0005219487,0.8468757,0.035080127,0.068752356,0.00084187917,0.00028496547],"about_ca_topic_score_codex":0.000012956913,"about_ca_topic_score_gemma":5.194659e-7,"teacher_disagreement_score":0.6117382,"about_ca_system_score_codex":0.000048467275,"about_ca_system_score_gemma":0.00011428795,"threshold_uncertainty_score":0.23559152},"labels":[],"label_agreement":null},{"id":"W2111609065","doi":"10.1109/icinfa.2009.5205107","title":"A non-time based action executor for the coordinated hybrid agent framework","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Executor; Control engineering; Computer science; Automation; Teleoperation; Robot; Control (management); Multi-agent system; Mobile robot; Action (physics); Engineering; Artificial intelligence","score_opus":0.026623915448171703,"score_gpt":0.2867016735602264,"score_spread":0.2600777581120547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111609065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007562303,0.00002046162,0.9907153,0.0066022496,0.00057297765,0.00041261018,0.0000028616923,0.00032485832,0.0005924508],"genre_scores_gemma":[0.22218004,0.0000013086042,0.77272826,0.0033268405,0.00016411746,0.000043464195,0.0000072261987,0.0000094069555,0.0015393045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989864,0.00002680513,0.00016357763,0.00031576763,0.00020523283,0.00030221356],"domain_scores_gemma":[0.99874973,0.00042334737,0.000073684416,0.00059526233,0.00008002625,0.000077968245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032759874,0.00013462453,0.00013429273,0.000058243,0.00021298032,0.00017178076,0.0007561958,0.00005516778,0.000024470017],"category_scores_gemma":[0.00010626945,0.00009098436,0.000084032305,0.00027712897,0.000018993544,0.00017915605,0.000037785456,0.00013643275,0.00010433379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013452534,0.00057917676,0.00013347047,0.00003742706,0.00015374142,0.00013007302,0.0010507702,0.11049271,0.008402183,0.011783565,0.3326068,0.5344956],"study_design_scores_gemma":[0.00026977374,0.00017931467,0.0012153962,0.000024218607,0.000009668107,0.00000994524,0.0000061191954,0.9882969,0.0053362497,0.0015122297,0.0029962184,0.00014393628],"about_ca_topic_score_codex":0.000011292472,"about_ca_topic_score_gemma":8.689512e-8,"teacher_disagreement_score":0.8778042,"about_ca_system_score_codex":0.00005317897,"about_ca_system_score_gemma":0.000063709434,"threshold_uncertainty_score":0.37102345},"labels":[],"label_agreement":null},{"id":"W2111638077","doi":"10.1109/tac.2004.837589","title":"Formations of Vehicles in Cyclic Pursuit","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":669,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nonholonomic system; Constraint (computer-aided design); Focus (optics); Modulo; Controller (irrigation); Stability (learning theory); Simple (philosophy); Control theory (sociology); Computer science; Constant (computer programming); Principal (computer security); Mathematics; Mathematical optimization; Control (management); Mobile robot; Artificial intelligence; Robot; Geometry; Discrete mathematics; Physics","score_opus":0.012998186268752671,"score_gpt":0.24278498113820252,"score_spread":0.22978679486944983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111638077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027352389,0.00001917939,0.97022563,0.0012181856,0.00038646793,0.00032204282,0.000009897282,0.00024573045,0.00022047052],"genre_scores_gemma":[0.9445278,0.0000025729003,0.055211585,0.0001332912,0.0000053347444,0.00007074891,3.6102173e-7,0.000007800276,0.000040476927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986934,0.000060538085,0.0004642911,0.00019562701,0.00033106076,0.00025505514],"domain_scores_gemma":[0.99907714,0.00023102129,0.000120363984,0.00046104274,0.000039987604,0.00007045694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027701087,0.00013516848,0.00026111348,0.0003539599,0.00008323038,0.000048651105,0.00053910085,0.00006664175,0.000011586426],"category_scores_gemma":[0.000014455346,0.0001312658,0.000095766365,0.00052684505,0.000044030727,0.00039987906,0.0000014291666,0.00014045286,0.00008513692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008903337,0.00067410455,0.00003398216,0.000071975344,0.00007022699,0.000025886873,0.0026669125,0.8975125,0.00603464,0.0024910222,0.000021724056,0.09038811],"study_design_scores_gemma":[0.0033027167,0.00016563172,0.0041008173,0.00020121597,0.000023213901,0.000032707016,0.000053095682,0.9802883,0.009031806,0.0025978114,0.000013365319,0.00018931426],"about_ca_topic_score_codex":0.00007307859,"about_ca_topic_score_gemma":0.000015594194,"teacher_disagreement_score":0.9171754,"about_ca_system_score_codex":0.0001063361,"about_ca_system_score_gemma":0.000113819966,"threshold_uncertainty_score":0.5352864},"labels":[],"label_agreement":null},{"id":"W2111984142","doi":"10.1504/ijcat.2006.010082","title":"CMM sequence optimisation with collision detection","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Applications in Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"OpenGL; Collision; Collision detection; Computer science; Path (computing); Heuristic; Coordinate-measuring machine; Algorithm; Kernel (algebra); Graphics; Sequence (biology); Artificial intelligence; Computer graphics (images); Mathematics; Engineering; Combinatorics; Visualization","score_opus":0.008934999885712884,"score_gpt":0.2634761151792576,"score_spread":0.25454111529354473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111984142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041369118,0.000048725018,0.95633286,0.001545647,0.00031711906,0.0001437696,0.0000012783185,0.000099460034,0.0001420087],"genre_scores_gemma":[0.5485301,0.0000066039192,0.45127615,0.000030005936,0.00012362833,0.000018427761,0.0000023558264,0.000004625875,0.000008122019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874675,0.00002812818,0.00047661306,0.0002160035,0.00039146317,0.0001410535],"domain_scores_gemma":[0.99858636,0.000086810236,0.0004338171,0.00027613138,0.0005910455,0.000025809835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023082508,0.00011054684,0.00015554258,0.0010375382,0.000041722287,0.00008696815,0.0014230238,0.00010547049,0.0000011852746],"category_scores_gemma":[0.000011843286,0.00009656112,0.000035788806,0.00082583807,0.00007992967,0.0004110936,0.00012983849,0.00026113412,0.000006659568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042205404,0.00054350914,0.013071972,0.00000796819,0.000086012486,0.0002191564,0.00020089913,0.45221218,0.009004554,0.12282873,0.00029274527,0.4014901],"study_design_scores_gemma":[0.002298511,0.0006499573,0.02152524,0.00021050479,0.000018170202,0.004078721,0.000059632384,0.82247025,0.049202442,0.08989516,0.009095726,0.00049569894],"about_ca_topic_score_codex":0.00002444734,"about_ca_topic_score_gemma":0.0000062687745,"teacher_disagreement_score":0.50716096,"about_ca_system_score_codex":0.00022997233,"about_ca_system_score_gemma":0.00008687496,"threshold_uncertainty_score":0.39376482},"labels":[],"label_agreement":null},{"id":"W2112022322","doi":"10.1109/cig.2007.368073","title":"Micro Robot Hockey Simulator - Game Engine Design","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Simulation; Computer science; Human–computer interaction; Robot kinematics; Mobile robot; Engineering; Artificial intelligence","score_opus":0.026907849852375532,"score_gpt":0.2610641424375525,"score_spread":0.23415629258517695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112022322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015736461,0.00010234465,0.9958113,0.00027099514,0.0005308521,0.00014670925,3.8147533e-7,0.00053860893,0.001025156],"genre_scores_gemma":[0.16937082,0.0000014235252,0.8286237,0.00046286057,0.00010759145,0.0000017957857,8.965673e-7,0.000011723555,0.0014191974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850863,0.000040712213,0.0002556096,0.0003915384,0.000279263,0.000524262],"domain_scores_gemma":[0.9986862,0.00038137307,0.000057182053,0.00061242847,0.00006625373,0.00019658927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008330439,0.00016895839,0.00017112498,0.00014154821,0.00006142364,0.000102567436,0.0008557806,0.00008365954,0.000025194797],"category_scores_gemma":[0.00008173209,0.00014931105,0.000051966832,0.0004127464,0.000026959415,0.00027617687,0.00015625573,0.00014658089,0.00041841168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021889538,0.00022097165,0.0012568583,0.000014518855,0.00006822911,0.0006747985,0.0016230532,0.8654315,0.0228192,0.00434159,0.008627591,0.09489983],"study_design_scores_gemma":[0.00043842132,0.00010842978,0.004427922,0.000015761661,0.000004884008,0.000065431195,0.0000105100935,0.94882613,0.04377231,0.00048175402,0.0015234597,0.0003250016],"about_ca_topic_score_codex":0.000021960177,"about_ca_topic_score_gemma":4.9645894e-7,"teacher_disagreement_score":0.16779716,"about_ca_system_score_codex":0.00005457866,"about_ca_system_score_gemma":0.000057439232,"threshold_uncertainty_score":0.6088728},"labels":[],"label_agreement":null},{"id":"W2112141968","doi":"10.1109/crv.2007.27","title":"Energy Efficient Robot Rendezvous","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rendezvous; Robot; Heading (navigation); Computer science; Mobile robot; Heuristic; Simplex algorithm; Mathematical optimization; Energy (signal processing); Point (geometry); Algorithm; Artificial intelligence; Linear programming; Mathematics; Engineering","score_opus":0.013289813409510597,"score_gpt":0.24077435369464073,"score_spread":0.22748454028513013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112141968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000508251,0.00006796071,0.9551372,0.00026616803,0.0006546734,0.000025563275,1.4684618e-7,0.00040027135,0.0429398],"genre_scores_gemma":[0.3695007,0.000001100065,0.6267858,0.00055649213,0.00008685815,0.0000015697423,8.3613304e-7,0.0000066067855,0.0030600305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998765,0.000020103273,0.00018844056,0.00031783737,0.0003113924,0.00039720818],"domain_scores_gemma":[0.9991458,0.00010773184,0.000044991175,0.00051911117,0.000044115106,0.00013826622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004761246,0.00010400298,0.00010202059,0.0001145491,0.0000772867,0.00006328962,0.0006818927,0.00005067595,0.000013285711],"category_scores_gemma":[0.000021212241,0.000088376946,0.00004004289,0.00038162892,0.000023017672,0.000074721516,0.00018189386,0.00007123126,0.00011472696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068520712,0.00024771667,0.00047559463,0.0000056995136,0.000029050281,0.00070115394,0.0007947097,0.15968831,0.0024130868,0.33692038,0.0073603205,0.49135712],"study_design_scores_gemma":[0.00022260378,0.00006540056,0.004496607,0.000008969464,0.0000022694735,0.00011766643,0.000018666853,0.9806479,0.008665045,0.00066174887,0.004852711,0.00024038368],"about_ca_topic_score_codex":0.00008345049,"about_ca_topic_score_gemma":0.0000036197887,"teacher_disagreement_score":0.8209596,"about_ca_system_score_codex":0.000044141583,"about_ca_system_score_gemma":0.00003255459,"threshold_uncertainty_score":0.36039072},"labels":[],"label_agreement":null},{"id":"W2112323931","doi":"10.1109/mfi.1994.398459","title":"Local path planning in dynamic environments with uncertainty","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Obstacle; Motion planning; Acceleration; Path (computing); Mobile robot; Computer science; Collision; Robot; Function (biology); Mathematical optimization; Collision avoidance; Obstacle avoidance; Simulation; Artificial intelligence; Mathematics; Computer security; Geography; Physics","score_opus":0.014618243919219497,"score_gpt":0.22162010063949827,"score_spread":0.20700185672027877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112323931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010890248,0.000070345406,0.9848462,0.00026471654,0.000069891525,0.00008749335,5.719695e-7,0.00009859984,0.0036719504],"genre_scores_gemma":[0.83656967,0.0000036044935,0.16197594,0.00021326439,0.000005943581,0.000008584612,0.0000016491172,0.000007708006,0.001213653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880946,0.000040439638,0.00015580335,0.00036897743,0.00028234263,0.00034296012],"domain_scores_gemma":[0.99943715,0.000053325686,0.000042330263,0.0003813442,0.000004093668,0.00008176976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258081,0.00013438505,0.00013440529,0.00008918542,0.000047765814,0.000045691188,0.0004926485,0.000047273188,0.000029272394],"category_scores_gemma":[0.000006358494,0.0001048152,0.000016397535,0.00024402657,0.000053445852,0.00024221075,0.00010672825,0.0001598881,0.00015713189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055505143,0.00016501547,0.012530102,0.000006363338,0.000015052025,0.0009017058,0.0021269848,0.9349644,0.00007456206,0.0007641107,0.00059788575,0.047848266],"study_design_scores_gemma":[0.0004026066,0.000093731906,0.0139581505,0.000040666928,0.000001285426,0.000044858924,0.00007010885,0.98471344,0.000021046117,0.000091911,0.00039978678,0.00016238863],"about_ca_topic_score_codex":0.00004485669,"about_ca_topic_score_gemma":0.0000028232569,"teacher_disagreement_score":0.8256794,"about_ca_system_score_codex":0.0001351962,"about_ca_system_score_gemma":0.000009865623,"threshold_uncertainty_score":0.42742398},"labels":[],"label_agreement":null},{"id":"W2112409596","doi":"10.1109/iros.1998.724879","title":"On sensor-based roadmap: a framework for motion planning for a manipulator arm in unknown environments","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Configuration space; Computer science; Robotic arm; Robot; Motion (physics); Graph; Robot manipulator; Space (punctuation); Simulation; Control engineering; Artificial intelligence; Engineering; Physics; Theoretical computer science","score_opus":0.06441978604810228,"score_gpt":0.28556729963455674,"score_spread":0.22114751358645446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112409596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007938818,0.000027359465,0.99000645,0.0005963736,0.0003414138,0.00069811795,0.0000049571286,0.00012716191,0.0002593706],"genre_scores_gemma":[0.26669416,4.993014e-7,0.7318103,0.0007971636,0.00006580558,0.00014789737,0.000006869226,0.000018125094,0.00045920655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847317,0.000038077724,0.00027199343,0.0005315672,0.00024548982,0.0004396827],"domain_scores_gemma":[0.99870557,0.00063895516,0.000098073084,0.00045097762,0.000012090717,0.00009436051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002540033,0.00019054461,0.0002016664,0.00017717846,0.00010138603,0.000076745135,0.00040134657,0.0001451479,0.000017911705],"category_scores_gemma":[0.00019435969,0.00018243946,0.000081991246,0.00018102983,0.000017183758,0.00015057893,0.000037830698,0.00014926451,0.000054775643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008351724,0.001264167,0.0042267255,0.00008366603,0.00004244405,0.00007076809,0.0018772563,0.8416893,0.00029419715,0.10798281,0.0022531864,0.04013197],"study_design_scores_gemma":[0.0010322814,0.00035132025,0.0013893275,0.00011547282,0.0000044154963,0.000003196559,0.000023532102,0.9884043,0.0005725771,0.0070252838,0.000850628,0.00022769431],"about_ca_topic_score_codex":0.0000051383918,"about_ca_topic_score_gemma":1.8161431e-7,"teacher_disagreement_score":0.25875536,"about_ca_system_score_codex":0.000120948665,"about_ca_system_score_gemma":0.000009141006,"threshold_uncertainty_score":0.7439665},"labels":[],"label_agreement":null},{"id":"W2112429204","doi":"","title":"Optimistic Shortest Paths on Uncertain Terrains","year":2004,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"K shortest path routing; Shortest path problem; Euclidean shortest path; Yen's algorithm; Shortest Path Faster Algorithm; Constrained Shortest Path First; Terrain; Computer science; Geodesic; Longest path problem; Path (computing); Widest path problem; Pathfinding; Mathematical optimization; Algorithm; Mathematics; Dijkstra's algorithm; Theoretical computer science; Geography; Graph; Geometry","score_opus":0.04657298695031612,"score_gpt":0.2724671985039716,"score_spread":0.2258942115536555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112429204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011590504,0.000026206593,0.95835793,0.0071164723,0.0009881295,0.0002958878,0.00011857795,0.00024242881,0.021263847],"genre_scores_gemma":[0.8854265,0.0000014358058,0.110649295,0.0035046956,0.00009976081,0.00001659365,0.00008880042,0.000016294789,0.00019659042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977904,0.000056438308,0.0002855296,0.00067109574,0.00057764293,0.00061889464],"domain_scores_gemma":[0.9981358,0.00028285707,0.000089003945,0.00049724325,0.00022435423,0.00077073026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002403203,0.00028059148,0.00023158615,0.00079759135,0.0002962969,0.00029994224,0.001018949,0.00013163387,0.00007576467],"category_scores_gemma":[0.00023453445,0.0002962633,0.00006690395,0.0008690935,0.00010291659,0.00017924797,0.000051989922,0.0003855715,0.0007048017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021258336,0.000045275152,0.0001496182,0.000004044925,0.000015175703,0.00031738754,0.00020177843,0.7546544,0.0000034359107,0.23224787,0.00056268455,0.0117962025],"study_design_scores_gemma":[0.0009626428,0.000797057,0.026065351,0.0002784869,0.000009155705,0.00013964983,0.00009544959,0.90519476,0.000028368877,0.06463924,0.0009185842,0.00087128376],"about_ca_topic_score_codex":0.0045022387,"about_ca_topic_score_gemma":0.00096742355,"teacher_disagreement_score":0.87383604,"about_ca_system_score_codex":0.0006699042,"about_ca_system_score_gemma":0.002581824,"threshold_uncertainty_score":0.999949},"labels":[],"label_agreement":null},{"id":"W2112695860","doi":"10.1142/s021819590800257x","title":"BOUNDED-VELOCITY APPROXIMATION OF MOBILE EUCLIDEAN 2-CENTRES","year":2008,"lang":"en","type":"article","venue":"International Journal of Computational Geometry & Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; McGill University","funders":"Mitacs; Simon Fraser University","keywords":"Euclidean geometry; Bounded function; Mathematics; Euclidean distance; Plane (geometry); RADIUS; Closeness; Combinatorics; Set (abstract data type); Trajectory; Geometry; Mathematical analysis; Computer science; Physics","score_opus":0.017897807115312497,"score_gpt":0.28662663567978275,"score_spread":0.2687288285644702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112695860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029751662,0.00025751413,0.96810436,0.00042098167,0.0003899717,0.00027007633,0.000028011193,0.00004834598,0.00072906545],"genre_scores_gemma":[0.56170005,0.000042512103,0.43772537,0.000109713474,0.0002839685,0.00003654547,0.000034474004,0.0000090801595,0.000058286823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703705,0.000071731345,0.0010313399,0.00024936732,0.00142901,0.00018147776],"domain_scores_gemma":[0.99570554,0.0004975302,0.0011596864,0.00025839056,0.0022453004,0.0001335183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048449,0.00016165989,0.00029567763,0.0007528035,0.00015056276,0.00008343391,0.0016600754,0.000069620786,0.000025885623],"category_scores_gemma":[0.00008929809,0.00016256639,0.00019362508,0.00082988926,0.00015958922,0.00070258253,0.00017385573,0.00023376258,0.00004069025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005009574,0.0014936303,0.0075772665,0.000048152942,0.0006494256,0.000083616345,0.0015648565,0.73342735,0.00054394454,0.137188,0.001859232,0.11551446],"study_design_scores_gemma":[0.0056768204,0.00078268175,0.1249439,0.00030657876,0.0001250907,0.0073335804,0.0004371998,0.5091983,0.005999454,0.27704227,0.06677017,0.001383941],"about_ca_topic_score_codex":0.00000938834,"about_ca_topic_score_gemma":2.0840419e-7,"teacher_disagreement_score":0.5319484,"about_ca_system_score_codex":0.00015359641,"about_ca_system_score_gemma":0.0004045412,"threshold_uncertainty_score":0.6629265},"labels":[],"label_agreement":null},{"id":"W2113109498","doi":"10.1109/19.893281","title":"Visual measurement of orientation error for a mobile robot","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mobile robot; Orientation (vector space); Computer science; Computer vision; Artificial intelligence; Robot; Mathematics; Geometry","score_opus":0.049243386451751296,"score_gpt":0.2940823803805593,"score_spread":0.24483899392880798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113109498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034777824,0.000035697656,0.9634502,0.00014028506,0.00054727943,0.00082811253,0.000009898381,0.000080782396,0.00012992679],"genre_scores_gemma":[0.93563145,0.000026753662,0.063727304,0.00011008421,0.000017535709,0.0004052116,0.0000026638593,0.000010767114,0.000068217676],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980428,0.00006936303,0.00039160572,0.00035284576,0.00093806494,0.0002053246],"domain_scores_gemma":[0.99926335,0.000024744022,0.00011068428,0.00020373602,0.00029627807,0.00010118907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006437721,0.00016137409,0.00017335657,0.00015318795,0.00019797792,0.000053471085,0.00014943488,0.00005009684,0.000047443507],"category_scores_gemma":[0.0000051330294,0.00016169978,0.0000763062,0.00022620332,0.000038064965,0.00031707593,9.205117e-7,0.000076171906,0.000011539119],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012720736,0.00053182535,0.000024059145,0.000065017724,0.000095795396,6.7148954e-7,0.0016985246,0.05277132,0.018523643,0.00014194433,0.000066184,0.9259538],"study_design_scores_gemma":[0.009053906,0.004173101,0.0031497544,0.00040257993,0.00021472908,0.000029757754,0.001104479,0.32126558,0.6573965,0.00041652337,0.0019574508,0.0008356182],"about_ca_topic_score_codex":0.000039560087,"about_ca_topic_score_gemma":0.00001327837,"teacher_disagreement_score":0.9251182,"about_ca_system_score_codex":0.00021139125,"about_ca_system_score_gemma":0.0001067027,"threshold_uncertainty_score":0.65939254},"labels":[],"label_agreement":null},{"id":"W2113302706","doi":"10.1109/cdc.2006.376831","title":"A Viability Problem for Control Affine Systems with Application to Collision Avoidance","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Affine transformation; Controller (irrigation); Control theory (sociology); Domain (mathematical analysis); Kernel (algebra); Boundary (topology); Collision avoidance; Computer science; Control (management); Mathematics; Collision; Artificial intelligence; Pure mathematics; Mathematical analysis; Computer security","score_opus":0.005486787245405587,"score_gpt":0.21906212537169029,"score_spread":0.2135753381262847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113302706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018640754,0.000022132104,0.9930989,0.0010718899,0.0000821067,0.0025180061,0.000008472509,0.00029630994,0.0010380875],"genre_scores_gemma":[0.48506558,5.416828e-8,0.5135341,0.00009074588,0.000067624234,0.0007099228,0.0000040079544,0.0000064095284,0.0005215465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874765,0.000037777914,0.00024124308,0.00047674606,0.00023893293,0.00025766992],"domain_scores_gemma":[0.9989019,0.00018306744,0.00009120568,0.000540754,0.00021079025,0.0000723008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049361563,0.00012236775,0.00018832638,0.000050343668,0.00010399837,0.00012685214,0.00043964808,0.00004298537,3.790267e-7],"category_scores_gemma":[0.00002147058,0.00009041456,0.00002365998,0.00035646788,0.000016323287,0.00018382193,0.00003687132,0.00004509215,0.000025871204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004781973,0.00014552144,0.002407247,0.00007480107,0.000008708009,0.0000018223794,0.000080673126,0.95483327,0.0018584676,0.03386908,0.00332121,0.0033514109],"study_design_scores_gemma":[0.00075217086,0.00023853403,0.0035250161,0.000029057914,0.000004596935,0.00000934983,0.000005775355,0.99133533,0.000482947,0.000593828,0.0028674675,0.00015594599],"about_ca_topic_score_codex":0.00031284743,"about_ca_topic_score_gemma":0.000017940214,"teacher_disagreement_score":0.4832015,"about_ca_system_score_codex":0.00008371998,"about_ca_system_score_gemma":0.00005224793,"threshold_uncertainty_score":0.36869988},"labels":[],"label_agreement":null},{"id":"W2113454633","doi":"10.1109/ccece.2005.1557016","title":"A proposal for self-recognition in robot programming","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Robot; Computer science; Process (computing); Artificial intelligence; A priori and a posteriori; Personal robot; Code (set theory); Human–computer interaction; Robot control; Social robot; Mobile robot; Computer vision; Set (abstract data type); Programming language","score_opus":0.017423122958311384,"score_gpt":0.2498184533278578,"score_spread":0.2323953303695464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113454633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018713938,0.000014505781,0.9950362,0.0006111653,0.0001649003,0.000525368,6.572673e-7,0.00046319756,0.0013126194],"genre_scores_gemma":[0.026116472,1.9429342e-7,0.9734226,0.00004598061,0.00008147519,0.00013211922,0.0000099638455,0.00000640796,0.00018482129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911207,0.000023731349,0.00017947289,0.00026918488,0.00012820952,0.00028733423],"domain_scores_gemma":[0.9996601,0.00006075134,0.00004271112,0.00015657359,0.000050626433,0.00002926897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027324507,0.000085262305,0.00009481615,0.00010094912,0.00004663588,0.00011257866,0.000256924,0.00004533429,0.0000014545107],"category_scores_gemma":[0.000019164972,0.000076912576,0.000031014417,0.00027430573,0.000009315525,0.0002867773,0.000045194993,0.00005938367,0.00003261249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015101435,0.0012441969,0.009118619,0.00013688473,0.000025108255,0.00010684154,0.0010835695,0.009455414,0.0009784907,0.057888154,0.0029241412,0.9170235],"study_design_scores_gemma":[0.0009762428,0.0001883531,0.0057859947,0.00004319981,0.000005601867,0.000039112212,0.00002120316,0.9643702,0.002054555,0.025244964,0.00096177513,0.00030879778],"about_ca_topic_score_codex":0.00012426102,"about_ca_topic_score_gemma":0.000031050524,"teacher_disagreement_score":0.9549148,"about_ca_system_score_codex":0.000043320677,"about_ca_system_score_gemma":0.000086919885,"threshold_uncertainty_score":0.3136404},"labels":[],"label_agreement":null},{"id":"W2113517870","doi":"10.21307/ijssis-2017-325","title":"Advanced Predictive Guidance Navigation for Mobile Robots: A Novel Strategy for Rendezvous in Dynamic Settings","year":2008,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Interception; Proportional navigation; Computer science; Trajectory; Position (finance); Novelty; Robot; Matching (statistics); Mobile robot; Control theory (sociology); Simulation; Artificial intelligence; Engineering; Aerospace engineering; Control (management); Mathematics","score_opus":0.03264218353701023,"score_gpt":0.3105030588939505,"score_spread":0.27786087535694026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113517870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04954212,0.00039288608,0.945729,0.0002952995,0.0033082697,0.0005821812,0.000033965396,0.0000567138,0.000059535425],"genre_scores_gemma":[0.9074245,0.00008890389,0.09161323,0.00014038279,0.00032615513,0.00004584893,0.000029010489,0.000023062248,0.00030887793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980943,0.000060196864,0.0006804477,0.00040523562,0.00046500776,0.00029480652],"domain_scores_gemma":[0.9982498,0.00040298045,0.0004387748,0.00016306582,0.00064094045,0.000104420295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006889178,0.00019691057,0.00026867303,0.00027115337,0.0002097866,0.0002263015,0.000372853,0.00008695098,2.501332e-7],"category_scores_gemma":[0.00017833305,0.00018460122,0.00009314614,0.00012765767,0.00004420551,0.00040084435,0.000043690263,0.00023632393,0.000002912232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022300975,0.00014070232,0.0002489627,0.000042121967,0.00012213935,0.00012360857,0.0020252385,0.95130026,0.0023056741,0.0009222817,0.00045396658,0.042092048],"study_design_scores_gemma":[0.00086792995,0.00059155794,0.0004047866,0.0010147488,0.0000053360054,0.0024362125,0.0002955331,0.99149173,0.0005179776,0.00031351257,0.0018671461,0.00019353155],"about_ca_topic_score_codex":0.000039711667,"about_ca_topic_score_gemma":0.0000021862759,"teacher_disagreement_score":0.8578824,"about_ca_system_score_codex":0.0003754979,"about_ca_system_score_gemma":0.00010938051,"threshold_uncertainty_score":0.75278187},"labels":[],"label_agreement":null},{"id":"W2113660339","doi":"10.1109/icsmc.2007.4413724","title":"Experiments on a novel modular cost-effective RFID-based mobile robot navigation system","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mobile robot navigation; Mobile robot; Computer science; Modular design; Robot; Artificial intelligence; Navigation system; Robotics; Radio navigation; Scheme (mathematics); SIGNAL (programming language); Computer vision; Robot control; Real-time computing; Global Positioning System; Telecommunications","score_opus":0.02458157968558724,"score_gpt":0.30250875320725645,"score_spread":0.27792717352166924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113660339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025565984,0.00002459304,0.96915746,0.000027343947,0.00073802564,0.0017246756,0.000002871726,0.0005769798,0.0021820432],"genre_scores_gemma":[0.7406887,3.4400752e-8,0.25863066,0.00014680422,0.00005825712,0.0003570872,0.000009375621,0.000013335803,0.00009574818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981298,0.00006186231,0.0002845195,0.0005648742,0.0005343853,0.00042459863],"domain_scores_gemma":[0.99869645,0.00024379863,0.00011947432,0.0006621179,0.00011492544,0.00016324045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071769004,0.0002162193,0.00021075834,0.00015471107,0.00014233022,0.000104682826,0.00056530576,0.000102852806,0.0000028042195],"category_scores_gemma":[0.000023450246,0.00019400568,0.0000684032,0.00043044673,0.00002883801,0.0002533016,0.000081113765,0.00015324433,0.00016987095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056729634,0.0007577344,0.0003991736,0.00007854504,0.00006204789,0.000370593,0.0013072701,0.8673947,0.07562583,0.012118153,0.00017127709,0.04165796],"study_design_scores_gemma":[0.0008472623,0.00032294804,0.0017381272,0.00015966925,0.0000041449307,0.000030096355,0.00011614094,0.8256621,0.17077427,0.0000091165475,0.000112977126,0.00022316398],"about_ca_topic_score_codex":0.00006727022,"about_ca_topic_score_gemma":2.8470862e-7,"teacher_disagreement_score":0.7151227,"about_ca_system_score_codex":0.00041320082,"about_ca_system_score_gemma":0.00004596736,"threshold_uncertainty_score":0.7911322},"labels":[],"label_agreement":null},{"id":"W2113784107","doi":"10.1109/ccece.2004.1345203","title":"Genetic algorithm for dynamic path planning","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Genetic algorithm; Motion planning; Path (computing); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Robot","score_opus":0.015865149804344687,"score_gpt":0.2685622060551077,"score_spread":0.25269705625076305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113784107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009518134,0.00016717803,0.9968021,0.00032208362,0.000538049,0.00023511381,0.0000041478947,0.0004169778,0.00056255556],"genre_scores_gemma":[0.013698687,0.000002327002,0.9855149,0.00033787583,0.00006556982,0.00003839708,0.000003884176,0.000014458203,0.00032392616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987328,0.000014118438,0.00020518155,0.00042186843,0.00021116961,0.00041484032],"domain_scores_gemma":[0.9992536,0.00007333258,0.000059434635,0.00044698504,0.000053568354,0.00011309132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015516943,0.00015169241,0.00015317134,0.00009161013,0.00013496076,0.0001166004,0.0007310945,0.00006418364,0.000002611741],"category_scores_gemma":[0.00002300428,0.00013807492,0.0000652294,0.00021615566,0.00002408607,0.00020757996,0.000116931486,0.00008886171,0.000063791624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002480986,0.00013533887,0.00026683754,0.000024633511,0.00005037988,0.0002763633,0.0015123434,0.20538713,0.0003723454,0.008788683,0.001044304,0.7821392],"study_design_scores_gemma":[0.0006751763,0.00013776169,0.003947463,0.000033438268,0.0000048499287,0.00009323613,0.000024455072,0.98474926,0.00017769898,0.009525936,0.00040266634,0.00022803618],"about_ca_topic_score_codex":0.000020605445,"about_ca_topic_score_gemma":2.1161095e-7,"teacher_disagreement_score":0.78191113,"about_ca_system_score_codex":0.00008154457,"about_ca_system_score_gemma":0.00010186719,"threshold_uncertainty_score":0.5630532},"labels":[],"label_agreement":null},{"id":"W2115043830","doi":"10.1142/s0218195902000724","title":"KINETIC COLLISION DETECTION FOR SIMPLE POLYGONS","year":2002,"lang":"en","type":"article","venue":"International Journal of Computational Geometry & Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Logarithm; Simple (philosophy); Regular polygon; Plane (geometry); Combinatorics; Certificate; Set (abstract data type); Collision detection; Upper and lower bounds; Collision; Discrete mathematics; Algorithm; Geometry; Computer science; Mathematical analysis","score_opus":0.021427093830425268,"score_gpt":0.29288042518449425,"score_spread":0.27145333135406896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115043830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019057378,0.000319135,0.99475455,0.0016146314,0.0006251941,0.00036517714,0.00004060418,0.00006088408,0.00031408048],"genre_scores_gemma":[0.6150868,0.000023328039,0.38361487,0.00030686185,0.00070929935,0.00010018852,0.000025814168,0.000013791686,0.00011905069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793226,0.00004152311,0.0006897144,0.0002592529,0.0008763531,0.00020091305],"domain_scores_gemma":[0.9966516,0.00073445006,0.00060150994,0.00021432247,0.0016555768,0.00014252702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034977167,0.00015026606,0.00019897611,0.0007432283,0.00017742135,0.00021580151,0.0013166957,0.000070337504,0.00003989432],"category_scores_gemma":[0.000129181,0.00015259195,0.00018850886,0.0007334519,0.00004885807,0.00051328674,0.000103595514,0.00018453141,0.000065945496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028839979,0.00055683294,0.0002950453,0.000016820302,0.0002767033,0.000017390717,0.00020839876,0.6195251,0.00076955266,0.028452385,0.0042082006,0.34564477],"study_design_scores_gemma":[0.0014544068,0.00026810257,0.0035890457,0.000034262895,0.000037573744,0.00075120624,0.00003385891,0.8130897,0.00096985034,0.07080266,0.108670846,0.0002984512],"about_ca_topic_score_codex":0.0000046558794,"about_ca_topic_score_gemma":4.1964745e-7,"teacher_disagreement_score":0.61318105,"about_ca_system_score_codex":0.00016560564,"about_ca_system_score_gemma":0.00007041714,"threshold_uncertainty_score":0.6222519},"labels":[],"label_agreement":null},{"id":"W2116191977","doi":"10.1109/iros.2003.1250745","title":"Simultaneous path planning and exploration for manipulators with eye and skin sensors","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Motion planning; Computer vision; Kinematics; Modalities; Artificial intelligence; Modality (human–computer interaction); Robot; Path (computing); Degrees of freedom (physics and chemistry); Physics","score_opus":0.02192071001353102,"score_gpt":0.2579573343064701,"score_spread":0.2360366242929391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116191977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20151821,0.00004612481,0.7977659,0.00022021892,0.00005150306,0.00018046069,8.8145123e-7,0.00012567574,0.00009100501],"genre_scores_gemma":[0.5909952,0.0000017685187,0.40883115,0.000065936794,0.000021621321,0.000007493838,0.0000017781039,0.000006698699,0.00006833594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992269,0.00001218112,0.00011456616,0.00032028748,0.00013220006,0.00019382757],"domain_scores_gemma":[0.9995429,0.00012205822,0.000048125763,0.00016511042,0.000038001548,0.000083821455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001104795,0.000120264755,0.000117089825,0.000059985505,0.00012651486,0.00013435647,0.0001066807,0.000042645443,2.561678e-7],"category_scores_gemma":[0.000033970966,0.000093888775,0.000009859035,0.00010575272,0.000030958276,0.00039789386,0.000046158722,0.000053929507,0.0000018302171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032941287,0.000044309578,0.004115161,0.00005120542,0.00003218713,0.0002933087,0.009198007,0.9557762,0.00022436843,0.012865193,0.000059755548,0.01730733],"study_design_scores_gemma":[0.0010507705,0.00042498374,0.0024705771,0.00007723545,0.000009225304,0.00012390639,0.0004553127,0.9915235,0.001122199,0.002345329,0.00012268641,0.00027426914],"about_ca_topic_score_codex":0.000021172742,"about_ca_topic_score_gemma":0.0000012835704,"teacher_disagreement_score":0.38947698,"about_ca_system_score_codex":0.00001992269,"about_ca_system_score_gemma":0.000023152299,"threshold_uncertainty_score":0.3828673},"labels":[],"label_agreement":null},{"id":"W2116608546","doi":"","title":"Path-planning for autonomous training on robot manipulators in space","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"","keywords":"Workspace; Motion planning; Robot; Robot manipulator; Computer science; Task (project management); Path (computing); Control engineering; Software; Space (punctuation); Manipulator (device); Mobile manipulator; Artificial intelligence; Simulation; Mobile robot; Engineering; Systems engineering; Operating system","score_opus":0.06540683307384162,"score_gpt":0.2984269535340139,"score_spread":0.23302012046017226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116608546","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008951864,0.000046813584,0.9791498,0.0025739293,0.00037426894,0.00026144067,9.756179e-7,0.00037212958,0.008268768],"genre_scores_gemma":[0.42517373,4.938836e-7,0.5734699,0.0005495464,0.00011745562,0.00002463985,0.0000018396136,0.000012993253,0.00064945646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845815,0.00003317502,0.0002805883,0.00049015455,0.00020772144,0.00053019443],"domain_scores_gemma":[0.9991603,0.00022823979,0.00007770583,0.00040085005,0.000023254643,0.00010968688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046209714,0.0001859127,0.00023159977,0.00021176401,0.0000927772,0.00010876178,0.0005977007,0.000121749224,0.0000065013246],"category_scores_gemma":[0.000067680514,0.00017711481,0.000059153594,0.00026823475,0.000015718942,0.00032935332,0.00007417838,0.00023589893,0.000061249404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010116904,0.00009615931,0.0013640535,0.000015416997,0.0000136547,0.000084618514,0.0073071215,0.8172382,0.00028195226,0.08521582,0.0018469259,0.086525925],"study_design_scores_gemma":[0.000556516,0.00012213695,0.0074770185,0.00008029947,0.0000021684102,0.000025170219,0.00015370459,0.98734224,0.00048671526,0.0005542344,0.0029255867,0.00027420998],"about_ca_topic_score_codex":0.000020103,"about_ca_topic_score_gemma":0.0000023130071,"teacher_disagreement_score":0.41622186,"about_ca_system_score_codex":0.00013413756,"about_ca_system_score_gemma":0.000077132994,"threshold_uncertainty_score":0.72225326},"labels":[],"label_agreement":null},{"id":"W2117456259","doi":"10.1109/cdc.2008.4739354","title":"Sensor selection and motion planning in robotic sensor networks under communication constraints","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Topology (electrical circuits); Wireless sensor network; Configuration space; Torus; Computer science; Network topology; Point (geometry); Collision avoidance; Robot; Motion (physics); Collision; Selection (genetic algorithm); Space (punctuation); Robot kinematics; Mobile robot; Computer vision; Artificial intelligence; Mathematics; Geometry; Computer network; Physics","score_opus":0.038519724951883064,"score_gpt":0.2637167606381138,"score_spread":0.22519703568623073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117456259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05803811,0.00008788506,0.940211,0.00040118262,0.00011053222,0.00014092047,1.4549443e-7,0.00020044655,0.00080977724],"genre_scores_gemma":[0.70888937,0.000023531593,0.29080263,0.00014006355,0.000020747073,0.0000040759464,0.0000037552434,0.0000061661526,0.00010966495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883944,0.00019131714,0.00024125245,0.00030537037,0.00015563481,0.00026698323],"domain_scores_gemma":[0.9992983,0.00020153096,0.00008241785,0.00029280197,0.000052950254,0.00007203921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031931102,0.00012894323,0.00016119341,0.00015224115,0.0002059835,0.00006328061,0.00021586359,0.00010471211,0.000005207715],"category_scores_gemma":[0.00003645523,0.00013049161,0.000018795161,0.00039109815,0.00010190725,0.00036221588,0.0000920514,0.0002571306,0.000010804189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028880634,0.000031453652,0.024068123,0.0000036539816,0.000007862349,0.000025645184,0.000657134,0.9696106,0.000098422104,0.0014336685,0.00008453962,0.0039760144],"study_design_scores_gemma":[0.00030385706,0.000023939157,0.14696711,0.000036821482,0.000002409475,0.00050581736,0.000099105855,0.851726,0.00004394812,0.00015856119,0.000004050661,0.00012836645],"about_ca_topic_score_codex":0.000072079296,"about_ca_topic_score_gemma":0.000006552418,"teacher_disagreement_score":0.65085125,"about_ca_system_score_codex":0.000070523965,"about_ca_system_score_gemma":0.000030299398,"threshold_uncertainty_score":0.53212935},"labels":[],"label_agreement":null},{"id":"W2117708288","doi":"10.1109/3dim.2005.58","title":"Multi-Resolution Modeling and Locally Refined Collision Detection for Haptic Interaction","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Collision detection; Polygon mesh; Computer science; Haptic technology; Rendering (computer graphics); Frame rate; Collision; Bounding overwatch; Space partitioning; Representation (politics); Computer graphics (images); k-d tree; Tree (set theory); Computer vision; Algorithm; Tree traversal; Artificial intelligence; Mathematics","score_opus":0.04234212158273363,"score_gpt":0.2896539278912124,"score_spread":0.2473118063084788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117708288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009648478,0.0000496871,0.98898506,0.0005237204,0.00030195733,0.00021119199,4.912805e-7,0.00022146113,0.00005794861],"genre_scores_gemma":[0.41220835,0.0000042731403,0.5875116,0.00008222207,0.000053872252,0.000015483272,0.0000012866626,0.0000045757765,0.00011836948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991718,0.00002565447,0.00019908448,0.00030658842,0.00012096667,0.0001759218],"domain_scores_gemma":[0.9995316,0.00006967425,0.000055635093,0.0001828758,0.00010133794,0.00005886517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028002737,0.000094987896,0.00009320951,0.00010843759,0.00016114644,0.0001086615,0.00013898412,0.00006643623,9.2816765e-7],"category_scores_gemma":[0.00008738095,0.00008801581,0.000027830261,0.0001227669,0.000009878752,0.00067355006,0.00006234807,0.000079790305,0.000014424406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020964044,0.000033605356,0.000005558895,0.000007940512,0.00000620901,6.375446e-7,0.00016989732,0.8830234,0.007660201,0.00025435592,0.000050118037,0.10876714],"study_design_scores_gemma":[0.0005385575,0.00010631051,0.00007619212,0.000026889566,0.0000056011513,0.000024943729,0.00002322624,0.9972625,0.001504401,0.00008653847,0.00023602147,0.00010882546],"about_ca_topic_score_codex":0.000062089784,"about_ca_topic_score_gemma":0.00004127452,"teacher_disagreement_score":0.40255988,"about_ca_system_score_codex":0.000101210135,"about_ca_system_score_gemma":0.000018541747,"threshold_uncertainty_score":0.35891807},"labels":[],"label_agreement":null},{"id":"W2117853178","doi":"10.1109/ccece.1995.528177","title":"Teleo-reactive autonomous mobile navigation","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Situated; Computer science; Formalism (music); Mobile robot; Architecture; Computation; Robot; Interpreter; Human–computer interaction; Programming language; Artificial intelligence","score_opus":0.01924915290571821,"score_gpt":0.23991808664546732,"score_spread":0.2206689337397491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117853178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013155353,0.000080480146,0.9357031,0.0003667629,0.00047831534,0.0001765167,9.5209623e-7,0.0006473513,0.049391143],"genre_scores_gemma":[0.60556734,0.0000019125832,0.39024323,0.00021106451,0.000073147836,0.000037176953,0.000002574549,0.0000073591427,0.003856206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991844,0.000027587866,0.00012815493,0.00027519543,0.00017893786,0.0002056783],"domain_scores_gemma":[0.99939215,0.00006552489,0.000049842787,0.00037817392,0.000043091353,0.00007121858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009953978,0.0000906389,0.00009158537,0.000050459686,0.0000762453,0.00008551136,0.00043299902,0.000044894434,0.000073297146],"category_scores_gemma":[0.000013717333,0.000081192695,0.00003111453,0.00023936655,0.000020885553,0.0004460434,0.000080041646,0.00010126906,0.0011100804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021805145,0.0005307983,0.0013560255,0.000018285846,0.0000641988,0.00031635244,0.0095494995,0.024180029,0.0033442988,0.027826658,0.026903257,0.9059084],"study_design_scores_gemma":[0.00015389241,0.000091537826,0.0014030291,0.000009883417,0.000002253225,0.00007508817,0.000029426314,0.99138504,0.0024028856,0.0005397267,0.0037446897,0.00016256972],"about_ca_topic_score_codex":0.000034395318,"about_ca_topic_score_gemma":1.7132935e-7,"teacher_disagreement_score":0.967205,"about_ca_system_score_codex":0.00005482424,"about_ca_system_score_gemma":0.000012038613,"threshold_uncertainty_score":0.99966764},"labels":[],"label_agreement":null},{"id":"W2117893626","doi":"10.1109/ssrr.2010.5981564","title":"Canine Assisted Robot Deployment for Urban Search and Rescue","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Urban search and rescue; Rescue robot; Robot; Search and rescue; Software deployment; Rubble; Computer science; Exploit; Mobile robot; Plan (archaeology); Human–computer interaction; Simulation; Artificial intelligence; Engineering; Computer security; Geography; Software engineering","score_opus":0.03183536201568977,"score_gpt":0.2865271199038469,"score_spread":0.2546917578881571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117893626","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03437578,0.000028255929,0.9616585,0.0020779131,0.00042384915,0.00027784347,0.0000026196747,0.00023623207,0.0009190194],"genre_scores_gemma":[0.2309913,9.709726e-7,0.76641494,0.00021243251,0.00007934882,0.000024980316,0.0000026187397,0.000009532865,0.0022638657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989984,0.00002401529,0.00013731574,0.00034386097,0.00019808531,0.00029832887],"domain_scores_gemma":[0.9992229,0.00010617068,0.000013459166,0.00042736807,0.00007549755,0.00015462865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004108184,0.00009930271,0.000118808304,0.00006819431,0.00012068647,0.00013583669,0.0004377909,0.000058751208,0.000007943256],"category_scores_gemma":[0.000059175258,0.0000818674,0.000025070016,0.00014106059,0.00003781629,0.00014799416,0.00018132759,0.00015988907,0.00001150043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069924106,0.00071620516,0.035285722,0.00019947774,0.00025154484,0.0002193521,0.0043422356,0.015387246,0.20702575,0.25389147,0.045862876,0.43674818],"study_design_scores_gemma":[0.0006549942,0.00017087712,0.06248651,0.0000112098105,0.0000060010007,0.00007775927,0.000016309326,0.9280847,0.0063135717,0.00013918086,0.0018237227,0.000215181],"about_ca_topic_score_codex":0.00036465452,"about_ca_topic_score_gemma":0.0001375642,"teacher_disagreement_score":0.91269743,"about_ca_system_score_codex":0.000016307438,"about_ca_system_score_gemma":0.00007116096,"threshold_uncertainty_score":0.3338456},"labels":[],"label_agreement":null},{"id":"W2117927826","doi":"10.1177/0278364911417911","title":"Optimal path planning for surveillance with temporal-logic constraints","year":2011,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":202,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research","keywords":"Linear temporal logic; Automaton; Computer science; Robot; Motion planning; Path (computing); Temporal logic; Graph; Function (biology); Upload; Finite-state machine; Mathematical optimization; Real-time computing; Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Programming language","score_opus":0.18890767408638054,"score_gpt":0.39012736863314196,"score_spread":0.20121969454676142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117927826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006139626,0.00011796513,0.98892885,0.002881418,0.00065543124,0.00016891539,0.0000057894167,0.000019842697,0.0010821397],"genre_scores_gemma":[0.47202006,0.000010899808,0.52757126,0.00007937354,0.00019582802,0.000004067541,0.0000013473749,0.000009293632,0.000107879474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728787,0.00019969272,0.00039353437,0.00019429212,0.0015418223,0.00038276974],"domain_scores_gemma":[0.9961303,0.00094098976,0.0003135694,0.00029831065,0.00219485,0.00012199409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004320728,0.00011782325,0.00019109112,0.0002791613,0.0001687695,0.00022682118,0.0033237166,0.00004859812,0.000011632853],"category_scores_gemma":[0.00048105756,0.000071097245,0.00007538258,0.0002621922,0.00034373577,0.0003031528,0.0002924378,0.0005432904,0.000009998062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013678316,0.0006829883,0.04047302,0.00004140087,0.0012034898,0.0037421566,0.012243563,0.7811383,0.0009776205,0.12338865,0.016168553,0.018572444],"study_design_scores_gemma":[0.004931234,0.004511648,0.029496225,0.000873327,0.00002810284,0.0076439604,0.002218018,0.9128012,0.002761351,0.03201491,0.0019392081,0.0007807828],"about_ca_topic_score_codex":0.000016116026,"about_ca_topic_score_gemma":5.479635e-7,"teacher_disagreement_score":0.46588045,"about_ca_system_score_codex":0.00010437061,"about_ca_system_score_gemma":0.00043715723,"threshold_uncertainty_score":0.6176348},"labels":[],"label_agreement":null},{"id":"W2118188083","doi":"10.1109/iros.1999.811659","title":"Real-time motion planning of car-like robots","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Holonomic; Artificial neural network; Robot; Computer science; Workspace; Motion planning; Nonholonomic system; Collision; Motion (physics); Lyapunov stability; Process (computing); Lyapunov function; Artificial intelligence; Control theory (sociology); Mobile robot","score_opus":0.021665732595448572,"score_gpt":0.25727529939016813,"score_spread":0.23560956679471956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118188083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011357387,0.00002713545,0.9385112,0.000054769742,0.00037669324,0.00008362374,5.89429e-7,0.00023414392,0.049354453],"genre_scores_gemma":[0.14234278,0.0000027515707,0.85492206,0.00005367548,0.000023279037,0.0000036790434,0.0000025010359,0.000009902053,0.0026393705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889505,0.000087558685,0.00022690996,0.00028138518,0.00026266245,0.0002464622],"domain_scores_gemma":[0.9992026,0.0000880252,0.00009708875,0.00046989208,0.000064473425,0.00007792053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003571995,0.00011000815,0.00017282256,0.00009889986,0.00005475429,0.00003510766,0.00039751103,0.00006073241,0.00002671593],"category_scores_gemma":[0.000058152516,0.000101074664,0.000043263684,0.00031093438,0.00002418432,0.00025576958,0.000060234684,0.00007430852,0.00010126768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011851868,0.00043559575,0.024993138,0.000099421,0.0001501015,0.00033067845,0.0068764235,0.69710034,0.08852143,0.1393106,0.019917889,0.022252515],"study_design_scores_gemma":[0.00073334714,0.00020450963,0.023584018,0.00010854504,0.000015899865,0.0001460396,0.00008430514,0.9464735,0.02517464,0.0024903556,0.00045273962,0.00053210015],"about_ca_topic_score_codex":0.000058216123,"about_ca_topic_score_gemma":1.2613766e-7,"teacher_disagreement_score":0.24937314,"about_ca_system_score_codex":0.000028323408,"about_ca_system_score_gemma":0.000050061044,"threshold_uncertainty_score":0.4121705},"labels":[],"label_agreement":null},{"id":"W2118379053","doi":"10.1109/isic.2002.1157841","title":"A real-time cooperative sweeping strategy for multiple cleaning robots","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Computer science; Artificial neural network; Trajectory; Path (computing); Motion planning; Mobile robot; Artificial intelligence; Real-time computing; Simulation","score_opus":0.04196509817799645,"score_gpt":0.2854685895672515,"score_spread":0.24350349138925506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118379053","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018539164,0.000016734908,0.9750937,0.0000896484,0.000197798,0.00034034046,0.0000019260133,0.00036819305,0.022037692],"genre_scores_gemma":[0.107039005,0.000002860577,0.8900654,0.0001228436,0.000042581312,0.000037939662,0.0000044329763,0.000017184455,0.0026677644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985326,0.00011453007,0.0002512492,0.0004771268,0.00017324895,0.00045124826],"domain_scores_gemma":[0.9987963,0.00046743467,0.00007789425,0.000392877,0.0001435841,0.000121901605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005029594,0.0001729929,0.00022132625,0.000073781615,0.00023605618,0.0002017288,0.00044765312,0.00006924049,0.000021258023],"category_scores_gemma":[0.00031329403,0.00015887247,0.000058864345,0.00029269903,0.000026608895,0.00044776898,0.00006154088,0.00009859759,0.00012385129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000152393595,0.0001639561,0.0005576964,0.00003189257,0.00009806337,0.000068289926,0.0022771286,0.74584204,0.026673142,0.20079386,0.0052629607,0.018215695],"study_design_scores_gemma":[0.00062323536,0.0001733236,0.00031856346,0.000027367234,0.000004554713,0.000025911275,0.00009566713,0.9916204,0.0059075337,0.00068768504,0.00025187776,0.0002638764],"about_ca_topic_score_codex":0.000049236045,"about_ca_topic_score_gemma":0.0000026456705,"teacher_disagreement_score":0.24577834,"about_ca_system_score_codex":0.000050899827,"about_ca_system_score_gemma":0.00013506481,"threshold_uncertainty_score":0.64786315},"labels":[],"label_agreement":null},{"id":"W2118684378","doi":"10.1109/robio.2006.340133","title":"An Extension of the Distance-Propagating Dynamic System for Robot Path Planning to Safe Obstacle Clearance","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Obstacle; Motion planning; Robot; Path (computing); Computer science; Margin (machine learning); Grid; Representation (politics); Obstacle avoidance; Penalty method; Path length; Mobile robot; Algorithm; Mathematical optimization; Real-time computing; Simulation; Artificial intelligence; Mathematics","score_opus":0.013293122008258242,"score_gpt":0.2608425543762455,"score_spread":0.24754943236798724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118684378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06434625,0.00006966352,0.9338489,0.00016255889,0.0005094158,0.0004404887,0.0000072713988,0.00019830241,0.00041714244],"genre_scores_gemma":[0.62167233,7.3956905e-8,0.37808168,0.00004850889,0.00003265293,0.000016524034,0.0000018259823,0.000010695204,0.00013572857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983662,0.00007693497,0.0003864249,0.00047744316,0.00033593705,0.00035706925],"domain_scores_gemma":[0.9986039,0.00012558622,0.00019641207,0.0008732501,0.00013216617,0.00006869477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004375681,0.00015520428,0.00022984997,0.000049701895,0.00025325033,0.00008788876,0.0009485165,0.000050726474,4.4634265e-7],"category_scores_gemma":[0.000050809544,0.00011037964,0.00006904063,0.00038613012,0.000029964784,0.00026357613,0.00013090545,0.00009165537,0.000005321547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031561045,0.000078631834,0.0029117644,0.00016991387,0.000008392609,0.000017028218,0.0007645623,0.92875206,0.037292812,0.014485268,0.0003287307,0.015159271],"study_design_scores_gemma":[0.00022578954,0.000103861006,0.04866591,0.00041544522,0.0000045424017,0.000017814391,0.00019375885,0.9461924,0.0037571152,0.00021917753,0.00003910337,0.00016509138],"about_ca_topic_score_codex":0.00009152866,"about_ca_topic_score_gemma":0.000005464315,"teacher_disagreement_score":0.5573261,"about_ca_system_score_codex":0.00009872653,"about_ca_system_score_gemma":0.000043435695,"threshold_uncertainty_score":0.4501151},"labels":[],"label_agreement":null},{"id":"W2118847825","doi":"10.1109/icsmc.1995.538431","title":"Time-optimal interception of objects moving along topologically varying paths","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Rendezvous; Trajectory; Motion planning; Context (archaeology); Interception; Robot; Computer science; Plan (archaeology); Point (geometry); Object (grammar); Artificial intelligence; Engineering; Mathematics; Geography; Aerospace engineering; Spacecraft","score_opus":0.025051726456580072,"score_gpt":0.23677710152908915,"score_spread":0.21172537507250908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118847825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110263705,0.000046540976,0.86926067,0.00019209248,0.00022379639,0.00009494801,5.852565e-7,0.00031376723,0.019603927],"genre_scores_gemma":[0.6043537,0.0000033059605,0.39470875,0.000117645446,0.00003109118,0.0000024661615,6.907673e-7,0.0000042826823,0.000777998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987911,0.0000698178,0.00029034895,0.00032500378,0.00024636902,0.00027734225],"domain_scores_gemma":[0.9992465,0.00013700082,0.00010719231,0.00037736286,0.000059453407,0.000072534574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025195605,0.00012517163,0.00018771534,0.00010261741,0.00006899307,0.000055029403,0.00062030944,0.00007633322,0.00032761783],"category_scores_gemma":[0.00010255951,0.000106390784,0.00006457821,0.0002442948,0.00005907847,0.0003820533,0.00027400863,0.00012856748,0.00035551356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002740184,0.0011718925,0.010686337,0.00014735942,0.00018514518,0.0008162085,0.040842168,0.16727452,0.11278168,0.009311275,0.008239933,0.64851606],"study_design_scores_gemma":[0.00017067898,0.00016137757,0.0024989129,0.000040490893,0.0000036846295,0.000052126983,0.000057126505,0.9952197,0.0015140238,0.00011609889,0.0000144117175,0.00015138654],"about_ca_topic_score_codex":0.000027162314,"about_ca_topic_score_gemma":3.1623648e-7,"teacher_disagreement_score":0.8279452,"about_ca_system_score_codex":0.00003327345,"about_ca_system_score_gemma":0.000010740059,"threshold_uncertainty_score":0.4569526},"labels":[],"label_agreement":null},{"id":"W2119293622","doi":"10.1109/cira.2003.1222322","title":"Area-covering operation of a cleaning robot in a dynamic environment with unforeseen obstacles","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Obstacle avoidance; Motion planning; Robot; Workspace; Computer science; Artificial neural network; Cover (algebra); Obstacle; Path (computing); Artificial intelligence; Mobile robot; Control engineering; Engineering; Geography","score_opus":0.009770094935839243,"score_gpt":0.2052175111011767,"score_spread":0.19544741616533745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119293622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10822788,0.000024328692,0.89085627,0.00011876634,0.000023474173,0.000117084106,4.6307488e-7,0.000047057078,0.00058471056],"genre_scores_gemma":[0.6124307,0.0000036502834,0.38750535,0.000019936979,0.0000022754698,0.000005203477,0.000001392953,0.0000042095103,0.000027292655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991051,0.000015954412,0.0001986189,0.0002470038,0.00023988305,0.00019342174],"domain_scores_gemma":[0.9995754,0.000027318354,0.000060301012,0.00028901067,0.000009238656,0.00003872578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001402294,0.00010054354,0.00013608033,0.000105110055,0.00003363942,0.000044122964,0.00032217056,0.00003112277,0.0000041848925],"category_scores_gemma":[0.000009516255,0.00008227645,0.00001700545,0.00016529072,0.000027725016,0.00039050932,0.00010721406,0.00008225715,0.000010584502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004131607,0.00003567612,0.0019400252,0.000005910524,0.000005514489,0.000029590072,0.0012730325,0.98968285,0.0017682569,0.001531079,5.406839e-7,0.0037233992],"study_design_scores_gemma":[0.00080602494,0.00019020811,0.04263888,0.0001280824,0.000002126864,0.00002649679,0.00020810662,0.9517438,0.003602519,0.00048558056,0.000004773517,0.00016343276],"about_ca_topic_score_codex":0.0002486736,"about_ca_topic_score_gemma":0.000041832463,"teacher_disagreement_score":0.5042028,"about_ca_system_score_codex":0.00015201843,"about_ca_system_score_gemma":0.000053424792,"threshold_uncertainty_score":0.3355136},"labels":[],"label_agreement":null},{"id":"W2119394765","doi":"10.1109/taes.2012.6324664","title":"Flatness-Based Trajectory Planning/Replanning for a Quadrotor Unmanned Aerial Vehicle","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":190,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Actuator; Control theory (sociology); Trajectory; Flatness (cosmology); Trajectory optimization; Engineering; Motion planning; Control engineering; Computer science; Control (management); Robot; Artificial intelligence; Physics","score_opus":0.025111859206958367,"score_gpt":0.27206321142197903,"score_spread":0.24695135221502068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119394765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07471201,0.0010555534,0.92042565,0.00018764545,0.0023966646,0.0008187982,0.000014092813,0.0003390642,0.00005050797],"genre_scores_gemma":[0.9927939,0.000007827954,0.0057892627,0.0001154063,0.00034038367,0.00049303676,0.0000033983795,0.000041012045,0.00041575084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975436,0.0001322887,0.00033209726,0.0005130338,0.00031795667,0.0011610321],"domain_scores_gemma":[0.9986888,0.00034294135,0.00015016072,0.0004985144,0.000059817816,0.00025972564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070309365,0.00032401123,0.00039042265,0.00019166632,0.0004783722,0.00019225106,0.00036833243,0.000192235,0.0000029732912],"category_scores_gemma":[0.000009823453,0.00031683667,0.00013025876,0.00032211212,0.000052187537,0.0003848528,0.0000022947584,0.00035150355,0.000021984983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013164661,0.0014475848,0.0022016992,0.00080625847,0.0007647966,0.000033349985,0.010820209,0.9094492,0.0522646,0.0055108843,0.0038187162,0.011566205],"study_design_scores_gemma":[0.00422258,0.0016725251,0.0005562494,0.00035668677,0.00010443329,0.000140386,0.0003788884,0.96479833,0.0221209,0.00003585873,0.0045836763,0.0010295113],"about_ca_topic_score_codex":0.000089921625,"about_ca_topic_score_gemma":0.0000046431046,"teacher_disagreement_score":0.91808194,"about_ca_system_score_codex":0.0002245707,"about_ca_system_score_gemma":0.00021054382,"threshold_uncertainty_score":0.99992836},"labels":[],"label_agreement":null},{"id":"W2119473414","doi":"10.1109/robot.2003.1242149","title":"Multi-agent cooperative manipulation with uncertainty: a neural net-based game theoretic approach","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Minimax; Computer science; Object (grammar); Trajectory; Mathematical optimization; Nash equilibrium; Potential game; Net (polyhedron); Artificial neural network; Game theory; Fictitious play; Multi-agent system; Artificial intelligence; Mathematics; Mathematical economics","score_opus":0.03375486743344562,"score_gpt":0.2556076886451256,"score_spread":0.22185282121167998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119473414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012537405,0.000019187602,0.985304,0.000547356,0.00010849195,0.00037826842,0.0000011106415,0.0003248942,0.0007793089],"genre_scores_gemma":[0.5467134,2.0513497e-7,0.45275536,0.00038989386,0.000016737278,0.000023157658,0.00001066075,0.000008409611,0.000082148035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853444,0.000094919684,0.00019178061,0.0005044287,0.00033765173,0.00033675577],"domain_scores_gemma":[0.99914944,0.000054349923,0.00007593417,0.00049172906,0.00011005601,0.00011851466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001879281,0.00021253734,0.00018010188,0.00009014954,0.00012347742,0.00017512849,0.0005048998,0.000054656597,0.000007225443],"category_scores_gemma":[0.000024621031,0.00014658691,0.000038315713,0.00042853737,0.000112817426,0.0002606452,0.000066768655,0.00015645275,0.000038618586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010970713,0.00011772455,0.00022228864,0.000006932598,0.00001066825,0.000035829213,0.0008387071,0.97926795,0.00007862208,0.018660689,0.000009671471,0.0007399588],"study_design_scores_gemma":[0.0015359757,0.00024694417,0.003523764,0.000023048195,0.000008158256,0.00004115822,0.0000807092,0.99378234,0.00029756126,0.00022002585,0.000007700433,0.0002326414],"about_ca_topic_score_codex":0.00012427712,"about_ca_topic_score_gemma":0.0000064781766,"teacher_disagreement_score":0.534176,"about_ca_system_score_codex":0.00012119172,"about_ca_system_score_gemma":0.00015260991,"threshold_uncertainty_score":0.5977641},"labels":[],"label_agreement":null},{"id":"W2120160155","doi":"10.1109/ccece.2004.1347634","title":"A new efficient control algorithm using potential field: extension to robot path tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Mobile robot; Robot; Field (mathematics); Obstacle; Artificial intelligence; Robotics; Robot control; Path (computing); Control engineering; Motion control; Tracking (education); Motion planning; Computer vision; Engineering; Mathematics","score_opus":0.0173079475394864,"score_gpt":0.26279200027489613,"score_spread":0.24548405273540974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120160155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004314302,0.000057688125,0.9919915,0.0016252046,0.0012597638,0.0003082896,0.000001163465,0.00033385208,0.00010822734],"genre_scores_gemma":[0.22004415,5.273034e-7,0.77818745,0.0014740243,0.00021305602,0.0000025349211,6.2878127e-7,0.000012750262,0.00006487778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978647,0.000047579255,0.0003306999,0.0006516925,0.0005356459,0.00056968775],"domain_scores_gemma":[0.99875677,0.00006795656,0.00008429173,0.00060637126,0.000117798445,0.0003668338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030988196,0.00022812985,0.0002712149,0.00018122359,0.00019918181,0.00026027177,0.00068012445,0.00010257834,0.00001573194],"category_scores_gemma":[0.000079584934,0.0002046029,0.00010598654,0.00047929067,0.000010996199,0.00022760684,0.0002045479,0.00018792464,0.000101194906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005216915,0.00005084453,0.000008343359,0.000002105265,0.000010412916,0.00024248507,0.00031392003,0.8533173,0.006619153,0.00043965018,0.000144121,0.1388464],"study_design_scores_gemma":[0.0013329933,0.00019625871,0.00086305715,0.000092633854,0.000014881149,0.00024337904,0.000023046154,0.9923126,0.004158462,0.00042176002,0.000036676014,0.00030425246],"about_ca_topic_score_codex":0.0005890731,"about_ca_topic_score_gemma":0.0000011492598,"teacher_disagreement_score":0.21572985,"about_ca_system_score_codex":0.00012496315,"about_ca_system_score_gemma":0.00022688066,"threshold_uncertainty_score":0.8343464},"labels":[],"label_agreement":null},{"id":"W2120650294","doi":"10.1109/iros.1999.811724","title":"Sensor-based roadmaps for motion planning for articulated robots in unknown environments: some experiments with an eye-in-hand system","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Motion planning; Robot; Artificial intelligence; Computer science; Planner; Motion (physics); Triangulation; Simulation; Mathematics","score_opus":0.030283995585794914,"score_gpt":0.27953183318223307,"score_spread":0.24924783759643815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120650294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17421177,0.000043858465,0.8244184,0.000044300472,0.00016935218,0.0009536873,0.0000025273341,0.000092863855,0.00006320454],"genre_scores_gemma":[0.6374532,1.364977e-7,0.36210433,0.00007144046,0.000016764476,0.00021729007,0.000017740265,0.000019299281,0.00009978234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980158,0.0001351979,0.00039248812,0.0006470549,0.0002596381,0.000549803],"domain_scores_gemma":[0.9991631,0.000106578314,0.00012616333,0.0004487808,0.000025496147,0.00012990578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004999046,0.00024217267,0.00029766923,0.00022407898,0.00012155427,0.00012942318,0.0002731735,0.00010479882,0.0000013037054],"category_scores_gemma":[0.00003583006,0.0002102065,0.000040030194,0.0002453746,0.00003121093,0.0005922701,0.000020530155,0.00008475604,0.0000052918954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008052887,0.00033755516,0.0074455077,0.000057098016,0.000020687321,0.00006430732,0.00085388054,0.9809468,0.00623989,0.0033527291,0.000012203211,0.00058884016],"study_design_scores_gemma":[0.003903627,0.00047700523,0.005326132,0.00021477482,0.000007817941,0.000010853045,0.0003356272,0.9628286,0.0264335,0.000075985634,0.00006165754,0.00032443416],"about_ca_topic_score_codex":0.000024624906,"about_ca_topic_score_gemma":0.0000017844646,"teacher_disagreement_score":0.46324146,"about_ca_system_score_codex":0.00025979936,"about_ca_system_score_gemma":0.000057734087,"threshold_uncertainty_score":0.8571973},"labels":[],"label_agreement":null},{"id":"W2120690363","doi":"10.1109/cgi.1997.601268","title":"Integrating flying and fish tank metaphors with cyclopean scale","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Fish <Actinopterygii>; Scale (ratio); Computer science; Marine engineering; Fishery; Environmental science; Engineering; Geography; Cartography; Biology","score_opus":0.01648564404984412,"score_gpt":0.20398447401767245,"score_spread":0.18749882996782832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120690363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026169993,0.00008092909,0.9271691,0.00087715517,0.000113180446,0.000095829535,6.6985234e-7,0.0003118909,0.045181256],"genre_scores_gemma":[0.22130975,0.000007263286,0.7767949,0.00043591412,0.000028776994,0.0000049715777,4.94477e-7,0.000008851629,0.0014090636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990257,0.000042187425,0.00014384752,0.000341575,0.00019812367,0.0002485533],"domain_scores_gemma":[0.99941534,0.0000774455,0.000050860428,0.00031823252,0.00003353126,0.0001046127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020631446,0.00012837363,0.0001447567,0.00007136188,0.00018275506,0.00021858142,0.00037100146,0.000029629404,0.000023760927],"category_scores_gemma":[0.000029613733,0.00009104519,0.000019337149,0.0002884918,0.000044030832,0.0004176758,0.00013716602,0.000136292,0.00002674113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005159474,0.00016072334,0.04238267,0.000048299382,0.00013003494,0.00043318892,0.02215159,0.0018150687,0.00077929266,0.010110456,0.013201278,0.90878224],"study_design_scores_gemma":[0.00025771305,0.00009909482,0.003705001,0.00006215467,0.00000833833,0.00017446147,0.0003174478,0.99392027,0.00038331177,0.00012582281,0.00069534814,0.00025105992],"about_ca_topic_score_codex":0.00005244783,"about_ca_topic_score_gemma":0.000016438831,"teacher_disagreement_score":0.9921052,"about_ca_system_score_codex":0.000016274851,"about_ca_system_score_gemma":0.0000069059734,"threshold_uncertainty_score":0.37127152},"labels":[],"label_agreement":null},{"id":"W2121065212","doi":"10.1299/jsmeicam.2010.5.118","title":"Path Planning and T racking of an Autonomous Underwater Vehicle using Virtual Way-points","year":2010,"lang":"en","type":"article","venue":"The Abstracts of the international conference on advanced mechatronics toward evolutionary fusion of IT and mechatronics ICAM","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Underwater; Motion planning; Computer science; Computer vision; Path (computing); Artificial intelligence; Tracking (education); Real-time computing; Geography; Robot; Computer network","score_opus":0.03594536295510565,"score_gpt":0.2884695878686818,"score_spread":0.2525242249135762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121065212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9476624,0.00016074658,0.047794748,0.0027356888,0.0009715238,0.0002791036,0.00004582037,0.00003594828,0.0003140221],"genre_scores_gemma":[0.91625196,0.00009012719,0.083467126,0.000089383495,0.00003836162,0.0000045881175,0.000008682618,0.000013857354,0.0000359394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791306,0.00007614874,0.0006110535,0.00040464877,0.0006973061,0.0002977654],"domain_scores_gemma":[0.9979686,0.00022698432,0.00074859144,0.000621381,0.0003370603,0.00009736604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082771684,0.00023355754,0.0003046832,0.0001364432,0.00020073782,0.000046515856,0.001439591,0.00016782338,0.000015574271],"category_scores_gemma":[0.00015897772,0.0001663902,0.00009375287,0.00011770725,0.00024072643,0.0005545028,0.0007454404,0.00067172345,0.0000013555822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020003185,0.00023217274,0.00012746261,0.000044351764,0.000085979955,0.0000033822107,0.0013211329,0.079659484,0.43174538,0.4719636,0.000016371898,0.014600626],"study_design_scores_gemma":[0.0009786664,0.00048779213,0.0029929606,0.00041146713,0.000032088363,0.00006154323,0.00092515914,0.8834274,0.046784803,0.06344704,0.00017179488,0.00027930384],"about_ca_topic_score_codex":0.00004573721,"about_ca_topic_score_gemma":0.0000052222335,"teacher_disagreement_score":0.8037679,"about_ca_system_score_codex":0.00007782056,"about_ca_system_score_gemma":0.0003301549,"threshold_uncertainty_score":0.6785195},"labels":[],"label_agreement":null},{"id":"W2121089220","doi":"10.1109/iros.2011.6094517","title":"Optimal deployment of robotic teams for autonomous wilderness search and rescue","year":2011,"lang":"en","type":"article","venue":"2011 IEEE/RSJ International Conference on Intelligent Robots and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Software deployment; Search and rescue; Terrain; Computer science; Robot; Wilderness; Mobile robot; Artificial intelligence; Real-time computing; Geography","score_opus":0.12145401036033047,"score_gpt":0.31185722481692885,"score_spread":0.19040321445659836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121089220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04310705,0.000235137,0.95194316,0.00020527282,0.002323225,0.0006698803,0.00001927132,0.00007764157,0.0014193614],"genre_scores_gemma":[0.9598134,0.00014496087,0.038367804,0.000040647657,0.00011492588,0.00009572055,0.000008672437,0.000020836607,0.0013930352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978347,0.00009698257,0.00060230814,0.00062216446,0.0004937635,0.00035012554],"domain_scores_gemma":[0.99871355,0.00011367652,0.00015587496,0.00039730512,0.0004243225,0.00019529361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065254315,0.00026551387,0.0003975037,0.0002554309,0.00010775855,0.00019288383,0.0008592775,0.00012024205,0.000030553823],"category_scores_gemma":[0.000030859792,0.00022665759,0.00007071169,0.000067638415,0.00014639247,0.00026599443,0.00017682502,0.00017765103,0.00002668875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002551202,0.0007457912,0.00534106,0.00040186135,0.0006056437,0.00007250894,0.010927752,0.23914507,0.001894552,0.70443076,0.0007850512,0.035394862],"study_design_scores_gemma":[0.00037314932,0.000600466,0.0017606741,0.0003221945,0.000016610158,0.00008308137,0.0004475348,0.99267876,0.0029911902,0.0003675521,0.000076193435,0.00028258716],"about_ca_topic_score_codex":0.0012580629,"about_ca_topic_score_gemma":0.00001011109,"teacher_disagreement_score":0.9167063,"about_ca_system_score_codex":0.00007981229,"about_ca_system_score_gemma":0.00013245961,"threshold_uncertainty_score":0.92428285},"labels":[],"label_agreement":null},{"id":"W2121315442","doi":"10.1109/isic.2002.1157849","title":"A coordination mechanism for model-based multi-sensor planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software deployment; Task (project management); Negotiation; Position (finance); Real-time computing; Artificial intelligence; Computer vision; Mechanism (biology); Engineering; Systems engineering","score_opus":0.06291181005626849,"score_gpt":0.30293706958730987,"score_spread":0.24002525953104137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121315442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005154597,0.000009392327,0.99726427,0.0003457572,0.0002782059,0.0002903904,0.000002137606,0.00032406597,0.0009703034],"genre_scores_gemma":[0.15036432,6.809495e-8,0.8477792,0.000533987,0.000011344798,0.000050642084,0.00000272618,0.000010491576,0.001247188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989717,0.000040240055,0.00017149949,0.0003486164,0.00017454696,0.00029338087],"domain_scores_gemma":[0.9992363,0.00013957007,0.00006692721,0.00032542853,0.00015058527,0.00008119265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003347673,0.0001277026,0.00012909151,0.00011067618,0.0001403592,0.00008895082,0.0003329938,0.00006942931,0.0000021089943],"category_scores_gemma":[0.00022344182,0.00011969852,0.000054364205,0.00017251665,0.0000110782585,0.00020411,0.00002199831,0.000067044624,0.000016723376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030275742,0.00008072121,0.00010202839,0.00001507463,0.000009945067,0.000009265039,0.00029910923,0.5244003,0.002634744,0.4708654,0.0006669967,0.0009133998],"study_design_scores_gemma":[0.0008297333,0.00005113357,0.000018229372,0.000013941985,0.0000040783375,0.0000059389026,0.000019165112,0.9800215,0.01158284,0.007217094,0.00006954839,0.00016684437],"about_ca_topic_score_codex":0.0000043257564,"about_ca_topic_score_gemma":2.8142068e-7,"teacher_disagreement_score":0.46364832,"about_ca_system_score_codex":0.000042280117,"about_ca_system_score_gemma":0.000092817005,"threshold_uncertainty_score":0.48811638},"labels":[],"label_agreement":null},{"id":"W2121938477","doi":"10.1109/cira.2003.1222321","title":"Path planning of multi-robot systems with cooperation","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Robot; Computer science; Path (computing); Mobile robot; Artificial intelligence; Computer network","score_opus":0.03284442083400938,"score_gpt":0.2607600041989146,"score_spread":0.22791558336490522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121938477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057363217,0.00010838485,0.9928835,0.00007482393,0.00020735084,0.00014498938,7.8924336e-7,0.00016840572,0.0006754181],"genre_scores_gemma":[0.52009904,5.958619e-7,0.47971466,0.000029527971,0.000016260465,0.000005650429,0.0000016852247,0.0000045796455,0.00012801567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991446,0.000027308544,0.00019619156,0.00023330118,0.00022342868,0.00017513966],"domain_scores_gemma":[0.999389,0.000029723864,0.000089317284,0.00032869476,0.00010532693,0.000057965466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001741549,0.00010160421,0.00015308439,0.00006715582,0.00006140283,0.000076762204,0.00034453603,0.000040249062,8.401756e-7],"category_scores_gemma":[0.000017156915,0.0000737096,0.000015373013,0.00025955477,0.000026561545,0.00030482464,0.000049296876,0.0000726323,0.000017827215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024290646,0.00005054327,0.0009773534,0.000021007989,0.000011754619,0.000043514417,0.0009032326,0.98789436,0.0009949062,0.008735954,0.00003931332,0.00032560813],"study_design_scores_gemma":[0.0010588345,0.0002611482,0.0037174511,0.00027053896,0.0000049362343,0.000105976695,0.00011606267,0.99157643,0.0026515173,0.000022953705,0.000026987229,0.00018716877],"about_ca_topic_score_codex":0.00017327076,"about_ca_topic_score_gemma":9.083374e-7,"teacher_disagreement_score":0.5143627,"about_ca_system_score_codex":0.00003469513,"about_ca_system_score_gemma":0.00009926554,"threshold_uncertainty_score":0.300579},"labels":[],"label_agreement":null},{"id":"W2122035096","doi":"10.1109/acc.2000.876667","title":"Modelling of redundancy in multiple mobile robots","year":2000,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Redundancy (engineering); Mobile robot; Robot; Computer science; Fault tolerance; Reliability (semiconductor); Reliability theory; Distributed computing; Control engineering; Reliability engineering; Artificial intelligence; Engineering; Failure rate","score_opus":0.04139418900968494,"score_gpt":0.24524299958038082,"score_spread":0.20384881057069587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122035096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076898456,0.000100456644,0.9156699,0.000035006575,0.00007751655,0.000113846996,5.350051e-7,0.00009234842,0.0070119225],"genre_scores_gemma":[0.4824942,0.000010447284,0.51649624,0.000020462245,0.00000970951,0.0000084711955,5.646187e-7,0.000003762834,0.00095611624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999106,0.000030123965,0.00024408239,0.00024505318,0.00016389338,0.00021088678],"domain_scores_gemma":[0.99943465,0.00007653338,0.000034277444,0.00038706136,0.000022413,0.000045043238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017044441,0.000079767735,0.00014581461,0.00008326679,0.0000254749,0.00002049681,0.00048619727,0.000042081207,0.000057356032],"category_scores_gemma":[0.000007752373,0.00007478876,0.000031101168,0.0003331968,0.000021122796,0.0002450269,0.0000461885,0.00008357683,0.000069141526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016097816,0.00004603052,0.00058887113,0.0000038492767,0.0000016611366,0.0000092521,0.00045401597,0.97025937,0.000057605306,0.0003114581,0.000039025195,0.028227266],"study_design_scores_gemma":[0.00023033078,0.00003524208,0.00034162792,0.000029661605,6.043473e-7,0.0000057682164,0.000010666412,0.9977754,0.000822193,0.0005696855,0.00009154991,0.000087299326],"about_ca_topic_score_codex":0.0003204439,"about_ca_topic_score_gemma":0.0000024248957,"teacher_disagreement_score":0.40559575,"about_ca_system_score_codex":0.000018525749,"about_ca_system_score_gemma":0.00003114217,"threshold_uncertainty_score":0.3049797},"labels":[],"label_agreement":null},{"id":"W2122128939","doi":"10.1613/jair.1789","title":"Learning in Real-Time Search: A Unifying Framework","year":2006,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Computer science; Heuristic; Scalability; Completeness (order theory); Function (biology); Convergence (economics); Control (management); Machine learning; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.17793919854438428,"score_gpt":0.42330659136146825,"score_spread":0.24536739281708397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122128939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16853705,0.00017760125,0.8265545,0.0018919406,0.00030922913,0.00016593572,3.6334944e-7,0.00006204361,0.0023013854],"genre_scores_gemma":[0.7215489,0.0001218905,0.27737537,0.000012580816,0.00059039675,0.0000028710733,4.874795e-7,0.000019383426,0.00032809746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99495184,0.0010366383,0.0009982968,0.00032788605,0.0017819877,0.00090335077],"domain_scores_gemma":[0.9962087,0.002142813,0.00021736814,0.0003776048,0.0008606186,0.00019284368],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.010336234,0.00014553046,0.0003328918,0.0013748626,0.00027054088,0.00046471282,0.0016548983,0.00016042677,0.00004245384],"category_scores_gemma":[0.0016492428,0.00013365805,0.000109336244,0.0026883313,0.00020704388,0.000637517,0.00032880635,0.0024273915,0.00031236085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013884786,0.0006022715,0.004509574,0.000048116155,0.000032214342,0.0031356805,0.006673924,0.50331444,0.03425399,0.10318595,0.00047098318,0.343634],"study_design_scores_gemma":[0.000059300935,0.000882573,0.0023488475,0.0006409806,0.0000032386959,0.0002218366,0.0015832857,0.8166136,0.023772722,0.15338787,0.00020195176,0.00028379515],"about_ca_topic_score_codex":0.0005258476,"about_ca_topic_score_gemma":0.000009373973,"teacher_disagreement_score":0.5530119,"about_ca_system_score_codex":0.0002888864,"about_ca_system_score_gemma":0.000558878,"threshold_uncertainty_score":0.99987406},"labels":[],"label_agreement":null},{"id":"W2122151059","doi":"10.1109/isic.2002.1157742","title":"Real-time torque control of nonholonomic mobile robots with obstacle avoidance","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Control theory (sociology); Nonholonomic system; Controller (irrigation); Robot; Computer science; Obstacle avoidance; Robot control; Lyapunov function; Torque; Lyapunov stability; Control engineering; Nonlinear system; Artificial intelligence; Engineering; Control (management)","score_opus":0.006167858454708406,"score_gpt":0.21161941565343115,"score_spread":0.20545155719872274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122151059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043594055,0.00006596138,0.9448233,0.0000517442,0.00015028182,0.00030275443,0.0000034856735,0.00020313382,0.010805237],"genre_scores_gemma":[0.49737278,0.000003802954,0.50133634,0.00006698465,0.000014448477,0.000028406728,8.116872e-7,0.000011544023,0.0011648991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874365,0.00010299415,0.00025318985,0.00037959788,0.00019988512,0.000320693],"domain_scores_gemma":[0.9987779,0.00018520595,0.00014261366,0.0007039599,0.000085804575,0.00010448959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003019639,0.00015477893,0.0002879138,0.00005998654,0.000056788173,0.000042082345,0.0005541854,0.000055892124,0.000041120617],"category_scores_gemma":[0.000032571897,0.00012516469,0.00003746527,0.00020614128,0.000068871625,0.00028838162,0.00003641958,0.000092928436,0.00017845722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007559219,0.0005947947,0.013385157,0.00006583643,0.00020084949,0.00022693494,0.0020417066,0.87926704,0.05095337,0.039847028,0.002534343,0.0108073335],"study_design_scores_gemma":[0.0060004303,0.0019626871,0.020100482,0.00016247874,0.00003801319,0.00039132292,0.000135752,0.9029608,0.06470546,0.00072308345,0.001502463,0.001317021],"about_ca_topic_score_codex":0.00007543558,"about_ca_topic_score_gemma":0.0000013203675,"teacher_disagreement_score":0.4537787,"about_ca_system_score_codex":0.000049256614,"about_ca_system_score_gemma":0.00015340772,"threshold_uncertainty_score":0.5104068},"labels":[],"label_agreement":null},{"id":"W2122509122","doi":"10.1109/icma.2006.257683","title":"Two-Stage Fuzzy Logic-Based Controller for Mobile Robot Navigation","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Fuzzy logic; Sonar; Mobile robot; Controller (irrigation); Control theory (sociology); Computer science; Robot; Fuzzy control system; Artificial intelligence; Measure (data warehouse); Obstacle avoidance; Control engineering; Engineering; Computer vision; Simulation; Control (management); Data mining","score_opus":0.01953758245057762,"score_gpt":0.28164820957644987,"score_spread":0.26211062712587224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122509122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008838977,0.00006478425,0.9914594,0.0003616476,0.0003035422,0.00062809436,0.000008433345,0.00040740968,0.0058827335],"genre_scores_gemma":[0.2477657,1.5357219e-7,0.7482902,0.0005268097,0.0001321122,0.00020338921,0.000036067166,0.000010635031,0.003034933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871904,0.00004271186,0.00026567272,0.00039604466,0.00023533349,0.00034118525],"domain_scores_gemma":[0.99910915,0.00023452558,0.00010864374,0.00036432728,0.00012310385,0.000060276096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034998145,0.00015207901,0.00019462258,0.00006940469,0.00013140203,0.00014575322,0.00048153507,0.000062731815,0.000010561566],"category_scores_gemma":[0.000022192924,0.000127765,0.00009160898,0.00020880971,0.000030941665,0.00023472263,0.000044515986,0.00007547544,0.00007096953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000137509,0.000120452336,0.0003301104,0.000015830763,0.000009076979,0.000015705078,0.00003543328,0.90313536,0.0032139693,0.08437402,0.002391459,0.006344823],"study_design_scores_gemma":[0.0019549306,0.00014993042,0.0004731587,0.000012682938,0.0000053069057,0.0000037344091,0.000010368156,0.98633915,0.0031684553,0.0062707593,0.0014126664,0.00019884767],"about_ca_topic_score_codex":0.00015162298,"about_ca_topic_score_gemma":0.0000027051872,"teacher_disagreement_score":0.2468818,"about_ca_system_score_codex":0.000050388644,"about_ca_system_score_gemma":0.000057055313,"threshold_uncertainty_score":0.5210105},"labels":[],"label_agreement":null},{"id":"W2122717665","doi":"10.1109/robot.1988.12048","title":"Optimal trajectory planning for planar n-link revolute manipulators in the presence of obstacles","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Revolute joint; Trajectory; Link (geometry); Optimal control; Planar; Computer science; Control theory (sociology); Mathematical optimization; State (computer science); Motion planning; Space (punctuation); Mathematics; Control (management); Robot; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.050424948609085085,"score_gpt":0.28480336049080124,"score_spread":0.23437841188171615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122717665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029515043,0.00038751468,0.96700776,0.00021080069,0.00030150873,0.00036684013,0.0000029813696,0.00006872589,0.002138823],"genre_scores_gemma":[0.29467314,0.0000030466551,0.704987,0.00010989805,0.000032421387,0.00002938806,0.0000016383188,0.000008234898,0.000155259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986135,0.0001372021,0.00031316877,0.00031910668,0.0002827761,0.0003342801],"domain_scores_gemma":[0.99858785,0.0007143751,0.00010338819,0.0005128158,0.00003770402,0.00004385677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008925305,0.00013210441,0.00018875215,0.0001147073,0.000073377065,0.00004264038,0.0010190547,0.00006432195,0.0000023096554],"category_scores_gemma":[0.00023296225,0.00009612086,0.00005938412,0.00032876042,0.00004743887,0.00024254435,0.00003123076,0.00014012319,0.0000031832176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004865559,0.000296235,0.044251684,0.0002254448,0.00008687474,0.0002575488,0.02631018,0.55507416,0.0011184746,0.3541721,0.012318433,0.005840239],"study_design_scores_gemma":[0.0014208986,0.0004428435,0.059829,0.000253179,0.000019315685,0.00015320655,0.0018642048,0.9233508,0.0029692722,0.0056812465,0.0033758902,0.0006401226],"about_ca_topic_score_codex":0.0000307458,"about_ca_topic_score_gemma":0.0000018644253,"teacher_disagreement_score":0.3682767,"about_ca_system_score_codex":0.000023244505,"about_ca_system_score_gemma":0.00007348298,"threshold_uncertainty_score":0.39196947},"labels":[],"label_agreement":null},{"id":"W2122875230","doi":"10.1109/iros.1998.727477","title":"A constraint-based controller for soccer-playing robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Java; Robot; Constraint (computer-aided design); Controller (irrigation); Robotics; Domain (mathematical analysis); Control engineering; Artificial intelligence; Task (project management); Distributed computing; Programming language; Systems engineering; Engineering","score_opus":0.04871050536317055,"score_gpt":0.25678194556966555,"score_spread":0.20807144020649498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122875230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011627102,0.00006644996,0.9889736,0.0023118295,0.00031417885,0.00033092435,0.0000037951693,0.00037793693,0.007505008],"genre_scores_gemma":[0.20107515,4.9538727e-7,0.7956253,0.001962286,0.0000654015,0.000054612832,0.0000014438773,0.000009822243,0.0012054669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876624,0.000034408484,0.00022516963,0.00036717905,0.00020138721,0.00040562125],"domain_scores_gemma":[0.9989574,0.00044509815,0.00007342176,0.00032500626,0.000081043174,0.00011801476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025780563,0.00014617843,0.00021210716,0.00008758516,0.0001712621,0.00015294286,0.00056095066,0.00006900073,0.000095088086],"category_scores_gemma":[0.00011093055,0.00012721319,0.00010246645,0.00016295654,0.000052600382,0.00019391706,0.00004291721,0.00008828562,0.0001607429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003311706,0.0006252898,0.002069392,0.00009120871,0.00023508686,0.00017506354,0.0015654429,0.15933375,0.0046496927,0.15376018,0.100847915,0.57661384],"study_design_scores_gemma":[0.0016419463,0.0000772263,0.00018524796,0.000015229481,0.000005707126,0.00002152,0.000009796927,0.99644065,0.00031699805,0.00043722434,0.00066685583,0.00018161081],"about_ca_topic_score_codex":0.000009627854,"about_ca_topic_score_gemma":4.948711e-7,"teacher_disagreement_score":0.8371069,"about_ca_system_score_codex":0.000032657797,"about_ca_system_score_gemma":0.00003145294,"threshold_uncertainty_score":0.5187603},"labels":[],"label_agreement":null},{"id":"W2123129415","doi":"10.1109/robot.1993.292157","title":"A system architecture for a mobile robot based on activities and a blackboard control unit","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Blackboard (design pattern); Blackboard system; Architecture; Computer science; Mobile robot; Implementation; Function (biology); Robot; Control unit; Systems architecture; Embedded system; Artificial intelligence; Human–computer interaction; Computer architecture; Software engineering; Operating system","score_opus":0.0177751466355562,"score_gpt":0.22596296026055787,"score_spread":0.20818781362500166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123129415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017609268,0.000048990794,0.99394387,0.00064553594,0.00013843022,0.00061804795,0.0000119439,0.0003718685,0.0024603836],"genre_scores_gemma":[0.7380293,4.058945e-7,0.26018387,0.0005796763,0.00005652078,0.00022491481,9.29609e-7,0.000012043845,0.0009123378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894214,0.00007469108,0.0001372138,0.0003562005,0.00019715459,0.00029262152],"domain_scores_gemma":[0.99875015,0.0006556028,0.00005759438,0.0004034227,0.00003062001,0.00010261181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001732259,0.00016434921,0.00023053469,0.00012971158,0.00011811861,0.0001423991,0.00032271154,0.00006259406,0.000007179816],"category_scores_gemma":[0.000027161333,0.00012952168,0.000056239023,0.00014259313,0.000041455125,0.00010127027,0.00003402674,0.000110715686,0.000013666406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011164612,0.00032041498,0.00041557752,0.00045348544,0.00011588603,0.00011010916,0.0029305462,0.8889887,0.00087580853,0.013551245,0.0046573877,0.0874692],"study_design_scores_gemma":[0.0012605056,0.00047866398,0.0000955824,0.000074017684,0.000009378135,0.000027798627,0.00009217398,0.9967726,0.000254093,0.000041503616,0.000730833,0.0001628634],"about_ca_topic_score_codex":0.000009972497,"about_ca_topic_score_gemma":0.0000017524276,"teacher_disagreement_score":0.73626834,"about_ca_system_score_codex":0.000029362578,"about_ca_system_score_gemma":0.000017935217,"threshold_uncertainty_score":0.5281741},"labels":[],"label_agreement":null},{"id":"W2123312258","doi":"10.1017/s0263574713000428","title":"Randomized path planning with preferences in highly complex dynamic environments","year":2013,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Université de Sherbrooke","funders":"University of Cambridge","keywords":"Probabilistic roadmap; Heuristic; Planner; Motion planning; Path (computing); Computer science; Any-angle path planning; Mathematical optimization; Probabilistic logic; Space (punctuation); Sampling (signal processing); Operations research; Artificial intelligence; Mathematics; Robot","score_opus":0.022149351289276083,"score_gpt":0.24536053465504296,"score_spread":0.22321118336576687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123312258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027053637,0.00009346086,0.96840525,0.001085278,0.00017908867,0.0007808761,0.0000011684903,0.00016289637,0.0022383197],"genre_scores_gemma":[0.6661865,0.000006147288,0.33330345,0.00012529964,0.000011699222,0.00008639395,0.000007793787,0.0000114732775,0.00026125874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790806,0.00027658898,0.00037738105,0.0005151154,0.00042488755,0.00049796293],"domain_scores_gemma":[0.99875337,0.0004214356,0.00014399084,0.0005268621,0.000018747785,0.00013557175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040648595,0.0002403233,0.0005272102,0.00014853556,0.00008709042,0.00017726273,0.000830322,0.000078064506,0.000041056108],"category_scores_gemma":[0.000060376446,0.00017615665,0.000048511803,0.00026827012,0.00015137217,0.0004708159,0.00018316196,0.00024167872,0.0003252982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001883642,0.0006382735,0.016293252,0.000083000436,0.00026712817,0.00045087258,0.0062814066,0.9457516,0.0017797848,0.009682743,0.001322631,0.015565679],"study_design_scores_gemma":[0.025017798,0.00008679018,0.05084384,0.00013234485,0.000011100526,0.000022041833,0.000038843962,0.9215603,0.00002034891,0.0019321521,0.000045185534,0.00028928046],"about_ca_topic_score_codex":0.000104845916,"about_ca_topic_score_gemma":0.0000014907313,"teacher_disagreement_score":0.63913286,"about_ca_system_score_codex":0.00006856248,"about_ca_system_score_gemma":0.00004590793,"threshold_uncertainty_score":0.718346},"labels":[],"label_agreement":null},{"id":"W2123761689","doi":"10.1109/tsmcb.2005.850172","title":"Solving Minimum Distance Problems With Convex or Concave Bodies Using Combinatorial Global Optimization Algorithms","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Mathematical optimization; Simulated annealing; Optimization problem; Regular polygon; Combinatorial optimization; Mathematics; Algorithm; Global optimization; Computer science; Geometry","score_opus":0.027052218396942124,"score_gpt":0.2511656824740416,"score_spread":0.22411346407709945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123761689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032201812,0.00053875736,0.9910546,0.00018241505,0.0027756568,0.0008899826,0.00006871123,0.00034393687,0.0009257712],"genre_scores_gemma":[0.8520793,0.0002200232,0.14507802,0.00012622964,0.00044357812,0.000103342594,0.000009364278,0.00007314956,0.001867056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625254,0.0002215575,0.0008445838,0.0010151743,0.00086888997,0.00079722976],"domain_scores_gemma":[0.9979217,0.00020350498,0.00040444487,0.0007765333,0.00030726948,0.0003865537],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003787651,0.0006091951,0.0006812971,0.00014825756,0.0005012452,0.00072169217,0.00062450906,0.00027862715,0.00001625811],"category_scores_gemma":[0.00001072669,0.0005326436,0.00009442428,0.00046243228,0.00033003898,0.00047808295,0.000019209749,0.00038158314,0.000031769392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077605255,0.00029205423,0.0001422564,0.00011907062,0.0001673179,0.00005569773,0.0015621992,0.9913103,0.000038125694,0.002646961,0.00025919,0.0033292507],"study_design_scores_gemma":[0.0021559831,0.0006172242,0.000044780165,0.0005720713,0.00012425364,0.00034141054,0.00024792875,0.9922722,0.00033729256,0.00009590219,0.0024331566,0.00075778103],"about_ca_topic_score_codex":0.00018979762,"about_ca_topic_score_gemma":0.00007367392,"teacher_disagreement_score":0.8488591,"about_ca_system_score_codex":0.000328879,"about_ca_system_score_gemma":0.00019982179,"threshold_uncertainty_score":0.9997125},"labels":[],"label_agreement":null},{"id":"W2124234792","doi":"10.1109/his.2006.53","title":"Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm","year":2006,"lang":"en","type":"article","venue":"International Conference Hybrid Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Crossover; Motion planning; Genetic algorithm; Mobile robot; Computer science; Robot; Path (computing); Algorithm; Mutation; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning","score_opus":0.035547290610147296,"score_gpt":0.28699099692383845,"score_spread":0.2514437063136912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124234792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022212584,0.00024191094,0.98799187,0.0001868859,0.005158661,0.00110533,0.00018768857,0.0003598064,0.002546603],"genre_scores_gemma":[0.7499639,0.000008659411,0.24671786,0.00020691156,0.0008131133,0.00079330354,0.00025211275,0.000047765116,0.0011964205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962417,0.00012812998,0.0009142792,0.0010200943,0.0010503711,0.0006454159],"domain_scores_gemma":[0.9975422,0.00040163775,0.00043378831,0.00076171855,0.00068346795,0.00017717002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052646885,0.0004604071,0.000447571,0.00043281532,0.00018973999,0.0008049745,0.0020259507,0.00007403402,0.00003768375],"category_scores_gemma":[0.000077537145,0.0004529356,0.0002133531,0.00017075242,0.000068558526,0.0003285271,0.00015828325,0.0002581654,0.00019085249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025971754,0.0002468299,0.00041045054,0.00003879417,0.00006336264,0.00029632758,0.00007723769,0.96274936,0.00014180434,0.005840298,0.00651447,0.023595111],"study_design_scores_gemma":[0.00048904394,0.00036860272,0.00025065008,0.00041656033,0.000011979808,0.00019670551,0.00004724435,0.9870815,0.003093283,0.0008984635,0.0066487463,0.0004972068],"about_ca_topic_score_codex":0.00020443306,"about_ca_topic_score_gemma":2.8796697e-7,"teacher_disagreement_score":0.7477426,"about_ca_system_score_codex":0.0003207631,"about_ca_system_score_gemma":0.0002504337,"threshold_uncertainty_score":0.9997922},"labels":[],"label_agreement":null},{"id":"W2124569463","doi":"10.1109/imtc.2010.5488117","title":"Evaluation of fish shoal inspired movement in collaborative robotic environments","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Shoal; Computer science; Obstacle avoidance; Cohesion (chemistry); Artificial intelligence; Robot; Obstacle; Human–computer interaction; Set (abstract data type); Movement (music); Simulation; Mobile robot; Geology; Geography; Oceanography","score_opus":0.025155121037799506,"score_gpt":0.27720372507986757,"score_spread":0.2520486040420681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124569463","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5132811,0.000025931395,0.47390795,0.0013133741,0.0012731652,0.0010398044,0.000005128864,0.00007033915,0.009083211],"genre_scores_gemma":[0.8485684,0.000001461557,0.15097177,0.00026492248,0.000017332524,0.00003529087,0.0000036354766,0.0000046714617,0.00013253659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828994,0.00013850421,0.00025848945,0.00026267782,0.0008828129,0.00016759378],"domain_scores_gemma":[0.99931306,0.000052493535,0.00010187406,0.00039321894,0.00009082598,0.00004852459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014084973,0.00009313291,0.00013707738,0.000108420034,0.000025381592,0.000026715767,0.00041263975,0.000056898327,0.000046490582],"category_scores_gemma":[0.00014416696,0.00008771585,0.00001762062,0.0003531209,0.00003836391,0.00027652018,0.00012085185,0.00012048577,0.000019266106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000125519255,0.0013043886,0.03220618,0.000017940121,0.00011176735,0.000027774,0.007590279,0.68663204,0.114236034,0.0139879575,0.0015501467,0.14232293],"study_design_scores_gemma":[0.0007116958,0.00006709204,0.123659916,0.000010364265,0.000009658614,7.3468726e-7,0.000050949177,0.8624231,0.011540829,0.0013832125,0.000038291877,0.0001041201],"about_ca_topic_score_codex":0.000046682617,"about_ca_topic_score_gemma":0.00006968042,"teacher_disagreement_score":0.33528727,"about_ca_system_score_codex":0.000082191225,"about_ca_system_score_gemma":0.00015573775,"threshold_uncertainty_score":0.35769486},"labels":[],"label_agreement":null},{"id":"W2125205873","doi":"10.1109/iembs.2005.1615605","title":"An Intelligent Assistive Robotic Manipulator","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of British Columbia","funders":"","keywords":"Computer science; Object (grammar); GRASP; Computer vision; Artificial intelligence; Orientation (vector space); Robotic arm; Robot; Position (finance); Rotation (mathematics); Point (geometry); Mathematics","score_opus":0.03149687805608702,"score_gpt":0.2871745680060019,"score_spread":0.25567768994991485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125205873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018901073,0.000040456795,0.9920701,0.0007789656,0.00034230357,0.00008514057,2.4779786e-7,0.00042751606,0.0043651825],"genre_scores_gemma":[0.3955579,0.000001175745,0.6030003,0.0004733756,0.00012244792,0.000004237273,0.0000014068661,0.0000063374737,0.00083281565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883795,0.000048611626,0.00019080426,0.00038537572,0.00024727505,0.0002899966],"domain_scores_gemma":[0.99903375,0.0000458858,0.000046594614,0.0006401727,0.000051460225,0.00018214961],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001955717,0.00012822605,0.00013168012,0.0000828726,0.000079551704,0.0001450063,0.0008787699,0.000048692247,0.00005818514],"category_scores_gemma":[0.000020244885,0.00011018556,0.000039026596,0.00020571906,0.000022606477,0.0005632923,0.00011019553,0.000109068984,0.00088546966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003717482,0.00042338672,0.0031252424,0.000007356263,0.000044239834,0.0000920587,0.0011629346,0.5858846,0.00041199598,0.10194862,0.0038932664,0.30300263],"study_design_scores_gemma":[0.00007752094,0.00007936941,0.01233818,0.000007655399,0.0000035628318,0.00003325134,0.000020439538,0.9847305,0.0013074683,0.00024883528,0.0009780511,0.00017517398],"about_ca_topic_score_codex":0.000017959108,"about_ca_topic_score_gemma":0.0000034395903,"teacher_disagreement_score":0.39884594,"about_ca_system_score_codex":0.000080922626,"about_ca_system_score_gemma":0.000041247382,"threshold_uncertainty_score":0.9998925},"labels":[],"label_agreement":null},{"id":"W2125690420","doi":"10.1109/robot.2000.846436","title":"Time-optimal rendezvous planning for pick-and-place task sharing","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rendezvous; Task (project management); Workspace; Computer science; Robot; SMT placement equipment; Relay; Point (geometry); Mode (computer interface); Key (lock); Simulation; Artificial intelligence; Engineering; Human–computer interaction; Mathematics","score_opus":0.03492664930977405,"score_gpt":0.2566438037763677,"score_spread":0.22171715446659365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125690420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007755253,0.00030768962,0.98342705,0.00075206,0.00024310513,0.00019953765,0.0000027482522,0.0005312722,0.0067813154],"genre_scores_gemma":[0.040438116,0.0000043217788,0.93648106,0.00033235629,0.00013035645,0.000030281888,0.0000037277514,0.000022045686,0.02255775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986004,0.000017219796,0.00020962764,0.00054209295,0.00019739872,0.00043321095],"domain_scores_gemma":[0.99912435,0.00020686808,0.00006789179,0.00043138443,0.00003672578,0.00013279669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028412626,0.00016491543,0.00018843506,0.0000950168,0.00019111988,0.00019750188,0.00066864444,0.00007333733,0.000039196835],"category_scores_gemma":[0.000073678944,0.000154156,0.000045332545,0.00016179793,0.000036610905,0.00038212098,0.00028806875,0.00012696399,0.0001762486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058677884,0.00044872882,0.004241344,0.00027536778,0.00035691157,0.0006550306,0.034981526,0.51401794,0.0036146531,0.024899002,0.2857986,0.1306522],"study_design_scores_gemma":[0.00036743496,0.00010222061,0.00018768692,0.000032926655,0.0000064682345,0.00009939609,0.000027512988,0.99605364,0.00013076978,0.00012715136,0.0026463063,0.0002185091],"about_ca_topic_score_codex":0.0000075764247,"about_ca_topic_score_gemma":1.07699734e-7,"teacher_disagreement_score":0.48203567,"about_ca_system_score_codex":0.000032515156,"about_ca_system_score_gemma":0.000010440417,"threshold_uncertainty_score":0.62863},"labels":[],"label_agreement":null},{"id":"W2125804107","doi":"10.1109/robot.2002.1014423","title":"Resistors, Markov chains and dynamic path planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Computer science; Convergence (economics); Sampling (signal processing); Motion planning; Mathematical optimization; Exponential function; Total harmonic distortion; Resistor; Tree (set theory); Harmonic; Markov process; Heuristic; Grid; Algorithm; Mathematics; Artificial intelligence; Engineering; Telecommunications; Machine learning; Statistics","score_opus":0.01178518023857749,"score_gpt":0.24298003980164484,"score_spread":0.23119485956306735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125804107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007901567,0.0004663372,0.96918434,0.00026484864,0.00035007388,0.00007669975,7.821282e-7,0.0002545447,0.021500813],"genre_scores_gemma":[0.27891606,0.000009315423,0.7162218,0.00029015224,0.000013945808,0.0000051953657,0.0000012968715,0.000009612926,0.004532609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898815,0.00007543487,0.00013798784,0.0003457861,0.0001746738,0.00027799307],"domain_scores_gemma":[0.99935824,0.000092460315,0.0000440011,0.00037447986,0.000020388457,0.000110461595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003529387,0.00011837129,0.00012406804,0.00008601133,0.00012796508,0.00009029238,0.00029434377,0.000050876257,0.000007166871],"category_scores_gemma":[0.000083014565,0.000105870444,0.000020915346,0.00019065983,0.000030801257,0.00019167566,0.00008110263,0.000108148306,0.000017997343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027193968,0.0004288115,0.09360353,0.0002110391,0.0002154189,0.0025988678,0.015124573,0.0075464062,0.003493391,0.6717402,0.05923914,0.14577143],"study_design_scores_gemma":[0.0006874579,0.00014419356,0.051758695,0.000095711584,0.000008878242,0.00031645095,0.00017755979,0.9337476,0.00019533346,0.0027865947,0.009439865,0.00064167636],"about_ca_topic_score_codex":0.000010459878,"about_ca_topic_score_gemma":5.7640955e-7,"teacher_disagreement_score":0.92620116,"about_ca_system_score_codex":0.000037801812,"about_ca_system_score_gemma":0.000043198434,"threshold_uncertainty_score":0.43172714},"labels":[],"label_agreement":null},{"id":"W2125970263","doi":"10.1109/icmech.2009.4957210","title":"Exploiting collision information in probabilistic roadmap planning","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Probabilistic logic; Computer science; Collision; Artificial intelligence; Computer security","score_opus":0.021421761138745894,"score_gpt":0.26355649648083146,"score_spread":0.24213473534208557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125970263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043673903,0.000017507375,0.9446327,0.00060130557,0.00016451912,0.00018614889,2.6026964e-7,0.00029715657,0.010426511],"genre_scores_gemma":[0.67511797,5.724387e-7,0.32431725,0.00048342458,0.00002296313,0.000006737442,0.0000039813112,0.0000018477211,0.000045289282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989874,0.000034454624,0.00031053636,0.0001658086,0.0002459244,0.00025592867],"domain_scores_gemma":[0.99947953,0.00008204023,0.00008118573,0.00025458948,0.000044983244,0.000057679128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003961869,0.00009625828,0.00011536875,0.00019667912,0.00006602184,0.00017945615,0.00040635504,0.000048539587,0.000002072761],"category_scores_gemma":[0.00021103071,0.000087585584,0.000018616573,0.00045990225,0.000007835332,0.0018138521,0.00006165667,0.000121080884,0.00006716145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000875802,0.00008531665,0.0029285909,0.000021033482,0.000003020512,0.00007480983,0.008979949,0.81536204,0.00017518667,0.035431616,0.0013112446,0.13561846],"study_design_scores_gemma":[0.0002732287,0.000083218016,0.022723366,0.000095920914,6.757396e-7,0.000018955014,0.00012487435,0.9727124,0.00020190881,0.0034372155,0.0001859792,0.00014224867],"about_ca_topic_score_codex":0.000016108528,"about_ca_topic_score_gemma":2.7366372e-7,"teacher_disagreement_score":0.63144404,"about_ca_system_score_codex":0.000071786686,"about_ca_system_score_gemma":0.000045952933,"threshold_uncertainty_score":0.35716364},"labels":[],"label_agreement":null},{"id":"W2126534328","doi":"10.1109/iros.2006.282637","title":"Integrated Autonomous System for Exploration and Navigation in Underground Mines","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Australian Centre for Field Robotics; Polytechnique Montréal","keywords":"Context (archaeology); Supervisor; Navigation system; Mode (computer interface); Planner; Computer science; Real-time computing; Robot; Mobile robot navigation; Human–computer interaction; Artificial intelligence; Mobile robot; Geography","score_opus":0.024311711892470094,"score_gpt":0.244807591212966,"score_spread":0.2204958793204959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126534328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015793068,0.000052306343,0.9827028,0.00039025117,0.00019476275,0.00028114623,9.358101e-7,0.00019068361,0.00039405347],"genre_scores_gemma":[0.67945564,5.49906e-7,0.31999195,0.000018314064,0.00003398597,0.0000651901,0.000028192757,0.0000048302263,0.0004013551],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993795,0.00003105296,0.00019269239,0.00020408643,0.00007437505,0.00011824922],"domain_scores_gemma":[0.9996444,0.00009546592,0.000053280695,0.00012838903,0.00005993011,0.000018514265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019764047,0.00007444011,0.000106815925,0.00008388622,0.00004498535,0.0001384191,0.00012412718,0.000041895528,1.2095371e-7],"category_scores_gemma":[0.000016982258,0.000063723775,0.000011819392,0.0002095338,0.0000117255295,0.00056680257,0.000022270342,0.000027770582,0.0000026364476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003060377,0.00027129706,0.004812967,0.0031518065,0.000033807453,0.000092896815,0.0029917203,0.081409626,0.0074938377,0.8681838,0.0025519512,0.028975712],"study_design_scores_gemma":[0.00029743026,0.00003782823,0.0007636341,0.00021944601,0.000002589252,0.000018054943,0.00019219765,0.9929369,0.0009198211,0.0044822353,0.00003507158,0.00009480519],"about_ca_topic_score_codex":0.00033133948,"about_ca_topic_score_gemma":0.000051447958,"teacher_disagreement_score":0.9115273,"about_ca_system_score_codex":0.000086206375,"about_ca_system_score_gemma":0.000029377317,"threshold_uncertainty_score":0.259858},"labels":[],"label_agreement":null},{"id":"W2126605958","doi":"10.1109/iros.2004.1389661","title":"Code reusability tools for programming mobile robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Reusability; Computer science; Code reuse; Mobile robot; Visual programming language; Reuse; Robot; Event-driven programming; Modular design; Reactive programming; Extensible programming; Robotics; Code (set theory); Software engineering; Programming language; Human–computer interaction; Programming paradigm; Inductive programming; Artificial intelligence; Software; Engineering","score_opus":0.046378687962969525,"score_gpt":0.31245141770729984,"score_spread":0.2660727297443303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126605958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016009726,0.000052696414,0.99468994,0.001045087,0.00021984977,0.0007335317,0.0000029782711,0.0005524652,0.0011024923],"genre_scores_gemma":[0.02203305,9.81737e-7,0.9762963,0.00025056791,0.00014649518,0.00028589572,0.0000043248847,0.000008554915,0.0009738533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986494,0.000032137516,0.00025325015,0.00045954192,0.00019968304,0.00040595885],"domain_scores_gemma":[0.99866074,0.0003593525,0.00006002769,0.0007173358,0.00009260775,0.00010991457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005750977,0.0001260663,0.00016202961,0.000038424296,0.00012085758,0.000295584,0.00081842276,0.00006272143,0.000011543939],"category_scores_gemma":[0.00023134648,0.00011012304,0.00007461586,0.00019561165,0.000034979217,0.00085420616,0.00016427127,0.00008602622,0.000078909296],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024695673,0.00010272275,0.0001934085,0.000012315562,0.000007832354,0.0000018616381,0.00028376403,0.024592724,0.000094750816,0.0037720792,0.0016820979,0.96925396],"study_design_scores_gemma":[0.00053334585,0.0002834187,0.0010274255,0.000019927702,0.0000069883777,0.000026298525,0.000049990707,0.78218913,0.0025337064,0.00068682164,0.21227637,0.0003665678],"about_ca_topic_score_codex":0.0000085520405,"about_ca_topic_score_gemma":0.000004654555,"teacher_disagreement_score":0.9688874,"about_ca_system_score_codex":0.000072210394,"about_ca_system_score_gemma":0.00005872145,"threshold_uncertainty_score":0.44906873},"labels":[],"label_agreement":null},{"id":"W2126700445","doi":"10.1109/robot.2004.1302471","title":"Robotic navigation using harmonic function-based probabilistic roadmaps","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Science Council","keywords":"Probabilistic roadmap; Motion planning; Probabilistic logic; Computer science; Path (computing); Laplace transform; Function (biology); Harmonic; Algorithm; Mathematical optimization; Topology (electrical circuits); A priori and a posteriori; Artificial intelligence; Mathematics; Robot; Physics","score_opus":0.034161612384857834,"score_gpt":0.2599644886335893,"score_spread":0.22580287624873147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126700445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01760644,0.00004069478,0.9800194,0.0005354248,0.00073878386,0.00023459364,4.467911e-7,0.00049013126,0.00033408916],"genre_scores_gemma":[0.54695,1.2815082e-7,0.4526855,0.00022998838,0.00004845864,0.000009016481,0.000006499397,0.000009380417,0.000061029394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851483,0.00005048445,0.0002684501,0.00045902262,0.00036200738,0.00034518243],"domain_scores_gemma":[0.99903107,0.000073336705,0.00009937724,0.0005606523,0.000114384566,0.00012120484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026202074,0.00017031311,0.00015923347,0.00011273993,0.00019109866,0.00017463636,0.0004783774,0.00007271829,0.0000119572305],"category_scores_gemma":[0.00006792969,0.0001579971,0.000063208965,0.0005947288,0.000046276753,0.00043021986,0.000065626766,0.00015324574,0.00016938275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020850225,0.00005883477,0.0001812318,0.000015887565,0.000007793079,0.000019822324,0.00006794914,0.9883947,0.0007708963,0.008920725,0.000022608187,0.0015374824],"study_design_scores_gemma":[0.0005440778,0.00010472267,0.0024932467,0.00008760651,0.000017232855,0.000035961286,0.000008487723,0.9842541,0.0010322414,0.011181839,0.000016768814,0.00022368753],"about_ca_topic_score_codex":0.00014704817,"about_ca_topic_score_gemma":0.0000017107602,"teacher_disagreement_score":0.52934355,"about_ca_system_score_codex":0.00027848466,"about_ca_system_score_gemma":0.00038408212,"threshold_uncertainty_score":0.6442934},"labels":[],"label_agreement":null},{"id":"W2126757501","doi":"10.1109/robot.2006.1641818","title":"Optimal path planning under defferent norms in continuous state spaces","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dijkstra's algorithm; Maxima and minima; Motion planning; Fast marching method; Mathematical optimization; Computer science; Path (computing); Shortest path problem; Any-angle path planning; State (computer science); Euclidean distance; Graph; Algorithm; Mathematics; Robot; Theoretical computer science; Artificial intelligence","score_opus":0.014839644439533438,"score_gpt":0.24482531064194973,"score_spread":0.22998566620241628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126757501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33642247,0.00009074805,0.660286,0.00022637253,0.00020244555,0.00009476465,0.0000011417305,0.00019481969,0.0024812121],"genre_scores_gemma":[0.6985593,0.0000013605219,0.30000114,0.00012235643,0.00004052164,0.0000090107,0.0000045303273,0.000010113786,0.0012516738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984656,0.000053135638,0.00028421375,0.00040561054,0.00029695392,0.0004944616],"domain_scores_gemma":[0.9993612,0.000115352654,0.0000823168,0.00033456116,0.000033823177,0.000072722905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026493875,0.00018160272,0.0002282561,0.00015224957,0.000060514612,0.00024038804,0.00053988973,0.00005310355,0.0000072068765],"category_scores_gemma":[0.000011441999,0.00014656896,0.00003764441,0.00029090024,0.00003213541,0.0003178104,0.00018068941,0.00017502173,0.00006380617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006546131,0.000112531656,0.053307667,0.000008568682,0.000009663359,0.00037569756,0.0007223286,0.9346039,0.00023665024,0.005261834,0.0022821974,0.003072367],"study_design_scores_gemma":[0.0007968309,0.00012208475,0.19463828,0.0000758263,0.000002889401,0.000049562503,0.00015423178,0.8001533,0.0008055826,0.0024942001,0.00029901924,0.00040819202],"about_ca_topic_score_codex":0.0006262341,"about_ca_topic_score_gemma":0.000009604703,"teacher_disagreement_score":0.3621368,"about_ca_system_score_codex":0.00006555927,"about_ca_system_score_gemma":0.000051473835,"threshold_uncertainty_score":0.5976908},"labels":[],"label_agreement":null},{"id":"W2126815750","doi":"10.1145/1815396.1815577","title":"Bark indication detection and release algorithm for the automatic delivery of packages by dogs","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Bark (sound); Computer science; Algorithm; Physics","score_opus":0.006817394043630515,"score_gpt":0.2256236396959691,"score_spread":0.21880624565233858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126815750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05121401,0.0000780747,0.9477719,0.00028497138,0.00027049117,0.00023170911,0.000004640279,0.000099552075,0.000044634522],"genre_scores_gemma":[0.52271783,0.000011692319,0.47703546,0.000079920515,0.00003151958,0.00003348114,0.0000019156757,0.000005118129,0.00008302793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.000026933605,0.00015786057,0.000185542,0.00014745397,0.00012349915],"domain_scores_gemma":[0.9990225,0.00043037668,0.00010969153,0.00032659323,0.00006292607,0.00004792642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036609077,0.000075359145,0.00008433852,0.000052066258,0.00012847462,0.000057839694,0.00031069384,0.000058991292,0.0000036354527],"category_scores_gemma":[0.00008799363,0.000051569914,0.000025311905,0.00013952496,0.000060715345,0.0002179414,0.000060275564,0.00010463771,0.00000433152],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.5579434e-7,0.000019155379,0.00010963561,0.000010666087,0.000011792479,3.532459e-7,0.00021067957,0.000015194985,0.014940345,0.00022121107,0.00060015416,0.98386014],"study_design_scores_gemma":[0.0001913637,0.00006291798,0.009796228,0.00000647901,0.000011347651,0.000015019862,0.000024099209,0.97293156,0.016258078,0.00048271698,0.00014948915,0.00007068857],"about_ca_topic_score_codex":0.00007922998,"about_ca_topic_score_gemma":0.0000047810745,"teacher_disagreement_score":0.98378944,"about_ca_system_score_codex":0.000007545739,"about_ca_system_score_gemma":0.00002666203,"threshold_uncertainty_score":0.210296},"labels":[],"label_agreement":null},{"id":"W2126861912","doi":"10.1109/robot.2010.5509859","title":"Distributed roadmaps for robot navigation in sensor networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Computer science; Node (physics); Path (computing); Wireless sensor network; Distributed computing; Robot; Overhead (engineering); Sensor node; Representation (politics); Real-time computing; Distributed algorithm; Key distribution in wireless sensor networks; Computer network; Artificial intelligence; Engineering; Telecommunications","score_opus":0.013073555056666228,"score_gpt":0.25860761778464064,"score_spread":0.2455340627279744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126861912","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076671313,0.00000650968,0.98999643,0.00085133035,0.00089433475,0.00023292503,0.0000046201453,0.00018497404,0.00016175742],"genre_scores_gemma":[0.27978268,3.5765666e-7,0.71974295,0.000101949285,0.00011716484,0.000029915662,0.00006395873,0.000006319445,0.00015467964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907213,0.000023785553,0.00019868353,0.000290802,0.00011974041,0.0002948687],"domain_scores_gemma":[0.9992621,0.0001650412,0.000055988807,0.00037033067,0.00007232122,0.000074239266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034885685,0.00009911542,0.00012106862,0.00004971159,0.000060487127,0.00009622005,0.00043228854,0.000103187114,0.0000038030876],"category_scores_gemma":[0.00009486889,0.00009010281,0.000035700195,0.00031326967,0.00001990801,0.00023333127,0.00007045713,0.00021579363,0.000015321571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015258162,0.000182923,0.012581758,0.000021212234,0.000019113297,0.00006234847,0.00043278778,0.8848547,0.0054309582,0.028849125,0.0062190057,0.061330833],"study_design_scores_gemma":[0.00032359423,0.000027184838,0.016638055,0.000011322171,0.0000014251822,0.000017547958,0.0000070850147,0.9812842,0.00050060754,0.0006668262,0.00039574114,0.00012640786],"about_ca_topic_score_codex":0.00004234254,"about_ca_topic_score_gemma":0.000012870737,"teacher_disagreement_score":0.27211556,"about_ca_system_score_codex":0.00002085776,"about_ca_system_score_gemma":0.00002901658,"threshold_uncertainty_score":0.3674286},"labels":[],"label_agreement":null},{"id":"W2127711722","doi":"10.1109/cimsa.2008.4595843","title":"Mobile robot navigation using particle swarm optimization and noisy RFID communication","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Particle swarm optimization; Computer science; Radio-frequency identification; Mobile robot; Robot; Trajectory; Noise (video); Scheme (mathematics); Real-time computing; Position (finance); Tracking (education); Swarm behaviour; Computer vision; Radio frequency; Artificial intelligence; Algorithm; Telecommunications","score_opus":0.03885897123193791,"score_gpt":0.27317451864251396,"score_spread":0.23431554741057606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127711722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16545887,0.00017716367,0.8337446,0.000079362275,0.000059219732,0.00012742607,3.2393277e-7,0.00016439107,0.00018862492],"genre_scores_gemma":[0.45194933,0.0000320776,0.5478808,0.000050684383,0.000010011624,0.00000722976,0.000005175796,0.0000042108995,0.000060473547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992086,0.00007579289,0.00018139675,0.0002127068,0.00016553323,0.00015596408],"domain_scores_gemma":[0.9992707,0.00005235638,0.00007781213,0.00045027726,0.0000813154,0.000067542205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018915466,0.00008411205,0.00009290762,0.000035097415,0.00029393943,0.00007293881,0.00028112324,0.000044191594,0.000003928784],"category_scores_gemma":[0.000017769444,0.00008318173,0.000015353377,0.00028057356,0.00006187259,0.0006944912,0.00015078436,0.00007386285,0.000011559732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.817466e-7,0.000031138938,0.0017746271,0.0000034129366,0.0000039354045,0.000004695963,0.0014862134,0.9933556,0.0010695215,0.0006122473,0.000035947814,0.0016216873],"study_design_scores_gemma":[0.00019758321,0.00004135179,0.001157702,0.000017626935,0.0000034305863,0.00011222076,0.00004113452,0.9937548,0.004443024,0.00010593735,0.000019415118,0.00010575952],"about_ca_topic_score_codex":0.000083620376,"about_ca_topic_score_gemma":3.825049e-7,"teacher_disagreement_score":0.28649047,"about_ca_system_score_codex":0.000038822396,"about_ca_system_score_gemma":0.000032374388,"threshold_uncertainty_score":0.33920527},"labels":[],"label_agreement":null},{"id":"W2127817500","doi":"10.1109/tsmcb.2005.862724","title":"An efficient dynamic system for real-time robot-path planning","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Grid; Motion planning; Robot; Computer science; Path (computing); Point (geometry); Shortest path problem; Grid reference; Any-angle path planning; Dynamic programming; Mobile robot; Algorithm; Artificial intelligence; Mathematics; Theoretical computer science; Graph","score_opus":0.012962337982991106,"score_gpt":0.24434759832696715,"score_spread":0.23138526034397605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127817500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037488658,0.00031914248,0.9556459,0.00006302731,0.002355123,0.0011785912,0.000112823895,0.0008638086,0.0019728846],"genre_scores_gemma":[0.962433,0.00002503645,0.03345473,0.000026815263,0.00023664182,0.00024707953,0.000030707808,0.00009926163,0.003446725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958218,0.00027342702,0.0009852072,0.001244751,0.00073034404,0.0009444897],"domain_scores_gemma":[0.99742645,0.000325572,0.00036385198,0.0012748003,0.00021075542,0.00039858045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00071274646,0.00062323356,0.00071359804,0.0003610825,0.0005542868,0.00066097395,0.00088154717,0.00032550242,0.0000041633875],"category_scores_gemma":[0.0000042732268,0.0006323613,0.00018569452,0.00040864618,0.00014375823,0.00018801256,0.000011479037,0.00033348554,0.00012303586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003295846,0.000349074,0.000037654554,0.00021742805,0.00007055361,0.00006131589,0.0006852272,0.990671,0.0023148723,0.0032782117,0.00058543455,0.0016962664],"study_design_scores_gemma":[0.0011271478,0.0005937559,0.00055129034,0.00056999864,0.00010673887,0.00022110062,0.00023520441,0.99382013,0.0011362117,0.00005824086,0.00083497644,0.00074523274],"about_ca_topic_score_codex":0.00042819604,"about_ca_topic_score_gemma":0.0000099670215,"teacher_disagreement_score":0.92494434,"about_ca_system_score_codex":0.00026387133,"about_ca_system_score_gemma":0.00009302841,"threshold_uncertainty_score":0.99961275},"labels":[],"label_agreement":null},{"id":"W2128140866","doi":"10.1109/iros.2006.281892","title":"A Configuration Space View of View Planning","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Configuration space; Entropy (arrow of time); Space (punctuation); Computer science; Robot; Mathematics; Artificial intelligence; Physics","score_opus":0.02234513117651666,"score_gpt":0.2654364396858439,"score_spread":0.24309130850932725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128140866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010416087,0.00061120646,0.95490533,0.00052475964,0.00015728005,0.00009289422,4.770234e-7,0.0001434971,0.04252296],"genre_scores_gemma":[0.4861744,0.00000828601,0.5112184,0.00018886966,0.00008197688,0.000008136631,0.000006093355,0.000008244227,0.0023055742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999162,0.000041306143,0.00022932408,0.00019852708,0.00020737262,0.00016145785],"domain_scores_gemma":[0.99942636,0.00007395793,0.00010354166,0.00030555314,0.00005940358,0.000031198106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021066166,0.000087550936,0.00015972974,0.000059991115,0.000040180203,0.000047544112,0.00033984397,0.000036459835,0.000016929065],"category_scores_gemma":[0.00001731617,0.000075474876,0.000033215212,0.00026773862,0.000021689199,0.00023410196,0.000050166404,0.00005688811,0.000060249502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005850089,0.00021085331,0.004759834,0.0002245528,0.000039180046,0.00013219396,0.0013975454,0.051695835,0.013420383,0.83205605,0.020622864,0.07543485],"study_design_scores_gemma":[0.0008957031,0.00024288932,0.03455109,0.0006102006,0.000020644426,0.00012575905,0.00007027059,0.8799283,0.038748737,0.013336774,0.030783817,0.00068582146],"about_ca_topic_score_codex":0.00011781345,"about_ca_topic_score_gemma":9.669856e-7,"teacher_disagreement_score":0.82823247,"about_ca_system_score_codex":0.000014027268,"about_ca_system_score_gemma":0.000039266466,"threshold_uncertainty_score":0.3077776},"labels":[],"label_agreement":null},{"id":"W2128247685","doi":"10.1109/icsmc.1995.537808","title":"Robot path planning using genetic algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Computer science; Genetic algorithm; Path (computing); Robot; Artificial intelligence; Algorithm; Machine learning; Computer network","score_opus":0.07233880266214743,"score_gpt":0.2767000770325934,"score_spread":0.204361274370446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128247685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002903675,0.0005414033,0.9909647,0.0001734019,0.000633704,0.00010562077,7.8155676e-7,0.0004818357,0.0041948943],"genre_scores_gemma":[0.05995094,0.000006063175,0.9387193,0.00034561148,0.0001624965,0.000004493265,6.400901e-7,0.000017449207,0.0007930385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981806,0.00006265132,0.00029014866,0.00052605406,0.00039828982,0.00054222735],"domain_scores_gemma":[0.9989277,0.0000775315,0.000090568545,0.0006745952,0.00005184839,0.00017776084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016390161,0.00020895859,0.00020686159,0.00014352593,0.00019656251,0.0002118146,0.0009259644,0.00008394357,0.00008830045],"category_scores_gemma":[0.000031565098,0.00019347832,0.00006419366,0.00046908754,0.000036703866,0.00038353587,0.00024858685,0.00017653647,0.00027579602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015945858,0.00022890356,0.0066833124,0.00002742938,0.000076523,0.001694675,0.0037325856,0.7524139,0.0025765954,0.0019068758,0.008055774,0.22260183],"study_design_scores_gemma":[0.00020974054,0.00004863813,0.0040198904,0.00003462741,0.0000058433466,0.0002759771,0.000021800599,0.99418795,0.00024974725,0.00022770066,0.0004401535,0.00027794737],"about_ca_topic_score_codex":0.0000411135,"about_ca_topic_score_gemma":5.2297725e-8,"teacher_disagreement_score":0.24177404,"about_ca_system_score_codex":0.000058390062,"about_ca_system_score_gemma":0.000021952106,"threshold_uncertainty_score":0.7889817},"labels":[],"label_agreement":null},{"id":"W2128374244","doi":"10.1109/robio.2004.1521769","title":"A New Optimization-Based Method for Motion Planning in Dynamic Environments","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Motion planning; Motion (physics); Artificial intelligence; Robot","score_opus":0.017693484098269326,"score_gpt":0.29744034245820744,"score_spread":0.2797468583599381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128374244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002328304,0.000024182249,0.9979561,0.0012713384,0.0000962605,0.00021049875,8.5083275e-7,0.00008949486,0.00032801626],"genre_scores_gemma":[0.0049591437,4.5921863e-7,0.9937589,0.00037048486,0.000027918546,0.000019532963,0.0000114917075,0.000008453344,0.00084360695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990978,0.00004280686,0.00018913235,0.00030299852,0.00014889459,0.00021833615],"domain_scores_gemma":[0.9994621,0.00016277794,0.00005792714,0.00024283573,0.0000063909197,0.0000679302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002969546,0.000098306635,0.00010805065,0.00014177497,0.0000437927,0.000050461764,0.00033390877,0.00005294992,0.00001538476],"category_scores_gemma":[0.000037559676,0.00009874097,0.00003008995,0.00017843553,0.0000047067665,0.0002779446,0.000036579815,0.000060941427,0.000015938087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020545701,0.000021137792,0.00021239225,0.000001812455,0.0000022737393,0.000001571396,0.00013405306,0.94257176,0.00005438416,0.0001783552,0.0001297849,0.05669041],"study_design_scores_gemma":[0.0006681794,0.000029299625,0.001005087,0.000016189151,0.0000022809627,0.0000022862866,0.0000060349676,0.9974158,0.00026408528,0.000141946,0.00033572744,0.00011310201],"about_ca_topic_score_codex":0.000022016811,"about_ca_topic_score_gemma":0.0000016908424,"teacher_disagreement_score":0.056577306,"about_ca_system_score_codex":0.000118933756,"about_ca_system_score_gemma":0.000048754122,"threshold_uncertainty_score":0.402654},"labels":[],"label_agreement":null},{"id":"W2129521256","doi":"10.1109/robio.2004.1521862","title":"A Simple yet Efficient Dynamic System for Robot Path Planning","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Workspace; Motion planning; Grid; Computer science; Robot; Path (computing); Any-angle path planning; Simple (philosophy); Occupancy grid mapping; Grid reference; Point (geometry); Mobile robot; Robot kinematics; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.01764295544899692,"score_gpt":0.2749577078658513,"score_spread":0.25731475241685436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129521256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048513305,0.00010755089,0.99158597,0.00029840152,0.0004978184,0.00033984712,0.000007390423,0.00074656494,0.0015651389],"genre_scores_gemma":[0.5345209,1.5721957e-7,0.4649559,0.000118121025,0.000055606906,0.000035439076,0.0000068587533,0.0000112325715,0.00029573566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839664,0.000036208912,0.0002981816,0.00047927984,0.0002824763,0.00050721405],"domain_scores_gemma":[0.9989285,0.00021673588,0.00009959018,0.00055716187,0.00006596599,0.00013202986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004269465,0.00017734175,0.00022032826,0.00011100968,0.00018464778,0.00015097803,0.00077599194,0.00006753661,0.000003029688],"category_scores_gemma":[0.000042948894,0.00015358403,0.00008030068,0.00023962685,0.000017168477,0.00015363442,0.00016341633,0.00009505918,0.0001087876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046294876,0.000054325214,0.00013162753,0.000048416387,0.000017160495,0.000025316484,0.0006734521,0.97632855,0.0003877349,0.007906425,0.0023742956,0.012048068],"study_design_scores_gemma":[0.00042037843,0.0000705167,0.00068243494,0.00006826267,0.0000060010975,0.000052894437,0.000110470966,0.99672306,0.0003109049,0.00005167942,0.0012929087,0.00021049973],"about_ca_topic_score_codex":0.000012798745,"about_ca_topic_score_gemma":6.749655e-7,"teacher_disagreement_score":0.52966964,"about_ca_system_score_codex":0.00018414432,"about_ca_system_score_gemma":0.000064314314,"threshold_uncertainty_score":0.6262975},"labels":[],"label_agreement":null},{"id":"W2129604054","doi":"10.1109/robot.2001.932564","title":"On eye-sensor based path planning for robots with non-trivial geometry/kinematics","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Observability; Kinematics; Completeness (order theory); Reachability; Configuration space; Robot; Computer science; Inverse kinematics; Robot kinematics; Computer vision; Mathematics; Geometry; Artificial intelligence; Mobile robot; Theoretical computer science; Mathematical analysis; Physics; Classical mechanics","score_opus":0.03259306086732413,"score_gpt":0.2617306445882835,"score_spread":0.22913758372095935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129604054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065493193,0.000014328527,0.98831755,0.00068267586,0.0003280029,0.0004349494,0.0000059879117,0.0003143344,0.0033528449],"genre_scores_gemma":[0.106526926,3.5866967e-7,0.89095044,0.00090459414,0.00011913062,0.00004101388,0.0000057538646,0.000027451315,0.0014243068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980666,0.000035244706,0.00029530938,0.0005341008,0.0005183248,0.00055044034],"domain_scores_gemma":[0.99821335,0.0006426529,0.00014812476,0.0007189954,0.00010808831,0.00016877947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028137994,0.0002733783,0.00031937743,0.00026714292,0.00018424251,0.00018622556,0.0007193646,0.000096577816,0.000036568774],"category_scores_gemma":[0.00016613156,0.00020530268,0.0000758318,0.00058496324,0.000034891524,0.00020522546,0.00006295806,0.00017490592,0.0001235725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046113706,0.000421114,0.0020101785,0.00011434428,0.00006310225,0.00024105047,0.00085219706,0.9612629,0.00021938246,0.002643089,0.026299376,0.0058271736],"study_design_scores_gemma":[0.0013696214,0.00074534415,0.0010828702,0.00015535309,0.00001236188,0.000012914833,0.000022433534,0.9953954,0.0005668106,0.00012047113,0.00018122434,0.00033518497],"about_ca_topic_score_codex":0.000006459122,"about_ca_topic_score_gemma":1.2508038e-7,"teacher_disagreement_score":0.09997761,"about_ca_system_score_codex":0.000048732112,"about_ca_system_score_gemma":0.000035189783,"threshold_uncertainty_score":0.83720005},"labels":[],"label_agreement":null},{"id":"W2130280189","doi":"10.1109/robot.1992.220295","title":"Real-time multi-robot path planner based on a heuristic approach","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Robot; Computer science; Heuristic; Path (computing); Trajectory; Motion planning; Robot control; Real-time computing; Arm solution; Collision; Simulation; Cartesian coordinate robot; Artificial intelligence; Mobile robot; Operating system","score_opus":0.02645811111930339,"score_gpt":0.24741133614829774,"score_spread":0.22095322502899437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130280189","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002143051,0.000007803765,0.93793106,0.0000951229,0.0002348254,0.00019996107,0.000003151128,0.0005123214,0.060801458],"genre_scores_gemma":[0.039957013,0.0000010935047,0.9555143,0.00043453005,0.000030257217,0.000026194388,0.000010586772,0.000020697149,0.0040053176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979495,0.00021874603,0.00025117357,0.00066940254,0.00042262222,0.00048859086],"domain_scores_gemma":[0.9984717,0.00023945529,0.00007926367,0.00095748395,0.000051807827,0.00020031782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050887046,0.00024829397,0.00024917102,0.00014387425,0.00012902722,0.00012750628,0.0007090429,0.00009860144,0.000039963827],"category_scores_gemma":[0.00018613141,0.00020180298,0.00007148674,0.00039265846,0.00003809435,0.00013940224,0.00005594373,0.00018624472,0.00068237394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038739738,0.0031635256,0.003414922,0.00010026453,0.00007855559,0.000842907,0.0013199223,0.9036913,0.0020519267,0.045376834,0.03340653,0.0065145525],"study_design_scores_gemma":[0.0006716221,0.0001297165,0.0016070718,0.000022498964,0.0000050288127,0.000029563673,0.000008513603,0.99666464,0.00022677449,0.000087475506,0.00025708094,0.00028998064],"about_ca_topic_score_codex":0.000038108967,"about_ca_topic_score_gemma":1.2212443e-7,"teacher_disagreement_score":0.09297335,"about_ca_system_score_codex":0.000057627272,"about_ca_system_score_gemma":0.000120405326,"threshold_uncertainty_score":0.8770764},"labels":[],"label_agreement":null},{"id":"W2130347196","doi":"10.1115/1.3124128","title":"Continuous Collision Detection of Cubic-Spline-Based Tethers in ROV Simulations","year":2009,"lang":"en","type":"article","venue":"Journal of Offshore Mechanics and Arctic Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dynamic Systems Analysis (Canada); University of Victoria; University of New Brunswick","funders":"","keywords":"Remotely operated underwater vehicle; Collision; Maxima and minima; Trajectory; Simulation; Marine engineering; Separation (statistics); Computer science; Remotely operated vehicle; Control theory (sociology); Aerospace engineering; Engineering; Physics; Mathematics; Artificial intelligence; Mobile robot; Robot","score_opus":0.008669550284272532,"score_gpt":0.22155855358949614,"score_spread":0.21288900330522362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130347196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15722242,0.0001762291,0.8421414,0.00016390887,0.00022942942,0.000051780407,5.4787915e-7,0.0000117251575,0.0000025621575],"genre_scores_gemma":[0.87846303,0.000011441263,0.12146607,0.000020200827,0.000032689386,2.6731826e-7,2.01934e-7,0.000004758893,0.0000013387981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917614,0.000018138993,0.00037567213,0.00009769808,0.0002016976,0.00013066211],"domain_scores_gemma":[0.99937475,0.00013002315,0.00019487769,0.0001193586,0.00011693863,0.00006404683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038865302,0.00009103506,0.00021646607,0.000281379,0.000023866372,0.000030081555,0.00017405905,0.000051863168,5.6183103e-7],"category_scores_gemma":[0.00016417445,0.0000832525,0.00004854686,0.00030475113,0.0000029430205,0.00017257102,0.000017391178,0.00019470295,1.11071174e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007833768,0.00003778706,0.00010071445,0.00001688924,0.000007925399,0.000025998977,0.00016347473,0.9540455,0.03788223,0.001004494,0.0000013442742,0.0067058476],"study_design_scores_gemma":[0.00045447602,0.0003172729,0.0013081993,0.00022754542,0.0000091263155,0.000049234623,0.00002203479,0.9865112,0.010291038,0.0007098085,0.000021034715,0.00007908179],"about_ca_topic_score_codex":0.0000035351943,"about_ca_topic_score_gemma":5.632516e-7,"teacher_disagreement_score":0.72124064,"about_ca_system_score_codex":0.0000473576,"about_ca_system_score_gemma":0.000033986646,"threshold_uncertainty_score":0.33949384},"labels":[],"label_agreement":null},{"id":"W2130852056","doi":"10.1109/icsmc.1995.538367","title":"Mobile robot navigation with generalized modus ponens type fuzzy reasoning","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Modus ponens; Artificial intelligence; Computer science; Rule of inference; Operator (biology); Mathematics; Inference; Fuzzy logic; Defuzzification; Mobile robot; Fuzzy set; Fuzzy number; Theoretical computer science; Robot","score_opus":0.024232033757991902,"score_gpt":0.24377168012065106,"score_spread":0.21953964636265916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130852056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04963164,0.00029978904,0.93252283,0.00022364977,0.00041261024,0.0002464473,8.528638e-7,0.00069058186,0.01597159],"genre_scores_gemma":[0.25222418,0.000010965136,0.7429312,0.00017485289,0.000073054434,0.000019132343,0.0000068419736,0.000014198551,0.0045456137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872935,0.000054925884,0.0001695166,0.00040264902,0.00032239285,0.0003211747],"domain_scores_gemma":[0.99909806,0.00003291606,0.00007827023,0.0005667088,0.00010995662,0.00011407062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014840186,0.00015417083,0.00016238166,0.000067634006,0.00013077483,0.0001360521,0.00046372754,0.00006235173,0.00004644219],"category_scores_gemma":[0.000018574112,0.00012231614,0.000027849519,0.00053400424,0.00002895596,0.00038951635,0.0000881575,0.00013257065,0.0003120136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034669818,0.00040312356,0.0052857134,0.00003863058,0.00015585027,0.0006737539,0.007626048,0.833046,0.008620338,0.04501191,0.016018923,0.08308499],"study_design_scores_gemma":[0.00044096878,0.00027515713,0.00073748466,0.000045775883,0.0000072697203,0.0001657782,0.000021731483,0.9963529,0.00090074685,0.00023476049,0.000569276,0.000248131],"about_ca_topic_score_codex":0.00007144832,"about_ca_topic_score_gemma":7.617923e-7,"teacher_disagreement_score":0.20259254,"about_ca_system_score_codex":0.000045023542,"about_ca_system_score_gemma":0.000020948004,"threshold_uncertainty_score":0.4987907},"labels":[],"label_agreement":null},{"id":"W2131142029","doi":"10.1109/robot.2007.363117","title":"Motion Planning of Multiple Agents in Virtual Environments on Parallel Architectures","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE International Conference on Robotics and Automation/Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Western Canada Research Grid","keywords":"Computer science; Distributed computing; Scalability; Multiprocessing; Voronoi diagram; Liveness; Deadlock; Motion planning; Path (computing); Parallel computing; Virtual machine; Process (computing); Robot; Artificial intelligence; Operating system","score_opus":0.058170990442982705,"score_gpt":0.304712197812367,"score_spread":0.2465412073693843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131142029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.779026,0.000008960481,0.21163422,0.0008689749,0.0005849114,0.000367939,0.000007933307,0.00016080833,0.0073402906],"genre_scores_gemma":[0.9528313,0.000012420033,0.046610933,0.00024160124,0.0001020733,0.00001748654,0.000007654712,0.000019614863,0.00015690668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733406,0.000004751793,0.00067514397,0.00065205235,0.0009210967,0.00041289063],"domain_scores_gemma":[0.99892443,0.00010979887,0.00050560856,0.000087622255,0.00022498591,0.0001475562],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006623023,0.00032342755,0.00030229866,0.00073050504,0.00012510177,0.00027896828,0.0007819168,0.0001539758,0.000007520362],"category_scores_gemma":[0.00020393911,0.00032198374,0.00005968088,0.00030819743,0.00009939805,0.0005175187,0.0001461634,0.00037580202,0.000014517405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000275491,0.0009081737,0.12543182,0.00014173843,0.00014903676,0.00003100573,0.009787655,0.30309632,0.023661202,0.5159768,0.00057406817,0.019966714],"study_design_scores_gemma":[0.0009003935,0.0003022805,0.14005896,0.0004378699,0.000006941254,0.000021419728,0.00033925707,0.84929544,0.0043492443,0.003937561,0.000038769205,0.00031182755],"about_ca_topic_score_codex":0.0000122988295,"about_ca_topic_score_gemma":5.614154e-7,"teacher_disagreement_score":0.54619914,"about_ca_system_score_codex":0.00014599535,"about_ca_system_score_gemma":0.000025054624,"threshold_uncertainty_score":0.9999232},"labels":[],"label_agreement":null},{"id":"W2131651363","doi":"10.1007/978-3-540-92182-0_56","title":"A Complete Approximation Algorithm for Shortest Bounded-Curvature Paths","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Shortest path problem; Bounded function; Longest path problem; Euclidean shortest path; Curvature; Mathematics; Path (computing); Distance; Yen's algorithm; K shortest path routing; Widest path problem; Combinatorics; Algorithm; Discrete mathematics; Computer science; Dijkstra's algorithm; Graph; Geometry; Mathematical analysis","score_opus":0.029715775452179155,"score_gpt":0.2537242041369665,"score_spread":0.22400842868478735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131651363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000032907033,0.00071517477,0.9930411,0.0005889991,0.0029086194,0.0012053464,0.000082581086,0.0003926854,0.0010622019],"genre_scores_gemma":[0.0006884719,0.000047738955,0.996563,0.0012516407,0.00089634804,0.000059516125,0.00009191031,0.00006559091,0.0003357623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99419457,0.000046155466,0.00081567804,0.0023314804,0.0014843618,0.0011277804],"domain_scores_gemma":[0.9959135,0.00088497973,0.0005381913,0.001854697,0.00052904343,0.00027958603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010337044,0.0008623864,0.00089288654,0.00092447805,0.00066415465,0.00075742503,0.0041625854,0.00060768705,0.0000052353857],"category_scores_gemma":[0.000147816,0.00082821137,0.00025123457,0.0007729843,0.0008209654,0.0008937049,0.0010058398,0.0011312594,0.00004410382],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032013488,0.000046083234,0.000010255097,0.000038542275,0.000024172994,0.00015547319,0.0010072956,0.021407446,0.000046776935,0.0036525563,0.00026309383,0.9733451],"study_design_scores_gemma":[0.00041544953,0.00026152193,0.000059380756,0.00033847068,0.000013654,0.00037558164,1.4199105e-7,0.94111276,0.000121563746,0.049809825,0.006587891,0.00090379326],"about_ca_topic_score_codex":0.000015512764,"about_ca_topic_score_gemma":0.000007720382,"teacher_disagreement_score":0.9724413,"about_ca_system_score_codex":0.00051301916,"about_ca_system_score_gemma":0.0008855327,"threshold_uncertainty_score":0.9994169},"labels":[],"label_agreement":null},{"id":"W2132369382","doi":"10.1109/csie.2009.855","title":"Adaptive 2D-Path Optimization of Steerable Bevel-Tip Needles in Uncertain Model of Brain Tissue","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bevel; Maxima and minima; Motion planning; Computer science; Path (computing); Robot; Artificial intelligence; Engineering; Mathematics; Structural engineering","score_opus":0.032991629491025236,"score_gpt":0.2761677127253012,"score_spread":0.24317608323427597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132369382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015933955,0.000050254497,0.993247,0.000392154,0.000043803917,0.00016743185,0.000004353893,0.00005465171,0.0044469554],"genre_scores_gemma":[0.29984206,0.0000036187253,0.6994542,0.00012796925,0.0000066557723,0.000002294929,0.0000027831506,0.0000040208465,0.0005564418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987614,0.000070853835,0.00037858167,0.000273906,0.0002894154,0.0002258486],"domain_scores_gemma":[0.99920195,0.00011554949,0.00015468067,0.00035852526,0.00011797948,0.000051318635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039815812,0.00012628378,0.00026881875,0.00020078637,0.00002405378,0.00001816653,0.00052814075,0.00007573014,0.000006319534],"category_scores_gemma":[0.000050098275,0.00011894616,0.000028637394,0.0005594069,0.000031000436,0.0003773844,0.000066879904,0.00008983506,0.0000022060983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006439635,0.0000775367,0.00005721216,0.0000060207885,0.0000036991428,0.0000040904506,0.00175558,0.98222446,0.0010676637,0.00842572,0.00017294074,0.0061986037],"study_design_scores_gemma":[0.00033294206,0.00023195073,0.00033199615,0.000059269845,0.0000022950421,0.0000027497024,0.00014004455,0.9932968,0.002794153,0.0026834793,0.0000016912479,0.0001226197],"about_ca_topic_score_codex":0.00020734275,"about_ca_topic_score_gemma":0.0000024425633,"teacher_disagreement_score":0.29824865,"about_ca_system_score_codex":0.00004225858,"about_ca_system_score_gemma":0.00013329857,"threshold_uncertainty_score":0.48504835},"labels":[],"label_agreement":null},{"id":"W2132398204","doi":"10.1109/robot.1987.1087799","title":"Some qualitative results on the collision-free joint space of a planar n-DOF linkage","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Yale University","keywords":"Linkage (software); Planar; Joint (building); Collision; Space (punctuation); Computer science; Computer graphics (images); Engineering; Structural engineering; Genetics; Biology; Computer security; Gene; Operating system","score_opus":0.058755170881373595,"score_gpt":0.3063977326979557,"score_spread":0.24764256181658212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132398204","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028347086,0.00025596638,0.7431683,0.18694504,0.0007834767,0.00088763254,0.0001481707,0.00048344623,0.03898091],"genre_scores_gemma":[0.10769976,0.000019170324,0.88429266,0.0020303943,0.00029172917,0.000017305614,0.0000050680233,0.000015536882,0.005628356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998019,0.00031029235,0.0004180148,0.0003774801,0.0005980692,0.00027714606],"domain_scores_gemma":[0.9966053,0.0016462269,0.00021581682,0.0013474083,0.00009838804,0.00008685429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016677991,0.00015689399,0.00023069106,0.00010975984,0.00011140558,0.000057613102,0.0013540764,0.00006646482,0.0000058211963],"category_scores_gemma":[0.0010616934,0.00009575439,0.000073414536,0.00034946567,0.00009074366,0.00025507703,0.00023421414,0.00021395693,0.0001875908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053980013,0.0002449466,0.0000044671283,0.00001426413,0.00005658777,0.000049112186,0.05510264,0.013644329,0.00080065744,0.7685695,0.15974325,0.0017162224],"study_design_scores_gemma":[0.005032157,0.0020087282,0.0025041332,0.0006821331,0.000025251515,0.00006153449,0.008009565,0.81701106,0.07089276,0.08248789,0.01015575,0.0011290137],"about_ca_topic_score_codex":0.00008153777,"about_ca_topic_score_gemma":0.0000058215487,"teacher_disagreement_score":0.8033668,"about_ca_system_score_codex":0.00005386824,"about_ca_system_score_gemma":0.00008130483,"threshold_uncertainty_score":0.39047506},"labels":[],"label_agreement":null},{"id":"W2132743588","doi":"10.1109/cec.2006.1688694","title":"Cooperative Transportation by Multiple Robots with Machine Learning","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Robot; Computer science; Task (project management); Probabilistic logic; Artificial intelligence; Robot learning; Genetic algorithm; Distributed computing; Mobile robot; Machine learning; Engineering; Systems engineering","score_opus":0.0062889308656442315,"score_gpt":0.1949112464710275,"score_spread":0.18862231560538328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132743588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00965218,0.0000700112,0.98802954,0.00026600418,0.000046927762,0.00010016325,0.0000053223357,0.00030900253,0.0015208451],"genre_scores_gemma":[0.6117024,0.0000013496704,0.3854283,0.00007518532,0.000018105484,0.00000888834,0.000107674256,0.000008456494,0.0026496744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991462,0.000041012012,0.00013932165,0.00027782618,0.00020259737,0.0001930091],"domain_scores_gemma":[0.99960387,0.00009128087,0.000052612773,0.00014741402,0.000061342405,0.000043502325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090826885,0.00011718517,0.00011157063,0.000037514244,0.00012677851,0.00007751361,0.00022276459,0.000032476564,0.000011125669],"category_scores_gemma":[0.000011542637,0.00008811404,0.000016668684,0.00024740543,0.000023732768,0.0003294544,0.000007945827,0.00013025347,0.000026106612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010640233,0.00009920542,0.05147046,0.000005428257,0.000016252508,0.000056876284,0.0006147504,0.93540287,0.0026903304,0.0031997603,0.0026094976,0.003823907],"study_design_scores_gemma":[0.00062938785,0.00015352019,0.015679521,0.000012033809,0.0000040718437,0.000009193601,0.000019104498,0.97778565,0.0044540516,0.000036638023,0.0010217933,0.00019504111],"about_ca_topic_score_codex":0.0010738948,"about_ca_topic_score_gemma":0.00015499476,"teacher_disagreement_score":0.60260123,"about_ca_system_score_codex":0.000018225408,"about_ca_system_score_gemma":0.000024035575,"threshold_uncertainty_score":0.3593186},"labels":[],"label_agreement":null},{"id":"W2133118325","doi":"10.1109/tsmcc.2007.897499","title":"Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Fuzzy logic; Mobile robot; Trajectory; Artificial intelligence; Obstacle avoidance; Robot; Artificial neural network; State (computer science); Control engineering; Control theory (sociology); Control (management); Engineering; Algorithm","score_opus":0.02259140461650461,"score_gpt":0.2612784282424843,"score_spread":0.2386870236259797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133118325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013089173,0.0016635347,0.99414045,0.000057655587,0.00016956165,0.0020352474,0.0000069498287,0.000047047215,0.0005706586],"genre_scores_gemma":[0.9051428,0.0021485265,0.08598121,0.00039432436,0.00010109488,0.004509718,0.000019977026,0.000039198247,0.0016631846],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837273,0.000102841164,0.00052475405,0.00053974637,0.00020010717,0.00025982145],"domain_scores_gemma":[0.9990414,0.00007637417,0.000119028984,0.00053617894,0.000020190011,0.00020687308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006610413,0.00019958646,0.00028819317,0.00017604945,0.00018100778,0.000101946556,0.0002453288,0.00008864426,8.7547016e-7],"category_scores_gemma":[0.0000015189955,0.00018136801,0.000044331064,0.00046039306,0.000054624335,0.00008955817,0.000005039465,0.00018672689,0.000051162246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020637824,0.0016430893,0.00026367122,0.0006766677,0.0000408378,0.000011318297,0.001352463,0.39398125,0.0022757517,0.0073269755,0.00039947874,0.5920079],"study_design_scores_gemma":[0.001304413,0.00051160477,0.0019707533,0.00088370865,0.000082253224,0.00013207404,0.00017727952,0.31598973,0.0014212438,0.000103987426,0.6763421,0.0010807924],"about_ca_topic_score_codex":0.00002617755,"about_ca_topic_score_gemma":0.0000029867947,"teacher_disagreement_score":0.9081592,"about_ca_system_score_codex":0.000046708257,"about_ca_system_score_gemma":0.000015286381,"threshold_uncertainty_score":0.73959726},"labels":[],"label_agreement":null},{"id":"W2133359599","doi":"10.1109/robot.1992.220093","title":"Heuristics for local path planning","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Motion planning; Heuristic; Computer science; Path (computing); Acceleration; Mobile robot; Mathematical optimization; Process (computing); Set (abstract data type); Robot; Product (mathematics); Artificial intelligence; Mathematics","score_opus":0.030926089927166764,"score_gpt":0.2775606346546577,"score_spread":0.2466345447274909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133359599","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008980715,0.000063725915,0.9886379,0.00009502562,0.00051559263,0.00010445996,0.0000013285347,0.0002047296,0.01028742],"genre_scores_gemma":[0.097059235,5.180181e-7,0.9016563,0.0003414258,0.000034499797,0.000011894943,0.0000015337514,0.000007446155,0.00088712055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991385,0.000028774535,0.00014344999,0.00025469557,0.00014673623,0.00028785985],"domain_scores_gemma":[0.99931175,0.00019932352,0.00003712416,0.00031342724,0.00005078056,0.00008759861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002770331,0.00009492526,0.000108843386,0.000041355037,0.00009607675,0.00007166654,0.00036508837,0.000045272696,0.000004750843],"category_scores_gemma":[0.00015278577,0.00008365839,0.00003586227,0.00013711525,0.00002356268,0.000116765594,0.000040177307,0.000069064175,0.00003863011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046990713,0.00010579559,0.0020639803,0.000035789264,0.000029846344,0.00014014261,0.0009338324,0.08279026,0.00007429677,0.8310095,0.051975608,0.030836252],"study_design_scores_gemma":[0.0003305611,0.000099734745,0.0003113953,0.000017028096,0.000003564207,0.000050189323,0.00005380546,0.9704692,0.0006196062,0.008533644,0.019318564,0.00019273107],"about_ca_topic_score_codex":0.0000027004678,"about_ca_topic_score_gemma":4.071933e-8,"teacher_disagreement_score":0.8876789,"about_ca_system_score_codex":0.00002282271,"about_ca_system_score_gemma":0.000060211572,"threshold_uncertainty_score":0.34114903},"labels":[],"label_agreement":null},{"id":"W2133424327","doi":"10.1109/ccece.2005.1557014","title":"A hybrid optimal-approximate path planning algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Path (computing); Algorithm; Computer science; Longest path problem; Terrain; Path length; Time complexity; Mathematical optimization; Graph; Mathematics; Theoretical computer science; Shortest path problem","score_opus":0.0133171925777672,"score_gpt":0.23873612586853662,"score_spread":0.22541893329076942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133424327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001957596,0.00015360808,0.9822911,0.00024992586,0.00041413342,0.0001364443,0.0000052115633,0.00095403916,0.013837955],"genre_scores_gemma":[0.018439373,0.0000012247901,0.97916764,0.00021678039,0.00019063041,0.000019296212,0.000014290157,0.000018438472,0.0019323286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980433,0.000049375503,0.00032019382,0.0005763523,0.0004129551,0.0005978427],"domain_scores_gemma":[0.99898374,0.00008168969,0.000104006554,0.00065223826,0.00006378307,0.00011453832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003430841,0.00023251423,0.00023154521,0.00013872373,0.00017076153,0.00028403866,0.00095126004,0.000051490788,0.000016548322],"category_scores_gemma":[0.000015989433,0.00020784179,0.000074569456,0.00027448955,0.000041025898,0.0005115594,0.00028750525,0.00018037329,0.00018521011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013306794,0.0008355592,0.0039537614,0.00007745993,0.00012745093,0.007032336,0.0014201518,0.32645854,0.0016701155,0.0891755,0.18452297,0.38471285],"study_design_scores_gemma":[0.00031052722,0.000053722997,0.0012689792,0.000029272938,0.000004208436,0.00027662163,0.000013481132,0.99134725,0.0017318026,0.002577823,0.0020731292,0.00031317995],"about_ca_topic_score_codex":0.000117390824,"about_ca_topic_score_gemma":6.281112e-8,"teacher_disagreement_score":0.66488874,"about_ca_system_score_codex":0.00004693294,"about_ca_system_score_gemma":0.00006004627,"threshold_uncertainty_score":0.8475542},"labels":[],"label_agreement":null},{"id":"W2133482720","doi":"10.1007/978-3-540-72665-4_46","title":"Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pathfinding; Computer science; Dijkstra's algorithm; Shortest path problem; Mathematical optimization; Motion planning; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Robot","score_opus":0.04220036228549223,"score_gpt":0.2939609903223324,"score_spread":0.2517606280368402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133482720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000080852136,0.00010909624,0.9914627,0.00058757875,0.002714407,0.001156687,0.000015542479,0.00040958842,0.0034635416],"genre_scores_gemma":[0.034860738,0.0000023134062,0.9610433,0.002777999,0.0009556287,0.000043606837,0.000017288563,0.0000775618,0.00022156484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99340916,0.000024297457,0.00084829476,0.002622635,0.0014729958,0.0016226376],"domain_scores_gemma":[0.9956748,0.0015390563,0.00031347421,0.0016582927,0.0002351247,0.0005792687],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022472625,0.00084743527,0.0008867544,0.0018714493,0.0005119963,0.0007691459,0.0048517976,0.00055824564,0.0000042571223],"category_scores_gemma":[0.0002295816,0.00083807315,0.00019122669,0.0010458924,0.00044376575,0.00041174775,0.0015772906,0.0012894145,0.000049372782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020585296,0.0000302691,0.0001849108,0.000041063773,0.000020368458,0.0002471717,0.0025051392,0.4087162,0.00024796673,0.022760535,0.00007460441,0.56515115],"study_design_scores_gemma":[0.00055803586,0.00043033875,0.0005151537,0.00092107215,0.000017191049,0.00014241193,9.769526e-7,0.9434274,0.0010222676,0.04820241,0.0030597472,0.0017029956],"about_ca_topic_score_codex":0.000011164433,"about_ca_topic_score_gemma":0.000010873988,"teacher_disagreement_score":0.5634482,"about_ca_system_score_codex":0.00061677996,"about_ca_system_score_gemma":0.0006480898,"threshold_uncertainty_score":0.999407},"labels":[],"label_agreement":null},{"id":"W2134374462","doi":"10.1155/2009/986207","title":"Collaborator: A Nonholonomic Multiagent Team for Tasks in a Dynamic Environment","year":2009,"lang":"en","type":"article","venue":"Journal of Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Nonholonomic system; Workspace; Motion planning; Task (project management); Robot; Class (philosophy); Lyapunov stability; Path (computing); Protocol (science); Field (mathematics); Lyapunov function; Negotiation; Scheme (mathematics); Distributed computing; Stability (learning theory); Artificial intelligence; Control (management); Mobile robot; Systems engineering; Machine learning","score_opus":0.012366157930944739,"score_gpt":0.2624319273498732,"score_spread":0.25006576941892844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134374462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012152559,0.00036627415,0.984639,0.0020310166,0.0005345254,0.00022333597,0.0000037567368,0.000015611962,0.000033931432],"genre_scores_gemma":[0.298671,0.000049563758,0.7009241,0.0002384797,0.00006407463,0.0000019412923,7.1432436e-7,0.000007635872,0.000042453492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986106,0.00005261587,0.000587183,0.00018670445,0.00026313937,0.00029973005],"domain_scores_gemma":[0.99894804,0.00011352169,0.00042961596,0.0003035087,0.000071884846,0.00013344086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006005331,0.00014592233,0.0003175508,0.0002230685,0.00004722232,0.000073810246,0.000686229,0.00007894402,0.0000010683833],"category_scores_gemma":[0.0000833487,0.0001340823,0.000096263604,0.00019612059,0.000021235765,0.00025403872,0.00005058844,0.00023005148,0.00001134727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026107527,0.0002816582,0.0004854663,0.0000071989925,0.0000209528,0.00011933728,0.0005902146,0.9851336,0.001099846,0.0003736015,0.0009862665,0.010875773],"study_design_scores_gemma":[0.0016323252,0.00091568055,0.014151871,0.000077494355,0.000020072754,0.00018621942,0.000053210653,0.98092884,0.00018289435,0.0008363043,0.0008119422,0.00020314545],"about_ca_topic_score_codex":9.557479e-7,"about_ca_topic_score_gemma":6.995382e-7,"teacher_disagreement_score":0.28651845,"about_ca_system_score_codex":0.00037731233,"about_ca_system_score_gemma":0.00017647538,"threshold_uncertainty_score":0.54677176},"labels":[],"label_agreement":null},{"id":"W2134549902","doi":"10.1109/cca.2005.1507126","title":"A fast algorithm for moving obstacle avoidance for vision-based mobile robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Obstacle avoidance; Motion planning; Computer science; Robot; Computer vision; Kinematics; Nonholonomic system; Obstacle; Artificial intelligence; Collision avoidance; Robot kinematics; Omnidirectional antenna; Lyapunov function; Transformation (genetics); Robot control; Algorithm; Control theory (sociology); Control (management)","score_opus":0.014024883104967911,"score_gpt":0.2834503978202169,"score_spread":0.269425514715249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134549902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022019373,0.00015594027,0.9964397,0.0008367313,0.00047499244,0.001072544,0.000029135686,0.00048639553,0.0002843541],"genre_scores_gemma":[0.0065921857,0.0000010611157,0.9892315,0.00095021864,0.0002888726,0.0006370517,0.000014197598,0.000028647182,0.0022562672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980214,0.0000309116,0.00035382356,0.0006799026,0.00028520846,0.0006287049],"domain_scores_gemma":[0.9980687,0.0007716289,0.00012611196,0.00066785247,0.00019680377,0.00016888764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047610432,0.00023317593,0.00026876148,0.00012043462,0.0002915196,0.00022543996,0.0009657866,0.00009598657,0.000009879777],"category_scores_gemma":[0.0000920407,0.0002164774,0.00016156935,0.00027774295,0.00003712952,0.0005713619,0.000110983754,0.00010370498,0.000052338808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042687043,0.00010969453,0.000014065384,0.000018500532,0.000009881695,0.0000024353203,0.00017904694,0.26295176,0.00041712727,0.00051667116,0.0038649808,0.7319116],"study_design_scores_gemma":[0.0010651137,0.00029558505,0.00009422841,0.00004193012,0.000006211877,0.000007093611,0.000026584357,0.9850254,0.0050500436,0.0002876977,0.0077969744,0.00030314914],"about_ca_topic_score_codex":0.000014561264,"about_ca_topic_score_gemma":0.0000026446376,"teacher_disagreement_score":0.73160845,"about_ca_system_score_codex":0.000109064604,"about_ca_system_score_gemma":0.00017448208,"threshold_uncertainty_score":0.8827692},"labels":[],"label_agreement":null},{"id":"W2135953026","doi":"10.1109/icsmc.1995.538310","title":"Jacobian transpose control: a foundation for multiagent manipulator control","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transpose; Jacobian matrix and determinant; Computer science; Representation (politics); Simple (philosophy); Robot; Multi-agent system; Control (management); Control theory (sociology); Artificial intelligence; Control engineering; Mathematics; Engineering; Eigenvalues and eigenvectors; Applied mathematics","score_opus":0.036848404819506596,"score_gpt":0.24353355130701426,"score_spread":0.20668514648750766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135953026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068487134,0.000056366305,0.99440086,0.0022571245,0.00053409196,0.000758071,0.000008017483,0.00034354496,0.00095707655],"genre_scores_gemma":[0.74309105,0.0000019499648,0.25465313,0.0011573154,0.000099437784,0.00008827576,0.0000038718194,0.000012322723,0.00089267764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986698,0.000052640444,0.00028451858,0.0003949931,0.00022863071,0.0003694246],"domain_scores_gemma":[0.999066,0.00021306812,0.00007888624,0.00041590846,0.00008916363,0.00013699238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026112839,0.00016106664,0.0002160162,0.00008228568,0.00014287968,0.0001429491,0.0005093929,0.0000690933,0.000083092076],"category_scores_gemma":[0.00005011441,0.0001445078,0.00011281466,0.00013429324,0.000023362385,0.00037811237,0.000012161396,0.00007409033,0.00024379873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022661436,0.0019189172,0.0051721977,0.00022133446,0.000789766,0.00025448314,0.00783498,0.11625088,0.012279115,0.18886293,0.02004054,0.64614826],"study_design_scores_gemma":[0.003885493,0.0001454939,0.0021204143,0.000010150759,0.000020459305,0.000012828054,0.0000075448256,0.98992884,0.00016383184,0.00024930254,0.0032636847,0.00019197575],"about_ca_topic_score_codex":0.000022632583,"about_ca_topic_score_gemma":0.0000046596133,"teacher_disagreement_score":0.87367797,"about_ca_system_score_codex":0.000049617935,"about_ca_system_score_gemma":0.000014447579,"threshold_uncertainty_score":0.58928573},"labels":[],"label_agreement":null},{"id":"W2136443579","doi":"10.1108/01439911211268660","title":"Autonomous golf ball picking robot design and development","year":2012,"lang":"en","type":"article","venue":"Industrial Robot the international journal of robotics research and application","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Regional Development Fund; Fundação para a Ciência e a Tecnologia","keywords":"Ball (mathematics); Robot; Simulation; Motion planning; Kinematics; Computer science; Engineering; Artificial intelligence; Mathematics","score_opus":0.20493196698943963,"score_gpt":0.37108515756919996,"score_spread":0.16615319057976033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136443579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033352338,0.0004490519,0.98963964,0.0056281732,0.0005961723,0.00028079862,5.38397e-7,0.000018646935,0.00005175126],"genre_scores_gemma":[0.60868603,0.00009794329,0.38988197,0.00009614646,0.0011055014,0.000018489527,0.000002614139,0.000012713208,0.00009858569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974426,0.00024100185,0.0005196479,0.00020543093,0.0011853618,0.00040595364],"domain_scores_gemma":[0.997595,0.00091249426,0.00033012807,0.00022884388,0.00067766476,0.0002558832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047794976,0.0001372117,0.00017862531,0.00027810305,0.00029004246,0.00044263172,0.0013768648,0.00011726286,0.000002033584],"category_scores_gemma":[0.00037087055,0.00010088079,0.00002706038,0.00025223996,0.00014472197,0.0005931538,0.0004996578,0.00071310607,0.000013865443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017927591,0.00046915596,0.0098668,0.000010781251,0.00039347177,0.00004205948,0.003012166,0.38491523,0.0064972686,0.02811391,0.0030736127,0.56342626],"study_design_scores_gemma":[0.009639618,0.0018058196,0.052276403,0.00085873046,0.000100258694,0.005151552,0.0013123633,0.8141038,0.045843095,0.016748631,0.05034319,0.0018165513],"about_ca_topic_score_codex":0.000020823301,"about_ca_topic_score_gemma":7.5876835e-7,"teacher_disagreement_score":0.6053508,"about_ca_system_score_codex":0.00018779357,"about_ca_system_score_gemma":0.00039467294,"threshold_uncertainty_score":0.4268308},"labels":[],"label_agreement":null},{"id":"W2138058145","doi":"10.1109/iros.2003.1248890","title":"An improved fuzzy logic based navigation system for mobile robots","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Fuzzy logic; Mobile robot navigation; Computer science; Navigation system; Robot; Artificial intelligence; Fuzzy control system; Real-time computing; Control engineering; Robot control; Engineering","score_opus":0.018729008599554772,"score_gpt":0.2751929953440802,"score_spread":0.2564639867445254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138058145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035744216,0.000021988208,0.99393743,0.00015130086,0.0005173955,0.0006188037,0.0000038803864,0.00075970014,0.00041509516],"genre_scores_gemma":[0.43312594,5.021772e-8,0.5665469,0.00009961691,0.000050996816,0.00012132191,0.00001721281,0.0000070820583,0.000030887586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884754,0.000033903354,0.00021897999,0.00043119275,0.00017287917,0.00029549148],"domain_scores_gemma":[0.99902046,0.000055368244,0.00009041678,0.0005971485,0.000116120595,0.00012049214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003399134,0.00013663383,0.00015293722,0.00006300621,0.00013411,0.0001515832,0.0006361891,0.0000813575,9.901067e-7],"category_scores_gemma":[0.000013775051,0.00011659904,0.000056250337,0.00022615757,0.00001780674,0.00042621026,0.000034231318,0.00006581554,0.000035329802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007619231,0.00013674983,0.000042099782,0.000095268086,0.0000090458625,0.000024778512,0.00024822954,0.9532263,0.010742327,0.027176665,0.00004217031,0.008248709],"study_design_scores_gemma":[0.00084519945,0.00048185067,0.00015467613,0.000051187413,0.000005111344,0.000024449577,0.000058815203,0.98755366,0.008988703,0.0016365942,0.000016345712,0.00018341088],"about_ca_topic_score_codex":0.00006508767,"about_ca_topic_score_gemma":7.9522493e-7,"teacher_disagreement_score":0.4295515,"about_ca_system_score_codex":0.00014914075,"about_ca_system_score_gemma":0.00012005408,"threshold_uncertainty_score":0.47547707},"labels":[],"label_agreement":null},{"id":"W2138132439","doi":"10.1109/icsmc.2003.1245650","title":"Making a mobile robot read textual messages","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Reading (process); Mobile robot; Zoom; Robot; Character (mathematics); Process (computing); Artificial intelligence; Human–computer interaction; Face (sociological concept); Mobile device; Computer vision; Multimedia; World Wide Web; Engineering","score_opus":0.039738257856668735,"score_gpt":0.3071179171783977,"score_spread":0.26737965932172897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138132439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008452658,0.000109187895,0.96362203,0.00047519561,0.00023774696,0.00012490481,8.1140087e-7,0.00056045793,0.034024425],"genre_scores_gemma":[0.33171934,0.0000026234993,0.666717,0.00034577944,0.000057384004,0.000020523963,0.0000011482315,0.000007806277,0.0011284184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998794,0.000027337808,0.00017722524,0.00037926077,0.00028244505,0.00033969746],"domain_scores_gemma":[0.9992233,0.000058193298,0.000054403317,0.0005527916,0.000038725484,0.00007256524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021238092,0.00013276777,0.00014360003,0.00008463527,0.00010940992,0.00015366597,0.0008360921,0.000057106878,0.00003443642],"category_scores_gemma":[0.00003217463,0.00011363164,0.00004608571,0.00031882746,0.00003534856,0.0003647628,0.000250176,0.00012650069,0.00046960815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062754257,0.0003500541,0.0007595793,0.000030659947,0.00007158732,0.001097219,0.0051163076,0.5566413,0.0022755405,0.18167527,0.0028817358,0.24909449],"study_design_scores_gemma":[0.007667319,0.0024395036,0.029244544,0.0011696797,0.00007739212,0.0043299925,0.0026753317,0.84932077,0.02153487,0.052976754,0.02346643,0.005097399],"about_ca_topic_score_codex":0.000052307332,"about_ca_topic_score_gemma":0.0000020081552,"teacher_disagreement_score":0.3308741,"about_ca_system_score_codex":0.00006066086,"about_ca_system_score_gemma":0.000078175086,"threshold_uncertainty_score":0.60360193},"labels":[],"label_agreement":null},{"id":"W2138156761","doi":"10.1109/ramech.2004.1438015","title":"A robot path planning approach based on generalized semi-infinite optimization","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Intersection (aeronautics); Motion planning; Obstacle avoidance; Mathematical optimization; Obstacle; Computer science; Path (computing); Transformation (genetics); Robot; Optimization problem; Representation (politics); Robotics; Artificial intelligence; Mathematics; Mobile robot; Engineering","score_opus":0.0286783069306427,"score_gpt":0.25651875479122216,"score_spread":0.22784044786057947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138156761","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001284998,0.0000309234,0.9769448,0.00082318176,0.0001679122,0.00017682479,0.0000014918526,0.0006149846,0.021111349],"genre_scores_gemma":[0.02366696,0.0000014005093,0.97233033,0.0031061927,0.00016281004,0.00002676048,0.000031853604,0.000018479728,0.0006552104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982134,0.000104236366,0.00029550752,0.00054735644,0.00044424745,0.00039526218],"domain_scores_gemma":[0.99889934,0.00010372143,0.0001099799,0.0006635561,0.00008044913,0.0001429317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003928751,0.00022189834,0.00021148486,0.0002039475,0.00015218754,0.00020310776,0.00067418674,0.00010098109,0.000029077675],"category_scores_gemma":[0.00006588813,0.00019450182,0.000066374574,0.00047382317,0.000020032317,0.0003669442,0.00009121456,0.00017644926,0.00006681026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005815031,0.00008269771,0.00011741152,0.00000446397,0.0000062150307,0.000010959828,0.00021670667,0.99295217,0.0000478154,0.0010462039,0.0019978266,0.0035117045],"study_design_scores_gemma":[0.00072601193,0.000069919806,0.00024744746,0.000031121304,0.0000046867103,0.000012563042,0.0000098260025,0.99770445,0.0002622817,0.000018474955,0.00065201335,0.00026123642],"about_ca_topic_score_codex":0.000010772043,"about_ca_topic_score_gemma":4.4504446e-8,"teacher_disagreement_score":0.02353846,"about_ca_system_score_codex":0.00007468451,"about_ca_system_score_gemma":0.000079863436,"threshold_uncertainty_score":0.79315543},"labels":[],"label_agreement":null},{"id":"W2138444350","doi":"10.1109/vl.1998.706166","title":"Visual programming for robot control","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Programmer; Reactive programming; Inductive programming; Functional reactive programming; Programming language; Visual programming language; Programming domain; Event-driven programming; Procedural programming; Programming paradigm; Domain (mathematical analysis); Artificial intelligence; Robot; Representation (politics); Process (computing); Symbolic programming; Human–computer interaction","score_opus":0.029019436040993316,"score_gpt":0.2681542722856719,"score_spread":0.23913483624467857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138444350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008748484,0.0000655856,0.99643487,0.0011110536,0.00029624018,0.00030557226,4.9297944e-7,0.0003901145,0.0013085935],"genre_scores_gemma":[0.1535451,5.8886707e-7,0.8441208,0.00044490784,0.00009954387,0.000063478816,6.223905e-7,0.0000069948815,0.0017179698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990974,0.000019218884,0.00014291069,0.00026328673,0.00015132768,0.0003258212],"domain_scores_gemma":[0.9994521,0.00014664528,0.000040803872,0.00022639275,0.000051430543,0.000082584826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017509634,0.000092059534,0.00012151985,0.000048575508,0.00009485072,0.0001389967,0.0004229335,0.000039356422,0.000016828268],"category_scores_gemma":[0.000058854268,0.00007847407,0.000054326872,0.00014910307,0.00001711321,0.00022318235,0.000041707368,0.000053478718,0.000115487455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030206766,0.00018872126,0.000497921,0.000014682474,0.00003490573,0.000023331075,0.00042585566,0.0041609504,0.0003011062,0.013641081,0.006622734,0.9740857],"study_design_scores_gemma":[0.00061112916,0.00015544036,0.00021110714,0.0000057091097,0.0000037791228,0.000015373527,0.000006816762,0.99257797,0.0001663704,0.00015249646,0.0059723253,0.00012147719],"about_ca_topic_score_codex":0.0000052364676,"about_ca_topic_score_gemma":2.684737e-7,"teacher_disagreement_score":0.988417,"about_ca_system_score_codex":0.000016841996,"about_ca_system_score_gemma":0.000008169404,"threshold_uncertainty_score":0.32000798},"labels":[],"label_agreement":null},{"id":"W2138813076","doi":"10.2478/s13230-010-0012-2","title":"A Streamlined Search Algorithm for Path Modifications of a Safe Robot Manipulator<sup>*</sup>","year":2010,"lang":"en","type":"article","venue":"Paladyn Journal of Behavioral Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Path (computing); Motion planning; Fast path; Algorithm; Robot; Obstacle; Computer science; Any-angle path planning; Path length; Obstacle avoidance; Mathematical optimization; Dimension (graph theory); Search algorithm; Mobile robot; Mathematics; Artificial intelligence","score_opus":0.05358977627274027,"score_gpt":0.32440760736047175,"score_spread":0.2708178310877315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138813076","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037748452,0.00006822229,0.9605187,0.00037300808,0.00080837833,0.00036128884,0.000051213792,0.000045680725,0.00002502767],"genre_scores_gemma":[0.31729847,0.000009897351,0.68233573,0.000016508002,0.00021505816,0.0000077241875,0.000009468375,0.000028156239,0.00007899561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672127,0.00011138321,0.001269356,0.00037910178,0.00092344644,0.00059546786],"domain_scores_gemma":[0.9962842,0.0002805435,0.0007785495,0.0008903192,0.0013378324,0.00042855897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010897599,0.00031956472,0.0006613606,0.0004259335,0.00018840015,0.000150459,0.0017427839,0.00023164297,0.000012507258],"category_scores_gemma":[0.000106156214,0.00029385433,0.0003680962,0.00056288374,0.00015385982,0.0005364799,0.0001916272,0.00085740996,0.000009550325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044724497,0.0019716227,0.0069549046,0.00007101833,0.00013327453,0.0002008307,0.003275818,0.77786475,0.009418715,0.0052623977,0.00051304075,0.19428891],"study_design_scores_gemma":[0.0012921558,0.0008288566,0.0044471566,0.00009929728,0.00015733915,0.00040953298,0.00024434883,0.99005353,0.0014636057,0.0006182695,0.00005509479,0.00033080523],"about_ca_topic_score_codex":0.00003417392,"about_ca_topic_score_gemma":0.0000022325323,"teacher_disagreement_score":0.27955002,"about_ca_system_score_codex":0.00007712505,"about_ca_system_score_gemma":0.00049374136,"threshold_uncertainty_score":0.99995136},"labels":[],"label_agreement":null},{"id":"W2138859646","doi":"10.1109/robot.2008.4543339","title":"Backbone-based roadmaps for robot navigation in sensor networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Path (computing); Wireless sensor network; Computer science; Mobile robot navigation; Robot; Shortest path problem; Mobile robot; Real-time computing; Field (mathematics); Distributed computing; Computer network; Artificial intelligence; Robot control; Theoretical computer science; Mathematics","score_opus":0.03422509686376619,"score_gpt":0.257899269220235,"score_spread":0.2236741723564688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138859646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061311005,0.000034889476,0.9918941,0.00074009906,0.00036315888,0.00028074757,8.933832e-7,0.00020060012,0.00035439292],"genre_scores_gemma":[0.20877884,0.0000014065029,0.7902793,0.00039730762,0.00008061948,0.000034755307,0.000017402721,0.000009103496,0.00040120477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988475,0.000049955026,0.00024320287,0.00034737407,0.00017782603,0.0003341323],"domain_scores_gemma":[0.99920154,0.00020955897,0.000064545435,0.00037404004,0.00007353199,0.00007679739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028253192,0.00012247068,0.00015984658,0.00009176491,0.00009972287,0.000039828454,0.00038450427,0.00008557278,0.0000037401705],"category_scores_gemma":[0.000044299173,0.000115628405,0.00005332466,0.0003882595,0.000029781295,0.00022325922,0.0000415146,0.00011549929,0.000027893493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006261004,0.00004818651,0.0034727827,0.000006170915,0.0000032979774,0.00005392671,0.00014376944,0.99026984,0.000100868725,0.0005967998,0.001563936,0.0037341695],"study_design_scores_gemma":[0.00065049005,0.000060430546,0.0125823775,0.000028465227,0.000001264156,0.000031658437,0.000005707777,0.9857133,0.0005109674,0.00011246281,0.00014680522,0.0001560865],"about_ca_topic_score_codex":0.000052901672,"about_ca_topic_score_gemma":0.0000028181041,"teacher_disagreement_score":0.20264775,"about_ca_system_score_codex":0.000054057666,"about_ca_system_score_gemma":0.000075523894,"threshold_uncertainty_score":0.47151896},"labels":[],"label_agreement":null},{"id":"W2139369605","doi":"10.1287/opre.1070.0474","title":"Visualizing and Constructing Cycles in the Simplex Method","year":2008,"lang":"en","type":"article","venue":"Operations Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Simplex; Simplex algorithm; Simple (philosophy); Dual (grammatical number); Cycling; Revised simplex method; Computer science; Mathematics; Algorithm; Combinatorics; Mathematical optimization; Linear programming; Geography; Philosophy; Archaeology","score_opus":0.24975057357634586,"score_gpt":0.4890805239239869,"score_spread":0.239329950347641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139369605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21156935,0.00017010856,0.7814553,0.0029117395,0.00005448261,0.00029812285,0.0000013892364,0.00004597341,0.003493539],"genre_scores_gemma":[0.41892993,0.00001964346,0.58083606,0.000073908835,0.000033551154,0.000025625654,0.0000010814928,0.0000032435896,0.00007698131],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980946,0.0008287363,0.00014540409,0.00024010308,0.00041288364,0.00027824743],"domain_scores_gemma":[0.99866253,0.0008662396,0.0000074285563,0.0003149416,0.00010754514,0.000041325384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027056101,0.000054768567,0.00007818478,0.00020231633,0.00076574367,0.00028150057,0.0005253167,0.000031076965,0.0000042888687],"category_scores_gemma":[0.00049627776,0.000040154948,0.00001085319,0.0007775962,0.00014125531,0.00032230007,0.00020454844,0.00031890764,0.000016078731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055163373,0.00032722112,0.04390989,0.000040117025,0.000030306705,0.0011119089,0.12135304,0.06615794,0.009792509,0.48370638,0.00242063,0.27114454],"study_design_scores_gemma":[0.00017795256,0.000037400692,0.011075601,0.000016672668,5.493954e-7,0.0006736276,0.002082919,0.98426414,0.0004799,0.0005485663,0.0005562489,0.000086400316],"about_ca_topic_score_codex":0.0003645229,"about_ca_topic_score_gemma":0.000022234819,"teacher_disagreement_score":0.9181062,"about_ca_system_score_codex":0.000024011772,"about_ca_system_score_gemma":0.00012863813,"threshold_uncertainty_score":0.5889559},"labels":[],"label_agreement":null},{"id":"W2139801213","doi":"10.3233/aic-140605","title":"Automatic move pruning for single-agent search","year":2014,"lang":"en","type":"article","venue":"AI Communications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pruning; Artificial intelligence; Machine learning; Information retrieval","score_opus":0.08222466232108644,"score_gpt":0.3340013739461056,"score_spread":0.25177671162501913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139801213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007290998,0.00010778494,0.9837636,0.009807671,0.00012891817,0.00027500873,0.0000024515564,0.00033730958,0.004848174],"genre_scores_gemma":[0.36520967,0.000004125619,0.6339485,0.00058418576,0.000020072475,0.00007006336,0.000012077225,0.000008991648,0.00014230006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989334,0.00021514138,0.00022164648,0.00019737902,0.0001734003,0.00025904315],"domain_scores_gemma":[0.99585646,0.00094176055,0.00007021215,0.0029105572,0.00013795604,0.00008305799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006837046,0.0000937753,0.00012728763,0.00009315835,0.00043428454,0.00016775716,0.002860392,0.000042644137,0.0000032570977],"category_scores_gemma":[0.00025181848,0.00009613895,0.00005197332,0.00028901393,0.00007621615,0.00025314366,0.0007542867,0.00017208129,0.00008938855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015064076,0.00052155816,0.0008353008,0.000070299306,0.00008016864,0.0000013476839,0.007662577,0.008795546,0.0031064786,0.40763387,0.004950296,0.56634104],"study_design_scores_gemma":[0.0001697306,0.0000608242,0.0009934652,0.00004103857,0.000006569492,0.00000903155,0.000032504173,0.987151,0.00027058474,0.0028136263,0.008336591,0.00011501099],"about_ca_topic_score_codex":0.000024861738,"about_ca_topic_score_gemma":0.0000021788928,"teacher_disagreement_score":0.97835547,"about_ca_system_score_codex":0.000060200884,"about_ca_system_score_gemma":0.000059589085,"threshold_uncertainty_score":0.53153676},"labels":[],"label_agreement":null},{"id":"W2140133974","doi":"10.1109/iros.1995.526171","title":"Experiments in sensing and communication for robot convoy navigation","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Hydro One (Canada); McGill University","funders":"","keywords":"Robot; Mobile robot; Computer science; Reliability (semiconductor); Implementation; Collision avoidance; Autonomous robot; Communications system; Human–computer interaction; Artificial intelligence; Simulation; Collision; Telecommunications; Computer security","score_opus":0.05959949260676626,"score_gpt":0.29249094933216535,"score_spread":0.2328914567253991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140133974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013992802,0.00021231,0.9839966,0.0006433395,0.00006448484,0.00015626478,2.076051e-7,0.00006525581,0.00086870743],"genre_scores_gemma":[0.4576721,0.0000050255485,0.5421117,0.00008603195,0.0000049159544,0.000003817296,0.000002060124,0.0000022451632,0.00011213506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995385,0.000034893903,0.00011516953,0.00014024551,0.00006688047,0.000104304134],"domain_scores_gemma":[0.999572,0.000096599484,0.000034794382,0.00024644917,0.000024563555,0.000025615951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015234326,0.000049531434,0.00006532276,0.0000403332,0.00005979414,0.00006374717,0.00015263747,0.000027744489,0.0000015785206],"category_scores_gemma":[0.00001881098,0.00004973193,0.000008264842,0.00009297141,0.000017107754,0.00023826097,0.0000643785,0.000040700354,0.000006354074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011290779,0.00033414626,0.0074120895,0.00007889622,0.000038547092,0.000042022704,0.037900947,0.01154574,0.025444284,0.034273956,0.0030432995,0.87987477],"study_design_scores_gemma":[0.00030515174,0.000018182845,0.0009943366,0.00003186026,6.630171e-7,0.000013678532,0.00005521856,0.9961956,0.0016176566,0.00063392933,0.000070196125,0.000063518644],"about_ca_topic_score_codex":0.000050884522,"about_ca_topic_score_gemma":0.0000017566019,"teacher_disagreement_score":0.9846499,"about_ca_system_score_codex":0.000022993927,"about_ca_system_score_gemma":0.0000030109522,"threshold_uncertainty_score":0.20280093},"labels":[],"label_agreement":null},{"id":"W2140393091","doi":"10.1109/robot.1990.125998","title":"The use of awareness in collision prediction","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collision; Trajectory; Computer science; Representation (politics); Object (grammar); Function (biology); Pairwise comparison; Acceleration; Theoretical computer science; Collision detection; Algorithm; Artificial intelligence; Programming language; Physics","score_opus":0.1048480624644637,"score_gpt":0.2578125454337647,"score_spread":0.15296448296930099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140393091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038773518,0.00005836052,0.95934933,0.000529503,0.00044631053,0.000101782396,0.0000013231233,0.00007904229,0.0006607966],"genre_scores_gemma":[0.68408495,0.00009536586,0.3127985,0.000109188906,0.000038634367,0.000014537944,0.000001103051,0.00000668802,0.002851004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994342,0.000043631415,0.00014478588,0.0001138258,0.00016165101,0.00010191424],"domain_scores_gemma":[0.9993928,0.00022106538,0.00003697474,0.00029711318,0.000033028566,0.000019027457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015207453,0.000038549922,0.000054043427,0.000042926855,0.000055861816,0.000051265004,0.00029725768,0.000025267642,0.0000025240818],"category_scores_gemma":[0.000072966475,0.000024619063,0.000013124973,0.0003031522,0.00002048447,0.00027507008,0.000068332956,0.000044997934,0.000011401721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010120933,0.00035634096,0.16146891,0.000019371279,0.000020345538,0.00005277165,0.0034458204,0.56734747,0.00062078977,0.024815982,0.039500326,0.20234175],"study_design_scores_gemma":[0.000093832445,0.000024340532,0.026306793,0.000013508839,4.5296872e-7,0.0000037148375,0.000006208821,0.9715526,0.00035930853,0.00009069651,0.0015217683,0.000026745422],"about_ca_topic_score_codex":0.000096670214,"about_ca_topic_score_gemma":0.0000065289087,"teacher_disagreement_score":0.64655083,"about_ca_system_score_codex":0.000016498325,"about_ca_system_score_gemma":0.00001034967,"threshold_uncertainty_score":0.10039362},"labels":[],"label_agreement":null},{"id":"W2140481024","doi":"10.1109/icar.2005.1507500","title":"Recharging robot teams: A tanker approach","year":2005,"lang":"en","type":"article","venue":"ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Mobile robot; Task (project management); Energy (signal processing); Work (physics); Battery (electricity); Computer science; Engineering; Simulation; Automotive engineering; Artificial intelligence; Systems engineering; Mechanical engineering; Power (physics)","score_opus":0.03708356334856221,"score_gpt":0.29223808047457306,"score_spread":0.25515451712601084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140481024","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065639534,0.00012786541,0.8986692,0.012440005,0.002521508,0.0006833928,0.000023815232,0.0010543513,0.083823495],"genre_scores_gemma":[0.19271271,0.00011070144,0.798324,0.0016949046,0.00089899515,0.000100820136,0.000040261948,0.000072815084,0.0060447953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994724,0.00003686222,0.00092161493,0.0016151494,0.001534223,0.0011682021],"domain_scores_gemma":[0.99722195,0.00008120699,0.0006149714,0.0006343143,0.000982835,0.0004647032],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006980863,0.0007065833,0.0006149653,0.0005584323,0.00033302166,0.0007429307,0.0031520785,0.0002824904,0.00015717078],"category_scores_gemma":[0.00041477475,0.00069859764,0.00020292583,0.0004800919,0.0001786669,0.002078503,0.00055665907,0.0010386591,0.00047453196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010925031,0.0007926567,0.00058942393,0.000062949206,0.00022555931,0.00005665228,0.0015383027,0.30432737,0.0051877033,0.62439305,0.009943306,0.052773766],"study_design_scores_gemma":[0.0013397965,0.000220944,0.00041673126,0.0003628764,0.000024296336,0.00018155127,0.00036717436,0.97833544,0.002561691,0.0032878134,0.011895918,0.001005785],"about_ca_topic_score_codex":0.000008245487,"about_ca_topic_score_gemma":0.00000203053,"teacher_disagreement_score":0.6740081,"about_ca_system_score_codex":0.0007444318,"about_ca_system_score_gemma":0.00026198226,"threshold_uncertainty_score":0.9995465},"labels":[],"label_agreement":null},{"id":"W2140607033","doi":"10.1109/icsmc.1995.538361","title":"Real-time control of soccer-playing robots using off-board vision: the dynamite testbed","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Testbed; Robot; Computer science; Mobile robot; Real-time computing; Orientation (vector space); Simulation; Artificial intelligence; Embedded system; Computer vision","score_opus":0.022835389708166596,"score_gpt":0.2598225978113546,"score_spread":0.236987208103188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140607033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015995257,0.00018587323,0.97238934,0.0014288101,0.00035842182,0.00029767977,0.0000034386949,0.0003136013,0.009027577],"genre_scores_gemma":[0.4180537,0.000019395375,0.58021975,0.0003795982,0.000105406085,0.0000061591695,0.0000010067737,0.000023009998,0.0011919857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998056,0.00017073935,0.000431745,0.00041069626,0.0005074741,0.00042334685],"domain_scores_gemma":[0.9980362,0.0005857435,0.00023328264,0.00091277406,0.00012794785,0.00010405174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054037303,0.00020397648,0.00033672215,0.0001044233,0.00024984768,0.00014250392,0.0011376926,0.00008805569,0.00011560867],"category_scores_gemma":[0.00009454132,0.00014011005,0.00011100513,0.0004904404,0.00010705131,0.00041079838,0.0002115204,0.00016829255,0.00016927723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002297286,0.0004794918,0.0059860707,0.000057641068,0.00030321509,0.00028655754,0.0038246412,0.69267774,0.11411514,0.007550558,0.007913516,0.16678247],"study_design_scores_gemma":[0.00049662055,0.00008391953,0.0036369192,0.000052865336,0.000018320416,0.00006546949,0.000014877916,0.9950992,0.0002070177,0.00011408984,0.00003328668,0.00017739966],"about_ca_topic_score_codex":0.0001576884,"about_ca_topic_score_gemma":9.021227e-7,"teacher_disagreement_score":0.40205845,"about_ca_system_score_codex":0.0000595292,"about_ca_system_score_gemma":0.00003504336,"threshold_uncertainty_score":0.5713522},"labels":[],"label_agreement":null},{"id":"W2141076647","doi":"10.1109/irds.2002.1041625","title":"Computing C-space entropy for view planning based on beam sensor model","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Entropy (arrow of time); Motion planning; Computation; Computer science; Planar; Robot; Configuration space; Artificial intelligence; Computer vision; Mathematics; Algorithm; Physics; Computer graphics (images)","score_opus":0.04034328691531827,"score_gpt":0.28890199226876706,"score_spread":0.2485587053534488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141076647","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008837991,0.000041939467,0.9924738,0.0005799963,0.00033416774,0.0003043188,0.000002348103,0.00037724045,0.005002414],"genre_scores_gemma":[0.099750854,4.4909484e-7,0.89804626,0.0015386654,0.000042586806,0.000010495946,0.0000031873044,0.000017844464,0.0005896706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983201,0.00006765023,0.00024571086,0.0005350928,0.00030980515,0.000521636],"domain_scores_gemma":[0.99861914,0.0005088432,0.00010301397,0.00054958457,0.000078852565,0.00014053898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000547717,0.00021494504,0.00024784505,0.00011670082,0.00022266543,0.00014054577,0.0004879815,0.00007130779,0.000003962503],"category_scores_gemma":[0.00018463691,0.0001922881,0.000091498274,0.0002444308,0.000020217578,0.00013956678,0.000048157057,0.00014711023,0.000034249962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003633783,0.000043967826,0.00013857408,0.000017359924,0.000006758873,0.000011079653,0.00018866795,0.9716542,0.00023192499,0.02495849,0.0014693876,0.0012759608],"study_design_scores_gemma":[0.0005952613,0.00011459143,0.00004511641,0.000086567874,0.000005564506,0.00001023165,0.000019984165,0.9945615,0.0025144883,0.00060047157,0.0011976237,0.0002486262],"about_ca_topic_score_codex":0.0000034459142,"about_ca_topic_score_gemma":5.1994302e-8,"teacher_disagreement_score":0.09886706,"about_ca_system_score_codex":0.000060146645,"about_ca_system_score_gemma":0.000114038274,"threshold_uncertainty_score":0.7841281},"labels":[],"label_agreement":null},{"id":"W2141551261","doi":"10.1109/fuzzy.1998.687499","title":"Dynamic fuzzy control and system stability for the Acrobot","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Aeronautics and Space Administration","keywords":"Control theory (sociology); Fuzzy control system; Stability (learning theory); Controller (irrigation); Fuzzy logic; Computer science; Robot; Fuzzy rule; Swing; Control engineering; Control (management); Artificial intelligence; Engineering; Machine learning","score_opus":0.02437774090637274,"score_gpt":0.2338402540187274,"score_spread":0.20946251311235467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141551261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096688524,0.00039128176,0.99412334,0.0022259227,0.0004311388,0.00044652147,0.0000061690425,0.00022192263,0.0011868029],"genre_scores_gemma":[0.86084646,0.0000030749416,0.13861868,0.00018234027,0.00002088149,0.000045525405,2.390272e-7,0.0000046500422,0.00027813925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914986,0.000049671416,0.00015411405,0.0002815346,0.00013051816,0.00023430472],"domain_scores_gemma":[0.99865466,0.0006763824,0.000045669847,0.00051563105,0.000050449653,0.000057174522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004758308,0.00009378753,0.00013460181,0.000018269682,0.00019114773,0.00013087515,0.00048655696,0.000036243142,0.0000036374404],"category_scores_gemma":[0.00005526441,0.000056839268,0.000037227946,0.00009687108,0.000050401297,0.00014482238,0.00007274296,0.00006087689,0.000021690275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007539118,0.0006004828,0.011326363,0.0011180091,0.00068539305,0.00009893005,0.013290993,0.036240455,0.0038729182,0.37522814,0.023286387,0.5341765],"study_design_scores_gemma":[0.00045512247,0.00004315875,0.002545226,0.000008406912,0.000008072449,0.000028990351,0.00009335011,0.99640685,0.000015330452,0.00015249132,0.00016346501,0.00007953669],"about_ca_topic_score_codex":0.000018759476,"about_ca_topic_score_gemma":0.0000031801046,"teacher_disagreement_score":0.9601664,"about_ca_system_score_codex":0.000041488936,"about_ca_system_score_gemma":0.000008348488,"threshold_uncertainty_score":0.2317838},"labels":[],"label_agreement":null},{"id":"W2142167813","doi":"10.1109/robot.1994.351307","title":"Path tracking, obstacle avoidance and position estimation by an autonomous, wheeled planetary rover","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Obstacle avoidance; Computer vision; Computer science; Position (finance); Odometry; Sensor fusion; Trajectory; Obstacle; Artificial intelligence; Inertial measurement unit; Path (computing); Orientation (vector space); Control theory (sociology); Mobile robot; Robot; Mathematics; Control (management); Geography","score_opus":0.01421114403616866,"score_gpt":0.217967736621522,"score_spread":0.20375659258535334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142167813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04976368,0.0003488025,0.94746417,0.00056046236,0.00016217303,0.00013782883,0.000009408062,0.000402302,0.0011511507],"genre_scores_gemma":[0.61113733,0.000011450762,0.3876434,0.00048500407,0.00003198405,0.0000054603834,0.000045713146,0.000009013458,0.0006306363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989269,0.00006172545,0.00018295342,0.00038972704,0.00020311259,0.00023560296],"domain_scores_gemma":[0.99939233,0.000056003846,0.00007546411,0.00033214732,0.000026203352,0.000117867734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014476852,0.00013660047,0.00012511264,0.000048649956,0.0001358328,0.00022535617,0.00028146117,0.00006730675,0.000040643037],"category_scores_gemma":[0.000017173621,0.00013222886,0.00001593462,0.00011397533,0.000026445756,0.0013136539,0.000040741375,0.00011323141,0.00007100749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014833704,0.0008378883,0.007948874,0.00010176337,0.000064948974,0.0002598386,0.009270277,0.056206487,0.009871334,0.00741256,0.0613438,0.8466674],"study_design_scores_gemma":[0.00024650508,0.00012756293,0.011883482,0.000017549304,0.0000043308282,0.00009115361,0.000009431879,0.98632187,0.0005891904,0.00034178258,0.00018379423,0.00018332993],"about_ca_topic_score_codex":0.000079732665,"about_ca_topic_score_gemma":0.0000021998949,"teacher_disagreement_score":0.9301154,"about_ca_system_score_codex":0.000037383765,"about_ca_system_score_gemma":0.000009814009,"threshold_uncertainty_score":0.53921366},"labels":[],"label_agreement":null},{"id":"W2142262023","doi":"10.1109/cdc.1998.758626","title":"A modified, hybrid, PDE-ODE controller with integrated directional and region avoidance constraints","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Ode; Controller (irrigation); Control theory (sociology); Set (abstract data type); Computer science; Control engineering; Mathematical optimization; Mathematics; Applied mathematics; Control (management); Engineering; Artificial intelligence","score_opus":0.021890547749485588,"score_gpt":0.199267420529713,"score_spread":0.1773768727802274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142262023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068300716,0.00014008512,0.9789455,0.00099974,0.0001352725,0.00015968164,0.0000025692466,0.000297476,0.012489605],"genre_scores_gemma":[0.8301671,0.000015562922,0.16551103,0.00044173058,0.000034401048,0.00001809382,0.0000013304092,0.00000817535,0.003802563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882406,0.000064200176,0.00017061523,0.00043547072,0.00023891871,0.00026672048],"domain_scores_gemma":[0.9993174,0.00014025082,0.00007330554,0.00024347167,0.00010276754,0.00012279925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013038614,0.00016634265,0.0001996546,0.000086356275,0.00013095878,0.00013208388,0.00026486217,0.000040743336,0.000023976154],"category_scores_gemma":[0.000048309797,0.000120941535,0.000024769834,0.0002220188,0.00020591903,0.00031069052,0.00005020134,0.0001778348,0.000041308165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028382952,0.00096154906,0.015570831,0.00007073718,0.00062242034,0.0027598226,0.003585311,0.047395416,0.0015082756,0.11375676,0.06758469,0.74590033],"study_design_scores_gemma":[0.0013568607,0.000103474966,0.0013168344,0.000049955008,0.000005987663,0.0012492391,0.000021568529,0.994531,0.00020669996,0.00049570954,0.0004457138,0.0002170046],"about_ca_topic_score_codex":0.000048772657,"about_ca_topic_score_gemma":0.0000023363464,"teacher_disagreement_score":0.9471355,"about_ca_system_score_codex":0.000034276003,"about_ca_system_score_gemma":0.000026171272,"threshold_uncertainty_score":0.49318528},"labels":[],"label_agreement":null},{"id":"W2142360609","doi":"10.1109/ccece.2004.1345318","title":"Robot path planning with multiresolution probabilistic representations: a comparative study","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Motion planning; Probabilistic roadmap; Probabilistic logic; Occupancy grid mapping; Computer science; Robot; Representation (politics); Path (computing); Encoding (memory); Task (project management); Collision avoidance; Artificial intelligence; Mobile robot; Computer vision; Collision; Engineering; Systems engineering","score_opus":0.06719397966020287,"score_gpt":0.3325637211836078,"score_spread":0.2653697415234049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142360609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06734478,0.00002951546,0.929584,0.00019076001,0.00010902974,0.00084473455,8.6018036e-7,0.0004333351,0.0014629764],"genre_scores_gemma":[0.60685706,1.2837307e-7,0.3929319,0.000031687174,0.000019231042,0.000073884,0.000002777385,0.000004971183,0.00007833116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830943,0.000110174355,0.00026482283,0.00057994504,0.00043009678,0.00030551324],"domain_scores_gemma":[0.99891406,0.00013262124,0.00011606963,0.0006027037,0.00013161283,0.00010292673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025242777,0.00018208411,0.00022081805,0.000113564536,0.00024030734,0.00016689925,0.00051177415,0.00003189408,0.0000024916162],"category_scores_gemma":[0.00005854179,0.0001385758,0.000025637073,0.0005760919,0.00006227978,0.00052888977,0.00013203404,0.00015966572,0.000052496474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015760947,0.00058802066,0.007768465,0.0000055443725,0.00004098729,0.00017502606,0.018064255,0.9679138,0.00006150821,0.004964891,0.000086541186,0.0003151876],"study_design_scores_gemma":[0.0036821775,0.0017157542,0.23409775,0.00017203785,0.000032554617,0.00014608182,0.0059384666,0.75081867,0.00023401898,0.0025999935,0.000015824697,0.0005466516],"about_ca_topic_score_codex":0.00029656483,"about_ca_topic_score_gemma":0.00001421101,"teacher_disagreement_score":0.53951234,"about_ca_system_score_codex":0.00012057796,"about_ca_system_score_gemma":0.00013598084,"threshold_uncertainty_score":0.5650957},"labels":[],"label_agreement":null},{"id":"W2142589911","doi":"","title":"A new approach to cooperative pathfinding (Short Paper)","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Plan (archaeology); Computer science; Fidelity; Path (computing); Human–computer interaction; Artificial intelligence; Operations research; Shortest path problem; Theoretical computer science; Engineering; Programming language; Geography","score_opus":0.05405079318970613,"score_gpt":0.26567593532992845,"score_spread":0.21162514214022232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142589911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001229759,0.00001582654,0.90403557,0.00032477247,0.00019300339,0.00017258442,4.4829835e-7,0.00027368162,0.09375433],"genre_scores_gemma":[0.058154047,0.0000024483195,0.93140495,0.0011131987,0.00010267968,0.000014231449,0.0000018245139,0.0000088268835,0.009197769],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865717,0.000041834475,0.00017318585,0.000490589,0.0003022555,0.00033495974],"domain_scores_gemma":[0.9990811,0.00005318235,0.000017274264,0.00050929247,0.000052021616,0.0002871645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001682612,0.00014577297,0.00017334292,0.00009402106,0.00019547458,0.00008318228,0.000795451,0.000049949736,0.000025921396],"category_scores_gemma":[0.000057238954,0.000121872756,0.000037944337,0.00059782964,0.000016809201,0.0004046311,0.0002401656,0.00012391916,0.00052169187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022128472,0.00061629113,0.009763685,0.000021339207,0.0001673532,0.000918264,0.05168902,0.09624832,0.00534787,0.18304475,0.49127093,0.16089004],"study_design_scores_gemma":[0.001110949,0.0004939464,0.038428936,0.000068254485,0.000013518519,0.0017368436,0.0003338467,0.92908645,0.0059572374,0.0004893788,0.020484965,0.001795653],"about_ca_topic_score_codex":0.00006331545,"about_ca_topic_score_gemma":5.874149e-7,"teacher_disagreement_score":0.8328381,"about_ca_system_score_codex":0.00004380632,"about_ca_system_score_gemma":0.00016796491,"threshold_uncertainty_score":0.6705467},"labels":[],"label_agreement":null},{"id":"W2142797591","doi":"10.1109/robot.1993.292076","title":"Robot navigation using the vector potential approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Workspace; Computer science; Set (abstract data type); Field (mathematics); Construct (python library); Robot; Artificial intelligence; Vector field; Scalar (mathematics); Control engineering; Computer vision; Engineering; Mathematics","score_opus":0.04874796244240961,"score_gpt":0.24761305981065776,"score_spread":0.19886509736824814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142797591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025022728,0.00005516778,0.99355215,0.00050739705,0.0004017463,0.00009635395,2.628483e-7,0.0001653118,0.002719352],"genre_scores_gemma":[0.41355592,6.122878e-7,0.58577734,0.00011543101,0.000110581546,0.0000027571318,0.0000010359612,0.0000044308604,0.00043192756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991271,0.00006117042,0.00012145581,0.00022982984,0.0002595728,0.0002008696],"domain_scores_gemma":[0.9994388,0.000026056256,0.00004784678,0.00041130392,0.00003347609,0.000042523676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017099596,0.00008354384,0.000071895825,0.000029056433,0.00019146658,0.00016889315,0.0006146806,0.000037987782,0.00001795408],"category_scores_gemma":[0.000011134394,0.000054953725,0.000037896607,0.00027792627,0.000031278334,0.00027795896,0.00011411794,0.00011132968,0.00006775665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016827667,0.00022424744,0.0002624207,0.000018385477,0.00005076073,0.000067467685,0.0033776157,0.92065823,0.009834724,0.020215934,0.0037626491,0.041525885],"study_design_scores_gemma":[0.00008413272,0.000011391009,0.0006347654,0.000004936137,0.000004005508,0.000119373595,0.000022245562,0.9985312,0.00032482087,0.00012955765,0.000050262322,0.0000832657],"about_ca_topic_score_codex":0.00003430645,"about_ca_topic_score_gemma":4.334476e-8,"teacher_disagreement_score":0.41105363,"about_ca_system_score_codex":0.000027390824,"about_ca_system_score_gemma":0.000009336412,"threshold_uncertainty_score":0.2240948},"labels":[],"label_agreement":null},{"id":"W2142842238","doi":"10.1109/taai.2010.43","title":"Application of Integrated Central and Distributed Decisions to Path Tracking Control of a Vision-based Intelligent Vehicle","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Flexibility (engineering); Computer science; Path (computing); Machine vision; Computer vision; Artificial intelligence; Motion planning; Controller (irrigation); Tracking (education); Real-time computing; Robot","score_opus":0.011586332805187604,"score_gpt":0.2645773989186361,"score_spread":0.25299106611344846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142842238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1415206,0.000008699564,0.85772485,0.0003076174,0.00009279106,0.00023945601,0.000039472823,0.00005095839,0.000015566522],"genre_scores_gemma":[0.81594265,4.985249e-7,0.18395886,0.00006562283,0.0000070985197,0.00000878462,0.000010648186,0.000003809907,0.0000020129164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989457,0.00003636054,0.0003440688,0.00026043894,0.00022298425,0.00019045724],"domain_scores_gemma":[0.9986521,0.00043335167,0.00012122087,0.0004166169,0.00022212214,0.00015456104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003031538,0.00009835082,0.00019633317,0.00009734682,0.000040015228,0.000035005778,0.000417437,0.000059930346,0.000004327911],"category_scores_gemma":[0.00037405136,0.00007819539,0.00003832045,0.0004427603,0.00004995623,0.00009723556,0.000059114358,0.0001279568,0.0000028797865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007151474,0.0005223939,0.041076317,0.000019658744,0.0000310791,0.000006663133,0.0006906709,0.059923973,0.3239745,0.012014912,0.0001516447,0.56151664],"study_design_scores_gemma":[0.00034324703,0.00010885728,0.07938604,0.00003443419,0.0000054006023,0.0000019460654,0.000021964492,0.900136,0.01957801,0.00022961805,0.00007732484,0.000077176715],"about_ca_topic_score_codex":0.00013978287,"about_ca_topic_score_gemma":0.000010201141,"teacher_disagreement_score":0.840212,"about_ca_system_score_codex":0.000017738406,"about_ca_system_score_gemma":0.00008005502,"threshold_uncertainty_score":0.31887156},"labels":[],"label_agreement":null},{"id":"W2143102370","doi":"10.1109/isvd.2007.43","title":"Voronoi diagram in optimal path planning","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voronoi diagram; Smoothness; Disjoint sets; Path (computing); Computer science; Centroidal Voronoi tessellation; Motion planning; Diagram; Relation (database); Algorithm; Computational geometry; Mathematical optimization; Path length; Mathematics; Data mining; Artificial intelligence; Combinatorics; Geometry","score_opus":0.01898327563348483,"score_gpt":0.28184114386057385,"score_spread":0.262857868227089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143102370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036292214,0.000074613534,0.9513901,0.00014055661,0.0003354627,0.00007634192,2.1149535e-7,0.00023889253,0.011451595],"genre_scores_gemma":[0.4284415,7.082426e-7,0.5710291,0.00018313334,0.000051089897,0.0000026377788,9.854336e-7,0.000005293064,0.0002855324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998717,0.000025209578,0.00023803367,0.0003207504,0.00022491519,0.00047408394],"domain_scores_gemma":[0.9993261,0.00014919665,0.000043981654,0.0003512352,0.00001985639,0.00010964013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084687717,0.00011497191,0.00013147193,0.0001570414,0.000045572237,0.000075737364,0.0006184291,0.000063799314,0.000007652473],"category_scores_gemma":[0.000052321095,0.00010410885,0.000029003715,0.00038003115,0.00002060891,0.00031153124,0.00016736623,0.00017366669,0.00008396271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026625412,0.00043839536,0.22506604,0.000026358655,0.00003199581,0.004721434,0.010682833,0.1855223,0.0008224268,0.055096816,0.008316246,0.50924855],"study_design_scores_gemma":[0.000401055,0.00008550285,0.18612923,0.00003974005,0.0000012183448,0.000049609036,0.000107525964,0.8109168,0.0007778999,0.00027155114,0.0009454263,0.000274481],"about_ca_topic_score_codex":0.00007025978,"about_ca_topic_score_gemma":0.000001279527,"teacher_disagreement_score":0.62539446,"about_ca_system_score_codex":0.000052448082,"about_ca_system_score_gemma":0.00003528275,"threshold_uncertainty_score":0.42454356},"labels":[],"label_agreement":null},{"id":"W2143492328","doi":"","title":"A New Approach to Manipulate Objects with a Team of Distributed Robots Based on Constrain-and-Move Strategy","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Robot; Task (project management); Object (grammar); Computer science; Mobile robot; Path (computing); Measure (data warehouse); Artificial intelligence; Group (periodic table); Series (stratigraphy); Distributed computing; Computer vision; Engineering; Data mining","score_opus":0.016890224768488053,"score_gpt":0.22902468568458645,"score_spread":0.2121344609160984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143492328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017625663,0.000006804774,0.95690376,0.00014941576,0.000034415476,0.00029770916,0.000012552361,0.00015252539,0.04068027],"genre_scores_gemma":[0.4473487,6.944061e-8,0.5521363,0.00011046995,0.000017516568,0.0000055331243,0.000012964728,0.0000070111423,0.00036138948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985111,0.000046462395,0.0002413241,0.0005084912,0.00035603376,0.00033662235],"domain_scores_gemma":[0.9990736,0.00009687926,0.000093567236,0.0004884566,0.000058883343,0.00018861814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016499277,0.00020682071,0.00026988544,0.00012668732,0.000049478855,0.00010367307,0.0004088543,0.00006480036,0.000005181354],"category_scores_gemma":[0.000016503112,0.00015806296,0.000032925862,0.0005248826,0.000046540226,0.00011733141,0.00006403546,0.00011523722,0.000013546212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034801727,0.00021640508,0.0007910312,0.00002534376,0.000019636334,0.000039048078,0.00013080686,0.9612101,0.0003589579,0.028095566,0.0018450338,0.007233286],"study_design_scores_gemma":[0.0011957489,0.0004914487,0.027745558,0.00007929896,0.000012711671,0.00006307176,0.000054684177,0.9678924,0.0015289299,0.00057416747,0.000036534493,0.00032545836],"about_ca_topic_score_codex":0.00048423168,"about_ca_topic_score_gemma":0.000010885658,"teacher_disagreement_score":0.44558617,"about_ca_system_score_codex":0.000033589222,"about_ca_system_score_gemma":0.00021924934,"threshold_uncertainty_score":0.64456207},"labels":[],"label_agreement":null},{"id":"W2143567544","doi":"10.1109/icma.2005.1626631","title":"A neural network based intelligent planner for the coordinated hybrid agent framework","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planner; Computer science; Artificial neural network; Multi-agent system; Robot; Plan (archaeology); Artificial intelligence; Intelligent agent; Distributed computing; Mobile robot","score_opus":0.023550498874996932,"score_gpt":0.2572524370434125,"score_spread":0.23370193816841556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143567544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004654746,0.00027639457,0.9922318,0.0040940237,0.0016608004,0.00047434008,0.0000049866835,0.00036058854,0.00043158582],"genre_scores_gemma":[0.3622223,0.0000015185167,0.6329805,0.002966519,0.000642655,0.00011399424,0.000019362978,0.000021205,0.0010319364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843335,0.000067025234,0.00029014377,0.00040277987,0.00024971392,0.0005569747],"domain_scores_gemma":[0.99782526,0.0012297761,0.0000947567,0.0006893897,0.00008370269,0.00007713815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004462889,0.00019590401,0.00017571078,0.000044697823,0.0002946804,0.0002335817,0.0011180599,0.000059532544,0.000023432585],"category_scores_gemma":[0.00007571836,0.00012564547,0.00011999919,0.00036470362,0.000049303766,0.00010424996,0.00014122883,0.00020064597,0.000048483394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011691427,0.000048187216,0.00048147768,0.00000513613,0.000017828464,0.00003058073,0.00003328224,0.87471604,0.0000043469963,0.015641728,0.098174214,0.010835506],"study_design_scores_gemma":[0.0001800507,0.000080846636,0.0011698209,0.000024749332,0.000011581606,0.000015234743,0.000006362848,0.98141104,0.00032765357,0.005851319,0.010734145,0.00018719726],"about_ca_topic_score_codex":0.0001067118,"about_ca_topic_score_gemma":0.000002748363,"teacher_disagreement_score":0.36175683,"about_ca_system_score_codex":0.000043452997,"about_ca_system_score_gemma":0.00004809553,"threshold_uncertainty_score":0.51236737},"labels":[],"label_agreement":null},{"id":"W2144876918","doi":"10.1109/icas.2009.26","title":"Optimization Architecture of a Modular Architecture for Robotic Control: MARC Control Structure Applied to a VTOL UAV","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Modular design; Architecture; Weighting; Computer science; Robotics; Mobile robot; Task (project management); Remotely operated underwater vehicle; Control (management); Control engineering; Control system; Engineering; Artificial intelligence; Robot; Systems engineering","score_opus":0.0062672252232069455,"score_gpt":0.21851692687239,"score_spread":0.21224970164918305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144876918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003375428,0.00004645602,0.99176496,0.005072104,0.00019010676,0.001976777,0.000035940262,0.00027872564,0.00029737214],"genre_scores_gemma":[0.4445597,2.829758e-7,0.5532013,0.0020497001,0.000081897364,0.00003424432,0.000011228,0.000014886942,0.000046743015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758893,0.000083430605,0.0004962913,0.00073648675,0.00048236057,0.0006124923],"domain_scores_gemma":[0.9982032,0.00025456483,0.00021210936,0.0008897451,0.00018702331,0.00025334005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024913094,0.00037858612,0.00067045685,0.0003338748,0.000118000855,0.000111454545,0.00115524,0.00019094445,0.000012129444],"category_scores_gemma":[0.00015030586,0.00030953294,0.00015634857,0.0005609628,0.000040745446,0.00010581474,0.000060862993,0.00026462376,0.0000032163507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119656026,0.000048226662,0.000012498575,0.000025916048,0.000050796392,0.0000055474125,0.0004442638,0.96583694,0.008468161,0.0058444603,0.00021624498,0.018927298],"study_design_scores_gemma":[0.0030340352,0.0005073896,0.0007568437,0.00003830113,0.00004577103,0.000041139865,0.000009443422,0.9844822,0.0010431585,0.00957305,0.00009898463,0.00036967773],"about_ca_topic_score_codex":0.000008341769,"about_ca_topic_score_gemma":0.000002591249,"teacher_disagreement_score":0.44422215,"about_ca_system_score_codex":0.000046259847,"about_ca_system_score_gemma":0.00009630455,"threshold_uncertainty_score":0.9999357},"labels":[],"label_agreement":null},{"id":"W2145250685","doi":"10.1109/mis.2000.895866","title":"Having a robot attend AAAI 2000","year":2000,"lang":"en","type":"article","venue":"IEEE Intelligent Systems and their Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Desk; Computer science; Robotics; Artificial intelligence; Personal robot; Human–computer interaction; Mobile robot; Multimedia; Simulation; Robot control","score_opus":0.022832845488292508,"score_gpt":0.2496884862800794,"score_spread":0.22685564079178688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145250685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019724853,0.0014413254,0.99151826,0.00028271636,0.0002589826,0.0005759654,0.000012933286,0.0002535633,0.0036837666],"genre_scores_gemma":[0.96411914,0.00022117222,0.025959002,0.00021552341,0.00047916744,0.0008995104,0.000011313682,0.000035481393,0.008059717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864113,0.000054083815,0.00036039404,0.00048282984,0.00015331192,0.00030822837],"domain_scores_gemma":[0.99882185,0.00011640372,0.00008734737,0.000755698,0.000058133668,0.00016055959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026181372,0.00019425742,0.00022278783,0.00008712797,0.00027027758,0.00025745246,0.0006327225,0.00007649667,0.000025696992],"category_scores_gemma":[0.0000030265678,0.00015897742,0.000057421585,0.00032046842,0.00005496081,0.00018382,0.00004249982,0.00013977292,0.00051533605],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073196466,0.00042411886,0.0007499104,0.00014872602,0.00020738761,0.000021178881,0.0089979675,0.07022588,0.0033042543,0.052949473,0.003944208,0.8590196],"study_design_scores_gemma":[0.00023990328,0.00008534625,0.00067704107,0.00023255576,0.000020530137,0.0004921984,0.00038681974,0.7635917,0.00245543,0.00301618,0.22803172,0.0007705566],"about_ca_topic_score_codex":0.00024068776,"about_ca_topic_score_gemma":0.0000024167275,"teacher_disagreement_score":0.96555924,"about_ca_system_score_codex":0.000054882195,"about_ca_system_score_gemma":0.00004819735,"threshold_uncertainty_score":0.6623774},"labels":[],"label_agreement":null},{"id":"W2145396857","doi":"10.1109/ccece.2009.5090303","title":"Path planning for multiple Unmanned Aerial Vehicles using genetic algorithms","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Motion planning; Computer science; Plan (archaeology); Aerospace; MATLAB; Process (computing); Genetic algorithm; Path (computing); Data collection; Real-time computing; Systems engineering; Artificial intelligence; Engineering; Machine learning; Aerospace engineering; Robot; Computer network","score_opus":0.04849125833164077,"score_gpt":0.29507472806861296,"score_spread":0.24658346973697218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145396857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044589393,0.00013665408,0.95331955,0.00023969574,0.0007772264,0.00035065002,0.0000055283717,0.00041189374,0.00016938335],"genre_scores_gemma":[0.19651413,0.0000012161947,0.80261904,0.00038412857,0.00037021431,0.000009507936,0.0000049540067,0.000013216164,0.00008361261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814,0.000049809725,0.00034474063,0.0005651733,0.0002940342,0.00060628867],"domain_scores_gemma":[0.99893296,0.00019582249,0.000119027995,0.00050253095,0.00009095518,0.00015872244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026677913,0.0002331434,0.0002643176,0.00012895727,0.00027070046,0.00022525837,0.00079861516,0.00010991583,0.0000025530353],"category_scores_gemma":[0.00010523613,0.0002181638,0.000097392935,0.00027538085,0.00002834212,0.00034454983,0.00010047726,0.00011042232,0.000014050462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013903223,0.0005681003,0.0056845727,0.00007932441,0.00013212937,0.00049635355,0.005014902,0.5631017,0.050012387,0.0053119957,0.006996752,0.36246273],"study_design_scores_gemma":[0.0009904316,0.00024779016,0.0070548505,0.00004615477,0.000009692148,0.00005391952,0.000034133667,0.9861866,0.0030546617,0.001694253,0.00030917476,0.00031833028],"about_ca_topic_score_codex":0.000031394542,"about_ca_topic_score_gemma":1.656564e-7,"teacher_disagreement_score":0.42308488,"about_ca_system_score_codex":0.00005864249,"about_ca_system_score_gemma":0.00008831602,"threshold_uncertainty_score":0.8896462},"labels":[],"label_agreement":null},{"id":"W2145673216","doi":"10.1109/iros.2009.5354816","title":"Adaptive mobile charging stations for multi-robot systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"DOCK; Robot; Mobile robot; Computer science; Task (project management); Motion planning; Real-time computing; Interference (communication); Robot kinematics; Simulation; Distributed computing; Engineering; Artificial intelligence; Computer network","score_opus":0.0748851001837004,"score_gpt":0.32168129395352596,"score_spread":0.24679619376982556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145673216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012088012,0.0001263968,0.9974756,0.00017532145,0.00044206326,0.0005824404,0.0000073748674,0.00035732746,0.00071256916],"genre_scores_gemma":[0.10627464,0.0000016753488,0.891637,0.00016929948,0.000061740444,0.00011668699,0.0000047968642,0.000005691244,0.0017284529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902976,0.000030207317,0.0001890517,0.00030685338,0.00015303225,0.00029110716],"domain_scores_gemma":[0.999293,0.00012041224,0.00006970137,0.00031767398,0.000114301205,0.00008487946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021714653,0.00010962591,0.00014199325,0.000088754976,0.00015426928,0.00013589082,0.00043542834,0.000038254115,0.000001798934],"category_scores_gemma":[0.000029905428,0.000097775024,0.000043506298,0.00020399786,0.000011652637,0.00034509657,0.0000314066,0.00006335269,0.000049761835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007995555,0.00033662448,0.00009831712,0.000024921692,0.00005093095,0.000029042285,0.004761485,0.7175747,0.0013569634,0.2000046,0.0049570827,0.07079733],"study_design_scores_gemma":[0.00032687723,0.0001911182,0.00070170674,0.000022319144,0.0000033316053,0.0000099229555,0.00018308892,0.99770194,0.00017089081,0.00019121422,0.0003599026,0.00013770747],"about_ca_topic_score_codex":0.000030149911,"about_ca_topic_score_gemma":5.812318e-7,"teacher_disagreement_score":0.28012723,"about_ca_system_score_codex":0.000046798166,"about_ca_system_score_gemma":0.00004887212,"threshold_uncertainty_score":0.398715},"labels":[],"label_agreement":null},{"id":"W2146076630","doi":"10.1109/icsmc.1997.637375","title":"A framework for the study of interactions between robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Mobile robot; Human–computer interaction; Architecture; Control (management); Conceptual framework; Conceptual architecture; Artificial intelligence; Decentralised system; Distributed computing","score_opus":0.1253068756203779,"score_gpt":0.3466707870866552,"score_spread":0.22136391146627732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146076630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032255624,0.00002641569,0.994118,0.0013106132,0.00034890088,0.00034686766,9.923725e-7,0.00007662797,0.0005460463],"genre_scores_gemma":[0.55863637,6.335329e-7,0.44077235,0.00005523375,0.00007129476,0.000031207066,1.1886394e-7,0.0000032634732,0.00042954204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935275,0.000031561212,0.0001696121,0.00016631978,0.00014659326,0.00013314419],"domain_scores_gemma":[0.9978717,0.0014587358,0.00006929236,0.0005192182,0.000050489107,0.00003054536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014796987,0.00006399493,0.00011073965,0.000044467764,0.0001165857,0.000047011938,0.00071753416,0.00002248555,0.00002236128],"category_scores_gemma":[0.00012469315,0.00004076902,0.000039069044,0.0002547605,0.000015089981,0.00013490245,0.00011073535,0.000113487935,0.000024743287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008734202,0.0029809582,0.07044738,0.000035933208,0.00089515623,0.000016650009,0.058907412,0.08442221,0.00005077845,0.099499546,0.030994711,0.65174055],"study_design_scores_gemma":[0.00039324453,0.0004803035,0.03490933,0.000027848515,0.000038703594,0.000005320882,0.00085753715,0.95724124,0.0000889919,0.0049831625,0.0008294464,0.00014488111],"about_ca_topic_score_codex":0.000034523957,"about_ca_topic_score_gemma":0.00000227226,"teacher_disagreement_score":0.872819,"about_ca_system_score_codex":0.000009890799,"about_ca_system_score_gemma":0.000004663483,"threshold_uncertainty_score":0.16625124},"labels":[],"label_agreement":null},{"id":"W2146226283","doi":"10.1109/iros.2004.1389659","title":"Situated robot design with prioritized constraints","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Situated; Robot; Computer science; Constraint (computer-aided design); Modular design; Mobile robot; Human–computer interaction; Control engineering; Artificial intelligence; Programming language; Engineering","score_opus":0.024152909616276405,"score_gpt":0.2361083878973365,"score_spread":0.2119554782810601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146226283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037399927,0.000018946288,0.98985314,0.0015069942,0.00008153785,0.00019883021,4.059665e-7,0.0006100967,0.007356041],"genre_scores_gemma":[0.14977354,0.0000012243522,0.84885913,0.0004421155,0.000044376284,0.000007559187,9.4249737e-7,0.0000075623807,0.0008635636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875945,0.00007650857,0.00017529058,0.00035667035,0.0002854762,0.000346623],"domain_scores_gemma":[0.9991551,0.00013944275,0.00005519391,0.00043228283,0.00008173084,0.00013622479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029891307,0.00014781648,0.00016700952,0.000074307405,0.00008626887,0.0001321301,0.00059919414,0.0000516811,0.000057695645],"category_scores_gemma":[0.000031913354,0.00011184381,0.000023832728,0.0003123202,0.0000964456,0.00038099903,0.000071822265,0.00011793136,0.00034504657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006237839,0.00038811375,0.0009215132,0.000015284151,0.00016631372,0.0006686283,0.0020841467,0.24093564,0.007714832,0.033934575,0.008479208,0.70462936],"study_design_scores_gemma":[0.0016517281,0.0001806007,0.002373023,0.00004147321,0.000009607889,0.00037409543,0.000026179856,0.9865629,0.00765428,0.00028181684,0.0004366099,0.0004076988],"about_ca_topic_score_codex":0.000007509527,"about_ca_topic_score_gemma":0.0000011425809,"teacher_disagreement_score":0.7456272,"about_ca_system_score_codex":0.000041314688,"about_ca_system_score_gemma":0.00016709795,"threshold_uncertainty_score":0.4560858},"labels":[],"label_agreement":null},{"id":"W2146487268","doi":"10.1109/ivs.2004.1336472","title":"Occlusions in obstacle detection for safe navigation","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; Computer vision; Computer science; Artificial intelligence; Trajectory; Collision avoidance; Obstacle avoidance; Robot; Object detection; Collision; Mobile robot; Computer security; Pattern recognition (psychology); Geography","score_opus":0.016934491757294022,"score_gpt":0.2664468906727005,"score_spread":0.24951239891540647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146487268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031167919,0.000010707617,0.9670712,0.0005786459,0.00032116042,0.00017910881,6.805807e-7,0.00012616556,0.000544441],"genre_scores_gemma":[0.5448523,3.956903e-7,0.45492396,0.00008771408,0.00002153365,0.000017878952,0.000002158083,0.0000028518032,0.000091175185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940795,0.00001246767,0.00012847417,0.00019037673,0.0001033288,0.0001574281],"domain_scores_gemma":[0.9996555,0.000056174584,0.00002954787,0.00018888479,0.00003307187,0.000036852074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017838863,0.00005507454,0.000060671093,0.000073327465,0.000087841545,0.000037700964,0.00021214028,0.000040505634,0.0000013112384],"category_scores_gemma":[0.000047552065,0.00005250217,0.000023695104,0.0002917951,0.000008566685,0.00028351642,0.000052143652,0.0000571717,0.000031812782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019333196,0.00034451627,0.0011197999,0.000042562348,0.000014672214,0.000063738196,0.005284846,0.4660141,0.034268733,0.09348892,0.00019405148,0.39914474],"study_design_scores_gemma":[0.0015647323,0.00018905062,0.016016888,0.000068463436,0.0000027737472,0.00005692754,0.0000765496,0.8969597,0.036655024,0.047674634,0.00047862096,0.00025661621],"about_ca_topic_score_codex":0.00012174419,"about_ca_topic_score_gemma":0.000024999757,"teacher_disagreement_score":0.51368445,"about_ca_system_score_codex":0.00010691958,"about_ca_system_score_gemma":0.00004128186,"threshold_uncertainty_score":0.21409763},"labels":[],"label_agreement":null},{"id":"W2147232932","doi":"10.1109/icsmc.1995.537949","title":"Dynamic path planning","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Motion planning; Maxima and minima; Plan (archaeology); Path (computing); Computation; Workstation; Any-angle path planning; A priori and a posteriori; Software; Function (biology); Distributed computing; Robot; Algorithm; Artificial intelligence; Operating system","score_opus":0.027291119775860996,"score_gpt":0.2505253485459864,"score_spread":0.22323422877012541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147232932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013795869,0.0001895806,0.95779485,0.0005741841,0.00032250548,0.000041114406,3.1297319e-7,0.00047997758,0.03921787],"genre_scores_gemma":[0.26452768,0.0000033995213,0.73009247,0.0004317778,0.000018750146,0.0000037432326,6.272461e-7,0.000006221734,0.004915317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991918,0.000020668596,0.00011388418,0.00024596765,0.0001818717,0.00024579858],"domain_scores_gemma":[0.9994311,0.000051108596,0.00003097453,0.0003961563,0.000016973996,0.00007373235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009886948,0.000085205946,0.00008538005,0.000061540595,0.000074237745,0.00008723091,0.0005887341,0.000034385237,0.00006480294],"category_scores_gemma":[0.00002125433,0.00007383592,0.000027021855,0.00021292559,0.000013917794,0.00025304122,0.000113047456,0.00009251976,0.0006897906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034090756,0.0005497864,0.01486705,0.000054893047,0.000120831595,0.00292843,0.015672633,0.08272537,0.0019197467,0.08467032,0.21089937,0.58558816],"study_design_scores_gemma":[0.00009937594,0.00002691219,0.0023762318,0.000012357635,9.353047e-7,0.000051279334,0.0000122489955,0.99545026,0.000025601026,0.00042606218,0.00140571,0.00011303851],"about_ca_topic_score_codex":0.0000044301905,"about_ca_topic_score_gemma":4.851402e-8,"teacher_disagreement_score":0.91272485,"about_ca_system_score_codex":0.000022473052,"about_ca_system_score_gemma":0.000005451966,"threshold_uncertainty_score":0.88660926},"labels":[],"label_agreement":null},{"id":"W2148332992","doi":"10.1109/tnn.2009.2029858","title":"Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Mobile robot; Robot; Artificial neural network; Path (computing); Motion planning; Shortest path problem; Event (particle physics); Topology (electrical circuits); Artificial intelligence; Graph; Engineering; Theoretical computer science; Physics; Computer network","score_opus":0.025007147238864467,"score_gpt":0.2536678519390162,"score_spread":0.22866070470015173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148332992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071830377,0.00003063345,0.9879872,0.0009993955,0.0014326568,0.0004983093,0.000006691454,0.0012390766,0.0006229989],"genre_scores_gemma":[0.92556906,0.000009957764,0.07140836,0.0023000108,0.0003400193,0.00005057987,0.000013137607,0.000054357748,0.0002545025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959434,0.00024775552,0.0006404527,0.0011327962,0.0007247485,0.0013107907],"domain_scores_gemma":[0.9975256,0.00047266815,0.00022095651,0.0012768164,0.00008797539,0.00041599805],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004029823,0.0006532076,0.00059806625,0.00026189693,0.00070599583,0.000377045,0.0012149998,0.00032659573,0.000014731421],"category_scores_gemma":[0.0000072066778,0.00063214847,0.0003280252,0.0010766704,0.000063369785,0.0006469357,0.0000060442076,0.001217319,0.000032369167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001786162,0.00020604087,0.0000038132853,0.0000033599506,0.000018840725,0.00015041932,0.00008893709,0.97738576,0.00028194647,0.00005039714,0.000600797,0.021031091],"study_design_scores_gemma":[0.0010405328,0.0007731057,0.00035856408,0.00012558448,0.00003782305,0.000037341848,0.000002646059,0.99674195,0.00006593735,0.00019420394,0.0000020849,0.0006202549],"about_ca_topic_score_codex":0.00003051446,"about_ca_topic_score_gemma":9.105311e-7,"teacher_disagreement_score":0.91838604,"about_ca_system_score_codex":0.00013738555,"about_ca_system_score_gemma":0.000077055105,"threshold_uncertainty_score":0.999613},"labels":[],"label_agreement":null},{"id":"W2148461563","doi":"10.1109/iros.2003.1249640","title":"Flexible binary space partitioning for robotic rescue","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Space (punctuation); Binary number; Operating system; Mathematics","score_opus":0.0336152924574271,"score_gpt":0.27944082419248367,"score_spread":0.24582553173505656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148461563","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006827106,0.00008037717,0.9908042,0.005128733,0.0004900122,0.00021183901,4.808242e-7,0.00054506806,0.002056585],"genre_scores_gemma":[0.09046351,0.000002512646,0.9068526,0.00037358,0.00009294129,0.000040868534,0.0000039084994,0.000010872292,0.0021591769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989188,0.000018153794,0.00016467772,0.00035113623,0.00017731414,0.00036997258],"domain_scores_gemma":[0.99927,0.00007981258,0.000037368005,0.00044748382,0.000061274885,0.00010404755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023990896,0.00011471142,0.00013514572,0.00008860108,0.0001946401,0.00013502332,0.0004916845,0.000051237632,0.0000067540527],"category_scores_gemma":[0.00007209539,0.00010634135,0.00005916449,0.00031515333,0.000028026296,0.000423048,0.00011654475,0.00008184235,0.00015449733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025530328,0.00004443771,0.000081121674,0.000012318251,0.00000881406,0.000021180587,0.00022985818,0.83808684,0.0005038148,0.1588546,0.001255202,0.000899259],"study_design_scores_gemma":[0.0015185607,0.00048035826,0.003228921,0.00015130489,0.000013952962,0.000090518275,0.00007135098,0.9245628,0.016905667,0.050626785,0.0018298138,0.0005199799],"about_ca_topic_score_codex":0.00004967604,"about_ca_topic_score_gemma":0.0000024001597,"teacher_disagreement_score":0.10822781,"about_ca_system_score_codex":0.000070082075,"about_ca_system_score_gemma":0.000111056856,"threshold_uncertainty_score":0.43364745},"labels":[],"label_agreement":null},{"id":"W2148837279","doi":"10.1109/robot.2007.363163","title":"Metric View Planning Problem with Traveling Cost and Visibility Range","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE International Conference on Robotics and Automation/Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Travelling salesman problem; Metric (unit); Mathematical optimization; Approximation algorithm; Range (aeronautics); Motion planning; Greedy algorithm; Set cover problem; Mathematics; Metric space; Visibility; Set (abstract data type); Computer science; Robot; Discrete mathematics; Artificial intelligence","score_opus":0.05214411790293259,"score_gpt":0.30865846916074213,"score_spread":0.2565143512578095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148837279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3507146,0.00020647737,0.60626155,0.0050255386,0.00071990315,0.0014729154,0.0000140581105,0.0010546524,0.034530304],"genre_scores_gemma":[0.7785639,0.000056271332,0.22071964,0.00033135514,0.00014686029,0.000034713717,0.0000045488914,0.000026728027,0.0001159515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672717,0.000004687498,0.0006937603,0.000998861,0.0009848392,0.00059068867],"domain_scores_gemma":[0.9975445,0.00013495168,0.00052485394,0.00010348976,0.0013736703,0.00031852524],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015134602,0.00044703236,0.0004244115,0.000585356,0.00036543788,0.0014795123,0.00077252375,0.00016818801,0.000008057037],"category_scores_gemma":[0.00016122063,0.00039364345,0.000050088893,0.00083464134,0.00015394509,0.0016274973,0.00017823446,0.0004688924,0.000010299457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002055816,0.00042467655,0.08699938,0.0007689047,0.000312759,0.000050433468,0.01013943,0.003059598,0.005008486,0.8256652,0.0007763609,0.0665892],"study_design_scores_gemma":[0.0014504083,0.0004890924,0.06252931,0.0012298023,0.000053517797,0.00033864946,0.00096623413,0.9224941,0.0016272201,0.007700273,0.00023782077,0.00088358193],"about_ca_topic_score_codex":0.000014936673,"about_ca_topic_score_gemma":7.41478e-7,"teacher_disagreement_score":0.9194345,"about_ca_system_score_codex":0.00015523634,"about_ca_system_score_gemma":0.000075153956,"threshold_uncertainty_score":0.9998515},"labels":[],"label_agreement":null},{"id":"W2149159441","doi":"10.1109/cira.2003.1222320","title":"Analysis and optimization of a non-time based motion controller for a nonholonomic mobile robot","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Nonholonomic system; Control theory (sociology); Controller (irrigation); Computer science; Mobile robot; Tracking error; Motion control; Teleoperation; Genetic algorithm; Robot; Control engineering; Engineering; Artificial intelligence; Control (management)","score_opus":0.007544560716962713,"score_gpt":0.2340714195890209,"score_spread":0.22652685887205817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149159441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029954822,0.000011339512,0.9963911,0.00011070488,0.00003014896,0.0003508871,0.000004962035,0.000053740507,0.000051637544],"genre_scores_gemma":[0.31163806,5.7437893e-7,0.6882225,0.000049695605,0.000007691415,0.000037785394,0.000011422371,0.000003517759,0.00002874414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993043,0.000017572193,0.00020223268,0.0002576775,0.00008899787,0.00012921577],"domain_scores_gemma":[0.9994647,0.00008433447,0.000109977176,0.00021179552,0.00008244698,0.0000467481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022783471,0.0000847173,0.00023963352,0.00023415367,0.000044151355,0.00004071644,0.00017731822,0.00004653069,0.0000070963138],"category_scores_gemma":[0.000022149723,0.00007671174,0.000073909,0.00037031094,0.000022854254,0.00017579811,0.000028964598,0.000025473402,0.0000039618485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008048918,0.00003474007,0.00023042517,0.000004883725,0.000060709295,4.5071053e-7,0.0000878019,0.99680537,0.00054958777,0.000032923585,0.0000055157066,0.0021795647],"study_design_scores_gemma":[0.0016216318,0.00014589044,0.0015468238,0.000006112324,0.00006897998,9.574758e-7,0.0000044337826,0.9951272,0.0013537318,0.000038587204,0.0000013691343,0.00008428344],"about_ca_topic_score_codex":0.000047908776,"about_ca_topic_score_gemma":7.9992776e-7,"teacher_disagreement_score":0.30864257,"about_ca_system_score_codex":0.000032821357,"about_ca_system_score_gemma":0.000042897016,"threshold_uncertainty_score":0.3128214},"labels":[],"label_agreement":null},{"id":"W2149803537","doi":"10.1109/ccece.2009.5090208","title":"Non-time based motion control for multiple intelligent vehicles","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Automation; Computer science; Control engineering; Controller (irrigation); Motion control; Intelligent control; Robot; Tracking (education); Mobile robot; Control (management); Motion (physics); Robot control; Track (disk drive); Robot kinematics; Engineering; Artificial intelligence","score_opus":0.0168023028529482,"score_gpt":0.24650785761735336,"score_spread":0.22970555476440516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149803537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001550856,0.000010822694,0.9955047,0.001684919,0.00016792255,0.00037513048,0.0000033762358,0.00025463122,0.00044767835],"genre_scores_gemma":[0.5436734,2.0352554e-7,0.45497954,0.0010466451,0.000048322523,0.000012749461,0.0000052304017,0.000003794228,0.00023012588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908197,0.000022916513,0.00018395805,0.00028932604,0.00016302077,0.00025878503],"domain_scores_gemma":[0.99921715,0.00022437988,0.00005424594,0.000337348,0.00008175015,0.00008513436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000300737,0.00011224829,0.00014664124,0.00007534553,0.00008092693,0.00008534583,0.00044658687,0.00005227015,0.0000074135914],"category_scores_gemma":[0.00008092091,0.00009695124,0.00007303752,0.00012253773,0.000012218043,0.00018336445,0.000016467571,0.000054404118,0.00011663389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013633083,0.0007070205,0.0038082663,0.000033248747,0.00005894428,0.000036783018,0.0007618966,0.3302076,0.030841462,0.005889176,0.016535452,0.61098385],"study_design_scores_gemma":[0.0008603836,0.00021880743,0.004784001,0.000011589435,0.0000041460935,0.0000018306648,0.0000026611176,0.9827871,0.010297135,0.00067100377,0.00023195504,0.00012939409],"about_ca_topic_score_codex":0.000008211553,"about_ca_topic_score_gemma":2.4499568e-7,"teacher_disagreement_score":0.6525795,"about_ca_system_score_codex":0.00003454673,"about_ca_system_score_gemma":0.000032172124,"threshold_uncertainty_score":0.3953557},"labels":[],"label_agreement":null},{"id":"W2150058539","doi":"10.1109/icet.2006.335934","title":"Path Finder in Unknown Environment","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Path (computing); Obstacle; Pathfinder; Set (abstract data type); Position (finance); Section (typography); Artificial intelligence; Data set; Point (geometry); Algorithm; Computer vision; Data mining; Mathematics; Computer network; World Wide Web; Geography","score_opus":0.007269052278299986,"score_gpt":0.1897648946758541,"score_spread":0.18249584239755412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150058539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005194156,0.000056619756,0.9734912,0.000823284,0.00012463797,0.00006683544,2.6337224e-7,0.00009645008,0.020146547],"genre_scores_gemma":[0.2920353,0.0000029175937,0.7015819,0.00036267986,0.000056175788,0.000010965788,0.000002401154,0.0000063027715,0.0059413393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992249,0.00002199493,0.0001359145,0.00023606593,0.00017176266,0.00020935222],"domain_scores_gemma":[0.99960256,0.000033225984,0.000022897724,0.00030908152,0.0000034545092,0.000028809056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013172868,0.00007377697,0.000075397,0.000055117333,0.000023020862,0.000036130325,0.0003392615,0.000033918386,0.00003065487],"category_scores_gemma":[0.0000036387855,0.00006371726,0.000018215236,0.00011454302,0.000014426904,0.00013077538,0.00010772149,0.00006844293,0.00028195095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053787708,0.0013128733,0.09627723,0.00003155071,0.000020135036,0.0012461492,0.0020163732,0.3566764,0.00221543,0.40882117,0.034199554,0.097177744],"study_design_scores_gemma":[0.0005336112,0.000048619717,0.23647028,0.000016525337,0.000001318864,0.000022119704,0.000008451568,0.73986703,0.0007394967,0.006234565,0.015755227,0.0003027541],"about_ca_topic_score_codex":0.00014604606,"about_ca_topic_score_gemma":0.000002271149,"teacher_disagreement_score":0.4025866,"about_ca_system_score_codex":0.000045418958,"about_ca_system_score_gemma":0.00001537251,"threshold_uncertainty_score":0.36240032},"labels":[],"label_agreement":null},{"id":"W2150189195","doi":"10.1109/ivs.1993.697333","title":"Dynamo: Real-time Experiments With Multiple Mobile Robots","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mobile robot; Dynamo; Computer science; Artificial intelligence; Robotics; Software; Transmitter; Real-time computing; Robot; Radio control; Tuner; Computer vision; Tracking (education); Radio frequency; Telecommunications; Operating system; Magnetic field","score_opus":0.011460421340882983,"score_gpt":0.2526743263179773,"score_spread":0.24121390497709433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150189195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031726405,0.000055012897,0.9331021,0.00027408812,0.00016596391,0.00038276252,0.0000016943127,0.0010180225,0.033274002],"genre_scores_gemma":[0.13308229,0.0000034400418,0.8587368,0.00015545414,0.0000728496,0.00006263389,0.000004213618,0.000015857206,0.007866446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985755,0.00003785456,0.00018724934,0.0004599271,0.00034697526,0.00039250843],"domain_scores_gemma":[0.9989369,0.00007888825,0.00006467389,0.0007084513,0.00005139494,0.00015969781],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013446345,0.00017778638,0.00017723578,0.000073719355,0.000103629194,0.000112788926,0.00073754834,0.000053256084,0.000084844796],"category_scores_gemma":[0.000010359094,0.00013678923,0.000033669796,0.0002455128,0.000036025107,0.00054352713,0.0001760333,0.00008410507,0.0010554708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005781431,0.0013680764,0.01125349,0.000022077127,0.00018673776,0.0003457455,0.007134007,0.7685882,0.040633596,0.0033293199,0.02164109,0.14543986],"study_design_scores_gemma":[0.00064931426,0.00019663325,0.002175023,0.000019484449,0.0000031313007,0.00004680533,0.000032260552,0.987377,0.0081983125,0.000015574466,0.0010094641,0.00027703572],"about_ca_topic_score_codex":0.000102396276,"about_ca_topic_score_gemma":0.000003960537,"teacher_disagreement_score":0.21878879,"about_ca_system_score_codex":0.000076667675,"about_ca_system_score_gemma":0.000050273255,"threshold_uncertainty_score":0.9997223},"labels":[],"label_agreement":null},{"id":"W2150953273","doi":"10.1109/iros.2003.1249282","title":"Textual message read by a mobile robot","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Mobile robot; Robot; Character (mathematics); Relation (database); Pentium; Artificial intelligence; Computer vision; Zoom; Social robot; Robot control; Human–computer interaction; Engineering","score_opus":0.010464610661259111,"score_gpt":0.24485819403116185,"score_spread":0.23439358336990274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150953273","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074870203,0.00013403223,0.96622396,0.00060477696,0.00014490102,0.00012970327,0.0000024630397,0.00048375185,0.03152774],"genre_scores_gemma":[0.17018496,0.000007504919,0.82197255,0.0009072027,0.000059293026,0.000043014046,0.000007494925,0.000013735135,0.00680424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880785,0.000025458516,0.00016950705,0.00038537467,0.00028150843,0.0003303049],"domain_scores_gemma":[0.9991849,0.000047017315,0.000044313336,0.0005677856,0.000031659227,0.0001243175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018202573,0.00013118848,0.00014054688,0.000050975763,0.00008535215,0.00011564215,0.00084346265,0.000061575345,0.00004377747],"category_scores_gemma":[0.000021641883,0.00011210618,0.000041066058,0.00028894073,0.00003689893,0.00033638295,0.000185154,0.00012272688,0.0005234293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012702532,0.001401039,0.00060904183,0.00004223925,0.00014463475,0.0012299684,0.005295218,0.26726308,0.024268279,0.19831048,0.13398883,0.3674345],"study_design_scores_gemma":[0.013378287,0.0042537255,0.0074340594,0.0004901715,0.00008011367,0.0021748429,0.0022739503,0.68726075,0.14305414,0.030691845,0.1023549,0.006553209],"about_ca_topic_score_codex":0.00014591949,"about_ca_topic_score_gemma":0.0000015698316,"teacher_disagreement_score":0.41999766,"about_ca_system_score_codex":0.00005688043,"about_ca_system_score_gemma":0.00007005583,"threshold_uncertainty_score":0.6727799},"labels":[],"label_agreement":null},{"id":"W2152048929","doi":"10.1109/robot.2006.1641999","title":"Self-motion graph in path planning for redundant robots along specified end-effector paths","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Probabilistic logic; Path (computing); Computer science; Robot end effector; Robot; Graph; Any-angle path planning; Motion (physics); Probabilistic roadmap; Set (abstract data type); Algorithm; Artificial intelligence; Theoretical computer science","score_opus":0.019198697761122582,"score_gpt":0.2524096987530597,"score_spread":0.2332110009919371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152048929","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026703583,0.00013761772,0.9685368,0.00021251058,0.0008645348,0.0006634343,0.0000065322306,0.00062300416,0.0022519785],"genre_scores_gemma":[0.41363168,0.0000023304738,0.58579713,0.00006821109,0.00021495394,0.000073270145,0.00003174365,0.000020091333,0.00016060768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770755,0.000112399044,0.0004846816,0.0006923327,0.00036286528,0.0006401527],"domain_scores_gemma":[0.9988111,0.00034307205,0.00015478427,0.0005284157,0.00006574337,0.00009685234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007698119,0.00026410475,0.0003116963,0.00032169264,0.0001464039,0.00019426142,0.00064359116,0.00012529868,0.000004888228],"category_scores_gemma":[0.000050315342,0.00024466583,0.00011096934,0.00057723856,0.00002611361,0.00046292756,0.00011893774,0.00018626059,0.000033791213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018843127,0.002884622,0.1835522,0.0006894497,0.00019390162,0.0026899634,0.00831347,0.47659254,0.013351797,0.18591036,0.033024915,0.09260834],"study_design_scores_gemma":[0.0018489298,0.0002637992,0.22863287,0.00016898004,0.000013034327,0.000082130784,0.00005468582,0.7589718,0.0030857092,0.005233581,0.0009737936,0.0006706581],"about_ca_topic_score_codex":0.00025061634,"about_ca_topic_score_gemma":0.000012255938,"teacher_disagreement_score":0.38692808,"about_ca_system_score_codex":0.00012774662,"about_ca_system_score_gemma":0.00006597899,"threshold_uncertainty_score":0.99771833},"labels":[],"label_agreement":null},{"id":"W2152311680","doi":"10.1109/crv.2011.21","title":"Practicality-Based Probabilistic Roadmaps Method","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic roadmap; Planner; Motion planning; Probabilistic logic; Computer science; Path (computing); Mathematical optimization; Sample (material); Robot; Artificial intelligence; Mathematics","score_opus":0.08765222374256378,"score_gpt":0.3139369566405871,"score_spread":0.2262847328980233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152311680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005303726,0.000006687453,0.9498255,0.00074463955,0.00027048946,0.00015592662,6.119971e-7,0.00047289964,0.048470166],"genre_scores_gemma":[0.01717173,1.1749166e-7,0.9814811,0.00083135354,0.00002686904,0.000021374097,0.0000010399839,0.000007641256,0.00045881292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984906,0.00024465378,0.00022791816,0.00044113226,0.0002790563,0.0003166443],"domain_scores_gemma":[0.99846256,0.00037883868,0.000080679165,0.00082527933,0.00009399298,0.00015867993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009906502,0.00013167092,0.00015865547,0.00007337944,0.00007124672,0.0000712106,0.00081631675,0.00006460369,0.00009989068],"category_scores_gemma":[0.00050081045,0.000106400264,0.0000530476,0.00033230055,0.00003951483,0.00032936828,0.00013123458,0.00015820467,0.00034766537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023977875,0.000960374,0.0019236448,0.00007255294,0.000060365106,0.0003519743,0.0029509484,0.0035802284,0.00029085783,0.91831064,0.0047407937,0.06673361],"study_design_scores_gemma":[0.00028470848,0.00014668074,0.0039205113,0.000014245661,0.000012814489,0.000050913324,0.000018416435,0.96349764,0.0025690973,0.027630502,0.0015736966,0.00028076058],"about_ca_topic_score_codex":0.00010018683,"about_ca_topic_score_gemma":9.994271e-7,"teacher_disagreement_score":0.9599174,"about_ca_system_score_codex":0.000033017266,"about_ca_system_score_gemma":0.00015290605,"threshold_uncertainty_score":0.44686508},"labels":[],"label_agreement":null},{"id":"W2152548017","doi":"10.1109/crv.2014.16","title":"Speed Daemon: Experience-Based Mobile Robot Speed Scheduling","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Computer science; Scheduling (production processes); Motion planning; Speedup; Robot; Real-time computing; Ackermann function; Odometry; Terrain; Path (computing); Schedule; Electronic speed control; Simulation; Artificial intelligence; Mathematical optimization; Engineering; Computer network; Parallel computing; Mathematics","score_opus":0.02739532617823242,"score_gpt":0.2744155550016438,"score_spread":0.24702022882341135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152548017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04078251,0.00003469366,0.9483814,0.0003603666,0.0008651617,0.00020558077,4.0097106e-7,0.00069959875,0.008670262],"genre_scores_gemma":[0.4546693,5.669373e-7,0.5440029,0.00059326895,0.00014152045,0.000007366443,0.0000023988587,0.000012276554,0.00057041086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782246,0.0001049577,0.00033602366,0.0006778174,0.0005080804,0.00055062876],"domain_scores_gemma":[0.99824214,0.00021263509,0.00011148802,0.0011009019,0.000094454415,0.00023836741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004877586,0.00024184221,0.00028039474,0.00014432553,0.00018123585,0.00028073564,0.001427927,0.000097102165,0.00008283275],"category_scores_gemma":[0.00013601407,0.0002147156,0.00009068561,0.0005213306,0.000082230654,0.00044409142,0.00022294666,0.00018675266,0.00048323695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009762308,0.0001922849,0.003388114,0.000023535587,0.000018646811,0.00006450601,0.0021841447,0.9335086,0.013583833,0.006015677,0.0006164737,0.04039447],"study_design_scores_gemma":[0.00048531903,0.00013227436,0.00084085535,0.000030808118,0.0000032018706,0.000015040317,0.00009081419,0.9781701,0.018818133,0.00020509369,0.0008935806,0.00031481052],"about_ca_topic_score_codex":0.000059280785,"about_ca_topic_score_gemma":7.525901e-7,"teacher_disagreement_score":0.4138868,"about_ca_system_score_codex":0.000050530147,"about_ca_system_score_gemma":0.000083307044,"threshold_uncertainty_score":0.8755848},"labels":[],"label_agreement":null},{"id":"W2152687842","doi":"10.1017/s0263574707003694","title":"Modified Newton's method applied to potential field-based navigation for nonholonomic robots in dynamic environments","year":2007,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Ontario Tech University","funders":"","keywords":"Robot; Nonholonomic system; Computer science; Control theory (sociology); Field (mathematics); Mobile robot; Control engineering; Artificial intelligence; Engineering; Mathematics; Control (management)","score_opus":0.011878808274935218,"score_gpt":0.29353746259179003,"score_spread":0.28165865431685483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152687842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041749706,0.0000082236265,0.99227387,0.0020341028,0.00046151175,0.0007619886,0.0000027352655,0.00009670385,0.00018589672],"genre_scores_gemma":[0.37319833,2.3855657e-7,0.6260898,0.0005024048,0.000037482685,0.000043889537,0.000018058008,0.000015893836,0.00009387262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979616,0.000057735168,0.00042301754,0.00067023153,0.0002747102,0.0006127185],"domain_scores_gemma":[0.99866897,0.00041604973,0.00011285911,0.0005944336,0.000017582464,0.00019011229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010238709,0.00022086939,0.00028460234,0.00025410022,0.00010301915,0.000075902935,0.0007413873,0.0001745583,0.000002920262],"category_scores_gemma":[0.00006283222,0.00024509113,0.00007787873,0.00031724718,0.000018375467,0.00014057374,0.00013597767,0.00020800915,0.0000644938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058124846,0.00007943212,0.00004060498,0.00001157031,0.000008917735,0.000026301548,0.00013536731,0.9665318,0.009757999,0.0015336723,0.00006163766,0.02175458],"study_design_scores_gemma":[0.0010373301,0.0001438305,0.0063580764,0.000033056782,0.000011804361,0.0000075411353,0.000010146418,0.9862372,0.0047539505,0.0010525039,0.00006998886,0.00028452015],"about_ca_topic_score_codex":0.000027888314,"about_ca_topic_score_gemma":0.0000047087105,"teacher_disagreement_score":0.36902335,"about_ca_system_score_codex":0.00022776802,"about_ca_system_score_gemma":0.00006368098,"threshold_uncertainty_score":0.9994526},"labels":[],"label_agreement":null},{"id":"W2153340051","doi":"10.1109/rose.2011.6058518","title":"An improved Artificial Potential Field approach to real-time mobile robot path planning in an unknown environment","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Potential field; Mobile robot; Motion planning; Robot; Trajectory; Computer science; Path (computing); Field (mathematics); Artificial intelligence; Computer vision; Simulation; Mathematics; Physics","score_opus":0.02861287975422475,"score_gpt":0.249628712567303,"score_spread":0.22101583281307827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153340051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09965572,0.000005604199,0.89445364,0.00003578898,0.00021149241,0.0004550968,0.0000018064572,0.00026004983,0.0049208016],"genre_scores_gemma":[0.41489372,8.0576143e-7,0.58459455,0.00012959827,0.000095309995,0.000074882795,0.000011686404,0.000015732485,0.00018373347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977313,0.00014192777,0.00039516276,0.00086172094,0.00029458778,0.0005752604],"domain_scores_gemma":[0.99855715,0.00003024139,0.00008146939,0.0009822823,0.000019587835,0.0003292754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048719448,0.00024409861,0.000263589,0.00017963062,0.00010717058,0.00013816034,0.0011273929,0.00014552893,0.00004893243],"category_scores_gemma":[0.000010463657,0.0002298523,0.000047016303,0.00021866939,0.00002621882,0.0006233765,0.00025130325,0.00020736452,0.00011413741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023604138,0.0051388666,0.0033897632,0.00002945765,0.00005159041,0.0005061088,0.037653293,0.58934623,0.2113573,0.004162005,0.00075475726,0.14737456],"study_design_scores_gemma":[0.00021674708,0.0012193607,0.008265945,0.000011545898,0.000005537396,0.000016599644,0.00022637348,0.98503584,0.0042626974,0.00032774484,0.000020699494,0.0003908921],"about_ca_topic_score_codex":0.0005544764,"about_ca_topic_score_gemma":0.0000023565683,"teacher_disagreement_score":0.3956896,"about_ca_system_score_codex":0.000060266637,"about_ca_system_score_gemma":0.000045960263,"threshold_uncertainty_score":0.9373105},"labels":[],"label_agreement":null},{"id":"W2153353727","doi":"10.1109/robot.2003.1241759","title":"Genetic algorithm based path planning for a mobile robot","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Motion planning; Computer science; Robot; Genetic algorithm; Path (computing); Chromosome; Algorithm; Any-angle path planning; Mobile robot navigation; Obstacle; Artificial intelligence; Real-time computing; Robot control; Computer network; Machine learning; Geography","score_opus":0.020121131171397924,"score_gpt":0.2689770755427387,"score_spread":0.2488559443713408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153353727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059966836,0.00016791926,0.99722534,0.00021444571,0.00045325112,0.0004947379,0.0000061888363,0.00049287314,0.0003455948],"genre_scores_gemma":[0.017367486,8.415626e-7,0.98146373,0.00063989323,0.00012293106,0.00021944405,0.000006134002,0.00001961158,0.00015995109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983924,0.000024972262,0.00026196547,0.0005320986,0.00028477405,0.0005037923],"domain_scores_gemma":[0.99895316,0.00014023273,0.00007934631,0.0005851148,0.00008206787,0.00016007932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022600219,0.00019846945,0.00020687506,0.00012138686,0.00016364089,0.00014969894,0.0008108664,0.00008218632,0.0000070999586],"category_scores_gemma":[0.0000320949,0.00018069045,0.00009625052,0.00030196432,0.00003024019,0.00020679542,0.000100174395,0.00010686018,0.000056841152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025566792,0.00009851729,0.00014279426,0.000015286292,0.000013714702,0.00009590453,0.00040987265,0.9234325,0.00022932669,0.0007646816,0.00061894156,0.07417587],"study_design_scores_gemma":[0.0011834735,0.00035134918,0.0015155827,0.000052087875,0.0000071795534,0.000039787865,0.000030119252,0.9921388,0.0014317721,0.0020277712,0.00092669454,0.0002953563],"about_ca_topic_score_codex":0.000037800575,"about_ca_topic_score_gemma":1.7938639e-7,"teacher_disagreement_score":0.073880516,"about_ca_system_score_codex":0.00008053441,"about_ca_system_score_gemma":0.00020540868,"threshold_uncertainty_score":0.7368342},"labels":[],"label_agreement":null},{"id":"W2153583758","doi":"10.1109/icra.2014.6907707","title":"A language for robot path planning in discrete environments: The TSP with Boolean satisfiability constraints","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Travelling salesman problem; Boolean satisfiability problem; Motion planning; Vertex (graph theory); Computer science; Satisfiability; Graph; Shortest path problem; Theoretical computer science; Path (computing); Mathematical optimization; Robot; Mathematics; Algorithm; Artificial intelligence","score_opus":0.011044736517641943,"score_gpt":0.2488754303803556,"score_spread":0.23783069386271366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153583758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015840182,0.000021058546,0.9812049,0.00057026296,0.00007327404,0.0004052005,0.0000042835222,0.00007836933,0.0018024215],"genre_scores_gemma":[0.71740174,2.9277322e-7,0.28198564,0.00034429185,0.000029449386,0.000047619727,0.0000044076432,0.000008628745,0.00017793884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986038,0.00013470146,0.00021951733,0.00043064094,0.00024043955,0.00037089596],"domain_scores_gemma":[0.9986999,0.0004724323,0.000084747844,0.00066054834,0.000007900468,0.00007444176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088765315,0.00016176957,0.0001854351,0.00004214123,0.00010834663,0.000092796574,0.00064651377,0.000045191067,0.000009160654],"category_scores_gemma":[0.000116571595,0.00009671122,0.000035606252,0.0001262926,0.00017689707,0.00020300104,0.00012683895,0.00015524532,0.000012394309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014853058,0.00048521927,0.28943083,0.0001636511,0.00019661785,0.00022925928,0.049509738,0.25172085,0.003657522,0.09928164,0.0018011559,0.30337498],"study_design_scores_gemma":[0.001525333,0.000365611,0.14781374,0.000108077504,0.00001118039,0.00004141729,0.0010855331,0.84618485,0.00060711574,0.0012575496,0.00058326835,0.0004163499],"about_ca_topic_score_codex":0.00006856056,"about_ca_topic_score_gemma":0.0000075391777,"teacher_disagreement_score":0.7015615,"about_ca_system_score_codex":0.00004598356,"about_ca_system_score_gemma":0.000029666086,"threshold_uncertainty_score":0.3943769},"labels":[],"label_agreement":null},{"id":"W2153651416","doi":"10.1109/cdc.2002.1184821","title":"On trajectory optimization for hybrid systems: theory and algorithms for fixed schedules","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Trajectory; Computer science; Mathematical optimization; Algorithm; Trajectory optimization; Mathematics; Optimal control","score_opus":0.0255281394193111,"score_gpt":0.26552721690471115,"score_spread":0.23999907748540006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153651416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056816655,0.00027088905,0.9968344,0.00006160876,0.0007385128,0.0007928246,0.000014708574,0.00019568852,0.0005232309],"genre_scores_gemma":[0.015388412,0.000006393418,0.98355633,0.00018015664,0.000055219312,0.00020677275,0.000010410053,0.000018378543,0.0005779418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987891,0.00015286944,0.00021046857,0.00042589384,0.00014458947,0.00027710406],"domain_scores_gemma":[0.99812067,0.0012890089,0.00008197231,0.00032077715,0.00010128428,0.00008626554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012017697,0.00015594163,0.00019650291,0.000105194886,0.00017183101,0.00015753771,0.00025439422,0.0000545203,0.0000036763697],"category_scores_gemma":[0.0005344682,0.00013309965,0.00005074718,0.000094444105,0.000032589982,0.00022484313,0.000020694075,0.00005351399,0.000003333464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018470939,0.000046977442,0.000010217606,0.000064806,0.000027306854,0.0000021934532,0.00009580013,0.29211617,0.000025255322,0.7016198,0.0007631895,0.005209813],"study_design_scores_gemma":[0.00077730854,0.0002423897,0.00001542605,0.000040231145,0.000010064247,0.000020744088,0.000062120416,0.9891463,0.0010084277,0.008122407,0.0003624717,0.00019210967],"about_ca_topic_score_codex":0.0000021884152,"about_ca_topic_score_gemma":7.120079e-8,"teacher_disagreement_score":0.6970301,"about_ca_system_score_codex":0.00003428629,"about_ca_system_score_gemma":0.000060006856,"threshold_uncertainty_score":0.5427646},"labels":[],"label_agreement":null},{"id":"W2154429683","doi":"","title":"Towards a higher level of human-robot interaction and integration","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Mobile robot; Computer science; Human–computer interaction; Mobile robot navigation; Software; Graphical user interface; Scheduling (production processes); Human–robot interaction; System integration; Robot control; Artificial intelligence; Engineering; Operating system","score_opus":0.11478310839367917,"score_gpt":0.3280475748350847,"score_spread":0.2132644664414055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154429683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016086696,0.000020899839,0.96875805,0.0003257274,0.00027464883,0.00005288318,8.784115e-7,0.00007858501,0.0144016165],"genre_scores_gemma":[0.7441035,4.747006e-7,0.25393298,0.000033080116,0.000039898565,0.0000025858503,0.0000027870387,0.0000026025907,0.001882079],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99942327,0.00002410815,0.00016793328,0.00016551322,0.0001299066,0.000089286616],"domain_scores_gemma":[0.9996607,0.000025465926,0.00006961146,0.0001682052,0.00005600681,0.000020061592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114344344,0.000069104244,0.000092953436,0.000079464706,0.000036902613,0.000052060303,0.00015832756,0.000028538705,0.00001389869],"category_scores_gemma":[0.000007148778,0.000056802688,0.00001662714,0.0001248035,0.000023288998,0.00033521483,0.00006344027,0.00006131974,0.0000070611613],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013759351,0.00038671275,0.0027532608,0.000043978493,0.00003390016,0.000021722768,0.0011044889,0.0026679882,0.19397372,0.57524365,0.00993024,0.2138266],"study_design_scores_gemma":[0.0010468919,0.0007377453,0.5645051,0.00013995875,0.000017081826,0.00006218445,0.00009064069,0.2982675,0.10971554,0.024367627,0.00057149667,0.0004782233],"about_ca_topic_score_codex":0.0012073257,"about_ca_topic_score_gemma":0.000022329197,"teacher_disagreement_score":0.7280168,"about_ca_system_score_codex":0.00002182683,"about_ca_system_score_gemma":0.000012958343,"threshold_uncertainty_score":0.23163465},"labels":[],"label_agreement":null},{"id":"W2154566375","doi":"10.1109/icma.2005.1626533","title":"Continuous guidance for the docking of autonomous vehicles using active task-space sensing","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Vehicle dynamics; Artificial intelligence; Computer vision; Novelty; Motion planning; Trajectory; Position (finance); Control theory (sociology); Simulation; Robot; Engineering; Aerospace engineering","score_opus":0.02207604583948407,"score_gpt":0.2634717892290427,"score_spread":0.2413957433895586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154566375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036284667,0.00021204998,0.96141034,0.00049997296,0.00031983334,0.00025601775,0.000003617228,0.0001346068,0.00087888894],"genre_scores_gemma":[0.49292052,5.4799864e-7,0.5067546,0.000057731322,0.0000898405,0.0000018958176,5.035773e-7,0.000008486235,0.00016587718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887,0.00004838787,0.00026585147,0.0002977656,0.00019789461,0.0003200855],"domain_scores_gemma":[0.99848044,0.00065226026,0.00024007623,0.00043630312,0.00016357297,0.000027349699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036580313,0.00013513153,0.00020851337,0.00005716538,0.00022428391,0.00009427014,0.0004906682,0.000049680588,5.982985e-7],"category_scores_gemma":[0.00007561746,0.00010005511,0.00007647555,0.00023906438,0.0000766465,0.00023529181,0.00013817611,0.00008610471,0.0000018718841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029607649,0.00010035576,0.001579943,0.000059098424,0.0001235752,0.0000497743,0.0028187914,0.6941923,0.10889731,0.035361968,0.0017161535,0.15507112],"study_design_scores_gemma":[0.00023519491,0.000028923425,0.0022123987,0.000050162995,0.000013795174,0.000038462444,0.00010861931,0.95705485,0.038611315,0.0010592623,0.0004494345,0.00013760023],"about_ca_topic_score_codex":0.00089100964,"about_ca_topic_score_gemma":0.000009486749,"teacher_disagreement_score":0.45663586,"about_ca_system_score_codex":0.00007258278,"about_ca_system_score_gemma":0.00010100857,"threshold_uncertainty_score":0.40801293},"labels":[],"label_agreement":null},{"id":"W2154776451","doi":"10.1109/icsmc.1997.635367","title":"A reactive planner for multiple robots involved in competitive tasks","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Context (archaeology); Trajectory; Relation (database); Task (project management); Representation (politics); Plan (archaeology); Artificial intelligence; Motion planning; Mobile robot; Scheme (mathematics); Planner; Computer vision; Mathematics; Engineering; Data mining","score_opus":0.04837116904462606,"score_gpt":0.25521605134198133,"score_spread":0.20684488229735526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154776451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003861161,0.00006958626,0.9834959,0.0011343809,0.00037320677,0.0005475579,0.00001545203,0.00025039277,0.0102523975],"genre_scores_gemma":[0.43935627,0.0000022138554,0.558927,0.00050898996,0.000059121485,0.00010180782,0.000011762317,0.000012432542,0.001020434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987252,0.000055036413,0.00021835562,0.00044297625,0.00017829266,0.0003801512],"domain_scores_gemma":[0.9988222,0.000585199,0.00007046982,0.00035908757,0.000073043666,0.000089966656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001989005,0.00015342105,0.00021459023,0.00015043275,0.00006526112,0.000064713175,0.0005329056,0.00007856406,0.000016764445],"category_scores_gemma":[0.00017886671,0.00013865124,0.000052397154,0.00030121044,0.000035590478,0.00037842733,0.00011576643,0.00014707088,0.000093614086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029162894,0.0051299417,0.1449824,0.00022773203,0.00049484405,0.0015542321,0.07119327,0.16659364,0.007737829,0.36704466,0.11829291,0.1164569],"study_design_scores_gemma":[0.0011997024,0.00011292089,0.0068708607,0.000040389594,0.0000024096582,0.000009045606,0.00019603675,0.98857725,0.00086270436,0.0007024189,0.0011945418,0.00023169504],"about_ca_topic_score_codex":0.00014339067,"about_ca_topic_score_gemma":0.000075651624,"teacher_disagreement_score":0.82198364,"about_ca_system_score_codex":0.00007894829,"about_ca_system_score_gemma":0.000018197818,"threshold_uncertainty_score":0.5654034},"labels":[],"label_agreement":null},{"id":"W2154859766","doi":"10.1017/s0263574708004876","title":"Trajectory and temporal planning of a wheeled mobile robot on an uneven surface","year":2008,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Mobile robot; Trajectory; Kinematics; Motion planning; Computer science; Robot; Simulation; Control theory (sociology); Path (computing); Artificial intelligence; Physics; Control (management)","score_opus":0.034439564076404576,"score_gpt":0.27325330998150005,"score_spread":0.23881374590509546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154859766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55407655,0.0004002986,0.44353476,0.00017439458,0.00033030644,0.0003337689,0.0000043367486,0.0003161018,0.0008294777],"genre_scores_gemma":[0.71834534,0.0000057923453,0.28139794,0.000060964732,0.00003503188,0.0000051417405,0.0000035803444,0.000014201871,0.00013202694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983071,0.0001526891,0.0003102316,0.0004975701,0.00037237574,0.00036003182],"domain_scores_gemma":[0.998784,0.00020680444,0.00013096536,0.0006201736,0.000060798233,0.00019724538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031943482,0.00020608962,0.00035250207,0.00010625178,0.00016147738,0.00003511012,0.00056560617,0.00010514684,0.0000053555436],"category_scores_gemma":[0.000043735286,0.00019556675,0.000049572416,0.0002938092,0.00013373012,0.00032924,0.00011639935,0.00023446933,0.000016817861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026113488,0.00026368236,0.01811058,0.000031539617,0.00003399561,0.00027297717,0.0046429215,0.9711165,0.0029745586,0.00080902537,0.00035765092,0.0013604694],"study_design_scores_gemma":[0.0010908992,0.0017094547,0.09166939,0.00018947187,0.000018098439,0.00030555105,0.00015637936,0.9010821,0.0028100372,0.00024078936,0.00013779427,0.00059006293],"about_ca_topic_score_codex":0.000056625628,"about_ca_topic_score_gemma":9.726982e-7,"teacher_disagreement_score":0.16426878,"about_ca_system_score_codex":0.000031123796,"about_ca_system_score_gemma":0.000119063385,"threshold_uncertainty_score":0.7974981},"labels":[],"label_agreement":null},{"id":"W2155328045","doi":"10.1109/crv.2006.76","title":"The Nomad 200 and the Nomad SuperScout: Reverse engineered and resurrected","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Computer science; Art; Engineering","score_opus":0.0035906614491047024,"score_gpt":0.17557401522845908,"score_spread":0.1719833537793544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155328045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19828911,0.0076085194,0.6989004,0.039369304,0.0027376253,0.0015082184,0.000017662456,0.0012764311,0.05029276],"genre_scores_gemma":[0.7799006,0.00017783698,0.20356028,0.0008593738,0.00036153395,0.000037783677,0.000006069233,0.000029029205,0.0150674675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894285,0.00010698951,0.0001877033,0.00028523713,0.00019846547,0.00027873562],"domain_scores_gemma":[0.9987031,0.0006481248,0.000039469636,0.00050323905,0.000050228227,0.000055822242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497021,0.00013412868,0.00013783525,0.000037250953,0.00032640298,0.00028524382,0.00047151116,0.00004760599,0.0000027660365],"category_scores_gemma":[0.000113499904,0.00006950916,0.000027728925,0.00023623082,0.0002081519,0.00017627024,0.00017449138,0.00014248092,0.000015618498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000291376,0.00022062195,0.015308771,0.00007220899,0.0003070531,0.00039522516,0.010797206,0.0064190063,0.0021630034,0.49554542,0.36711487,0.10136524],"study_design_scores_gemma":[0.0029017918,0.000057365996,0.055545546,0.000031289,0.000023410399,0.0002336191,0.00021805073,0.92224246,0.0002356135,0.0029623318,0.015164579,0.0003839477],"about_ca_topic_score_codex":0.0005021365,"about_ca_topic_score_gemma":0.000020958898,"teacher_disagreement_score":0.91582346,"about_ca_system_score_codex":0.000014321907,"about_ca_system_score_gemma":0.000029745703,"threshold_uncertainty_score":0.2834501},"labels":[],"label_agreement":null},{"id":"W2155682648","doi":"10.1109/ific.2000.859866","title":"Sensor data fusion using Kalman filter","year":2000,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer vision; Odometry; Sensor fusion; Kalman filter; Extended Kalman filter; Computer science; Sonar; Artificial intelligence; Mobile robot; SIGNAL (programming language); Robot; Simultaneous localization and mapping","score_opus":0.0898081006643325,"score_gpt":0.3046306442318942,"score_spread":0.2148225435675617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155682648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0167872,0.000028868923,0.9693333,0.0005049014,0.00021959051,0.00007508567,0.0000071981594,0.0002916781,0.012752165],"genre_scores_gemma":[0.0066951313,0.0000041430153,0.9886128,0.00057672476,0.00011338432,4.809813e-7,0.000012923645,0.000007163309,0.0039772913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989252,0.000042551375,0.00014063095,0.00043095555,0.00021857367,0.00024210897],"domain_scores_gemma":[0.99863166,0.000040464434,0.000024584617,0.0012094139,0.00001810163,0.000075776356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002038756,0.00009468437,0.00009651561,0.00004324827,0.00010439134,0.000111220346,0.0013832441,0.00003975157,0.00047443004],"category_scores_gemma":[0.000013684505,0.000078997684,0.00001692919,0.00019945437,0.000018363568,0.00058061874,0.00043048867,0.00008051494,0.00059468765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010054161,0.00019282028,0.0013548235,0.000020472271,0.000042187785,0.000559213,0.0011919843,0.023872266,0.0071842317,0.001857185,0.052683424,0.91103137],"study_design_scores_gemma":[0.00011196712,0.0000135218215,0.000939921,0.000013694773,0.0000028658696,0.00009096578,0.0000052440787,0.9894509,0.00023697298,0.00011715126,0.008889887,0.00012693333],"about_ca_topic_score_codex":0.00008876275,"about_ca_topic_score_gemma":7.6156005e-7,"teacher_disagreement_score":0.9655786,"about_ca_system_score_codex":0.000015283138,"about_ca_system_score_gemma":0.000029455881,"threshold_uncertainty_score":0.7643705},"labels":[],"label_agreement":null},{"id":"W2155878205","doi":"10.1109/robot.1994.351147","title":"A simple method for the collision avoidance of telerobotic manipulators","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collision avoidance; Moore–Penrose pseudoinverse; Simple (philosophy); Obstacle avoidance; Gravitational singularity; Computer science; Control theory (sociology); Artificial intelligence; Collision; Robot; Mathematics; Mobile robot; Control (management)","score_opus":0.05188850541686858,"score_gpt":0.2967432493454329,"score_spread":0.24485474392856432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155878205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037752467,0.00018343731,0.99756706,0.0006681473,0.00024677636,0.00030638403,0.0000016230942,0.000090866604,0.00055820536],"genre_scores_gemma":[0.087559216,0.0000071801824,0.91124386,0.0002292956,0.000036482434,0.000024032757,3.9146562e-7,0.000007907581,0.0008916482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989849,0.000057571022,0.0002351133,0.00025754794,0.00023068943,0.0002341965],"domain_scores_gemma":[0.99807936,0.0010808788,0.00010450691,0.00061192806,0.00007623531,0.000047084595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049637654,0.00010248375,0.00016708784,0.00004948914,0.0001340282,0.000045379104,0.0008820528,0.000042499934,0.000018475304],"category_scores_gemma":[0.00014730812,0.000065270455,0.00007672924,0.00036082856,0.000027743903,0.0001502597,0.00012194963,0.000070958085,0.000022876693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009227887,0.0002227902,0.0012740132,0.000089566995,0.00010433472,0.0000128258525,0.0024598842,0.71613514,0.0015911068,0.09561426,0.041365284,0.14112155],"study_design_scores_gemma":[0.00020451976,0.00007116357,0.0013146581,0.000010830764,0.000008042382,0.000011829527,0.000029651232,0.9935542,0.0022071751,0.0013425063,0.0011571223,0.00008829892],"about_ca_topic_score_codex":0.000049404447,"about_ca_topic_score_gemma":0.0000017534553,"teacher_disagreement_score":0.27741906,"about_ca_system_score_codex":0.000019388475,"about_ca_system_score_gemma":0.000012062354,"threshold_uncertainty_score":0.2661652},"labels":[],"label_agreement":null},{"id":"W2156068714","doi":"10.1109/itsc.2006.1707447","title":"Motion planning for autonomous rendezvous with vehicle convoys","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rendezvous; Collision avoidance; Computer science; Obstacle avoidance; Obstacle; Motion planning; Matching (statistics); Collision; Trajectory; Vehicle dynamics; Simulation; Artificial intelligence; Real-time computing; Mobile robot; Engineering; Robot; Automotive engineering; Aerospace engineering; Mathematics","score_opus":0.01705287086522691,"score_gpt":0.2385731655979195,"score_spread":0.22152029473269258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156068714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006962738,0.000040068393,0.984833,0.00043648767,0.00018252998,0.00019695114,0.0000018868257,0.0005243672,0.006821949],"genre_scores_gemma":[0.4258826,9.6369796e-8,0.572109,0.00012934812,0.00009513026,0.000026773203,0.000008515193,0.000010495564,0.001738029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989795,0.000018724753,0.00016618476,0.00034649178,0.0001779101,0.00031117687],"domain_scores_gemma":[0.9993757,0.00009966906,0.00007531898,0.00032918694,0.000068475376,0.000051678406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018060402,0.0001210264,0.0001306168,0.000069636066,0.00013437128,0.00012927891,0.000362179,0.000050073173,0.0000030266349],"category_scores_gemma":[0.000011941748,0.00009830333,0.000030061208,0.00017549418,0.00002573009,0.00029696515,0.00004668998,0.00006978577,0.000023499982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006892537,0.00041147097,0.022764487,0.00008227143,0.00008518927,0.00034094363,0.0017494659,0.6736648,0.003249272,0.18627091,0.024317939,0.08699435],"study_design_scores_gemma":[0.00074291,0.00021568919,0.013236744,0.000021447273,0.0000065800723,0.00006384718,0.000022389077,0.97965175,0.0025316146,0.0020589398,0.0012223626,0.00022571275],"about_ca_topic_score_codex":0.00009814936,"about_ca_topic_score_gemma":0.0000023822788,"teacher_disagreement_score":0.4189199,"about_ca_system_score_codex":0.00004793555,"about_ca_system_score_gemma":0.000050500304,"threshold_uncertainty_score":0.40086937},"labels":[],"label_agreement":null},{"id":"W2156373593","doi":"10.1109/tsmcb.2006.877792","title":"Rendezvous-Guidance Trajectory Planning for Robotic Dynamic Obstacle Avoidance and Interception","year":2006,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendezvous; Interception; Obstacle avoidance; Obstacle; Collision avoidance; Computer science; Trajectory; Position (finance); Robot; Motion planning; Control theory (sociology); Path (computing); Simulation; Artificial intelligence; Collision; Mobile robot; Engineering; Control (management); Aerospace engineering; Geography; Physics","score_opus":0.02571331981470601,"score_gpt":0.25282068338734087,"score_spread":0.22710736357263486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156373593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010415529,0.003079467,0.981747,0.0070219315,0.0046807057,0.0013850427,0.00015042025,0.00046056794,0.00043336116],"genre_scores_gemma":[0.6946588,0.0013501382,0.11054857,0.056188267,0.0052307765,0.0026744984,0.0005593812,0.0012175791,0.12757196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946987,0.00036652375,0.0012006116,0.0018137199,0.0008064095,0.0011140108],"domain_scores_gemma":[0.9969575,0.0006675339,0.0006206569,0.0012581489,0.0002502669,0.0002459309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004995409,0.0010261157,0.0011139304,0.00051039003,0.0004699827,0.0007775232,0.0009404178,0.0010991106,0.0000057705715],"category_scores_gemma":[0.000014578086,0.0011103984,0.00025250623,0.00031834174,0.00031889742,0.00027502762,0.000021513555,0.0018437445,0.000050027513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006578273,0.00037660697,0.00008998306,0.003076308,0.00061316916,0.00056343403,0.004003361,0.5097413,0.0004592555,0.00031186786,0.46184087,0.018858057],"study_design_scores_gemma":[0.0019738956,0.0010426452,0.00076446164,0.0035310062,0.0004251423,0.00065215875,0.00018079497,0.8999226,0.0002117356,0.00042492914,0.08833585,0.0025347825],"about_ca_topic_score_codex":0.00016754141,"about_ca_topic_score_gemma":0.00003627045,"teacher_disagreement_score":0.87119836,"about_ca_system_score_codex":0.00030582285,"about_ca_system_score_gemma":0.000108136766,"threshold_uncertainty_score":0.9991346},"labels":[],"label_agreement":null},{"id":"W2157286417","doi":"10.1109/robot.2003.1242037","title":"Path planning using learned constraints and preferences","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Motion planning; Path (computing); Mathematical optimization; Artificial intelligence; Robot; Mathematics; Computer network","score_opus":0.07997498138627972,"score_gpt":0.3062817273573546,"score_spread":0.22630674597107486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157286417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13567884,0.00008528911,0.85999393,0.00015247782,0.00013684762,0.000056073583,5.095041e-7,0.00017094797,0.0037250721],"genre_scores_gemma":[0.5392483,0.0000016473653,0.46062455,0.00008276399,0.000015175207,7.4301124e-7,3.2433138e-7,0.0000024465119,0.0000240732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991762,0.000024428606,0.00012512328,0.00028858558,0.00016140565,0.00022425043],"domain_scores_gemma":[0.9996059,0.0000486459,0.000046726465,0.00018569453,0.000024092542,0.00008893112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017159393,0.00009801412,0.000111884576,0.000058625064,0.00011302251,0.00014929299,0.00027964925,0.000044437566,0.000006392047],"category_scores_gemma":[0.000035861565,0.00008268027,0.000014177195,0.00013544678,0.00009802506,0.00030553632,0.0001270724,0.00009918246,0.000011959445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001766238,0.0002803124,0.06280601,0.0001469063,0.00017347612,0.0013330559,0.024751222,0.26080802,0.011451446,0.22895269,0.00030860052,0.40897062],"study_design_scores_gemma":[0.0024291072,0.00037349723,0.03996313,0.0006308482,0.00002119968,0.0011370975,0.0010714446,0.885638,0.0038339696,0.06369098,0.00013503159,0.0010757182],"about_ca_topic_score_codex":0.000050237126,"about_ca_topic_score_gemma":2.1568627e-7,"teacher_disagreement_score":0.62482995,"about_ca_system_score_codex":0.00002144667,"about_ca_system_score_gemma":0.0000925671,"threshold_uncertainty_score":0.33716038},"labels":[],"label_agreement":null},{"id":"W2157638946","doi":"10.1109/robot.2007.363070","title":"View Planning Problem with Combined View and Traveling Cost","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE International Conference on Robotics and Automation/Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Workspace; Motion planning; Robot; Mathematical optimization; Rounding; Approximation algorithm; Computer science; Ackermann function; Greedy algorithm; Linear programming; Algorithm; Mathematics; Artificial intelligence","score_opus":0.04738791226301796,"score_gpt":0.2980496576572515,"score_spread":0.2506617453942335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157638946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2854403,0.00030213263,0.63035834,0.013297098,0.0012599899,0.0022569813,0.000017213475,0.0017635467,0.0653044],"genre_scores_gemma":[0.77252877,0.00010910496,0.2262845,0.00062431715,0.00015818393,0.000046891197,0.000007943765,0.000036282523,0.00020402708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996928,0.0000039884103,0.00069419143,0.00089268084,0.0008918934,0.0005892644],"domain_scores_gemma":[0.997728,0.00009745605,0.00053376134,0.00009374442,0.0012332614,0.0003137864],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010162954,0.0004704155,0.00044146797,0.0003835096,0.0003878282,0.0015644298,0.00079259486,0.00016247305,0.000008625304],"category_scores_gemma":[0.00007979996,0.00041857836,0.000045762758,0.0005109938,0.00015665717,0.0014842825,0.00017920032,0.00046012644,0.000013432782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001126842,0.00021532564,0.013702179,0.00048387295,0.0001888989,0.000034449728,0.00565032,0.0014107786,0.0043359613,0.9392325,0.0007301338,0.033902843],"study_design_scores_gemma":[0.0017985089,0.00071783905,0.02003538,0.0027071643,0.00005773792,0.00045755075,0.0010454506,0.95905125,0.0020617659,0.010376686,0.00064052583,0.0010501661],"about_ca_topic_score_codex":0.00000933416,"about_ca_topic_score_gemma":5.9961843e-7,"teacher_disagreement_score":0.95764047,"about_ca_system_score_codex":0.000113671966,"about_ca_system_score_gemma":0.00007747667,"threshold_uncertainty_score":0.9998266},"labels":[],"label_agreement":null},{"id":"W2158427612","doi":"10.1109/robot.2002.1014251","title":"Mission planning for the Sun-Synchronous Navigation Field Experiment","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Traverse; Tempest; Computer science; Motion planning; Real-time computing; Path (computing); Resource (disambiguation); Terrain; Aerospace engineering; Simulation; Engineering; Robot; Artificial intelligence; Geography; Geodesy","score_opus":0.03866599311047129,"score_gpt":0.3148148049987244,"score_spread":0.2761488118882531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158427612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013912033,0.0003713851,0.99304724,0.0011583838,0.0006272888,0.00023751464,2.1072508e-7,0.00011756338,0.003049198],"genre_scores_gemma":[0.32654214,0.000002341805,0.6713297,0.00094816496,0.00008352462,0.000049641556,0.0000024370204,0.00000885298,0.0010332478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992456,0.00003418747,0.0001357629,0.000219681,0.00015758585,0.00020721839],"domain_scores_gemma":[0.9988655,0.00065088825,0.00004691643,0.00035305312,0.000033394277,0.00005023797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003000642,0.00008783773,0.00007419072,0.000023123413,0.00023368286,0.00009650212,0.00037865737,0.00004402952,0.000014673114],"category_scores_gemma":[0.00011737011,0.000056799738,0.000038788767,0.000100641766,0.000010491888,0.00014388649,0.00004052402,0.00006989034,0.000014720014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049780545,0.00045472546,0.00298731,0.00010472483,0.0001786924,0.00011415438,0.028977683,0.18516825,0.018840052,0.29576367,0.16831693,0.299044],"study_design_scores_gemma":[0.0004971867,0.0002626313,0.00023365964,0.000071300085,0.000007042398,0.00005171718,0.00040803428,0.8543575,0.11736065,0.0017600273,0.024744432,0.00024585825],"about_ca_topic_score_codex":0.00001607464,"about_ca_topic_score_gemma":5.902869e-8,"teacher_disagreement_score":0.6691892,"about_ca_system_score_codex":0.000030357076,"about_ca_system_score_gemma":0.00004324307,"threshold_uncertainty_score":0.2316226},"labels":[],"label_agreement":null},{"id":"W2158626063","doi":"10.1109/ia.2009.4927504","title":"Finding an unknown goal in an environment by a group of agents","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Order (exchange); Human–computer interaction; Intelligent agent; Multi-agent system; Artificial intelligence; Business","score_opus":0.025049638277934303,"score_gpt":0.27089902518224634,"score_spread":0.24584938690431204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158626063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3790371,0.000032377608,0.6191704,0.00015279665,0.00008931082,0.000114742754,0.0000015890748,0.0000709187,0.0013307119],"genre_scores_gemma":[0.74970824,0.000002550992,0.24986675,0.00023046536,0.00001352436,0.000001999327,0.000007022648,0.000003340238,0.00016611592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896526,0.00006437897,0.00019804845,0.000307846,0.00023415069,0.00023030773],"domain_scores_gemma":[0.9994213,0.00001758133,0.00005477164,0.00040234378,0.000004261715,0.000099724144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025007236,0.0000943686,0.00012543342,0.00007914852,0.0000284722,0.00003645079,0.00059991964,0.000044564284,0.000018363638],"category_scores_gemma":[0.0000053589824,0.00008839303,0.000018507804,0.00013525532,0.000016888165,0.00047777809,0.00006241706,0.00007714982,0.000022080376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033003373,0.005667278,0.06910074,0.000025693264,0.000032991422,0.00039606955,0.0126533145,0.07725833,0.07515932,0.02658424,0.006070864,0.7270182],"study_design_scores_gemma":[0.0005290525,0.00080117106,0.15798783,0.000021170188,0.0000022089303,0.00000810538,0.000040508214,0.83729124,0.0019169989,0.00074698415,0.00042544687,0.00022930402],"about_ca_topic_score_codex":0.000035856385,"about_ca_topic_score_gemma":0.0000019554868,"teacher_disagreement_score":0.7600329,"about_ca_system_score_codex":0.000046294066,"about_ca_system_score_gemma":0.000009015258,"threshold_uncertainty_score":0.36045632},"labels":[],"label_agreement":null},{"id":"W2158680534","doi":"10.1177/0278364904036094","title":"Planning Velocities of Free Sliding Objects as a Free Boundary Value Problem","year":2004,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Impulse (physics); Boundary value problem; Boundary (topology); Free boundary problem; Mathematical optimization; Motion (physics); Computer science; Initial value problem; Control theory (sociology); Mathematics; Robot; Artificial intelligence; Mathematical analysis; Control (management); Classical mechanics; Physics","score_opus":0.07678122860902109,"score_gpt":0.375537080379475,"score_spread":0.2987558517704539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158680534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042710226,0.0016380935,0.91596496,0.02858946,0.0025021897,0.00031458784,0.000012059814,0.00005807005,0.0082103275],"genre_scores_gemma":[0.5327672,0.000071561204,0.4661814,0.00016637653,0.0005379521,0.0000034085901,0.0000012050662,0.000021355085,0.00024958144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948691,0.000253441,0.0006745795,0.00020281004,0.0035640425,0.00043601842],"domain_scores_gemma":[0.9957301,0.0010667549,0.000450818,0.0006688672,0.0019497311,0.00013373024],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0043350817,0.00014640715,0.00026365154,0.0006677947,0.00025674718,0.00045290028,0.0077625634,0.0000759389,0.000005718663],"category_scores_gemma":[0.00198397,0.00010846567,0.00013480496,0.0005122177,0.00025263752,0.0005911877,0.0017031269,0.0010290837,0.000015859508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007769102,0.0001288122,0.00027094342,0.000025708823,0.00029383617,0.0007781529,0.009919685,0.8394528,0.0026826775,0.14298154,0.00207842,0.0013096991],"study_design_scores_gemma":[0.0035473749,0.001262436,0.0016507192,0.0023172456,0.00002955773,0.003627011,0.0026750434,0.078756586,0.021768965,0.8835248,0.00046649724,0.00037376428],"about_ca_topic_score_codex":0.00017070121,"about_ca_topic_score_gemma":0.000003116363,"teacher_disagreement_score":0.76069623,"about_ca_system_score_codex":0.0004102877,"about_ca_system_score_gemma":0.0014979831,"threshold_uncertainty_score":0.9976059},"labels":[],"label_agreement":null},{"id":"W2158912029","doi":"10.1109/med.2013.6608928","title":"Linear Model Predictive Control for the encirclement of a target using a quadrotor aircraft","year":2013,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Royal Military College of Canada","funders":"","keywords":"MATLAB; Controller (irrigation); Computer science; Control theory (sociology); Path (computing); Task (project management); Trajectory; Control (management); Control engineering; Simulation; Engineering; Artificial intelligence; Physics","score_opus":0.030392144558010887,"score_gpt":0.2714574936132986,"score_spread":0.24106534905528768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158912029","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012874843,0.00004101139,0.996105,0.00070680777,0.00014743947,0.0015083587,0.000017752489,0.0000790896,0.00010705902],"genre_scores_gemma":[0.32688704,5.02595e-7,0.67249,0.0002684131,0.000044899443,0.00021119408,5.9384354e-7,0.0000063269586,0.00009101447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889994,0.000036000543,0.00027298531,0.00024917122,0.00026800449,0.00027389987],"domain_scores_gemma":[0.99882823,0.00032232917,0.000120880155,0.00044854323,0.00021443026,0.00006557358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037749732,0.000111450456,0.00017944921,0.000045494136,0.00010342049,0.000039848375,0.00067585765,0.000041522064,0.000011763208],"category_scores_gemma":[0.000077895995,0.00007353596,0.000081903454,0.00011692786,0.00004646093,0.00028292384,0.000099872865,0.00007071262,0.000009824413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006924815,0.000052304855,0.0002183252,0.000011479346,0.00005959919,4.5902834e-7,0.0008484157,0.99379104,0.0017134398,0.001753615,0.0006717179,0.000872687],"study_design_scores_gemma":[0.00060248247,0.00012680124,0.00029672188,0.000014348151,0.000014535713,0.0000025566126,0.00007258906,0.9956199,0.0009888553,0.0021509712,0.000021367188,0.00008887003],"about_ca_topic_score_codex":0.00016685226,"about_ca_topic_score_gemma":4.150942e-7,"teacher_disagreement_score":0.32559955,"about_ca_system_score_codex":0.000039683655,"about_ca_system_score_gemma":0.00013521044,"threshold_uncertainty_score":0.29987094},"labels":[],"label_agreement":null},{"id":"W2159555600","doi":"10.1109/robot.2002.1014284","title":"Waiting with Jose, a vision-based mobile robot","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mobile robot; Computer science; Computer vision; Artificial intelligence; Machine vision; Robot vision; Computer graphics (images); Robot; Human–computer interaction","score_opus":0.00912963952609315,"score_gpt":0.23865815948331282,"score_spread":0.22952851995721968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159555600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041995356,0.000036953177,0.97784084,0.00015969266,0.0001381857,0.00015104834,3.104269e-7,0.00035767048,0.017115789],"genre_scores_gemma":[0.38587782,2.1792609e-7,0.6132303,0.00033475348,0.00001246597,0.000020312984,6.768938e-7,0.000008044437,0.0005154261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874115,0.00007260794,0.00015677944,0.00038202523,0.0003126169,0.00033483564],"domain_scores_gemma":[0.9990378,0.000179864,0.000060025006,0.0005343972,0.00007027075,0.000117616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003089527,0.0001369776,0.00013857485,0.00008177265,0.00012690944,0.00015050902,0.0004504438,0.000040267154,0.00003991943],"category_scores_gemma":[0.000049585073,0.000100057245,0.000031840595,0.00044633404,0.000032094827,0.00023972064,0.00003948103,0.000113732756,0.0001194574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008812087,0.00034620124,0.004519056,0.000031574305,0.000037003836,0.00038784952,0.0006847526,0.9071602,0.0017778458,0.040898144,0.0028292004,0.041319326],"study_design_scores_gemma":[0.00047733742,0.000280457,0.00040381445,0.000044699573,0.0000033092563,0.00004716057,0.000040962936,0.9919251,0.0052035036,0.00017351724,0.0011677208,0.0002323988],"about_ca_topic_score_codex":0.000019829407,"about_ca_topic_score_gemma":0.0000012857198,"teacher_disagreement_score":0.38167828,"about_ca_system_score_codex":0.000028965533,"about_ca_system_score_gemma":0.00014203001,"threshold_uncertainty_score":0.4080216},"labels":[],"label_agreement":null},{"id":"W2159683855","doi":"10.1109/robot.2005.1570827","title":"Learning to Steer on Winding Tracks Using Semi-Parametric Control Policies","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voronoi diagram; Parametric statistics; Nonholonomic system; Representation (politics); Computer science; Control (management); Set (abstract data type); Truck; Node (physics); Reinforcement learning; State space; Vehicle dynamics; Control theory (sociology); State (computer science); Space (punctuation); Artificial intelligence; Mobile robot; Engineering; Robot; Mathematics; Algorithm","score_opus":0.02052727492655067,"score_gpt":0.2697594599214931,"score_spread":0.2492321849949424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159683855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21443464,0.000019412708,0.77709216,0.00038770633,0.00027112837,0.00015187605,7.662695e-7,0.00032797258,0.007314338],"genre_scores_gemma":[0.8361421,2.0092953e-7,0.16136205,0.00055971846,0.00016101834,0.0000037556506,6.952173e-7,0.000013549206,0.0017568553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982704,0.00009312157,0.00026369543,0.00041170386,0.0004306068,0.00053047],"domain_scores_gemma":[0.9990348,0.0003599534,0.000088192995,0.00033932412,0.000059261627,0.000118422795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040014856,0.00018500036,0.00023294234,0.00056941895,0.00023231772,0.00030486265,0.0005411783,0.000073826566,0.00000833709],"category_scores_gemma":[0.00015557569,0.00016713225,0.00006200201,0.0012689474,0.000019610075,0.0002645206,0.00009008121,0.00024256286,0.00015269606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003260729,0.00003704434,0.0073856493,0.000003053542,0.000009553992,0.000026966118,0.00040100436,0.9832839,0.0015961792,0.0029715667,0.0004944992,0.0037873257],"study_design_scores_gemma":[0.0004443619,0.00019840254,0.017728364,0.000044192566,0.000008120849,0.00003787419,0.00008660287,0.9788367,0.0015456993,0.00016478461,0.00060088746,0.00030400112],"about_ca_topic_score_codex":0.0004807785,"about_ca_topic_score_gemma":0.0000015687151,"teacher_disagreement_score":0.6217075,"about_ca_system_score_codex":0.00013007979,"about_ca_system_score_gemma":0.00004097799,"threshold_uncertainty_score":0.6815455},"labels":[],"label_agreement":null},{"id":"W2160231487","doi":"10.1109/iros.1994.407553","title":"Map updating and path planning for real-time mobile robot navigation","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mobile robot; Computer science; Grid reference; Motion planning; Mobile robot navigation; Histogram; Computer vision; Artificial intelligence; Representation (politics); Grid; Robot; Plan (archaeology); Path (computing); Real-time computing; Robot control; Image (mathematics); Geography","score_opus":0.023769901353081237,"score_gpt":0.26457279038858017,"score_spread":0.24080288903549893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160231487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012476267,0.00011910185,0.98181874,0.00023446955,0.00021256741,0.0003503063,0.0000033179458,0.000431039,0.00435419],"genre_scores_gemma":[0.061601736,0.000004226854,0.936589,0.00009014083,0.000092313625,0.000059297636,0.000014466293,0.000012382231,0.0015364207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900043,0.000028611774,0.00019777726,0.00034885682,0.00015595257,0.00026837285],"domain_scores_gemma":[0.9993251,0.00019572888,0.00008596757,0.0002666822,0.000045031575,0.00008147265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027277903,0.00011678543,0.0001376536,0.000050227078,0.00017265411,0.0001529778,0.00026786228,0.000054112585,0.00002165033],"category_scores_gemma":[0.000022859878,0.00010874715,0.00002626538,0.00010556392,0.000019628475,0.00039387855,0.000101528036,0.00006913721,0.000089043904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023033586,0.00047257094,0.008592354,0.0005323175,0.0001731483,0.0003140108,0.03273328,0.21388313,0.042162232,0.03925649,0.12033947,0.541518],"study_design_scores_gemma":[0.00024587667,0.000115240204,0.00045228476,0.00006163788,0.0000038070223,0.000028182323,0.00009113919,0.99760354,0.00043647105,0.0004235078,0.00037942932,0.00015889584],"about_ca_topic_score_codex":0.000015798201,"about_ca_topic_score_gemma":3.090373e-8,"teacher_disagreement_score":0.7837204,"about_ca_system_score_codex":0.000022506978,"about_ca_system_score_gemma":0.000007762502,"threshold_uncertainty_score":0.443458},"labels":[],"label_agreement":null},{"id":"W2160393454","doi":"10.1109/robot.2005.1570696","title":"Rendezvous Guidance for the Autonomous Interception of Moving Objects in Cluttered Environments","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interception; Rendezvous; Computer science; Computer vision; Trajectory; Artificial intelligence; Collision avoidance; Object (grammar); Collision; Decomposition; Simulation; Real-time computing; Engineering; Aerospace engineering; Spacecraft","score_opus":0.014820626007731577,"score_gpt":0.23797810322196639,"score_spread":0.22315747721423482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160393454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624844,0.00007906472,0.98435485,0.00026550287,0.00021725305,0.00022938078,0.0000015307911,0.000043415206,0.0011841579],"genre_scores_gemma":[0.7677638,0.0000018224744,0.2312486,0.000083245824,0.000033731652,0.000026941789,0.0000015647184,0.0000051608217,0.000835138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909294,0.00003567545,0.00028258492,0.0002391131,0.00014461607,0.00020504849],"domain_scores_gemma":[0.99930066,0.0001681938,0.0001135841,0.00039249397,0.000009292354,0.000015748697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032495783,0.00008855081,0.00011533809,0.000063607054,0.000044644763,0.00003112133,0.00056839257,0.00003916879,0.0000038371736],"category_scores_gemma":[0.000030268075,0.00006648842,0.00004074694,0.00012324017,0.00003350051,0.0001676544,0.00012731455,0.00006161625,0.000012741107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005594672,0.0007195175,0.028537996,0.00011527183,0.00006765994,0.000049954408,0.00846182,0.59478986,0.077601485,0.014850976,0.0067065414,0.26804298],"study_design_scores_gemma":[0.0004117721,0.000066088025,0.10878542,0.000037208254,0.0000032299167,0.000007632056,0.00005308068,0.8844897,0.004632462,0.0011807485,0.0002202484,0.00011239773],"about_ca_topic_score_codex":0.00028144073,"about_ca_topic_score_gemma":0.000030484342,"teacher_disagreement_score":0.75413895,"about_ca_system_score_codex":0.00008182449,"about_ca_system_score_gemma":0.000019746822,"threshold_uncertainty_score":0.2711319},"labels":[],"label_agreement":null},{"id":"W2160402878","doi":"10.1109/ical.2008.4636261","title":"A fuzzy inference controller with Accelerate/Brake Module for mobile robots","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mobile robot; Controller (irrigation); Obstacle avoidance; Computer science; Fuzzy control system; Robot; Motion planning; Control engineering; Brake; Fuzzy inference; Fuzzy logic; Path (computing); Obstacle; Inference; Artificial intelligence; Adaptive neuro fuzzy inference system; Control theory (sociology); Engineering; Control (management); Automotive engineering","score_opus":0.037451102278450825,"score_gpt":0.26934609856775926,"score_spread":0.23189499628930843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160402878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010765924,0.00005903041,0.98245716,0.00024064924,0.0001693761,0.00069516286,0.000003613949,0.0003918178,0.0052172393],"genre_scores_gemma":[0.48644984,0.0000059618533,0.5104339,0.00042312304,0.000057989677,0.00028953227,0.000002936761,0.000013116611,0.0023235586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984622,0.00003207326,0.00023176045,0.00051477284,0.00029582073,0.00046335833],"domain_scores_gemma":[0.9988038,0.0002384963,0.00008953529,0.00051007944,0.00020771846,0.00015039828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015966644,0.00020900674,0.00029937434,0.00008954365,0.00024043108,0.00011637068,0.0007806691,0.00007267194,0.000010523444],"category_scores_gemma":[0.00004539705,0.00015133325,0.000058820948,0.00032694382,0.00007007642,0.0005429538,0.000116043935,0.00011650989,0.00008270119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022684941,0.0008508189,0.016373271,0.00007273701,0.00027758884,0.00035011175,0.0035270392,0.85446864,0.0017613946,0.05376427,0.02366451,0.044662748],"study_design_scores_gemma":[0.003097277,0.00088779203,0.0083724735,0.00003389055,0.000009730529,0.00016670761,0.000024728088,0.9823599,0.0015557957,0.0018189,0.0011465693,0.0005262507],"about_ca_topic_score_codex":0.00003767848,"about_ca_topic_score_gemma":0.0000032520832,"teacher_disagreement_score":0.47568393,"about_ca_system_score_codex":0.000030809064,"about_ca_system_score_gemma":0.00015973569,"threshold_uncertainty_score":0.6171191},"labels":[],"label_agreement":null},{"id":"W2160609653","doi":"10.5772/54933","title":"Mobile Robot Collision Avoidance in Human Environments","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Hatch (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Collision avoidance; Robot; Mobile robot; Holonomic; Obstacle avoidance; Piecewise; Nonholonomic system; Artificial intelligence; Motion (physics); Motion planning; Collision; Simulation; Computer vision; Mathematics","score_opus":0.010758678589432183,"score_gpt":0.26738506666206463,"score_spread":0.25662638807263244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160609653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05893651,0.00094250345,0.9336419,0.00020465459,0.0055333185,0.00039215872,0.0000011554229,0.00002750053,0.0003202899],"genre_scores_gemma":[0.89231163,0.000059206377,0.10656548,0.000060693586,0.00025844903,0.000037766706,0.0000020356422,0.000015278172,0.0006894848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973508,0.0001321835,0.00096840935,0.00027212294,0.0009971047,0.0002793582],"domain_scores_gemma":[0.99839365,0.00014310282,0.00076719903,0.00032011353,0.00023970382,0.00013622087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045355875,0.00018253153,0.00035983053,0.00037538735,0.000054809487,0.00021923812,0.0017710489,0.00007273811,0.0000099340805],"category_scores_gemma":[0.00009016876,0.00016222883,0.00009264772,0.00020496345,0.00004064865,0.001409635,0.0001777397,0.00028494003,0.00008220846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049369096,0.000118631266,0.0016171209,0.0000060395055,0.00003802113,0.00017360075,0.0003707049,0.97949976,0.01388293,0.00054216443,0.00018309266,0.003563025],"study_design_scores_gemma":[0.005823349,0.0012720838,0.051831897,0.002472774,0.000019128505,0.002465101,0.0010121331,0.9229592,0.0050463323,0.0028396444,0.0033028726,0.0009554623],"about_ca_topic_score_codex":0.000059164315,"about_ca_topic_score_gemma":6.250561e-7,"teacher_disagreement_score":0.8333751,"about_ca_system_score_codex":0.0004050073,"about_ca_system_score_gemma":0.000055061137,"threshold_uncertainty_score":0.6615499},"labels":[],"label_agreement":null},{"id":"W2160697303","doi":"10.1109/acc.2003.1243399","title":"Planning for dynamic multi-agent planar manipulation with uncertainty: a game theoretic approach","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Minimax; Computer science; Mathematical optimization; Trajectory; Controller (irrigation); Object (grammar); Multi-agent system; Linearization; Potential game; Game theory; Control theory (sociology); Nash equilibrium; Control (management); Mathematics; Artificial intelligence; Nonlinear system","score_opus":0.037535213135520384,"score_gpt":0.2791054047012774,"score_spread":0.24157019156575704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160697303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005061542,0.00004832655,0.9929841,0.0002724004,0.00012776424,0.00056219735,0.00000322614,0.00036336583,0.00057707453],"genre_scores_gemma":[0.40950516,7.1681717e-7,0.59012955,0.00012414304,0.000016903236,0.000054817043,0.000025818557,0.0000131904735,0.00012969227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985511,0.000030605195,0.0002138411,0.0005227452,0.0002760868,0.00040565676],"domain_scores_gemma":[0.9991991,0.0000816098,0.00010121406,0.0004557769,0.000059285998,0.00010303077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021011729,0.0002062627,0.00019430528,0.00012514241,0.00012768221,0.00012960064,0.0005523364,0.000071450755,0.0000013001134],"category_scores_gemma":[0.000024730707,0.0001544471,0.000048975788,0.00024660802,0.000052970303,0.00024019179,0.00004977501,0.00011754504,0.000015037012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019657873,0.00008528691,0.00025754067,0.000029952953,0.000026923288,0.000025538113,0.0019717277,0.94409114,0.000059322938,0.0520902,0.00001626984,0.0013264347],"study_design_scores_gemma":[0.0015078646,0.00020360449,0.0026564456,0.0000650229,0.00001340058,0.000093393726,0.00019004496,0.9904206,0.000046719357,0.0045149075,0.000029813922,0.00025819748],"about_ca_topic_score_codex":0.00004566651,"about_ca_topic_score_gemma":0.0000029591683,"teacher_disagreement_score":0.40444362,"about_ca_system_score_codex":0.00016321192,"about_ca_system_score_gemma":0.00008867674,"threshold_uncertainty_score":0.629817},"labels":[],"label_agreement":null},{"id":"W2161024278","doi":"10.1109/robot.1996.503818","title":"Octree-based hierarchical distance maps for collision detection","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Octree; Computer science; Collision detection; Artificial intelligence; Representation (politics); Computer vision; Collision; Voxel; Graphics; Computer graphics (images); Distance transform; Data structure; Path (computing); Pattern recognition (psychology); Image (mathematics)","score_opus":0.026379284042980913,"score_gpt":0.23914631773064574,"score_spread":0.2127670336876648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161024278","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006517944,0.00003990877,0.9956293,0.0012042782,0.00043379352,0.0002157762,0.0000047106582,0.0003083665,0.001512067],"genre_scores_gemma":[0.2975591,0.0000014560678,0.7004919,0.00037850442,0.00008178231,0.00004696991,0.000003269516,0.000008577526,0.0014284323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989683,0.000037612695,0.00016052004,0.0003470616,0.00022511423,0.00026136314],"domain_scores_gemma":[0.9991506,0.00026408647,0.000045194316,0.00039623858,0.00005172604,0.00009217155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017634933,0.00010316317,0.00011016416,0.00007498589,0.0001566631,0.000096764954,0.00042789316,0.000060449805,0.000012317346],"category_scores_gemma":[0.00008883088,0.00008972259,0.000058702062,0.00029885973,0.000029201488,0.00017896305,0.00003957471,0.00009117224,0.000065121836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009271273,0.00070877204,0.0010918453,0.00010369198,0.000038391605,0.00009194351,0.0007313413,0.055833817,0.0046527265,0.05702628,0.047878984,0.8317495],"study_design_scores_gemma":[0.00046401971,0.00014987821,0.0003579943,0.000012932655,0.0000021893661,0.000006378438,0.000002155896,0.98451996,0.0053550904,0.001319098,0.0076807896,0.0001294978],"about_ca_topic_score_codex":0.000008028125,"about_ca_topic_score_gemma":0.0000036061394,"teacher_disagreement_score":0.92868614,"about_ca_system_score_codex":0.000052805808,"about_ca_system_score_gemma":0.000013499756,"threshold_uncertainty_score":0.3658781},"labels":[],"label_agreement":null},{"id":"W2161163752","doi":"10.1109/isic.1995.525095","title":"Variable learning rate neuromorphic guidance controller for automated transit vehicles","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Controller (irrigation); Computer science; Track (disk drive); Control theory (sociology); Artificial neural network; Feed forward; Variable (mathematics); Vehicle dynamics; Backpropagation; Neuromorphic engineering; Control engineering; Simulation; Artificial intelligence; Engineering; Control (management); Automotive engineering; Mathematics","score_opus":0.03733696853820441,"score_gpt":0.2336965654463048,"score_spread":0.1963595969081004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161163752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059459155,0.00017246488,0.9923816,0.0014217821,0.00029656364,0.0002911749,0.0000023261314,0.002135616,0.0027039272],"genre_scores_gemma":[0.21900335,0.0000057144243,0.7712055,0.0011355848,0.00007974166,0.00006299791,0.0000026980222,0.00002627011,0.008478138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984749,0.00015066513,0.0002876363,0.0004525037,0.0001784756,0.00045583406],"domain_scores_gemma":[0.99895644,0.0004296762,0.00009313814,0.00030043052,0.0001162218,0.00010411784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005435343,0.00017389188,0.00026052244,0.000076309305,0.00024018763,0.0001935888,0.0006279061,0.00007486685,0.00004230718],"category_scores_gemma":[0.000190408,0.000157367,0.00006485372,0.00036913887,0.00003042582,0.00035179706,0.000047957874,0.0001609694,0.00015319802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003060845,0.00021575794,0.0005043538,0.00007029832,0.00014069215,0.000107769934,0.0011060992,0.865161,0.033197004,0.03580711,0.044645317,0.019013958],"study_design_scores_gemma":[0.0011968982,0.00013257228,0.00065695646,0.00002218346,0.000010550665,0.000025402203,0.0000040163313,0.9920474,0.00045310284,0.0004559666,0.004794954,0.0001999976],"about_ca_topic_score_codex":0.000016126101,"about_ca_topic_score_gemma":1.9776179e-7,"teacher_disagreement_score":0.22117604,"about_ca_system_score_codex":0.000020074272,"about_ca_system_score_gemma":0.000020381558,"threshold_uncertainty_score":0.641724},"labels":[],"label_agreement":null},{"id":"W2161298439","doi":"10.1177/0278364914547786","title":"Sampling-based A* algorithm for robot path-planning","year":2014,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Probabilistic roadmap; Computer science; Mathematical optimization; Probabilistic logic; Algorithm; Simulated annealing; Graph; Entropy (arrow of time); Cross entropy; Heuristic; Adaptive simulated annealing; Robot; Artificial intelligence; Mathematics; Theoretical computer science; Principle of maximum entropy","score_opus":0.14159887998377177,"score_gpt":0.4227342927869594,"score_spread":0.28113541280318766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161298439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013201326,0.00019806267,0.98710126,0.010331149,0.0018376847,0.0001539675,0.0000040192767,0.000028042768,0.00021383219],"genre_scores_gemma":[0.031741247,0.000012547308,0.966417,0.0004220013,0.0012068168,0.000008470171,0.0000039056417,0.000020917563,0.00016708358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961327,0.0003271054,0.00055933587,0.00023741365,0.0022639174,0.0004795781],"domain_scores_gemma":[0.9927089,0.0035058195,0.0003446067,0.00044673943,0.0028324556,0.00016146744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00753239,0.00014380467,0.00023599024,0.00048663723,0.00027782586,0.0005422656,0.004652269,0.00007556584,0.000004648274],"category_scores_gemma":[0.0013166247,0.0001014788,0.00016923828,0.0003444924,0.00013694342,0.00028811867,0.00035710176,0.000731776,0.000021191707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003503277,0.00010544908,0.00013867971,0.000007769549,0.00011306903,0.00005331271,0.00034435152,0.7734774,0.00052827253,0.008637451,0.003749896,0.21280931],"study_design_scores_gemma":[0.00076342485,0.00034163563,0.00034247848,0.00015177291,0.000007766757,0.00015661781,0.000044161414,0.98258406,0.0007179552,0.010026797,0.0047485777,0.00011476385],"about_ca_topic_score_codex":0.000011049698,"about_ca_topic_score_gemma":3.0259622e-7,"teacher_disagreement_score":0.21269456,"about_ca_system_score_codex":0.00018923546,"about_ca_system_score_gemma":0.0004022403,"threshold_uncertainty_score":0.8645151},"labels":[],"label_agreement":null},{"id":"W2161754147","doi":"","title":"Incremental Phi*: incremental any-angle path planning on grids","year":2009,"lang":"en","type":"article","venue":"Scholarly Commons (University of Pennsylvania)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Laboratory; Army Research Office; University of Alberta; Northrop Grumman; National Science Foundation","keywords":"Grid; Motion planning; Any-angle path planning; Vertex (graph theory); Path (computing); Computer science; Tree (set theory); Algorithm; Series (stratigraphy); Reuse; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Combinatorics; Geometry; Engineering","score_opus":0.02923367094946445,"score_gpt":0.2392544537485168,"score_spread":0.21002078279905237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161754147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84719086,0.00015847714,0.13178481,0.0018715953,0.0004620117,0.00037367616,0.00005521997,0.00047748958,0.017625833],"genre_scores_gemma":[0.89634925,0.0000052218115,0.10302342,0.0003286136,0.00005202331,2.495295e-7,0.000024258876,0.000011132622,0.00020582914],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99758464,0.00021777867,0.0002360409,0.0006095691,0.00082142564,0.00053055736],"domain_scores_gemma":[0.9983062,0.000116732845,0.0002505984,0.0009191517,0.00013413797,0.00027316975],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006984674,0.00028238542,0.00037996145,0.00044410958,0.0006858703,0.00028090045,0.0023675067,0.00015097813,0.000050315564],"category_scores_gemma":[0.000059830778,0.0003586959,0.00015831183,0.00072200684,0.000111078945,0.0031407515,0.00057231047,0.00070048694,0.00016638404],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001373041,0.008413371,0.20745853,0.00019792197,0.0011317105,0.010130799,0.06397512,0.008424161,0.22047998,0.13228735,0.115340024,0.23078799],"study_design_scores_gemma":[0.007961548,0.005781821,0.90340936,0.00082720595,0.00015106563,0.0002471049,0.0072964146,0.057443257,0.00386435,0.004884644,0.005854329,0.002278921],"about_ca_topic_score_codex":0.00017994814,"about_ca_topic_score_gemma":0.000011789064,"teacher_disagreement_score":0.6959508,"about_ca_system_score_codex":0.00023639739,"about_ca_system_score_gemma":0.000107811036,"threshold_uncertainty_score":0.9998865},"labels":[],"label_agreement":null},{"id":"W2162178110","doi":"10.1109/robot.2006.1641770","title":"Heuristic search for coordinating robot agents in adversarial domains","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Scalability; Heuristic; Pruning; Set (abstract data type); Robot; Task (project management); Artificial intelligence; Domain (mathematical analysis); Adversarial system; Function (biology); Mathematical optimization; Machine learning; Engineering; Mathematics","score_opus":0.037146428822137735,"score_gpt":0.30282371121103124,"score_spread":0.2656772823888935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162178110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067075505,0.000011799917,0.9862605,0.00038595623,0.00047367022,0.00028325224,0.0000026562714,0.0001350255,0.005739619],"genre_scores_gemma":[0.47556612,2.1911102e-7,0.5232879,0.00007503886,0.00013659426,0.000019227356,0.000006432125,0.00000787883,0.0009006306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986243,0.00005971144,0.00025283365,0.00036260928,0.00026264958,0.0004378746],"domain_scores_gemma":[0.99925816,0.0003049558,0.000043512297,0.0002842623,0.00005497495,0.00005412226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005301441,0.00011271745,0.00015404286,0.00014643537,0.000104959414,0.000105199084,0.0005915182,0.000055278746,0.0000067866354],"category_scores_gemma":[0.00007212328,0.00010938211,0.000045329347,0.000360714,0.000025078192,0.00021091354,0.00016183163,0.00010990449,0.000033161425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000376799,0.0005541237,0.04447988,0.00009941511,0.000033403223,0.0003955459,0.0010785317,0.72957045,0.0012286138,0.17455643,0.01651504,0.031450864],"study_design_scores_gemma":[0.001192313,0.00006333852,0.02412071,0.000021585183,0.000002350926,0.000009710315,0.000018908228,0.9715654,0.0004781323,0.0021453898,0.00021699826,0.00016513225],"about_ca_topic_score_codex":0.0005771333,"about_ca_topic_score_gemma":0.000017302567,"teacher_disagreement_score":0.46885857,"about_ca_system_score_codex":0.00009404979,"about_ca_system_score_gemma":0.00006654609,"threshold_uncertainty_score":0.4460473},"labels":[],"label_agreement":null},{"id":"W2163062020","doi":"10.1109/icsmc.2009.5346794","title":"An integrated ACO-AHP approach for resource management optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Analytic hierarchy process; Multiple-criteria decision analysis; Mathematical optimization; Weighted sum model; Computer science; Ranking (information retrieval); Vagueness; Fuzzy set; Set (abstract data type); Operations research; Decision problem; Ant colony optimization algorithms; Fuzzy logic; Optimal decision; Artificial intelligence; Mathematics; Decision tree; Algorithm","score_opus":0.0184359574339241,"score_gpt":0.2588206938677421,"score_spread":0.240384736433818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163062020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014157793,0.000008746436,0.97523123,0.00034402456,0.000056295943,0.00033811812,9.453027e-7,0.0004518729,0.02355462],"genre_scores_gemma":[0.0041573057,0.0000011721957,0.9935556,0.000709493,0.00003512154,0.000020343317,0.00006212223,0.0000061655883,0.0014526456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901074,0.00004077347,0.00015635754,0.000388175,0.0001706066,0.00023332602],"domain_scores_gemma":[0.99929076,0.000018345881,0.000046526802,0.0005119516,0.00005090259,0.00008153572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002921829,0.00011305363,0.00010379855,0.000108333996,0.00009574637,0.00017184688,0.0007473226,0.00004862743,0.0000029343894],"category_scores_gemma":[0.0000092645205,0.00009542106,0.000029995834,0.00034953936,0.00001033062,0.00032880463,0.000038345595,0.00005738463,0.000004758527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004693392,0.00009910101,0.0000062606705,0.0000043710743,0.000007052167,0.000003200746,0.0001538994,0.90536124,0.000009941107,0.012439994,0.0029071576,0.07900309],"study_design_scores_gemma":[0.00027807488,0.00016972714,0.00019049963,0.0000061709875,0.0000052825967,0.0000052108753,0.00009465068,0.9974704,0.00007037899,0.00022477194,0.0013521629,0.00013269029],"about_ca_topic_score_codex":0.0000029703724,"about_ca_topic_score_gemma":2.1401897e-8,"teacher_disagreement_score":0.09210914,"about_ca_system_score_codex":0.000034700148,"about_ca_system_score_gemma":0.000011725312,"threshold_uncertainty_score":0.38911578},"labels":[],"label_agreement":null},{"id":"W2163341694","doi":"10.1109/cca.2005.1507104","title":"Motion planning for mobile robots using a fuzzy layered goal-oriented approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Fuzzy logic; Motion planning; Computer science; Planner; Robot; Point (geometry); Motion (physics); Artificial intelligence; Control engineering; Simulation; Engineering; Mathematics","score_opus":0.03919894155608339,"score_gpt":0.2990255714362144,"score_spread":0.259826629880131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163341694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009040068,0.00010299541,0.9866692,0.00008676147,0.0003343823,0.00056551746,0.0000031929455,0.00047234728,0.0027255542],"genre_scores_gemma":[0.1511709,4.6102335e-7,0.8479389,0.00014202045,0.00022172068,0.00008010835,0.000014022037,0.000016942924,0.0004149289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982757,0.000050555205,0.0003120602,0.00057037687,0.00028942083,0.0005018602],"domain_scores_gemma":[0.99904776,0.000089328336,0.00012304563,0.00050276436,0.00010595013,0.00013114593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041018418,0.0001987174,0.00022453665,0.00015734383,0.00022333236,0.00013701657,0.0005330046,0.00010391013,0.0000035188436],"category_scores_gemma":[0.000044905228,0.00018650123,0.00008483898,0.00041118756,0.000024757504,0.0006718399,0.00015061046,0.00012631164,0.000022325466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000600464,0.00010716214,0.00029616253,0.000014717559,0.000016767186,0.0000035306814,0.0009679882,0.9787822,0.0011939134,0.002832912,0.00048405788,0.015294583],"study_design_scores_gemma":[0.00059165474,0.00007349089,0.00036061075,0.000024506677,0.000007977805,0.000049821556,0.00010836005,0.99676764,0.0010718117,0.00016869251,0.000538584,0.00023683299],"about_ca_topic_score_codex":0.000020956046,"about_ca_topic_score_gemma":2.8413336e-7,"teacher_disagreement_score":0.14213082,"about_ca_system_score_codex":0.00011945002,"about_ca_system_score_gemma":0.000054516466,"threshold_uncertainty_score":0.76052994},"labels":[],"label_agreement":null},{"id":"W2163418689","doi":"10.1109/aici.2009.463","title":"Intelligent Planning and Execution of Tasks Using Hybrid Agents","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Multi-agent system; Control (management); Distributed computing; Range (aeronautics); Autonomous agent; Work (physics); Intelligent agent; Artificial intelligence; Systems engineering; Engineering","score_opus":0.05961403000457072,"score_gpt":0.31393010000434085,"score_spread":0.25431606999977013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163418689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10545873,0.00013215195,0.8931576,0.00007632257,0.0001301008,0.000055277862,5.8523614e-7,0.000061253384,0.0009279839],"genre_scores_gemma":[0.6780201,0.0000025841523,0.3217967,0.00012401662,0.000015234768,2.3644182e-7,9.079827e-7,0.0000018086621,0.000038370978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999281,0.000023817227,0.00017590755,0.00019913568,0.00016332058,0.00015683894],"domain_scores_gemma":[0.9996054,0.00002552435,0.00007240177,0.00020697138,0.00003063214,0.000059072372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017037604,0.00007896786,0.00011307579,0.00008892453,0.000049220856,0.000040346695,0.00022676177,0.000022077318,0.0000027931183],"category_scores_gemma":[0.00002082724,0.00007228703,0.000019759373,0.0001255953,0.00001963231,0.00020489929,0.0000722256,0.00005773567,0.0000031157256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035688958,0.00050941634,0.018335806,0.000091720365,0.000115351104,0.0006518302,0.010581426,0.2754247,0.052921444,0.02689312,0.005545883,0.60889363],"study_design_scores_gemma":[0.00009560739,0.00008037109,0.00870411,0.00005719531,0.0000040424834,0.00007732616,0.000029366769,0.9757329,0.013938298,0.0011239139,0.00005974943,0.00009709634],"about_ca_topic_score_codex":0.000019977908,"about_ca_topic_score_gemma":2.488678e-8,"teacher_disagreement_score":0.7003082,"about_ca_system_score_codex":0.000021194033,"about_ca_system_score_gemma":0.000018675226,"threshold_uncertainty_score":0.29477796},"labels":[],"label_agreement":null},{"id":"W2163942697","doi":"10.1109/irds.2002.1041489","title":"Stereo vision based navigation for Sun-synchronous exploration","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Traverse; Terrain; Computer science; Robot; Artificial intelligence; Mobile robot; Computer vision; Remote sensing; Geography; Geodesy; Cartography","score_opus":0.03329925835463108,"score_gpt":0.29004549065197466,"score_spread":0.25674623229734356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163942697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013696775,0.000015696223,0.995714,0.00047178933,0.00047116194,0.00030225716,0.0000011111764,0.00022226216,0.0014320617],"genre_scores_gemma":[0.2430659,3.8913817e-7,0.7563755,0.0002125158,0.000028753433,0.000046494984,0.000019286146,0.0000074222307,0.00024373739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906373,0.000067880515,0.00018019494,0.000298874,0.0001924229,0.00019690432],"domain_scores_gemma":[0.9992806,0.00013241236,0.00006874592,0.00035899554,0.0000994421,0.000059857302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039844617,0.00009892771,0.00009422317,0.000066731554,0.00011958094,0.00014033649,0.00024234258,0.000052226707,0.0000089010955],"category_scores_gemma":[0.000063685125,0.00008989197,0.00003942523,0.00020618761,0.000012238471,0.0008038919,0.00001772934,0.000046759942,0.000059405127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047502956,0.00087550137,0.0016548628,0.00023552643,0.000052784984,0.000052445823,0.0035106558,0.22262049,0.011611268,0.32954833,0.018863665,0.41092697],"study_design_scores_gemma":[0.0005341779,0.0002178184,0.00022099588,0.000028112408,0.0000030418823,0.0000053815575,0.00002316471,0.9834572,0.010027613,0.003335594,0.0019959935,0.00015088353],"about_ca_topic_score_codex":0.000006104961,"about_ca_topic_score_gemma":6.781264e-7,"teacher_disagreement_score":0.7608367,"about_ca_system_score_codex":0.000053067273,"about_ca_system_score_gemma":0.000077968114,"threshold_uncertainty_score":0.3665688},"labels":[],"label_agreement":null},{"id":"W2164098927","doi":"10.1109/acc.2011.5991400","title":"Collision-free trajectory generation of robotic manipulators using receding horizon strategy","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Collision avoidance; Collision; Control theory (sociology); Computation; Computer science; Set (abstract data type); Horizon; Robot; State space; Motion planning; Robot manipulator; Mathematical optimization; Mathematics; Artificial intelligence; Algorithm; Control (management)","score_opus":0.19505813632168226,"score_gpt":0.2844243667386415,"score_spread":0.08936623041695926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164098927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20832337,0.000048385493,0.78825414,0.000008869438,0.0007786345,0.000114009774,5.7182626e-7,0.000115205796,0.002356824],"genre_scores_gemma":[0.5351868,0.0000023946836,0.46467137,0.000009515531,0.00006497416,0.0000015881632,0.0000010895154,0.000008315781,0.000053924483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998633,0.000072156305,0.00036328993,0.00035190408,0.00032123935,0.0002583883],"domain_scores_gemma":[0.9989387,0.000041842126,0.00016124528,0.0006622912,0.00010443183,0.00009152446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003498869,0.00014869824,0.00020883285,0.00017778747,0.00009718575,0.0000489658,0.00078067236,0.00009309496,0.000019354009],"category_scores_gemma":[0.00005779601,0.0001378969,0.00005508512,0.0004176544,0.000033956814,0.00053090823,0.00015561175,0.0001026077,0.000008234595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001482544,0.00031882597,0.0047880304,0.00007744731,0.000111710455,0.00011505204,0.0036259275,0.77984214,0.1238638,0.06386712,0.0015379948,0.021837119],"study_design_scores_gemma":[0.00020227069,0.00022533306,0.0025837675,0.00003185436,0.0000120515415,0.000027337335,0.000066848064,0.9571936,0.03877989,0.0006940964,0.0000014033751,0.00018154524],"about_ca_topic_score_codex":0.00020977386,"about_ca_topic_score_gemma":0.000006899854,"teacher_disagreement_score":0.32686344,"about_ca_system_score_codex":0.000066337394,"about_ca_system_score_gemma":0.0001019286,"threshold_uncertainty_score":0.56232727},"labels":[],"label_agreement":null},{"id":"W2164522163","doi":"10.1115/1.4030145","title":"Tangy the Robot Bingo Facilitator: A Performance Review1","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Devices","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Facilitator; Computer science; Robot; Psychology; Artificial intelligence; Social psychology","score_opus":0.06327472626937508,"score_gpt":0.3101868884751922,"score_spread":0.24691216220581713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164522163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05214008,0.044211034,0.790513,0.10342824,0.006359032,0.00025726817,0.0000015968477,0.000121372264,0.002968413],"genre_scores_gemma":[0.8031029,0.002389717,0.18156868,0.010233958,0.0022726718,0.000009346395,8.465182e-7,0.000023939854,0.000397899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996364,0.00023016421,0.00058751815,0.00013784535,0.0024303633,0.00025011844],"domain_scores_gemma":[0.99821967,0.00032102724,0.0004094935,0.0003132182,0.0002621632,0.00047444497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058367667,0.000117528296,0.0002965599,0.00007998352,0.00008024991,0.00012548204,0.0027247693,0.00008469572,0.000017263159],"category_scores_gemma":[0.0014036533,0.000061359555,0.00009467681,0.00035776477,0.00009520693,0.00052486075,0.00033643708,0.000529996,0.00010584929],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024840461,0.00019475739,0.009296939,0.00024338778,0.00017350378,0.00087854255,0.0067397985,0.0032468396,0.000025070452,0.0019700925,0.104215115,0.8729911],"study_design_scores_gemma":[0.0018189184,0.0011285238,0.01416013,0.002348582,0.00006759707,0.0051731523,0.000683213,0.489458,0.000115311996,0.001018963,0.48356947,0.0004581519],"about_ca_topic_score_codex":0.000010275158,"about_ca_topic_score_gemma":0.0000013238524,"teacher_disagreement_score":0.87253296,"about_ca_system_score_codex":0.000050159106,"about_ca_system_score_gemma":0.0005042672,"threshold_uncertainty_score":0.5063345},"labels":[],"label_agreement":null},{"id":"W2164561380","doi":"10.1109/tsmcb.2005.850177","title":"A Layered Goal-Oriented Fuzzy Motion Planning Strategy for Mobile Robot Navigation","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Motion planning; Mobile robot; Fuzzy logic; Computer science; Robot; Mobile robot navigation; Artificial intelligence; Planner; Visibility; Computer vision; Real-time computing; Simulation; Robot control; Geography","score_opus":0.02858775443073196,"score_gpt":0.27600739365175353,"score_spread":0.24741963922102156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164561380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029488122,0.0005759471,0.9653588,0.0001334108,0.001667158,0.0013833449,0.00007077249,0.00041395496,0.00090850145],"genre_scores_gemma":[0.97111815,0.000057224064,0.024766743,0.00007873565,0.00034585042,0.00051052123,0.000034052042,0.00006205875,0.0030266405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967125,0.00018723788,0.0008431681,0.00094791374,0.00059614517,0.0007130231],"domain_scores_gemma":[0.99808085,0.00024259115,0.00032060724,0.00076853274,0.00024330834,0.0003441292],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051338715,0.00048730607,0.0004931578,0.00026437177,0.0004219208,0.00043557034,0.0004930956,0.00030454106,0.0000077065215],"category_scores_gemma":[0.000007841244,0.0005127426,0.00015417041,0.00041691418,0.00010924678,0.00040647126,0.000010194909,0.00040302417,0.000094967494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037342565,0.00029738015,0.000051530602,0.000105857034,0.00009397689,0.000014488004,0.0017763062,0.96138585,0.00084079837,0.0025635683,0.00054219266,0.0322907],"study_design_scores_gemma":[0.0019973624,0.00094998063,0.00040602084,0.0004833427,0.00009779797,0.00016923055,0.0004889503,0.98147696,0.0047359806,0.00028628905,0.008100477,0.00080762804],"about_ca_topic_score_codex":0.00010205291,"about_ca_topic_score_gemma":0.0000124498065,"teacher_disagreement_score":0.94163007,"about_ca_system_score_codex":0.00016378531,"about_ca_system_score_gemma":0.000070838134,"threshold_uncertainty_score":0.99973243},"labels":[],"label_agreement":null},{"id":"W2165020484","doi":"10.1109/tvcg.2010.114","title":"GPU-Accelerated Minimum Distance and Clearance Queries","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nvidia; University of Waterloo; University of California Berkeley","keywords":"Computer science; Coprocessor; Kernel (algebra); Graphics; Algorithm; Point (geometry); Surface (topology); Minimum bounding box; Computer graphics (images); Computational science; Artificial intelligence; Parallel computing; Geometry; Mathematics; Image (mathematics)","score_opus":0.042633075094690644,"score_gpt":0.26647973416014853,"score_spread":0.2238466590654579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165020484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008907738,0.00008465549,0.9898659,0.000031578114,0.0005942559,0.00013050612,0.0000053021145,0.00029669062,0.00008336053],"genre_scores_gemma":[0.97035474,0.00029073306,0.028525822,0.00069134805,0.000028297112,0.000016541395,0.000002287396,0.000017703867,0.00007250815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.00008609819,0.00023663742,0.0004273293,0.00018468857,0.00020294686],"domain_scores_gemma":[0.9993837,0.00004934638,0.000077528726,0.00027179075,0.00009792649,0.00011969265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117382595,0.00018785142,0.00017349364,0.00019323673,0.00030387816,0.00016323212,0.0002201424,0.00009034882,0.0000051572915],"category_scores_gemma":[0.0000015127961,0.00018885171,0.000035297453,0.00058113376,0.00014824672,0.00045980548,0.000005305307,0.00014269704,0.000004782067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009215584,0.000524902,0.0008964255,0.0001066887,0.000113053255,0.00004225848,0.012808165,0.0010058597,0.00004576265,0.9439732,0.0002884638,0.04010307],"study_design_scores_gemma":[0.0003890303,0.0002399193,0.0039003466,0.000058706693,0.000010994823,0.000034589222,0.000029932718,0.99288255,0.0010664316,0.00080878724,0.00031887513,0.00025986147],"about_ca_topic_score_codex":0.00002006712,"about_ca_topic_score_gemma":0.0000061972296,"teacher_disagreement_score":0.99187666,"about_ca_system_score_codex":0.000009279722,"about_ca_system_score_gemma":0.000023021725,"threshold_uncertainty_score":0.7701149},"labels":[],"label_agreement":null},{"id":"W2165761395","doi":"10.5772/51855","title":"Real-Time Navigation of Nonholonomic Mobile Robots under Velocity Vector Control","year":2012,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mobile robot; Computer science; Kinematics; Obstacle; Workspace; Robot; Acceleration; Nonholonomic system; Control theory (sociology); Mobile robot navigation; Obstacle avoidance; Position (finance); Artificial intelligence; Computer vision; Robot control; Law; Physics; Control (management)","score_opus":0.011902991194887937,"score_gpt":0.2757845160483486,"score_spread":0.26388152485346067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165761395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089364305,0.0005583264,0.9013435,0.00016461416,0.0081219515,0.00024532186,0.000008321942,0.000044476757,0.00014913228],"genre_scores_gemma":[0.8913033,0.000023275228,0.10787669,0.000024221898,0.00066186156,0.0000098442815,0.000005050117,0.000016600025,0.00007917634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751186,0.00017903236,0.0010010328,0.00018604797,0.000816347,0.0003056479],"domain_scores_gemma":[0.99686927,0.0004050513,0.0014330932,0.0002968119,0.0008156149,0.00018014525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088998803,0.0001857236,0.0004908776,0.00023826798,0.00004531193,0.00007466232,0.0011542859,0.00008904734,0.000007771466],"category_scores_gemma":[0.00010261573,0.00016651688,0.00014540268,0.00015679713,0.000054737542,0.0012370009,0.00008537387,0.00019986129,0.00005378759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039145136,0.00013156771,0.0011336952,0.000015861015,0.00020140303,0.000025451089,0.000494951,0.9782701,0.01659229,0.0013295012,0.00008330806,0.0016827163],"study_design_scores_gemma":[0.006423309,0.0008871989,0.054263163,0.0016482654,0.000111636255,0.003805782,0.0006578896,0.9255589,0.0050398284,0.00048128274,0.0003157427,0.0008069663],"about_ca_topic_score_codex":0.00003453484,"about_ca_topic_score_gemma":9.034853e-8,"teacher_disagreement_score":0.80193895,"about_ca_system_score_codex":0.00038686485,"about_ca_system_score_gemma":0.00015913219,"threshold_uncertainty_score":0.67903614},"labels":[],"label_agreement":null},{"id":"W2166177494","doi":"10.1109/tsmcb.2003.811769","title":"A Neural Network Approach to Complete Coverage Path Planning","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":443,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Motion planning; Artificial neural network; Path (computing); Computer science; Workspace; Obstacle; Obstacle avoidance; Artificial intelligence; Mobile robot; Geography; Computer network","score_opus":0.03547404330118932,"score_gpt":0.24236988124271855,"score_spread":0.20689583794152921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166177494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014066993,0.00044266199,0.9760297,0.00022752002,0.0024838785,0.00088193174,0.00004677467,0.00042707653,0.0053934557],"genre_scores_gemma":[0.9525336,0.00004918067,0.0448218,0.0006432889,0.0003655759,0.00014963253,0.000010036417,0.00007246256,0.0013544199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960331,0.00022188172,0.00077077706,0.0011458688,0.00079285784,0.0010355427],"domain_scores_gemma":[0.997756,0.00017744496,0.00021444258,0.0010925372,0.00011540829,0.0006441835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047546098,0.00059505075,0.0006565978,0.00023872903,0.0004962022,0.0006512867,0.0009340074,0.00023478626,0.0000039826327],"category_scores_gemma":[0.0000073560627,0.0006028394,0.00015245026,0.0006850662,0.00012475679,0.00022139239,0.000028457807,0.0006153176,0.00016681617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002102977,0.00021231144,0.00007668216,0.00005554279,0.000074600146,0.00007221128,0.0019282476,0.9894128,0.00006454931,0.004480017,0.0011822707,0.0024197283],"study_design_scores_gemma":[0.0029675746,0.0012427222,0.0017805332,0.000905771,0.00013031009,0.0008477668,0.00028911306,0.9702678,0.00024326664,0.0010021842,0.018484013,0.0018389865],"about_ca_topic_score_codex":0.0002557013,"about_ca_topic_score_gemma":0.0000063943194,"teacher_disagreement_score":0.9384666,"about_ca_system_score_codex":0.00015026618,"about_ca_system_score_gemma":0.0000806202,"threshold_uncertainty_score":0.9996423},"labels":[],"label_agreement":null},{"id":"W2166572019","doi":"10.1109/icsmc.1998.726538","title":"A neural network approach to real-time path planning with safety consideration","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Workspace; Computer science; Robot; Motion planning; Artificial neural network; Trajectory; Path (computing); Mobile robot; State space; Lyapunov function; Control theory (sociology); Artificial intelligence; Control (management); Mathematics","score_opus":0.02998820241351054,"score_gpt":0.22893490666429592,"score_spread":0.19894670425078537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166572019","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019352573,0.000025156802,0.9048633,0.0007947679,0.000118671465,0.00025155398,7.749035e-7,0.0004992201,0.09151129],"genre_scores_gemma":[0.07372344,0.0000017612809,0.9231375,0.0008279464,0.00016112285,0.000017617795,0.000005574433,0.00001408388,0.0021109562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984348,0.00009007029,0.00022806761,0.00048459228,0.0003174801,0.00044501462],"domain_scores_gemma":[0.99907494,0.00012810041,0.000068259935,0.00049452786,0.000055702923,0.0001784478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026311164,0.00017733559,0.00020623232,0.00006119654,0.0002264466,0.00021714569,0.0003935678,0.000053217827,0.000021529218],"category_scores_gemma":[0.000023778744,0.00013864473,0.000026213622,0.0004761785,0.000023773267,0.00034684577,0.0001191266,0.0001300183,0.00016372585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010357709,0.000051011382,0.0012859548,0.00000446803,0.00001657404,0.00007504004,0.0015153694,0.9414216,0.000058053713,0.0049338695,0.04785857,0.0027691259],"study_design_scores_gemma":[0.00025366768,0.0001467867,0.003160574,0.000027861759,0.000004246048,0.00016142573,0.000020057712,0.9956325,0.000010928162,0.00007666387,0.00028278012,0.00022250049],"about_ca_topic_score_codex":0.00003280775,"about_ca_topic_score_gemma":2.6606173e-7,"teacher_disagreement_score":0.08940033,"about_ca_system_score_codex":0.00003692212,"about_ca_system_score_gemma":0.000021014057,"threshold_uncertainty_score":0.56537676},"labels":[],"label_agreement":null},{"id":"W2167011086","doi":"10.1109/tnn.2003.820618","title":"Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Guelph","funders":"","keywords":"Artificial neural network; Computer science; Mobile robot; Motion planning; Robot; Collision; Artificial intelligence; Stability (learning theory); Control theory (sociology); Machine learning","score_opus":0.02357518814957928,"score_gpt":0.25696351778645976,"score_spread":0.23338832963688047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167011086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042724643,0.00003431952,0.9552565,0.000032207063,0.00097529823,0.00047297272,0.000016358395,0.0002997972,0.00018788312],"genre_scores_gemma":[0.7813134,0.0000031165594,0.21845682,0.00005044926,0.000036998586,0.00004438951,0.000007686694,0.000034183257,0.00005294257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738085,0.00035678895,0.0005526882,0.0006581368,0.00048045669,0.0005710865],"domain_scores_gemma":[0.9980837,0.0003386956,0.0002587032,0.0010256676,0.00011319543,0.00018004037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039168636,0.00034786802,0.00043980408,0.00030125404,0.0003493159,0.00011114074,0.0008112577,0.00021373152,0.000008135309],"category_scores_gemma":[0.000013416712,0.0003498227,0.00021395623,0.0011267388,0.00009969665,0.00041245233,0.000006914773,0.0005300656,0.0000030345705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003247247,0.00022173853,0.000034328405,0.000015294421,0.000021585716,0.000024413597,0.00011315088,0.99500257,0.0004417237,0.000040958534,0.00002898273,0.004022796],"study_design_scores_gemma":[0.0007924455,0.0002812061,0.00008250222,0.00007063992,0.000037758855,0.000091334965,0.00002931472,0.99740165,0.0008721744,0.000032901542,0.0000013166672,0.00030676796],"about_ca_topic_score_codex":0.00007173444,"about_ca_topic_score_gemma":0.0000017561372,"teacher_disagreement_score":0.73858875,"about_ca_system_score_codex":0.00017563872,"about_ca_system_score_gemma":0.00007348698,"threshold_uncertainty_score":0.9998954},"labels":[],"label_agreement":null},{"id":"W2167333020","doi":"","title":"Optimal schedules for 2-guard room search","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Guard (computer science); Visibility; Schedule; Visibility polygon; Computer science; Polygon (computer graphics); Mathematical optimization; Simple polygon; Boundary (topology); Point (geometry); Shortest path problem; Algorithm; Mathematics; Theoretical computer science; Computer network; Regular polygon; Graph","score_opus":0.03819005015790636,"score_gpt":0.31515171860367575,"score_spread":0.2769616684457694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167333020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012064789,0.00003992782,0.9830777,0.00067066756,0.00031319927,0.00016457011,0.0000011353457,0.0002663353,0.0034016904],"genre_scores_gemma":[0.037833054,6.9111115e-7,0.9604707,0.00018460293,0.00012681515,0.0000057689995,0.0000019303814,0.0000075105613,0.0013689594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878496,0.000017655517,0.00016465709,0.00030844787,0.00025775758,0.00046652995],"domain_scores_gemma":[0.99911654,0.0002561265,0.000023867091,0.00038320498,0.00009488533,0.000125377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011902226,0.00009475243,0.00010808718,0.00010216042,0.00010858077,0.000108399945,0.00072556507,0.00005717533,0.0000089910245],"category_scores_gemma":[0.0000565035,0.000082330145,0.00005321926,0.00021911298,0.000022443075,0.0002322821,0.0001559258,0.00011094909,0.00011625616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041994812,0.0002173266,0.004874242,0.00005037422,0.00007190446,0.0001446072,0.0012218708,0.058928926,0.0020029948,0.7225603,0.0056059873,0.2042795],"study_design_scores_gemma":[0.00047818804,0.00018554283,0.0064314483,0.000014180039,0.0000031647453,0.000031541622,0.00009422174,0.9725814,0.016317386,0.0009570162,0.0026632631,0.00024267833],"about_ca_topic_score_codex":0.000017697423,"about_ca_topic_score_gemma":0.0000011560289,"teacher_disagreement_score":0.9136524,"about_ca_system_score_codex":0.000030484307,"about_ca_system_score_gemma":0.000062860774,"threshold_uncertainty_score":0.33573258},"labels":[],"label_agreement":null},{"id":"W2167337641","doi":"10.1017/s0263574703005617","title":"On interacting with physics-based models of graphical objects","year":2004,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Representation (politics); Computer science; Object (grammar); Graphical user interface; Trajectory; Motion (physics); Interface (matter); Computer graphics; Computer graphics (images); Artificial intelligence; Physics; Programming language","score_opus":0.020569853792164725,"score_gpt":0.24539812099857394,"score_spread":0.22482826720640922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167337641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052533746,0.000007726787,0.9922643,0.000550372,0.00016743183,0.000103022656,7.702792e-7,0.00014047913,0.0015125303],"genre_scores_gemma":[0.6222851,2.1926108e-7,0.37752983,0.00014629844,0.000020530617,0.0000035016624,9.865194e-7,0.000008669617,0.0000048633606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987294,0.000042201595,0.00019094645,0.0003543525,0.00039905033,0.00028406823],"domain_scores_gemma":[0.9988124,0.0003415086,0.00011713603,0.0005455483,0.000087022716,0.00009638108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013942599,0.00015585712,0.00022106721,0.00009446388,0.00006833676,0.000047214053,0.0005170865,0.000053040516,0.0000012117131],"category_scores_gemma":[0.000070713,0.00012615402,0.00006127321,0.00046682724,0.000069391506,0.00026950694,0.00006386008,0.0002534749,0.000016979577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000133987005,0.00013670782,0.000033461572,0.000012462678,0.000013217576,0.000039010505,0.00030567765,0.90212077,0.00019646338,0.09647269,0.000006263585,0.00064989],"study_design_scores_gemma":[0.000740409,0.00048309413,0.00027330112,0.0004063007,0.000009702884,0.000014832847,0.000012395907,0.9629114,0.004419517,0.03054673,5.7384545e-7,0.0001816986],"about_ca_topic_score_codex":0.000024511437,"about_ca_topic_score_gemma":0.0000010599755,"teacher_disagreement_score":0.61703175,"about_ca_system_score_codex":0.000043776086,"about_ca_system_score_gemma":0.00015326159,"threshold_uncertainty_score":0.51444113},"labels":[],"label_agreement":null},{"id":"W2167549499","doi":"10.1111/0824-7935.00136","title":"Architectural Methodology Based on Intentional Configuration of Behaviors","year":2001,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Architecture; Mechanism (biology); Artificial intelligence; Control (management); Robot; Object (grammar); Human–computer interaction; Fuzzy logic; Behavioral pattern; Software engineering","score_opus":0.11090979690513524,"score_gpt":0.36240733125684155,"score_spread":0.2514975343517063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167549499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059142653,0.000016933916,0.99210715,0.0007587563,0.00042686556,0.00011700079,0.000004261773,0.00008243561,0.0005723431],"genre_scores_gemma":[0.5477698,4.6913888e-7,0.4519189,0.00021573658,0.00002190299,0.0000073639226,0.000022141805,0.0000036960562,0.00004002704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844074,0.0002127177,0.000388279,0.0003361958,0.0004377701,0.0001843184],"domain_scores_gemma":[0.9981299,0.001079396,0.00017506556,0.00024455108,0.00030275856,0.000068321206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004488844,0.00013251451,0.0001720427,0.00025622032,0.00007287486,0.000034442903,0.0005976817,0.000055204662,0.000052944994],"category_scores_gemma":[0.00025578556,0.00012777657,0.000082893646,0.00041958052,0.00014058994,0.00013190409,0.000056261128,0.00015987604,0.00007069511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001709881,0.00007053088,0.00060136575,0.000004134738,0.0000065185527,0.000015783598,0.00015433229,0.88321435,0.00016834188,0.06418013,0.000035014516,0.05153238],"study_design_scores_gemma":[0.00008605981,0.00019763563,0.013547222,0.00003552371,0.0000043376485,0.000079900194,0.000021018113,0.9520424,0.0029825529,0.030851658,0.00002640155,0.00012531519],"about_ca_topic_score_codex":0.000016217045,"about_ca_topic_score_gemma":5.00902e-7,"teacher_disagreement_score":0.5418555,"about_ca_system_score_codex":0.000041002622,"about_ca_system_score_gemma":0.00011533407,"threshold_uncertainty_score":0.5210577},"labels":[],"label_agreement":null},{"id":"W2167554916","doi":"10.1109/icra.2011.5979707","title":"Optimal complete terrain coverage using an Unmanned Aerial Vehicle","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Terrain; Computer science; Trajectory; Artificial intelligence; Robotics; Drone; Real-time computing; Computer vision; Domain (mathematical analysis); Remotely operated underwater vehicle; Fixed wing; Mobile robot; Motion planning; Path (computing); Robot; Aerospace engineering; Engineering; Geography; Wing; Mathematics; Cartography","score_opus":0.0937143608320587,"score_gpt":0.2765359387435016,"score_spread":0.18282157791144288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167554916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2378412,0.00000422765,0.7576567,0.000036904363,0.0005264066,0.000092270566,0.0000033017789,0.00027257533,0.0035664157],"genre_scores_gemma":[0.42624196,2.0229427e-7,0.57333,0.00022946713,0.00009894465,0.0000019066748,0.000002788328,0.000009487,0.00008524071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860334,0.00010827916,0.00022213392,0.00042276582,0.00023617127,0.00040728934],"domain_scores_gemma":[0.9990559,0.000029974957,0.000073802075,0.00062351115,0.000044111046,0.0001726567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028501535,0.00015813412,0.0001846502,0.000080105565,0.00015938008,0.00013472425,0.00093255326,0.00006432498,0.00008591199],"category_scores_gemma":[0.000017920103,0.00014823707,0.00004554402,0.00019915607,0.00005274692,0.0007839521,0.00026279473,0.00011246401,0.000101316116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005104375,0.0042883805,0.013832532,0.00011161027,0.000482217,0.0038347044,0.10895851,0.38473,0.26064023,0.13235258,0.00504492,0.085213855],"study_design_scores_gemma":[0.0004980832,0.0002087492,0.0038998427,0.000009455047,0.0000045546094,0.000058043297,0.000039703616,0.992349,0.0020400747,0.0005063033,0.00013587621,0.00025029917],"about_ca_topic_score_codex":0.00041600672,"about_ca_topic_score_gemma":0.0000031021773,"teacher_disagreement_score":0.607619,"about_ca_system_score_codex":0.000038340535,"about_ca_system_score_gemma":0.000059808994,"threshold_uncertainty_score":0.60449326},"labels":[],"label_agreement":null},{"id":"W2167825918","doi":"10.1109/icsmc.1995.537902","title":"Rubber band paradigm: a method for environment reconstruction for global planning of a mobile robot","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mobile robot; Computer science; Motion planning; Frame (networking); Robot; Natural rubber; Variety (cybernetics); Simple (philosophy); Path (computing); Artificial intelligence; Telecommunications; Computer network","score_opus":0.0421908272732834,"score_gpt":0.2970057889376463,"score_spread":0.2548149616643629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167825918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005716597,0.00022067492,0.9970243,0.00017581633,0.0003203969,0.0006344881,0.0000158492,0.00007505108,0.0009617442],"genre_scores_gemma":[0.0095238155,0.000004484238,0.98972684,0.000061444945,0.000056562065,0.00023683415,0.000002882739,0.0000069885405,0.00038012472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988421,0.000037565726,0.0003049467,0.00038154967,0.00015191176,0.0002819264],"domain_scores_gemma":[0.9991714,0.00025819856,0.00014660292,0.00032801993,0.00002104948,0.000074755284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003366482,0.00013230633,0.00023850826,0.000043925906,0.00008463168,0.00003860285,0.00031904422,0.00007270222,0.000023744147],"category_scores_gemma":[0.000028880158,0.00012103326,0.0001017446,0.00010952463,0.00003057915,0.00020509891,0.000042678555,0.000041908108,0.0000070569436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036091045,0.00023432814,0.0017567127,0.00010200005,0.00014784196,0.0000061802334,0.0017757689,0.37277105,0.0013998551,0.012797117,0.0043745227,0.6045985],"study_design_scores_gemma":[0.0007500202,0.00032494115,0.00032065745,0.000028743434,0.000018091076,0.00010307704,0.00004948794,0.9891983,0.0023136633,0.0047372594,0.0019888603,0.00016685782],"about_ca_topic_score_codex":0.000009754461,"about_ca_topic_score_gemma":1.9464908e-7,"teacher_disagreement_score":0.6164273,"about_ca_system_score_codex":0.0000698739,"about_ca_system_score_gemma":0.0000136963345,"threshold_uncertainty_score":0.4935593},"labels":[],"label_agreement":null},{"id":"W2167868723","doi":"10.1109/iros.2006.282010","title":"A Hybrid Two-layered Approach to Real-Time Motion Planning of Multiple Agents in Virtual Environments","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Voronoi diagram; Motion planning; Computer science; Path (computing); Distributed computing; Plan (archaeology); Planner; Motion (physics); Virtual machine; Real-time computing; Computer vision; Artificial intelligence; Robot; Mathematics","score_opus":0.027733222777137023,"score_gpt":0.2523381831514102,"score_spread":0.2246049603742732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167868723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21840152,0.000003574364,0.77334464,0.000025056279,0.00007684792,0.00022455373,0.000004157055,0.000073918294,0.007845752],"genre_scores_gemma":[0.6913143,3.1710312e-7,0.30741552,0.000031023395,0.000025651518,0.000014685318,0.000019251469,0.000009391927,0.0011699002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827415,0.000090943875,0.00038949528,0.00048790773,0.0004055325,0.00035197494],"domain_scores_gemma":[0.9992628,0.00007295268,0.00010954598,0.0004572286,0.00001071831,0.000086767235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033400548,0.000165009,0.00023555831,0.00022869643,0.00004265889,0.000037211306,0.0005904293,0.00004084481,0.000005371661],"category_scores_gemma":[0.0000310869,0.0001659224,0.00004073695,0.00025302393,0.000022779786,0.0002644677,0.00023528036,0.000090717134,0.00010307615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010630305,0.00038630166,0.016335486,0.000005097837,0.000008651453,0.000034637615,0.0004676297,0.96498525,0.014193757,0.0006437594,0.00088189245,0.0020468796],"study_design_scores_gemma":[0.00079143076,0.000066225395,0.09103195,0.000025078747,0.0000020409661,0.000011657832,0.00001827496,0.90347815,0.0042525865,0.000121712634,0.000029793437,0.00017107434],"about_ca_topic_score_codex":0.000772648,"about_ca_topic_score_gemma":7.1115795e-7,"teacher_disagreement_score":0.47291273,"about_ca_system_score_codex":0.000093678165,"about_ca_system_score_gemma":0.000016694108,"threshold_uncertainty_score":0.67661196},"labels":[],"label_agreement":null},{"id":"W2168300466","doi":"10.5772/58705","title":"Reactive Robot Navigation Utilizing Nonlinear Control","year":2014,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Calgary","funders":"","keywords":"Computer science; Robot; Heuristic; Collision avoidance; Mobile robot; Nonlinear system; Position (finance); Fuzzy logic; Control theory (sociology); Encoding (memory); Path (computing); Motion planning; Stability (learning theory); Collision; Artificial intelligence; Real-time computing; Computer vision; Control (management)","score_opus":0.012730789158048521,"score_gpt":0.27487844631411973,"score_spread":0.2621476571560712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168300466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053496337,0.00024139979,0.9832376,0.00073927874,0.00968401,0.00014196656,0.0000023419223,0.00006208223,0.00054166355],"genre_scores_gemma":[0.7600503,0.000006215358,0.23877722,0.00011447345,0.00095872156,0.000004272681,0.0000035469027,0.000015089358,0.000070148715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973036,0.0002186149,0.0008763236,0.00026687817,0.0010772476,0.00025734626],"domain_scores_gemma":[0.9965502,0.00053905987,0.0011711135,0.0003098658,0.0012759871,0.00015378383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010059323,0.00019221532,0.00041548314,0.00027884313,0.00007207101,0.00022827636,0.0014561285,0.00007840481,0.000001655461],"category_scores_gemma":[0.0005179026,0.00016841358,0.00014605309,0.00019311745,0.000039991868,0.0011531187,0.00008081628,0.00031763886,0.000030448635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003380151,0.00006075259,0.00042925228,0.000010051001,0.000149782,0.000120762954,0.00035127578,0.98081076,0.0034960604,0.0035798496,0.00005528106,0.01090236],"study_design_scores_gemma":[0.0018838971,0.00023569592,0.00127963,0.0006776846,0.000022283615,0.0017887946,0.00022295584,0.9914671,0.00057017873,0.00050823943,0.0011314325,0.00021209125],"about_ca_topic_score_codex":0.000014873144,"about_ca_topic_score_gemma":2.1875064e-7,"teacher_disagreement_score":0.75470066,"about_ca_system_score_codex":0.00024752604,"about_ca_system_score_gemma":0.00010955587,"threshold_uncertainty_score":0.6867706},"labels":[],"label_agreement":null},{"id":"W2168602518","doi":"10.1109/crv.2012.65","title":"BRaVO: Biased Reciprocal Velocity Obstacles Break Symmetry in Dense Robot Populations","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Reciprocal; Collision avoidance; Computer science; Crowds; Collision; Mobile robot; Task (project management); Simulation; Artificial intelligence; Engineering; Computer security","score_opus":0.07201516832975675,"score_gpt":0.3024673553498134,"score_spread":0.23045218702005665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168602518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09302883,0.00015918646,0.9023509,0.00074202253,0.00089208875,0.00016379102,0.0000027063602,0.00033897063,0.0023215292],"genre_scores_gemma":[0.56297976,0.0000010928941,0.4364251,0.0002594764,0.00007191578,0.000006893015,0.0000052979135,0.000007690636,0.00024281455],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983277,0.00013193086,0.00032445436,0.00032872366,0.00031707995,0.0005701567],"domain_scores_gemma":[0.9988958,0.00021637348,0.00007365042,0.00054971257,0.000048356957,0.00021607771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005666058,0.00016438743,0.0001996413,0.00022761138,0.00011525293,0.00009367197,0.00057150243,0.00011350748,0.000019145558],"category_scores_gemma":[0.00024675633,0.00015303186,0.000053189957,0.0008344204,0.00003719037,0.0007767548,0.00020003977,0.0002165129,0.00019226602],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001579874,0.0009727784,0.7174112,0.000038364255,0.000039174054,0.00010789166,0.0038603495,0.013327624,0.0009816401,0.12860139,0.006089319,0.12855448],"study_design_scores_gemma":[0.00033181818,0.000024862238,0.75928473,0.000031584328,0.000004782915,0.000054953543,0.000049351893,0.23780926,0.0006163566,0.0013075279,0.0001988968,0.00028584801],"about_ca_topic_score_codex":0.0004870161,"about_ca_topic_score_gemma":0.00003964015,"teacher_disagreement_score":0.4699509,"about_ca_system_score_codex":0.000115634226,"about_ca_system_score_gemma":0.00006317379,"threshold_uncertainty_score":0.6240458},"labels":[],"label_agreement":null},{"id":"W2169226396","doi":"10.1109/ccece.1999.804896","title":"An efficient neural network model for path planning of car-like robots in dynamic environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Workspace; Computer science; Motion planning; Robot; Path (computing); Convergence (economics); Obstacle avoidance; Obstacle; Collision; Collision avoidance; Stability (learning theory); Lyapunov function; Lyapunov stability; Artificial intelligence; Mobile robot; Control (management); Machine learning; Computer network","score_opus":0.021416390228174313,"score_gpt":0.25900869662134446,"score_spread":0.23759230639317014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169226396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07098655,0.00011300602,0.92814726,0.000027960059,0.00024600376,0.00027956816,0.000002162426,0.000058659025,0.00013885213],"genre_scores_gemma":[0.54011244,8.94613e-7,0.45973057,0.000060376628,0.00000691592,0.000017731516,0.0000032126234,0.000008121889,0.000059719958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840355,0.00007977577,0.00033156006,0.0004541482,0.0002511356,0.0004798267],"domain_scores_gemma":[0.99914926,0.000091358525,0.000103314065,0.0005417928,0.000012012081,0.00010227855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050219236,0.0001660228,0.00023239634,0.000079329264,0.00006637063,0.000031970376,0.0005153723,0.000065982174,0.0000016742375],"category_scores_gemma":[0.00001316373,0.00015487592,0.00005314632,0.0001717003,0.000029095529,0.00012674766,0.00006693141,0.00010792754,0.0000023616672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038299145,0.00008378046,0.0011259362,0.0000058450655,0.0000038290746,0.000008961332,0.0006906118,0.9957547,0.00014309582,0.0013112125,0.000046922294,0.0008212416],"study_design_scores_gemma":[0.0004017929,0.0001197868,0.0023061025,0.000024127394,0.0000038411117,0.000007926467,0.000034234115,0.9964314,0.00004119643,0.00044512452,0.000008620684,0.0001758571],"about_ca_topic_score_codex":0.000012811573,"about_ca_topic_score_gemma":0.0000011285917,"teacher_disagreement_score":0.4691259,"about_ca_system_score_codex":0.000080321086,"about_ca_system_score_gemma":0.00004697437,"threshold_uncertainty_score":0.63156563},"labels":[],"label_agreement":null},{"id":"W2169422916","doi":"10.1109/iros.2008.4651035","title":"Occlusion-free path planning with a probabilistic roadmap","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visibility; Probabilistic roadmap; Computer vision; Motion planning; Probabilistic logic; Computer science; Artificial intelligence; Path (computing); Line-of-sight; Robot; Line (geometry); Mathematics; Geography; Engineering; Geometry","score_opus":0.024272981414541428,"score_gpt":0.23224567330638463,"score_spread":0.2079726918918432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169422916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016433388,0.00007224365,0.9652102,0.00044135752,0.00027737368,0.00021946347,0.000001401256,0.0006964871,0.01664811],"genre_scores_gemma":[0.17803025,0.0000017554435,0.8198837,0.00042495978,0.00010295091,0.000016424872,0.0000025156248,0.00001561127,0.001521859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833006,0.000047722784,0.00020557755,0.00049630535,0.00050942856,0.00041091227],"domain_scores_gemma":[0.99837816,0.00013866072,0.000077603276,0.0011050893,0.00013480699,0.00016568959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019832299,0.00018938926,0.00019926432,0.00009527867,0.0002963848,0.00006461405,0.0013221623,0.000055971257,0.000012852304],"category_scores_gemma":[0.00024278149,0.000134372,0.000034583984,0.00044325326,0.000086532564,0.000355525,0.00048101813,0.00017058515,0.00008883916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016570972,0.001491385,0.17759757,0.00022341732,0.00029077704,0.030178906,0.051565643,0.37865654,0.0005463557,0.08121954,0.26274776,0.015316384],"study_design_scores_gemma":[0.001766811,0.0007534935,0.058041856,0.00022635606,0.000013149086,0.0029664089,0.00009255873,0.92876345,0.00021202127,0.0038497278,0.002394627,0.00091956015],"about_ca_topic_score_codex":0.000043734366,"about_ca_topic_score_gemma":7.8690033e-7,"teacher_disagreement_score":0.5501069,"about_ca_system_score_codex":0.000046761295,"about_ca_system_score_gemma":0.00018328965,"threshold_uncertainty_score":0.5479532},"labels":[],"label_agreement":null},{"id":"W2169620233","doi":"10.1109/robot.2005.1570148","title":"Motion Planning of Multiple Agents in Virtual Environments using Coordination Graphs","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Motion planning; Motion (physics); Human–computer interaction; Distributed computing; Artificial intelligence; Robot","score_opus":0.03890475539599992,"score_gpt":0.2694232692816011,"score_spread":0.23051851388560118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169620233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34538943,0.000008993613,0.6541726,0.00001186017,0.000111686604,0.0000669187,7.2354544e-7,0.000020478548,0.00021732644],"genre_scores_gemma":[0.8985228,2.6089455e-7,0.1012997,0.000014209595,0.000011067413,0.0000020913947,0.000007004425,0.000004749896,0.00013809843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901724,0.0000539371,0.00026237083,0.00023102734,0.0002576122,0.0001778024],"domain_scores_gemma":[0.99960595,0.000053438605,0.00011453909,0.00019161576,0.00001013767,0.000024324185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021689982,0.00008942066,0.00011488265,0.00022118639,0.00004085128,0.00002181988,0.00024893196,0.000050912506,0.000002981393],"category_scores_gemma":[0.00002260887,0.000093045004,0.000026636999,0.00028709392,0.000023559063,0.00037679708,0.000083236344,0.00006370846,0.0000059778718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002126373,0.00011432115,0.25026354,0.000003422243,0.0000038310777,0.000015197907,0.00021577034,0.7344907,0.0110360775,0.0018726917,0.000052885403,0.0019294329],"study_design_scores_gemma":[0.00036266024,0.000022310429,0.22366197,0.000023327053,0.0000014751274,0.0000040212285,0.0000191258,0.7711466,0.0042597465,0.00041252348,0.000010672996,0.00007559016],"about_ca_topic_score_codex":0.00034328195,"about_ca_topic_score_gemma":0.000002131731,"teacher_disagreement_score":0.55313337,"about_ca_system_score_codex":0.00006704865,"about_ca_system_score_gemma":0.000009549081,"threshold_uncertainty_score":0.3794265},"labels":[],"label_agreement":null},{"id":"W2170111365","doi":"10.21307/ijssis-2017-307","title":"Guidance-Based On-Line Motion Planning For Autonomous Highway Overtaking","year":2008,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Overtaking; Rendezvous; Obstacle; Shadow (psychology); Line (geometry); Context (archaeology); Computer science; Position (finance); Scheme (mathematics); Simulation; Collision avoidance; Real-time computing; Collision; Engineering; Aerospace engineering; Transport engineering; Computer security","score_opus":0.07728811089776272,"score_gpt":0.31142173552267255,"score_spread":0.23413362462490983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170111365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017059106,0.00017014895,0.9720714,0.0009231628,0.008939268,0.00016988543,0.0000064284077,0.00012489143,0.00053568895],"genre_scores_gemma":[0.9630367,0.000018282202,0.03461904,0.0007301817,0.0012242834,0.0000035281098,0.000010068465,0.000022381464,0.000335496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978392,0.00011579305,0.0006165136,0.00038574342,0.0007399742,0.00030275338],"domain_scores_gemma":[0.9982721,0.00046733342,0.00042725092,0.00023290224,0.000444382,0.00015603045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007281813,0.00022866836,0.0002678708,0.00041665498,0.00040219087,0.00041167697,0.0004391878,0.00008850435,0.0000013936443],"category_scores_gemma":[0.0002263484,0.00019983681,0.00012284548,0.00010518785,0.00004137873,0.00022671957,0.000043591866,0.00029964963,0.000023100316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010874432,0.0001015086,0.001231925,0.0000150845,0.00010979645,0.00058907724,0.00073930615,0.96718687,0.00024860568,0.0021834234,0.0023827145,0.025102925],"study_design_scores_gemma":[0.00050357,0.00037488158,0.00052694243,0.00084680144,0.0000057493326,0.0018166112,0.000032441054,0.98468447,0.001404121,0.00010619793,0.0094843935,0.00021383369],"about_ca_topic_score_codex":0.00004226285,"about_ca_topic_score_gemma":2.0260785e-7,"teacher_disagreement_score":0.9459776,"about_ca_system_score_codex":0.00029615522,"about_ca_system_score_gemma":0.00010057348,"threshold_uncertainty_score":0.8149109},"labels":[],"label_agreement":null},{"id":"W2170141090","doi":"10.1109/tase.2011.2123888","title":"Some Complexity Results for Metric View Planning Problem With Traveling Cost and Visibility Range","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metric (unit); Motion planning; Range (aeronautics); Travelling salesman problem; Approximation algorithm; Mathematical optimization; Visibility; Mathematics; Set cover problem; Set (abstract data type); Cover (algebra); Metric space; Robot; Computer science; Algorithm; Discrete mathematics; Artificial intelligence; Engineering","score_opus":0.07460280181598752,"score_gpt":0.2791214548209429,"score_spread":0.2045186530049554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170141090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0216495,0.000056746405,0.9772662,0.00007822752,0.00017984681,0.00041908762,0.000009610987,0.00029041927,0.0000503387],"genre_scores_gemma":[0.6198951,0.0000077523955,0.38000676,0.0000280851,0.000009871412,0.000042568656,3.8813397e-7,0.000005174782,0.000004287002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986605,0.00001452346,0.00024643965,0.00045131327,0.00033435074,0.00029285572],"domain_scores_gemma":[0.9992763,0.00014772496,0.000068239286,0.00023862562,0.00012892896,0.00014020616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012625244,0.00014103708,0.00016263944,0.00035330997,0.00036938064,0.0001690692,0.00025913576,0.000037765447,4.112572e-7],"category_scores_gemma":[0.000036577607,0.00012221132,0.000019190811,0.0010093346,0.0001248603,0.0013026526,0.0000039726256,0.00012136823,0.0000013663933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012955719,0.00025534222,0.0001620874,0.0005381646,0.00006695616,0.000017079552,0.022041503,0.71841365,0.0049937526,0.0052395635,0.000022825905,0.2481195],"study_design_scores_gemma":[0.0005470713,0.00014667874,0.009424569,0.000119502656,0.000009764324,0.000030404322,0.00004477826,0.9861221,0.0032127707,0.00015432552,0.00001737304,0.00017066194],"about_ca_topic_score_codex":0.000018455603,"about_ca_topic_score_gemma":8.3719107e-7,"teacher_disagreement_score":0.5982456,"about_ca_system_score_codex":0.000066419016,"about_ca_system_score_gemma":0.00006973345,"threshold_uncertainty_score":0.49836332},"labels":[],"label_agreement":null},{"id":"W2170236440","doi":"10.1109/imtc.2007.379157","title":"Autonomous Dead-Reckoning Mobile Robot Navigation System With Intelligent Precision Calibration","year":2007,"lang":"en","type":"article","venue":"Conference proceedings - IEEE Instrumentation/Measurement Technology Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Odometry; Mobile robot; Computer science; Mobile robot navigation; Artificial intelligence; Dead reckoning; Computer vision; Fuzzy logic; Robot; Key (lock); Robot control; Global Positioning System","score_opus":0.03876975368117131,"score_gpt":0.2642219180281107,"score_spread":0.22545216434693938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170236440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23665798,0.000055278382,0.7586316,0.0003145968,0.00061891787,0.0012292956,0.0000029637447,0.0015892513,0.0009001292],"genre_scores_gemma":[0.8498079,0.000009462336,0.14959872,0.000040515333,0.00006134798,0.0003836003,0.000018016322,0.000033972952,0.00004643218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99484426,0.000043689335,0.0011709445,0.001388558,0.0015837401,0.0009688256],"domain_scores_gemma":[0.9956476,0.00006432992,0.0009260905,0.00060084584,0.0025024053,0.00025874758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018507862,0.0006248674,0.00054871815,0.00093660055,0.0005939961,0.00080340897,0.0017897391,0.00045821583,0.000014088331],"category_scores_gemma":[0.00011372134,0.0005950787,0.00006997326,0.0016966701,0.0003327444,0.0020445974,0.00024774292,0.00070563616,0.00006381627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019895719,0.00051711773,0.035614394,0.0005602284,0.00031073045,0.00013390288,0.009627368,0.0036452168,0.19808477,0.18042944,0.00014551643,0.57073236],"study_design_scores_gemma":[0.0017004322,0.001599255,0.0026679672,0.002572597,0.000086943524,0.00074324716,0.008953098,0.5209132,0.45678017,0.002472838,0.0001221568,0.001388134],"about_ca_topic_score_codex":0.00005295321,"about_ca_topic_score_gemma":0.00001757109,"teacher_disagreement_score":0.61314994,"about_ca_system_score_codex":0.0011470848,"about_ca_system_score_gemma":0.00072639115,"threshold_uncertainty_score":0.99965006},"labels":[],"label_agreement":null},{"id":"W2170388107","doi":"10.1109/robot.2003.1242224","title":"Real-time path planning with deadlock avoidance of multiple cleaning robots","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Deadlock; Computer science; Path (computing); Motion planning; Artificial neural network; Trajectory; Deadlock prevention algorithms; Real-time computing; Mobile robot; Artificial intelligence; Distributed computing; Computer network","score_opus":0.014863770674933175,"score_gpt":0.2345545645722345,"score_spread":0.21969079389730134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170388107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092980295,0.000030468185,0.9010829,0.000114987066,0.00009109243,0.00013769856,0.0000014298666,0.00040636046,0.0051548225],"genre_scores_gemma":[0.38445464,0.0000018238869,0.615261,0.000043317974,0.000025625643,0.000004389098,0.0000017972467,0.000013101775,0.00019430103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833,0.000045456396,0.00030643132,0.00047101968,0.00041603175,0.00043103864],"domain_scores_gemma":[0.99878865,0.0001663175,0.00019071701,0.000626621,0.000098718905,0.00012898228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002960746,0.00020401264,0.00029086825,0.00010448744,0.0001227395,0.00006366432,0.00078106666,0.00006764468,0.0000045760426],"category_scores_gemma":[0.0000612769,0.00016644018,0.00004453128,0.00044735373,0.000059749567,0.00043367984,0.00017085855,0.00016235326,0.00006859019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020513513,0.00009143988,0.009424894,0.0000255661,0.000033284603,0.00021440242,0.0021881906,0.97042155,0.011533769,0.003985164,0.00019902535,0.0018622156],"study_design_scores_gemma":[0.0028381806,0.0007655971,0.04848461,0.0009541401,0.000018443729,0.00026817794,0.00021705405,0.9162409,0.028128542,0.0012144973,0.0000459592,0.00082390144],"about_ca_topic_score_codex":0.00028418537,"about_ca_topic_score_gemma":0.0000018879919,"teacher_disagreement_score":0.29147434,"about_ca_system_score_codex":0.00006657288,"about_ca_system_score_gemma":0.0001383602,"threshold_uncertainty_score":0.67872334},"labels":[],"label_agreement":null},{"id":"W2170641183","doi":"10.1109/optim.1998.708003","title":"Autonomous Mobile Vehicles For Motion On Uneven And Inclined Surfaces","year":2005,"lang":"en","type":"article","venue":"Proceedings of the 6th International Conference on Optimization of Electrical and Electronic Equipments","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mobile robot; Motion (physics); Computer science; Computer vision; Artificial intelligence; Robot","score_opus":0.020336292183957525,"score_gpt":0.27275615668007896,"score_spread":0.2524198644961214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170641183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4663456,0.00030284552,0.51372176,0.011650299,0.00036535924,0.0018046005,0.000026904565,0.0001672016,0.005615415],"genre_scores_gemma":[0.9781115,0.00013823241,0.021271573,0.00011706173,0.00002931083,0.000034314617,0.00000395178,0.000006488533,0.0002875604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893266,0.000009404097,0.0002795676,0.00027203237,0.0002951464,0.00021117195],"domain_scores_gemma":[0.9992326,0.00007365495,0.00027536985,0.00006462452,0.00031254278,0.00004121201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002484782,0.00012005177,0.00015584876,0.000118703065,0.000085736276,0.000061083796,0.0004588957,0.000058475798,0.000004626036],"category_scores_gemma":[0.00011084511,0.00009283835,0.000034004897,0.00016565155,0.00004720503,0.00025954965,0.00010301777,0.00012015792,5.3607465e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003594254,0.0006501206,0.0020432342,0.00006435556,0.00017922497,1.2716538e-7,0.0005838,0.36380512,0.010942409,0.5184994,0.00033520433,0.10253761],"study_design_scores_gemma":[0.0005805432,0.00074442703,0.00048290667,0.000055431003,0.000008625017,0.0000029642906,0.000007899396,0.98085964,0.011093677,0.005963385,0.00010507054,0.000095420284],"about_ca_topic_score_codex":0.000003718708,"about_ca_topic_score_gemma":1.6464455e-7,"teacher_disagreement_score":0.6170545,"about_ca_system_score_codex":0.0000935604,"about_ca_system_score_gemma":0.000065631335,"threshold_uncertainty_score":0.37858382},"labels":[],"label_agreement":null},{"id":"W2170769545","doi":"10.1109/cira.2001.1013182","title":"Real-time collision-free path planning and tracking control of a nonholonomic mobile robot using a biologically inspired approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Guelph","funders":"","keywords":"Backstepping; Mobile robot; Computer science; Kinematics; Artificial neural network; Control theory (sociology); Nonholonomic system; Motion planning; Trajectory; Robot; Controller (irrigation); Obstacle avoidance; Control engineering; Artificial intelligence; Engineering; Adaptive control; Control (management); Physics","score_opus":0.04089357161163703,"score_gpt":0.25176874027270413,"score_spread":0.2108751686610671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170769545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22047928,0.00023137033,0.77738965,0.00003129594,0.00006483621,0.00036320265,0.000009444882,0.00020048505,0.0012304198],"genre_scores_gemma":[0.50498664,0.000008285062,0.49488357,0.000041612086,0.000024328558,0.000013859552,0.0000015908331,0.00000885292,0.00003126661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820334,0.00014046612,0.0004696943,0.0005755007,0.00021814916,0.00039283966],"domain_scores_gemma":[0.99857587,0.00031968785,0.000252374,0.0006493007,0.00007257406,0.00013017401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005253856,0.00022264791,0.0004819215,0.00015077341,0.00014660458,0.000115329894,0.0008520544,0.00014521721,0.000011247604],"category_scores_gemma":[0.00011353036,0.00018200742,0.000060958453,0.00028558268,0.000111954745,0.00032683997,0.0002939493,0.00014511654,0.000009152405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000659085,0.0006417644,0.013696199,0.00007689218,0.00014674386,0.00019608451,0.00502207,0.8641442,0.09515149,0.0008870646,0.00046250693,0.019509077],"study_design_scores_gemma":[0.0011869014,0.00027659957,0.0045158286,0.000058640617,0.000011484524,0.00010269341,0.000060159888,0.99321294,0.00027032103,0.0000662239,0.000010647447,0.00022755012],"about_ca_topic_score_codex":0.00006427812,"about_ca_topic_score_gemma":7.1663735e-8,"teacher_disagreement_score":0.28450736,"about_ca_system_score_codex":0.00004589483,"about_ca_system_score_gemma":0.000032912132,"threshold_uncertainty_score":0.7422047},"labels":[],"label_agreement":null},{"id":"W2170942374","doi":"10.1109/med.2008.4602109","title":"UAV collision avoidance using cooperative predictive control","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Université Laval","funders":"","keywords":"Autopilot; Model predictive control; Control theory (sociology); Trajectory; Collision avoidance; Computer science; Flexibility (engineering); Obstacle avoidance; Control engineering; Stability (learning theory); Engineering; Control (management); Collision; Mobile robot; Mathematics; Robot; Artificial intelligence","score_opus":0.03027154613219007,"score_gpt":0.2524859455816797,"score_spread":0.22221439944948962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170942374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023560042,0.00008216019,0.97248226,0.00018640468,0.00038100727,0.0002322906,0.00000573122,0.00025209095,0.002818019],"genre_scores_gemma":[0.64227223,0.000006187717,0.3565323,0.00042816802,0.00007352934,0.00000868102,9.197189e-7,0.0000072301445,0.0006707524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867415,0.00011576669,0.00020050073,0.00039637228,0.0003260219,0.00028720227],"domain_scores_gemma":[0.99904937,0.00018557964,0.00007563928,0.00037624448,0.00020365938,0.00010952518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019096801,0.00014483687,0.00020474194,0.00006845128,0.00034955042,0.000053846554,0.00051202014,0.000061362996,0.000007843168],"category_scores_gemma":[0.00010372111,0.00012069961,0.000038553808,0.00038511324,0.000094847324,0.0005723468,0.00010407871,0.00014738746,0.00007370538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039661787,0.00016716472,0.0056441645,0.000006096261,0.00007502236,0.0005124533,0.004385224,0.97335005,0.0042357766,0.006157126,0.0045471448,0.0008800926],"study_design_scores_gemma":[0.0006639804,0.00013492559,0.004065656,0.000022603526,0.000003877653,0.00016983389,0.00002683632,0.9922207,0.0023400928,0.00007392455,0.00012325533,0.00015433192],"about_ca_topic_score_codex":0.000042792904,"about_ca_topic_score_gemma":4.5760882e-7,"teacher_disagreement_score":0.6187122,"about_ca_system_score_codex":0.00008524738,"about_ca_system_score_gemma":0.00017989302,"threshold_uncertainty_score":0.49219874},"labels":[],"label_agreement":null},{"id":"W2171157557","doi":"10.1109/cdc.2008.4738911","title":"Efficient dynamic programming for optimal multi-location robot rendezvous","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rendezvous; Correctness; Dynamic programming; Computer science; Robot; Tree (set theory); Node (physics); Mathematical optimization; Robot kinematics; Algorithm; Mobile robot; Artificial intelligence; Mathematics; Engineering","score_opus":0.038450427431431795,"score_gpt":0.2883308256371076,"score_spread":0.2498803982056758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171157557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056240195,0.0000804174,0.99262416,0.00024975112,0.0003838137,0.00046328054,8.227265e-7,0.0004994497,0.00007425465],"genre_scores_gemma":[0.23767926,9.2396306e-7,0.76137334,0.00004359929,0.000022340653,0.00007074342,0.000005525944,0.00000959998,0.0007946887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867433,0.000025986898,0.00022800096,0.00044125816,0.00023789307,0.00039253497],"domain_scores_gemma":[0.99915856,0.00008497888,0.00008145985,0.00043009952,0.0001497057,0.00009521698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024237014,0.0001406957,0.00014131195,0.00009872758,0.0002575694,0.00006319222,0.00054167537,0.00006081349,0.0000012533135],"category_scores_gemma":[0.0000885131,0.00012792225,0.000057275247,0.00035638764,0.000045706893,0.000099752324,0.00010654976,0.00008435837,0.000040575684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000387253,0.00018635343,0.00012859891,0.000015002594,0.000011628896,0.000023625995,0.0010052329,0.9399818,0.00023301465,0.00065734226,0.00012190771,0.057631627],"study_design_scores_gemma":[0.00046214944,0.00010658849,0.0035576723,0.00001398638,0.0000037253396,0.00011718982,0.000026242518,0.99516684,0.00022394564,0.0000070425585,0.00014088783,0.00017375161],"about_ca_topic_score_codex":0.000021820833,"about_ca_topic_score_gemma":0.0000014579628,"teacher_disagreement_score":0.23205525,"about_ca_system_score_codex":0.00008663164,"about_ca_system_score_gemma":0.00009851889,"threshold_uncertainty_score":0.5216518},"labels":[],"label_agreement":null},{"id":"W2171189639","doi":"10.1109/iros.1991.174752","title":"Collision avoidance system concept for mobile servicing system","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Collision; Computer science; Testbed; Collision avoidance system; Redundancy (engineering); International Space Station; Octree; Kinematics; Real-time computing; Simulation; Engineering; Aerospace engineering; Artificial intelligence; Operating system; Computer network; Computer security","score_opus":0.018999297999750957,"score_gpt":0.22670448557020556,"score_spread":0.2077051875704546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171189639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002080546,0.00041368685,0.9892501,0.00009402487,0.0011995336,0.00072346034,0.000007731711,0.0011099546,0.005120922],"genre_scores_gemma":[0.64910305,0.0000012947954,0.34853673,0.000102169266,0.00014432252,0.00016821551,0.00000200357,0.00001543086,0.001926806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841297,0.000067622954,0.000337829,0.00048891,0.000301384,0.00039128886],"domain_scores_gemma":[0.99868524,0.0002441096,0.00013375186,0.0006723088,0.00014573078,0.00011886011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033183832,0.00016993018,0.00027543766,0.0000698973,0.00025192404,0.0001694488,0.0008705359,0.00008343919,0.000004316789],"category_scores_gemma":[0.000017687516,0.00014629329,0.000071247574,0.0003442568,0.000018606981,0.00034513389,0.00013556043,0.00008334702,0.00020702202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001670441,0.00025217645,0.0005441416,0.0033063435,0.00013904578,0.00033196734,0.013946111,0.5976757,0.0018851684,0.28755388,0.049808543,0.044540253],"study_design_scores_gemma":[0.0004143659,0.000111775356,0.000030390454,0.00033133564,0.000005702674,0.000089508816,0.00073901727,0.99523175,0.0017784245,0.000006231771,0.0010722476,0.0001892704],"about_ca_topic_score_codex":0.00003677795,"about_ca_topic_score_gemma":5.5703634e-7,"teacher_disagreement_score":0.6470225,"about_ca_system_score_codex":0.0002031939,"about_ca_system_score_gemma":0.000017491659,"threshold_uncertainty_score":0.59656674},"labels":[],"label_agreement":null},{"id":"W2171388963","doi":"10.1109/isvd.2007.33","title":"Pursuit-Evasion Voronoi Diagrams in \\ell_1","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voronoi diagram; Pursuer; Pursuit-evasion; Traverse; Plane (geometry); Combinatorics; Partition (number theory); Point (geometry); Path (computing); Mathematics; Time complexity; Metric (unit); Computer science; Algorithm; Discrete mathematics; Mathematical optimization; Geometry; Engineering; Geography","score_opus":0.013283535889272976,"score_gpt":0.2565565891737532,"score_spread":0.2432730532844802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171388963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01752061,0.00006842938,0.96502525,0.0005480118,0.0005635786,0.000110293855,1.2830328e-7,0.00022716892,0.01593654],"genre_scores_gemma":[0.46293882,0.0000064410324,0.5346927,0.0004926517,0.00009689881,0.0000045672227,0.000001808653,0.000009456585,0.0017566484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986578,0.00003055151,0.00023216796,0.00033614162,0.00031310393,0.0004302213],"domain_scores_gemma":[0.9991903,0.00015740558,0.000041062223,0.0004724037,0.000025403917,0.00011343575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011285774,0.000109706765,0.00012463541,0.00015044557,0.00004183767,0.00008327533,0.00084420457,0.000070993665,0.000015600424],"category_scores_gemma":[0.00006220975,0.00009391872,0.00003432298,0.00048381733,0.000022804676,0.00030191583,0.00020867522,0.00014420765,0.0002866852],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009136865,0.00028912487,0.047895785,0.000014424442,0.000010730534,0.000651479,0.0017470894,0.0033176322,0.0019502621,0.04689224,0.0038228424,0.89339924],"study_design_scores_gemma":[0.0012052619,0.00023206725,0.47064117,0.00007796963,0.000003959758,0.000085907806,0.00012592787,0.5066082,0.008863011,0.005063388,0.0063668014,0.0007263511],"about_ca_topic_score_codex":0.00013008076,"about_ca_topic_score_gemma":0.00004765273,"teacher_disagreement_score":0.8926729,"about_ca_system_score_codex":0.0000665111,"about_ca_system_score_gemma":0.000028870716,"threshold_uncertainty_score":0.38298944},"labels":[],"label_agreement":null},{"id":"W2171730706","doi":"10.1109/robot.1996.506851","title":"Obstacle count independent real-time collision avoidance","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Workspace; Collision detection; Computer science; Teleoperation; Collision avoidance; Collision; Obstacle; Computer vision; Artificial intelligence; Voxel; Motion planning; Obstacle avoidance; Robot; Real-time computing; Simulation; Mobile robot","score_opus":0.019732061024760293,"score_gpt":0.22752796370976305,"score_spread":0.20779590268500275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171730706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0247632,0.00029738087,0.8830968,0.0011117666,0.0011299027,0.00032016652,0.000005312991,0.0010398569,0.088235624],"genre_scores_gemma":[0.21927865,0.00018886986,0.70638293,0.00066353625,0.00020450485,0.00003474141,0.000005028374,0.000034778273,0.07320696],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983697,0.000058562528,0.00022689457,0.00043083707,0.0005568316,0.00035720313],"domain_scores_gemma":[0.9988658,0.0001299424,0.00007769349,0.00070436543,0.00008388086,0.00013835743],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027908102,0.00014253588,0.00016298382,0.00007335205,0.00013880579,0.00022398801,0.00086467434,0.000076140175,0.00014336637],"category_scores_gemma":[0.000042687287,0.00012577084,0.000036724272,0.00034180784,0.000028349757,0.00044529067,0.0002080414,0.0001330685,0.0027843218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001944074,0.0014435335,0.0050027096,0.000057787125,0.0001269359,0.0017303082,0.007452222,0.2744941,0.01720906,0.10018391,0.5163677,0.07591234],"study_design_scores_gemma":[0.00025746875,0.00006502913,0.0031092921,0.00001681671,0.0000021687495,0.000055333163,0.000012519558,0.9922634,0.0005800246,0.00018556739,0.0032537775,0.00019863244],"about_ca_topic_score_codex":0.0001069106,"about_ca_topic_score_gemma":6.1176013e-7,"teacher_disagreement_score":0.71776927,"about_ca_system_score_codex":0.00009700825,"about_ca_system_score_gemma":0.00002324779,"threshold_uncertainty_score":0.9979921},"labels":[],"label_agreement":null},{"id":"W2171877226","doi":"10.1109/tro.2009.2035745","title":"Path Planning for Improved Visibility Using a Probabilistic Road Map","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Motion planning; Planner; Computer vision; Computer science; Visibility; Artificial intelligence; Probabilistic logic; Probabilistic roadmap; Path (computing); Point (geometry); Software; Robot; Mathematics; Geography","score_opus":0.036854988874796574,"score_gpt":0.29683657875132735,"score_spread":0.25998158987653075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171877226","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040779687,0.000009957939,0.99014425,0.0004486622,0.004103087,0.00075113523,0.000034940975,0.0004027623,0.00002720599],"genre_scores_gemma":[0.17549786,5.023585e-7,0.8240157,0.00017271559,0.000112429705,0.00005051266,0.0000028242664,0.00003243045,0.000115032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979614,0.000059051134,0.00043962235,0.0007167646,0.00025132662,0.000571858],"domain_scores_gemma":[0.99807817,0.000322637,0.0001483592,0.0010489075,0.00019292373,0.00020900452],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050148333,0.00030005287,0.00031498863,0.00016772664,0.00045487864,0.00019888925,0.00074338575,0.00020809309,0.0000054638035],"category_scores_gemma":[0.000053141943,0.00029778152,0.00017589919,0.00033751788,0.00009628554,0.00035802493,0.0000062798035,0.00064475863,0.00001774114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020652138,0.00024790288,0.000012190586,0.00006111306,0.000029541141,0.0000073963647,0.00033492577,0.9823535,0.008036794,0.00032197533,0.000056149398,0.008517853],"study_design_scores_gemma":[0.00055577187,0.00020684295,0.0001228351,0.000048218593,0.00004581799,0.00003806378,0.00001542602,0.9950137,0.0021292919,0.0014340646,0.0000475125,0.00034245165],"about_ca_topic_score_codex":0.000035300818,"about_ca_topic_score_gemma":0.000005873495,"teacher_disagreement_score":0.17141989,"about_ca_system_score_codex":0.00010423746,"about_ca_system_score_gemma":0.0002138031,"threshold_uncertainty_score":0.9999474},"labels":[],"label_agreement":null},{"id":"W2171913316","doi":"10.1109/iros.2009.5354019","title":"Planning collision-free and occlusion-free paths for industrial manipulators with eye-to-hand configuration","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Context (archaeology); Task (project management); Collision; Motion planning; Quadtree; Robot; Probabilistic logic; Object (grammar); Motion (physics); Pixel; Engineering","score_opus":0.03332332644958443,"score_gpt":0.2728092533032707,"score_spread":0.23948592685368628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171913316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092602246,0.000032702173,0.90266246,0.002434195,0.00035914913,0.00075260835,0.000011815512,0.00019155782,0.00095325],"genre_scores_gemma":[0.48252997,0.000001246598,0.51552117,0.0010374697,0.00028631405,0.00002496456,0.000015567792,0.000017473732,0.0005658298],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832153,0.000037609796,0.00029173787,0.0005487167,0.00042745183,0.00037292115],"domain_scores_gemma":[0.9984857,0.00021644922,0.000112724934,0.00082125317,0.00012720413,0.00023670188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038600274,0.00022360077,0.00025549415,0.00018052506,0.0003906642,0.0004042681,0.0009138421,0.00014172446,0.00000350491],"category_scores_gemma":[0.0003603941,0.0001737752,0.00003071456,0.000387526,0.000036724487,0.0004381646,0.00024227545,0.00014333277,0.0000053263943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002051521,0.0008782765,0.049128912,0.00011191943,0.00031853208,0.0013519512,0.022398861,0.22329278,0.01415568,0.14063424,0.32556766,0.22010967],"study_design_scores_gemma":[0.015955556,0.0066774646,0.10076881,0.00068886805,0.00007639844,0.00026518028,0.0004930541,0.83666986,0.014300709,0.016942328,0.0052045016,0.0019572736],"about_ca_topic_score_codex":0.000022404816,"about_ca_topic_score_gemma":0.0000026303724,"teacher_disagreement_score":0.6133771,"about_ca_system_score_codex":0.00004490151,"about_ca_system_score_gemma":0.00010950688,"threshold_uncertainty_score":0.7086347},"labels":[],"label_agreement":null},{"id":"W2172167902","doi":"10.1109/tsmcb.2011.2132716","title":"Target-Motion Prediction for Robotic Search and Rescue in Wilderness Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrain; Search and rescue; Wilderness; Motion (physics); Wilderness area; Computer science; Artificial intelligence; Modular design; Robot; Mechanism (biology); Space (punctuation); Simulation; Computer vision; Machine learning; Human–computer interaction; Geography; Ecology; Biology; Cartography; Physics","score_opus":0.03977241979494068,"score_gpt":0.23674941604695598,"score_spread":0.1969769962520153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172167902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037461672,0.0003244255,0.95932496,0.00006848976,0.0013421922,0.0010225857,0.000027453798,0.00011175892,0.00031644478],"genre_scores_gemma":[0.97888523,0.00026375623,0.018550163,0.00003921139,0.000083890016,0.0001977728,0.000008053814,0.00004557007,0.0019263459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997269,0.00022886234,0.00061576976,0.0008568191,0.00045576878,0.00057379424],"domain_scores_gemma":[0.99888283,0.00011712536,0.00008622855,0.000595948,0.000049366317,0.00026851927],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061287545,0.00035312594,0.0004027824,0.00031713012,0.00022075266,0.0001722951,0.0003759212,0.0002504706,0.0000073312103],"category_scores_gemma":[0.0000071801915,0.00036947627,0.000068563575,0.00024525763,0.00016831336,0.00028692323,0.00001461421,0.00034722764,0.000029434652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022234258,0.0019549222,0.0060432204,0.00069212704,0.00033387588,0.000111696725,0.017778037,0.9171357,0.0015218734,0.0074172285,0.00036331743,0.04642564],"study_design_scores_gemma":[0.0017853087,0.00071859144,0.016798874,0.0003359515,0.00006598734,0.000116727366,0.00030259247,0.975431,0.0029659772,0.0003923353,0.0005430968,0.0005435253],"about_ca_topic_score_codex":0.00041323013,"about_ca_topic_score_gemma":0.000043286567,"teacher_disagreement_score":0.9414236,"about_ca_system_score_codex":0.00010984597,"about_ca_system_score_gemma":0.000034197805,"threshold_uncertainty_score":0.9998757},"labels":[],"label_agreement":null},{"id":"W2172246582","doi":"10.1109/cimsa.2006.250741","title":"Multiple Waypoint Path Planning for a Mobile Robot using Genetic Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Genetic algorithm; Waypoint; Path (computing); Motion planning; Planner; Mobile robot; Computer science; Any-angle path planning; Robot; MATLAB; Mathematical optimization; Algorithm; Task (project management); Real-time computing; Artificial intelligence; Mathematics; Engineering; Machine learning; Computer network","score_opus":0.03449466503188944,"score_gpt":0.2778759382008582,"score_spread":0.24338127316896876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172246582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014785272,0.00028186652,0.9828869,0.000045645094,0.00059587636,0.0006127288,0.000008339169,0.00046154877,0.00032181607],"genre_scores_gemma":[0.09311784,8.5622537e-7,0.9060025,0.00011031811,0.00029047113,0.00011025266,0.000010244566,0.000028394494,0.00032910996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978444,0.00004769944,0.0004342729,0.0006702739,0.00032628977,0.0006770538],"domain_scores_gemma":[0.99874145,0.00026835472,0.00014226772,0.0006150192,0.00011257012,0.000120360055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002999189,0.0002652668,0.00028994467,0.00015053748,0.00026668733,0.0002212636,0.00073687633,0.00010473767,0.000005720319],"category_scores_gemma":[0.00004136392,0.0002453953,0.00012506089,0.00032398757,0.000040039944,0.0003077273,0.00021377568,0.00012454213,0.000024614768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005547251,0.00011177193,0.0027747632,0.000022869579,0.000017459755,0.00008057413,0.00032937678,0.9765776,0.0032431206,0.00047504378,0.0012269876,0.015134917],"study_design_scores_gemma":[0.0007095752,0.00013775734,0.0050723823,0.0000436339,0.000010410828,0.00010287716,0.000045930374,0.98984087,0.0019687377,0.0010066434,0.00070498453,0.00035620734],"about_ca_topic_score_codex":0.00037748853,"about_ca_topic_score_gemma":0.0000015679494,"teacher_disagreement_score":0.078332566,"about_ca_system_score_codex":0.00009701441,"about_ca_system_score_gemma":0.00009213068,"threshold_uncertainty_score":0.9999998},"labels":[],"label_agreement":null},{"id":"W2184609577","doi":"","title":"Comparing the Performance of Genetic Algorithm and Ant Colony Optimization Algorithm for Mobile Robot Path Planning in the Dynamic Environments with Different Complexities","year":2013,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Algorithm; Path (computing); Genetic algorithm; Motion planning; Variable (mathematics); Chromosome; Robot; MATLAB; Mobile robot; Artificial intelligence; Machine learning; Mathematics","score_opus":0.027880566671355078,"score_gpt":0.26482240999819123,"score_spread":0.23694184332683615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184609577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43189302,0.0029668934,0.5646004,0.000148523,0.000040297135,0.00034111348,0.0000014041186,0.0000035620951,0.0000047671815],"genre_scores_gemma":[0.74626577,0.0014558921,0.2520882,0.00007323557,0.000037116923,0.00006794609,6.743325e-7,0.0000059228196,0.000005243772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894714,0.00004435623,0.00041677468,0.0001325244,0.0002635028,0.00019568714],"domain_scores_gemma":[0.99909127,0.00033575666,0.00041109187,0.000101899124,0.000028502813,0.00003148226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033801715,0.00015122317,0.00035958068,0.000065893655,0.00020363547,0.000038476952,0.00035421742,0.000043885764,1.6690686e-7],"category_scores_gemma":[0.0000059654776,0.00007610177,0.000020246653,0.00008411526,0.00022808726,0.00014915863,0.00013713299,0.00031019488,8.1760355e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027414997,0.00007197425,0.012165755,0.00009472959,0.00021626797,0.000004431656,0.024748534,0.86544544,0.0003173015,0.000089725305,0.0001233147,0.09669515],"study_design_scores_gemma":[0.0006720684,0.00035222954,0.099658996,0.00014059851,0.00003102733,0.00008200832,0.0056266664,0.8930986,0.00007010472,0.00016266615,0.000010801699,0.00009422491],"about_ca_topic_score_codex":0.0000021280837,"about_ca_topic_score_gemma":5.564549e-8,"teacher_disagreement_score":0.31437275,"about_ca_system_score_codex":0.000029759138,"about_ca_system_score_gemma":0.000012083271,"threshold_uncertainty_score":0.31033403},"labels":[],"label_agreement":null},{"id":"W2184832316","doi":"","title":"Path Following and Stabilization of an Autonomous Bicycle","year":2013,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Control theory (sociology); Setpoint; Controller (irrigation); Linearization; Torque; Angular velocity; Feedback linearization; PID controller; Nonlinear system; Engineering; Control engineering; Computer science; Control (management)","score_opus":0.009109268099368914,"score_gpt":0.2040285264891717,"score_spread":0.19491925838980279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184832316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939066,0.00007526522,0.005210431,0.00006398532,0.00030883722,0.00019725905,0.0000075607018,0.000079902034,0.00015012945],"genre_scores_gemma":[0.53157204,0.000042846033,0.38421237,0.0000116695655,0.00003350145,9.1002346e-7,0.0004432275,0.000041628147,0.0836418],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897087,0.00006906244,0.00013145634,0.0003841564,0.00025950992,0.00018495561],"domain_scores_gemma":[0.9990357,0.000032639386,0.0002904533,0.00042229047,0.00012474446,0.00009418314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015634966,0.00016594441,0.00033813767,0.00023812297,0.00011408964,0.0000356402,0.0005803238,0.00018944973,0.000010751023],"category_scores_gemma":[0.000013668273,0.00019610145,0.00008053482,0.00020205263,0.0000402141,0.000749012,0.0000952137,0.0001164269,0.000007819273],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006778647,0.00043358334,0.0065732277,0.0014917097,0.00036400964,0.00021944597,0.8745569,0.0030022387,0.033071704,0.0024808263,0.00032922914,0.07740934],"study_design_scores_gemma":[0.0021335995,0.0011253409,0.16992825,0.0011033904,0.0003235132,0.000014670144,0.1653757,0.64698625,0.010049503,0.0014643527,0.000061963096,0.0014334804],"about_ca_topic_score_codex":0.013350257,"about_ca_topic_score_gemma":0.00076220033,"teacher_disagreement_score":0.7091812,"about_ca_system_score_codex":0.000038187216,"about_ca_system_score_gemma":0.000094211806,"threshold_uncertainty_score":0.9932199},"labels":[],"label_agreement":null},{"id":"W2187178535","doi":"","title":"Robofoot ÉPM Team Description - RoboCup2005 MiddleSize League","year":2005,"lang":"fr","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Obstacle; Robot; Computer science; League; Supervisor; Artificial intelligence; Field (mathematics); Human–computer interaction; Obstacle avoidance; Mechanism (biology); Mobile robot","score_opus":0.06375337275805164,"score_gpt":0.2665765865255058,"score_spread":0.20282321376745416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187178535","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009688219,0.008346457,0.89837056,0.024580834,0.0058997106,0.00039356487,0.000008785932,0.000672265,0.060758974],"genre_scores_gemma":[0.028238174,0.00015763269,0.70951945,0.002028055,0.0021392882,0.000019862577,0.000010106936,0.000048026086,0.2578394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99539894,0.00033819277,0.00085520215,0.0011710606,0.00080078276,0.0014358173],"domain_scores_gemma":[0.9974105,0.0002275016,0.00026043924,0.0013021176,0.0002524084,0.00054704014],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010563253,0.0005829282,0.00054146716,0.00024880632,0.0003713717,0.0008178569,0.0015285863,0.0004513778,0.0006514097],"category_scores_gemma":[0.0001964104,0.0006103185,0.0002451087,0.0008493115,0.00027046932,0.0027870205,0.0004551968,0.0006850482,0.008165396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017627972,0.000887073,0.0011991715,0.000097125114,0.000117623604,0.00015975819,0.0031342092,0.25065604,0.0013116102,0.05981987,0.21720116,0.46539873],"study_design_scores_gemma":[0.00075181224,0.0002101312,0.0039665173,0.00026146675,0.000050467097,0.0004984206,0.00009025523,0.8241595,0.0013709419,0.00089900923,0.16697237,0.0007690813],"about_ca_topic_score_codex":0.00078369235,"about_ca_topic_score_gemma":0.00011042726,"teacher_disagreement_score":0.5735035,"about_ca_system_score_codex":0.000506358,"about_ca_system_score_gemma":0.00036613696,"threshold_uncertainty_score":0.9996348},"labels":[],"label_agreement":null},{"id":"W2188156513","doi":"","title":"Symbol Recognition and Artificial Emotion for Making an Autonomoius Robot Attend the AAAI Conference.","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Computer science; Human–computer interaction; Mobile robot; sort; Artificial intelligence; Compass; Pentium; Social robot; Frame (networking); Robot control; Computer vision","score_opus":0.275299898719469,"score_gpt":0.3672453488869614,"score_spread":0.09194545016749239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188156513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021521924,0.000017748522,0.9677881,0.0047371285,0.0006410143,0.00076479133,0.000077951125,0.00023517277,0.004216198],"genre_scores_gemma":[0.96194386,0.000015359057,0.036566548,0.0007162447,0.0003660622,0.000117772695,0.00007521128,0.000016605205,0.00018230436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972446,0.00020921355,0.00060802547,0.0008020134,0.00069646613,0.000439661],"domain_scores_gemma":[0.99805784,0.0004538913,0.0001995467,0.00037108056,0.0007895376,0.00012812686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011198091,0.00028477795,0.00024393953,0.0001820872,0.00065376,0.000844525,0.00080953975,0.00016369448,0.0003913783],"category_scores_gemma":[0.00030076894,0.00024777022,0.00007344723,0.0003691924,0.00023114884,0.00077258353,0.000059913345,0.0003297041,0.0005987948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005858457,0.0001257712,0.000005559213,0.000007270374,0.000013385188,0.000003298427,0.0011050468,0.0044057034,0.0009408861,0.23114197,0.000058107013,0.76213443],"study_design_scores_gemma":[0.000040113715,0.0002515111,0.0005273636,0.00006930433,0.000009543337,0.000020865733,0.00020353662,0.6851203,0.0030975484,0.31025657,0.00013279979,0.00027056006],"about_ca_topic_score_codex":0.00003080582,"about_ca_topic_score_gemma":0.000033253666,"teacher_disagreement_score":0.94042194,"about_ca_system_score_codex":0.000103877996,"about_ca_system_score_gemma":0.00032559232,"threshold_uncertainty_score":0.99999744},"labels":[],"label_agreement":null},{"id":"W2190168015","doi":"10.13053/cys-19-3-2014","title":"Cockpit Design for First Person View Flight for a Remotely Operated Quadrotor Helicopter","year":2015,"lang":"en","type":"article","venue":"Computación y Sistemas","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Cockpit; Heading (navigation); Computer science; Aeronautics; Flight management system; Key (lock); Graphical user interface; Interface (matter); Human–computer interaction; Workload; Simulation; Flight simulator; Systems engineering; Engineering; Aerospace engineering; Computer security; Operating system","score_opus":0.18173801918746957,"score_gpt":0.308477145389287,"score_spread":0.12673912620181743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190168015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061050913,0.000957157,0.99033386,0.002547926,0.0016884179,0.0031050823,0.000019515093,0.00050443236,0.0002330944],"genre_scores_gemma":[0.034486245,0.000004725384,0.9625378,0.0006925231,0.00043821544,0.0005303952,0.00003280521,0.00006665782,0.001210593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970963,0.00018979995,0.0005562495,0.0009711348,0.00044734243,0.0007391621],"domain_scores_gemma":[0.9969441,0.00075933716,0.00023944545,0.00092804566,0.00068789657,0.00044117402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011714875,0.00043342658,0.0006997201,0.00017294052,0.0003716932,0.00045700354,0.0014541819,0.00019414857,0.000003175849],"category_scores_gemma":[0.0003897256,0.00039037084,0.00024454077,0.00047147882,0.00005974748,0.00043726264,0.00018205192,0.00014491762,0.00015641798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001206782,0.0017539376,0.0008001753,0.005118239,0.001275894,0.00046440822,0.026020776,0.415172,0.0015983073,0.03124213,0.41424522,0.101102106],"study_design_scores_gemma":[0.0021753532,0.0009089084,0.000114040246,0.0003564694,0.000040136387,0.000101658625,0.000051985946,0.9556701,0.00070480944,0.0005800418,0.038726907,0.0005695648],"about_ca_topic_score_codex":0.000016271688,"about_ca_topic_score_gemma":0.0000022012912,"teacher_disagreement_score":0.5404981,"about_ca_system_score_codex":0.00022336727,"about_ca_system_score_gemma":0.0003138414,"threshold_uncertainty_score":0.9998548},"labels":[],"label_agreement":null},{"id":"W2205612522","doi":"10.5772/61555","title":"Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments","year":2015,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Motion planning; Path (computing); Underwater; Grid reference; Key (lock); Grid; Artificial intelligence; Position (finance); Algorithm; Robot; Mobile robot","score_opus":0.030155451700461033,"score_gpt":0.2807250885677465,"score_spread":0.25056963686728545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205612522","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035142514,0.0004370748,0.98256254,0.0005107436,0.0126138395,0.00019843971,0.0000021493383,0.00004171627,0.00011922589],"genre_scores_gemma":[0.40847328,0.000008830511,0.5901929,0.00024933665,0.0008539933,0.000008956867,0.000005231655,0.000026194584,0.0001812603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651736,0.00025876184,0.0010446858,0.00035990917,0.0013911774,0.0004281265],"domain_scores_gemma":[0.9979187,0.00027912707,0.00093618646,0.00034753323,0.00026609725,0.00025236016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011454377,0.00026017317,0.0004479695,0.00043697737,0.00005184486,0.00019629845,0.0016355217,0.00009304537,0.0000013731873],"category_scores_gemma":[0.00030870741,0.00023394784,0.00012431538,0.00034275648,0.000043356293,0.00074930117,0.0001513536,0.00038897805,0.000026847978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002721326,0.00015620602,0.004365142,0.0000038005448,0.000040326053,0.0006118647,0.00015232334,0.9784215,0.0001340561,0.00008589365,0.00008306619,0.015918598],"study_design_scores_gemma":[0.002877226,0.0002485497,0.006402227,0.0005556453,0.0000071316185,0.00029890542,0.00007280078,0.9885144,0.00005246462,0.000049485694,0.00069996435,0.0002211914],"about_ca_topic_score_codex":0.000031598094,"about_ca_topic_score_gemma":0.0000014100904,"teacher_disagreement_score":0.40495902,"about_ca_system_score_codex":0.00056690624,"about_ca_system_score_gemma":0.00015600448,"threshold_uncertainty_score":0.9540116},"labels":[],"label_agreement":null},{"id":"W2210721416","doi":"10.1007/s12369-015-0310-2","title":"Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning","year":2015,"lang":"en","type":"article","venue":"International Journal of Social Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Artificial intelligence; Robotics; Computer science; Motion planning; Path (computing); Reinforcement; Machine learning; Psychology; Robot; Social psychology","score_opus":0.07762826465960976,"score_gpt":0.3289133293213839,"score_spread":0.25128506466177414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2210721416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0203176,0.00002592763,0.9769975,0.0006189733,0.0012602323,0.000058567264,5.7833057e-7,0.000013695791,0.0007069115],"genre_scores_gemma":[0.8369039,0.000003263187,0.16221322,0.0001813386,0.00055511744,6.1905513e-7,0.0000028066227,0.000012798122,0.00012688334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977407,0.0001347879,0.0005953645,0.00014983975,0.0011372883,0.0002420151],"domain_scores_gemma":[0.99878997,0.00005391702,0.00072624994,0.000070373484,0.00023674913,0.00012274369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078748586,0.00014028126,0.00023535984,0.00026768007,0.00011411794,0.00011756999,0.00089190115,0.00009309619,0.0000027278445],"category_scores_gemma":[0.00014770185,0.00015108095,0.00009358987,0.00014665604,0.000060674847,0.00063286815,0.00026516544,0.0004777609,0.000006852266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021415703,0.000055583423,0.0036770625,0.0000010662636,0.000078225334,0.0004540255,0.007287051,0.98284113,0.0004828217,0.004562823,0.00019232508,0.00034649548],"study_design_scores_gemma":[0.0022443854,0.00030941883,0.0046018288,0.00018246255,0.000023713854,0.00011874031,0.001656707,0.9852243,0.00012900775,0.004760636,0.00044232185,0.00030644843],"about_ca_topic_score_codex":0.000026543776,"about_ca_topic_score_gemma":8.162862e-7,"teacher_disagreement_score":0.8165864,"about_ca_system_score_codex":0.0009129055,"about_ca_system_score_gemma":0.00034335614,"threshold_uncertainty_score":0.6160903},"labels":[],"label_agreement":null},{"id":"W2211905658","doi":"10.1109/iros.2015.7354186","title":"Robust environment mapping using flux skeletons","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Topological map; Occupancy grid mapping; Robot; Artificial intelligence; Context (archaeology); Noise (video); Motion planning; Heuristic; Simultaneous localization and mapping; Global Map; Algorithm; Mobile robot; Topology (electrical circuits); Computer vision; Mathematics; Geography","score_opus":0.15881233759191107,"score_gpt":0.2563700483586651,"score_spread":0.09755771076675404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211905658","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008523404,0.00004303187,0.9855374,0.00032864895,0.00032309306,0.00007512179,2.6108785e-7,0.0001957083,0.0049733208],"genre_scores_gemma":[0.041292153,6.530467e-7,0.9573972,0.00019217301,0.000060806942,0.0000027119495,0.0000018224775,0.00000716191,0.0010452643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989029,0.000041988016,0.00015796376,0.000302073,0.00030435043,0.00029073376],"domain_scores_gemma":[0.9992191,0.00002843729,0.00004688262,0.0004920508,0.000019081826,0.00019444921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031844847,0.000108545944,0.000112170055,0.00007906289,0.00007697625,0.00012036044,0.0005584878,0.000042020467,0.000017400282],"category_scores_gemma":[0.000023961618,0.00010000044,0.000031444084,0.000168828,0.000027099424,0.00039936273,0.00030432022,0.00008908442,0.00040702347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.6207774e-7,0.000096675874,0.0014774785,0.0000065959025,0.000029467266,0.00013455606,0.00330766,0.97168326,0.0032019056,0.002687532,0.00484013,0.01253389],"study_design_scores_gemma":[0.00017298017,0.00002326655,0.0006039608,0.000010389256,0.0000024492074,0.00005609666,0.00017740205,0.99613005,0.00046967602,0.00017043666,0.0020249456,0.0001583228],"about_ca_topic_score_codex":0.000057259917,"about_ca_topic_score_gemma":2.338355e-7,"teacher_disagreement_score":0.032768752,"about_ca_system_score_codex":0.00012493893,"about_ca_system_score_gemma":0.00007050047,"threshold_uncertainty_score":0.5231599},"labels":[],"label_agreement":null},{"id":"W2217070656","doi":"10.1109/iros.2015.7354245","title":"A localization aware sampling strategy for motion planning under uncertainty","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Sampling (signal processing); Path (computing); Computer science; Reduction (mathematics); Sample (material); Planner; Mathematical optimization; Artificial intelligence; Mathematics; Robot; Computer vision","score_opus":0.19512547625117208,"score_gpt":0.36094245426991367,"score_spread":0.1658169780187416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217070656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049149216,0.000037543836,0.9978003,0.00025049612,0.00037679795,0.00019282977,0.0000020700438,0.00033832854,0.0005101667],"genre_scores_gemma":[0.5524156,4.722933e-7,0.4466659,0.00040714553,0.00014416472,0.000026678941,0.00005013878,0.000014092179,0.00027577992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989683,0.00003679318,0.00018903527,0.00031609365,0.00022881202,0.0002609882],"domain_scores_gemma":[0.9992361,0.00010098708,0.00007184773,0.00026189684,0.00020104201,0.00012809767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040412007,0.00011378094,0.000117608346,0.00008062349,0.00011088334,0.00016901926,0.0003288666,0.000071955394,0.0000019945373],"category_scores_gemma":[0.00007496769,0.000102877006,0.000031091637,0.0002451911,0.000015628179,0.00039683314,0.00006404852,0.00006453294,0.000015459224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031541097,0.000013107933,0.00026254292,0.000006404591,0.000005773654,0.0000016563338,0.000323486,0.98244566,0.0000070284045,0.008837057,0.0008010233,0.0072931005],"study_design_scores_gemma":[0.00038296945,0.00008484656,0.00020828264,0.000024832856,0.0000037973384,0.000010307831,0.00037111618,0.98717797,0.000050393486,0.01123644,0.00030977113,0.00013928997],"about_ca_topic_score_codex":0.00006204054,"about_ca_topic_score_gemma":0.000001898117,"teacher_disagreement_score":0.5519241,"about_ca_system_score_codex":0.00010336621,"about_ca_system_score_gemma":0.0001259396,"threshold_uncertainty_score":0.41952026},"labels":[],"label_agreement":null},{"id":"W2218001763","doi":"","title":"Smart autoflight control systems","year":2014,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Air traffic control; Computer science; Process (computing); Abstraction; Control (management); Systems engineering; Terrain; Air traffic management; State (computer science); Command and control; Aeronautics; Operations research; Engineering; Aerospace engineering; Artificial intelligence","score_opus":0.005830372271036928,"score_gpt":0.1847058754212535,"score_spread":0.17887550315021658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218001763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031899905,0.00016784546,0.99328476,0.0001088729,0.002393117,0.000106186984,5.7935455e-7,0.00072029623,0.000028345052],"genre_scores_gemma":[0.4173458,0.0000020324267,0.5821994,0.00020204447,0.0002132442,0.000009927373,3.1020042e-7,0.000008816208,0.000018419842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823046,0.000030216263,0.00019802897,0.0005429589,0.0005147696,0.00048359574],"domain_scores_gemma":[0.9987014,0.0002964656,0.00004828933,0.00054653146,0.00016063773,0.0002466837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014802337,0.00017306671,0.00021737641,0.00023757985,0.00023709518,0.000651245,0.0011205534,0.000046601024,2.9502138e-7],"category_scores_gemma":[0.00033362495,0.0001573231,0.000026026675,0.0006275838,0.00009531386,0.00078526244,0.00030150946,0.00013644525,0.000020494888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012945186,0.00003568041,0.005910162,0.000115230665,0.000026659725,0.00005745609,0.0010603718,0.7756423,0.00068138493,0.04037238,0.00088278286,0.1752143],"study_design_scores_gemma":[0.00020028521,0.00005342933,0.008279827,0.00005550059,0.0000024327783,0.000079791964,8.4182585e-7,0.9882878,0.000057322344,0.0000320945,0.0027420023,0.00020868583],"about_ca_topic_score_codex":0.000008672355,"about_ca_topic_score_gemma":4.103041e-8,"teacher_disagreement_score":0.4141558,"about_ca_system_score_codex":0.000051637966,"about_ca_system_score_gemma":0.00008078772,"threshold_uncertainty_score":0.641545},"labels":[],"label_agreement":null},{"id":"W2220117090","doi":"10.1007/978-3-540-45058-0_10","title":"View Planning via Maximal C-space Entropy Reduction","year":2004,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Entropy (arrow of time); Mathematics; Configuration space; Computation; Planar; Motion planning; Computer science; Algorithm; Artificial intelligence; Robot; Mathematical optimization; Physics","score_opus":0.022269121375077427,"score_gpt":0.2608963674211135,"score_spread":0.23862724604603608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220117090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025020445,0.0028118084,0.9517261,0.0006329407,0.004270605,0.0006378234,0.000004840012,0.0005745789,0.039316278],"genre_scores_gemma":[0.0042499974,0.00049763644,0.9683276,0.000116289186,0.00051544356,0.00001890644,0.00002778736,0.00017562878,0.026070733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957758,0.00004067863,0.0009550012,0.0014053389,0.00085793977,0.0009651952],"domain_scores_gemma":[0.99731624,0.00012511987,0.0007129189,0.0014193006,0.00014613532,0.00028028293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003717237,0.00087237725,0.00094751915,0.0005981712,0.00016962511,0.00017692978,0.0013940166,0.00063698454,0.000022007705],"category_scores_gemma":[0.00007401068,0.0009808857,0.00021324462,0.00023531013,0.00013745026,0.0008084958,0.00039537202,0.0018865617,0.00021959996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017793363,0.00006505048,0.000016600772,0.0001181643,0.00004342733,0.0008060604,0.00036656854,0.8119972,0.00033226213,0.15915236,0.000024615654,0.02705994],"study_design_scores_gemma":[0.008874668,0.0016894977,0.0037024152,0.025344847,0.00046125127,0.0031539898,0.0001005925,0.2589293,0.0032246653,0.6112461,0.07109017,0.012182508],"about_ca_topic_score_codex":0.00000898953,"about_ca_topic_score_gemma":0.0000010357803,"teacher_disagreement_score":0.55306786,"about_ca_system_score_codex":0.00093576306,"about_ca_system_score_gemma":0.00031332095,"threshold_uncertainty_score":0.9992642},"labels":[],"label_agreement":null},{"id":"W2221873305","doi":"10.1007/s10514-015-9536-y","title":"Localization aware sampling and connection strategies for incremental motion planning under uncertainty","year":2015,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Sample (material); Sampling (signal processing); Path (computing); Queue; Measure (data warehouse); Motion planning; Reduction (mathematics); Node (physics); Connection (principal bundle); Uncertainty reduction theory; Mathematical optimization; Data mining; Artificial intelligence; Mathematics; Detector; Telecommunications; Robot","score_opus":0.08960031543776135,"score_gpt":0.3191506130857991,"score_spread":0.22955029764803775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221873305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011246704,0.00011034077,0.98716146,0.00022853092,0.00054770976,0.00027520678,0.0000035062894,0.0003019895,0.0001245724],"genre_scores_gemma":[0.87244445,0.0000013467363,0.12712517,0.00019293466,0.0001027736,0.00003074666,0.00006057708,0.000013985191,0.000028035463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889696,0.00005292789,0.00022453368,0.00038544,0.00017625577,0.00026390757],"domain_scores_gemma":[0.9993329,0.00010180535,0.00012071737,0.00020244258,0.00012778642,0.000114347175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039817323,0.00015244857,0.00015697138,0.00010483133,0.00024955958,0.00030093288,0.00019665576,0.000089252724,8.5865383e-7],"category_scores_gemma":[0.000045901998,0.00015762963,0.000025916484,0.0001649919,0.00003591845,0.0007609599,0.00009330318,0.00008295171,0.00000494639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064964233,0.00001634065,0.00079746847,0.000013047053,0.000012655036,0.0000021045698,0.0010550284,0.9862879,0.00007972201,0.0065944484,0.00011369664,0.0050211083],"study_design_scores_gemma":[0.00059154874,0.0001408808,0.0012847054,0.000039694274,0.000009838914,0.00003537876,0.0013191375,0.9865492,0.00010217987,0.009614486,0.00012162912,0.00019131636],"about_ca_topic_score_codex":0.00020438865,"about_ca_topic_score_gemma":0.000008647385,"teacher_disagreement_score":0.8611977,"about_ca_system_score_codex":0.00020319325,"about_ca_system_score_gemma":0.00017710883,"threshold_uncertainty_score":0.64279497},"labels":[],"label_agreement":null},{"id":"W2236202049","doi":"10.4271/2003-01-2597","title":"Autonomous Rover Navigation","year":2003,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Computer science; Computer vision; Human–computer interaction; Artificial intelligence","score_opus":0.013995968986232187,"score_gpt":0.2511025303329267,"score_spread":0.2371065613466945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2236202049","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27016342,0.0021283268,0.0030898028,0.023864834,0.0059943423,0.0059425314,0.00013738172,0.034664936,0.6540144],"genre_scores_gemma":[0.8669079,0.000050390794,0.12932117,0.0022801904,0.000119647055,0.00020688279,0.000027593545,0.000090473426,0.0009957323],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99383044,0.00035649768,0.0012000767,0.0018781328,0.0013674258,0.0013674223],"domain_scores_gemma":[0.9955327,0.000633119,0.00035006533,0.0027394,0.0001622905,0.0005824426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012153699,0.00088895444,0.000926009,0.00026038976,0.0005649534,0.0003471969,0.002553507,0.00090643857,0.00019099045],"category_scores_gemma":[0.0011555194,0.00080505986,0.00045560385,0.0014194574,0.0007119982,0.0012519968,0.0005782073,0.001564337,0.0006275622],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003909778,0.0004070673,0.00016911341,0.000030668187,0.000040562685,0.00023129034,0.000067583765,0.00027096274,0.8237858,0.16195191,0.0047239885,0.008281962],"study_design_scores_gemma":[0.0008400375,0.0011756996,0.9047464,0.00023322331,0.000045865872,0.0005751998,0.000035105357,0.000004288282,0.00037384304,0.011829671,0.07898543,0.001155246],"about_ca_topic_score_codex":0.000031146992,"about_ca_topic_score_gemma":0.0017086682,"teacher_disagreement_score":0.90457726,"about_ca_system_score_codex":0.0005140487,"about_ca_system_score_gemma":0.00028238518,"threshold_uncertainty_score":0.99944},"labels":[],"label_agreement":null},{"id":"W2242756877","doi":"10.1609/socs.v1i1.18151","title":"Simultaneously Searching with Multiple Settings: An Alternative to Parameter Tuning for Suboptimal Single-Agent Search Algorithms","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parallelizable manifold; Algorithm; Domain (mathematical analysis); Mathematical optimization; Mathematics","score_opus":0.026249021842736097,"score_gpt":0.28531714361391786,"score_spread":0.25906812177118177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242756877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92425185,0.0000026467787,0.060547404,0.006991976,0.0052031954,0.0017051676,0.00005010439,0.00019646615,0.001051191],"genre_scores_gemma":[0.8406617,6.961605e-7,0.15809296,0.00022031805,0.0007365569,0.00009237273,0.0000068857835,0.00005030927,0.00013819561],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99550116,0.000048937083,0.00047336047,0.0009691491,0.0022481813,0.0007592175],"domain_scores_gemma":[0.9957575,0.0012945443,0.00023138517,0.00041904184,0.001937142,0.00036037507],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018253515,0.00035099548,0.00033681476,0.000289143,0.0004278605,0.00077370304,0.004309813,0.00013399702,0.0000052630667],"category_scores_gemma":[0.0010471807,0.00027000316,0.00014371789,0.0005371181,0.00016875754,0.00074833987,0.0010434098,0.0009389502,0.000011775251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017695077,0.001978652,0.008928552,0.00013897104,0.00042242507,0.00003037562,0.01303345,0.068559535,0.8073879,0.08436458,0.00044838013,0.012937687],"study_design_scores_gemma":[0.0016178393,0.0019768346,0.00034033085,0.0001838155,0.000012303285,0.000054562996,0.00019343483,0.7599496,0.23327422,0.0014386396,0.00058519334,0.0003731922],"about_ca_topic_score_codex":0.00024614623,"about_ca_topic_score_gemma":0.000004926989,"teacher_disagreement_score":0.6913901,"about_ca_system_score_codex":0.00028353787,"about_ca_system_score_gemma":0.00014262543,"threshold_uncertainty_score":0.9999752},"labels":[],"label_agreement":null},{"id":"W2245459950","doi":"10.1109/smc.2015.49","title":"Conflict Resolution of Cluttered Multi-robot Systems Using Metaheuristic Optimization Algorithms","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robot; Liveness; Computer science; Collision avoidance; Metaheuristic; Genetic algorithm; Collision; Artificial intelligence; Optimization problem; Motion planning; Algorithm; Mathematical optimization; Mathematics; Distributed computing; Machine learning","score_opus":0.20093562623365915,"score_gpt":0.32903053400812227,"score_spread":0.1280949077744631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245459950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025849938,0.0003986582,0.99721766,0.000060840248,0.0011995039,0.0002820374,0.000004564278,0.00023348442,0.00034475146],"genre_scores_gemma":[0.04128443,0.0000042892502,0.9582785,0.000034672245,0.000078674646,0.0000073304927,0.000009697253,0.0000150286505,0.00028742693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980027,0.0002314232,0.00053436746,0.00040854918,0.0005053818,0.00031757582],"domain_scores_gemma":[0.9983591,0.000087897955,0.00030877718,0.0006209297,0.00043414303,0.00018918385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008693105,0.00018280659,0.0003402888,0.00021108898,0.000072829724,0.00012016981,0.00061629625,0.00010647232,0.0000022531428],"category_scores_gemma":[0.00022787366,0.00016744681,0.000054757722,0.0005129865,0.00005995855,0.0004924555,0.0001865871,0.000106075386,0.000017620538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033904305,0.000050209757,0.00010989346,0.000016601649,0.000024537063,0.000012513238,0.0003775779,0.99798167,0.00028021508,0.00063855795,0.00017252819,0.0003323048],"study_design_scores_gemma":[0.0007274658,0.00007653234,0.00015988814,0.000054264503,0.000023513696,0.00007418225,0.0000776753,0.998223,0.00031300788,0.000013536635,0.0000670807,0.00018985935],"about_ca_topic_score_codex":0.00063232123,"about_ca_topic_score_gemma":6.2737314e-7,"teacher_disagreement_score":0.041025933,"about_ca_system_score_codex":0.00012808047,"about_ca_system_score_gemma":0.0001605012,"threshold_uncertainty_score":0.6828283},"labels":[],"label_agreement":null},{"id":"W2247306252","doi":"10.1007/10991459_23","title":"Topological Analysis of Robotic N-Wheeled Ground Vehicles","year":2006,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Climbing; Terrain; Robot; Mechanism (biology); Robustness (evolution); Engineering; Power consumption; Robot locomotion; Control engineering; Architecture; Mechanism design; Topology (electrical circuits); Mobile robot; Computer science; Simulation; Power (physics); Artificial intelligence; Electrical engineering; Robot control; Geography; Mathematics; Physics; Structural engineering","score_opus":0.02397969433239377,"score_gpt":0.2615533456691506,"score_spread":0.23757365133675684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247306252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062894484,0.0014297165,0.9336764,0.00023682886,0.0013040982,0.00050263415,0.000017094033,0.00032288776,0.0618814],"genre_scores_gemma":[0.07458837,0.000239831,0.8781107,0.00013676748,0.00016806315,0.000013167777,0.00008024251,0.00011031654,0.046552554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99565583,0.00006548464,0.0014033645,0.0012122464,0.00090741075,0.0007556413],"domain_scores_gemma":[0.9964296,0.0006315909,0.0009191036,0.0016421451,0.00020083386,0.00017667902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046092973,0.00072471023,0.001871748,0.0014584267,0.00008814244,0.00009640217,0.0017102901,0.0007013233,0.000020991874],"category_scores_gemma":[0.00013786857,0.0007311818,0.00051773083,0.0007783904,0.000261603,0.00037691067,0.00043265286,0.0011268194,0.000033208697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009230683,0.00011906287,0.00046417906,0.00004963621,0.0003550438,0.00032134922,0.00007087845,0.88233757,0.00007677257,0.110890806,0.000021647616,0.005283796],"study_design_scores_gemma":[0.0016726552,0.00046817237,0.08801749,0.0010384999,0.0021454794,0.000048924343,0.000020639938,0.8480168,0.00018797406,0.053628065,0.0018149909,0.0029403039],"about_ca_topic_score_codex":0.000041094616,"about_ca_topic_score_gemma":0.000035870566,"teacher_disagreement_score":0.087553315,"about_ca_system_score_codex":0.00032356233,"about_ca_system_score_gemma":0.00014297616,"threshold_uncertainty_score":0.9995139},"labels":[],"label_agreement":null},{"id":"W2248489652","doi":"10.1609/socs.v1i1.18169","title":"Portal-Based True-Distance Heuristics for Path Finding","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation","keywords":"Heuristics; Heuristic; Computer science; Path (computing); Domain (mathematical analysis); Algorithm; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence","score_opus":0.01715329244094359,"score_gpt":0.27468542826688014,"score_spread":0.25753213582593654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248489652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71785897,0.000021935622,0.11680989,0.03285681,0.08675823,0.0030031702,0.00021225978,0.00063090445,0.041847803],"genre_scores_gemma":[0.9725756,0.0000011558542,0.026401311,0.00012835592,0.0005821101,0.000047065023,0.0000043034456,0.000020371037,0.00023970433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775046,0.0000084499825,0.0003492023,0.00042248078,0.0011462088,0.00032319763],"domain_scores_gemma":[0.9981658,0.00036130936,0.00025884912,0.00025410153,0.0008624509,0.00009750726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009022553,0.00017332383,0.00018780497,0.000099442346,0.00021338378,0.0002514192,0.0029455135,0.00010876626,0.0000068505224],"category_scores_gemma":[0.00062443194,0.00014180166,0.00014308094,0.00028894667,0.000098455006,0.0002328917,0.0003089623,0.0004399486,0.0000066020893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010661229,0.00028113797,0.004721742,0.000053813543,0.000038036447,0.0000022527222,0.00020522994,0.00043915436,0.063333124,0.9274329,0.0028914034,0.000494607],"study_design_scores_gemma":[0.0039225654,0.0007621753,0.0032412438,0.00033982578,0.00002762509,0.000027622196,0.000041032505,0.52740896,0.38790137,0.0634737,0.012189263,0.0006646437],"about_ca_topic_score_codex":0.000018203527,"about_ca_topic_score_gemma":4.3687615e-7,"teacher_disagreement_score":0.8639592,"about_ca_system_score_codex":0.000091172406,"about_ca_system_score_gemma":0.00013174918,"threshold_uncertainty_score":0.5782504},"labels":[],"label_agreement":null},{"id":"W2258099928","doi":"10.4108/icst.bict.2014.257925","title":"Terrain Leveling by a Swarm of Simple Agents","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Terrain; Underwater; Computer science; Swarm behaviour; Simple (philosophy); Remotely operated underwater vehicle; Real-time computing; Artificial intelligence; Mobile robot; Robot; Geology; Geography","score_opus":0.07134075332936098,"score_gpt":0.30309425392236844,"score_spread":0.23175350059300748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258099928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019691892,0.000038351835,0.97429615,0.00029325316,0.00020112532,0.00005697603,0.000002778337,0.00011386023,0.0053056297],"genre_scores_gemma":[0.51965725,5.7918487e-7,0.47875082,0.00035652693,0.00002725339,0.0000029390121,0.000004328484,0.0000062140907,0.0011940789],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991292,0.00003425185,0.00017671824,0.00019532407,0.00027474965,0.00018977081],"domain_scores_gemma":[0.99937916,0.000039070314,0.00006106148,0.00033561233,0.00006190539,0.00012319093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036223434,0.000074757605,0.00011765282,0.000046566565,0.00002195934,0.0000313742,0.0005955839,0.000032602722,0.0000064346464],"category_scores_gemma":[0.00009260431,0.000064874854,0.000024661347,0.00018486194,0.000018140485,0.00017879854,0.0001818415,0.00005311385,0.000057458787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010214567,0.00040489488,0.0093881935,0.000040625033,0.000090194866,0.00009163587,0.017527765,0.027878365,0.0052016857,0.0049818465,0.8448462,0.08953838],"study_design_scores_gemma":[0.0004891422,0.00010652551,0.00058163976,0.000014145592,0.0000029454268,0.000014522493,0.00015486173,0.98277533,0.0076162363,0.0020610134,0.006006071,0.00017754713],"about_ca_topic_score_codex":0.00018010229,"about_ca_topic_score_gemma":8.0392175e-7,"teacher_disagreement_score":0.954897,"about_ca_system_score_codex":0.00002372674,"about_ca_system_score_gemma":0.000061216,"threshold_uncertainty_score":0.26455197},"labels":[],"label_agreement":null},{"id":"W2259697407","doi":"10.1145/2822013.2822043","title":"ACCLMesh","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of British Columbia","funders":"","keywords":"Polygon mesh; Computer science; Collision avoidance; Curvature; Computer vision; Artificial intelligence; Computer graphics (images); Collision; Geometry; Mathematics","score_opus":0.07000709988147788,"score_gpt":0.2734060782686126,"score_spread":0.2033989783871347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259697407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037639448,0.000021949003,0.9044353,0.0009132624,0.0004032216,0.000022018095,6.4039e-8,0.0002941739,0.093533635],"genre_scores_gemma":[0.050433844,2.6818262e-7,0.94462556,0.0005811237,0.00005236819,0.0000019991255,3.1793348e-7,0.000002534837,0.004301976],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995206,0.000014784094,0.000057960948,0.00012855028,0.00015461147,0.00012351856],"domain_scores_gemma":[0.9995133,0.000018323273,0.000013263752,0.00030152156,0.000038239945,0.00011534068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017452902,0.00004023604,0.000046307177,0.000028230357,0.000015734628,0.00005945908,0.0005035821,0.000017936985,0.0000051001953],"category_scores_gemma":[0.0000446087,0.000032318352,0.000011527732,0.00014504949,0.00000791527,0.00022722162,0.00012893023,0.000036462166,0.0007362334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024091578,0.00010066353,0.006685006,0.000003937729,0.00002069776,0.00023821689,0.0029388592,0.004904095,0.00015578972,0.3714344,0.46371958,0.14979635],"study_design_scores_gemma":[0.0005184154,0.00010276097,0.0035034388,0.000006356166,0.0000017385157,0.00011041855,0.00006318929,0.94337976,0.0011477682,0.01645763,0.034440756,0.00026776787],"about_ca_topic_score_codex":0.000013138349,"about_ca_topic_score_gemma":1.5125408e-7,"teacher_disagreement_score":0.93847567,"about_ca_system_score_codex":0.00001456521,"about_ca_system_score_gemma":0.000051989944,"threshold_uncertainty_score":0.94630367},"labels":[],"label_agreement":null},{"id":"W2259985174","doi":"10.4271/2004-01-0786","title":"Collision-Free CMM Path Planning using Swept Volumes","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Path (computing); Collision; Computer science; Motion planning; Artificial intelligence; Robot; Computer security; Operating system","score_opus":0.022240082783898957,"score_gpt":0.2694349594397616,"score_spread":0.24719487665586262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259985174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8471933,0.0043448624,0.008941767,0.024886617,0.004987144,0.0055511636,0.00029616267,0.029533625,0.07426535],"genre_scores_gemma":[0.721306,0.00005690232,0.27579492,0.0021935767,0.00022821281,0.00011187003,0.000019311869,0.000114400646,0.00017477409],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99197316,0.00023926437,0.0015410675,0.0023114355,0.0020734156,0.0018616745],"domain_scores_gemma":[0.99374443,0.0007070589,0.0004869826,0.004055083,0.00021750508,0.0007889578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001214046,0.0011835048,0.0013450991,0.00045545225,0.00093082216,0.00052153325,0.0053406903,0.0011104733,0.00007679915],"category_scores_gemma":[0.0018087985,0.001070771,0.000587285,0.002010842,0.0010580315,0.0015514027,0.0023582778,0.0019589916,0.0002246239],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116733434,0.0005532283,0.00030987017,0.00005200904,0.00006981604,0.0008069587,0.00020182642,0.008815097,0.9510412,0.02882549,0.0057131867,0.0034945586],"study_design_scores_gemma":[0.0022230486,0.0019207066,0.9573736,0.0012338548,0.00009251589,0.00091515726,0.00014427734,0.000030939216,0.0003368915,0.019028516,0.014820739,0.0018797126],"about_ca_topic_score_codex":0.00012705725,"about_ca_topic_score_gemma":0.0018955895,"teacher_disagreement_score":0.9570638,"about_ca_system_score_codex":0.00084837293,"about_ca_system_score_gemma":0.0004839402,"threshold_uncertainty_score":0.99917424},"labels":[],"label_agreement":null},{"id":"W2277824931","doi":"10.5772/62117","title":"A Method Based on Bottleneck-Linear Assignment for Forming Complex Transport Formations","year":2016,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Bottleneck; Robot; Computer science; Task (project management); Robotics; Path (computing); Controller (irrigation); Point (geometry); Moment (physics); Artificial intelligence; Algorithm; Mathematical optimization; Mathematics; Engineering","score_opus":0.04160621714141659,"score_gpt":0.3346187077362342,"score_spread":0.2930124905948176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277824931","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018613275,0.00003686517,0.9930021,0.0027848121,0.003399308,0.00031296074,0.000024290144,0.000046717705,0.00020678966],"genre_scores_gemma":[0.25147444,0.0000047446615,0.74780095,0.00019098037,0.00033126582,0.000028913282,0.0000049117657,0.000015432488,0.00014834481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974737,0.000088301924,0.0009417624,0.0002317794,0.0009850791,0.0002793389],"domain_scores_gemma":[0.99696684,0.0009981254,0.0008406363,0.0002781543,0.0007712935,0.00014494875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009917627,0.0001887223,0.00036516183,0.00036763423,0.00009083143,0.00006229571,0.0012005839,0.000058362286,0.0000054973257],"category_scores_gemma":[0.00019108545,0.00012722847,0.0002264358,0.0001304703,0.000023466839,0.00081673777,0.000031678337,0.000106085674,0.000009767932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006584332,0.00008977841,0.00012882905,0.00001722624,0.00009752965,0.000040613155,0.00013246905,0.9661032,0.002237201,0.0054115728,0.00020877685,0.025466947],"study_design_scores_gemma":[0.0028390335,0.00044455082,0.00075608725,0.0008714855,0.000021542715,0.0003212514,0.000064228516,0.9889851,0.0008497231,0.00061309536,0.0040328894,0.00020097301],"about_ca_topic_score_codex":0.0000036327335,"about_ca_topic_score_gemma":3.3357847e-7,"teacher_disagreement_score":0.2512883,"about_ca_system_score_codex":0.0003682909,"about_ca_system_score_gemma":0.0001714311,"threshold_uncertainty_score":0.51882267},"labels":[],"label_agreement":null},{"id":"W2288108818","doi":"10.1186/s40638-016-0033-3","title":"Single-step collision-free trajectory planning of biped climbing robots in spatial trusses","year":2016,"lang":"en","type":"article","venue":"Robotics and Biomimetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China-Guangdong Joint Fund; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Climbing; Robot; Computer science; Motion planning; Control theory (sociology); Trajectory; Piecewise; Simulation; Mathematics; Artificial intelligence; Engineering","score_opus":0.029023809059714974,"score_gpt":0.24809872350241902,"score_spread":0.21907491444270405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288108818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063523166,0.0005364681,0.9341352,0.0006620645,0.00066867267,0.0001806472,0.0000130993,0.0000805218,0.00020015334],"genre_scores_gemma":[0.566228,0.00006374825,0.43353456,0.000040370564,0.00006496532,0.000001560665,0.0000016966993,0.000017107679,0.000048017588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807847,0.00010002362,0.00056786864,0.00045278773,0.0003695765,0.00043128716],"domain_scores_gemma":[0.99823713,0.000658488,0.00024021235,0.0006368147,0.00008924807,0.00013811744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004647636,0.00023224669,0.0004057248,0.00035466522,0.000077653545,0.000080158374,0.0007532365,0.00015998914,0.0000016949252],"category_scores_gemma":[0.00034410116,0.00017268021,0.00006201048,0.000481585,0.00014641567,0.00019639515,0.00041001246,0.000116446485,0.0000034891075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020414314,0.0017747415,0.11691985,0.00046095878,0.00020199399,0.0007033795,0.0038574622,0.27627915,0.1866132,0.016878994,0.0011248365,0.3949813],"study_design_scores_gemma":[0.006803074,0.0016997935,0.051526383,0.0020415795,0.00008537886,0.00011621241,0.00017471233,0.8818603,0.0501696,0.0037460336,0.00035676654,0.0014201715],"about_ca_topic_score_codex":0.0000537469,"about_ca_topic_score_gemma":0.000007158086,"teacher_disagreement_score":0.60558116,"about_ca_system_score_codex":0.00006152501,"about_ca_system_score_gemma":0.00008594436,"threshold_uncertainty_score":0.70416945},"labels":[],"label_agreement":null},{"id":"W2288656671","doi":"10.14288/1.0051643","title":"Decision graphs : algorithms and applications to influence diagram evaluation and high-level path planning under uncertainty","year":2009,"lang":"en","type":"article","venue":"Open Collections","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Path (computing); Algorithm; Computer science; Influence diagram; Diagram; Decision tree; Mathematics; Data mining; Artificial intelligence; Statistics","score_opus":0.047535306858117195,"score_gpt":0.3387141783244965,"score_spread":0.29117887146637933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288656671","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044199266,0.00010625721,0.99117863,0.0006574104,0.0001458373,0.001486226,0.000019347217,0.00010183554,0.0018845391],"genre_scores_gemma":[0.18017326,0.00003481414,0.8159351,0.0010275136,0.000060867093,0.00079516234,0.000017745679,0.000014073185,0.0019414813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836665,0.000095364354,0.00026124297,0.0006305191,0.00037771044,0.00026852483],"domain_scores_gemma":[0.9986664,0.00027914456,0.00008726468,0.00048121295,0.0002621245,0.00022384114],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00068449345,0.0001697922,0.00019269793,0.00019019545,0.002027908,0.0018108629,0.00059981795,0.00007749019,0.000006176431],"category_scores_gemma":[0.00015129468,0.00016716972,0.000020966587,0.0019331321,0.000040521772,0.0006262706,0.00030426116,0.00015241189,0.000005913858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016083137,0.00014186792,0.00024000311,0.000003384715,0.000027533673,0.0000054893826,0.0007461404,0.46193454,0.00012022209,0.0046991,0.0136052435,0.5184604],"study_design_scores_gemma":[0.0011281157,0.00040399042,0.09108623,0.00011982331,0.000042863652,0.000094868585,0.00015773658,0.770566,0.000035694968,0.13396758,0.0019273787,0.0004696995],"about_ca_topic_score_codex":0.0012121995,"about_ca_topic_score_gemma":0.000047140493,"teacher_disagreement_score":0.5179907,"about_ca_system_score_codex":0.00011262958,"about_ca_system_score_gemma":0.00017696696,"threshold_uncertainty_score":0.99927133},"labels":[],"label_agreement":null},{"id":"W2293312031","doi":"10.1109/csci.2015.92","title":"C-Theta*: Cluster Based Path-Planning on Grids","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Grid; Motion planning; Shortest path problem; Path (computing); Computer science; Cluster analysis; Constraint (computer-aided design); Widest path problem; Grid reference; Any-angle path planning; Grid method multiplication; Algorithm; Cluster (spacecraft); Path length; Mathematical optimization; K shortest path routing; Mathematics; Artificial intelligence; Theoretical computer science; Mobile robot; Graph; Geometry","score_opus":0.061721754994656514,"score_gpt":0.2878198593003833,"score_spread":0.22609810430572677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293312031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017965253,0.000026233309,0.94169855,0.0014674768,0.0011301416,0.000102984755,8.791196e-7,0.0004911261,0.053286094],"genre_scores_gemma":[0.27221122,2.0889962e-7,0.71841264,0.0057938965,0.0002568099,0.00001624303,0.000005946892,0.000021023214,0.0032819945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984853,0.00009777582,0.00018699959,0.00039805434,0.00048049074,0.000351324],"domain_scores_gemma":[0.9987737,0.00015240774,0.000057596386,0.0006911732,0.00007972878,0.00024539104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006072819,0.00016230637,0.00015698254,0.0001377806,0.00006753259,0.00015297138,0.00079412863,0.000070104405,0.0000105625995],"category_scores_gemma":[0.00010181958,0.00012848403,0.000047797064,0.0003019112,0.000024354089,0.00026501628,0.00013731122,0.00016597613,0.000682967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060459315,0.00040259908,0.00889792,0.000025836689,0.00004647185,0.00074963114,0.0045099356,0.56440175,0.00016942577,0.024317862,0.37006992,0.026348213],"study_design_scores_gemma":[0.0007253066,0.00024609023,0.0010970661,0.00004457462,0.0000029446255,0.000021478343,0.00003616583,0.99079496,0.0004850304,0.00091158925,0.0053969002,0.0002378829],"about_ca_topic_score_codex":0.0000134724405,"about_ca_topic_score_gemma":9.832894e-8,"teacher_disagreement_score":0.42639324,"about_ca_system_score_codex":0.00005484827,"about_ca_system_score_gemma":0.000118676435,"threshold_uncertainty_score":0.87783873},"labels":[],"label_agreement":null},{"id":"W2294023638","doi":"","title":"Hardness Results for Two-Dimensional Curvature-Constrained Motion Planning","year":2011,"lang":"en","type":"article","venue":"TU/e Research Portal (Eindhoven University of Technology)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Curvature; Domain (mathematical analysis); Path (computing); Motion planning; Simple (philosophy); Mathematics; Shortest path problem; Motion (physics); Algorithm; Mathematical optimization; Computer science; Geometry; Combinatorics; Mathematical analysis; Artificial intelligence; Robot","score_opus":0.08347746228870857,"score_gpt":0.313665502521208,"score_spread":0.23018804023249945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294023638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3352729,0.00041766764,0.6097236,0.004625041,0.0010729908,0.0025617168,0.0003227895,0.0020949664,0.04390832],"genre_scores_gemma":[0.7198661,0.0000019513375,0.27914426,0.0000104986475,0.000026500355,0.0000024327687,0.000030557614,0.000011577319,0.000906136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977028,0.00008804522,0.00026474893,0.00065584155,0.00060470006,0.0006838911],"domain_scores_gemma":[0.9981411,0.00014775254,0.00021479125,0.00076258666,0.000584121,0.00014962174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015064295,0.00017270294,0.00030826672,0.0010609955,0.0003894064,0.000020017736,0.0017789381,0.00031665608,0.000014764337],"category_scores_gemma":[0.00041049646,0.00019497279,0.000107459404,0.0011424434,0.0005911834,0.00041451267,0.0006998715,0.0006548865,0.000028280556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004748118,0.0051389206,0.026983144,0.00073589897,0.0024374926,0.013500135,0.023433289,0.014610106,0.057346188,0.4460713,0.15240273,0.25259268],"study_design_scores_gemma":[0.04018039,0.009378201,0.08416577,0.0030915935,0.00029947722,0.0016563964,0.012976261,0.5415149,0.14713845,0.13696882,0.01769613,0.0049336255],"about_ca_topic_score_codex":0.00020792517,"about_ca_topic_score_gemma":0.000009081576,"teacher_disagreement_score":0.5269048,"about_ca_system_score_codex":0.000076806486,"about_ca_system_score_gemma":0.00026838825,"threshold_uncertainty_score":0.79507595},"labels":[],"label_agreement":null},{"id":"W2294243368","doi":"","title":"Optimal Average Case Strategy for Looking around a Corner","year":2012,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Dalhousie University","funders":"","keywords":"Robot; Vertex (graph theory); Mathematics; Regular polygon; Path (computing); Function (biology); Motion planning; Computer science; Mathematical optimization; Artificial intelligence; Combinatorics; Geometry; Graph","score_opus":0.06612095846838448,"score_gpt":0.2950670190433908,"score_spread":0.2289460605750063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294243368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042473257,0.00007236462,0.95253646,0.0004647487,0.00079688395,0.0002797327,0.000104268904,0.00008843155,0.0031838634],"genre_scores_gemma":[0.84928083,5.291412e-7,0.14934179,0.0007631653,0.00023820426,0.000028721939,0.00005965752,0.000014867067,0.0002722324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981762,0.00005474775,0.0002609369,0.0004161468,0.00029622673,0.0007957109],"domain_scores_gemma":[0.99807495,0.00042235525,0.000104445884,0.00030818998,0.00026444453,0.00082563865],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004044891,0.00022572074,0.00020718224,0.0005396946,0.00035169633,0.0003536942,0.0005051362,0.000112919755,0.00006619189],"category_scores_gemma":[0.00010248459,0.0002453135,0.00007048617,0.00048920483,0.000059279217,0.0005121206,0.000048138256,0.00022173824,0.00015323295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005754933,0.000054794404,0.0010300766,0.000019640614,0.000047826907,0.0007084728,0.0004550588,0.74538296,0.0000045501147,0.22562842,0.0017676936,0.024894712],"study_design_scores_gemma":[0.00039857585,0.00012265459,0.0029563468,0.00003237888,0.000007583837,0.0013578909,0.00012028048,0.99088484,0.000011943172,0.002679766,0.0010619077,0.00036583477],"about_ca_topic_score_codex":0.002728355,"about_ca_topic_score_gemma":0.00040446533,"teacher_disagreement_score":0.8068076,"about_ca_system_score_codex":0.00027534817,"about_ca_system_score_gemma":0.001132368,"threshold_uncertainty_score":0.99999994},"labels":[],"label_agreement":null},{"id":"W2294988606","doi":"10.1609/icaps.v25i1.13709","title":"Moving Target Search with Subgoal Graphs","year":2015,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Path (computing); Abstraction; Robotics; Spacetime; Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Robot","score_opus":0.057875168532856985,"score_gpt":0.2929854708873217,"score_spread":0.2351103023544647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294988606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97301435,0.000079016216,0.013830091,0.0021745274,0.00041439832,0.00014027118,0.0000046888076,0.0005783904,0.009764289],"genre_scores_gemma":[0.8362729,0.0000017456593,0.16351259,0.00007595158,0.000026812331,0.000004985699,0.0000016471002,0.000008500493,0.00009489476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984374,0.000012742305,0.00022515772,0.0003382319,0.000746244,0.00024019726],"domain_scores_gemma":[0.9989035,0.000060604947,0.00018895481,0.0001155679,0.0006086823,0.00012270686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006352242,0.00016333103,0.00017339767,0.00018902648,0.00012590009,0.00034801534,0.0011579079,0.00006484289,0.0000020540697],"category_scores_gemma":[0.00018725156,0.000113103866,0.000030618165,0.00028845487,0.00008770403,0.00041642055,0.00028492618,0.00030328013,0.0000038118344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032379554,0.00020699558,0.2844292,0.00013062642,0.00037467614,0.000055769193,0.015581105,0.13795665,0.023513103,0.5341228,0.0012011632,0.0021041043],"study_design_scores_gemma":[0.00039774633,0.00012237689,0.0048270174,0.0005471423,0.0000055124583,0.00008438993,0.000785837,0.984618,0.0057690805,0.0026726588,0.000014013611,0.0001561961],"about_ca_topic_score_codex":0.000027017264,"about_ca_topic_score_gemma":9.4244015e-8,"teacher_disagreement_score":0.8466614,"about_ca_system_score_codex":0.000040567324,"about_ca_system_score_gemma":0.00014083125,"threshold_uncertainty_score":0.46122417},"labels":[],"label_agreement":null},{"id":"W2296260978","doi":"10.1109/robot.1995.525404","title":"Remote teleassistance","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Teleoperation; Robot; Computer science; Telerobotics; Computer vision; Servomotor; Latency (audio); Task (project management); Context (archaeology); Artificial intelligence; Robot control; Simulation; Human–computer interaction; Mobile robot; Engineering","score_opus":0.030745456417611047,"score_gpt":0.2227104157603577,"score_spread":0.19196495934274665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296260978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001323867,0.00011352769,0.87207913,0.0014075396,0.00022558513,0.000027134407,1.154174e-7,0.00033187307,0.12568268],"genre_scores_gemma":[0.04022725,0.000006967081,0.9424358,0.00057339127,0.000042166146,5.392078e-7,9.748107e-8,0.0000036932468,0.016710073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993288,0.000017282786,0.00009124511,0.00021209737,0.00016479415,0.00018581274],"domain_scores_gemma":[0.999399,0.00003637233,0.000024572793,0.00045485602,0.000022036174,0.000063146],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007512011,0.00006209915,0.000066265726,0.00003391625,0.000053439366,0.00006620309,0.00056104077,0.000024619429,0.000087071705],"category_scores_gemma":[0.00002982623,0.000053350977,0.000022743923,0.00023277506,0.000015289188,0.00019883923,0.00006794654,0.00006339837,0.0010365424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.1500706e-7,0.000052382627,0.0004302867,0.000006492935,0.000011696859,0.00019074544,0.00053603307,0.0007088439,0.00024177038,0.039843608,0.08182563,0.876152],"study_design_scores_gemma":[0.000086380925,0.000015698952,0.0013384031,0.0000069049747,6.211959e-7,0.000034843706,0.000002599039,0.9855393,0.00019719281,0.0009516617,0.011725957,0.00010040461],"about_ca_topic_score_codex":0.00000812785,"about_ca_topic_score_gemma":2.9487327e-7,"teacher_disagreement_score":0.9848305,"about_ca_system_score_codex":0.000016513779,"about_ca_system_score_gemma":0.0000043695086,"threshold_uncertainty_score":0.99974126},"labels":[],"label_agreement":null},{"id":"W2302592392","doi":"10.1007/978-3-319-24729-8_4","title":"The Rendezvous Problem: Fixed Neighbours","year":2015,"lang":"en","type":"book-chapter","venue":"Springer briefs in electrical and computer engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Rendezvous; Robot; Visibility; A priori and a posteriori; Computer science; Kinematics; Graph; Artificial intelligence; Control theory (sociology); Engineering; Geography; Theoretical computer science; Aerospace engineering; Physics; Control (management)","score_opus":0.013546344359836582,"score_gpt":0.2027387053988923,"score_spread":0.1891923610390557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2302592392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011166915,0.009206659,0.9695272,0.00060806307,0.0010844982,0.00036587194,0.0000017111404,0.00050117966,0.018693684],"genre_scores_gemma":[0.0033317243,0.0016085028,0.9312446,0.0004918558,0.0026675914,0.00010215667,0.0000142367635,0.00031230637,0.06022703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975545,0.00002192,0.0004790577,0.00074259215,0.0004959679,0.00070596306],"domain_scores_gemma":[0.998446,0.00028415205,0.00012903936,0.0008254337,0.00008064894,0.00023473601],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047691583,0.000471026,0.0004730836,0.00024703547,0.00010687938,0.00033818738,0.0014536601,0.000283874,0.0000010794402],"category_scores_gemma":[0.00002721285,0.00038834527,0.000093437804,0.00019789315,0.000060543556,0.00015255863,0.00088248594,0.0010709303,0.00002110461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007668654,0.000022002305,0.000035558423,0.000047391364,0.00009453037,0.00036087076,0.00016216222,0.011553631,0.000008060161,0.5093948,0.0025717467,0.47574154],"study_design_scores_gemma":[0.00028611603,0.00013621575,0.0002112884,0.00019454538,0.000011465475,0.00015595532,1.7748269e-7,0.8666814,0.0000059935487,0.012056474,0.11969321,0.0005671703],"about_ca_topic_score_codex":0.00001444368,"about_ca_topic_score_gemma":0.0000015711419,"teacher_disagreement_score":0.85512775,"about_ca_system_score_codex":0.00019452262,"about_ca_system_score_gemma":0.00011686806,"threshold_uncertainty_score":0.9998568},"labels":[],"label_agreement":null},{"id":"W2314137716","doi":"10.2514/6.2004-5427","title":"Formation Control of Holonomic Air-Bearing Robots Using Potential Field Interaction Laws","year":2004,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference and Exhibit","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Holonomic; Bearing (navigation); Robot; Field (mathematics); Control (management); Computer science; Control engineering; Control theory (sociology); Engineering; Artificial intelligence; Mathematics","score_opus":0.015439834963370922,"score_gpt":0.24712442515408234,"score_spread":0.23168459019071141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314137716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27996942,0.00027138618,0.71872514,0.00050475256,0.00020925411,0.00016956011,0.0000031145212,0.00003994814,0.00010741677],"genre_scores_gemma":[0.9895531,0.000033066364,0.009956407,0.000355403,0.000065407854,0.000012010994,0.000005913769,0.0000062673075,0.000012452389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885076,0.0000552801,0.00042992798,0.00030433596,0.00013628126,0.00022340473],"domain_scores_gemma":[0.99915856,0.00007295765,0.00027141572,0.00022097552,0.00020586404,0.000070239446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027606933,0.00016064089,0.00028463936,0.00010676315,0.00019960878,0.00014589535,0.00019440117,0.000101541315,0.0000015271298],"category_scores_gemma":[0.000033437984,0.0001602958,0.000045711105,0.00013322721,0.000069907095,0.0011450432,0.000045279594,0.0001470957,0.0000032397866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056043256,0.00026883394,0.020025335,0.00046954514,0.00038263606,0.000062245505,0.0076850103,0.41375586,0.30445644,0.107221544,0.00013366657,0.14497843],"study_design_scores_gemma":[0.0051903827,0.00025699066,0.01428486,0.00062987074,0.00007573008,0.00012420103,0.00023160236,0.95953673,0.010615414,0.008653449,0.00007410811,0.0003266789],"about_ca_topic_score_codex":0.00029052456,"about_ca_topic_score_gemma":0.000004399127,"teacher_disagreement_score":0.70958364,"about_ca_system_score_codex":0.00003869268,"about_ca_system_score_gemma":0.00009159601,"threshold_uncertainty_score":0.6536673},"labels":[],"label_agreement":null},{"id":"W2315153454","doi":"10.1115/detc2007-34178","title":"Design of Wheeled Mobile Robots With Advanced Drives","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Mobile robot; Robot; Dual (grammatical number); Computer science; Transmission (telecommunications); Rotation (mathematics); Isotropy; Control engineering; Simulation; Engineering; Artificial intelligence; Telecommunications","score_opus":0.01364137214735525,"score_gpt":0.2512195881381092,"score_spread":0.23757821599075393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315153454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004963032,0.00006697144,0.992269,0.000027340115,0.000100721314,0.000233104,1.4286893e-7,0.00017927236,0.0021604549],"genre_scores_gemma":[0.18472601,0.0000025997983,0.8148515,0.000044102017,0.000010993227,0.000007977611,3.5872816e-7,0.000006163376,0.00035024757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896055,0.00003132443,0.00019850442,0.0002667387,0.00026135534,0.00028154292],"domain_scores_gemma":[0.99907976,0.00020962131,0.00009630486,0.00044198387,0.000090245376,0.000082052306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003846083,0.00011129825,0.0001687282,0.00009341257,0.000039943137,0.000022115975,0.0005460723,0.000036739355,0.000008114689],"category_scores_gemma":[0.00001668376,0.00008194776,0.000020175952,0.0003683705,0.000048253576,0.00029261518,0.00007177941,0.00006983058,0.000019006347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042596923,0.00014042383,0.0010735128,0.000014093788,0.000033621647,0.000101286074,0.0016140824,0.87770367,0.018580483,0.0031224554,0.00018984076,0.09738395],"study_design_scores_gemma":[0.0018374851,0.0021853016,0.012174695,0.00012755033,0.000013792509,0.00010709431,0.0002827604,0.8262837,0.1554135,0.00078885036,0.00016109574,0.0006241973],"about_ca_topic_score_codex":0.000007286931,"about_ca_topic_score_gemma":8.1175295e-7,"teacher_disagreement_score":0.17976297,"about_ca_system_score_codex":0.000018013192,"about_ca_system_score_gemma":0.000054637483,"threshold_uncertainty_score":0.33417323},"labels":[],"label_agreement":null},{"id":"W2316273436","doi":"10.1061/40722(153)21","title":"Path Tracking Control for Tracked Based Machines Using Sensor Fusion","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Controller (irrigation); Path (computing); Position (finance); Sensor fusion; Tracking (education); Computer vision; Artificial intelligence; Motion planning; Control system; Control theory (sociology); Real-time computing; Control engineering; Control (management); Engineering; Robot","score_opus":0.035506923519572575,"score_gpt":0.282045327663707,"score_spread":0.24653840414413442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316273436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03200292,0.000028043441,0.9658645,0.00086733274,0.00042160004,0.00037565565,0.000007639003,0.000328314,0.000103987775],"genre_scores_gemma":[0.41843268,1.9398244e-7,0.58093673,0.0005086255,0.000075754935,0.0000072361086,0.0000023249204,0.000010225611,0.00002619742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986884,0.000044729524,0.00025645402,0.00039681405,0.00024393052,0.0003697008],"domain_scores_gemma":[0.9991416,0.00021525995,0.000099686695,0.0003350167,0.0001070126,0.000101440484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037828524,0.00017344873,0.00021950521,0.00010285004,0.00021155766,0.00015037613,0.00041343953,0.00007414217,0.0000052834807],"category_scores_gemma":[0.00010509653,0.0001444262,0.00010644439,0.00020660946,0.000023687935,0.00031886055,0.000029827323,0.00009329611,0.000010219491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063433734,0.00032994573,0.0014252828,0.00010000074,0.0000425752,0.00016300128,0.0011437071,0.85141206,0.08371951,0.008057188,0.00010217131,0.053441133],"study_design_scores_gemma":[0.0025717875,0.000090206224,0.0011283824,0.000058086036,0.000012436714,0.00003201665,0.000015794936,0.99101585,0.003908292,0.0009271798,0.00003041675,0.0002095513],"about_ca_topic_score_codex":0.000074473246,"about_ca_topic_score_gemma":0.0000017976597,"teacher_disagreement_score":0.38642976,"about_ca_system_score_codex":0.000070222624,"about_ca_system_score_gemma":0.000118447395,"threshold_uncertainty_score":0.58895296},"labels":[],"label_agreement":null},{"id":"W2316950857","doi":"10.1115/detc2010-28036","title":"The Direct Kinematics of Objects Suspended From Cables","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Polygon (computer graphics); Linkage (software); Position (finance); Planar; Nonlinear system; Parallel manipulator; Four-bar linkage; Symmetry (geometry); Polynomial; Kinematics equations; Forward kinematics; Computer science; Motion (physics); Kinematic chain; Algorithm; Mathematics; Robot kinematics; Robot; Geometry; Inverse kinematics; Computer vision; Artificial intelligence; Mobile robot; Mathematical analysis; Physics; Classical mechanics; Computer graphics (images); Frame (networking)","score_opus":0.007332167084456891,"score_gpt":0.22440541483645549,"score_spread":0.2170732477519986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316950857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08050788,0.00019260001,0.67815286,0.0019070072,0.004923873,0.00040906636,0.000010741476,0.00062408776,0.23327191],"genre_scores_gemma":[0.4409582,0.0000035235346,0.55796844,0.00004526499,0.0000551702,0.000003119717,0.0000010639227,0.000004652799,0.0009605692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917793,0.000037501657,0.00018877028,0.00016826973,0.00024740008,0.00018010117],"domain_scores_gemma":[0.9982427,0.00080088346,0.00009090174,0.00075135095,0.00006364555,0.000050514063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031167892,0.00008161989,0.00013222819,0.000026184845,0.00009816535,0.00009036897,0.0010039668,0.000044104952,0.00002458346],"category_scores_gemma":[0.0002612344,0.00004854486,0.000035034125,0.00016466912,0.000060772993,0.000111388086,0.00017204543,0.00013192839,0.0000350626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025556654,0.00061005994,0.015486482,0.00009066801,0.0004841004,0.00019409356,0.016802842,0.010607639,0.19112082,0.56826466,0.0490703,0.14724277],"study_design_scores_gemma":[0.0004199736,0.00006851645,0.011913889,0.000036558184,0.000015322747,0.000015197264,0.00017684551,0.84246844,0.114758,0.02865478,0.0011616587,0.0003108446],"about_ca_topic_score_codex":0.0003369237,"about_ca_topic_score_gemma":0.00009877181,"teacher_disagreement_score":0.8318608,"about_ca_system_score_codex":0.00000475465,"about_ca_system_score_gemma":0.000055931796,"threshold_uncertainty_score":0.1979602},"labels":[],"label_agreement":null},{"id":"W2318539806","doi":"10.5623/cig2011-024","title":"Development of a Web-Based Geographic Information System for Planetary Geological Mission Planning and Analysis","year":2011,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Geospatial analysis; Plan (archaeology); Traverse; Geographic information system; Computer science; Information system; World Wide Web; Data science; Geography; Remote sensing; Engineering; Archaeology; Cartography","score_opus":0.03722823436966654,"score_gpt":0.23419444527972602,"score_spread":0.19696621091005947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318539806","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17405951,0.00003336004,0.8252909,0.00001704756,0.00004784088,0.00016754877,0.0000052833866,0.000112938455,0.0002655366],"genre_scores_gemma":[0.46339178,1.211196e-7,0.5365483,0.000018417844,0.0000026703888,0.000010556906,0.000025819132,0.0000012965742,0.00000102253],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989809,0.000034225577,0.00042621433,0.00015869238,0.00020140337,0.00019859364],"domain_scores_gemma":[0.9992973,0.00011235287,0.00020846199,0.0002405787,0.000056590252,0.00008468279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047074593,0.00010932986,0.0002484043,0.00038280664,0.00011277773,0.000029564024,0.00028882726,0.00007202852,0.000003830416],"category_scores_gemma":[0.000038712817,0.000087382236,0.000051084855,0.00037286818,0.000030888117,0.0002062426,0.00007094906,0.000047407884,0.0000054059806],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033652945,0.0004300056,0.75449383,0.0052303835,0.002597826,0.000102052734,0.058052037,0.06918226,0.00070077466,0.009334387,0.0003906545,0.09914925],"study_design_scores_gemma":[0.00033139344,0.00005057079,0.17659226,0.00010419921,0.00006937778,0.00000683627,0.00011532842,0.822209,0.00030346477,0.00005404262,0.00005818038,0.00010535067],"about_ca_topic_score_codex":0.000008297761,"about_ca_topic_score_gemma":2.8651968e-7,"teacher_disagreement_score":0.7530267,"about_ca_system_score_codex":0.000012553842,"about_ca_system_score_gemma":0.000062562816,"threshold_uncertainty_score":0.3563344},"labels":[],"label_agreement":null},{"id":"W2318567786","doi":"10.1109/ccdc.2014.6852516","title":"Quasi-harmonic approach to non-holonomic robot motion control with concave obstacles avoidance","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Holonomic; Collision avoidance; Mobile robot; Robot; Motion (physics); Harmonic; Control theory (sociology); Obstacle avoidance; Motion control; Collision; Computer science; Motion planning; Control (management); Physics; Computer vision; Artificial intelligence; Acoustics","score_opus":0.011271873999360008,"score_gpt":0.212573335984033,"score_spread":0.201301461984673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318567786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012812418,0.00002244867,0.97995394,0.00067139405,0.00024132447,0.00042135152,0.0000017600125,0.00032625417,0.0055491053],"genre_scores_gemma":[0.60676455,5.563004e-7,0.3921614,0.0006682789,0.00006666929,0.000040640232,0.0000022314982,0.000013075993,0.0002825904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980863,0.0001278502,0.00027173644,0.0007236524,0.00029144302,0.00049903407],"domain_scores_gemma":[0.9985442,0.00017851364,0.00012252448,0.0008301429,0.00009358782,0.00023099186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005421012,0.00024580487,0.00033833526,0.00011439177,0.00014060763,0.00019769066,0.0009858574,0.00007700826,0.0000027666736],"category_scores_gemma":[0.000056383284,0.0001973741,0.000050343835,0.00029502483,0.000055695917,0.00037189998,0.0001101318,0.00019164942,0.0003087245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117032454,0.0011497264,0.008243542,0.000090328605,0.00018896694,0.000034807774,0.004232941,0.8220277,0.005810449,0.04706154,0.003834743,0.107208245],"study_design_scores_gemma":[0.0011723234,0.00048297993,0.023033308,0.000024245852,0.00001012604,0.000035634817,0.000040392846,0.97344726,0.0011020364,0.00017092878,0.00014766239,0.00033311037],"about_ca_topic_score_codex":0.00006993828,"about_ca_topic_score_gemma":0.000002328356,"teacher_disagreement_score":0.5939521,"about_ca_system_score_codex":0.0000955595,"about_ca_system_score_gemma":0.00006181417,"threshold_uncertainty_score":0.8048682},"labels":[],"label_agreement":null},{"id":"W2319069892","doi":"10.2514/6.2011-6294","title":"Prognostics-enhanced Receding Horizon Mission Planning for Field Unmanned Vehicles","year":2011,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Prognostics; Field (mathematics); Computer science; Horizon; Aerospace engineering; Aeronautics; Engineering; Physics","score_opus":0.04299454466691423,"score_gpt":0.27476240984943495,"score_spread":0.23176786518252074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319069892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031991106,0.0004690923,0.9639446,0.00053166115,0.0005529499,0.00054719637,0.000005946793,0.00021063618,0.0017468071],"genre_scores_gemma":[0.91337335,0.000018949097,0.08592556,0.00027804615,0.000100469944,0.00009104499,0.00001094986,0.00001298919,0.00018864524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983226,0.00007279244,0.00039289318,0.00057748304,0.00021505346,0.0004191507],"domain_scores_gemma":[0.99832517,0.00047977365,0.00024487797,0.00036446183,0.00044707593,0.00013863044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047709292,0.00023653569,0.00031013557,0.00009652958,0.0003686791,0.0001923814,0.00057792355,0.0001505896,0.0000034824775],"category_scores_gemma":[0.00038604432,0.0002235563,0.000053032072,0.00023931474,0.00007258863,0.0005051293,0.000071166236,0.00017606898,0.0000053254894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006936697,0.00028915543,0.042087805,0.0005211332,0.00031366272,0.000070661335,0.015983133,0.0006745476,0.0998463,0.24845771,0.006484035,0.58457816],"study_design_scores_gemma":[0.006654084,0.0025257522,0.024397615,0.002681458,0.00013259343,0.000034256827,0.0006031688,0.78265935,0.09179815,0.083701335,0.0034470863,0.0013651344],"about_ca_topic_score_codex":0.00002821584,"about_ca_topic_score_gemma":7.342424e-7,"teacher_disagreement_score":0.8813822,"about_ca_system_score_codex":0.000023852226,"about_ca_system_score_gemma":0.00012916526,"threshold_uncertainty_score":0.9116361},"labels":[],"label_agreement":null},{"id":"W2319427155","doi":"10.11159/jacr.2014.004","title":"A Reactive Bearing Angle Only Obstacle Avoidance Technique for Unmanned Ground Vehicles","year":2014,"lang":"en","type":"article","venue":"Journal of Automation and Control Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstacle avoidance; Obstacle; Unmanned ground vehicle; Computer science; Bearing (navigation); Computer vision; Global Positioning System; Artificial intelligence; Collision avoidance; Robot; Mobile robot; Control theory (sociology); Simulation; Real-time computing; Computer security","score_opus":0.03957857535550733,"score_gpt":0.3405793456285785,"score_spread":0.30100077027307115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319427155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04617279,0.0001311357,0.9508984,0.0021449318,0.00008950576,0.00030648848,0.0000012968981,0.000034234406,0.00022123005],"genre_scores_gemma":[0.8952752,0.0000054083475,0.10438209,0.0000795221,0.00012174231,0.000021949389,3.437449e-7,0.000006838451,0.00010691758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839646,0.0003240681,0.0003081021,0.00016078475,0.00054019777,0.0002704098],"domain_scores_gemma":[0.99755013,0.0012240863,0.00022625798,0.00018053806,0.0007024421,0.00011656163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004520076,0.00007635877,0.00020312761,0.00029413143,0.00023231265,0.00025264098,0.0004127532,0.000064832115,8.827971e-7],"category_scores_gemma":[0.0010777608,0.000064443106,0.000052897984,0.0002458275,0.000057875463,0.00064774376,0.00005556627,0.00032287833,0.0000031011407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057779864,0.00051049056,0.0038339798,0.00025167747,0.00025304276,0.000084152416,0.0049662595,0.0025750648,0.34710038,0.072273776,0.0025202997,0.56505305],"study_design_scores_gemma":[0.0026924843,0.0012850295,0.06425984,0.00023384018,0.0000105744975,0.00028471067,0.00016839441,0.9018195,0.0074396525,0.02020041,0.0014470563,0.00015847763],"about_ca_topic_score_codex":0.000016062671,"about_ca_topic_score_gemma":0.0000012721156,"teacher_disagreement_score":0.8992445,"about_ca_system_score_codex":0.000084359875,"about_ca_system_score_gemma":0.00016483584,"threshold_uncertainty_score":0.26279137},"labels":[],"label_agreement":null},{"id":"W2322019043","doi":"10.2514/6.2005-6369","title":"Receding Horizons with Heading Constraints for Collision Avoidance","year":2005,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference and Exhibit","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Collision avoidance; Heading (navigation); Model predictive control; Trajectory; Control theory (sociology); Computer science; Horizon; Control (management); Sample (material); Range (aeronautics); Collision; Artificial intelligence; Engineering; Mathematics; Aerospace engineering","score_opus":0.01369144722465897,"score_gpt":0.24319162959276522,"score_spread":0.22950018236810624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322019043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07992954,0.00086866424,0.91515833,0.0024035266,0.00015373818,0.00049210497,0.000016957163,0.00012447481,0.0008526545],"genre_scores_gemma":[0.91753703,0.00008007145,0.081486024,0.00046991665,0.00013652514,0.000091705755,0.000009178216,0.000011509446,0.00017801384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984104,0.000051553292,0.00035702507,0.000587896,0.0001867337,0.0004063874],"domain_scores_gemma":[0.9987444,0.0002949902,0.00017518061,0.0002779587,0.0003517471,0.0001556973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005258563,0.00023609684,0.00034116543,0.00009060058,0.00050159695,0.00037016865,0.0002789804,0.00010248147,0.000002132272],"category_scores_gemma":[0.00007499889,0.00020598456,0.00003354,0.0002446817,0.00025681045,0.00075467624,0.000043335982,0.00015451953,0.0000048460847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028757198,0.00012797218,0.027298853,0.00023609157,0.00021396141,0.00002536107,0.0046601035,0.0020984644,0.016812097,0.36684906,0.002239547,0.5791509],"study_design_scores_gemma":[0.010558999,0.0010595461,0.015711023,0.0023393335,0.00012180961,0.00023911064,0.0006969499,0.934577,0.005892218,0.011871838,0.015668934,0.001263258],"about_ca_topic_score_codex":0.000017545055,"about_ca_topic_score_gemma":0.000007958992,"teacher_disagreement_score":0.9324785,"about_ca_system_score_codex":0.0000379025,"about_ca_system_score_gemma":0.00016004461,"threshold_uncertainty_score":0.83998066},"labels":[],"label_agreement":null},{"id":"W2322497546","doi":"10.1115/imece2012-89585","title":"Dynamics and Motion Control of a 6-DOF Robot Manipulator","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Workspace; Mobile manipulator; Kinematics; Computer science; Path (computing); SMT placement equipment; Robot; Motion planning; Robotic arm; Simulation; Trajectory; Parallel manipulator; Robot kinematics; Collision; Motion control; Object (grammar); Motion (physics); Control engineering; Mobile robot; Engineering; Artificial intelligence","score_opus":0.015487475952700962,"score_gpt":0.23484064824330875,"score_spread":0.21935317229060777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322497546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009473269,0.00009079996,0.9889824,0.0002724309,0.00020978006,0.0000748647,0.0000012126947,0.000065803724,0.0008294576],"genre_scores_gemma":[0.779663,0.0000010292754,0.22011605,0.000059855036,0.00002494177,0.0000017254109,7.377514e-7,0.0000028949862,0.00012970569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941397,0.000028560045,0.00013196855,0.00011598408,0.00012949615,0.00018000584],"domain_scores_gemma":[0.99955565,0.000050973005,0.00005823984,0.00022289221,0.000030770767,0.00008147695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002279792,0.000065899825,0.000116278185,0.000047897687,0.00002575922,0.000019409987,0.00017818186,0.00003722099,0.000004142293],"category_scores_gemma":[0.00002345434,0.000056344255,0.000019096971,0.000094142226,0.0000221245,0.00032186048,0.000061997474,0.000045158868,0.000010738913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008617781,0.00028157904,0.2553545,0.000066695575,0.00007225776,0.00000814034,0.0012637938,0.008434304,0.00201599,0.6371346,0.00020434384,0.09515518],"study_design_scores_gemma":[0.00024569462,0.000026687421,0.092425555,0.0000063593247,0.0000050875233,0.000023912497,0.000013448299,0.90663266,0.0002605566,0.00029003827,0.000005192919,0.00006480848],"about_ca_topic_score_codex":0.000027058128,"about_ca_topic_score_gemma":8.0569805e-7,"teacher_disagreement_score":0.89819837,"about_ca_system_score_codex":0.000020906355,"about_ca_system_score_gemma":0.000007993833,"threshold_uncertainty_score":0.2297652},"labels":[],"label_agreement":null},{"id":"W2323151717","doi":"10.2514/6.2015-0404","title":"Using the DIMMACSS-PSG Intelligent Robotic Middleware to Control Real-World and Simulated Multi-Agent Systems","year":2015,"lang":"en","type":"article","venue":"AIAA Modeling and Simulation Technologies Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Middleware (distributed applications); Control (management); Multi-agent system; Embedded system; Distributed computing; Artificial intelligence","score_opus":0.21044731322387297,"score_gpt":0.3444912001585046,"score_spread":0.13404388693463162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323151717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10449502,0.0005025028,0.89272636,0.00060959725,0.00027836356,0.0005309648,0.0000027408191,0.00083885307,0.000015602478],"genre_scores_gemma":[0.93317133,0.00004291732,0.066624075,0.00006514692,0.000015703023,0.000013392528,0.0000020615753,0.000013152076,0.000052224084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816245,0.000111612324,0.00044992706,0.0005843896,0.00031668902,0.00037496223],"domain_scores_gemma":[0.99833125,0.00028397347,0.00014736241,0.00068330613,0.00042446508,0.00012962881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053244614,0.0002641357,0.00034363812,0.0002712605,0.00027589765,0.00043230798,0.00065504276,0.00015030416,2.3031691e-7],"category_scores_gemma":[0.00045891,0.00019819498,0.00002854384,0.00046712815,0.00010524221,0.00028021718,0.00043519228,0.00025981627,0.0000052234627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007936852,0.000015025277,0.0008981373,0.000012522137,0.000018931118,0.000006262047,0.00091354206,0.98739827,0.00006200364,0.002155419,0.000001956697,0.008509968],"study_design_scores_gemma":[0.00044508028,0.000070382084,0.00007193438,0.00014388259,0.000021891998,0.0000074811733,0.0011194964,0.99651074,0.00007246837,0.0012622515,0.000017532764,0.0002568307],"about_ca_topic_score_codex":0.0005797295,"about_ca_topic_score_gemma":0.000012731652,"teacher_disagreement_score":0.8286763,"about_ca_system_score_codex":0.000097260476,"about_ca_system_score_gemma":0.000087756234,"threshold_uncertainty_score":0.8082157},"labels":[],"label_agreement":null},{"id":"W2325591534","doi":"10.2514/6.2010-7883","title":"Kinodynamic Motion Planning for Holonomic UAVs in Complex 3D Environments","year":2010,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Holonomic; Computer science; Motion planning; Motion (physics); Artificial intelligence; Computer vision; Robot","score_opus":0.01882893798987427,"score_gpt":0.25954074239346114,"score_spread":0.24071180440358686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325591534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23160188,0.00007521308,0.76694417,0.00034450766,0.00033187852,0.0003894985,0.000013216561,0.000058320722,0.00024130978],"genre_scores_gemma":[0.9462735,0.0000057074476,0.053175338,0.00025199403,0.000059081496,0.00011214845,0.000043681557,0.000011072344,0.000067442554],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847436,0.00006402171,0.00040822552,0.00053891964,0.00015766713,0.00035680327],"domain_scores_gemma":[0.9990841,0.000169194,0.00019618223,0.00040337854,0.00006287628,0.0000842397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004739135,0.00020226561,0.00028088762,0.00012166551,0.00018756757,0.0001754382,0.0005277847,0.00012486595,0.0000038379176],"category_scores_gemma":[0.000060206898,0.00021424692,0.000035945144,0.00014496091,0.00012873301,0.0004484753,0.000060019967,0.00024555935,0.000013090153],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016046398,0.0003480075,0.38131672,0.00016377853,0.00013405233,0.000053244512,0.004041954,0.019693904,0.25532576,0.1386556,0.00047822925,0.19962826],"study_design_scores_gemma":[0.0016346045,0.00004603811,0.363797,0.00005417765,0.000007931665,0.000009681864,0.000017840488,0.6288588,0.00011520091,0.004454235,0.0008269912,0.0001775131],"about_ca_topic_score_codex":0.000046152534,"about_ca_topic_score_gemma":0.000008289255,"teacher_disagreement_score":0.7146717,"about_ca_system_score_codex":0.000043453496,"about_ca_system_score_gemma":0.00007605084,"threshold_uncertainty_score":0.87367356},"labels":[],"label_agreement":null},{"id":"W2326049206","doi":"10.2514/6.2009-5779","title":"Cooperative UAV Search for Moving Targets Using a Modified Diffusion Uncertainty Model","year":2009,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Diffusion; Artificial intelligence; Physics","score_opus":0.03539236239605666,"score_gpt":0.291246541999305,"score_spread":0.25585417960324836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326049206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0655311,0.00037065696,0.9319825,0.00081854674,0.00013707555,0.0006588147,0.000022147102,0.00012899327,0.00035013343],"genre_scores_gemma":[0.9102765,0.00001797512,0.088722594,0.00071321387,0.000059988914,0.000044856533,0.000016760609,0.0000097066295,0.00013843505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979392,0.00012407929,0.0004380805,0.0006709773,0.00032733497,0.00050028],"domain_scores_gemma":[0.99821454,0.00020327876,0.00015571299,0.00042073958,0.0008534458,0.00015230488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005841588,0.00027421545,0.0003930378,0.000110154564,0.00059795,0.00036065764,0.0005657207,0.00012615122,0.0000012033358],"category_scores_gemma":[0.00009194051,0.000254409,0.00006217556,0.00031167667,0.00011041892,0.0006198557,0.00006805758,0.00019886931,0.000001931156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010821684,0.00010801128,0.0006537113,0.00004379958,0.000049088132,0.0000104525425,0.0028380246,0.7343318,0.0530173,0.17770751,0.00013874735,0.030993368],"study_design_scores_gemma":[0.0020399645,0.00014531874,0.0016362388,0.00019174647,0.000019803556,0.000008204737,0.00006889901,0.9801456,0.00080140034,0.014628949,0.000025445062,0.00028846337],"about_ca_topic_score_codex":0.00007295075,"about_ca_topic_score_gemma":0.0000019542424,"teacher_disagreement_score":0.8447454,"about_ca_system_score_codex":0.000073599156,"about_ca_system_score_gemma":0.00041452175,"threshold_uncertainty_score":0.9999908},"labels":[],"label_agreement":null},{"id":"W2327781871","doi":"10.2514/6.2013-5231","title":"Kinodynamic Motion Planning for UAVs: A Minimum Energy Approach","year":2013,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control (GNC) Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Control theory (sociology); Motion planning; Computer science; Robot; Trajectory; Heuristic; Mathematical optimization; Mobile robot; Mathematics; Artificial intelligence","score_opus":0.01402671384659073,"score_gpt":0.22890723158995058,"score_spread":0.21488051774335984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327781871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074665598,0.0008168196,0.98782665,0.00073234993,0.0004221535,0.00062068854,0.000013684473,0.000239917,0.001861164],"genre_scores_gemma":[0.9228824,0.000014702588,0.07515848,0.0005674869,0.000141918,0.0005976934,0.00006321677,0.000020411962,0.0005536952],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761367,0.00012931316,0.0005669658,0.0008098644,0.00031983838,0.0005603387],"domain_scores_gemma":[0.99807936,0.0002787264,0.00030846492,0.0005772745,0.00058482017,0.00017136498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003328173,0.00033442976,0.0004309258,0.00015353259,0.00036412268,0.00053386745,0.00077409047,0.00018232102,0.0000042606534],"category_scores_gemma":[0.00009149315,0.00032087762,0.00008404118,0.0003407051,0.00014543477,0.00091768964,0.00006947153,0.00016445314,0.000014653598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011088018,0.0004318246,0.018779444,0.00041811462,0.0004109795,0.00002423118,0.0063083232,0.018020112,0.015742078,0.666719,0.00627561,0.26675937],"study_design_scores_gemma":[0.0018680382,0.00011702455,0.020451657,0.00018068451,0.00002905741,0.000025423798,0.0001400879,0.956219,0.000150964,0.019493828,0.0009477161,0.0003765261],"about_ca_topic_score_codex":0.00030073163,"about_ca_topic_score_gemma":0.000002099947,"teacher_disagreement_score":0.93819886,"about_ca_system_score_codex":0.000055604858,"about_ca_system_score_gemma":0.00015244361,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W2327970053","doi":"10.7763/ijcce.2013.v2.185","title":"Exact Solution for Search-and-Rescue Path Planning","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer and Communication Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Université TÉLUQ; Defence Research and Development Canada","funders":"","keywords":"Path (computing); Computer science; Search and rescue; Motion planning; Operations research; Artificial intelligence; Mathematics; Computer network","score_opus":0.019318451650396754,"score_gpt":0.26812650630624824,"score_spread":0.24880805465585148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327970053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03237932,0.0006335857,0.9651374,0.0013892062,0.00034445614,0.00007066078,7.2663073e-7,0.000029580266,0.000015071125],"genre_scores_gemma":[0.5170923,0.000057159883,0.4826731,0.000063201616,0.00010039596,0.0000037830196,0.000001615758,0.000004116391,0.0000043259342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993133,0.000030345762,0.0002627156,0.00008696177,0.00019912794,0.00010754816],"domain_scores_gemma":[0.99918205,0.0001958843,0.000069837915,0.00016514106,0.00031604533,0.00007105141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033682043,0.00007871986,0.000112388065,0.00016376676,0.000053979802,0.00024217003,0.00071013265,0.000030555428,0.0000010012245],"category_scores_gemma":[0.000027924549,0.00007390065,0.000036952737,0.000047170935,0.000016950598,0.00066383363,0.0002407665,0.00015313986,0.0000011546701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015860314,0.00008014072,0.0012618311,0.000041280888,0.00028510464,0.000015196668,0.0040239976,0.5791079,0.0021781046,0.022713667,0.0010407417,0.3892362],"study_design_scores_gemma":[0.00034489293,0.00005737808,0.014749183,0.00014100182,0.000003351323,0.00019368243,0.000011612677,0.98350453,0.00008704324,0.00042527626,0.00040679376,0.0000752365],"about_ca_topic_score_codex":0.000017960523,"about_ca_topic_score_gemma":6.948557e-8,"teacher_disagreement_score":0.484713,"about_ca_system_score_codex":0.000031646894,"about_ca_system_score_gemma":0.000020100002,"threshold_uncertainty_score":0.3013581},"labels":[],"label_agreement":null},{"id":"W2328842223","doi":"10.2514/6.2010-8361","title":"Modeling Urban Environments for Communication-Aware UAV Swarm Path Planning","year":2010,"lang":"en","type":"article","venue":"AIAA Modeling and Simulation Technologies Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Swarm behaviour; Motion planning; Computer science; Path (computing); Simulation; Human–computer interaction; Real-time computing; Artificial intelligence; Computer network; Robot","score_opus":0.05216715858887103,"score_gpt":0.2895948780423068,"score_spread":0.2374277194534358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328842223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11977798,0.00018444611,0.8783279,0.00043504784,0.00011938013,0.0002530525,0.000005553718,0.00085386744,0.000042777345],"genre_scores_gemma":[0.73626757,0.000035543537,0.26355675,0.0000319588,0.000012672457,0.000044150907,0.000017487342,0.000011469215,0.000022388413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853605,0.00002640802,0.00036022734,0.00052811153,0.00021613142,0.00033305486],"domain_scores_gemma":[0.9985047,0.00023435852,0.00011466736,0.0009778619,0.00011553067,0.00005286181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035203295,0.0002187804,0.00022542547,0.00016072411,0.0004683041,0.00022222509,0.0010418468,0.00027317848,8.033403e-7],"category_scores_gemma":[0.00027536033,0.00021717609,0.000041760217,0.00013991146,0.000087842214,0.0005044594,0.00042518735,0.00045434674,0.0000038732937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036371066,0.000016201328,0.0003682872,0.0000087876015,0.00000844481,8.673818e-7,0.0005757905,0.9753113,0.00031700265,0.0055872328,0.000006207278,0.017796252],"study_design_scores_gemma":[0.00030882223,0.000041187046,0.000016573113,0.000068235364,0.000008178055,0.0000029503324,0.00036285323,0.9803235,0.00022662082,0.018308815,0.000072940406,0.0002593253],"about_ca_topic_score_codex":0.000021428592,"about_ca_topic_score_gemma":8.0717575e-7,"teacher_disagreement_score":0.6164896,"about_ca_system_score_codex":0.00002235451,"about_ca_system_score_gemma":0.00004691995,"threshold_uncertainty_score":0.8856184},"labels":[],"label_agreement":null},{"id":"W2329089337","doi":"10.1115/detc2003/cie-48284","title":"Exploration of Evolutionary Path Planning of Part Handling in Sheet Metal Bending","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Motion planning; Bending; Sheet metal; Intersection (aeronautics); Path (computing); Robot; Computer science; Collision; Motion (physics); Algorithm; Simulation; Engineering; Artificial intelligence; Structural engineering; Aerospace engineering","score_opus":0.061886579524688005,"score_gpt":0.2806571876236294,"score_spread":0.2187706080989414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329089337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038437277,0.0002526901,0.9570436,0.00003201374,0.00030696284,0.0000857876,0.0000014037664,0.000043914075,0.0037963502],"genre_scores_gemma":[0.6649564,0.0000031156305,0.33496618,0.000008713137,0.000011006877,0.000004501907,0.0000027295669,0.000003348867,0.000043999666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989031,0.00009379136,0.00036767672,0.00020803079,0.0002515195,0.00017586657],"domain_scores_gemma":[0.9994287,0.00012213233,0.00014208093,0.00022343431,0.000050168775,0.000033503056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005018242,0.00008451538,0.00018887948,0.00019129697,0.000035133533,0.0000120321565,0.00022645738,0.000042339325,0.000008087863],"category_scores_gemma":[0.00014414189,0.000081032566,0.00003598743,0.00041689698,0.000023984267,0.0007848985,0.000051804986,0.00007798556,0.0000027768158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012257216,0.0002863772,0.069595516,0.00008813136,0.000052019594,0.000072420946,0.008112936,0.76632696,0.005782381,0.14528105,0.0007024865,0.0036874409],"study_design_scores_gemma":[0.0006765771,0.00016324515,0.010599102,0.00036286935,0.0000098002865,0.000024575626,0.00080476666,0.9635628,0.017438527,0.005903073,0.00018534927,0.0002693398],"about_ca_topic_score_codex":0.000021354694,"about_ca_topic_score_gemma":4.5358132e-7,"teacher_disagreement_score":0.62651914,"about_ca_system_score_codex":0.000028901384,"about_ca_system_score_gemma":0.00006585931,"threshold_uncertainty_score":0.3304412},"labels":[],"label_agreement":null},{"id":"W2330019931","doi":"10.2514/6.2008-6836","title":"A Dynamic Path Generation Method for a UAV Swarm in the Urban Environment","year":2008,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation and Control Conference and Exhibit","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Army Research Office","keywords":"Swarm behaviour; Computer science; Path (computing); Artificial intelligence; Computer network","score_opus":0.024650757747964408,"score_gpt":0.2613885724591193,"score_spread":0.23673781471115488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330019931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047717217,0.0007453934,0.94901943,0.0018026875,0.00007794739,0.00048532287,0.0000066205607,0.00002862769,0.000116774834],"genre_scores_gemma":[0.91731393,0.000108600056,0.08109185,0.0011336105,0.000036786463,0.00019925542,0.000017846547,0.0000052595897,0.00009284192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988111,0.00014912183,0.00027504683,0.00038666927,0.00015998316,0.00021806119],"domain_scores_gemma":[0.99938613,0.00015561818,0.00010359359,0.00025709142,0.000048517766,0.000049076247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006387288,0.00014694058,0.00019914059,0.00006013593,0.00024585522,0.00012533247,0.00023325083,0.0000709985,0.0000010413497],"category_scores_gemma":[0.000027583383,0.000113645096,0.00003299019,0.00012192083,0.00007179235,0.00025200372,0.000032109918,0.00011663611,0.0000035442922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025648938,0.0006493602,0.031324893,0.00022680266,0.00022253636,0.00018898123,0.051467773,0.0095399935,0.064531274,0.279182,0.004913613,0.5574963],"study_design_scores_gemma":[0.0016014436,0.00012769173,0.0130966045,0.000045865916,0.0000129286655,0.000047945003,0.00012372053,0.9803012,0.0001440858,0.0025107516,0.0018314702,0.0001562638],"about_ca_topic_score_codex":0.00003512349,"about_ca_topic_score_gemma":0.0000032289875,"teacher_disagreement_score":0.97076124,"about_ca_system_score_codex":0.000026703843,"about_ca_system_score_gemma":0.000057781752,"threshold_uncertainty_score":0.46343127},"labels":[],"label_agreement":null},{"id":"W2330511976","doi":"10.1007/s10479-016-2145-0","title":"Static target search path planning optimization with heterogeneous agents","year":2016,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Mathematical optimization; Computer science; Motion planning; Integer programming; Key (lock); Relaxation (psychology); Theory of computation; Path (computing); Quadratic programming; Quadratic equation; Computational complexity theory; Mathematics; Algorithm; Artificial intelligence","score_opus":0.24273312120667526,"score_gpt":0.4363235730292383,"score_spread":0.19359045182256304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330511976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03362188,0.000076728094,0.96128356,0.004208808,0.000045165027,0.00031730268,0.00001844426,0.000057165988,0.00037091432],"genre_scores_gemma":[0.56612504,0.00005119682,0.43301356,0.00011570878,0.000033363394,0.000044323282,0.000011971217,0.000016255748,0.00058859016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971852,0.00045468746,0.00028404614,0.00040641087,0.0011061565,0.000563556],"domain_scores_gemma":[0.9976628,0.0002855379,0.000029020859,0.0006512922,0.0011975955,0.0001737979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015008794,0.000120273704,0.00016793329,0.00040939965,0.00035130564,0.00020247282,0.0008347963,0.00005300814,0.00005634952],"category_scores_gemma":[0.00026285095,0.00007908837,0.000031187476,0.00081002497,0.0001600617,0.0006851336,0.00023438406,0.00016778769,0.000060365157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013245576,0.000084564635,0.0006051825,0.000012589593,0.000030939682,0.0000694526,0.0009445287,0.99308455,0.0005859172,0.00073529565,0.0015059431,0.0023277928],"study_design_scores_gemma":[0.00030788122,0.0005191991,0.00064470834,0.0001771209,0.0000012713455,0.000026240155,0.000055925993,0.98520017,0.012656004,0.00010633734,0.00016875331,0.0001363648],"about_ca_topic_score_codex":0.00006415376,"about_ca_topic_score_gemma":9.1669824e-7,"teacher_disagreement_score":0.5325031,"about_ca_system_score_codex":0.000031982625,"about_ca_system_score_gemma":0.00034077922,"threshold_uncertainty_score":0.322513},"labels":[],"label_agreement":null},{"id":"W2331965471","doi":"10.1017/s0263574714001921","title":"Waypoints guidance of differential-drive mobile robots with kinematic and precision constraints","year":2014,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Kinematics; Control theory (sociology); Tree traversal; Controller (irrigation); Acceleration; Computer science; Mobile robot; Convergence (economics); Control engineering; Robot; Engineering; Artificial intelligence; Real-time computing; Control (management); Algorithm; Physics","score_opus":0.007532902025011056,"score_gpt":0.22378975259065986,"score_spread":0.2162568505656488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331965471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017084593,0.000051067516,0.9808965,0.00012447483,0.00017056397,0.00030208193,0.0000019940323,0.000098460274,0.0012703112],"genre_scores_gemma":[0.5654425,0.0000036583726,0.43442428,0.000027790316,0.00002057561,0.000013216693,0.0000013853747,0.000009096185,0.000057502002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827373,0.00014135572,0.00036936344,0.0004789894,0.00041846104,0.00031807084],"domain_scores_gemma":[0.998363,0.0004339175,0.00021328768,0.000700359,0.00013501402,0.00015438092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003067789,0.00020931079,0.00041058217,0.00009872141,0.000082043334,0.00007372473,0.0005633176,0.000078479396,0.000018317276],"category_scores_gemma":[0.00018259668,0.00015944307,0.0000389375,0.00023407386,0.00027771218,0.00021122412,0.0002295013,0.00014271915,0.00002722286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008986109,0.00096030976,0.012794692,0.0009799193,0.00031358807,0.00011282,0.005662676,0.6164496,0.019526869,0.08367533,0.0008243841,0.25860998],"study_design_scores_gemma":[0.0010297393,0.0006582428,0.0380246,0.0007273742,0.000036111298,0.0001232318,0.0000259567,0.95504206,0.0022060208,0.001761583,0.000018690003,0.00034640636],"about_ca_topic_score_codex":0.000006597467,"about_ca_topic_score_gemma":8.694263e-7,"teacher_disagreement_score":0.5483579,"about_ca_system_score_codex":0.000017543025,"about_ca_system_score_gemma":0.000041828473,"threshold_uncertainty_score":0.65018994},"labels":[],"label_agreement":null},{"id":"W2334016672","doi":"10.2514/6.2016-1984","title":"Inexpensive, Efficient, Light-weight Vision-based Collision Avoidance System for Small Unmanned Aerial Vehicles","year":2016,"lang":"en","type":"article","venue":"AIAA Infotech @ Aerospace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collision avoidance; Computer science; Collision avoidance system; Computer vision; Machine vision; Remotely operated underwater vehicle; Artificial intelligence; Collision; Mobile robot; Robot; Computer security","score_opus":0.012395137532637171,"score_gpt":0.23511621292817922,"score_spread":0.22272107539554203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334016672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09013751,0.000114921335,0.9000364,0.00494516,0.002464096,0.0010726191,0.000037676306,0.0009934566,0.00019813766],"genre_scores_gemma":[0.7072363,0.0000065984973,0.2910077,0.00042779185,0.00041314485,0.00022118882,0.000009417679,0.000067550165,0.00061032816],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965863,0.00015779572,0.00063094095,0.0010441418,0.0006591086,0.000921671],"domain_scores_gemma":[0.9960994,0.001081451,0.00046179962,0.0015135045,0.0005363013,0.0003075169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008096058,0.00048328785,0.00056126557,0.00024911202,0.00048274858,0.00029860396,0.0017305474,0.00034167006,0.0000027517067],"category_scores_gemma":[0.00057061913,0.00035109976,0.00020242667,0.000754451,0.00011729318,0.00037719464,0.0003799253,0.00020155094,0.00025496914],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013646011,0.0011382092,0.0021044947,0.0011238,0.00027846586,0.0005129996,0.0029896083,0.15341209,0.67637736,0.051532388,0.066725135,0.04244085],"study_design_scores_gemma":[0.0068111983,0.0011384878,0.0014376555,0.0021177945,0.000035985304,0.00004429462,0.000073996875,0.49727458,0.4734481,0.00016925485,0.016143002,0.0013056628],"about_ca_topic_score_codex":0.00004635232,"about_ca_topic_score_gemma":0.0000055143714,"teacher_disagreement_score":0.61709875,"about_ca_system_score_codex":0.00034471502,"about_ca_system_score_gemma":0.00028384445,"threshold_uncertainty_score":0.9998941},"labels":[],"label_agreement":null},{"id":"W2334125968","doi":"10.13111/2066-8201.2012.5.3.9","title":"Algorithm for the aircraft trajectories considering No Fly Zones for a Flight Management System","year":2013,"lang":"en","type":"article","venue":"INCAS BULLETIN","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Obstacle; Dimension (graph theory); Aerospace engineering; Flight plan; Plan (archaeology); Computer science; Trajectory; Space (punctuation); Fly-by-wire; Aeronautics; On the fly; Meteorology; Flight simulator; Engineering; Geology; Geography; Mathematics; Physics","score_opus":0.015035508427073884,"score_gpt":0.2270662136683404,"score_spread":0.21203070524126652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334125968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006938003,0.00034503543,0.98950815,0.0051509244,0.0019211253,0.0019712201,0.000014931627,0.0004120423,0.0006071936],"genre_scores_gemma":[0.0025093786,0.000007354575,0.99181557,0.0004052042,0.00040981898,0.0020087566,0.000004254655,0.000030801784,0.0028088584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823904,0.000053564272,0.0003775645,0.00050368096,0.00028814326,0.00053798937],"domain_scores_gemma":[0.99790543,0.00094805245,0.00013638602,0.00067533023,0.00024314216,0.00009167258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005439957,0.0002479862,0.00027368826,0.000080025005,0.00045544494,0.0003823092,0.00096867967,0.000077371005,0.00002098085],"category_scores_gemma":[0.000072602066,0.00018127823,0.00013549431,0.00014489898,0.00006658317,0.00012190471,0.00024464238,0.00010240694,0.00049434294],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027490247,0.0001785697,0.00006411525,0.0011747055,0.00068938604,0.00015268657,0.0029228926,0.0038546228,0.00016633706,0.038241297,0.35578564,0.5967423],"study_design_scores_gemma":[0.0008568534,0.00015116113,0.0002191436,0.0001653225,0.00004386091,0.000090668684,0.0004647428,0.6581416,0.0003338421,0.0005503049,0.33861572,0.00036680084],"about_ca_topic_score_codex":0.00011973103,"about_ca_topic_score_gemma":8.97816e-7,"teacher_disagreement_score":0.654287,"about_ca_system_score_codex":0.00008269088,"about_ca_system_score_gemma":0.000033105203,"threshold_uncertainty_score":0.7392311},"labels":[],"label_agreement":null},{"id":"W2335537248","doi":"10.2514/6.2004-6464","title":"Automated Contingency Management Design for UAVs","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"Gruppo Nazionale per il Calcolo Scientifico; Defense Advanced Research Projects Agency; College of Computing; Zhejiang University","keywords":"Computer science; Contingency; Systems engineering; Engineering","score_opus":0.035667203558012894,"score_gpt":0.27871096030981707,"score_spread":0.24304375675180417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335537248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011612317,0.00002932973,0.99157447,0.0006361304,0.00035408133,0.0004417134,3.6294014e-7,0.0018139178,0.005033869],"genre_scores_gemma":[0.01546865,0.0000026668092,0.9832822,0.0002715723,0.000022813056,0.00005577363,0.000001182935,0.00000702882,0.00088810734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991104,0.000017451486,0.00015763921,0.0002840166,0.00014916134,0.00028131597],"domain_scores_gemma":[0.99946284,0.0000532068,0.000043900436,0.0003377463,0.000043398482,0.000058890964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022353588,0.00009689115,0.00009989935,0.00007046845,0.0000902436,0.00008826271,0.00058222446,0.000031974225,0.0000031180284],"category_scores_gemma":[0.000018798228,0.000085434694,0.00003655938,0.0002020969,0.000012017628,0.00016683138,0.00009574675,0.000031467822,0.00010522511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013949476,0.00031569993,0.00008726753,0.00009601773,0.00017940562,0.0002481944,0.0016790434,0.3621585,0.000595078,0.5184412,0.028765036,0.087420635],"study_design_scores_gemma":[0.001040083,0.00012525971,0.0009034369,0.0000309085,0.00000884439,0.000014672261,0.000022714292,0.9810406,0.0021517207,0.013674438,0.0007836193,0.00020368135],"about_ca_topic_score_codex":0.000009353548,"about_ca_topic_score_gemma":1.3281067e-7,"teacher_disagreement_score":0.6188821,"about_ca_system_score_codex":0.000044495264,"about_ca_system_score_gemma":0.000028797032,"threshold_uncertainty_score":0.34839258},"labels":[],"label_agreement":null},{"id":"W2335884387","doi":"","title":"Parallel Particle Swarm Optimization on Graphical Processing Unit for Pose Estimation","year":2012,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Speedup; CUDA; Computer science; Parallel computing; Particle swarm optimization; Computation; Central processing unit; Graphics processing unit; Parallel processing; General-purpose computing on graphics processing units; Algorithm; Parallel algorithm; Graphics; Computer graphics (images); Computer hardware","score_opus":0.04671528690260239,"score_gpt":0.30644536421185603,"score_spread":0.2597300773092536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335884387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037725673,0.000028971264,0.9942707,0.0008576945,0.00021227638,0.00021221049,5.204024e-7,0.0002719506,0.00037314728],"genre_scores_gemma":[0.33952722,6.526914e-7,0.66006577,0.00022994826,0.000054534437,0.000027964616,0.00000554111,0.000005945594,0.00008242941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990998,0.000033961485,0.00016428305,0.0001903879,0.00019222363,0.00031934347],"domain_scores_gemma":[0.99942225,0.0001036252,0.00006370666,0.00021806035,0.00006993663,0.00012241249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033652218,0.00009652114,0.00008901569,0.00005792309,0.00015346016,0.00011144635,0.00023734868,0.00005284785,0.000003983058],"category_scores_gemma":[0.00010100983,0.0000823175,0.000029155763,0.0003040391,0.000019836021,0.0007790285,0.000037683403,0.00006207102,0.000026963868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011234261,0.00013131066,0.000729706,0.000011714791,0.0000041227113,6.077373e-7,0.0003915377,0.95226246,0.000025825806,0.024623549,0.00019886864,0.021609075],"study_design_scores_gemma":[0.00031042716,0.00008442542,0.002145883,0.000017511977,0.0000054281295,0.0000063373514,0.000012472581,0.9958379,0.0007140351,0.00070257427,0.000045284633,0.00011766741],"about_ca_topic_score_codex":0.0000031287552,"about_ca_topic_score_gemma":1.0597208e-7,"teacher_disagreement_score":0.33575466,"about_ca_system_score_codex":0.000018399727,"about_ca_system_score_gemma":0.000026917116,"threshold_uncertainty_score":0.33568102},"labels":[],"label_agreement":null},{"id":"W2337425961","doi":"10.1139/cjce-2015-0110","title":"Real-time planning of a lifting scheme in mobile crane mounted controllers","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scheme (mathematics); Controller (irrigation); Motion planning; Computer science; Real-time computing; Software; Path (computing); Object (grammar); Simulation; Embedded system; Control engineering; Engineering; Operating system; Artificial intelligence","score_opus":0.007876647606310842,"score_gpt":0.21224200832091453,"score_spread":0.20436536071460368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337425961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35997218,0.0017685591,0.63436514,0.00037194867,0.001073255,0.00023771207,0.000014071409,0.00007785318,0.002119289],"genre_scores_gemma":[0.96363896,0.00000973572,0.036188874,0.000015868905,0.00008352383,0.0000026803732,2.4895039e-7,0.000017027294,0.000043070257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987145,0.00002766985,0.0005130302,0.00013294509,0.00019529536,0.00041655902],"domain_scores_gemma":[0.9988065,0.00024611945,0.00022878998,0.00020589892,0.00012351084,0.0003892038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062229467,0.00013312914,0.0003551898,0.0006871837,0.00002793871,0.00003620079,0.00056039315,0.000065795735,0.00002057733],"category_scores_gemma":[0.000392923,0.000112330395,0.00007137932,0.00037879444,0.000025202047,0.00036103645,0.000020843145,0.00017640718,0.0000044987196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000116154015,0.000013970231,0.007704572,0.000051550727,0.00009690173,0.0015492293,0.0026128858,0.8970613,0.086647704,0.0004027447,0.00054692867,0.0033006303],"study_design_scores_gemma":[0.002657243,0.000251651,0.009518962,0.0030842714,0.0000124037315,0.00045804083,0.00008483756,0.9804935,0.001616571,0.000079579666,0.0013223904,0.00042055006],"about_ca_topic_score_codex":0.00038523538,"about_ca_topic_score_gemma":0.00038143,"teacher_disagreement_score":0.6036668,"about_ca_system_score_codex":0.00022170666,"about_ca_system_score_gemma":0.0005525409,"threshold_uncertainty_score":0.45807004},"labels":[],"label_agreement":null},{"id":"W2337610099","doi":"","title":"Vision-Based Obstacle Run for Teams of Humanoid Robots (Demonstration)","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Traverse; Humanoid robot; Robot; Obstacle; Computer science; Artificial intelligence; Mobile robot; Obstacle avoidance; Computer vision; Set (abstract data type); Human–computer interaction; Geography","score_opus":0.036836038284001955,"score_gpt":0.27018901757232106,"score_spread":0.2333529792883191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337610099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030876242,0.000018901614,0.9886734,0.00011510334,0.00028056445,0.000233334,0.0000025588458,0.00016478769,0.0074237203],"genre_scores_gemma":[0.3049228,1.8504807e-7,0.69454515,0.00011671582,0.00001834632,0.000015323376,0.0000030581587,0.0000060288835,0.0003724313],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898267,0.000027749136,0.0002814622,0.00028696173,0.00019946406,0.00022170575],"domain_scores_gemma":[0.99902004,0.00015775603,0.00011783368,0.0005045079,0.00012515958,0.00007471249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026283122,0.00010994109,0.00015851537,0.00008923661,0.00008806155,0.000033386972,0.0006408383,0.00005769725,0.00003455712],"category_scores_gemma":[0.00005756798,0.00009528412,0.000074863005,0.00018867674,0.000052328345,0.00029603555,0.00005433971,0.00005337666,0.000030953466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013168652,0.003274878,0.023994371,0.0003624689,0.00020451909,0.00007436177,0.007332279,0.07888383,0.028673023,0.47989792,0.021403752,0.35576692],"study_design_scores_gemma":[0.0007055885,0.0005017229,0.016038693,0.000041494164,0.000008842347,0.0000056191407,0.000038582675,0.93604475,0.04198001,0.0042764647,0.000117740696,0.00024046167],"about_ca_topic_score_codex":0.000047404184,"about_ca_topic_score_gemma":0.0000019938036,"teacher_disagreement_score":0.8571609,"about_ca_system_score_codex":0.000018560431,"about_ca_system_score_gemma":0.00013571573,"threshold_uncertainty_score":0.38855737},"labels":[],"label_agreement":null},{"id":"W2346846240","doi":"10.5555/2981324.2981342","title":"Stippling with aerial robots","year":2016,"lang":"en","type":"article","venue":"Non-Photorealistic Animation and Rendering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robot; Computer science; Voronoi diagram; Kalman filter; Artificial intelligence; Computer vision; Motion control; Mathematics","score_opus":0.015928487417139918,"score_gpt":0.23164574510233682,"score_spread":0.2157172576851969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346846240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009263887,0.000015342972,0.98813653,0.0001941572,0.0002162923,0.00011026509,0.00000431115,0.00016465504,0.0018945637],"genre_scores_gemma":[0.7842028,0.00001091495,0.21543373,0.000066720546,0.000118637145,0.000015193623,0.0000034065686,0.00001206278,0.00013650052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901855,0.000021810893,0.00017594072,0.00032068277,0.00023156103,0.00023147362],"domain_scores_gemma":[0.9993904,0.00011275245,0.00009430175,0.00024160855,0.000048140784,0.00011275115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019096554,0.00013555736,0.0001465517,0.000076346536,0.00014314718,0.000118400676,0.0002088054,0.000051500603,0.000007931448],"category_scores_gemma":[0.00003962573,0.00008476569,0.000016729702,0.00012986657,0.000047293946,0.00030454228,0.000082970495,0.000056133405,0.000009819075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004293172,0.00028777742,0.010092899,0.000698242,0.0005206325,0.00087915396,0.01767668,0.018663157,0.24750581,0.21345216,0.0030548833,0.48673928],"study_design_scores_gemma":[0.0034714737,0.0004942821,0.04465835,0.0009234018,0.000053607462,0.00053132506,0.00019577872,0.936576,0.008370515,0.0029593515,0.00069947075,0.0010664249],"about_ca_topic_score_codex":0.000062447325,"about_ca_topic_score_gemma":0.0000037632915,"teacher_disagreement_score":0.91791284,"about_ca_system_score_codex":0.00004874464,"about_ca_system_score_gemma":0.00004973671,"threshold_uncertainty_score":0.34566444},"labels":[],"label_agreement":null},{"id":"W2353342479","doi":"","title":"LP-based Path Planning Method in Acceleration Space for Mobile Robot","year":2007,"lang":"en","type":"article","venue":"Acta Automatica Sinica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Motion planning; Mobile robot; Acceleration; Path (computing); Mathematical optimization; Computer science; Linear programming; Planner; Set (abstract data type); Robot; Control theory (sociology); Real-time computing; Simulation; Artificial intelligence; Mathematics; Physics","score_opus":0.03562015923479298,"score_gpt":0.36270996739411854,"score_spread":0.32708980815932553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2353342479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008680796,0.000042858763,0.98772246,0.0011123325,0.00036441718,0.00082855934,0.0000032871922,0.00048356634,0.000761747],"genre_scores_gemma":[0.31921208,5.2968824e-7,0.68025094,0.00031200188,0.000055870456,0.00009505262,0.000010669477,0.000018059587,0.000044810466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755156,0.00015803998,0.00066439755,0.0005939001,0.0003919772,0.000640152],"domain_scores_gemma":[0.9957897,0.002894943,0.00027160498,0.0008043015,0.00008746481,0.00015198268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029791684,0.00023839275,0.00039350148,0.00027446388,0.00014426866,0.0001993598,0.00089992746,0.00017162811,0.00000842279],"category_scores_gemma":[0.0006548197,0.00023300444,0.00009631433,0.0006067522,0.00003391952,0.0004801544,0.00012236713,0.00023485749,0.000021486989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024980117,0.0024703618,0.021015376,0.00070637977,0.00027907922,0.0006707716,0.016822504,0.5502844,0.046466548,0.018409917,0.019028073,0.3235968],"study_design_scores_gemma":[0.00081179227,0.00022275695,0.02800018,0.0001374343,0.000012476729,0.000012473661,0.000057290334,0.9658792,0.0028669087,0.00070877915,0.0010035132,0.0002871851],"about_ca_topic_score_codex":0.00001742942,"about_ca_topic_score_gemma":0.0000017207278,"teacher_disagreement_score":0.41559482,"about_ca_system_score_codex":0.00013218616,"about_ca_system_score_gemma":0.00020777533,"threshold_uncertainty_score":0.9501645},"labels":[],"label_agreement":null},{"id":"W2355272074","doi":"10.1111/coin.12092","title":"An Empirical Comparison of Search Approaches for Moving Agents","year":2016,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Range (aeronautics); Computer science; Domain (mathematical analysis); Grid; Path (computing); Search algorithm; Search problem; Hyperparameter optimization; Artificial intelligence; Random search; Algorithm; Mathematical optimization; Data mining; Machine learning; Mathematics","score_opus":0.3099315419918374,"score_gpt":0.4372872349717034,"score_spread":0.127355692979866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2355272074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014173919,0.00004555034,0.9847546,0.00047959996,0.00018117583,0.0001851945,0.000009909454,0.0000781209,0.00009192517],"genre_scores_gemma":[0.55940956,4.979125e-7,0.4404802,0.00003600619,0.000030961557,0.000009749901,0.000005282509,0.0000050288245,0.000022703935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984522,0.000090602785,0.0003844416,0.00040925492,0.00041538657,0.00024810416],"domain_scores_gemma":[0.99822325,0.0009926752,0.00011389526,0.00031593913,0.00023990199,0.00011437099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004345273,0.000117867,0.0001957461,0.00012180116,0.000095943506,0.00005192089,0.0010060901,0.00005430519,0.000008007069],"category_scores_gemma":[0.00012233315,0.00009009726,0.000060991828,0.0002520074,0.00011531963,0.0003492652,0.00012963385,0.00006795181,0.00003227878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009518357,0.00019084425,0.010539575,0.00002074114,0.000016815327,0.0000015830793,0.0015525488,0.7163682,0.00009712695,0.025445491,0.000255393,0.24550219],"study_design_scores_gemma":[0.000080745594,0.0001785668,0.009564729,0.000041251653,0.000002616968,0.000005361269,0.000076070995,0.9688295,0.0035246098,0.01753153,0.000042041083,0.00012293842],"about_ca_topic_score_codex":0.0000041630215,"about_ca_topic_score_gemma":2.5238964e-7,"teacher_disagreement_score":0.54523563,"about_ca_system_score_codex":0.00004713908,"about_ca_system_score_gemma":0.00012189675,"threshold_uncertainty_score":0.36740595},"labels":[],"label_agreement":null},{"id":"W2360553802","doi":"10.1007/978-3-642-03259-2_10","title":"Rover path planning","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Geology","score_opus":0.07459342506079573,"score_gpt":0.2774029904879918,"score_spread":0.20280956542719603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2360553802","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3146125e-7,0.0004991614,0.39387083,0.00009395492,0.00071708875,0.00007568235,0.0000039116044,0.00035184243,0.6043874],"genre_scores_gemma":[0.0000337896,0.000004734792,0.23315847,0.0002444429,0.00026863045,0.0000025252318,0.000013891926,0.000034837532,0.7662387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980656,0.00001227317,0.00029137745,0.00063117285,0.00066682015,0.0003327736],"domain_scores_gemma":[0.9983823,0.00007843949,0.00018107623,0.001000768,0.00013257147,0.00022485893],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030858407,0.00036121652,0.00037559678,0.00015791932,0.000059809892,0.00015237456,0.0012892329,0.00032370715,0.00011498001],"category_scores_gemma":[0.0000273797,0.00031982476,0.00009903194,0.000035584944,0.000037381833,0.0002481376,0.0005124533,0.00043922692,0.0016802931],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017508529,0.000007798952,0.000012001606,0.000013242247,0.00005290971,0.00068480533,0.0003021065,0.0014667516,0.0000017340042,0.63587743,0.34912193,0.012457541],"study_design_scores_gemma":[0.00030620198,0.00013187897,0.00003479353,0.00032819164,0.000021571397,0.00015638747,0.0000049099335,0.079110414,0.0000052086757,0.08148655,0.8374865,0.00092739426],"about_ca_topic_score_codex":0.000008650952,"about_ca_topic_score_gemma":1.04667365e-7,"teacher_disagreement_score":0.55439085,"about_ca_system_score_codex":0.000107699285,"about_ca_system_score_gemma":0.00022001816,"threshold_uncertainty_score":0.9999254},"labels":[],"label_agreement":null},{"id":"W2362050632","doi":"","title":"Region Segmentation of UAV Path Planning","year":2012,"lang":"en","type":"article","venue":"Jisuanji fangzhen","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Path (computing); Motion planning; Diagonal; Computer science; Point (geometry); Block (permutation group theory); Line segment; Any-angle path planning; Segmentation; Line (geometry); Plane (geometry); Computer vision; Artificial intelligence; Algorithm; Mathematics; Geometry","score_opus":0.050361900474347264,"score_gpt":0.30264483492114,"score_spread":0.2522829344467928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2362050632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08431812,0.00044320308,0.9115585,0.00014923008,0.0007146851,0.00014928431,0.00000152642,0.00018021557,0.002485221],"genre_scores_gemma":[0.7896879,0.000003577128,0.20980637,0.00011920326,0.00016629307,0.000009750946,0.000007924114,0.000010278142,0.00018876874],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99886817,0.00006704228,0.00023546524,0.00019372409,0.00030344437,0.00033217404],"domain_scores_gemma":[0.99920183,0.00008226101,0.00018122407,0.00038053136,0.000044752924,0.00010938196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031810754,0.00011622871,0.00015936623,0.00010536579,0.000065680695,0.000032115593,0.00042112975,0.000064619395,0.000003931585],"category_scores_gemma":[0.000040159346,0.000108227025,0.00005053792,0.0002744557,0.000025564334,0.0006567354,0.00012641436,0.00009586047,0.000052350577],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032905533,0.0005824506,0.7527233,0.0001670662,0.00016671959,0.00012385384,0.043840762,0.004930312,0.013190364,0.027684173,0.026105782,0.13045236],"study_design_scores_gemma":[0.0021933906,0.0005652534,0.74782515,0.00055552815,0.00008863255,0.00034543325,0.0013186818,0.19781877,0.037591368,0.0028827735,0.0073775863,0.0014374463],"about_ca_topic_score_codex":0.00003156489,"about_ca_topic_score_gemma":5.6107982e-8,"teacher_disagreement_score":0.7053697,"about_ca_system_score_codex":0.00004580024,"about_ca_system_score_gemma":0.000025295409,"threshold_uncertainty_score":0.44133702},"labels":[],"label_agreement":null},{"id":"W2363688215","doi":"","title":"Error Analyzing and Solutions in Robot Soccer System","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Robot; Computer vision; Artificial intelligence; Line (geometry); Image (mathematics); Simulation; Mathematics; Geometry","score_opus":0.013051500753091678,"score_gpt":0.23086632298540394,"score_spread":0.21781482223231227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2363688215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001010985,0.0003760145,0.9965537,0.0006312547,0.000027211378,0.00038300795,0.000005475939,0.00031312223,0.0006992097],"genre_scores_gemma":[0.12308595,0.0000022871584,0.8763403,0.00007867063,0.00012971762,0.00023621264,0.000016515758,0.000011068196,0.00009925459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986934,0.00004658112,0.00031534198,0.00048645996,0.00010590703,0.00035231237],"domain_scores_gemma":[0.999327,0.00007535025,0.00008046613,0.00040933344,0.000044076147,0.0000637955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000211383,0.0001454442,0.00017375775,0.00022836999,0.00024315322,0.00017301641,0.00053540146,0.00007334739,7.590241e-7],"category_scores_gemma":[3.3553744e-7,0.00015517673,0.00003565495,0.00060931075,0.000045268996,0.00022328134,0.0002743636,0.00015410945,0.00006589745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030890103,0.0006848676,0.018687038,0.00024368319,0.00007402351,0.00006816974,0.0013314643,0.14670675,0.01681757,0.46589774,0.009866088,0.33961952],"study_design_scores_gemma":[0.0005759227,0.000016542272,0.068620145,0.00007782077,0.000015800335,0.00021465395,0.000040189192,0.89778703,0.00028918442,0.0020635582,0.029821897,0.00047723218],"about_ca_topic_score_codex":0.00020751519,"about_ca_topic_score_gemma":0.0000174838,"teacher_disagreement_score":0.75108033,"about_ca_system_score_codex":0.00010560434,"about_ca_system_score_gemma":0.00003900788,"threshold_uncertainty_score":0.63279235},"labels":[],"label_agreement":null},{"id":"W2366410293","doi":"","title":"Development of talking and mobile robot","year":2001,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Mobile robot; Robot; Engineering; China; Path (computing); Electrical engineering; Telecommunications; Computer science; Geography; Artificial intelligence; Operating system","score_opus":0.020807593451845307,"score_gpt":0.2565232409120847,"score_spread":0.23571564746023943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366410293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08612413,0.00007252527,0.91061544,0.000030812753,0.00007628308,0.00004715535,4.046728e-8,0.00006513652,0.0029684834],"genre_scores_gemma":[0.26562682,0.0000020153384,0.73406667,0.000024048912,0.000006265158,0.0000040140208,1.974112e-7,0.0000017151214,0.0002682266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994763,0.000007693426,0.00013335115,0.00014407751,0.000120768476,0.000117798416],"domain_scores_gemma":[0.99972177,0.000027457581,0.000035133893,0.00015478642,0.000020699781,0.000040172363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012809997,0.000050849656,0.00007972931,0.00004342668,0.000046615856,0.000023104303,0.00021372025,0.000019186355,0.000007306745],"category_scores_gemma":[0.0000070729793,0.000043463668,0.0000082693905,0.00013500747,0.000014384953,0.00012117192,0.00014200751,0.000030593947,0.0000106933],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014052468,0.00007400608,0.008525614,0.000015038782,0.000017868395,0.000029977002,0.009306096,0.003415211,0.0068931673,0.0028455395,0.00013309409,0.96874297],"study_design_scores_gemma":[0.0008311803,0.00018098771,0.072113946,0.00013093914,0.0000068032605,0.00026744825,0.00066938385,0.8546823,0.048887376,0.0007769138,0.020772552,0.00068016787],"about_ca_topic_score_codex":0.0000051881652,"about_ca_topic_score_gemma":6.940026e-7,"teacher_disagreement_score":0.9680628,"about_ca_system_score_codex":0.000009505321,"about_ca_system_score_gemma":0.000031674,"threshold_uncertainty_score":0.17723969},"labels":[],"label_agreement":null},{"id":"W2369149136","doi":"","title":"Design and Implementation of Maze-solving Micromouse","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Realization (probability); Simple (philosophy); Control unit; Software; Computer hardware; Power (physics); Embedded system; Computer engineering; Algorithm; Parallel computing; Programming language","score_opus":0.021023251380609648,"score_gpt":0.2660109339567883,"score_spread":0.24498768257617867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369149136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012066173,0.00014156966,0.98690534,0.00021224664,0.000012912217,0.00051959633,0.0000027648484,0.000119187855,0.000020237867],"genre_scores_gemma":[0.044604372,0.000026197024,0.95507604,0.000111562076,0.000032061635,0.00010896325,0.0000055342603,0.00000843372,0.00002684203],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990937,0.000040715287,0.00025742204,0.00032025948,0.00010627842,0.00018163203],"domain_scores_gemma":[0.9993224,0.00009359808,0.00011880449,0.0003306324,0.00007343789,0.00006111924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014101689,0.00010874676,0.00013429839,0.00011239278,0.00011530262,0.000036149184,0.0004622137,0.00003575281,0.0000026320683],"category_scores_gemma":[2.8820787e-7,0.00011594207,0.000026822589,0.0002756291,0.000057208654,0.00019552199,0.00019038921,0.00007325074,0.000020162142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046536666,0.00031669647,0.005852077,0.00010456119,0.00011848809,0.000030391504,0.022635624,0.004611977,0.3158535,0.009813762,0.006761934,0.6338963],"study_design_scores_gemma":[0.0036940575,0.00042996753,0.09673108,0.00009792795,0.000073459996,0.0026867616,0.00023837666,0.3212164,0.4781458,0.005128464,0.08977024,0.0017874446],"about_ca_topic_score_codex":0.00003301797,"about_ca_topic_score_gemma":3.5307082e-7,"teacher_disagreement_score":0.63210887,"about_ca_system_score_codex":0.00002177599,"about_ca_system_score_gemma":0.00006502642,"threshold_uncertainty_score":0.47279805},"labels":[],"label_agreement":null},{"id":"W2369340877","doi":"","title":"Automatic guided vehicle's guide based on sorts of sensors","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Automated guided vehicle; Mobile robot; Computer science; Control engineering; Engineering; Artificial intelligence; Robot; Simulation","score_opus":0.02052102064930267,"score_gpt":0.273658201679793,"score_spread":0.2531371810304903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369340877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080369264,0.000015619684,0.8640029,0.0050279517,0.00035910058,0.00025774963,0.0000018522696,0.00088205066,0.049083535],"genre_scores_gemma":[0.30782637,3.3920273e-7,0.6898769,0.001031779,0.00004374446,0.0000039077026,9.398633e-7,0.000008452698,0.0012075778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984632,0.00007385773,0.00041483468,0.00031094896,0.0004604094,0.00027672524],"domain_scores_gemma":[0.9986465,0.00021488182,0.00012699608,0.0008354706,0.000076429256,0.00009971882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042444398,0.00014175528,0.00020689734,0.00015528777,0.000048465005,0.000039319926,0.0006464895,0.00005709196,0.000082497354],"category_scores_gemma":[0.00013671223,0.000120326185,0.000069545735,0.00030153655,0.00003339797,0.00017472592,0.00007680163,0.000082802835,0.00023772958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032778566,0.00031699636,0.0016569986,0.000045554978,0.000028881574,0.00009281998,0.0005480914,0.7747108,0.0017456493,0.0056314445,0.056846652,0.15837286],"study_design_scores_gemma":[0.00031738097,0.00007211492,0.0041136886,0.00004710148,0.0000038453163,0.000010900946,0.0000051463776,0.9859685,0.0082625775,0.000084479885,0.0009751037,0.00013917121],"about_ca_topic_score_codex":0.000034972727,"about_ca_topic_score_gemma":8.783016e-7,"teacher_disagreement_score":0.2274571,"about_ca_system_score_codex":0.000054782857,"about_ca_system_score_gemma":0.00009361344,"threshold_uncertainty_score":0.49067596},"labels":[],"label_agreement":null},{"id":"W2369796141","doi":"10.1002/cav.1710","title":"ACCLMesh: curvature‐based navigation mesh generation","year":2016,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polygon mesh; Computer science; Curvature; Navigation system; Computer vision; Artificial intelligence; Computer graphics (images); Geometry; Mathematics","score_opus":0.028992687368685755,"score_gpt":0.2661105117011151,"score_spread":0.23711782433242934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369796141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0112230275,0.00003915898,0.9853952,0.0020230636,0.0007390853,0.00013052153,0.0000034120094,0.00027337883,0.00017315416],"genre_scores_gemma":[0.71530104,0.0000052287314,0.28291485,0.00094891625,0.00042024688,0.0000123588825,0.000028997909,0.000010698004,0.00035764347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987563,0.000106541804,0.0002530879,0.00040599273,0.00027709172,0.00020100828],"domain_scores_gemma":[0.99923545,0.00012029077,0.00011140578,0.00031654557,0.000108229724,0.00010807396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029538875,0.00016061906,0.000143914,0.00014027338,0.0001656932,0.00025004303,0.00030998111,0.00007645109,0.000013337208],"category_scores_gemma":[0.00001983114,0.00011908595,0.000039067432,0.0002932327,0.000040206633,0.0008154956,0.00011213566,0.00008244707,0.00007773228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011590004,0.00009415849,0.0011061103,0.000019247827,0.000030443762,0.000016715754,0.0007865066,0.0016728445,0.017681729,0.12125912,0.018210547,0.839111],"study_design_scores_gemma":[0.0006344256,0.00016767415,0.008718297,0.00008283428,0.0000055400146,0.000015435891,0.0000021731703,0.9855606,0.0020650495,0.00041207607,0.0021130142,0.00022286976],"about_ca_topic_score_codex":0.0000019314853,"about_ca_topic_score_gemma":6.722555e-7,"teacher_disagreement_score":0.9838878,"about_ca_system_score_codex":0.000045859586,"about_ca_system_score_gemma":0.000042903986,"threshold_uncertainty_score":0.4856184},"labels":[],"label_agreement":null},{"id":"W2398897972","doi":"10.2316/journal.206.2015.5.206-4200","title":"SELECTIVE TOPOLOGICAL APPROACH TO MOBILE ROBOT NAVIGATION WITH RECURRENT NEURAL NETWORKS","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mobile robot; Computer science; Artificial neural network; Artificial intelligence; Topology (electrical circuits); Mobile robot navigation; Robot; Robot control; Mathematics; Combinatorics","score_opus":0.025449795444845807,"score_gpt":0.283942015544546,"score_spread":0.2584922200997002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398897972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023845633,0.0000784993,0.97421116,0.0006543985,0.0008785134,0.00014058003,7.3146515e-7,0.00003540427,0.00015505498],"genre_scores_gemma":[0.65100485,0.0000038865956,0.34871328,0.00008188058,0.00017277755,0.000006169744,0.000005123114,0.0000038196217,0.000008181926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878794,0.000070641596,0.00030945503,0.00015895428,0.0005451405,0.00012788038],"domain_scores_gemma":[0.9987025,0.000057446236,0.00029620985,0.00008746917,0.0006979361,0.00015841286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041145124,0.00010432803,0.00014990555,0.00014518153,0.000041069816,0.00020670907,0.00041502778,0.000049776725,3.292074e-7],"category_scores_gemma":[0.000056902412,0.0000778469,0.00003053254,0.00019825358,0.000026266247,0.00048768125,0.000087910215,0.00018471976,0.0000013544001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026250109,0.00007804796,0.0005542845,0.0000018264867,0.00003182927,0.000018159037,0.0005841404,0.97150314,0.000018659335,0.0025530471,0.00014120905,0.024489427],"study_design_scores_gemma":[0.00043661144,0.0006625191,0.00478765,0.000052516265,0.000008311718,0.0006112383,0.000083494044,0.9926152,0.000025273273,0.00058377994,0.000034966364,0.00009843753],"about_ca_topic_score_codex":0.0000045678426,"about_ca_topic_score_gemma":1.8808865e-7,"teacher_disagreement_score":0.62715924,"about_ca_system_score_codex":0.00011949677,"about_ca_system_score_gemma":0.00006144623,"threshold_uncertainty_score":0.31745043},"labels":[],"label_agreement":null},{"id":"W2399757108","doi":"10.2316/journal.206.2015.3.206-4247","title":"A MULTI-AGENT ARCHITECTURE WITH HIERARCHICAL FUZZY CONTROLLER FOR A MOBILE ROBOT","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Architecture; Computer science; Mobile robot; Controller (irrigation); Robot; Artificial intelligence; Geography; Biology","score_opus":0.024999976969936295,"score_gpt":0.2873393838420523,"score_spread":0.26233940687211604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399757108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004072568,0.00013891178,0.9924643,0.0024641359,0.00062607107,0.00017601239,0.000004050558,0.000029416013,0.00002453109],"genre_scores_gemma":[0.28607166,0.000005924126,0.7135563,0.00014093428,0.00016779409,0.00000791663,0.0000029478344,0.0000063131133,0.000040205177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988461,0.00004484659,0.0003320575,0.00013960621,0.0005065192,0.00013087632],"domain_scores_gemma":[0.9986118,0.00013652345,0.00032960353,0.000086855856,0.0006889912,0.00014619557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043348808,0.000108209206,0.00018488299,0.00019080231,0.00004025246,0.00019505632,0.00042876886,0.000045992856,4.3067504e-7],"category_scores_gemma":[0.00012976007,0.00007742203,0.000057459943,0.000077701676,0.000035329136,0.00027205597,0.00006198704,0.0001446382,0.0000016675622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008741683,0.00011962315,0.00030534356,0.0000055088217,0.0001301648,0.00004800616,0.0011471914,0.95152974,0.00034495824,0.0024366577,0.00021149847,0.043633882],"study_design_scores_gemma":[0.0031631903,0.0005769208,0.0020714437,0.00008676614,0.000018722858,0.00067086396,0.00004768356,0.9892013,0.00007215198,0.0035168922,0.00046314238,0.00011092006],"about_ca_topic_score_codex":0.0000029140604,"about_ca_topic_score_gemma":7.7432446e-7,"teacher_disagreement_score":0.2819991,"about_ca_system_score_codex":0.00006631028,"about_ca_system_score_gemma":0.00013380102,"threshold_uncertainty_score":0.31571788},"labels":[],"label_agreement":null},{"id":"W2401571671","doi":"10.2316/journal.206.2015.3.206-4230","title":"POSITION-BASED VISUAL SERVOING IN ROBOTIC CAPTURE OF MOVING TARGET ENHANCED BY KALMAN FILTER","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual servoing; Kalman filter; Computer vision; Artificial intelligence; Computer science; Position (finance); Extended Kalman filter; Image (mathematics)","score_opus":0.011707926191637744,"score_gpt":0.2672259480799232,"score_spread":0.2555180218882855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401571671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06548606,0.00012543562,0.9319042,0.0016734938,0.000673967,0.00004908198,0.0000024892754,0.000016223832,0.00006905705],"genre_scores_gemma":[0.7374562,0.0000032803307,0.26233402,0.00012473576,0.000050895993,6.4648697e-7,0.000009587962,0.0000051571237,0.000015484828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998588,0.00007054946,0.00051930506,0.00012619032,0.0005753247,0.000120601],"domain_scores_gemma":[0.99868536,0.00010940753,0.0005150423,0.00008176784,0.0005239981,0.00008442982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046603166,0.00010372125,0.00019525924,0.00029767686,0.000021303083,0.000103842474,0.00038946234,0.00006034793,0.0000023811322],"category_scores_gemma":[0.00013681654,0.00009730533,0.000045300338,0.00015416405,0.000023532824,0.0005885292,0.000058035326,0.00015066739,0.0000018736636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012798932,0.000096258256,0.0016555979,0.000011696626,0.000028564771,0.000029923824,0.0006902003,0.98921895,0.006107843,0.0004818675,0.00022219685,0.0014440984],"study_design_scores_gemma":[0.00082106807,0.00011998121,0.0035639931,0.0002612883,0.000006930888,0.000054444547,0.00006547918,0.98749894,0.006564322,0.0009370248,0.0000071779305,0.000099358724],"about_ca_topic_score_codex":0.000022220878,"about_ca_topic_score_gemma":0.0000011793989,"teacher_disagreement_score":0.6719701,"about_ca_system_score_codex":0.00011835237,"about_ca_system_score_gemma":0.00014659492,"threshold_uncertainty_score":0.39679962},"labels":[],"label_agreement":null},{"id":"W2404015305","doi":"10.2312/conf/eg2012/tutorials/t5","title":"Recent Advances in Real-Time Collision and Proximity Computations for Games and Simulations","year":2012,"lang":"en","type":"article","venue":"Eurographics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Collision; Computation; Algorithm; Computer security","score_opus":0.03010135307002285,"score_gpt":0.30777010291432033,"score_spread":0.27766874984429746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404015305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28778794,0.00912166,0.6994921,0.0012812178,0.00045642728,0.0010983114,0.000034183035,0.0002911748,0.00043700327],"genre_scores_gemma":[0.27241018,0.0040226337,0.72333586,0.00009187351,0.000057341793,0.000026880822,0.000018261831,0.000012118522,0.000024870915],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926883,0.00006320497,0.00015318871,0.00019932106,0.00010618461,0.0002092844],"domain_scores_gemma":[0.99915403,0.0004805851,0.000060798393,0.00014743778,0.000066847526,0.0000903207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002992573,0.00008465638,0.00010898535,0.00015354826,0.00013128754,0.00006421423,0.00010974066,0.000036414378,4.5084795e-7],"category_scores_gemma":[0.000119827855,0.000082183986,0.000014804024,0.0005107029,0.00006435229,0.0006883546,0.000070149064,0.00006905577,0.0000012029473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026424064,0.00064414454,0.44820318,0.00013308601,0.000032315926,0.000008093314,0.006340217,0.061916023,0.00021330769,0.053691216,0.0005427915,0.4282492],"study_design_scores_gemma":[0.00043609343,0.000095878015,0.1172735,0.00003656048,0.000007853367,0.000010324796,0.000023628276,0.86717844,0.0000198856,0.003195194,0.011570634,0.00015203333],"about_ca_topic_score_codex":0.0000019121135,"about_ca_topic_score_gemma":0.0000020881473,"teacher_disagreement_score":0.8052624,"about_ca_system_score_codex":0.00000781826,"about_ca_system_score_gemma":0.000015926946,"threshold_uncertainty_score":0.33513656},"labels":[],"label_agreement":null},{"id":"W2408178243","doi":"10.2316/journal.206.2015.3.206-3956","title":"LOWER BOUNDS FOR A VEHICLE ROUTING PROBLEM WITH MOTION CONSTRAINTS","year":2015,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Path (computing); Vehicle routing problem; Motion (physics); Routing (electronic design automation); Set (abstract data type); Mathematical optimization; Computer science; Mathematics; Artificial intelligence; Computer network; Robot","score_opus":0.024318380202568796,"score_gpt":0.2735373691974018,"score_spread":0.249218988994833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408178243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012164175,0.00002527849,0.9845571,0.0022580726,0.0006877964,0.000082443716,0.0000018640209,0.000028194421,0.00019506276],"genre_scores_gemma":[0.5698479,0.0000015829088,0.4299696,0.00005553219,0.00010351132,0.0000011270669,0.0000019481586,0.0000036305053,0.000015156892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990605,0.000024186342,0.00029025137,0.00010337391,0.0004178209,0.00010383111],"domain_scores_gemma":[0.99853444,0.000068356894,0.00037850803,0.00006518822,0.00087077904,0.000082756356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005651091,0.0000743857,0.000108243665,0.00011999986,0.00004265952,0.00026311417,0.00027538298,0.000034174573,7.583004e-7],"category_scores_gemma":[0.00010354394,0.00005974395,0.000030114144,0.000074654774,0.00003905063,0.0006323908,0.00003922726,0.00007779176,0.0000013617364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114553244,0.00027529628,0.0056520673,0.000026374852,0.00028517048,0.00014806676,0.0031825805,0.64008826,0.00076630496,0.077369966,0.000831292,0.27126008],"study_design_scores_gemma":[0.0013179766,0.0003450833,0.0027180284,0.00014052939,0.00001300262,0.0005173347,0.00006847958,0.9893855,0.0001483048,0.0051550698,0.0000983768,0.00009232058],"about_ca_topic_score_codex":0.0000024039257,"about_ca_topic_score_gemma":3.1431887e-7,"teacher_disagreement_score":0.5576837,"about_ca_system_score_codex":0.00007800359,"about_ca_system_score_gemma":0.00012898988,"threshold_uncertainty_score":0.25372162},"labels":[],"label_agreement":null},{"id":"W2408456090","doi":"","title":"Generalized Watchman Route Problem with Discrete View Cost.","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Polygon (computer graphics); Approximation algorithm; Viewpoints; Mathematics; Combinatorics; Mathematical optimization; Time complexity; Computer science; Boundary (topology); Algorithm; Discrete mathematics","score_opus":0.018109947431515346,"score_gpt":0.26697386524159417,"score_spread":0.24886391781007883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408456090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001563893,0.00007512526,0.97367,0.0011975016,0.00016113465,0.00036089183,0.0000010306094,0.00045312016,0.022517283],"genre_scores_gemma":[0.027212102,0.000007902958,0.96748084,0.0008119443,0.00007522449,0.000019236106,0.0000070128035,0.00001595002,0.0043698032],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826145,0.000043906257,0.00027163606,0.00046243944,0.00040072497,0.0005598435],"domain_scores_gemma":[0.9989137,0.000061811894,0.00008760022,0.0006565279,0.00007182476,0.00020854862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006620836,0.00019511775,0.00022234864,0.00007665326,0.00011594201,0.00016046542,0.00082906254,0.000053822387,0.000019406609],"category_scores_gemma":[0.000007823357,0.00012970866,0.000043824162,0.00045405267,0.000045281606,0.0004124465,0.00017703454,0.000140627,0.00012604323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012329103,0.0003645394,0.028133646,0.00016961354,0.00035203027,0.0021930824,0.0054170727,0.020991163,0.0017960919,0.4724038,0.019395685,0.44866],"study_design_scores_gemma":[0.008780687,0.0016913138,0.056032427,0.0007415101,0.00011377872,0.0013390814,0.00018347494,0.6892609,0.017689997,0.010822872,0.20911951,0.0042244317],"about_ca_topic_score_codex":0.00015960608,"about_ca_topic_score_gemma":0.00003133903,"teacher_disagreement_score":0.66826975,"about_ca_system_score_codex":0.000056121557,"about_ca_system_score_gemma":0.000061205,"threshold_uncertainty_score":0.52893656},"labels":[],"label_agreement":null},{"id":"W2409593918","doi":"10.1109/icra.2016.7487435","title":"Development and experimental validation of a reorientation algorithm for a free-floating serial manipulator","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Revolute joint; Serial manipulator; Robot; Trajectory; Control theory (sociology); Computer science; Planar; Actuator; Rotation (mathematics); Motion planning; Nonlinear system; Algorithm; Parallel manipulator; Simulation; Artificial intelligence; Control (management); Physics","score_opus":0.03084406834388498,"score_gpt":0.27723314042773395,"score_spread":0.24638907208384897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409593918","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16007537,0.000009434343,0.8392845,0.000057071873,0.00026654886,0.00018417508,0.000002726704,0.000056565317,0.00006363184],"genre_scores_gemma":[0.071932934,2.1816184e-7,0.9278572,0.000015728474,0.00004925033,0.000025754533,0.0000032333317,0.000005279163,0.00011037671],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991954,0.000019182618,0.00023987026,0.00023610186,0.00017403133,0.00013541555],"domain_scores_gemma":[0.9995265,0.000076911434,0.00010971595,0.00018265573,0.000058321944,0.000045909645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022994936,0.000081298844,0.00010052917,0.00005616626,0.000068367386,0.000034141667,0.00019804806,0.00003303406,0.0000038528797],"category_scores_gemma":[0.000041818163,0.00005990832,0.000017229688,0.0000744655,0.00001772089,0.00036122373,0.000120149874,0.000013317838,0.0000023336738],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029128736,0.00015160671,0.0006661284,0.000035977893,0.000056216195,0.000004748413,0.011488281,0.00009137919,0.27990255,0.019877197,0.00037781752,0.687319],"study_design_scores_gemma":[0.0012774583,0.00009426135,0.00037544584,0.00003831315,0.0000025484806,0.000007972953,0.00017406204,0.04900233,0.948366,0.00041289823,0.00012782073,0.00012089326],"about_ca_topic_score_codex":0.000009276204,"about_ca_topic_score_gemma":2.7437426e-7,"teacher_disagreement_score":0.6871981,"about_ca_system_score_codex":0.000045545887,"about_ca_system_score_gemma":0.00004803589,"threshold_uncertainty_score":0.24429904},"labels":[],"label_agreement":null},{"id":"W2414011888","doi":"10.1007/978-3-319-68792-6_28","title":"Robots as Individuals in the Humanoid League","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Robot; League; Humanoid robot; Competition (biology); Computer science; Field (mathematics); Scheme (mathematics); Artificial intelligence; Point (geometry); Focus (optics); Ranking (information retrieval); Human–computer interaction; Mathematics","score_opus":0.03316723841147458,"score_gpt":0.2856812481062277,"score_spread":0.2525140096947531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414011888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008046824,0.00044298,0.97748417,0.0020326115,0.0021616602,0.00054435874,0.000003955686,0.00012545638,0.017124353],"genre_scores_gemma":[0.12557817,0.000058292837,0.86455643,0.006330518,0.0013298697,0.00004460949,0.000013182774,0.00007598356,0.0020129452],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99466985,0.00011571949,0.0006351601,0.0018393078,0.0017544114,0.0009855465],"domain_scores_gemma":[0.9947898,0.00091794634,0.00053321756,0.003470347,0.00014328994,0.00014542606],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0029324675,0.00065918115,0.00067211187,0.0009806134,0.0006358003,0.0017404975,0.013172998,0.0004437978,0.000012118841],"category_scores_gemma":[0.00036128933,0.000500414,0.00014507561,0.00039065987,0.0010077913,0.00087365496,0.002071488,0.0016856734,0.00020989195],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041350313,0.000101073834,0.0010611846,0.000056612276,0.000027007214,0.0017994631,0.009704662,0.2192244,0.000043679658,0.04416187,0.00034025862,0.72347564],"study_design_scores_gemma":[0.0008807685,0.00051300455,0.008306148,0.0016872032,0.000021194166,0.00088001613,0.0000011764976,0.43852708,0.00039437535,0.54333687,0.0033434674,0.0021087027],"about_ca_topic_score_codex":0.00011605834,"about_ca_topic_score_gemma":0.00006556743,"teacher_disagreement_score":0.72136694,"about_ca_system_score_codex":0.00022983011,"about_ca_system_score_gemma":0.0008849459,"threshold_uncertainty_score":0.9997448},"labels":[],"label_agreement":null},{"id":"W2418333579","doi":"10.1109/icra.2016.7487789","title":"Safe and robust robot maneuvers based on reach control","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Control theory (sociology); Robotics; Control engineering; Robust control; Computer science; Robot; Controller (irrigation); Trajectory; Control system; Engineering; Artificial intelligence; Control (management)","score_opus":0.015618117964966583,"score_gpt":0.2077222606456198,"score_spread":0.1921041426806532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2418333579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030303595,0.000009351913,0.9808382,0.0058675394,0.00022809913,0.00010504774,0.00000202748,0.00019264042,0.012454107],"genre_scores_gemma":[0.44761884,0.0000023450878,0.5473443,0.0022343525,0.00005500022,0.000009410775,5.833517e-7,0.000010139615,0.0027250121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892557,0.00006474501,0.0001307324,0.0003810293,0.00023470413,0.00026322098],"domain_scores_gemma":[0.99892735,0.00037972146,0.00004345525,0.0004915503,0.00003102624,0.00012686396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027580984,0.0001239865,0.00013942203,0.000084903404,0.000068982234,0.00006278935,0.0003932403,0.000053128322,0.00002893799],"category_scores_gemma":[0.00006869839,0.00007573589,0.0000279213,0.00010295681,0.00004501065,0.00018746583,0.00004960314,0.00006316617,0.00013508354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017316375,0.0004340943,0.024267685,0.000046230965,0.00011140103,0.00074011955,0.0003981621,0.2824881,0.009002038,0.059447363,0.044390194,0.5785014],"study_design_scores_gemma":[0.0018879547,0.00020534843,0.014288165,0.000054559325,0.000005383132,0.000019506784,0.0000039997076,0.98183376,0.00046953486,0.0002087918,0.00080618955,0.00021679769],"about_ca_topic_score_codex":0.000021377806,"about_ca_topic_score_gemma":8.9211335e-7,"teacher_disagreement_score":0.69934565,"about_ca_system_score_codex":0.000039246894,"about_ca_system_score_gemma":0.00003598137,"threshold_uncertainty_score":0.308842},"labels":[],"label_agreement":null},{"id":"W2441808524","doi":"10.1049/iet-cta.2015.1246","title":"Quadrotor circumnavigation of an unknown moving target using camera vision‐based measurements","year":2016,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Position (finance); Computer vision; Tracking (education); Point target; Computer science; Artificial intelligence; Point (geometry); Control theory (sociology); Trajectory; Phase (matter); Physics; Mathematics; Control (management)","score_opus":0.02149999591564184,"score_gpt":0.2896991757509615,"score_spread":0.26819917983531966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2441808524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015408397,0.00006765194,0.9835787,0.00020167886,0.00004895595,0.00046028424,0.000021159825,0.0000830864,0.00013008183],"genre_scores_gemma":[0.90648353,7.418964e-7,0.093154676,0.00014599723,0.000059714268,0.00012545043,0.000003455655,0.000008414011,0.000017990104],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877715,0.00026809968,0.00025203373,0.0003184061,0.00021078483,0.00017353786],"domain_scores_gemma":[0.9986987,0.00036479408,0.00018200888,0.00050982647,0.00014081072,0.00010384248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009058657,0.00011260523,0.00016315628,0.00007458306,0.00019684857,0.00004638135,0.00037415954,0.000051768355,0.000006466868],"category_scores_gemma":[0.000067445166,0.00008645947,0.000037478883,0.00018753464,0.00010056816,0.00039435728,0.000030952444,0.000052835632,0.0000060154503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050539013,0.00028650233,0.0012716936,0.00002934625,0.0000601461,0.0000017912371,0.0003320687,0.009139567,0.6023212,0.18825324,0.000011434402,0.19824246],"study_design_scores_gemma":[0.0043525565,0.00025926798,0.0066751237,0.0002913548,0.000085775435,0.000017758672,0.000061686995,0.8201538,0.04069873,0.12575659,0.0010282506,0.00061909645],"about_ca_topic_score_codex":0.000009537085,"about_ca_topic_score_gemma":3.234144e-7,"teacher_disagreement_score":0.89107513,"about_ca_system_score_codex":0.000031423755,"about_ca_system_score_gemma":0.00007186903,"threshold_uncertainty_score":0.3525715},"labels":[],"label_agreement":null},{"id":"W2462685666","doi":"10.1109/icuas.2016.7502535","title":"Dynamics of a quadrotor undergoing impact with a wall","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Collision; Computer science; Collision avoidance; Dynamics (music); Vehicle dynamics; Simulation; Orientation (vector space); Aerospace engineering; Marine engineering; Engineering; Computer security; Physics","score_opus":0.011758371681734367,"score_gpt":0.250446338390183,"score_spread":0.2386879667084486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462685666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022904621,0.000004843702,0.9734885,0.0014454594,0.000039687427,0.00008820097,0.0000017665843,0.00012166931,0.001905226],"genre_scores_gemma":[0.637091,0.000001282805,0.3621855,0.000047093483,0.000008353612,0.0000036395904,3.095092e-7,0.0000060412613,0.0006568114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921876,0.000025270438,0.00013387568,0.00020286566,0.00020544876,0.00021376545],"domain_scores_gemma":[0.99914265,0.00021193101,0.000080467034,0.000434166,0.000054189877,0.00007661283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013799187,0.0000950495,0.00013273364,0.00007154668,0.000023657576,0.00002558794,0.00047517216,0.00002992575,0.000015376623],"category_scores_gemma":[0.000036279005,0.000046029578,0.00003852586,0.00019848812,0.000036117424,0.00027674157,0.00008026885,0.000033753728,0.000028558714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019589759,0.00075885444,0.358219,0.00020363327,0.0007410595,0.00047424057,0.0036886544,0.01272121,0.0104393335,0.4071941,0.0030372748,0.20232673],"study_design_scores_gemma":[0.0014594286,0.0011450369,0.032870412,0.000358757,0.0000096657695,0.0001602259,0.000029117184,0.9577378,0.0014767372,0.0043387283,0.00002406647,0.00039003184],"about_ca_topic_score_codex":0.00010137853,"about_ca_topic_score_gemma":0.000037419064,"teacher_disagreement_score":0.94501656,"about_ca_system_score_codex":0.00009979789,"about_ca_system_score_gemma":0.00016776199,"threshold_uncertainty_score":0.18770316},"labels":[],"label_agreement":null},{"id":"W2475133691","doi":"","title":"Applying artificial potential fields to path planning for mobile robotics and to haptic rendering for minimally invasive surgery","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Motion planning; Robot; Rendering (computer graphics); Haptic technology; Control reconfiguration; Embedded system","score_opus":0.045231486175951595,"score_gpt":0.2823515510047392,"score_spread":0.23712006482878759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475133691","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028130466,0.000042272994,0.9684082,0.0012865808,0.0004995585,0.0014153577,0.000007234833,0.00016143944,0.00004885152],"genre_scores_gemma":[0.24051547,9.710536e-7,0.7572849,0.00097022724,0.0003587381,0.0007194424,0.000004965013,0.000018005881,0.00012732713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824464,0.000019762618,0.00039409837,0.0005751698,0.00021048046,0.00055587344],"domain_scores_gemma":[0.9983698,0.0008855642,0.00007962649,0.00030778671,0.000104650986,0.0002525725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048146307,0.00019997224,0.0003049609,0.00020622248,0.00024199627,0.00027326212,0.00036470007,0.000098171986,0.0000017992588],"category_scores_gemma":[0.00031462114,0.0002053537,0.00008915771,0.00019687664,0.000013800037,0.0002686051,0.00023118776,0.00008691585,0.000010246667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005548492,0.00004975399,0.00017816905,0.00007299514,0.00003316465,0.000025435429,0.0020697056,0.8771084,0.005075254,0.0011452344,0.005840124,0.10834628],"study_design_scores_gemma":[0.00023560681,0.0003676364,0.0002152647,0.00012287933,0.000018972656,0.00003335962,0.0003163145,0.99269056,0.00374239,0.00050287426,0.001305337,0.00044881157],"about_ca_topic_score_codex":0.000009873,"about_ca_topic_score_gemma":0.0000070774354,"teacher_disagreement_score":0.212385,"about_ca_system_score_codex":0.000046645186,"about_ca_system_score_gemma":0.000105538325,"threshold_uncertainty_score":0.83740807},"labels":[],"label_agreement":null},{"id":"W2476783402","doi":"10.1007/978-3-319-31293-4_12","title":"Design and FPGA Implementation of a Fuzzy-PI Controller for Omnidirectional Robot System","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; VHDL; Fuzzy logic; Computer science; Controller (irrigation); Control engineering; Hardware description language; MATLAB; Robot; Embedded system; Fuzzy electronics; PID controller; Fuzzy control system; Computer hardware; Adaptive neuro fuzzy inference system; Engineering; Artificial intelligence; Programming language","score_opus":0.02935539528507013,"score_gpt":0.29589188575462166,"score_spread":0.2665364904695515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2476783402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011608051,0.014502684,0.9793124,0.000020425123,0.0013537492,0.0011595009,0.0000134383135,0.000069453665,0.0035566972],"genre_scores_gemma":[0.4576953,0.002841237,0.5236674,0.00006979292,0.0014989795,0.0003400139,0.000023667002,0.00017487204,0.013688735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978515,0.00007663074,0.00090859615,0.0006016514,0.0002771285,0.0002844773],"domain_scores_gemma":[0.9977962,0.0009895652,0.0007609992,0.00021059407,0.00017331736,0.00006934615],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009210265,0.00030940323,0.000676976,0.00024596154,0.00012450338,0.00008315955,0.00027784533,0.0001400655,8.0590286e-7],"category_scores_gemma":[0.000016575716,0.00026013347,0.00006838533,0.000046072688,0.000061554376,0.00022920185,0.00013890487,0.000121627236,0.0000022515082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037658207,0.000012197593,0.00016226593,0.001178723,0.00013767376,0.000012632504,0.00068739266,0.044998094,0.000039050512,0.46150473,0.000043627337,0.49118596],"study_design_scores_gemma":[0.002013394,0.00064993533,0.00007722551,0.009294128,0.00007244196,0.00031523377,0.00064304785,0.9626592,0.0004168951,0.0135286255,0.009321295,0.001008593],"about_ca_topic_score_codex":0.00002908149,"about_ca_topic_score_gemma":0.0000020405605,"teacher_disagreement_score":0.9176611,"about_ca_system_score_codex":0.0001337481,"about_ca_system_score_gemma":0.00005898386,"threshold_uncertainty_score":0.9999851},"labels":[],"label_agreement":null},{"id":"W2478074252","doi":"10.4018/978-1-60566-406-4.ch012","title":"Computational and Robotic Pool","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada); Queen's University; Université de Sherbrooke","funders":"","keywords":"Table (database); Computer science; Robot; Artificial intelligence; Position (finance); Human–computer interaction; Simulation; Data mining","score_opus":0.018870114450817897,"score_gpt":0.24650252926733268,"score_spread":0.22763241481651478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478074252","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028138109,0.00031950083,0.29429835,0.00019668258,0.00035982122,0.00017354162,0.000014648498,0.00025189543,0.7043827],"genre_scores_gemma":[0.022160795,0.00000559711,0.7904316,0.0036102748,0.0006185507,0.00000973991,0.000018179151,0.000066931665,0.18307832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99812907,0.000019238212,0.00033152074,0.00066901493,0.0005182873,0.00033283926],"domain_scores_gemma":[0.99894345,0.00006510574,0.00018725864,0.0004915473,0.00009958458,0.00021305135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011137984,0.00039259705,0.00041968943,0.000081960934,0.00011677649,0.00022749003,0.0006958651,0.000284659,0.00000398793],"category_scores_gemma":[0.000012937221,0.00040704256,0.00010019198,0.000022121429,0.00009060854,0.00008923585,0.0002637079,0.00028128666,0.00018265133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019050883,0.000004666039,0.0000042818024,0.00000949791,0.000034742163,0.00020979407,0.000035068246,0.0069971825,3.6258018e-7,0.9471206,0.001369486,0.044212412],"study_design_scores_gemma":[0.0003038729,0.00012780017,0.00040012907,0.00019124703,0.000034414796,0.00054475706,8.4060974e-7,0.0894564,0.000001148013,0.90578514,0.0026245974,0.0005296401],"about_ca_topic_score_codex":0.000011717812,"about_ca_topic_score_gemma":0.000001040644,"teacher_disagreement_score":0.5213044,"about_ca_system_score_codex":0.00014095212,"about_ca_system_score_gemma":0.00021048871,"threshold_uncertainty_score":0.9998381},"labels":[],"label_agreement":null},{"id":"W2487370392","doi":"10.1017/cbo9780511780929.004","title":"Locomotion and Perception","year":2010,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Variety (cybernetics); Artificial intelligence; Field (mathematics); Computer science; Robot; Mobile robot; Human–computer interaction; Perception; Key (lock); State (computer science); Range (aeronautics); Engineering; Psychology; Mathematics; Computer security","score_opus":0.018725707103645597,"score_gpt":0.1935295679441053,"score_spread":0.1748038608404597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2487370392","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065337685,0.000024370502,0.20314056,0.000034203247,0.0003861711,0.0001985083,0.000021942216,0.00023691606,0.795892],"genre_scores_gemma":[0.0002763049,0.00003509165,0.037448663,0.000042525346,0.00011657408,2.9479287e-7,0.000018234337,0.00002027245,0.96204203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99883854,0.000027999333,0.00012045931,0.0005701707,0.00023587378,0.00020693376],"domain_scores_gemma":[0.99891514,0.00004042854,0.00015367138,0.000628269,0.000103218204,0.00015926991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011698991,0.00026560453,0.00024345155,0.00017583955,0.00020420189,0.00010148046,0.00067923014,0.000428547,0.0000014507009],"category_scores_gemma":[0.000005834136,0.00031998786,0.00008040879,0.0000066650455,0.00017573232,0.00019816046,0.0005517449,0.0006217617,0.000024251141],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071400964,0.0000064218343,0.0000027466224,0.00004255005,0.00003672479,0.000269984,0.00014731802,0.000005616498,0.0004386156,0.9771749,0.0041763554,0.017691651],"study_design_scores_gemma":[0.0006281971,0.000107563304,0.0004379068,0.00020892185,0.00010595802,0.0002016686,0.0000138935075,0.012415856,0.00017269215,0.00009986921,0.9847783,0.00082917296],"about_ca_topic_score_codex":0.00004636278,"about_ca_topic_score_gemma":3.59753e-7,"teacher_disagreement_score":0.98060197,"about_ca_system_score_codex":0.00009090702,"about_ca_system_score_gemma":0.000056179928,"threshold_uncertainty_score":0.9999252},"labels":[],"label_agreement":null},{"id":"W2491243770","doi":"10.1017/cbo9780511780929.012","title":"Mapping and Related Tasks","year":2010,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Variety (cybernetics); Artificial intelligence; Field (mathematics); Key (lock); Computer science; Robot; Mobile robot; Human–computer interaction; State (computer science); Range (aeronautics); Graduate students; Multimedia; Engineering; Mathematics; Psychology; Computer security","score_opus":0.01922068119304192,"score_gpt":0.1892887730584815,"score_spread":0.17006809186543959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491243770","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040676998,0.00012440339,0.046760604,0.000041919895,0.00066272746,0.00023233039,0.000034820932,0.00037747616,0.95172507],"genre_scores_gemma":[0.00016666821,0.00003162658,0.023292998,0.000046209207,0.000060217077,2.7695205e-7,0.0000140039765,0.000028600056,0.9763594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998468,0.000032016982,0.00019149331,0.0007298485,0.00026606565,0.00031260715],"domain_scores_gemma":[0.99845,0.00008952791,0.00024179307,0.00088642346,0.00010522425,0.00022704407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015745082,0.0003514735,0.00036656027,0.00027516356,0.000259374,0.00011230445,0.0011090086,0.0005999045,0.0000015248633],"category_scores_gemma":[0.000013045798,0.00042551546,0.00010890601,0.00001437492,0.00024823536,0.0001739171,0.0010404855,0.0010423725,0.000024986808],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043290497,0.0000039313977,0.0000019973313,0.000040389656,0.000100684105,0.0013628306,0.00024816606,0.0000051646234,0.00017408254,0.9888614,0.0038373703,0.005359665],"study_design_scores_gemma":[0.00059857307,0.000044574914,0.000094234296,0.00027484834,0.000061820334,0.00033065848,0.000013591049,0.006461001,0.00016213696,0.0001384963,0.9910348,0.00078524486],"about_ca_topic_score_codex":0.000044292392,"about_ca_topic_score_gemma":1.6293437e-7,"teacher_disagreement_score":0.9887229,"about_ca_system_score_codex":0.000075668446,"about_ca_system_score_gemma":0.00009894672,"threshold_uncertainty_score":0.99981964},"labels":[],"label_agreement":null},{"id":"W2493528340","doi":"10.1017/cbo9780511780929.016","title":"Probability and Statistics","year":2010,"lang":"en","type":"other","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Robotics; Variety (cybernetics); Artificial intelligence; Computer science; Robot; Key (lock); Field (mathematics); Mobile robot; Range (aeronautics); State (computer science); Human–computer interaction; Data science; Engineering; Mathematics; Algorithm; Computer security","score_opus":0.02260883107268802,"score_gpt":0.2472778570748043,"score_spread":0.22466902600211627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493528340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.0930931e-7,0.000039673792,0.6380569,0.00004899282,0.000325681,0.00008568286,0.000014011238,0.0002671277,0.36116183],"genre_scores_gemma":[4.588336e-7,0.0000040232794,0.6160283,0.000040822295,0.00004632817,0.000002724418,0.0000027259769,0.00003323805,0.3838414],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930567,0.000019482617,0.00008295916,0.00032188138,0.00013397656,0.00013604085],"domain_scores_gemma":[0.99927497,0.000045687302,0.000060267594,0.0005328195,0.000015203069,0.00007104099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010842493,0.00012044329,0.0001413314,0.000057837224,0.000017541937,0.000069242094,0.0003857395,0.00017245993,0.00022746083],"category_scores_gemma":[0.000045869634,0.00009852843,0.00000879776,0.00005314116,0.000058336365,0.000026475991,0.00017037275,0.00019838019,0.0000963512],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.1434766e-7,0.000015854534,0.00013622546,0.000044697135,0.000009522053,0.000016716313,0.000036871923,0.000001119253,0.0000029006653,0.08553316,0.8535395,0.060663324],"study_design_scores_gemma":[0.00016723719,0.000045804973,0.0006228051,0.000056367124,0.000008808801,0.00003907473,0.0000011079727,0.054166406,0.000009446024,0.023872763,0.9205515,0.0004587053],"about_ca_topic_score_codex":0.00010997435,"about_ca_topic_score_gemma":0.000031372118,"teacher_disagreement_score":0.067011975,"about_ca_system_score_codex":0.0000055937453,"about_ca_system_score_gemma":0.00004752528,"threshold_uncertainty_score":0.40178725},"labels":[],"label_agreement":null},{"id":"W2497286804","doi":"10.1007/s13218-016-0450-z","title":"Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees","year":2016,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Humanoid robot; Robot; Motion (physics); Computer science; Motion planning; Obstacle; Context (archaeology); Artificial intelligence; Planner; Computer vision; Tree (set theory); Random tree; Degrees of freedom (physics and chemistry); Simulation; Mathematics; Geography","score_opus":0.1430913394168787,"score_gpt":0.32465077586430086,"score_spread":0.18155943644742215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497286804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037717402,0.0004415793,0.958338,0.00033417006,0.0017914289,0.0005393774,0.0000074236423,0.0005667733,0.0002637991],"genre_scores_gemma":[0.5488717,0.00004970504,0.44895434,0.00018378314,0.0010323006,0.00023022869,0.0000104524315,0.00009614011,0.00057132944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685335,0.00014862137,0.000708737,0.0009239655,0.00047771228,0.00088759814],"domain_scores_gemma":[0.9974821,0.0008668269,0.00030734058,0.00091049145,0.00020463397,0.00022859598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010121411,0.00043125078,0.0005116812,0.00038352795,0.0004087435,0.00026644074,0.0012960364,0.00017112256,0.000015282621],"category_scores_gemma":[0.00043307777,0.00033655885,0.00024929346,0.0004275593,0.00009418482,0.0013282577,0.00027653817,0.0001995798,0.00013438026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054519606,0.00037841738,0.0027079522,0.00016240023,0.00045965714,0.00019348486,0.0085079055,0.28288332,0.2796351,0.009594477,0.002444677,0.41248742],"study_design_scores_gemma":[0.0035708742,0.0005949348,0.0013749909,0.0009947551,0.00008862321,0.00019524671,0.00027583604,0.8123364,0.17202017,0.0039236546,0.0032174434,0.0014070595],"about_ca_topic_score_codex":0.000028986637,"about_ca_topic_score_gemma":0.000001982238,"teacher_disagreement_score":0.5294531,"about_ca_system_score_codex":0.00021680884,"about_ca_system_score_gemma":0.0001070106,"threshold_uncertainty_score":0.9999086},"labels":[],"label_agreement":null},{"id":"W2499884016","doi":"10.1109/hsi.2016.7529633","title":"Interactive collision avoidance system for indoor mobile robots based on human-robot interaction","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Robot; Mobile robot; Computer science; Collision; Human–computer interaction; Collision avoidance system; Human–robot interaction; Social robot; Gesture; Robot control; Work (physics); Simulation; Artificial intelligence; Engineering; Computer security","score_opus":0.036049783560840626,"score_gpt":0.32227414064775595,"score_spread":0.2862243570869153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499884016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060605276,0.0000070397386,0.9886713,0.0003785504,0.0017178314,0.0008119351,0.000010413612,0.00050173653,0.0018406351],"genre_scores_gemma":[0.8436253,4.77773e-7,0.15460439,0.00019625478,0.00013052385,0.0003760359,0.0000048110496,0.000020673237,0.0010414991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980912,0.00013363559,0.00037606218,0.00069103023,0.0003395451,0.00036853566],"domain_scores_gemma":[0.99755126,0.001140466,0.0002560003,0.00071826455,0.0002151834,0.00011880903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004258168,0.000241274,0.0002779209,0.00022945908,0.00022919259,0.00015621902,0.0007378852,0.000100740435,0.000011879974],"category_scores_gemma":[0.0001261475,0.00016096029,0.00011467198,0.00024407069,0.000031292704,0.0008019153,0.00011729458,0.00013544205,0.00021175675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009592371,0.0016617948,0.0016531486,0.00046645128,0.00020941785,0.00017161592,0.002182211,0.5790318,0.15983757,0.028424155,0.016292267,0.2091103],"study_design_scores_gemma":[0.0021389585,0.0012903155,0.0011123493,0.0013221508,0.000010926459,0.000024380004,0.00014362755,0.88582504,0.106662504,0.00012806647,0.0008938194,0.0004478724],"about_ca_topic_score_codex":0.000022187482,"about_ca_topic_score_gemma":0.000002499533,"teacher_disagreement_score":0.8375648,"about_ca_system_score_codex":0.0004912685,"about_ca_system_score_gemma":0.00006714225,"threshold_uncertainty_score":0.656377},"labels":[],"label_agreement":null},{"id":"W2502729453","doi":"","title":"Intelligent Robotics and Applications: 5th International Conference, ICIRA 2012, Montreal, Canada, October 3-5, 2012, Proceedings, Part I","year":2012,"lang":"en","type":"book","venue":"Springer eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mechatronics; Robotics; Artificial intelligence; Automation; Engineering; Automotive industry; Engineering management; Rehabilitation robotics; Control engineering; Computer science; Systems engineering; Robot; Mechanical engineering; Aerospace engineering","score_opus":0.030099956115007164,"score_gpt":0.24647218865501652,"score_spread":0.21637223254000937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502729453","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000060270827,0.002598915,0.17916265,0.00046476713,0.002635055,0.0011489529,0.00007623163,0.00030851533,0.8135989],"genre_scores_gemma":[0.0003500506,0.0002154945,0.04746095,0.00056549837,0.0021344714,0.0002951007,0.00008253903,0.000100130856,0.94879574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99687755,0.000020787906,0.000644567,0.00091551844,0.00085060456,0.00069094606],"domain_scores_gemma":[0.99782985,0.00008183719,0.00051033346,0.00070424215,0.00043619372,0.00043756736],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002995728,0.00058785133,0.00053633295,0.0002499493,0.00017819034,0.00038253426,0.0016465707,0.00035405604,0.00005601872],"category_scores_gemma":[0.000018066565,0.000596872,0.00008596232,0.00005687505,0.0001580576,0.00035577553,0.0008353197,0.0007263289,0.00010156429],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012338066,0.00012291803,0.0007199628,0.00029462285,0.00058265275,0.00006575082,0.0008622607,0.00027885233,0.000021432548,0.42349905,0.3896491,0.18389109],"study_design_scores_gemma":[0.00024286202,0.000033907912,0.0005729234,0.0003799032,0.0000938901,0.00008197002,0.00003163579,0.006946592,0.000056098674,0.0067888573,0.98383033,0.00094101514],"about_ca_topic_score_codex":0.008734234,"about_ca_topic_score_gemma":0.011089152,"teacher_disagreement_score":0.59418124,"about_ca_system_score_codex":0.0005156935,"about_ca_system_score_gemma":0.0014549332,"threshold_uncertainty_score":0.9996483},"labels":[],"label_agreement":null},{"id":"W2503304684","doi":"10.1109/robot.1998.677098","title":"Automatic path planning for coordinate measuring machines","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Computer science; Heuristics; Octree; Robustness (evolution); Cartesian coordinate system; Grid; Representation (politics); Collision detection; Path (computing); Rendering (computer graphics); Any-angle path planning; Algorithm; Computer vision; Artificial intelligence; Collision; Robot; Mathematics","score_opus":0.06417665733720057,"score_gpt":0.2636881923981832,"score_spread":0.19951153506098263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503304684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005928487,0.00020780976,0.98627496,0.00057339406,0.0004561863,0.00019695013,0.0000013405883,0.0008185371,0.0055423663],"genre_scores_gemma":[0.27558628,0.0000010261209,0.72261953,0.00022461925,0.00006959748,0.000035932186,0.0000010261072,0.000013464737,0.0014485163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988384,0.00003254569,0.00021775419,0.00032372703,0.00022223796,0.00036530217],"domain_scores_gemma":[0.9991919,0.00019956252,0.00007410339,0.00039145304,0.000050124687,0.00009286076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031521346,0.00014744264,0.000179485,0.000099761426,0.00017909784,0.00015906803,0.00066896935,0.00003511431,0.000031896794],"category_scores_gemma":[0.00011752447,0.00012507291,0.0000608594,0.00021199138,0.000015727364,0.0003435897,0.00011481214,0.00008510504,0.00008750065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005226251,0.00032740214,0.010850025,0.00037693646,0.00017892459,0.00041378738,0.007964507,0.059817232,0.0010955412,0.02785026,0.07220134,0.8189188],"study_design_scores_gemma":[0.000298421,0.000049792794,0.0010462209,0.000059651902,0.000004638581,0.000052503707,0.000011479573,0.99664354,0.00020726788,0.0009303152,0.0005147352,0.0001814062],"about_ca_topic_score_codex":0.00000876001,"about_ca_topic_score_gemma":9.058143e-8,"teacher_disagreement_score":0.93682635,"about_ca_system_score_codex":0.00002902743,"about_ca_system_score_gemma":0.000009445093,"threshold_uncertainty_score":0.51003253},"labels":[],"label_agreement":null},{"id":"W2504374753","doi":"10.1142/9789813149137_0063","title":"LOCOMOTION MODE TRANSITION STUDY OF GROUND HYBRID ROBOTS","year":2016,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Transition (genetics); Mode (computer interface); Robot; Computer science; Human–computer interaction; Artificial intelligence; Biology","score_opus":0.03186916614939924,"score_gpt":0.25201361917920256,"score_spread":0.22014445302980332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504374753","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074748846,0.00006427354,0.45727426,0.000113814276,0.0043759122,0.001238601,0.000059311078,0.00035132968,0.535775],"genre_scores_gemma":[0.053864066,2.843089e-7,0.011753798,0.000030626044,0.00017667051,0.000025153464,0.000024169745,0.000052199517,0.93407303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959923,0.00009828919,0.00078119617,0.0013559929,0.0013193594,0.00045284413],"domain_scores_gemma":[0.99693257,0.00010661432,0.0005547968,0.001850173,0.00037901683,0.00017683096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093806174,0.0004656735,0.00061017415,0.0010669928,0.0003707995,0.00035274035,0.0015233795,0.00011528361,0.00003709458],"category_scores_gemma":[0.0000069965113,0.0004137667,0.00018043636,0.0001322575,0.0003310283,0.00028218524,0.00026080944,0.00036479137,0.00019204288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010552218,0.001536679,0.000023358094,0.00051995233,0.00089172483,0.0013819794,0.030530807,0.018324068,0.005766499,0.6311858,0.031001778,0.2787318],"study_design_scores_gemma":[0.011792661,0.0034429363,0.000435214,0.010077038,0.0011315283,0.0005350473,0.00025401925,0.31060764,0.0073648836,0.5053375,0.13974547,0.009276097],"about_ca_topic_score_codex":0.000015101464,"about_ca_topic_score_gemma":0.00015041936,"teacher_disagreement_score":0.44552046,"about_ca_system_score_codex":0.00016125671,"about_ca_system_score_gemma":0.00018731329,"threshold_uncertainty_score":0.99983144},"labels":[],"label_agreement":null},{"id":"W2514137274","doi":"10.2495/sdp-v11-n4-616-626","title":"Setting up a mixed reality simulator for using teams of autonomous UAVs in air pollution monitoring","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Xunta de Galicia","keywords":"Simulation; Pollution; Environmental science; Air pollution; Computer science","score_opus":0.028746830191111922,"score_gpt":0.3043696063518202,"score_spread":0.2756227761607083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514137274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5625832,0.00009038121,0.4363702,0.0002808484,0.00058242516,0.00006743399,0.0000012310153,0.000008925156,0.00001534898],"genre_scores_gemma":[0.83300155,0.0000023123825,0.16675949,0.000015684285,0.00013301802,0.000002196243,7.3942783e-7,0.0000055937107,0.00007939054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99855417,0.00004279999,0.0006197819,0.00016037558,0.00035282734,0.00027002752],"domain_scores_gemma":[0.99843657,0.00026802908,0.0005280276,0.00007161436,0.0006304004,0.00006534952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013224016,0.00011336308,0.00020545194,0.0004757257,0.00008158589,0.000043729084,0.00040395206,0.000056714973,4.6135955e-7],"category_scores_gemma":[0.0004035983,0.00009133065,0.00004068072,0.00014397352,0.00002180043,0.0007284302,0.0001398872,0.00009452396,1.4645299e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007774405,0.00025311962,0.3257446,0.0003811008,0.00070943724,0.0031764905,0.05140002,0.37714997,0.00944451,0.012297106,0.00024783734,0.21841837],"study_design_scores_gemma":[0.011105749,0.00038976967,0.5726666,0.007181661,0.00005060948,0.0008891184,0.034383364,0.26689318,0.092410795,0.009004805,0.0037437307,0.0012806267],"about_ca_topic_score_codex":0.000022951328,"about_ca_topic_score_gemma":1.9100575e-7,"teacher_disagreement_score":0.27041835,"about_ca_system_score_codex":0.00049686467,"about_ca_system_score_gemma":0.00050029345,"threshold_uncertainty_score":0.37243557},"labels":[],"label_agreement":null},{"id":"W2514332889","doi":"10.1109/med.2016.7535886","title":"Real-time autonomous take-off, tracking and landing of UAV on a moving UGV platform","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Unmanned ground vehicle; Tracking (education); Controller (irrigation); Computer science; Linear-quadratic regulator; Control theory (sociology); Vehicle dynamics; Remotely operated underwater vehicle; Control engineering; Engineering; Real-time computing; Control (management); Artificial intelligence; Aerospace engineering; Mobile robot; Robot","score_opus":0.02541095206517559,"score_gpt":0.24987342828848652,"score_spread":0.22446247622331092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514332889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4157763,0.00003925901,0.5509006,0.00057182915,0.0002792163,0.00019066143,0.0000047556773,0.00052525173,0.031712126],"genre_scores_gemma":[0.7756848,0.000020038198,0.22240709,0.000058237314,0.00006362429,0.000003948091,6.180621e-7,0.000014128608,0.0017475246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889404,0.000020509196,0.00024244565,0.0003343768,0.00021729058,0.00029134715],"domain_scores_gemma":[0.9989971,0.0003913582,0.000120198274,0.0003625638,0.00003660553,0.00009220058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038023698,0.00013268937,0.00020850493,0.00013693354,0.00007912783,0.00007503841,0.00037351745,0.00006221908,0.000018847883],"category_scores_gemma":[0.00008931923,0.00008714615,0.000034766636,0.00013916315,0.00003901862,0.00050087104,0.0001424633,0.000064621614,0.000053001408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002759697,0.00011485539,0.010016933,0.000051234045,0.00007254507,0.00016596344,0.0036419206,0.0007473427,0.12204194,0.037987746,0.0007374507,0.82439446],"study_design_scores_gemma":[0.002181173,0.0008396172,0.06809593,0.0010793282,0.000021697319,0.0002417849,0.000100706864,0.8873576,0.034690183,0.004090595,0.00039811322,0.00090325676],"about_ca_topic_score_codex":0.000055840526,"about_ca_topic_score_gemma":0.0000013006044,"teacher_disagreement_score":0.88661027,"about_ca_system_score_codex":0.000056277284,"about_ca_system_score_gemma":0.000044474877,"threshold_uncertainty_score":0.3553717},"labels":[],"label_agreement":null},{"id":"W2520930588","doi":"10.7210/jrsj.20.464","title":"","year":2002,"lang":"","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Psychology","score_opus":0.03976078937127005,"score_gpt":0.24208952869876757,"score_spread":0.2023287393274975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520930588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06517418,0.009669147,0.81822497,0.0943689,0.0118011115,0.0006299775,0.000013061235,0.000040829433,0.00007783249],"genre_scores_gemma":[0.45019424,0.0018948804,0.5448577,0.0013224314,0.0011058403,8.5295295e-7,1.419116e-7,0.000059439084,0.00056450063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9859119,0.0012286543,0.005455873,0.0008115243,0.004752613,0.0018394173],"domain_scores_gemma":[0.9796156,0.0014263616,0.012225227,0.0030813043,0.0027157187,0.0009357398],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0057371357,0.00109298,0.0025958025,0.00022930847,0.0013136487,0.0006088959,0.012807912,0.0007712094,0.000020550557],"category_scores_gemma":[0.00096541917,0.00080757606,0.006188003,0.0029638496,0.0018578481,0.0022670503,0.002513424,0.0031883416,0.000006572156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002405489,0.0014948926,0.016058413,0.00032291687,0.0015717143,0.00001612896,0.05239622,0.9004507,0.0019549527,0.00037694207,0.01592199,0.009411052],"study_design_scores_gemma":[0.0033534914,0.0017563122,0.016151408,0.002543431,0.0008451246,0.0025261869,0.0024813425,0.96641207,0.0016694827,0.001335738,0.00013575103,0.0007896825],"about_ca_topic_score_codex":0.000036256974,"about_ca_topic_score_gemma":0.0000012474554,"teacher_disagreement_score":0.38502008,"about_ca_system_score_codex":0.0007155995,"about_ca_system_score_gemma":0.0006428847,"threshold_uncertainty_score":0.9999865},"labels":[],"label_agreement":null},{"id":"W2521744731","doi":"10.1109/cdc.2016.7799192","title":"On efficient computation of shortest Dubins paths through three consecutive points","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heading (navigation); Midpoint; Discretization; Path (computing); Shortest path problem; Computation; Mathematical optimization; Mathematics; Point (geometry); Computer science; Control theory (sociology); Algorithm; Artificial intelligence; Combinatorics; Geometry; Engineering; Mathematical analysis","score_opus":0.03589367622704229,"score_gpt":0.29022331733924894,"score_spread":0.2543296411122067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521744731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012373517,0.000057645837,0.9697904,0.0008584737,0.001935254,0.00060225517,0.00006056969,0.00032079912,0.014001108],"genre_scores_gemma":[0.73240495,0.0000042122765,0.26714835,0.00024780765,0.000063476204,0.000025637268,0.00001854938,0.000022060685,0.00006498889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697155,0.00015222355,0.0006351096,0.0010443671,0.00078687904,0.00040984785],"domain_scores_gemma":[0.997172,0.0006979411,0.00053519633,0.0011497053,0.0003403588,0.00010478988],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044925403,0.00042026074,0.00059203524,0.00018219373,0.000084884494,0.000094268085,0.0013639637,0.00029106266,0.00001386178],"category_scores_gemma":[0.00018104145,0.000317993,0.00018221582,0.00021319542,0.0001784688,0.00009675264,0.001438913,0.00043910195,0.0001722199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051938696,0.0010886614,0.0014199509,0.00021437237,0.00036113258,0.00027861568,0.0059152343,0.53684616,0.0002511717,0.42686936,0.0051844846,0.021518882],"study_design_scores_gemma":[0.0005862294,0.00027427883,0.0069790655,0.0011540568,0.000023126775,0.00002209559,0.000026522004,0.8590744,0.0015198388,0.12978277,0.000012574727,0.00054503675],"about_ca_topic_score_codex":0.00008996483,"about_ca_topic_score_gemma":0.000002896195,"teacher_disagreement_score":0.7200314,"about_ca_system_score_codex":0.00015947444,"about_ca_system_score_gemma":0.00034817605,"threshold_uncertainty_score":0.9999272},"labels":[],"label_agreement":null},{"id":"W2531452208","doi":"10.1155/2016/2306818","title":"Experimental Test of Artificial Potential Field-Based Automobiles Automated Perpendicular Parking","year":2016,"lang":"en","type":"article","venue":"International Journal of Vehicular Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council; Concordia University","keywords":"Perpendicular; Path (computing); Track (disk drive); Kinematics; Trajectory; Unmanned ground vehicle; Engineering; Turning radius; Simulation; Computer science; Automotive engineering; Artificial intelligence; Aerospace engineering; Mathematics; Physics; Mechanical engineering","score_opus":0.008724039864334697,"score_gpt":0.2734088812322098,"score_spread":0.2646848413678751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531452208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3729183,0.00019665285,0.6209638,0.0045390227,0.0010836924,0.00005766589,0.000004290468,0.00021613334,0.000020453697],"genre_scores_gemma":[0.9151066,0.000004138529,0.08467224,0.000082723214,0.00011542451,0.000003680589,8.5922017e-7,0.000008576762,0.00000575441],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982545,0.000050659026,0.0006238276,0.00020866512,0.00065726595,0.00020512863],"domain_scores_gemma":[0.99839014,0.00017871344,0.00061033224,0.0002700314,0.00049631525,0.000054454213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032107116,0.00013799124,0.00025463852,0.0007035494,0.000041328814,0.000042740987,0.0016421013,0.00017860113,0.00002869709],"category_scores_gemma":[0.00042331556,0.000100555095,0.00016080412,0.00022506781,0.00012940905,0.00025295158,0.00019838692,0.00018006662,0.000013158341],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003134693,0.00041616772,0.0027977854,0.000003577005,0.00016360957,0.0018520593,0.00005960744,0.0034345263,0.9542514,0.0027061224,0.00026228043,0.034021486],"study_design_scores_gemma":[0.0008070357,0.00057600456,0.00085188,0.00021106038,0.000014660273,0.0011800588,0.000037360205,0.15933345,0.8355992,0.00095749216,0.00029217536,0.00013962293],"about_ca_topic_score_codex":0.0000048803217,"about_ca_topic_score_gemma":1.7702534e-7,"teacher_disagreement_score":0.5421883,"about_ca_system_score_codex":0.00010676446,"about_ca_system_score_gemma":0.00015769934,"threshold_uncertainty_score":0.41005176},"labels":[],"label_agreement":null},{"id":"W2532332080","doi":"10.1109/icm.2007.4497654","title":"An RFID-Based Robot Navigation System with a Customized RFID Tag Architecture","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Robot; Microcontroller; Mobile robot; Modular design; Fuzzy logic; Workspace; Embedded system; Mobile robot navigation; Trajectory; Real-time computing; Computer hardware; Robot control; Artificial intelligence","score_opus":0.008162699578810408,"score_gpt":0.24279848869892537,"score_spread":0.23463578912011496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532332080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024107458,0.000015261061,0.9723065,0.00024321412,0.00026928145,0.00033264002,0.0000018515802,0.0010969961,0.0016267787],"genre_scores_gemma":[0.48503074,2.5641604e-8,0.51465756,0.00014184661,0.00006754024,0.000008902361,0.000011306322,0.000012381601,0.000069698486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997925,0.0001286188,0.00031303216,0.0005744672,0.00056712446,0.0004917448],"domain_scores_gemma":[0.9983675,0.00017462441,0.00015624502,0.0009051787,0.00014001002,0.00025644636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009633367,0.00024057105,0.0002722929,0.00019596331,0.00016447317,0.00019023362,0.0008666866,0.000109379136,0.000004352016],"category_scores_gemma":[0.000017395285,0.00017511,0.00005082729,0.00067927584,0.000053622152,0.00030985975,0.00004594083,0.00024462497,0.000053073647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002793479,0.00022544838,0.0025507528,0.00015713106,0.000058692352,0.0010815265,0.0017910117,0.9419901,0.0070140436,0.0119047025,0.00018750192,0.032759763],"study_design_scores_gemma":[0.0020328374,0.00033103826,0.0036207126,0.00020537521,0.000013316909,0.0002949907,0.00014211642,0.98712003,0.0057246354,0.000054312604,0.00007478756,0.00038583542],"about_ca_topic_score_codex":0.00014889531,"about_ca_topic_score_gemma":0.000012405753,"teacher_disagreement_score":0.46092328,"about_ca_system_score_codex":0.00013041685,"about_ca_system_score_gemma":0.00014757006,"threshold_uncertainty_score":0.7140779},"labels":[],"label_agreement":null},{"id":"W2532645520","doi":"","title":"Path/action planning for a mobile robot","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrain; Motion planning; Fidelity; Mobile robot; Computer science; Robot; Path (computing); Engineering; Real-time computing; Artificial intelligence; Simulation; Operations research; Geography; Computer network; Telecommunications; Cartography","score_opus":0.04181941651573025,"score_gpt":0.33147881157222253,"score_spread":0.2896593950564923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532645520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021916558,0.00033367073,0.98610497,0.000025139632,0.0038126558,0.0010285735,0.0000046580267,0.0005527874,0.005945906],"genre_scores_gemma":[0.0049876263,0.00001366057,0.9122315,0.00013396675,0.00044791546,0.0017854503,0.0008659888,0.000064773856,0.07946909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983012,0.000029602883,0.00033777344,0.000630482,0.00030961796,0.0003913407],"domain_scores_gemma":[0.9987222,0.0001702702,0.0002667175,0.0005388589,0.00019818274,0.00010378597],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018793419,0.0002971035,0.00032152171,0.00019800213,0.00016876365,0.00029371819,0.000786868,0.00032454022,0.000028780034],"category_scores_gemma":[0.000050433184,0.0002763719,0.00013352546,0.00019361838,0.00000736565,0.00045976223,0.00003922561,0.00025037903,0.00020365135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089426016,0.00047448164,0.00023359907,0.0022793773,0.00045702746,0.00010058888,0.019337142,0.0798145,0.01046606,0.013953931,0.20559803,0.66719586],"study_design_scores_gemma":[0.00057709153,0.00043634948,0.0020661873,0.00048913545,0.000046480567,0.000026119065,0.0013504082,0.9819784,0.004094074,0.0019968103,0.00592064,0.001018268],"about_ca_topic_score_codex":0.00007222299,"about_ca_topic_score_gemma":0.0000022652237,"teacher_disagreement_score":0.9021639,"about_ca_system_score_codex":0.000071578375,"about_ca_system_score_gemma":0.00013748363,"threshold_uncertainty_score":0.9999688},"labels":[],"label_agreement":null},{"id":"W2535596849","doi":"10.1016/j.robot.2016.10.013","title":"Control, localization and human interaction with an autonomous lighter-than-air performer","year":2016,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; McGill University; Université Laval","funders":"Université du Québec à Montréal","keywords":"Computer science; Performing arts; Context (archaeology); Robotics; Mechatronics; Embodied cognition; Robot; Mainstream; Field (mathematics); Human–computer interaction; Control (management); Artificial intelligence; Visual arts","score_opus":0.012995899249617708,"score_gpt":0.23571152655569855,"score_spread":0.22271562730608083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535596849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038987298,0.00012840296,0.9590891,0.00043564948,0.00049417105,0.0003223216,0.000004919806,0.00022237936,0.00031575243],"genre_scores_gemma":[0.9870385,0.000008006965,0.012185592,0.000072990544,0.00015909677,0.0000214538,0.0000052419123,0.000024714867,0.000484414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983829,0.00010970667,0.00037041848,0.0005728431,0.00020813482,0.00035599672],"domain_scores_gemma":[0.99890673,0.00008291977,0.00023221731,0.0004542094,0.00011346657,0.00021043004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003270789,0.00025838122,0.00034002532,0.00014298648,0.00033478803,0.00035632498,0.0002631101,0.000112181675,0.0000017227838],"category_scores_gemma":[0.000009865069,0.0001691209,0.000024788595,0.00011503104,0.000089106536,0.0009052705,0.00007539754,0.00010254953,0.000014011196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011743983,0.0006946946,0.085204676,0.00053502514,0.00050427666,0.00031101913,0.005545953,0.6204912,0.0071401694,0.2239231,0.00047406537,0.05505834],"study_design_scores_gemma":[0.0012055471,0.00096881535,0.0062730974,0.0002967816,0.000032930144,0.00042810926,0.00008154706,0.98894763,0.00012599147,0.00014699125,0.0010710948,0.00042144468],"about_ca_topic_score_codex":0.00021437448,"about_ca_topic_score_gemma":0.000013346606,"teacher_disagreement_score":0.9480512,"about_ca_system_score_codex":0.00010647683,"about_ca_system_score_gemma":0.0000669987,"threshold_uncertainty_score":0.689655},"labels":[],"label_agreement":null},{"id":"W2541293124","doi":"10.48550/arxiv.1610.06283","title":"Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Trajectory; Computer science; Controller (irrigation); Tracking (education); Control theory (sociology); Artificial neural network; PID controller; Nonlinear system; Artificial intelligence; Control engineering; Control (management); Engineering","score_opus":0.060545669879584485,"score_gpt":0.2021864631556094,"score_spread":0.1416407932760249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541293124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019685926,0.00012785177,0.9769465,0.000054798045,0.0018813206,0.0008507794,0.000023495064,0.0002666159,0.00016271087],"genre_scores_gemma":[0.97473615,0.000020211291,0.024700211,0.00004309998,0.00024087328,0.0000062027525,0.0000132272135,0.000038261394,0.00020176092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742866,0.00013876807,0.00039382128,0.0013046614,0.000094177834,0.0006399282],"domain_scores_gemma":[0.9971303,0.00038480613,0.0006359756,0.0013750604,0.00026956393,0.00020432309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004257666,0.00043676514,0.00060003024,0.0002945892,0.00014137165,0.00007840169,0.0024835903,0.00046046023,0.0000057390907],"category_scores_gemma":[0.00006766945,0.00044038458,0.00044574228,0.00034841493,0.0001545765,0.00041879824,0.0010325498,0.0005333852,0.0000040399304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066824585,0.00009249654,0.0012935109,0.00015382099,0.00016321814,0.00008294947,0.00026018356,0.97590464,0.00026511739,0.008755986,0.00007515049,0.012886103],"study_design_scores_gemma":[0.0007433325,0.00014451171,0.0011139509,0.00011289055,0.00006869925,0.000006724139,0.000022426162,0.99135876,0.00017525701,0.005735922,0.00002516723,0.00049237197],"about_ca_topic_score_codex":0.000057034424,"about_ca_topic_score_gemma":0.000007491276,"teacher_disagreement_score":0.95505023,"about_ca_system_score_codex":0.00019609071,"about_ca_system_score_gemma":0.0001495341,"threshold_uncertainty_score":0.9998048},"labels":[],"label_agreement":null},{"id":"W2545198579","doi":"10.2316/journal.206.2016.6.206-4878","title":"LATERAL CONTROL FOR AUTONOMOUS LAND VEHICLES VIA DUAL HEURISTIC PROGRAMMING","year":2016,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Dual (grammatical number); Heuristic; Computer science; Control (management); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.011688396670864853,"score_gpt":0.2579203690090657,"score_spread":0.24623197233820082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545198579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016228177,0.000055660734,0.9779031,0.0046631233,0.0010117543,0.0000881806,0.0000058329024,0.00003656889,0.000007552287],"genre_scores_gemma":[0.74703497,0.00000734791,0.25259584,0.00007371724,0.00024353313,0.0000025191334,0.0000014009203,0.000005187618,0.000035501293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908775,0.000029648754,0.00035719312,0.00011199625,0.0002844295,0.00012895554],"domain_scores_gemma":[0.9988755,0.00024330411,0.00035182567,0.000070423106,0.0003952742,0.00006368699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000355664,0.00008418043,0.00013778967,0.00013356797,0.00004920228,0.00018152529,0.00027976543,0.000039816525,0.0000010332061],"category_scores_gemma":[0.00012304945,0.000057403784,0.000054571086,0.000037642854,0.000026501995,0.00045618456,0.000037805097,0.000052154013,0.0000026475734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000834324,0.00016987842,0.014861649,0.000027938422,0.00035103204,0.0001843092,0.000575687,0.043577455,0.00511921,0.0151357455,0.00036084815,0.9195528],"study_design_scores_gemma":[0.0025144473,0.00030334474,0.02744621,0.00018050555,0.000026205737,0.0007074476,0.0000038015562,0.9598263,0.0002817654,0.007896695,0.0006648978,0.0001483933],"about_ca_topic_score_codex":0.0000025071838,"about_ca_topic_score_gemma":3.6114554e-7,"teacher_disagreement_score":0.91940445,"about_ca_system_score_codex":0.000054082233,"about_ca_system_score_gemma":0.000048364815,"threshold_uncertainty_score":0.23408583},"labels":[],"label_agreement":null},{"id":"W2553326346","doi":"10.1109/coase.2016.7743548","title":"On minimizing turns in robot coverage path planning","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Workspace; Polygon (computer graphics); Computer science; Convex polygon; Motion planning; Path (computing); Robot; Regular polygon; Cover (algebra); Mathematical optimization; Artificial intelligence; Mathematics; Engineering","score_opus":0.022913145790494596,"score_gpt":0.2596733748816744,"score_spread":0.23676022909117983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553326346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020877376,0.000047412635,0.96620524,0.0010223084,0.00042650718,0.00008622223,0.0000011806741,0.000225204,0.011108545],"genre_scores_gemma":[0.82997257,0.0000040688783,0.16834453,0.0006910766,0.000046951416,0.0000076705155,4.5686252e-7,0.0000099846775,0.00092269824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865526,0.00006256805,0.00021735928,0.00041311086,0.00026455135,0.00038712416],"domain_scores_gemma":[0.99888283,0.0004921179,0.000060334085,0.00045664312,0.000017930706,0.00009014045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002975207,0.00014391166,0.00016387805,0.00016489513,0.000038343987,0.000049116345,0.00061696477,0.000061740735,0.000018096882],"category_scores_gemma":[0.00013537398,0.000093219445,0.000035587043,0.00024068392,0.000017443806,0.00024024292,0.00014857453,0.00011509718,0.00019432828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011637362,0.0008752796,0.108410195,0.00008420992,0.000097183016,0.006404905,0.014582562,0.26605427,0.019161856,0.1726894,0.034248244,0.37727553],"study_design_scores_gemma":[0.0061236876,0.0009310542,0.17568779,0.002427842,0.000007870065,0.00019244046,0.0001165036,0.7721905,0.0060410975,0.031718623,0.0026164544,0.0019461049],"about_ca_topic_score_codex":0.000022127098,"about_ca_topic_score_gemma":8.728665e-7,"teacher_disagreement_score":0.8090952,"about_ca_system_score_codex":0.0000794748,"about_ca_system_score_gemma":0.000042479358,"threshold_uncertainty_score":0.38013786},"labels":[],"label_agreement":null},{"id":"W2555341372","doi":"10.1016/j.ifacol.2017.08.1980","title":"Autonomous Landing of a Multirotor Micro Air Vehicle on a High Velocity Ground Vehicle","year":2017,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Quadcopter; Multirotor; Extended Kalman filter; Software deployment; Computer science; Micro air vehicle; Aerospace engineering; Range (aeronautics); Automotive engineering; Controller (irrigation); Obstacle avoidance; Kalman filter; Simulation; Aeronautics; Engineering; Aerodynamics; Artificial intelligence; Robot; Mobile robot","score_opus":0.026606543826665704,"score_gpt":0.27542605926451713,"score_spread":0.2488195154378514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555341372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767068,0.000053919746,0.018706828,0.002453886,0.0008664216,0.00033921705,0.00005837002,0.0002513207,0.0005632481],"genre_scores_gemma":[0.534534,0.0000034136272,0.4645509,0.0002442438,0.00018952513,0.000012681062,0.000007517389,0.000017334429,0.00044039535],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807644,0.00006925352,0.00035106114,0.00060785265,0.000400095,0.00049530197],"domain_scores_gemma":[0.99779475,0.00018811152,0.0003887748,0.0013558088,0.000101995785,0.00017056076],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037623735,0.00026970558,0.00040908498,0.000108523236,0.0005206192,0.00018188496,0.0016934248,0.00014281334,0.000011387757],"category_scores_gemma":[0.00024122525,0.0002487864,0.00012262202,0.00013135797,0.00015328039,0.000484856,0.00039192053,0.0003029709,0.000110009154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055429724,0.0038544086,0.08120376,0.00053369417,0.00072774803,0.0019327911,0.017219327,0.012927936,0.5424029,0.010724409,0.00026202295,0.32765666],"study_design_scores_gemma":[0.00277034,0.0005169016,0.4402277,0.00024428402,0.00002548047,0.000034465756,0.00007544974,0.534385,0.020558132,0.0002407148,0.00032540594,0.00059611927],"about_ca_topic_score_codex":0.0012135989,"about_ca_topic_score_gemma":0.000023001714,"teacher_disagreement_score":0.5218448,"about_ca_system_score_codex":0.00013266799,"about_ca_system_score_gemma":0.0001266114,"threshold_uncertainty_score":0.9999964},"labels":[],"label_agreement":null},{"id":"W2557813544","doi":"10.1109/cec.2016.7744359","title":"Reliable multiple robot-assisted sensor relocation using multi-objective optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Relocation; Computer science; Robot; Artificial intelligence; Operating system","score_opus":0.047029888594299483,"score_gpt":0.2726709585178846,"score_spread":0.2256410699235851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557813544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009941948,0.000017176239,0.99699414,0.00035693133,0.00048621267,0.00022952928,0.0000017137445,0.00049202115,0.00042809072],"genre_scores_gemma":[0.07380019,0.000004644205,0.92407894,0.00008893767,0.000046799752,0.00001040836,0.0000030013096,0.00001654596,0.0019505068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985312,0.00008942878,0.0002791446,0.0005202556,0.00025566746,0.0003242815],"domain_scores_gemma":[0.9987289,0.00016670983,0.00015474211,0.0005778597,0.0002728146,0.000099004435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002779624,0.00016444158,0.00016330359,0.00014989499,0.00018759294,0.00009612304,0.00039133566,0.000103501676,0.0000134223055],"category_scores_gemma":[0.00033423686,0.00011793771,0.00004479291,0.00048697524,0.00003748543,0.000848141,0.00013410252,0.000070329326,0.0000897332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008138585,0.00010215544,0.004798985,0.0000064694405,0.000022278176,0.000013214413,0.000290091,0.96473676,0.016999872,0.0004638484,0.00013187948,0.012426325],"study_design_scores_gemma":[0.00079379743,0.000032834047,0.012242624,0.000068410605,0.000006489224,0.000034330813,0.000026292926,0.980397,0.0061336644,0.00001941829,0.00003378847,0.00021134566],"about_ca_topic_score_codex":0.00019789017,"about_ca_topic_score_gemma":0.000002998889,"teacher_disagreement_score":0.07291517,"about_ca_system_score_codex":0.00022436361,"about_ca_system_score_gemma":0.00010026539,"threshold_uncertainty_score":0.48093602},"labels":[],"label_agreement":null},{"id":"W2559795797","doi":"10.1139/tcsme-2009-0041","title":"OBSTACLE AVOIDANCE OF REDUNDANT DISCRETELY-ACTUATED MANIPULATORS USING WORKSPACE DENSITY FUNCTIONS","year":2009,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Workspace; Obstacle avoidance; Obstacle; Kinematics; Planar; Control theory (sociology); Computation; Computer science; Point (geometry); Manipulator (device); Inverse kinematics; Mathematics; Robot; Artificial intelligence; Algorithm; Mobile robot; Geometry; Control (management); Physics","score_opus":0.020115243347624257,"score_gpt":0.22202443065854224,"score_spread":0.20190918731091798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559795797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016140256,0.000046556714,0.982342,0.00061388355,0.0005158017,0.0002341431,0.000030865926,0.00007121802,0.0000052752494],"genre_scores_gemma":[0.6713418,0.0000014775369,0.32854757,0.000040685885,0.000014833977,0.0000037209406,0.0000011802931,0.000009673063,0.000039080824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989638,0.0000131915485,0.0002656639,0.00022230824,0.00019056132,0.00034451712],"domain_scores_gemma":[0.99905974,0.00007888448,0.00009035293,0.00048157453,0.00008890634,0.00020051775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021578874,0.00014222667,0.00022089957,0.00005735937,0.00029661125,0.000028243312,0.0005343729,0.00012738947,0.0000026033872],"category_scores_gemma":[0.000044629178,0.000133575,0.00043619002,0.0006056324,0.000033909255,0.00018270954,0.000010358458,0.0002453433,3.6477977e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003782342,0.000027404305,0.0000059414147,0.00003714609,0.000110260546,9.049607e-7,0.0004783811,0.9676013,0.02330077,0.0064684814,0.000056481684,0.0019091379],"study_design_scores_gemma":[0.00018971194,0.00004836929,0.0003499447,0.00009211459,0.00005589138,0.000017712642,0.000054328484,0.9854117,0.0132082375,0.00035361006,0.00006362741,0.00015473278],"about_ca_topic_score_codex":0.0033852456,"about_ca_topic_score_gemma":0.0008178322,"teacher_disagreement_score":0.65520155,"about_ca_system_score_codex":0.00032514674,"about_ca_system_score_gemma":0.00024539596,"threshold_uncertainty_score":0.544703},"labels":[],"label_agreement":null},{"id":"W2560844049","doi":"10.1109/tcyb.2016.2633331","title":"A New Conflict Resolution Method for Multiple Mobile Robots in Cluttered Environments With Motion-Liveness","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Liveness; Robot; Computer science; Scalability; Testbed; Mobile robot; Motion (physics); Distributed computing; Cluster analysis; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.026001393961779574,"score_gpt":0.2702439619893414,"score_spread":0.24424256802756183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560844049","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014031708,0.000024165529,0.99705,0.00029291533,0.00039363737,0.00068533147,0.000028073697,0.00009532322,0.000027389213],"genre_scores_gemma":[0.2121889,0.000024609875,0.7859567,0.000071132636,0.000036359725,0.00021982379,0.0000020379018,0.000026701025,0.0014737572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836254,0.000121354,0.0002927442,0.00054172956,0.00029728576,0.0003843648],"domain_scores_gemma":[0.9986425,0.0005314461,0.00010123587,0.00054983434,0.000029627185,0.0001453464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025250815,0.00021399281,0.00022187401,0.00018110927,0.00010465795,0.000044628687,0.00042354318,0.00012392366,0.000010408295],"category_scores_gemma":[0.000009705636,0.00016699852,0.0000689026,0.00024733887,0.000041543215,0.0002488473,0.0000038558287,0.00013624418,0.000041041367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008553069,0.00022405975,0.00013213023,0.000009126824,0.000035595534,0.000009746441,0.00070068566,0.7820165,0.0058986982,0.000090101275,0.00006853621,0.21072926],"study_design_scores_gemma":[0.0051950794,0.00085398386,0.0033988277,0.00022956937,0.000040355997,0.00004127306,0.000029348505,0.92857873,0.05872377,0.000271664,0.0021345557,0.000502839],"about_ca_topic_score_codex":0.00016702482,"about_ca_topic_score_gemma":0.00006074122,"teacher_disagreement_score":0.2110933,"about_ca_system_score_codex":0.00020991822,"about_ca_system_score_gemma":0.00007006692,"threshold_uncertainty_score":0.68100023},"labels":[],"label_agreement":null},{"id":"W2564378273","doi":"10.1109/iros.2017.8206147","title":"An anticipative kinematic limitation avoidance algorithm for collaborative robots: Three-dimensional case","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Robot; Kinematics; Task (project management); Computer science; Focus (optics); Robot kinematics; Position (finance); Collision avoidance; Space (punctuation); Human–computer interaction; Artificial intelligence; Simulation; Mobile robot; Engineering; Collision","score_opus":0.05702392948121184,"score_gpt":0.3359218361744631,"score_spread":0.27889790669325126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564378273","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080886055,0.000032935204,0.98938125,0.00073272537,0.0005038652,0.0007331059,0.000038296814,0.00019026584,0.00029892544],"genre_scores_gemma":[0.08948647,7.619142e-7,0.90985036,0.0001756081,0.000114612645,0.00013500096,0.000018128929,0.000016312359,0.00020273603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982108,0.00009890919,0.0003347675,0.0006235033,0.00033700973,0.00039502495],"domain_scores_gemma":[0.9970515,0.00047918517,0.00041370845,0.0011970222,0.0006803906,0.0001782071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059054495,0.00024137307,0.00031218954,0.00010329659,0.0011664984,0.00056840014,0.0009811117,0.00009061788,0.0000055380706],"category_scores_gemma":[0.00038608935,0.0002103106,0.00005824078,0.00018376281,0.00013721336,0.0018754157,0.0001571211,0.0001243617,0.000050198432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006184401,0.0015951318,0.00249638,0.00015921262,0.00048128178,0.006061121,0.014363346,0.26070917,0.0014288143,0.07006707,0.008031224,0.6345454],"study_design_scores_gemma":[0.000778896,0.00035778704,0.00485708,0.000060346367,0.000017836148,0.00025785482,0.00013777934,0.9896878,0.00051281124,0.0030173706,0.000016881919,0.0002975321],"about_ca_topic_score_codex":0.00012751496,"about_ca_topic_score_gemma":0.00008232415,"teacher_disagreement_score":0.72897863,"about_ca_system_score_codex":0.000055594082,"about_ca_system_score_gemma":0.0001745689,"threshold_uncertainty_score":0.89718807},"labels":[],"label_agreement":null},{"id":"W2566278123","doi":"10.1109/iros.2016.7759499","title":"The constriction decomposition method for coverage path planning","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Decomposition; Heuristic; Computer science; Motion planning; Path (computing); Constriction; Decomposition method (queueing theory); Mathematical optimization; Task (project management); Algorithm; Artificial intelligence; Mathematics; Engineering; Computer network; Systems engineering","score_opus":0.021541570625787723,"score_gpt":0.32462250565699946,"score_spread":0.30308093503121175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566278123","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001684525,0.000040487466,0.99518806,0.0017525587,0.0006246226,0.00017784411,0.0000032186128,0.00019856192,0.0018462159],"genre_scores_gemma":[0.030786738,0.000008273634,0.9682106,0.00023992712,0.000087560235,0.000040231636,0.0000014448979,0.000005809497,0.0006194643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917215,0.00007891948,0.00015496986,0.00022710074,0.00014231207,0.00022453988],"domain_scores_gemma":[0.9979319,0.001601769,0.000077109566,0.00027711038,0.00006385191,0.000048280996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006110367,0.00008048072,0.000078886274,0.000037004593,0.00032640036,0.00012887969,0.0003624647,0.00003909808,0.0000018306746],"category_scores_gemma":[0.0000961258,0.000040545907,0.000044176755,0.00011087703,0.000019321997,0.00027461295,0.00006121825,0.000038805523,0.000021452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018558565,0.000025067991,0.0008409063,0.000004782858,0.000032907017,0.000010078436,0.00020996777,0.0016474458,0.0070404164,0.13492958,0.013940293,0.8413],"study_design_scores_gemma":[0.0009769453,0.00019779745,0.0021531906,0.00006819913,0.000009227738,0.00009539755,0.000023946406,0.9502595,0.0077493964,0.021221796,0.01701033,0.00023427741],"about_ca_topic_score_codex":0.000006875282,"about_ca_topic_score_gemma":1.415867e-7,"teacher_disagreement_score":0.94861203,"about_ca_system_score_codex":0.000046543715,"about_ca_system_score_gemma":0.00003626041,"threshold_uncertainty_score":0.25104406},"labels":[],"label_agreement":null},{"id":"W2568385324","doi":"10.14288/1.0357150","title":"Improved action and path synthesis using gradient sampling","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gradient descent; Computer science; Stationary point; Sampling (signal processing); Path (computing); Mathematical optimization; State (computer science); Particle filter; Algorithm; Trajectory; Point (geometry); Motion planning; Control theory (sociology); Filter (signal processing); Robot; Mathematics; Artificial intelligence; Mathematical analysis; Geometry; Computer vision","score_opus":0.038626424090366394,"score_gpt":0.2272570578112492,"score_spread":0.1886306337208828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568385324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73846,0.000032975764,0.2610093,0.00004516075,0.00020816705,0.00007726653,0.000013529146,0.00005382173,0.00009976804],"genre_scores_gemma":[0.8777079,0.0000324948,0.12217059,0.0000071821646,0.00002013833,1.6801575e-7,6.187069e-7,0.0000052484506,0.000055641485],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99916184,0.00003196814,0.00007702927,0.0003751188,0.00014753843,0.00020651199],"domain_scores_gemma":[0.9990353,0.00004318668,0.00021227961,0.00052439445,0.000081695776,0.00010315507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021619722,0.00003752117,0.00017886463,0.000033644672,0.00088126445,0.00045688238,0.0006630791,0.00006750039,0.0000027074047],"category_scores_gemma":[0.000084407184,0.00013384293,0.00005170841,0.00006525213,0.0001631951,0.00083830656,0.000367854,0.00008989763,0.0000018507676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001658789,0.00003175581,0.006353779,0.000034986046,0.000022087024,0.00008578842,0.00018159687,0.000054147706,0.0016159026,0.000002620439,0.00003675736,0.99157894],"study_design_scores_gemma":[0.0002320912,0.000024696003,0.83862805,0.00014683383,0.00001861552,0.00010163779,0.00017654563,0.16039272,0.000005047004,0.00012972119,0.000014734405,0.00012930653],"about_ca_topic_score_codex":0.088841274,"about_ca_topic_score_gemma":0.007874628,"teacher_disagreement_score":0.9914496,"about_ca_system_score_codex":0.00005881842,"about_ca_system_score_gemma":0.000048062862,"threshold_uncertainty_score":0.9172262},"labels":[],"label_agreement":null},{"id":"W2577209731","doi":"10.1080/713745635","title":"Computational Intelligence Based Approach for the Joint Trajectory Generation of Cooperative Robotic Systems","year":2002,"lang":"en","type":"article","venue":"Systems Analysis Modelling Simulation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inverse kinematics; Trajectory; Kinematics; Joint (building); Degrees of freedom (physics and chemistry); Robot; Robotics; Artificial intelligence; Control engineering; Inverse dynamics; Engineering","score_opus":0.16620654506641494,"score_gpt":0.28820712805557075,"score_spread":0.12200058298915581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577209731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003635739,0.00096640823,0.9972991,0.000029775898,0.00033609566,0.0008902691,0.000009372,0.000076086304,0.000029322873],"genre_scores_gemma":[0.8181322,0.0000031101974,0.18149662,0.000010880137,0.00011777038,0.00008379009,0.00007518201,0.000011432597,0.00006899697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974799,0.00031182414,0.0008734906,0.0005064384,0.00060671876,0.00022160304],"domain_scores_gemma":[0.9973158,0.00080759014,0.00052276213,0.0005379596,0.0007602382,0.000055655102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011300353,0.00020578381,0.0004914866,0.0004106988,0.00031455496,0.00025717652,0.00040349082,0.000097783195,0.0000019183572],"category_scores_gemma":[0.00005449544,0.0001588689,0.00023727068,0.0012801836,0.00004697727,0.0002955408,0.000021747228,0.0001070984,0.000003291281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000203258,0.000058182948,0.000039211325,0.00005678423,0.00032829773,3.0960757e-7,0.0006651625,0.9946012,0.000024409635,0.004022506,0.00001417649,0.00018771952],"study_design_scores_gemma":[0.00014308174,0.00004523591,0.00001449477,0.000028380096,0.00029902224,0.0000010637771,0.000091523834,0.9991543,0.000022697128,0.000024484558,0.0000038943945,0.00017180813],"about_ca_topic_score_codex":0.00014203465,"about_ca_topic_score_gemma":7.443519e-7,"teacher_disagreement_score":0.81776863,"about_ca_system_score_codex":0.00010902709,"about_ca_system_score_gemma":0.000043800363,"threshold_uncertainty_score":0.6478486},"labels":[],"label_agreement":null},{"id":"W2577228585","doi":"10.1609/socs.v7i1.18394","title":"Searching with a Corrupted Heuristic","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ciência sem Fronteiras; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Heuristics; Computer science; Exploit; Heuristic; Energy consumption; Limit (mathematics); Bounded function; Code (set theory); Class (philosophy); Error detection and correction; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Computer security","score_opus":0.015251217783203715,"score_gpt":0.25275234394018214,"score_spread":0.23750112615697844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577228585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8242232,0.00005224029,0.00950676,0.03837372,0.014314351,0.0008152067,0.00001790234,0.00039369334,0.11230291],"genre_scores_gemma":[0.98980904,0.0000058924993,0.00902759,0.00016543841,0.00026822957,0.000014121536,0.000002107637,0.000017058454,0.0006905338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973555,0.00002822487,0.00024739598,0.00043343526,0.0016404644,0.00029495638],"domain_scores_gemma":[0.99811184,0.00023435365,0.00013641891,0.00024273874,0.0011765067,0.00009810852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005275716,0.00014971009,0.00017922094,0.00008409599,0.00017400394,0.00032240935,0.0022332072,0.000056064535,0.000009908329],"category_scores_gemma":[0.00031109503,0.00010906792,0.000078779005,0.00060489704,0.00008725655,0.0002847438,0.0008335675,0.0004187085,0.000018151935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001577881,0.00046702585,0.0078313565,0.0000557762,0.00015259818,0.00003981244,0.00095521135,0.0016681972,0.059210565,0.9273682,0.00085057714,0.0012428956],"study_design_scores_gemma":[0.0056410227,0.0013579384,0.014448211,0.0014427692,0.000049228496,0.00061702414,0.00030428157,0.19895896,0.7226543,0.04834952,0.005202069,0.0009747125],"about_ca_topic_score_codex":0.000054974134,"about_ca_topic_score_gemma":3.8092764e-7,"teacher_disagreement_score":0.87901866,"about_ca_system_score_codex":0.00016542332,"about_ca_system_score_gemma":0.0001970842,"threshold_uncertainty_score":0.44476607},"labels":[],"label_agreement":null},{"id":"W2582928123","doi":"10.1109/iscid.2016.1023","title":"Research on Real-Time Flight Path Planning of UAV Based on Grey Prediction","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Computer science; Motion planning; Path (computing); Artificial intelligence; Real-time computing; Robot","score_opus":0.061110873150926955,"score_gpt":0.33280258263216383,"score_spread":0.2716917094812369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582928123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030815396,0.00000791741,0.8365575,0.0020508715,0.0005788232,0.00035696127,0.000022109089,0.0006194417,0.12899095],"genre_scores_gemma":[0.82064795,0.0000064408023,0.17105821,0.00018792438,0.00024298883,0.00003353713,0.000007807636,0.00003100406,0.007784148],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975264,0.00028043566,0.0002648321,0.00050129,0.0010113819,0.00041565174],"domain_scores_gemma":[0.99768823,0.001043539,0.00008120918,0.00086585415,0.0001921813,0.0001289791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001565032,0.0001324801,0.00017386937,0.0004105401,0.00012477407,0.00004716352,0.0007173864,0.000097663884,0.00004758074],"category_scores_gemma":[0.00018706838,0.00008324181,0.00004569379,0.00049942266,0.000067839974,0.00022707145,0.00010990781,0.00016918268,0.00041138893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092972204,0.0024862099,0.031684082,0.00019850052,0.000175621,0.0010581306,0.003485898,0.13756014,0.2136812,0.09693966,0.34522906,0.16657178],"study_design_scores_gemma":[0.0010244133,0.0020289205,0.035794273,0.0009832529,0.000003425911,0.000007097501,0.000021675725,0.9289556,0.029680608,0.0007281196,0.0005457478,0.00022686794],"about_ca_topic_score_codex":0.00004921085,"about_ca_topic_score_gemma":1.2242921e-7,"teacher_disagreement_score":0.7913955,"about_ca_system_score_codex":0.000104187624,"about_ca_system_score_gemma":0.00012765796,"threshold_uncertainty_score":0.5287709},"labels":[],"label_agreement":null},{"id":"W2583894240","doi":"","title":"A hybrid collision avoidance system for indoor mobile robots based on human-robot interaction","year":2016,"lang":"en","type":"article","venue":"International Conference on Mechatronics - Mechatronika","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Robot; Obstacle avoidance; Mobile robot; Computer science; Collision; Artificial intelligence; Collision avoidance system; Human–robot interaction; Path (computing); Robot control; Simulation; Human–computer interaction; Computer security; Computer network","score_opus":0.06103784358017829,"score_gpt":0.3357629303494463,"score_spread":0.27472508676926805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583894240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006532716,0.000028588938,0.9833522,0.002182133,0.004202969,0.0011183397,0.00016800228,0.0005573252,0.0018577141],"genre_scores_gemma":[0.9241695,0.000015540514,0.073573336,0.000277715,0.00031866576,0.0008019419,0.00007061298,0.00005632011,0.0007163824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99569327,0.00020455473,0.00075436715,0.0013322551,0.0012424533,0.0007731059],"domain_scores_gemma":[0.99674344,0.0005931389,0.0006146127,0.0011490049,0.0006714126,0.0002284022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009670429,0.0005447332,0.00048635516,0.00057538,0.0003664272,0.00042910269,0.0023458062,0.0001933976,0.00006203872],"category_scores_gemma":[0.00018149329,0.00043836312,0.00028637602,0.0001912035,0.000056880628,0.0008373346,0.00024543973,0.00041132502,0.00035285793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029410288,0.0003385438,0.000027265507,0.0000494281,0.00009745627,0.000034845507,0.00008895157,0.06502115,0.021487758,0.8855662,0.00067195774,0.026322339],"study_design_scores_gemma":[0.0030642461,0.0018231646,0.00002853077,0.0013578464,0.000022076376,0.00003366199,0.00009843771,0.9240278,0.057313368,0.009249277,0.0022926861,0.0006889288],"about_ca_topic_score_codex":0.000021709486,"about_ca_topic_score_gemma":0.0000039700426,"teacher_disagreement_score":0.91763675,"about_ca_system_score_codex":0.0016392078,"about_ca_system_score_gemma":0.00043246034,"threshold_uncertainty_score":0.9998068},"labels":[],"label_agreement":null},{"id":"W2586623916","doi":"10.1017/s0001924000011325","title":"Autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles","year":2015,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle avoidance; Obstacle; Turning radius; Streamlines, streaklines, and pathlines; Vector field; Wing; Potential field; Computer science; Control theory (sociology); Curvature; Bounding overwatch; Wedge (geometry); Boundary (topology); Fixed wing; Aerospace engineering; Artificial intelligence; Geology; Mathematics; Geometry; Mobile robot; Engineering; Mathematical analysis; Geography; Robot; Control (management)","score_opus":0.058435895666073684,"score_gpt":0.28936264739077927,"score_spread":0.2309267517247056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586623916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035961755,0.00040577957,0.9485986,0.011532192,0.002568077,0.00026134544,0.000010228292,0.00020239162,0.00045962617],"genre_scores_gemma":[0.6047236,0.0000043671434,0.3917638,0.0012074811,0.0018427119,0.000023462668,0.000004168204,0.000030021254,0.0004004299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803656,0.00019423141,0.0003824583,0.00026714784,0.0004957585,0.00062381965],"domain_scores_gemma":[0.9982557,0.0005570412,0.00015480086,0.00046385545,0.00017884957,0.00038969508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015207628,0.0001777544,0.00023554897,0.00004891662,0.00045693194,0.00044326796,0.0016077566,0.00008189561,0.000009540927],"category_scores_gemma":[0.00071696506,0.000120383,0.00011709021,0.000181522,0.00010774935,0.00043954956,0.00027232865,0.00045801402,0.00013327535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000902324,0.000947276,0.00095522474,0.00007575256,0.0005574456,0.0010078808,0.018948039,0.12500016,0.00711785,0.22498393,0.17155235,0.44795176],"study_design_scores_gemma":[0.0031804687,0.0007629248,0.0031502584,0.00012400413,0.000048036152,0.002237658,0.00021895107,0.8980483,0.0019188399,0.071637765,0.018129814,0.00054302707],"about_ca_topic_score_codex":0.0000042811666,"about_ca_topic_score_gemma":4.5852136e-7,"teacher_disagreement_score":0.7730481,"about_ca_system_score_codex":0.00015610593,"about_ca_system_score_gemma":0.0003505574,"threshold_uncertainty_score":0.49090764},"labels":[],"label_agreement":null},{"id":"W2587182257","doi":"10.2316/journal.206.2017.1.206-4673","title":"AN ACCURATE PATH PLANNING ALGORITHM BASED ON TRIANGULAR MESHES IN ROBOTIC FIBRE PLACEMENT","year":2017,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Offset (computer science); Polygon mesh; Algorithm; Path (computing); Computer science; Motion planning; Variable (mathematics); Triangle mesh; Mathematics; Artificial intelligence; Robot; Computer graphics (images); Mathematical analysis","score_opus":0.026088157707085682,"score_gpt":0.31886136966908,"score_spread":0.29277321196199435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587182257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010038107,0.00005505746,0.98616713,0.0022266272,0.0013060899,0.0000851758,0.0000030267224,0.000026523412,0.000092239534],"genre_scores_gemma":[0.6286398,0.000011429949,0.37101582,0.00014041043,0.00016608965,0.0000015131978,0.0000060381713,0.0000074831587,0.000011402566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839336,0.00008882761,0.00048722432,0.00020053893,0.0006645613,0.0001654701],"domain_scores_gemma":[0.99843484,0.00012751794,0.0007840666,0.0003125398,0.00024081444,0.00010022546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079070835,0.0001441841,0.00021920752,0.00034416263,0.00014636874,0.0007883044,0.0010810714,0.00006523417,0.0000028965517],"category_scores_gemma":[0.000192635,0.00012755617,0.000054342712,0.000058278252,0.0000322737,0.0011592142,0.00008126883,0.00019747287,0.0000027621493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019027688,0.000101700876,0.001691428,0.0000048029688,0.000026033662,0.0003098273,0.00030914956,0.9572322,0.00011508938,0.0008000636,0.00007162067,0.039319064],"study_design_scores_gemma":[0.0012488186,0.00028725303,0.048164617,0.0003810087,0.000008193876,0.000073231495,0.00003759631,0.9486786,0.00017617406,0.00079188595,0.000025643425,0.00012698366],"about_ca_topic_score_codex":0.000010025898,"about_ca_topic_score_gemma":3.9432499e-7,"teacher_disagreement_score":0.61860174,"about_ca_system_score_codex":0.000108449174,"about_ca_system_score_gemma":0.000110561734,"threshold_uncertainty_score":0.7601637},"labels":[],"label_agreement":null},{"id":"W25902527","doi":"10.1007/978-3-642-10238-7_6","title":"Voronoi-assisted Parallel Bidirectional Heuristic Search","year":2009,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Heuristic; Speedup; Computer science; Overhead (engineering); Voronoi diagram; Process (computing); Heuristics; Variety (cybernetics); Exponential function; Algorithm; Parallel computing; Artificial intelligence; Mathematics","score_opus":0.06442006760290148,"score_gpt":0.31304050211553885,"score_spread":0.24862043451263738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W25902527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007787218,0.00032548912,0.7942951,0.0013360961,0.00034177935,0.00026795885,0.000007904004,0.00016260696,0.20325528],"genre_scores_gemma":[0.004458705,0.0013405209,0.9883785,0.0010316181,0.0000661946,0.000019463461,0.00007570017,0.000009728121,0.0046195737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788934,0.00005141442,0.00068521465,0.000360586,0.00070002547,0.00031344],"domain_scores_gemma":[0.9967286,0.00026969393,0.00025377912,0.0021612453,0.00043388136,0.00015281321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012731116,0.00024286161,0.00027252728,0.0011639444,0.00059754035,0.00073539204,0.003892515,0.00014267967,0.0000064733044],"category_scores_gemma":[0.0000513752,0.00025081763,0.00004987182,0.00055706897,0.00072209706,0.004634963,0.0017449036,0.0005963161,0.00014377812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015262171,0.000022275322,0.00002882263,0.000013386966,0.000005601725,0.0000016344035,0.00080350466,0.0031028264,0.0000011665228,0.3784911,0.0004386165,0.61708957],"study_design_scores_gemma":[0.0002524807,0.0000685646,0.008950988,0.00018040137,0.0000041578264,0.00010321781,0.000005889025,0.92298496,0.0000016681777,0.0058333618,0.061267003,0.0003473342],"about_ca_topic_score_codex":0.000016308622,"about_ca_topic_score_gemma":0.0000020541754,"teacher_disagreement_score":0.9198821,"about_ca_system_score_codex":0.00023128772,"about_ca_system_score_gemma":0.00050767785,"threshold_uncertainty_score":0.9999944},"labels":[],"label_agreement":null},{"id":"W2591696620","doi":"10.1109/robio.2016.7866478","title":"Sequence-modification based collision-free motion planning of multiple robots workcell","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Workcell; Robot; Workspace; Motion planning; Computer science; Sequence (biology); Scheduling (production processes); Heuristic; Path (computing); Collision; Distributed computing; Collision avoidance; Mathematical optimization; Artificial intelligence; Mathematics; Programming language","score_opus":0.07496948776012394,"score_gpt":0.29734119742238196,"score_spread":0.22237170966225803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591696620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01097671,0.000023828661,0.9863994,0.0010248659,0.0002988181,0.00017462503,0.000008061945,0.00023513244,0.0008585203],"genre_scores_gemma":[0.54034686,0.0000011700704,0.45933154,0.000051337418,0.000024173585,0.000009945122,0.000003981256,0.0000061090323,0.00022486428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998519,0.00008682987,0.00033175605,0.00040245088,0.00040323957,0.00025673036],"domain_scores_gemma":[0.9979782,0.0005423526,0.00019884338,0.0010307309,0.00015582447,0.00009406252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004422313,0.00013479256,0.00016937072,0.00015992601,0.000079628895,0.000041868665,0.0010584892,0.000087336324,0.000011318412],"category_scores_gemma":[0.0004125365,0.00009346879,0.00005275054,0.00040310092,0.000055769793,0.00045192803,0.000139689,0.00006192401,0.000049500122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005087576,0.00038149251,0.05187913,0.00006430683,0.00003725846,0.000053845866,0.00095066166,0.6483077,0.15200742,0.012041033,0.0058887857,0.12833752],"study_design_scores_gemma":[0.0008794409,0.00006360283,0.015472662,0.00019332401,0.0000038641747,0.0000047624007,0.000012779648,0.932858,0.04917137,0.0010936891,0.00007065073,0.00017588618],"about_ca_topic_score_codex":0.00004619878,"about_ca_topic_score_gemma":9.81064e-7,"teacher_disagreement_score":0.5293701,"about_ca_system_score_codex":0.00007521218,"about_ca_system_score_gemma":0.00008124466,"threshold_uncertainty_score":0.3811547},"labels":[],"label_agreement":null},{"id":"W2592597155","doi":"10.1155/2017/4961383","title":"Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"State Key Laboratory of Robotics and System; State Key Laboratory of Robotics; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Mobile robot; Kinematics; Motion planning; Omnidirectional antenna; Computer science; Robot; Motion (physics); Process (computing); Field (mathematics); Maxima and minima; Simulation; Potential field; Motion control; Scheme (mathematics); Control engineering; Artificial intelligence; Engineering; Mathematics","score_opus":0.01500894201142085,"score_gpt":0.3223234953310405,"score_spread":0.30731455331961965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592597155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017069284,0.00015548513,0.9804318,0.00041396712,0.0016955874,0.00016689792,0.0000090995845,0.000030199419,0.000027695007],"genre_scores_gemma":[0.30631858,0.000012662795,0.69334686,0.000048813716,0.00018098489,0.000013662971,0.00001357285,0.000008339299,0.00005654917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882936,0.00003095913,0.00044495027,0.00019614836,0.0003217557,0.00017683403],"domain_scores_gemma":[0.99840623,0.0001419797,0.00085926766,0.00022229846,0.00028922196,0.00008099099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045075206,0.000116629126,0.00022065459,0.00009659618,0.00032677985,0.00011916212,0.0004866364,0.000075713986,0.0000040862706],"category_scores_gemma":[0.0000917214,0.00011248248,0.00014324057,0.000055764576,0.000014172618,0.0014859568,0.0000048917855,0.00018138424,8.202539e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012548799,0.00006999639,0.0005250817,0.000022258426,0.000048105645,0.00003655171,0.00077883084,0.8583814,0.03012311,0.00021118051,0.00054821203,0.10912978],"study_design_scores_gemma":[0.013411182,0.0044161924,0.31312978,0.00078630314,0.00035026166,0.00043325726,0.00073402066,0.46747833,0.17497426,0.016433617,0.006495019,0.0013577419],"about_ca_topic_score_codex":0.000006765932,"about_ca_topic_score_gemma":9.15974e-7,"teacher_disagreement_score":0.39090306,"about_ca_system_score_codex":0.000036181587,"about_ca_system_score_gemma":0.00005116725,"threshold_uncertainty_score":0.45869023},"labels":[],"label_agreement":null},{"id":"W2592666947","doi":"10.1007/s10586-017-0815-6","title":"Collision detection for virtual environment using particle swarm optimization with adaptive cauchy mutation","year":2017,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Collision detection; Algorithm; Mathematical optimization; Adaptive mutation; Cauchy distribution; Mutation; Collision; Swarm behaviour; Genetic algorithm; Mathematics; Artificial intelligence","score_opus":0.03538594963211208,"score_gpt":0.2655798658036606,"score_spread":0.23019391617154852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592666947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16519707,0.000008875511,0.83386904,0.00009785294,0.0003576219,0.00036415167,0.000001176643,0.00008451855,0.000019705683],"genre_scores_gemma":[0.55027133,2.78084e-7,0.44958755,0.00003167131,0.000079723395,0.000005582937,0.0000018281326,0.000009823576,0.0000121947805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987637,0.00006355497,0.00022448318,0.00042526002,0.00023980126,0.00028322605],"domain_scores_gemma":[0.9988735,0.00013543168,0.00035996342,0.00047739194,0.00008080173,0.00007293512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000375654,0.00014709307,0.00014682759,0.00004984688,0.0011653124,0.00038112883,0.00039609475,0.000059026603,5.3351965e-7],"category_scores_gemma":[0.000064775915,0.00013839264,0.00003614641,0.00006905945,0.000057196175,0.00066819414,0.00024478775,0.000086343425,0.000005152234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028760991,0.000023230003,0.00015080361,0.0000047840967,0.000012495946,0.000007173637,0.0008393507,0.9808712,0.0002661251,0.00006229272,0.0000036766378,0.017730087],"study_design_scores_gemma":[0.0009523168,0.0003573618,0.0011367566,0.000068559355,0.000018049039,0.00004297585,0.00009268906,0.99032015,0.00676529,0.00005375051,0.0000074329428,0.00018468253],"about_ca_topic_score_codex":0.000038099606,"about_ca_topic_score_gemma":0.0000018023705,"teacher_disagreement_score":0.38507426,"about_ca_system_score_codex":0.00016315655,"about_ca_system_score_gemma":0.00003646737,"threshold_uncertainty_score":0.8962759},"labels":[],"label_agreement":null},{"id":"W2594548486","doi":"10.23919/acc.2017.7963402","title":"Clustering in discrete path planning for approximating minimum length paths","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Travelling salesman problem; Mathematical optimization; Reduction (mathematics); Computation; Motion planning; Graph; Metric (unit); Path (computing); Computer science; Mathematics; Time complexity; Algorithm; Robot; Artificial intelligence; Combinatorics","score_opus":0.04723947475807791,"score_gpt":0.31762135374308564,"score_spread":0.27038187898500776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594548486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011963543,0.000030148634,0.98145854,0.00044458613,0.0005264734,0.00030786812,0.0000028615032,0.0001707221,0.0050952802],"genre_scores_gemma":[0.3660495,7.206864e-7,0.6334283,0.000068881985,0.000081554426,0.000044992663,0.0000020689745,0.000012206052,0.0003118132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838376,0.000032723616,0.0003586892,0.00049891305,0.00020799531,0.0005179467],"domain_scores_gemma":[0.9984575,0.00020645339,0.00025213457,0.0009619473,0.00003275672,0.00008920889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007932597,0.00018009292,0.00025581443,0.00009625692,0.00046143596,0.00057488075,0.0015172018,0.00007411386,0.0000012815184],"category_scores_gemma":[0.00040017618,0.00016157913,0.000060253224,0.00006434191,0.00003664226,0.000835543,0.00060380896,0.00015711084,0.000008552339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117432275,0.0004021073,0.18623559,0.0010551799,0.00017250844,0.0012385315,0.045081895,0.15533732,0.0055566733,0.037204478,0.0040889783,0.5635093],"study_design_scores_gemma":[0.00058864587,0.000058862388,0.009948115,0.00019804743,0.00000229552,0.000014135014,0.00015678408,0.9877183,0.00018742804,0.00077421905,0.00011401302,0.00023915594],"about_ca_topic_score_codex":0.000058103466,"about_ca_topic_score_gemma":0.0000029677424,"teacher_disagreement_score":0.83238095,"about_ca_system_score_codex":0.0000421438,"about_ca_system_score_gemma":0.000041172847,"threshold_uncertainty_score":0.65890056},"labels":[],"label_agreement":null},{"id":"W2607444396","doi":"10.1142/s2301385017500042","title":"Experimental Test of Unmanned Ground Vehicle Delivering Goods Using RRT Path Planning Algorithm","year":2017,"lang":"en","type":"article","venue":"Unmanned Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council; Concordia University","keywords":"Motion planning; Software deployment; Random tree; Path (computing); Kinematics; Computer science; Position (finance); Simulation; Collision avoidance; Collision; Unmanned ground vehicle; Tree (set theory); Engineering; Artificial intelligence; Computer security; Mathematics; Robot","score_opus":0.052198751540754845,"score_gpt":0.30609015909682674,"score_spread":0.25389140755607187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607444396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42041272,0.0014046247,0.57193893,0.00004385501,0.003719801,0.0005477117,0.000028681732,0.00035197305,0.0015517158],"genre_scores_gemma":[0.8983621,0.0000020799457,0.101037815,0.000019407815,0.0003791714,0.00001898166,0.0000055417017,0.00004066551,0.00013424823],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697626,0.00013244734,0.00072329753,0.0007473984,0.0007008741,0.0007197048],"domain_scores_gemma":[0.9968534,0.00020096195,0.0008335088,0.0017506001,0.00013535419,0.0002261622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076743076,0.00038350417,0.00061285903,0.00020444093,0.0009603942,0.0007456636,0.0022162294,0.00017863615,0.0000024418312],"category_scores_gemma":[0.0001401677,0.0003907066,0.00013472703,0.00019688926,0.00015285799,0.0012643924,0.00074865395,0.00024417095,0.00002945077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110629335,0.0024729804,0.15369771,0.0012208952,0.0010545409,0.0048437202,0.038321804,0.24826795,0.5129584,0.008909062,0.0017676231,0.026374668],"study_design_scores_gemma":[0.00087906374,0.0002797898,0.008073786,0.00072680885,0.000016695005,0.00018050692,0.0006526195,0.97685766,0.0117287375,0.000031281295,0.00010532061,0.0004677411],"about_ca_topic_score_codex":0.001416587,"about_ca_topic_score_gemma":5.951505e-7,"teacher_disagreement_score":0.7285897,"about_ca_system_score_codex":0.00021457432,"about_ca_system_score_gemma":0.00010040128,"threshold_uncertainty_score":0.9998545},"labels":[],"label_agreement":null},{"id":"W2609024616","doi":"10.2514/1.g002703","title":"Autonomous Landing of a Quadcopter on a High-Speed Ground Vehicle","year":2017,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Aeronautics; Operations research; Engineering","score_opus":0.012690873478621965,"score_gpt":0.2531644698527159,"score_spread":0.24047359637409393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609024616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34850973,0.0002679029,0.6478394,0.0019713289,0.00077865866,0.000072233765,0.000007533965,0.00001496522,0.0005382262],"genre_scores_gemma":[0.9644398,0.000019000001,0.035184488,0.00013317024,0.00012884967,4.6777106e-7,1.9124269e-7,0.000005979157,0.00008806403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897414,0.000042053493,0.00040552727,0.00014043075,0.00025472732,0.00018313651],"domain_scores_gemma":[0.99828047,0.00014883038,0.00090712274,0.00039435452,0.00017530278,0.0000939336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000590311,0.00011295169,0.00035711942,0.00009686941,0.00017455425,0.00022473442,0.0007226896,0.00005681726,6.8042164e-7],"category_scores_gemma":[0.00018939734,0.00008848501,0.00007888679,0.00003838765,0.0000751322,0.0005138495,0.00007472832,0.00017909601,0.0000019940576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018412968,0.0010160666,0.18931425,0.00028226138,0.0011306242,0.0025000921,0.0035553763,0.088797435,0.0187898,0.2025105,0.00079712877,0.48946518],"study_design_scores_gemma":[0.0021752212,0.00052661385,0.1826065,0.00016744854,0.000019579758,0.00010020514,0.000012855591,0.8116707,0.000031214095,0.002552633,0.00003141889,0.00010559578],"about_ca_topic_score_codex":0.000054122407,"about_ca_topic_score_gemma":0.00000510444,"teacher_disagreement_score":0.7228733,"about_ca_system_score_codex":0.00006762124,"about_ca_system_score_gemma":0.000065055916,"threshold_uncertainty_score":0.3608314},"labels":[],"label_agreement":null},{"id":"W2610614794","doi":"10.1145/3027063.3054949","title":"Never Alone","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Generative grammar; Computer science; Scale (ratio); Multimedia; Human–computer interaction; Architectural engineering; Geography; Artificial intelligence; Engineering; Cartography","score_opus":0.03155159516248617,"score_gpt":0.2829822584258395,"score_spread":0.25143066326335334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610614794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097531977,0.0000104797655,0.9210556,0.0033107437,0.00055054156,0.00002931973,1.5470432e-7,0.00016304625,0.07390478],"genre_scores_gemma":[0.23165655,0.0000011823278,0.7559471,0.0006878367,0.0000813001,0.0000022378586,2.2242482e-7,0.0000034933448,0.011620041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950945,0.0000076617725,0.00005685164,0.00016384613,0.00011970551,0.00014250747],"domain_scores_gemma":[0.99880433,0.0000166643,0.000042526666,0.001062984,0.000020239355,0.000053257612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010671576,0.000047800077,0.0000605955,0.000017970086,0.00023625544,0.0002897052,0.0013476732,0.000022237493,0.00001716228],"category_scores_gemma":[0.000051836883,0.00003888013,0.000018570909,0.000020342673,0.000026259495,0.00078896235,0.0002995525,0.00004613396,0.0005850806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034081468,0.00014188117,0.028269207,0.000012109082,0.000057570785,0.0006635308,0.00082440506,0.0008996635,0.0016514444,0.4230017,0.12204009,0.422435],"study_design_scores_gemma":[0.00051059294,0.000044167362,0.28687844,0.000015404132,0.000002853843,0.000047003483,0.0000047685357,0.687487,0.0024496382,0.0070472583,0.015213706,0.0002991665],"about_ca_topic_score_codex":0.00007474169,"about_ca_topic_score_gemma":6.510436e-7,"teacher_disagreement_score":0.68658733,"about_ca_system_score_codex":0.00000686381,"about_ca_system_score_gemma":0.000016867787,"threshold_uncertainty_score":0.75202227},"labels":[],"label_agreement":null},{"id":"W2610801907","doi":"10.1117/12.2263514","title":"Unobtrusive and assistive obstacle avoidance for tele-operation of ground vehicles","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"Ontario Centres of Excellence","keywords":"SAFER; Obstacle avoidance; Obstacle; Computer science; Collision avoidance; Operator (biology); Robot; Kinematics; Unmanned ground vehicle; Simulation; Interference (communication); Mobile robot; Engineering; Control engineering; Artificial intelligence; Computer security; Telecommunications","score_opus":0.016859714547084122,"score_gpt":0.24787381826764227,"score_spread":0.23101410372055814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610801907","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99004066,0.00009837391,0.006427392,0.0020523379,0.00027816204,0.000528555,0.000047688438,0.000050159484,0.00047668788],"genre_scores_gemma":[0.47811413,0.000034444474,0.5213697,0.000043132466,0.00020041747,0.000102105456,0.000003184727,0.000024911491,0.000107973465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982411,1.9301678e-8,0.0005221116,0.0004251222,0.0005060716,0.00030557177],"domain_scores_gemma":[0.9969732,0.00025332498,0.0006607042,0.00011229254,0.0019087131,0.00009175071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065577973,0.00023874683,0.00038229857,0.00006819736,0.0002469998,0.00030003884,0.001537037,0.00014262875,8.2129816e-7],"category_scores_gemma":[0.0012713128,0.00020635797,0.00026956302,0.000120886456,0.0003208642,0.0012093771,0.00031382713,0.0001878107,4.0989028e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005992959,0.000092488175,0.0012782479,0.00047251838,0.0003064666,1.3145666e-7,0.0006126305,0.00037285025,0.32995477,0.6639255,0.00071022345,0.0022142022],"study_design_scores_gemma":[0.0023317966,0.00080035307,0.038163133,0.0006977285,0.00015450259,0.000032638964,0.00095076684,0.72784543,0.22101954,0.006651633,0.0007509403,0.0006015222],"about_ca_topic_score_codex":0.000019491561,"about_ca_topic_score_gemma":1.1324394e-7,"teacher_disagreement_score":0.7274726,"about_ca_system_score_codex":0.00009172099,"about_ca_system_score_gemma":0.000047476693,"threshold_uncertainty_score":0.8415034},"labels":[],"label_agreement":null},{"id":"W2613500262","doi":"10.1109/irc.2017.74","title":"Sliding Local Planners for Sampling-Based Path Planning","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"University of Victoria; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Workspace; Motion planning; Planner; Benchmark (surveying); Path (computing); Computer science; Sampling (signal processing); Robot; Boundary (topology); Mathematical optimization; Artificial intelligence; Mathematics; Computer vision","score_opus":0.09831942216085099,"score_gpt":0.3508832476118815,"score_spread":0.2525638254510305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613500262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001033486,0.000026640724,0.9945719,0.0009216036,0.0008617114,0.00018545175,0.0000054589696,0.00030879964,0.0020848964],"genre_scores_gemma":[0.38587567,1.7806892e-7,0.61349005,0.00032155082,0.000104838466,0.000022032502,0.000005677848,0.000011818504,0.0001681787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985736,0.00001925598,0.0002076923,0.00047354613,0.00024361476,0.00048227323],"domain_scores_gemma":[0.9982713,0.00032862017,0.0001784719,0.0010103906,0.00006497969,0.00014625004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005019339,0.00017396743,0.00020212411,0.00008461631,0.0008494575,0.0005805705,0.0016878347,0.0000865868,0.0000030261451],"category_scores_gemma":[0.0002497375,0.0001560549,0.000082286904,0.000051471416,0.00005406685,0.00043239957,0.00019250734,0.00012569872,0.000027023372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007855738,0.00015529465,0.014590924,0.00014143607,0.0001003858,0.00028738126,0.0015517749,0.7644384,0.0006457944,0.09298023,0.019971706,0.105058104],"study_design_scores_gemma":[0.0006238664,0.00009978192,0.0025995525,0.00009266025,0.00000526072,0.000009796514,0.000043600918,0.99209225,0.0009865279,0.0010431703,0.002149569,0.00025397033],"about_ca_topic_score_codex":0.00003683074,"about_ca_topic_score_gemma":5.3676536e-7,"teacher_disagreement_score":0.3848422,"about_ca_system_score_codex":0.000049222857,"about_ca_system_score_gemma":0.00011092639,"threshold_uncertainty_score":0.65334266},"labels":[],"label_agreement":null},{"id":"W2613560943","doi":"10.1016/s1474-6670(17)31087-x","title":"A Fuzzy-Braitenberg Navigation Strategy for Differential Drive Mobile Robots 1 1This research was supported in part by the National Sciences and Engineering Research Council (NSERC) of Canada under grants RGPINI345 and RGPIN227612, and the Canada Foundation for Innovation under the New Opportunities Program.","year":2004,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Mobile robot; Robot; Obstacle avoidance; Scheme (mathematics); Engineering; Fuzzy logic; Differential (mechanical device); Mobile robot navigation; Obstacle; Control engineering; Computer science; Robot control; Artificial intelligence; Mathematics; Aerospace engineering","score_opus":0.184867060019179,"score_gpt":0.34786944197228764,"score_spread":0.16300238195310865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613560943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96605545,0.000373086,0.010746432,0.01930927,0.00021146717,0.0030940862,0.000045323795,0.00003496823,0.00012992152],"genre_scores_gemma":[0.99592745,0.000033052467,0.0030622047,0.00011143045,0.000071289025,0.0005768048,0.000023888071,0.000011338717,0.00018253841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969872,0.00005142163,0.0003686383,0.00038870896,0.0017346819,0.00046931044],"domain_scores_gemma":[0.996491,0.0009172318,0.00015296543,0.00008685863,0.002282982,0.000068940884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047014304,0.00014642502,0.00017634631,0.00013980447,0.0008929426,0.00053778774,0.0004180165,0.00006412432,7.3898343e-7],"category_scores_gemma":[0.00070441794,0.000093425515,0.000013088406,0.00096577295,0.00064449303,0.0005023081,0.00012739073,0.0002671453,2.9609224e-8],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038924726,0.00023297044,0.0026535126,0.0008901938,0.00031360667,0.00000379111,0.021778334,0.055862177,0.0050837887,0.7924309,0.058985375,0.061376106],"study_design_scores_gemma":[0.0053360416,0.00095797516,0.023577692,0.00055972405,0.00003129292,0.00006360557,0.020059636,0.80720127,0.0016144973,0.13683167,0.0031785204,0.00058809004],"about_ca_topic_score_codex":0.40717694,"about_ca_topic_score_gemma":0.24408159,"teacher_disagreement_score":0.7513391,"about_ca_system_score_codex":0.0005897802,"about_ca_system_score_gemma":0.0071626343,"threshold_uncertainty_score":0.99846584},"labels":[],"label_agreement":null},{"id":"W2613988712","doi":"10.1109/icit.2017.7915472","title":"Optimal collision free path planning for an autonomous articulated vehicle with two trailers","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion planning; Collision; Collision avoidance; Control theory (sociology); Vehicle dynamics; Computer science; Path (computing); Optimal control; Point (geometry); Motion control; Mobile robot; Simulation; Robot; Engineering; Control (management); Artificial intelligence; Mathematical optimization; Mathematics; Automotive engineering","score_opus":0.043635957424747684,"score_gpt":0.30587044104202477,"score_spread":0.2622344836172771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613988712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31750828,0.000008756489,0.6799992,0.0003424362,0.00019712356,0.00028428523,0.0000059883128,0.0003083065,0.0013456627],"genre_scores_gemma":[0.49109182,1.2291486e-7,0.50856334,0.000072691044,0.000047565187,0.000021843985,0.0000043983473,0.000012272057,0.0001859649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849457,0.000032440195,0.00020494462,0.00053732615,0.00027070765,0.00046003523],"domain_scores_gemma":[0.9978976,0.00008651273,0.00017399093,0.0015269563,0.000103989594,0.00021090353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036248472,0.00018758126,0.0002140326,0.00006115539,0.00073681085,0.0007133481,0.0017078129,0.00006559624,0.0000030751594],"category_scores_gemma":[0.00008483699,0.00014640485,0.000044426037,0.00008181697,0.00007982798,0.0011159295,0.00021619968,0.00011758609,0.000011788468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103996936,0.00019061669,0.0034191266,0.000015012088,0.00006511313,0.00024248737,0.0032341026,0.9679349,0.0024052043,0.0044079022,0.0027578303,0.015223707],"study_design_scores_gemma":[0.0018226162,0.0006660849,0.020587735,0.000049475853,0.00001127425,0.00003615754,0.00006952811,0.9743507,0.0017513151,0.00022416766,0.00016597702,0.00026499218],"about_ca_topic_score_codex":0.00014783585,"about_ca_topic_score_gemma":0.0000069738658,"teacher_disagreement_score":0.17358354,"about_ca_system_score_codex":0.000043702294,"about_ca_system_score_gemma":0.00011364424,"threshold_uncertainty_score":0.6878832},"labels":[],"label_agreement":null},{"id":"W2615199095","doi":"10.1016/s1474-6670(17)33456-0","title":"Neuromorphic processing of image relevance function for path planning and navigation","year":2003,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Artificial intelligence; Relevance (law); Computer vision; Computer science; Neuromorphic engineering; Image (mathematics); Image processing; Motion planning; Property (philosophy); Robot; Artificial neural network","score_opus":0.02370767771873528,"score_gpt":0.2521036673746056,"score_spread":0.22839598965587032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615199095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23158243,0.00062873284,0.7664725,0.0001047665,0.00022181313,0.00030266072,0.0000026590703,0.00019238859,0.00049199944],"genre_scores_gemma":[0.65060824,0.000004098667,0.34918886,0.000039032875,0.00003438287,0.000030924668,0.0000021332182,0.000013865937,0.00007847547],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880916,0.000009916498,0.00026674088,0.00042115914,0.00022810293,0.00026490676],"domain_scores_gemma":[0.9991779,0.000070187736,0.00027777915,0.00010074269,0.00030758965,0.00006583032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041164763,0.00015000637,0.0001927605,0.000088992725,0.00018312626,0.00017234181,0.00021075203,0.00006689212,5.1495476e-7],"category_scores_gemma":[0.0003866773,0.00015285438,0.000031334774,0.0003175167,0.0000687023,0.00109736,0.000043924283,0.00012070422,0.0000016367284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022578036,0.0004591629,0.17463301,0.0061371014,0.00014000396,0.00004988057,0.024268026,0.0017851986,0.41405508,0.070823155,0.0068243677,0.30059925],"study_design_scores_gemma":[0.0013626328,0.0008413152,0.027635885,0.001104032,0.0000573804,0.00024166018,0.0005493775,0.92105263,0.024365231,0.019689739,0.0024229607,0.00067715487],"about_ca_topic_score_codex":0.0000025754707,"about_ca_topic_score_gemma":1.5512237e-8,"teacher_disagreement_score":0.9192674,"about_ca_system_score_codex":0.000021010412,"about_ca_system_score_gemma":0.00005251275,"threshold_uncertainty_score":0.62332207},"labels":[],"label_agreement":null},{"id":"W2618658041","doi":"10.1007/s10846-017-0567-9","title":"Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; Nanyang Technological University","keywords":"Path (computing); Motion planning; Random tree; Nonholonomic system; Tree (set theory); Baseline (sea); Computer science; Mathematics; Artificial intelligence; Robot; Mobile robot; Geology; Mathematical analysis","score_opus":0.058896679806674984,"score_gpt":0.3062608302347605,"score_spread":0.2473641504280855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618658041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18745516,0.00053780345,0.8080878,0.00010521139,0.0034635882,0.000252256,0.0000015770927,0.000019678218,0.000076924014],"genre_scores_gemma":[0.8431532,0.000009558341,0.1563369,0.000054382373,0.0003736822,0.000003274423,8.1118077e-7,0.000035205587,0.000032976102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959035,0.00031676306,0.0017222262,0.0005217181,0.00078807207,0.00074771966],"domain_scores_gemma":[0.99570435,0.00033582267,0.0025408664,0.0010306573,0.00009094186,0.00029735314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002346568,0.00042483502,0.0009099975,0.0006403633,0.00039117222,0.0009570335,0.0021811246,0.00019008204,0.0000039503484],"category_scores_gemma":[0.00032351745,0.00038633542,0.00025567954,0.00016849692,0.00013048109,0.0010238928,0.00027413853,0.0006174471,0.000021174434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016082107,0.000111269874,0.03160596,0.000059244525,0.00007201417,0.001141008,0.0007879503,0.95913315,0.005026552,0.00009118782,0.000027305912,0.0019282739],"study_design_scores_gemma":[0.0008810415,0.00022014145,0.009190876,0.0037230784,0.00003973207,0.001013799,0.00036493945,0.9825223,0.0015112243,0.000040961546,0.00007776245,0.0004141629],"about_ca_topic_score_codex":0.00013501034,"about_ca_topic_score_gemma":4.1172555e-7,"teacher_disagreement_score":0.65569806,"about_ca_system_score_codex":0.0007127789,"about_ca_system_score_gemma":0.00031037966,"threshold_uncertainty_score":0.99985886},"labels":[],"label_agreement":null},{"id":"W2620408778","doi":"10.1002/rob.21722","title":"Adaptive continuous‐space informative path planning for online environmental monitoring","year":2017,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Path (computing); Real-time computing; Mobile robot; Field (mathematics); Parameterized complexity; Planner; Focus (optics); Scheme (mathematics); Robot; Distributed computing; Computation; Artificial intelligence; Algorithm; Mathematics","score_opus":0.040616116560668186,"score_gpt":0.3027307588796727,"score_spread":0.2621146423190045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620408778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010904582,0.00018271912,0.985745,0.0011210671,0.001706933,0.00011321401,0.0000064505125,0.000016710628,0.00020328302],"genre_scores_gemma":[0.3202519,0.000024445044,0.6790422,0.000080158294,0.0004739398,0.0000010306846,6.526353e-7,0.0000072772627,0.00011836003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988568,0.000023964718,0.00042179058,0.00012002558,0.00031373106,0.00026367957],"domain_scores_gemma":[0.99802095,0.00035783104,0.0010169066,0.00038746063,0.000091356764,0.00012547063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033449862,0.00014673156,0.00029976628,0.000083699204,0.0003365902,0.00022323606,0.0011099766,0.000099972975,0.00000124757],"category_scores_gemma":[0.00030860826,0.00012658925,0.00012511146,0.000027464674,0.000039427825,0.0009973672,0.00021203584,0.0003910321,0.000002506808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003841248,0.0007350169,0.09156932,0.000108293,0.00080180954,0.0012879644,0.02332644,0.69513375,0.001675542,0.0048099975,0.012122023,0.1680457],"study_design_scores_gemma":[0.0040745926,0.004305721,0.07882933,0.0013763984,0.00013292668,0.00068486074,0.0038725787,0.89463913,0.0069772713,0.0023452921,0.0018700314,0.0008918275],"about_ca_topic_score_codex":0.0000034025097,"about_ca_topic_score_gemma":9.442116e-8,"teacher_disagreement_score":0.30934733,"about_ca_system_score_codex":0.000061325445,"about_ca_system_score_gemma":0.000065560576,"threshold_uncertainty_score":0.516216},"labels":[],"label_agreement":null},{"id":"W2624462169","doi":"10.2514/6.2017-3435","title":"3D Reference Trajectory Optimization Using Particle Swarm Optimization","year":2017,"lang":"en","type":"article","venue":"17th AIAA Aviation Technology, Integration, and Operations Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Particle swarm optimization; Trajectory; Multi-swarm optimization; Computer science; Trajectory optimization; Mathematical optimization; Algorithm; Mathematics; Physics","score_opus":0.04593741456466238,"score_gpt":0.29225244674016004,"score_spread":0.24631503217549766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624462169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012237378,0.00008552185,0.98399,0.0020252846,0.0003304378,0.0003978102,0.000012017947,0.00045271544,0.00046881294],"genre_scores_gemma":[0.55199444,0.00008287599,0.4475925,0.00006268944,0.000030929918,0.000050961055,0.000035561898,0.000009631976,0.00014039362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812853,0.000116126386,0.00050864293,0.00065765035,0.00027052072,0.00031850516],"domain_scores_gemma":[0.997524,0.000043962154,0.00030257643,0.0011115061,0.00091209234,0.000105824874],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00043952698,0.0002673912,0.00025385778,0.0003177817,0.0019320846,0.0012670488,0.0009426433,0.00030590672,0.00003357479],"category_scores_gemma":[0.00068860414,0.00026197408,0.000030029309,0.00041072277,0.0003105045,0.0024865817,0.0001861672,0.00031179131,0.0000129885775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043827217,0.000082007,0.0021721197,0.000008629984,0.000020966354,0.0000034754435,0.00079003355,0.8649991,0.0026609295,0.10218786,0.000020875836,0.027049582],"study_design_scores_gemma":[0.00039647828,0.00008400808,0.0020317547,0.000059168866,0.000021902662,0.000023524097,0.00019837817,0.988924,0.0071283015,0.0007973817,0.000038554706,0.0002964956],"about_ca_topic_score_codex":0.00016755905,"about_ca_topic_score_gemma":0.00013238135,"teacher_disagreement_score":0.5397571,"about_ca_system_score_codex":0.000104787796,"about_ca_system_score_gemma":0.0002803942,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W272403580","doi":"","title":"A Hybrid Randomized/Nonlinear Programming Technique For Small Aerial Vehicle Trajectory Planning in 3D","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Collision avoidance; Motion planning; Key (lock); Trajectory; Nonlinear system; Probabilistic logic; Nonlinear programming; Artificial intelligence; Mathematical optimization; Robot; Collision; Mathematics","score_opus":0.023928529136710144,"score_gpt":0.2737832324403851,"score_spread":0.24985470330367496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W272403580","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008374649,0.000084686544,0.98808086,0.00029858103,0.0002875647,0.0018206625,0.0000017042972,0.000454957,0.00059631484],"genre_scores_gemma":[0.044786908,0.0000013526517,0.9542792,0.00027845922,0.00021685209,0.00031885243,0.000006580874,0.000013155213,0.00009868476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981433,0.00013832562,0.0004736752,0.00049855484,0.0001920469,0.0005540941],"domain_scores_gemma":[0.998905,0.00044712605,0.00011830551,0.00037331536,0.000055002263,0.00010123484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016073029,0.00021381858,0.0005030505,0.00022589827,0.000089797286,0.00014965585,0.0006883034,0.00008823464,0.0000019274314],"category_scores_gemma":[0.00029115478,0.00018948544,0.00013631182,0.00025101798,0.00004511259,0.00024561404,0.000064697146,0.00021629508,0.0000050214367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.018617319,0.001987634,0.0008328139,0.00022433042,0.00016825729,0.0014626213,0.008005019,0.06528662,0.031588692,0.015534437,0.001694152,0.8545981],"study_design_scores_gemma":[0.044978105,0.00034832777,0.00017896091,0.0001560644,0.000012185622,0.000083899904,0.000021564732,0.9375167,0.013416382,0.001996564,0.00082623085,0.0004650543],"about_ca_topic_score_codex":0.000038573413,"about_ca_topic_score_gemma":0.00000126273,"teacher_disagreement_score":0.87223005,"about_ca_system_score_codex":0.00006539263,"about_ca_system_score_gemma":0.00012014711,"threshold_uncertainty_score":0.77269924},"labels":[],"label_agreement":null},{"id":"W2724632096","doi":"10.1609/aaai.v31i1.11054","title":"Deterministic versus Probabilistic Methods for Searching for an Evasive Target","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Probabilistic logic; Computer science; Task (project management); Key (lock); Class (philosophy); Bayesian probability; Constraint (computer-aided design); Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.2936157113745732,"score_gpt":0.45980466385428453,"score_spread":0.16618895247971133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724632096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005450459,0.000008330595,0.98826694,0.0013231328,0.001499216,0.0015036094,0.000019475929,0.000088300134,0.0018405325],"genre_scores_gemma":[0.47757077,0.0000018035402,0.5219958,0.000037032503,0.00011288354,0.00018896279,9.2107825e-7,0.000015558675,0.00007629507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978592,0.0000398242,0.000505863,0.0007318153,0.00032743585,0.00053588167],"domain_scores_gemma":[0.99626344,0.0009997499,0.0007033346,0.0007901825,0.0010893517,0.0001539235],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00190471,0.00026259656,0.00036288306,0.00010051966,0.0010278559,0.0008696013,0.0045793727,0.00010374858,0.0000040106756],"category_scores_gemma":[0.009088306,0.00020753068,0.00016592845,0.00013362052,0.0004098762,0.0006931859,0.00047261443,0.00023526211,0.000009333739],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032261264,0.00013484103,0.000030047668,0.0001436352,0.00002993603,3.527854e-7,0.0017386036,0.0005633825,0.014834199,0.70413053,0.00004808075,0.27802378],"study_design_scores_gemma":[0.00007814166,0.0007880373,0.00006184359,0.00012479832,0.000019865309,0.000002267477,0.00014103245,0.6027743,0.105723225,0.29005107,0.000048454873,0.00018691867],"about_ca_topic_score_codex":0.000023232049,"about_ca_topic_score_gemma":0.000003384636,"teacher_disagreement_score":0.60221094,"about_ca_system_score_codex":0.000062056424,"about_ca_system_score_gemma":0.00023007029,"threshold_uncertainty_score":0.9992586},"labels":[],"label_agreement":null},{"id":"W2728976606","doi":"10.25046/aj020383","title":"Human Robot Interaction for Hybrid Collision Avoidance System for Indoor Mobile Robots","year":2017,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst","keywords":"Collision avoidance; Robot; Mobile robot; Human–computer interaction; Computer science; Human–robot interaction; Collision; Simulation; Artificial intelligence; Computer security","score_opus":0.012874815207321144,"score_gpt":0.30296849254634906,"score_spread":0.2900936773390279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728976606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06828826,0.0018914326,0.9251007,0.000094620766,0.003913586,0.0004989556,0.000002758286,0.00017891897,0.00003079337],"genre_scores_gemma":[0.86879265,0.000041611544,0.13079923,0.0000024932956,0.00011358421,0.00021538405,2.4787593e-7,0.000009181996,0.000025590116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984089,0.0000124897,0.00041604452,0.00044506328,0.0002192301,0.00049824064],"domain_scores_gemma":[0.99865824,0.000106628744,0.0004218898,0.00053288136,0.00019034429,0.000090042704],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0016612764,0.00016776174,0.00030677003,0.0010101157,0.0014596062,0.0005784198,0.0016860714,0.00009555822,5.0025637e-8],"category_scores_gemma":[0.0004232668,0.00015359424,0.000036766458,0.00042013972,0.0002533775,0.0027111704,0.00018871596,0.00032358122,8.3766224e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006775755,0.000019429819,0.0012851729,0.00021763821,0.000007493934,0.0000330537,0.00012525062,0.9514457,0.011795009,0.022961862,0.000013846188,0.012088757],"study_design_scores_gemma":[0.00080626877,0.00032550047,0.00073269464,0.0012042517,0.0000054014226,0.0017768912,0.0002480813,0.98670363,0.005715448,0.0007193636,0.0014875681,0.00027491778],"about_ca_topic_score_codex":0.0000032408902,"about_ca_topic_score_gemma":6.711635e-7,"teacher_disagreement_score":0.80050445,"about_ca_system_score_codex":0.00022811958,"about_ca_system_score_gemma":0.000063556676,"threshold_uncertainty_score":0.9998404},"labels":[],"label_agreement":null},{"id":"W2731063979","doi":"10.1609/socs.v8i1.18433","title":"An Analysis and Enhancement of the Gap Heuristic for the Pancake Puzzle","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Heuristic; Benchmark (surveying); Solver; Consistent heuristic; Computer science; Incremental heuristic search; Algorithm; Function (biology); Mathematical optimization; Mathematics; Beam search; Artificial intelligence; Search algorithm","score_opus":0.020568013359232213,"score_gpt":0.28538701171315084,"score_spread":0.26481899835391864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731063979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90949476,0.00015075572,0.039128028,0.035342835,0.011170851,0.0012555899,0.000054671567,0.000055147095,0.003347357],"genre_scores_gemma":[0.9971589,0.00001821003,0.0022677102,0.000089803296,0.00017277102,0.000033955053,0.0000014823435,0.000006849516,0.00025033994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821144,0.000029475497,0.00028791183,0.00031742145,0.0009761562,0.00017757867],"domain_scores_gemma":[0.9981173,0.00041248757,0.0002131862,0.0003110629,0.00090198277,0.000043971206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008435696,0.00010784144,0.0001814789,0.000056795194,0.00023958016,0.00017323598,0.0021859324,0.000042960815,0.00000573118],"category_scores_gemma":[0.00030155783,0.00006147047,0.00015181073,0.00059012615,0.00011329994,0.00014530226,0.00060103816,0.00016135183,6.867308e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016687748,0.00094674085,0.034749296,0.00013194192,0.0013130986,0.0000012737531,0.0029606645,0.0029480194,0.17026162,0.7832794,0.00077828084,0.002462797],"study_design_scores_gemma":[0.0010939023,0.000306445,0.023058182,0.00013273244,0.00021634264,0.000009256559,0.00022420137,0.2917966,0.6644287,0.017765675,0.0007750145,0.00019297558],"about_ca_topic_score_codex":0.00007835432,"about_ca_topic_score_gemma":0.0000025722147,"teacher_disagreement_score":0.7655137,"about_ca_system_score_codex":0.00007887496,"about_ca_system_score_gemma":0.00007352975,"threshold_uncertainty_score":0.40620425},"labels":[],"label_agreement":null},{"id":"W2736316887","doi":"10.1109/icra.2017.7989234","title":"Falling in line: Visual route following on extreme terrain for a tethered mobile robot","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Terrain; Traverse; Robot; Mobile robot; Obstacle; Computer science; Bang-bang robot; Computer vision; Artificial intelligence; Simulation; Search and rescue; Trajectory; Obstacle avoidance; Heading (navigation); Robot kinematics; Engineering; Geodesy; Geology; Aerospace engineering; Physics; Geography","score_opus":0.06659100822760844,"score_gpt":0.3461917873120153,"score_spread":0.27960077908440684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736316887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054009717,0.000032645403,0.9421573,0.00048113376,0.0009515298,0.0005201714,0.000001764881,0.00019812856,0.0016475533],"genre_scores_gemma":[0.6142036,9.435719e-7,0.3842898,0.00016628091,0.00010802025,0.00010153129,0.0000023820915,0.000015822317,0.001111644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836636,0.000043524833,0.0002961732,0.0005571639,0.00026317043,0.00047362616],"domain_scores_gemma":[0.9986092,0.000271956,0.00013834269,0.0008574395,0.00003006608,0.00009301008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070856436,0.00019551766,0.0002697785,0.00012946117,0.00032412403,0.00044079236,0.0013979836,0.00009756216,0.0000033580823],"category_scores_gemma":[0.00029352438,0.00017385026,0.0001356395,0.00009138337,0.000023152505,0.0005046512,0.00023790097,0.00016454326,0.000032635937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010740205,0.0010405871,0.019321188,0.00008687551,0.0001873886,0.0007175252,0.009425161,0.2660102,0.0202364,0.0050046956,0.0010130828,0.6768495],"study_design_scores_gemma":[0.0011246309,0.0003156965,0.0040899366,0.00015422488,0.000004417039,0.000004043987,0.00006868322,0.9915532,0.0014753741,0.0007091195,0.00021790124,0.000282768],"about_ca_topic_score_codex":0.00021227794,"about_ca_topic_score_gemma":0.000041121035,"teacher_disagreement_score":0.725543,"about_ca_system_score_codex":0.000075737946,"about_ca_system_score_gemma":0.000066224,"threshold_uncertainty_score":0.7089408},"labels":[],"label_agreement":null},{"id":"W2737337772","doi":"10.1109/iros.2017.8202161","title":"A framework for multi-vehicle navigation using feedback-based motion primitives","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Workspace; Computer science; Robustness (evolution); Motion planning; Motion (physics); Affine transformation; Set (abstract data type); Motion control; Control theory (sociology); Computer vision; Control (management); Algorithm; Artificial intelligence; Mathematics; Robot","score_opus":0.15418251260877264,"score_gpt":0.38211272420416453,"score_spread":0.2279302115953919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737337772","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006693861,0.00005590683,0.9895467,0.00046673545,0.0017947397,0.000916975,0.000021748327,0.000425999,0.0000773089],"genre_scores_gemma":[0.08121422,0.0000013349724,0.91817963,0.000116525436,0.00022025795,0.00008227396,0.00005981925,0.000026974174,0.00009899414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791384,0.0000965347,0.00033888998,0.0009250661,0.00034185164,0.00038380278],"domain_scores_gemma":[0.9973065,0.00023813605,0.0005800739,0.001464287,0.0002991512,0.000111846086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005422488,0.00031924044,0.00035242576,0.00013159867,0.00041441075,0.0007415828,0.0015014781,0.00052266655,0.0000025902714],"category_scores_gemma":[0.000416445,0.0003207811,0.00018983817,0.00008029919,0.00007698781,0.00035772062,0.0006655138,0.0005516866,0.000017856204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040586263,0.00068164733,0.0039850916,0.0012190396,0.00020579678,0.00004302281,0.002999545,0.80534416,0.001891636,0.035702925,0.00020218726,0.14768434],"study_design_scores_gemma":[0.00040786393,0.000030727824,0.004415597,0.0009875117,0.000021708027,0.0000023000855,0.000011371988,0.9751488,0.0036017592,0.014992358,0.000013165217,0.00036683914],"about_ca_topic_score_codex":0.00010795766,"about_ca_topic_score_gemma":6.417948e-7,"teacher_disagreement_score":0.1698046,"about_ca_system_score_codex":0.00018114124,"about_ca_system_score_gemma":0.00031510936,"threshold_uncertainty_score":0.9999244},"labels":[],"label_agreement":null},{"id":"W2737943439","doi":"10.1109/icra.2017.7989685","title":"Quadrotor collision characterization and recovery control","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Collision avoidance; Propeller; SAFER; Collision; Drone; Computer science; Obstacle; Pipeline (software); Range (aeronautics); Forcing (mathematics); Engineering; Simulation; Aerospace engineering; Marine engineering; Computer security","score_opus":0.014137013822657371,"score_gpt":0.245906705031925,"score_spread":0.23176969120926763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737943439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03743353,0.000006089095,0.95961386,0.001158411,0.00046316255,0.00014897718,0.0000030451838,0.00008562251,0.0010872788],"genre_scores_gemma":[0.82134926,0.000020608457,0.17406641,0.00061877,0.00014382032,0.00002132724,0.000005432861,0.000008425069,0.0037659418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994428,0.000023675744,0.00009697923,0.00020628842,0.000113166956,0.00011708562],"domain_scores_gemma":[0.9992331,0.000043346434,0.00010875578,0.00052179414,0.00003558079,0.000057419344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019414796,0.0000638226,0.0000953033,0.000031994532,0.00029147134,0.0005032166,0.0004327441,0.000039298404,0.000003803333],"category_scores_gemma":[0.00010761095,0.000054043798,0.0000142434155,0.000022942852,0.000025327932,0.00082944694,0.00011057711,0.000040708837,0.00003705115],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007360449,0.00016958204,0.076123826,0.000050882623,0.00008086461,0.00016578073,0.0008957579,0.0005501065,0.08309743,0.036329236,0.0020472824,0.80041564],"study_design_scores_gemma":[0.00061493844,0.00008726217,0.43960524,0.000025159741,0.0000031083482,0.000018071514,0.0000021809212,0.5567396,0.0010469893,0.00030640198,0.001411555,0.0001394438],"about_ca_topic_score_codex":0.000023771352,"about_ca_topic_score_gemma":4.7445116e-7,"teacher_disagreement_score":0.8002762,"about_ca_system_score_codex":0.000010887623,"about_ca_system_score_gemma":0.000020050713,"threshold_uncertainty_score":0.48525292},"labels":[],"label_agreement":null},{"id":"W2738525387","doi":"10.1142/s0219843617500189","title":"Cooperative Vision-Based Object Transportation by Two Humanoid Robots in a Cluttered Environment","year":2017,"lang":"en","type":"article","venue":"International Journal of Humanoid Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Mitacs","keywords":"Computer science; Computer vision; Robot; Artificial intelligence; Odometry; Humanoid robot; Mobile robot; Object (grammar); Obstacle; Trajectory; Visual odometry; Motion planning; Robot control","score_opus":0.016047347951528786,"score_gpt":0.3033904396673732,"score_spread":0.28734309171584443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738525387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03284655,0.00019434695,0.9603435,0.004083555,0.0020059196,0.00018911588,0.000023349592,0.00002855224,0.0002851322],"genre_scores_gemma":[0.85419846,0.00003636329,0.14496902,0.0003361702,0.00021578422,0.000004521985,0.000022534177,0.000019417841,0.000197713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730694,0.00012083208,0.00089126546,0.00033564566,0.0010577347,0.00028760647],"domain_scores_gemma":[0.99774075,0.00017056517,0.0011038625,0.00051889603,0.00033595267,0.0001299742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006027661,0.00024873394,0.000356246,0.00032796492,0.0002531587,0.0005606814,0.0026806367,0.00008344569,0.000026921876],"category_scores_gemma":[0.00013638174,0.00023299259,0.00015481112,0.000058899568,0.00013795958,0.0010937555,0.00009302317,0.00042597367,0.000026984839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005889816,0.0004152426,0.0044044177,0.0000059263084,0.00010064777,0.0006679776,0.0008300207,0.98284256,0.004888462,0.0010629161,0.0015589227,0.0031639768],"study_design_scores_gemma":[0.010974421,0.0011887234,0.13533989,0.00077381945,0.00006743637,0.00017190573,0.000097088545,0.8370361,0.009588198,0.0026780174,0.0010675249,0.001016884],"about_ca_topic_score_codex":0.000050276405,"about_ca_topic_score_gemma":0.000021883598,"teacher_disagreement_score":0.82135195,"about_ca_system_score_codex":0.0003068981,"about_ca_system_score_gemma":0.00023562988,"threshold_uncertainty_score":0.9501162},"labels":[],"label_agreement":null},{"id":"W2738630525","doi":"10.1155/2017/4683912","title":"Intelligent Controller Design for Quad-Rotor Stabilization in Presence of Parameter Variations","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"PID controller; Control theory (sociology); Robustness (evolution); Payload (computing); Nonlinear system; Trajectory; Control engineering; Computer science; Robust control; Control system; Engineering; Simulation; Control (management); Artificial intelligence","score_opus":0.044542800085726815,"score_gpt":0.316154122682454,"score_spread":0.2716113225967272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738630525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034913253,0.000049770093,0.96393275,0.00019617533,0.00041640628,0.00047533773,0.000005676893,0.0000068671857,0.0000037435416],"genre_scores_gemma":[0.5340734,0.000016583106,0.46585742,0.000007263439,0.000018648696,0.000016015123,0.0000015365263,0.000003772341,0.0000053794383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99877423,0.00005220828,0.00066499866,0.0001345753,0.00024868417,0.0001253072],"domain_scores_gemma":[0.99769986,0.0005053703,0.00109012,0.00023107146,0.00042783923,0.00004575699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062894647,0.00008383086,0.00023951959,0.00014364994,0.000075095784,0.000045399276,0.0004649056,0.000045064065,0.0000013605181],"category_scores_gemma":[0.0006244694,0.00007543743,0.00007907913,0.00009474231,0.000029929348,0.001150942,0.0000034015682,0.00008892446,3.2626178e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019176597,0.000118488795,0.00455446,0.000032489257,0.000024499974,0.000006634927,0.0031055931,0.9659902,0.0044426234,0.0030894878,0.00000911827,0.01843464],"study_design_scores_gemma":[0.0034512137,0.0007721086,0.55213815,0.0003086315,0.000043395466,0.0000052357755,0.0001517841,0.4092134,0.010652058,0.023010707,0.000063714484,0.00018956805],"about_ca_topic_score_codex":0.000011353237,"about_ca_topic_score_gemma":0.000012463224,"teacher_disagreement_score":0.5567768,"about_ca_system_score_codex":0.000039263112,"about_ca_system_score_gemma":0.000096009855,"threshold_uncertainty_score":0.3076249},"labels":[],"label_agreement":null},{"id":"W2743144131","doi":"10.2316/journal.206.2017.4.206-5054","title":"VERTICAL CLIMBING LOCOMOTION OF A NEW GECKO ROBOT USING DRY ADHESIVE MATERIAL","year":2017,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Gecko; Climbing; Adhesive; Robot; Materials science; Computer science; Geology; Composite material; Artificial intelligence; Structural engineering; Engineering; Paleontology","score_opus":0.03840580082748222,"score_gpt":0.31592681761560415,"score_spread":0.2775210167881219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743144131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17430282,0.000022627784,0.8223589,0.0011647984,0.0020584725,0.000042103176,0.0000015529637,0.000012620128,0.000036108533],"genre_scores_gemma":[0.6614961,0.000013991119,0.33815524,0.000022488783,0.00029631687,1.3825014e-7,0.0000013420436,0.000004772398,0.000009638594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987399,0.000042207055,0.0004905186,0.00011915041,0.0004932165,0.00011499188],"domain_scores_gemma":[0.9985784,0.000051765746,0.0007089731,0.00018412447,0.00039096235,0.000085746906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003812448,0.000094507246,0.0001911364,0.00016608318,0.00010994428,0.00040324553,0.0007261906,0.00006225589,0.00000411734],"category_scores_gemma":[0.0002255126,0.000087649954,0.00006591759,0.000034243756,0.00004996116,0.0009947103,0.00018743017,0.00010557837,0.0000017116055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017071661,0.00040480733,0.030317886,0.0000736223,0.00058935775,0.00042776688,0.0029960722,0.6310327,0.07130547,0.036646713,0.00037391557,0.22566095],"study_design_scores_gemma":[0.0007441161,0.00012697758,0.052888073,0.00035633362,0.00002864845,0.00024606803,0.000020798154,0.93490237,0.006735668,0.0038316883,0.0000145058,0.00010476798],"about_ca_topic_score_codex":0.000043063057,"about_ca_topic_score_gemma":7.6177884e-7,"teacher_disagreement_score":0.4871933,"about_ca_system_score_codex":0.00006868734,"about_ca_system_score_gemma":0.00013349159,"threshold_uncertainty_score":0.3888506},"labels":[],"label_agreement":null},{"id":"W2743295872","doi":"10.2316/journal.206.2017.4.206-4998","title":"A MEMETIC ALGORITHM WITH VARIABLE LENGTH CHROMOSOME FOR ROBOT PATH PLANNING UNDER DYNAMIC ENVIRONMENTS","year":2017,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Memetic algorithm; Motion planning; Computer science; Path (computing); Variable (mathematics); Chromosome; Path length; Algorithm; Mathematical optimization; Artificial intelligence; Robot; Mathematics; Local search (optimization); Biology; Genetics","score_opus":0.01609915289129845,"score_gpt":0.2782613534441433,"score_spread":0.2621622005528449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743295872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029589934,0.000093351344,0.9942889,0.0015792967,0.0009048046,0.00010680669,0.000007681609,0.00001921196,0.000040928397],"genre_scores_gemma":[0.24751179,0.000026258114,0.7521557,0.00007319836,0.0001192617,0.0000035241549,0.000006059452,0.000010874261,0.00009331168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987309,0.000025687308,0.00035037796,0.00018914188,0.00053173245,0.00017217665],"domain_scores_gemma":[0.99856955,0.00012630814,0.00081282685,0.00023752784,0.00018316528,0.00007062828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040820273,0.00013783977,0.0001946954,0.00014877776,0.00023690471,0.0005613427,0.00085682323,0.000056828892,0.0000020856826],"category_scores_gemma":[0.000092125534,0.00011376829,0.00004531361,0.0000348391,0.000053379987,0.0009972922,0.00012842746,0.0001325852,0.0000018584312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002167372,0.00012762535,0.00052029407,0.00001574234,0.0003287333,0.00010451681,0.00041160538,0.9394115,0.0013212102,0.0066010156,0.00008926785,0.05104678],"study_design_scores_gemma":[0.0009795222,0.00024445102,0.013028467,0.00029139098,0.000032861506,0.0003918974,0.000027546617,0.97980875,0.00009809662,0.004790624,0.00016415957,0.00014221245],"about_ca_topic_score_codex":0.0000045392194,"about_ca_topic_score_gemma":1.6045463e-7,"teacher_disagreement_score":0.2445528,"about_ca_system_score_codex":0.000110797126,"about_ca_system_score_gemma":0.00008471643,"threshold_uncertainty_score":0.54130405},"labels":[],"label_agreement":null},{"id":"W2744743129","doi":"10.1109/icuas.2017.7991332","title":"Collision-free trajectory generation for UAVs using Markov decision process","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Trajectory; Markov decision process; Computer science; Backtracking; Collision; Markov process; Process (computing); Control theory (sociology); Markov chain; Motion planning; Simulation; Robot; Artificial intelligence; Algorithm; Mathematics; Control (management); Physics","score_opus":0.07676250419945706,"score_gpt":0.3477957806624212,"score_spread":0.2710332764629641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744743129","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06521841,0.000033085194,0.9322148,0.00023912788,0.0011422079,0.00030456364,0.0000041648877,0.000103028564,0.000740597],"genre_scores_gemma":[0.107978545,0.0000024583883,0.89131266,0.00009901715,0.00026821732,0.000021122178,0.0000025567065,0.000010914506,0.0003045376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987409,0.000020949197,0.00021505417,0.00043744824,0.0003361674,0.0002494808],"domain_scores_gemma":[0.9980118,0.00013971746,0.00016762006,0.0014026057,0.00019183045,0.000086384985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004005559,0.00012735275,0.00015012162,0.00007958084,0.0008399627,0.0006012411,0.0018392949,0.00008154613,0.0000040112736],"category_scores_gemma":[0.0006446094,0.000108992426,0.000053942393,0.00007471162,0.000033769466,0.0009210655,0.00024067095,0.000062694584,0.000008009989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113523376,0.00048659343,0.0051906817,0.00017891078,0.000109197565,0.00012159505,0.0027847325,0.23975433,0.043467842,0.011826209,0.06317659,0.6327898],"study_design_scores_gemma":[0.00055519864,0.000052288735,0.0016337428,0.000035596953,0.000005500697,0.0000151504555,0.000009271386,0.99075174,0.004578735,0.0020969713,0.00011717517,0.000148653],"about_ca_topic_score_codex":0.000022883043,"about_ca_topic_score_gemma":0.000008279564,"teacher_disagreement_score":0.75099736,"about_ca_system_score_codex":0.0000527213,"about_ca_system_score_gemma":0.00014390981,"threshold_uncertainty_score":0.6460399},"labels":[],"label_agreement":null},{"id":"W2745657785","doi":"","title":"Inverse kinematics and path planning methods for discretely-actuated hyper-redundant manipulators","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Motion planning; Kinematics; Inverse kinematics; Path (computing); Computer science; Control theory (sociology); Actuator; Solver; Robot end effector; Artificial intelligence; Robot; Mathematical optimization; Mathematics; Control (management); Physics","score_opus":0.1321882567601975,"score_gpt":0.3559350937904907,"score_spread":0.22374683703029322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745657785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010729007,0.000056440833,0.98658603,0.00007689864,0.0004154749,0.00029710543,0.0000021359267,0.0002808927,0.0015559873],"genre_scores_gemma":[0.017795313,0.0000025557915,0.98168117,0.00020185763,0.000028518129,0.000024985098,0.000004146211,0.000018613757,0.00024286628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867445,0.00009150582,0.0003101427,0.00041633344,0.00013771796,0.0003698597],"domain_scores_gemma":[0.9989167,0.00025130395,0.00012116887,0.00046811698,0.000058698577,0.00018399986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065030524,0.00020782955,0.0002772906,0.00011726885,0.00014361084,0.0001011018,0.0005214493,0.000084619176,0.0000107038095],"category_scores_gemma":[0.00017187305,0.00016505348,0.00005415428,0.00020941692,0.000059922793,0.0003538572,0.00024757665,0.00010815365,0.0000085336815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002001539,0.0008466089,0.02919238,0.0011189533,0.0009217924,0.0005307478,0.13183875,0.008964742,0.021140832,0.28344992,0.016402157,0.50539297],"study_design_scores_gemma":[0.0004135876,0.00017741011,0.002884092,0.00005870895,0.000024432356,0.00007396613,0.000250072,0.98672944,0.0027165452,0.005920574,0.00043167212,0.00031951154],"about_ca_topic_score_codex":0.00004952074,"about_ca_topic_score_gemma":3.1692815e-7,"teacher_disagreement_score":0.97776467,"about_ca_system_score_codex":0.000022265178,"about_ca_system_score_gemma":0.00003841809,"threshold_uncertainty_score":0.6730685},"labels":[],"label_agreement":null},{"id":"W2745711124","doi":"10.1007/978-3-319-56991-8_40","title":"Motion Control of a Terrain Following Unmanned Aerial Vehicle Under Uncertainty","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Terrain; Aeronautics; Motion control; Computer science; Environmental science; Artificial intelligence; Geography; Engineering; Cartography; Robot","score_opus":0.01736252800749023,"score_gpt":0.24095160624477988,"score_spread":0.22358907823728966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745711124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016220336,0.0037473075,0.98968756,0.00015799639,0.0033426706,0.000517821,0.000008812595,0.000060197384,0.0023154246],"genre_scores_gemma":[0.9967473,0.000029120398,0.0012143657,0.000087334614,0.00088257703,0.00001436686,0.00001711125,0.00003347329,0.0009743591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980508,0.00011760808,0.00054831896,0.00058032025,0.00033614598,0.00036681522],"domain_scores_gemma":[0.9980814,0.00045149555,0.00054845976,0.00077306794,0.000059674672,0.000085936736],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073845714,0.00036959955,0.00086684,0.00013973362,0.00014976914,0.00023507353,0.0006257227,0.0006656297,0.0000015476058],"category_scores_gemma":[0.000081364844,0.00031906896,0.00016245665,0.00003584932,0.00008071273,0.00012559218,0.00012492159,0.0004965642,0.0000015975172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001987851,0.0000078683615,0.0001632888,0.00005893173,0.000104676394,0.00006654296,0.00026983907,0.97985,0.00002317546,0.005430628,0.00003076028,0.013974394],"study_design_scores_gemma":[0.0009880795,0.00009408902,0.00013367279,0.0012078893,0.000035846962,0.000023318635,0.0000022361141,0.9913008,0.0000014274282,0.0055584623,0.00033380816,0.0003203325],"about_ca_topic_score_codex":0.0002950461,"about_ca_topic_score_gemma":0.000053124335,"teacher_disagreement_score":0.9965851,"about_ca_system_score_codex":0.00008597318,"about_ca_system_score_gemma":0.00005800772,"threshold_uncertainty_score":0.99992615},"labels":[],"label_agreement":null},{"id":"W2751063851","doi":"","title":"Path Constraints on Movement of Objects","year":2014,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Movement (music); Path (computing); Computer science; Computer vision; Physics; Acoustics; Computer network","score_opus":0.012155542256098327,"score_gpt":0.2365605865294895,"score_spread":0.22440504427339117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751063851","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056959905,0.00046089286,0.14329523,0.00027164898,0.0024566953,0.0007181332,0.000050524246,0.00024482625,0.7955421],"genre_scores_gemma":[0.89844817,0.00008054412,0.09088559,0.000044179866,0.000035907953,3.2935264e-7,0.000086351814,0.000016140533,0.01040281],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987254,0.00007045435,0.00012912307,0.0003807844,0.0004839492,0.00021026285],"domain_scores_gemma":[0.99857396,0.00007717892,0.0005031262,0.0005942343,0.00015758615,0.00009391282],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019005143,0.00019823614,0.00041991152,0.00007283185,0.00007699772,0.000010977503,0.0009843331,0.0001942145,0.0008593943],"category_scores_gemma":[0.000026258624,0.00024937207,0.00013588807,0.00006459883,0.000077438635,0.00020299287,0.00008754603,0.00013910109,0.000024910061],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003093759,0.00077788514,0.0003451741,0.0016570553,0.00071520003,0.00024765148,0.15183662,0.0015081144,0.004799455,0.041120887,0.006130707,0.7905519],"study_design_scores_gemma":[0.005527849,0.004051968,0.8330049,0.005029278,0.00040656189,0.000009069024,0.064752996,0.076212965,0.0069707367,0.00072969677,0.0006295893,0.0026744069],"about_ca_topic_score_codex":0.016215809,"about_ca_topic_score_gemma":0.002266572,"teacher_disagreement_score":0.84148824,"about_ca_system_score_codex":0.00017725019,"about_ca_system_score_gemma":0.00017324806,"threshold_uncertainty_score":0.9999958},"labels":[],"label_agreement":null},{"id":"W2752067137","doi":"10.11159/cdsr17.130","title":"Disturbance Rejection in Multi-Rotor Unmanned Aerial Vehicles Using a Novel Rotor Geometry","year":2017,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadcopter; Lift (data mining); Rotor (electric); Decoupling (probability); Thrust; Aerodynamics; Control theory (sociology); Coaxial; Computer science; Aerospace engineering; Propulsion; Orientation (vector space); Engineering; Control engineering; Mechanical engineering; Artificial intelligence; Geometry; Mathematics","score_opus":0.051613138863352864,"score_gpt":0.2909251698723582,"score_spread":0.23931203100900536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752067137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51293963,0.00015779024,0.48071793,0.001060815,0.0037523233,0.00097405235,0.00005313265,0.00004442891,0.00029989102],"genre_scores_gemma":[0.95981675,0.000015897405,0.039888814,0.000013075564,0.0000839028,0.000017750315,8.517939e-7,0.00000992255,0.00015304059],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998372,0.000014560233,0.00057957583,0.00033454664,0.00048791352,0.00021140146],"domain_scores_gemma":[0.997686,0.00007835522,0.0011753602,0.00026096866,0.00074903894,0.000050253126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005719392,0.00018239442,0.00040896228,0.00017131263,0.00018375322,0.00038098093,0.0017623187,0.000111563255,5.388853e-7],"category_scores_gemma":[0.00060339284,0.0001458726,0.00007043121,0.00009681831,0.00019427815,0.0006834077,0.00035308732,0.00017323703,3.9892433e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049367745,0.00073197536,0.34516183,0.0010759581,0.00057627674,0.0000070625224,0.0021477996,0.12114543,0.3303461,0.19658843,0.000040024846,0.0016854225],"study_design_scores_gemma":[0.0016167681,0.00005040373,0.06106593,0.00078642776,0.000019125648,0.000031395597,0.000121443256,0.9355468,0.00027712886,0.00033445685,0.0000061669502,0.00014395028],"about_ca_topic_score_codex":0.00047509896,"about_ca_topic_score_gemma":0.000013393323,"teacher_disagreement_score":0.8144014,"about_ca_system_score_codex":0.000118719094,"about_ca_system_score_gemma":0.000107272965,"threshold_uncertainty_score":0.5948512},"labels":[],"label_agreement":null},{"id":"W2752387064","doi":"10.1007/s10514-017-9667-4","title":"Optimized vision-based robot motion planning from multiple demonstrations","year":2017,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Workspace; Computer vision; Robot; Artificial intelligence; Traverse; Robotic arm; Domain (mathematical analysis); Motion planning; Constraint (computer-aided design); Trajectory; Field of view; Mathematics","score_opus":0.03402992701805586,"score_gpt":0.2955141584016386,"score_spread":0.26148423138358273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752387064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004252478,0.00008183731,0.9903313,0.0017346154,0.0011611655,0.00028505322,0.000015648006,0.0005965816,0.0015413233],"genre_scores_gemma":[0.4598775,7.724365e-7,0.53971153,0.00013536481,0.00009664362,0.00002562173,0.000034729703,0.000016124672,0.00010172876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978211,0.000108546556,0.00043082028,0.000778689,0.00035933722,0.000501533],"domain_scores_gemma":[0.9967771,0.000460613,0.0004434848,0.0019953905,0.000103512924,0.00021987868],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00033725513,0.0002962109,0.00035373756,0.00014346305,0.0013906074,0.0010813847,0.0020032977,0.00017665392,0.000020195743],"category_scores_gemma":[0.00041582398,0.00030348537,0.00013292542,0.00011266106,0.00012034711,0.0011469239,0.0003073957,0.00028618006,0.00020979949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007776699,0.000094777206,0.0061214156,0.0000031559796,0.000026942573,0.00008721817,0.00024786236,0.97097224,0.0008384291,0.00024936438,0.00029986107,0.021050945],"study_design_scores_gemma":[0.0011703855,0.00005220754,0.110096686,0.000077419296,0.000015460388,0.0000076229912,0.000009568343,0.88645786,0.0012332322,0.00048796463,0.00007577757,0.00031579792],"about_ca_topic_score_codex":0.00052394904,"about_ca_topic_score_gemma":0.0000083956,"teacher_disagreement_score":0.455625,"about_ca_system_score_codex":0.000113832015,"about_ca_system_score_gemma":0.00023767776,"threshold_uncertainty_score":0.9999556},"labels":[],"label_agreement":null},{"id":"W2754453099","doi":"10.1109/icra.2018.8460730","title":"Learning Sampling Distributions for Robot Motion Planning","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia; National Aeronautics and Space Administration","keywords":"Sampling (signal processing); Motion planning; Computer science; State space; Mathematical optimization; Exploit; Representation (politics); Convergence (economics); Adaptive sampling; Space (punctuation); Artificial intelligence; Robot; Mathematics; Computer vision; Statistics","score_opus":0.08737266373194558,"score_gpt":0.3420058092987185,"score_spread":0.2546331455667729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754453099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059208093,0.00008396884,0.9940867,0.00047729263,0.0022951313,0.00046543093,0.000026866957,0.0010548366,0.0009176823],"genre_scores_gemma":[0.051895596,0.0000030619003,0.9463285,0.00004673984,0.0006414794,0.00012696724,0.00038927322,0.000026063053,0.0005422864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976574,0.000088345645,0.00043017472,0.00096407725,0.00030230312,0.0005576609],"domain_scores_gemma":[0.99811,0.00036559277,0.0003151381,0.0007860902,0.00028556658,0.00013760713],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008116547,0.00032849313,0.000363559,0.00017654111,0.0005274384,0.0005327343,0.0012843879,0.00034026714,0.000008430639],"category_scores_gemma":[0.0005226368,0.00033457385,0.00018736382,0.00019855183,0.000052144827,0.00022709306,0.0014554352,0.0007298328,0.00005328179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004729337,0.000050941704,0.0013319426,0.00012829808,0.00007993642,0.000007642986,0.000926862,0.96685,0.00012569437,0.007169515,0.0019924452,0.021331988],"study_design_scores_gemma":[0.00018348274,0.0000943784,0.0027042963,0.00029082998,0.000023460601,0.000015251976,0.000030407055,0.9830289,0.00031010862,0.011714445,0.0011687834,0.00043565794],"about_ca_topic_score_codex":0.000029007977,"about_ca_topic_score_gemma":3.2064753e-7,"teacher_disagreement_score":0.051303517,"about_ca_system_score_codex":0.00017068382,"about_ca_system_score_gemma":0.00015278977,"threshold_uncertainty_score":0.99991065},"labels":[],"label_agreement":null},{"id":"W2754711610","doi":"10.1155/2017/6716820","title":"Reactive Path Planning Approach for Docking Robots in Unknown Environment","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Motion planning; Robot; Obstacle avoidance; Docking (animal); Obstacle; Path (computing); Computer science; Simulation; Engineering; Mobile robot; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","score_opus":0.02721557712626425,"score_gpt":0.28589569533458786,"score_spread":0.2586801182083236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754711610","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09024185,0.00011011114,0.90897834,0.00011087859,0.00029442148,0.00016490223,0.0000021534404,0.00001024792,0.00008706354],"genre_scores_gemma":[0.49143982,0.000010180811,0.50846404,0.000010480369,0.000050636136,0.000006962183,0.0000046223586,0.0000060215716,0.0000072565435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998873,0.000020770289,0.00043468032,0.0002017367,0.00027438928,0.00019544199],"domain_scores_gemma":[0.99866647,0.000074107294,0.0008727569,0.0002610518,0.00006195633,0.000063662446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004179073,0.00011487205,0.00023101938,0.00011999385,0.00015533352,0.000068827656,0.0005163696,0.000050672057,3.331783e-7],"category_scores_gemma":[0.00004663096,0.00010677353,0.0000792353,0.00004671067,0.000023784445,0.0014040084,0.0000064229666,0.00019851763,3.5012562e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005211404,0.00006414846,0.004728381,0.000017222152,0.000014690286,0.000074084666,0.0030802807,0.97543496,0.0015416269,0.00047118618,0.0000050899444,0.014516191],"study_design_scores_gemma":[0.0032527277,0.00040562876,0.845705,0.00038416754,0.000032596014,0.000040918432,0.0004630934,0.14405824,0.0021218755,0.002871829,0.00035350147,0.00031042413],"about_ca_topic_score_codex":0.0000037697787,"about_ca_topic_score_gemma":4.9964837e-7,"teacher_disagreement_score":0.8409766,"about_ca_system_score_codex":0.00008578305,"about_ca_system_score_gemma":0.000043721113,"threshold_uncertainty_score":0.4354098},"labels":[],"label_agreement":null},{"id":"W2759395049","doi":"10.1109/iros.2017.8206397","title":"A unified approach to configuration-based dynamic analysis of quadcopters for optimal stability","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"","keywords":"Quadcopter; Redundancy (engineering); Fuselage; Computer science; Control theory (sociology); Longitudinal static stability; Stability (learning theory); Wing; Aerospace engineering; Control engineering; Aerodynamics; Engineering; Control (management); Artificial intelligence","score_opus":0.06090189287633928,"score_gpt":0.3198829712998394,"score_spread":0.2589810784235001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759395049","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057389154,0.000011002329,0.98951876,0.00047397503,0.00034888196,0.0011788836,0.00016540197,0.00015578931,0.002408416],"genre_scores_gemma":[0.41973212,2.4103974e-7,0.57973564,0.000049716746,0.000008698713,0.00014595,0.00022277064,0.000007676234,0.000097195116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730754,0.0001183067,0.0006189948,0.0011668245,0.0004499043,0.00033843462],"domain_scores_gemma":[0.9953694,0.00038237651,0.00057842693,0.0030174004,0.00048127084,0.00017114454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011433496,0.0003233607,0.0009110182,0.00061918807,0.00013338016,0.0003020805,0.0026684918,0.0002494544,0.000008153053],"category_scores_gemma":[0.00032938313,0.0003054712,0.00046519216,0.00042673605,0.00009218326,0.00011604541,0.0005117709,0.00023471168,0.0000034395307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002389582,0.0001470278,0.00024832445,0.00015516317,0.0006039472,9.104102e-7,0.0007184593,0.9947838,0.00014010219,0.0014002487,0.000053250955,0.0017248747],"study_design_scores_gemma":[0.0002500478,0.00006555882,0.0047593503,0.000029570114,0.00034366062,1.9144554e-7,0.000045260625,0.9932836,0.00074166665,0.00014921362,0.00001096903,0.00032089997],"about_ca_topic_score_codex":0.0004444958,"about_ca_topic_score_gemma":0.00001866506,"teacher_disagreement_score":0.4139932,"about_ca_system_score_codex":0.00016073184,"about_ca_system_score_gemma":0.00062640256,"threshold_uncertainty_score":0.99993974},"labels":[],"label_agreement":null},{"id":"W2760932507","doi":"10.1016/j.engappai.2017.08.020","title":"Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control","year":2017,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Siemens; Agence Universitaire de la Francophonie; National Authority for Scientific Research and Innovation","keywords":"Computer science; Nonlinear system; Action (physics); Control (management); Mathematical optimization; Control theory (sociology); Artificial intelligence; Mathematics","score_opus":0.056738124348324905,"score_gpt":0.3351751344320317,"score_spread":0.27843701008370675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760932507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019897494,0.000014823569,0.97945696,0.00004411099,0.000075184886,0.0003771176,0.000019454888,0.00008478981,0.000030067491],"genre_scores_gemma":[0.49731055,5.066008e-7,0.50253505,0.00000194795,0.00004644356,0.000090090776,0.0000025963568,0.000008457256,0.0000043648874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900466,0.00000810441,0.0003219746,0.00029726053,0.00017045595,0.00019756619],"domain_scores_gemma":[0.9983659,0.00021493272,0.00030351774,0.00079467293,0.00024650572,0.00007448138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002531778,0.00013657856,0.0002151579,0.00005611023,0.00022879284,0.00010551746,0.0008712255,0.000065148575,0.0000017202303],"category_scores_gemma":[0.000116353556,0.00013643179,0.000044481792,0.00012749326,0.00011958234,0.00035275472,0.000047263773,0.00012237922,0.000006267379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045043278,0.0000796357,0.00012524636,0.00002919396,0.00003063501,7.348287e-7,0.00039004374,0.95877796,0.002377439,0.012842184,0.0000016391222,0.025300233],"study_design_scores_gemma":[0.000048542912,0.0002327736,0.00030480028,0.00005377696,0.000018272556,0.000007212526,0.00010315961,0.96392435,0.034648,0.00048960873,0.000029902933,0.00013960827],"about_ca_topic_score_codex":0.000048271704,"about_ca_topic_score_gemma":9.939839e-7,"teacher_disagreement_score":0.47741306,"about_ca_system_score_codex":0.000021311953,"about_ca_system_score_gemma":0.00007019524,"threshold_uncertainty_score":0.5563527},"labels":[],"label_agreement":null},{"id":"W2761657772","doi":"10.1109/iris.2016.8066082","title":"Genetic algorithm based direction finder on the manifold for singularity free paths","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Singularity; Workspace; Gravitational singularity; Manifold (fluid mechanics); Holonomic; Path (computing); Degrees of freedom (physics and chemistry); Computer science; Limit (mathematics); Control theory (sociology); Trajectory; Motion (physics); Topology (electrical circuits); Algorithm; Robot; Mathematics; Artificial intelligence; Mathematical analysis; Control (management); Engineering","score_opus":0.02508564374833157,"score_gpt":0.23483190255225714,"score_spread":0.20974625880392556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761657772","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005971469,0.000016154101,0.9895375,0.0074888053,0.00074331585,0.0003367203,0.000010086511,0.0002732774,0.0009969891],"genre_scores_gemma":[0.023265002,0.0000013701642,0.97333944,0.0018366188,0.00018879633,0.00007849435,9.028198e-7,0.00001564065,0.001273708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985878,0.00009535653,0.00017901779,0.00043685018,0.0003165291,0.0003844886],"domain_scores_gemma":[0.9978847,0.0008351268,0.000071652656,0.001038026,0.00008699877,0.00008348863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054455106,0.00015638424,0.00012618041,0.00006602047,0.00021658326,0.00011046573,0.00092560414,0.000078949975,0.000028703009],"category_scores_gemma":[0.0002898854,0.00008101817,0.000090904505,0.00016546446,0.00002948883,0.0001255985,0.000112774054,0.000075586795,0.0000716536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015781863,0.0003002804,0.0013243045,0.000016732518,0.00005959585,0.000043568958,0.00021330002,0.000731322,0.0012366928,0.07977158,0.04193589,0.87435097],"study_design_scores_gemma":[0.0010528228,0.00030814426,0.027048692,0.00006653893,0.000012680295,0.00001623925,0.0000066434386,0.9368926,0.0057659736,0.023049003,0.0054356055,0.00034505356],"about_ca_topic_score_codex":0.000056296096,"about_ca_topic_score_gemma":0.0000035843523,"teacher_disagreement_score":0.9361613,"about_ca_system_score_codex":0.00010728722,"about_ca_system_score_gemma":0.00006869995,"threshold_uncertainty_score":0.3303825},"labels":[],"label_agreement":null},{"id":"W2762836557","doi":"10.1109/iris.2016.8066092","title":"Task switching for specialized mobile robots working in cooperative formation","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mobile robot; Robot; Task (project management); Kinematics; Nonholonomic system; Robot kinematics; Computer science; Controller (irrigation); Robot control; Control engineering; Control theory (sociology); Simulation; Control (management); Engineering; Artificial intelligence; Physics","score_opus":0.030339840056012597,"score_gpt":0.2770817845437663,"score_spread":0.24674194448775372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762836557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034584748,0.000024214374,0.9933736,0.0004645167,0.00038373278,0.00045521115,6.748954e-7,0.0001349832,0.0017046422],"genre_scores_gemma":[0.5102334,0.000005736852,0.48877743,0.00017645016,0.00012698535,0.00014667703,0.0000017763366,0.0000085189695,0.00052300905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899566,0.00005206105,0.0002612427,0.00026172248,0.00014781194,0.0002815079],"domain_scores_gemma":[0.99927455,0.00030469586,0.00007914459,0.00023973422,0.000054692533,0.00004717616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041779075,0.0001066963,0.00015573416,0.00012144113,0.00008885983,0.00011017094,0.00039164827,0.000047003465,0.000007974246],"category_scores_gemma":[0.00009567324,0.000070461465,0.00003452847,0.0002523199,0.000010008557,0.00084462133,0.00009325757,0.00005212496,0.000042636028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047724956,0.00013201806,0.0015704383,0.000020233649,0.000025092844,0.000029544968,0.008055096,0.04452285,0.03842094,0.059267472,0.0025883191,0.8453203],"study_design_scores_gemma":[0.0030481445,0.00019300831,0.0013856955,0.0003542582,0.000003898205,0.000020797095,0.00014778346,0.97485054,0.011321978,0.0046208156,0.0036005094,0.00045254975],"about_ca_topic_score_codex":0.000012426669,"about_ca_topic_score_gemma":0.000015975518,"teacher_disagreement_score":0.9303277,"about_ca_system_score_codex":0.000106501844,"about_ca_system_score_gemma":0.000040968927,"threshold_uncertainty_score":0.28733352},"labels":[],"label_agreement":null},{"id":"W2766094726","doi":"10.1155/2017/7816263","title":"Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Liveness; Motion planning; Random tree; Computer science; Path (computing); Robotics; Artificial intelligence; Underwater; Process (computing); Reachability; Probabilistic logic; Robot; Simulation; Operations research; Mathematical optimization; Distributed computing; Engineering; Algorithm; Mathematics; Computer network","score_opus":0.027817214578351002,"score_gpt":0.3001607787101489,"score_spread":0.2723435641317979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766094726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04884452,0.00007220622,0.9494314,0.00046706814,0.00097297237,0.0001496883,0.00000905198,0.00004134682,0.000011743963],"genre_scores_gemma":[0.4110932,0.0000037317832,0.588697,0.000044778935,0.00011737135,0.000006042068,0.000010782063,0.000010480895,0.000016574872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987829,0.00002311918,0.00047458132,0.00020416881,0.00029646314,0.00021876572],"domain_scores_gemma":[0.9982942,0.00010104424,0.000900202,0.00030633103,0.00030744338,0.00009081687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003882304,0.0001406769,0.00024326336,0.00014319402,0.00033300245,0.00016337143,0.0006232602,0.0000716477,0.0000010927173],"category_scores_gemma":[0.000034365512,0.00012883429,0.00013741729,0.000055451994,0.000038052796,0.001510633,0.000004634081,0.00015138826,0.0000016905358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046513618,0.00007302986,0.0017860628,0.00003697199,0.00003409709,0.00011725949,0.0010822274,0.68449515,0.002661391,0.0002189285,0.0000140795055,0.3094343],"study_design_scores_gemma":[0.0039200364,0.0006847911,0.41020095,0.00043539883,0.00007283898,0.000038446684,0.00013992042,0.5565215,0.02158266,0.0054160175,0.00061410887,0.00037335098],"about_ca_topic_score_codex":0.0000045793686,"about_ca_topic_score_gemma":0.0000011468337,"teacher_disagreement_score":0.40841487,"about_ca_system_score_codex":0.00006787687,"about_ca_system_score_gemma":0.000113505455,"threshold_uncertainty_score":0.52537096},"labels":[],"label_agreement":null},{"id":"W2766175108","doi":"10.1007/s40815-017-0403-1","title":"An Optimized Fuzzy Control Algorithm for Three-Dimensional AUV Path Planning","year":2017,"lang":"en","type":"article","venue":"International Journal of Fuzzy Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Motion planning; Sonar; Computer science; Path (computing); Underwater; Fuzzy logic; Acceleration; Fuzzy control system; Boundary (topology); Level set (data structures); Algorithm; Control theory (sociology); Mathematical optimization; Artificial intelligence; Control (management); Robot; Mathematics","score_opus":0.030012057113177205,"score_gpt":0.31538749240949654,"score_spread":0.2853754352963193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766175108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039541577,0.00043905783,0.9815423,0.00089061307,0.0123795485,0.00031851596,0.00007056348,0.000057631078,0.0003476274],"genre_scores_gemma":[0.55290395,0.0000027770388,0.44410485,0.00015296262,0.002678083,0.000024361649,0.000009601269,0.000026493683,0.00009690695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968269,0.00012809371,0.000976719,0.00036212575,0.0013369618,0.00036918768],"domain_scores_gemma":[0.99513346,0.0004033322,0.0018737167,0.00068081403,0.0016283719,0.00028033464],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019617274,0.0002533669,0.0005413097,0.00029970767,0.00037839107,0.0013755505,0.0039622253,0.00013106111,0.0000024169653],"category_scores_gemma":[0.0002673773,0.0002140667,0.00025288993,0.000047472713,0.0000753922,0.0017460736,0.00015020615,0.00028659473,0.000013124764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000582317,0.00066523405,0.008927625,0.000044414865,0.0021981348,0.003491828,0.0010205734,0.7945121,0.002206289,0.010372609,0.010849877,0.16512898],"study_design_scores_gemma":[0.004641825,0.00038798194,0.0052209664,0.00042544687,0.000031495198,0.0013874163,0.00003064773,0.9853112,0.000045995377,0.0015430925,0.0007137754,0.00026017037],"about_ca_topic_score_codex":0.000077139484,"about_ca_topic_score_gemma":5.7921653e-7,"teacher_disagreement_score":0.5489498,"about_ca_system_score_codex":0.00016428315,"about_ca_system_score_gemma":0.0002898269,"threshold_uncertainty_score":0.9996611},"labels":[],"label_agreement":null},{"id":"W2766504869","doi":"10.1007/s10846-017-0729-9","title":"A Global Path Planning Algorithm for Fixed-wing UAVs","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Dijkstra's algorithm; Delaunay triangulation; Motion planning; Genetic algorithm; Any-angle path planning; Mathematical optimization; Path (computing); Algorithm; Shortest path problem; Yen's algorithm; Computer science; Voronoi diagram; Partition (number theory); Mathematics; Artificial intelligence; Theoretical computer science; Robot; Graph","score_opus":0.05097427091060251,"score_gpt":0.325032863588882,"score_spread":0.2740585926782795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766504869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066565594,0.0020227297,0.9847888,0.00030961458,0.011300417,0.00038344902,0.00000767204,0.00006374345,0.00045795456],"genre_scores_gemma":[0.21930404,0.00003924066,0.77769977,0.000102664664,0.0023568207,0.000020912214,0.0000023533487,0.00003797995,0.00043621683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658936,0.0001352475,0.001323234,0.00041610346,0.00087534415,0.0006607224],"domain_scores_gemma":[0.9953973,0.00035539686,0.0021719155,0.001065998,0.00065434026,0.00035501775],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0020618825,0.000342313,0.000821098,0.00019151368,0.0006202258,0.0013722549,0.0031358462,0.00016568636,0.0000020637442],"category_scores_gemma":[0.0006178469,0.0002824253,0.00039111436,0.00016649198,0.00007486678,0.0009824224,0.00030083183,0.00028261053,0.00002529259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003965714,0.00029052372,0.011797982,0.00024801603,0.000704545,0.0016534082,0.0020694423,0.78617644,0.00008768542,0.0062769726,0.012657889,0.17799743],"study_design_scores_gemma":[0.00064251386,0.0005117246,0.002826571,0.0015707404,0.000065484775,0.002322792,0.00040845934,0.9888261,0.00009859265,0.0006306269,0.0017351212,0.00036129513],"about_ca_topic_score_codex":0.00007673749,"about_ca_topic_score_gemma":4.068315e-7,"teacher_disagreement_score":0.21863839,"about_ca_system_score_codex":0.00036943398,"about_ca_system_score_gemma":0.00028735396,"threshold_uncertainty_score":0.9999628},"labels":[],"label_agreement":null},{"id":"W2766763133","doi":"10.1115/detc2017-67025","title":"Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion planning; Obstacle avoidance; Vehicle dynamics; Control theory (sociology); Yaw; Path (computing); Acceleration; Collision avoidance; Computer science; Optimal control; Sigmoid function; MATLAB; Trajectory; Simulation; Collision; Engineering; Mobile robot; Mathematical optimization; Mathematics; Control (management); Robot; Automotive engineering; Artificial neural network; Artificial intelligence","score_opus":0.07153374280047213,"score_gpt":0.3321407302197756,"score_spread":0.26060698741930344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766763133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06178447,0.000040885312,0.9349616,0.0004877931,0.00079909107,0.00043277696,0.000017737168,0.00047143264,0.0010041837],"genre_scores_gemma":[0.20631176,8.4163366e-7,0.79236937,0.00017411435,0.00010833239,0.00004575888,0.000007956005,0.000019903862,0.00096196507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981459,0.00004867583,0.00029872212,0.00065421435,0.00029275205,0.0005597456],"domain_scores_gemma":[0.99667263,0.0001986629,0.00023440414,0.0024987157,0.00013624183,0.00025935692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060325646,0.00022961304,0.000296579,0.00007805006,0.001133074,0.00094974187,0.0038906331,0.00012291272,0.00000416932],"category_scores_gemma":[0.00038202005,0.00020612117,0.000085672335,0.00006567657,0.0000835385,0.0012248497,0.00078928,0.00016086604,0.000037650258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002603116,0.0025568183,0.040343598,0.00017011922,0.00033067467,0.0010169519,0.015167784,0.68203175,0.009878586,0.04227685,0.093382485,0.11258409],"study_design_scores_gemma":[0.0019815264,0.0003527546,0.022761934,0.00003502079,0.0000071581862,0.000019755498,0.0000596251,0.9724489,0.0010518701,0.00022221381,0.0007486922,0.00031054806],"about_ca_topic_score_codex":0.00012982325,"about_ca_topic_score_gemma":0.000003368927,"teacher_disagreement_score":0.29041716,"about_ca_system_score_codex":0.000057032477,"about_ca_system_score_gemma":0.00013159918,"threshold_uncertainty_score":0.91583824},"labels":[],"label_agreement":null},{"id":"W2766852415","doi":"10.1155/2017/2521638","title":"Autonomous Path Planning for Road Vehicles in Narrow Environments: An Efficient Continuous Curvature Approach","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Planner; Motion planning; Path (computing); Curvature; Computer science; Process (computing); Mathematical optimization; Simulation; Artificial intelligence; Mathematics; Robot","score_opus":0.017309791769828702,"score_gpt":0.27345043306918454,"score_spread":0.2561406412993558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766852415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47112006,0.00023656207,0.5279474,0.00010215361,0.0003739404,0.0001783789,0.0000064966503,0.000014645592,0.00002033824],"genre_scores_gemma":[0.6588061,0.000003862177,0.34106386,0.000023235489,0.00006107187,0.000008110868,0.000011622076,0.0000092602295,0.000012925736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986385,0.000033367,0.00051332026,0.00025853777,0.0003110805,0.00024517364],"domain_scores_gemma":[0.9985721,0.000038698396,0.0008794631,0.000348031,0.00006114785,0.00010052673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004957316,0.00014885464,0.00028781805,0.0001226602,0.00019364603,0.00010702651,0.00071432086,0.00008366337,3.094642e-7],"category_scores_gemma":[0.000046347974,0.0001371486,0.00008410658,0.000059185324,0.000038474805,0.0010839117,0.000005842441,0.00025104178,4.458023e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007316742,0.0001882911,0.0044056983,0.000019380715,0.000013451168,0.000099328994,0.0058887354,0.9639674,0.0025738378,0.00021882908,0.000009337828,0.022542553],"study_design_scores_gemma":[0.0025590793,0.00046385676,0.77357787,0.00016430917,0.000023582796,0.00003336548,0.00049092225,0.22104567,0.00069996493,0.0004163281,0.0002914959,0.0002335487],"about_ca_topic_score_codex":0.0000057232405,"about_ca_topic_score_gemma":9.222462e-7,"teacher_disagreement_score":0.7691722,"about_ca_system_score_codex":0.00007043688,"about_ca_system_score_gemma":0.000056369314,"threshold_uncertainty_score":0.55927575},"labels":[],"label_agreement":null},{"id":"W2771631450","doi":"10.1109/ssd.2017.8166929","title":"Fast path planning for unmanned aerial vehicle using embedded GPU System","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Motion planning; Path (computing); Real-time computing; Terrain; Speedup; Point (geometry); Simulation; Parallel computing; Robot; Artificial intelligence; Operating system","score_opus":0.060048126261357415,"score_gpt":0.316477620768478,"score_spread":0.2564294945071206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771631450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05292943,0.000019631701,0.9414486,0.0001411056,0.0022858586,0.0003233264,0.0000074058735,0.00041405173,0.0024305366],"genre_scores_gemma":[0.5411065,1.1153225e-7,0.45817825,0.000053497468,0.0003815733,0.000015572243,0.0000023680884,0.000015784408,0.00024628744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836135,0.000045056568,0.0002901543,0.0005056592,0.00027790706,0.00051989994],"domain_scores_gemma":[0.9982064,0.000099342804,0.00028199595,0.0011654685,0.00010314237,0.00014365767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047290826,0.00019579181,0.00028660567,0.00007259683,0.00103699,0.00085187476,0.0016019983,0.00014457616,0.0000019115098],"category_scores_gemma":[0.000116478586,0.00017775227,0.00008818763,0.00006794871,0.00004810794,0.00072171394,0.00038354812,0.00013934192,0.000030517424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006325715,0.0007281453,0.028818792,0.0017810409,0.0008854831,0.002523771,0.028497532,0.4709056,0.10959868,0.23608899,0.027182667,0.09235673],"study_design_scores_gemma":[0.0009178525,0.00007781945,0.0014650073,0.00017260361,0.000011030838,0.0000456806,0.00020874258,0.9945891,0.0019956003,0.00013035939,0.00013026064,0.0002559486],"about_ca_topic_score_codex":0.00008078853,"about_ca_topic_score_gemma":4.760479e-7,"teacher_disagreement_score":0.5236835,"about_ca_system_score_codex":0.000089103116,"about_ca_system_score_gemma":0.000097346725,"threshold_uncertainty_score":0.82146484},"labels":[],"label_agreement":null},{"id":"W2773175808","doi":"10.1386/tear.15.1.53_1","title":"Capricious creatures: Animal behaviour as a model for robotic art","year":2017,"lang":"en","type":"article","venue":"Technoetic Arts","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Creatures; Instinct; Counterpoint; Robot; Human–computer interaction; Cognitive science; Computer science; Aesthetics; Artificial intelligence; Art; Psychology; Natural (archaeology); History; Biology; Ecology","score_opus":0.039664634937828236,"score_gpt":0.31163772458913014,"score_spread":0.2719730896513019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773175808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009701586,0.000064461274,0.98340344,0.0024527232,0.00031003114,0.00051446335,0.0000039550164,0.000706522,0.0028428128],"genre_scores_gemma":[0.6054454,0.0000024832484,0.3910731,0.00015971065,0.00006603577,0.00008986668,0.0000027892386,0.000021718442,0.0031389345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820256,0.000009746954,0.00025016323,0.00061577366,0.00035897453,0.00056280027],"domain_scores_gemma":[0.9974592,0.00006478205,0.00021886229,0.001996691,0.000102209655,0.00015827306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028573742,0.00024982673,0.00029487006,0.00013239289,0.0007349044,0.00038886853,0.002187053,0.00018546398,0.0000032150485],"category_scores_gemma":[0.00024680144,0.00023980241,0.00011238195,0.00007889126,0.00014579737,0.00036822693,0.00047406295,0.00023497628,0.00020404856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001554274,0.0017791026,0.0082141245,0.0002920514,0.0002551812,0.002146371,0.009794267,0.1763204,0.009839253,0.5992033,0.14138491,0.0506156],"study_design_scores_gemma":[0.00041970005,0.00020060677,0.0016765725,0.00006716542,0.00003196872,0.00013737947,0.000016608705,0.9804418,0.0010924758,0.015262681,0.00032442558,0.00032858457],"about_ca_topic_score_codex":0.000034506622,"about_ca_topic_score_gemma":0.0000048456795,"teacher_disagreement_score":0.80412143,"about_ca_system_score_codex":0.00006182081,"about_ca_system_score_gemma":0.00012541271,"threshold_uncertainty_score":0.97788584},"labels":[],"label_agreement":null},{"id":"W2773418006","doi":"10.1109/smc.2017.8123205","title":"A hexagonal grid-based sampling planner for aquatic environmental monitoring using unmanned surface vehicles","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sampling (signal processing); Real-time computing; Motion planning; Computer science; Planner; Grid; Path (computing); Remote sensing; Environmental science; Computer network; Artificial intelligence; Computer vision; Geography; Robot; Filter (signal processing)","score_opus":0.10685076901515624,"score_gpt":0.329394157080359,"score_spread":0.2225433880652028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773418006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37414715,0.0000394679,0.6244418,0.00021599533,0.00084484194,0.00016922444,0.000009934384,0.00009796941,0.00003359797],"genre_scores_gemma":[0.49487892,5.865228e-7,0.5048154,0.000035787634,0.00017628314,0.000005585969,0.0000041706576,0.000012827098,0.0000704738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984052,0.00003390503,0.0002495698,0.00049225206,0.00034036417,0.0004787179],"domain_scores_gemma":[0.99850804,0.00031393347,0.00020150948,0.00082947145,0.0000129052705,0.0001341515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004086672,0.00021035374,0.00021614997,0.0000547995,0.00093642477,0.00050408795,0.0011678634,0.000065768996,0.0000033968818],"category_scores_gemma":[0.00008296692,0.00020010846,0.00009009111,0.00003899992,0.000075526754,0.00056299823,0.00022953784,0.00012121913,0.000025222835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005648335,0.00025418148,0.16153663,0.0000716228,0.00010867539,0.000080062906,0.00071400066,0.7593673,0.064592965,0.00047670346,0.00019582802,0.01254555],"study_design_scores_gemma":[0.0006684037,0.000061209765,0.015595551,0.00007767738,0.000012522448,0.000012412704,0.000042708318,0.9690708,0.013851767,0.00024492174,0.000083571336,0.00027844158],"about_ca_topic_score_codex":0.000096476746,"about_ca_topic_score_gemma":7.1457623e-7,"teacher_disagreement_score":0.20970352,"about_ca_system_score_codex":0.00011735282,"about_ca_system_score_gemma":0.00007534691,"threshold_uncertainty_score":0.81601864},"labels":[],"label_agreement":null},{"id":"W2774384911","doi":"10.1109/iros.2017.8206454","title":"Topologically distinct trajectory predictions for probabilistic pursuit","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Trajectory; Computer science; Artificial intelligence; Physics","score_opus":0.0531553574393154,"score_gpt":0.3016748647307192,"score_spread":0.2485195072914038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774384911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008969543,0.000011446527,0.9769452,0.0019886503,0.00091967685,0.0003752145,0.000010029616,0.0003798497,0.018472977],"genre_scores_gemma":[0.39157057,8.2184084e-7,0.60289025,0.00012305865,0.0002032558,0.0001659799,0.0000040235136,0.000007774648,0.0050342297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989507,0.00002282994,0.0001691083,0.00038345068,0.00018267048,0.00029122343],"domain_scores_gemma":[0.998462,0.00021172818,0.00010579123,0.0010308336,0.00008200156,0.00010759881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032822584,0.00011183411,0.00013941627,0.000031842734,0.00074206834,0.00041807722,0.0018309149,0.000063705986,0.0000193802],"category_scores_gemma":[0.00091622066,0.00008650165,0.0000718782,0.00003728728,0.0001399551,0.0003126063,0.00023220078,0.00008644164,0.00003335501],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044317272,0.00096081593,0.017187404,0.00016577369,0.00017142338,0.00013102223,0.001173015,0.011809362,0.00056683616,0.7813527,0.053803526,0.1326338],"study_design_scores_gemma":[0.00055799284,0.0003951174,0.23352815,0.000023065732,0.00001822496,0.00003894315,0.000011416649,0.7368991,0.000060332874,0.02390497,0.0042688427,0.00029387168],"about_ca_topic_score_codex":0.000024971338,"about_ca_topic_score_gemma":0.000008714148,"teacher_disagreement_score":0.7574477,"about_ca_system_score_codex":0.000036875586,"about_ca_system_score_gemma":0.00007329669,"threshold_uncertainty_score":0.5707465},"labels":[],"label_agreement":null},{"id":"W2775176094","doi":"10.1504/ijvp.2018.10009700","title":"Optimal path planning for unmanned ground vehicles using potential field method and optimal control method","year":2017,"lang":"en","type":"article","venue":"International Journal of Vehicle Performance","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion planning; Optimal control; Field (mathematics); Path (computing); Potential field; Control (management); Computer science; Aerospace engineering; Control theory (sociology); Mathematical optimization; Engineering; Physics; Mathematics; Artificial intelligence; Robot; Geophysics","score_opus":0.03373568873278731,"score_gpt":0.3597245771635071,"score_spread":0.32598888843071977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775176094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36654934,0.000104797444,0.6313943,0.0009223898,0.0009235702,0.00006881718,0.0000052751643,0.000012853037,0.000018618846],"genre_scores_gemma":[0.5002452,0.000010648806,0.49917206,0.00014407084,0.00040096635,0.000002054739,4.8553716e-7,0.0000076800625,0.000016814422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983066,0.00007924315,0.0005051875,0.00026128747,0.0005529203,0.00029478112],"domain_scores_gemma":[0.9978257,0.0003737404,0.00086340256,0.0002697944,0.0005415684,0.00012583069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015048845,0.00017488925,0.00030737545,0.00017995962,0.00043551746,0.0006917238,0.0016400757,0.00009231037,0.0000027345855],"category_scores_gemma":[0.00024344384,0.00015897596,0.00012592645,0.000039232586,0.00005194322,0.0018341016,0.0002556578,0.0002998821,0.0000012273243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007483422,0.00010249789,0.010831566,0.000035139852,0.00044833316,0.00035366806,0.0010413913,0.8067323,0.016078513,0.00054926303,0.00023632684,0.16284265],"study_design_scores_gemma":[0.0022469112,0.0004119072,0.018746346,0.00018190574,0.000036510864,0.0010636542,0.00003785502,0.9716636,0.005042242,0.0002001176,0.0001926848,0.00017628194],"about_ca_topic_score_codex":0.000024204033,"about_ca_topic_score_gemma":6.3621684e-8,"teacher_disagreement_score":0.16493127,"about_ca_system_score_codex":0.000067972665,"about_ca_system_score_gemma":0.00011369457,"threshold_uncertainty_score":0.6670308},"labels":[],"label_agreement":null},{"id":"W2775615184","doi":"10.1504/ijvp.2018.10009708","title":"Advanced control techniques for unmanned ground vehicle: literature survey","year":2017,"lang":"en","type":"article","venue":"International Journal of Vehicle Performance","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Unmanned ground vehicle; Aerospace engineering; Control (management); Aeronautics; Computer science; Systems engineering; Remote sensing; Environmental science; Engineering; Geology; Telecommunications; Artificial intelligence","score_opus":0.020332818347738906,"score_gpt":0.3092517567214546,"score_spread":0.2889189383737157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775615184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57403666,0.00038458116,0.4177559,0.0035616283,0.0035960036,0.00026526174,0.00006035329,0.00007708804,0.0002625135],"genre_scores_gemma":[0.9236721,0.00010568001,0.07519013,0.00031352314,0.0005278547,0.000010552759,0.0000055725695,0.000012891897,0.00016173282],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983582,0.000058659796,0.00049769593,0.00022079975,0.00060594414,0.00025870863],"domain_scores_gemma":[0.99658,0.00023899974,0.00094717095,0.00046294078,0.001667238,0.00010365836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012972116,0.00016441786,0.00026269717,0.00018231003,0.00028781875,0.00080166024,0.0032168266,0.00008933811,0.0000022981556],"category_scores_gemma":[0.0004943015,0.00014150138,0.00012269095,0.000079505604,0.00007197793,0.0026771727,0.00016005614,0.0003065549,0.000006925517],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014923197,0.00039263425,0.15061237,0.00007270386,0.000533084,0.00033069862,0.001058411,0.0043701823,0.011887541,0.0018527242,0.0026276547,0.8247697],"study_design_scores_gemma":[0.0034230642,0.0007629835,0.6246933,0.00062935456,0.000013432136,0.00033872234,0.000007398657,0.35518843,0.010432668,0.00079383363,0.0033859846,0.0003308757],"about_ca_topic_score_codex":0.000011632908,"about_ca_topic_score_gemma":0.0000019950207,"teacher_disagreement_score":0.8244388,"about_ca_system_score_codex":0.000103940474,"about_ca_system_score_gemma":0.0001151563,"threshold_uncertainty_score":0.77304286},"labels":[],"label_agreement":null},{"id":"W2782799514","doi":"10.1109/iris.2017.8250106","title":"A cooperative exploration strategy with efficient backtracking for mobile robots","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Backtracking; Mobile robot; Computer science; Robot; Node (physics); Tree (set theory); Random tree; Distributed computing; Artificial intelligence; Engineering; Motion planning; Algorithm; Mathematics","score_opus":0.06025845480772201,"score_gpt":0.31717040468665925,"score_spread":0.25691194987893723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782799514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00568549,0.000016084652,0.9905024,0.0002755923,0.00024304556,0.0005535566,0.0000016109797,0.00013568027,0.0025865203],"genre_scores_gemma":[0.50936663,7.257556e-7,0.48985153,0.000046840672,0.000042804742,0.00016837935,0.000003340632,0.00000829635,0.0005114417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990056,0.000025271245,0.0001423298,0.00037769214,0.00019681467,0.00025226697],"domain_scores_gemma":[0.9987982,0.000072163566,0.00014017276,0.00069244375,0.00020262499,0.0000943748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023942035,0.00013165652,0.00014191598,0.000041065392,0.00049282215,0.00085352355,0.0007241511,0.000040280407,0.0000024606143],"category_scores_gemma":[0.00005344407,0.00009638369,0.000026382051,0.00006572869,0.000053420867,0.000873293,0.00009896559,0.000068347545,0.000030276618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009992501,0.000050620318,0.00008817406,0.000005239362,0.000011910047,0.000012573173,0.0009587864,0.9779666,0.0001149951,0.010733894,0.00017072276,0.0098764775],"study_design_scores_gemma":[0.0006321026,0.00053340563,0.00061375217,0.000034032277,0.0000043927657,0.000011240318,0.00024354171,0.99464273,0.0028743665,0.00016783408,0.00006800764,0.00017460361],"about_ca_topic_score_codex":0.000030461295,"about_ca_topic_score_gemma":0.000008350587,"teacher_disagreement_score":0.5036811,"about_ca_system_score_codex":0.000031736625,"about_ca_system_score_gemma":0.00011906434,"threshold_uncertainty_score":0.82305473},"labels":[],"label_agreement":null},{"id":"W2783975637","doi":"10.1109/cdc.2017.8264097","title":"A nerve-theoretic result on the problem of a topological obstruction in reach control","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Topology (electrical circuits); Homotopy; Simplex; Control (management); State (computer science); Control theory (sociology); Facet (psychology); Computer science; Pure mathematics; Combinatorics; Algorithm; Artificial intelligence","score_opus":0.029925659310822212,"score_gpt":0.27136129811641996,"score_spread":0.24143563880559774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783975637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39603513,0.000030256271,0.50285316,0.027196124,0.00047519876,0.00089245953,0.000003719013,0.00019338816,0.07232057],"genre_scores_gemma":[0.92671007,0.0000010755592,0.07292834,0.000118734664,0.000023919942,0.00001666992,2.0766505e-7,0.0000024626006,0.00019854364],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990144,0.00014646597,0.00021591979,0.00024031369,0.00020041071,0.0001825398],"domain_scores_gemma":[0.9986366,0.00028381217,0.00016640368,0.0008415757,0.000041150277,0.000030461551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007924794,0.00008206662,0.00014735322,0.000042900752,0.00014073313,0.0000956597,0.0011206443,0.00006168394,0.000007965488],"category_scores_gemma":[0.00028508625,0.00004559738,0.000036718233,0.000072716655,0.00018310698,0.00013044124,0.00011545192,0.00016507709,0.000014853126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025131407,0.00006462807,0.0057085687,0.0000058172545,0.0000075151684,0.00003209324,0.000484976,0.0014171031,0.0003980465,0.98202187,0.00015951479,0.00967472],"study_design_scores_gemma":[0.0019939307,0.0006246714,0.32133457,0.00014626993,0.000008066047,0.00006784712,0.00011748753,0.4933381,0.0020221737,0.17999671,0.00008966146,0.00026049287],"about_ca_topic_score_codex":0.00019798761,"about_ca_topic_score_gemma":0.0000040970294,"teacher_disagreement_score":0.80202514,"about_ca_system_score_codex":0.000022890466,"about_ca_system_score_gemma":0.000023780445,"threshold_uncertainty_score":0.20824546},"labels":[],"label_agreement":null},{"id":"W2784154204","doi":"10.1109/iris.2017.8250126","title":"Robot exploration in unknown cluttered environments when dealing with uncertainty","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Partially observable Markov decision process; Robot; Computer science; Task (project management); Artificial intelligence; Urban search and rescue; Process (computing); Rescue robot; Markov decision process; Mobile robot; Search and rescue; Actuator; Controller (irrigation); Robotics; Markov process; Control engineering; Machine learning; Markov chain; Engineering; Markov model; Systems engineering","score_opus":0.04379728761437457,"score_gpt":0.2638918008948922,"score_spread":0.22009451328051766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784154204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075420514,0.0000087410735,0.98788965,0.0018107012,0.00014364251,0.0001405208,3.6068192e-7,0.00006371285,0.002400613],"genre_scores_gemma":[0.59327304,0.0000041372164,0.40589076,0.0001293735,0.000028059589,0.000013799759,0.0000027331039,0.0000070120127,0.0006510848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989195,0.000036408543,0.00017169649,0.00036566018,0.00025449946,0.00025223175],"domain_scores_gemma":[0.9988496,0.000031847907,0.00013690843,0.00090164517,0.000010981457,0.00006903714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025834126,0.00012496814,0.0001344923,0.000063963926,0.00022673584,0.00028640233,0.00091648364,0.000045449182,0.000008674789],"category_scores_gemma":[0.000030439121,0.00010038248,0.000016974955,0.000035277633,0.00004746301,0.0011847807,0.00021324994,0.00010683531,0.00006193552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037305937,0.00018655282,0.0363584,0.000015092421,0.000046667745,0.00032988001,0.005161987,0.8085803,0.0024036807,0.020605627,0.00028579554,0.12598872],"study_design_scores_gemma":[0.0009800113,0.00010819563,0.040773068,0.00008029599,0.000004155745,0.000012220178,0.000048782647,0.95213693,0.0014736683,0.0034913975,0.00058892305,0.00030233918],"about_ca_topic_score_codex":0.00032949017,"about_ca_topic_score_gemma":0.0000894547,"teacher_disagreement_score":0.58573097,"about_ca_system_score_codex":0.00006410476,"about_ca_system_score_gemma":0.000024982623,"threshold_uncertainty_score":0.4093479},"labels":[],"label_agreement":null},{"id":"W2784607208","doi":"10.1139/tcsme-2013-0041","title":"UNMANNED VEHICLE OBSTACLE DETECTION AND AVOIDANCE USING DANGER ZONE APPROACH","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstacle; Obstacle avoidance; Intersection (aeronautics); Collision avoidance; Computer science; Boundary (topology); Control theory (sociology); Algebraic number; Artificial intelligence; Computer vision; Collision; Simulation; Mobile robot; Mathematics; Engineering; Aerospace engineering; Robot; Geography; Computer security","score_opus":0.014896134052925511,"score_gpt":0.19677545600433005,"score_spread":0.18187932195140455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784607208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019230377,0.000059099966,0.9798323,0.00024666745,0.00025906716,0.00029880813,0.000008383342,0.000059540493,0.000005786886],"genre_scores_gemma":[0.6365073,0.0000012580296,0.36335036,0.00004681892,0.0000169616,0.000031251016,3.1670282e-7,0.00001074264,0.000035032102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925417,0.000010157403,0.00014595297,0.0001954665,0.0001157561,0.000278526],"domain_scores_gemma":[0.999409,0.000058517497,0.00003669416,0.0002761009,0.00005257257,0.00016707928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015615103,0.00010136119,0.0001226518,0.000036060635,0.00027808477,0.000054341377,0.0003161886,0.00010130008,0.0000018558843],"category_scores_gemma":[0.00001745374,0.000094090654,0.00016653187,0.0002914479,0.000026327763,0.00025331226,0.00001221681,0.00018006194,6.71644e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018932072,0.000036624893,0.000005411285,0.00018035264,0.000170848,3.9214157e-7,0.0013086757,0.8144494,0.15660588,0.0025703341,0.00004714003,0.024623008],"study_design_scores_gemma":[0.00016609322,0.000018566318,0.00010720497,0.000019356565,0.000018340243,0.000012804195,0.00004766816,0.98901093,0.010303492,0.00011949911,0.00007033168,0.00010573103],"about_ca_topic_score_codex":0.009326332,"about_ca_topic_score_gemma":0.00055432174,"teacher_disagreement_score":0.61727685,"about_ca_system_score_codex":0.00018620065,"about_ca_system_score_gemma":0.000072550916,"threshold_uncertainty_score":0.99727064},"labels":[],"label_agreement":null},{"id":"W2789990415","doi":"10.1155/2018/6392697","title":"Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Trajectory; Point (geometry); Work (physics); Robotics; Software; Computer science; Path (computing); Robot; Collision avoidance; Collision; Simulation; Real-time computing; Artificial intelligence; Engineering","score_opus":0.06841923027206204,"score_gpt":0.3728782159994038,"score_spread":0.30445898572734176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789990415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5184189,0.00006467424,0.48111472,0.00006375429,0.0001404208,0.00017618849,0.0000032056691,0.000011623112,0.0000064808182],"genre_scores_gemma":[0.7965456,0.0000013908669,0.20335437,0.000021894377,0.000059976788,0.000007967018,0.0000019392573,0.0000049216537,0.0000019154131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986016,0.000040717936,0.00046518154,0.00020914077,0.00042128685,0.00026208392],"domain_scores_gemma":[0.9990249,0.00019891812,0.00020637597,0.00012612571,0.00035353564,0.00009011147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011372599,0.00009738332,0.00020187441,0.00020762827,0.00011505367,0.000053428856,0.00027951828,0.000048291295,3.990257e-7],"category_scores_gemma":[0.000051283612,0.0000884423,0.00003498987,0.00043466117,0.00007594393,0.0006450591,0.000005554686,0.00017641249,3.8707768e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009505865,0.0005409654,0.14818057,0.000055570254,0.000038084254,0.00054224755,0.016916484,0.6897174,0.0004716368,0.0008200528,0.000061790684,0.14170462],"study_design_scores_gemma":[0.0037701249,0.0034009076,0.8521309,0.00020632205,0.000010849796,0.000020353165,0.0020166305,0.1347243,0.0002553134,0.0032508941,0.000080855716,0.00013257451],"about_ca_topic_score_codex":0.000019500767,"about_ca_topic_score_gemma":0.000046918125,"teacher_disagreement_score":0.7039503,"about_ca_system_score_codex":0.00013304436,"about_ca_system_score_gemma":0.00020219405,"threshold_uncertainty_score":0.36065722},"labels":[],"label_agreement":null},{"id":"W2790412092","doi":"10.1109/taes.2018.2807558","title":"Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":256,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Motion planning; Computer science; Fitness function; Genetic algorithm; Path (computing); Real-time computing; Graphics processing unit; Genetic programming; Simulation; Parallel computing; Robot; Artificial intelligence","score_opus":0.01591906434887642,"score_gpt":0.24702384613126901,"score_spread":0.2311047817823926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790412092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019076038,0.001460219,0.9766717,0.00012536906,0.0018702169,0.0005527185,0.000028906825,0.00018848514,0.00002634854],"genre_scores_gemma":[0.80622274,0.00013163073,0.19185081,0.00015643708,0.0005981869,0.00014663968,0.0000032587736,0.000059636357,0.00083068025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769056,0.00009877678,0.00032770468,0.00068572833,0.00029992036,0.0008973315],"domain_scores_gemma":[0.9989526,0.00013641795,0.00008811013,0.0005422686,0.00011348556,0.00016707348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003444671,0.00030606243,0.0003387376,0.00016398652,0.00063183164,0.00013508565,0.00039510985,0.00015918732,0.0000025339418],"category_scores_gemma":[0.0000040177756,0.00029275945,0.00010643935,0.00034747925,0.000092080845,0.00024440346,0.0000044563144,0.00027440427,0.000023786159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019495937,0.0007314164,0.00017968629,0.0005169045,0.0010476186,0.000097624536,0.01095435,0.6495322,0.02643889,0.00095019885,0.00339649,0.30595964],"study_design_scores_gemma":[0.0006925857,0.00083873776,0.00005854644,0.00015355826,0.00004539876,0.0002728771,0.00019752927,0.994513,0.002039424,0.00005173294,0.0007677896,0.00036883217],"about_ca_topic_score_codex":0.00039516628,"about_ca_topic_score_gemma":0.000025752634,"teacher_disagreement_score":0.7871467,"about_ca_system_score_codex":0.00021936325,"about_ca_system_score_gemma":0.00019011027,"threshold_uncertainty_score":0.99995244},"labels":[],"label_agreement":null},{"id":"W2790625987","doi":"10.2514/1.i010565","title":"Toward Smarter Autoflight Control System Infrastructure","year":2018,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Flight plan; Aviation; Air traffic control; Control (management); Plan (archaeology); Systems engineering; Computer science; The Internet; Human-in-the-loop; Engineering; Real-time computing","score_opus":0.008696341027396828,"score_gpt":0.21577128069591933,"score_spread":0.2070749396685225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790625987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034584657,0.00011801837,0.98603296,0.0006507645,0.0068224915,0.00021855705,0.0000064305655,0.0001303482,0.002561957],"genre_scores_gemma":[0.9603847,0.000002800527,0.038424287,0.00023801904,0.0008630944,0.0000041455337,0.0000011079022,0.0000070913734,0.00007472197],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974854,0.00012577749,0.0011323681,0.000099896686,0.0008601441,0.0002964338],"domain_scores_gemma":[0.9964003,0.000080351274,0.0016750962,0.00039652947,0.0012485298,0.00019917182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011471361,0.00018811066,0.00042607926,0.0003127395,0.00013826403,0.00073120365,0.0009227031,0.00014538791,0.0000033547565],"category_scores_gemma":[0.000094662966,0.00013954991,0.00011454041,0.0003804571,0.000050629213,0.004101569,0.000063605126,0.0002677408,0.000261618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040293392,0.00013696369,0.020505577,0.0023859884,0.0015561032,0.00044454169,0.11449517,0.18813048,0.001924586,0.15356207,0.49413887,0.022316702],"study_design_scores_gemma":[0.0033038417,0.00089200254,0.009717509,0.0009228285,0.000046146397,0.006344654,0.002939445,0.9055681,0.00042199242,0.000040852057,0.069314525,0.00048808695],"about_ca_topic_score_codex":0.000011197009,"about_ca_topic_score_gemma":1.0839368e-7,"teacher_disagreement_score":0.9569263,"about_ca_system_score_codex":0.00025186836,"about_ca_system_score_gemma":0.00021227777,"threshold_uncertainty_score":0.7051014},"labels":[],"label_agreement":null},{"id":"W2791636136","doi":"10.1109/icamechs.2017.8316535","title":"Real-time path planning and following for nonholonomic unmanned ground vehicles","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Unmanned ground vehicle; Path (computing); Planner; Holonomic; Nonholonomic system; Computer science; Kinematics; Artificial intelligence; Real-time computing; Robot; Mobile robot; Computer network; Physics","score_opus":0.030678354458391347,"score_gpt":0.29405529855452345,"score_spread":0.2633769440961321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791636136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.643935,0.0000611621,0.35050178,0.0006854888,0.0005660658,0.0002714511,0.000003312766,0.0003050116,0.0036707171],"genre_scores_gemma":[0.51790965,0.000002823085,0.48085663,0.00006995109,0.000095888994,0.00001763113,0.0000029439213,0.000014515724,0.0010299723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889064,0.00002199699,0.00017728664,0.0004426833,0.00011911499,0.00034825786],"domain_scores_gemma":[0.998784,0.00020971487,0.00013711692,0.00074119895,0.000022172417,0.00010577648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048206703,0.00014820941,0.00021767843,0.000058161266,0.00075288035,0.0007116758,0.0009082433,0.00006848175,0.0000010329082],"category_scores_gemma":[0.00009823182,0.00013677908,0.000067350746,0.000028548759,0.000044605524,0.00077818433,0.00032922052,0.00006864847,0.000028588665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046217442,0.0006367107,0.26119885,0.00049475,0.0015322905,0.0029001855,0.02736441,0.010252586,0.20444973,0.10951239,0.04704751,0.3341484],"study_design_scores_gemma":[0.0010412205,0.00019187022,0.1474614,0.00011712739,0.00002141514,0.000034866385,0.00005301605,0.8457704,0.0008959809,0.0036735677,0.00030797173,0.0004311636],"about_ca_topic_score_codex":0.00008662208,"about_ca_topic_score_gemma":3.710989e-7,"teacher_disagreement_score":0.8355178,"about_ca_system_score_codex":0.000027866286,"about_ca_system_score_gemma":0.000036961683,"threshold_uncertainty_score":0.6862706},"labels":[],"label_agreement":null},{"id":"W2791820620","doi":"10.1177/0278364918754677","title":"Mobile manipulator planning under uncertainty in unknown environments","year":2018,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mobile manipulator; Manipulator (device); Motion planning; Computer science; Mobile robot; Control engineering; Artificial intelligence; Engineering; Robot","score_opus":0.11520060307555305,"score_gpt":0.4079702508878658,"score_spread":0.29276964781231274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791820620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082886875,0.00041905086,0.90835136,0.0044755,0.0024566592,0.00022319707,0.0000016038607,0.000017505841,0.001168274],"genre_scores_gemma":[0.9597465,0.000046908124,0.03867375,0.00020121865,0.0006309378,0.0000039478355,9.4127233e-7,0.000011982415,0.0006838267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967135,0.00027618476,0.0004462312,0.00018793235,0.002006098,0.0003700625],"domain_scores_gemma":[0.9984154,0.00050146953,0.00018917282,0.00034813734,0.0004421238,0.00010367266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031950984,0.000103137565,0.0001505517,0.0004546403,0.00012864296,0.0002284967,0.003447183,0.000058031037,0.00001886565],"category_scores_gemma":[0.00021227881,0.00007269032,0.000058672704,0.00035571493,0.00024232805,0.000320228,0.00067616516,0.0007127112,0.00008829438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004098987,0.0001043317,0.0012838559,0.0000015405565,0.00006906887,0.00024176396,0.0015329468,0.98532504,0.0015078424,0.0063803876,0.00091046124,0.0026017686],"study_design_scores_gemma":[0.0009449862,0.0006288184,0.0061797854,0.00022921844,0.000005035037,0.00048094813,0.0004791298,0.9710995,0.00086086325,0.014612402,0.004308341,0.00017095328],"about_ca_topic_score_codex":0.00003278321,"about_ca_topic_score_gemma":0.0000034437442,"teacher_disagreement_score":0.8768596,"about_ca_system_score_codex":0.00042577044,"about_ca_system_score_gemma":0.00020941907,"threshold_uncertainty_score":0.6405781},"labels":[],"label_agreement":null},{"id":"W2793015826","doi":"10.1109/itsc.2017.8317881","title":"Aggressive vehicle control using polynomial spiral curves","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Planner; Controller (irrigation); Control theory (sociology); Polynomial; Computer science; Spiral (railway); Vehicle dynamics; Automotive engineering; Control (management); Towing; Engineering; Simulation; Artificial intelligence; Mathematics; Mechanical engineering","score_opus":0.03914720201211396,"score_gpt":0.3026956251672403,"score_spread":0.26354842315512633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793015826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067167744,0.00045514855,0.9220718,0.0025946286,0.0015697893,0.00022512325,0.000004968302,0.00026124396,0.005649531],"genre_scores_gemma":[0.8333025,0.0000059173053,0.16479397,0.0011571887,0.00027062013,0.0000038212497,4.6970663e-7,0.00000884403,0.00045669088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989534,0.000038388996,0.00015659626,0.0003039532,0.0002272396,0.0003204295],"domain_scores_gemma":[0.9985133,0.000047396945,0.0003084478,0.00096475234,0.000058071375,0.00010804373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021057064,0.00012028501,0.00017999204,0.000038115097,0.0004919597,0.00037623142,0.0014761372,0.00004822517,0.000012608371],"category_scores_gemma":[0.00022065199,0.000103187966,0.00005739675,0.00003917767,0.00008729881,0.00075473095,0.0002793926,0.00011017275,0.000080520455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015184542,0.00084238517,0.3364366,0.00036141518,0.00071321416,0.0063302005,0.0028225102,0.02709598,0.052490182,0.015729163,0.11301622,0.4440103],"study_design_scores_gemma":[0.0010326918,0.000056595938,0.040532153,0.000181425,0.000010977235,0.00006206514,0.000004605215,0.9561199,0.0014461799,0.00012302231,0.0001954015,0.0002349844],"about_ca_topic_score_codex":0.00026850883,"about_ca_topic_score_gemma":0.0000016524084,"teacher_disagreement_score":0.9290239,"about_ca_system_score_codex":0.000028272798,"about_ca_system_score_gemma":0.00008930839,"threshold_uncertainty_score":0.42078832},"labels":[],"label_agreement":null},{"id":"W2793353622","doi":"10.5539/jmr.v10n2p122","title":"Route Planning Based on Genetic Algorithm","year":2018,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crossover; Genetic algorithm; Mathematical optimization; Population; Simulated annealing; Fuel efficiency; Smoothing; Point (geometry); Algorithm; Shortest path problem; Mathematics; Path (computing); Computer science; Engineering; Graph; Artificial intelligence; Statistics; Automotive engineering","score_opus":0.1368333516922947,"score_gpt":0.41618464734490657,"score_spread":0.27935129565261185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793353622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005215853,0.00007312167,0.9903694,0.0006282412,0.0004320543,0.00010764985,0.0000012733481,0.00002934678,0.0031430735],"genre_scores_gemma":[0.03218714,0.0000040524224,0.9669018,0.00009633564,0.0005452546,0.000002620649,1.9651246e-7,0.000018431718,0.0002441878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630445,0.00026139422,0.00057107117,0.0002068506,0.002134561,0.0005216718],"domain_scores_gemma":[0.9967152,0.0011084975,0.0002982262,0.0006577029,0.0009803349,0.00024002814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043954155,0.00013662636,0.00028591568,0.00073921727,0.00022921734,0.0002810687,0.0017908282,0.00008348715,0.000022974553],"category_scores_gemma":[0.0007417428,0.00010551182,0.00009421725,0.0006799174,0.00015347969,0.00020824891,0.00020162495,0.0007107426,0.0002000896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014153282,0.0052227546,0.002589129,0.0007168258,0.0005488195,0.010982311,0.032274727,0.11375933,0.004971222,0.013792556,0.09417571,0.7208251],"study_design_scores_gemma":[0.00044062117,0.001143791,0.00084372127,0.00042634734,0.0000048175543,0.0002895615,0.00011160941,0.9878547,0.0014952663,0.006400462,0.00087351835,0.00011556598],"about_ca_topic_score_codex":0.0000035445503,"about_ca_topic_score_gemma":8.245606e-8,"teacher_disagreement_score":0.8740954,"about_ca_system_score_codex":0.00013695203,"about_ca_system_score_gemma":0.0003734787,"threshold_uncertainty_score":0.4302647},"labels":[],"label_agreement":null},{"id":"W2793901471","doi":"10.1007/s10846-018-0802-z","title":"Collision-Free Trajectory Generation and Tracking for UAVs Using Markov Decision Process in a Cluttered Environment","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Trajectory; Control theory (sociology); Markov decision process; Tracking (education); Backtracking; Process (computing); Computer science; Collision; Controller (irrigation); Stability (learning theory); Markov process; Markov chain; Engineering; Control engineering; Simulation; Artificial intelligence; Control (management); Mathematics; Algorithm; Physics","score_opus":0.06059458382613537,"score_gpt":0.30707737647220507,"score_spread":0.2464827926460697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793901471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20582445,0.0010630931,0.79075444,0.00007107035,0.0018689006,0.00039993896,0.0000011861622,0.000010311781,0.0000066049943],"genre_scores_gemma":[0.692486,0.00006064137,0.30662906,0.000035242483,0.00074462715,0.000009109332,6.5753306e-7,0.000018655357,0.000016007618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742347,0.00015343448,0.0011469705,0.00033947214,0.0006207106,0.00031595546],"domain_scores_gemma":[0.99812704,0.00036445906,0.0007121783,0.00037984794,0.0002712514,0.00014522918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015539334,0.00020533692,0.00045740738,0.00040864715,0.00014092162,0.00026464942,0.0007115564,0.00011474011,0.0000020789266],"category_scores_gemma":[0.00033109396,0.0001717745,0.000090169895,0.0002398595,0.000055915843,0.000530161,0.00009572502,0.00016784965,0.0000029142943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007547186,0.0001502983,0.0016664121,0.00010224741,0.000053372558,0.000066249966,0.003322643,0.97408724,0.0040654163,0.00009065689,0.00033163727,0.01598833],"study_design_scores_gemma":[0.0006759682,0.0005021426,0.0009776356,0.0008626422,0.000024924513,0.000652578,0.00035408692,0.993323,0.002181152,0.0002108332,0.000049574286,0.00018543786],"about_ca_topic_score_codex":0.000016091182,"about_ca_topic_score_gemma":0.0000034616887,"teacher_disagreement_score":0.48666155,"about_ca_system_score_codex":0.00033019995,"about_ca_system_score_gemma":0.00013657263,"threshold_uncertainty_score":0.7004761},"labels":[],"label_agreement":null},{"id":"W2794978095","doi":"10.1504/ijdsss.2018.10011960","title":"Massively parallel hybrid algorithm on embedded graphics processing unit for unmanned aerial vehicle path planning","year":2018,"lang":"en","type":"article","venue":"International Journal of Digital Signals and Smart Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Graphics processing unit; Massively parallel; Computer science; Motion planning; Unit (ring theory); Path (computing); Graphics; Parallel processing; Real-time computing; Computer graphics (images); Parallel computing; Artificial intelligence; Mathematics; Computer network","score_opus":0.03658986226919345,"score_gpt":0.29469456534494914,"score_spread":0.25810470307575567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794978095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064903535,0.0002974643,0.9311025,0.00033706558,0.0026926894,0.00019570862,0.00006712938,0.0000427501,0.00036116558],"genre_scores_gemma":[0.97847253,0.0000067714636,0.01964226,0.00014031859,0.0016080164,0.000008769398,0.0000111050995,0.000017937491,0.00009228606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795693,0.000051177907,0.00066975644,0.00027453378,0.00076949503,0.00027812886],"domain_scores_gemma":[0.9977173,0.0002382674,0.00069065613,0.0001256976,0.0010520562,0.0001759801],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005423139,0.00020020813,0.00032108836,0.00026764296,0.00016885423,0.0013061602,0.00079102133,0.0000637546,0.0000011918219],"category_scores_gemma":[0.00013680114,0.00016498781,0.000115086244,0.0001318875,0.00008584629,0.0011358178,0.00010240208,0.00016069681,0.0000057154257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048257476,0.0033064557,0.038419403,0.0006306202,0.005412829,0.0073707965,0.020827731,0.08089934,0.00835088,0.041650936,0.038990874,0.74931437],"study_design_scores_gemma":[0.0029395479,0.0020910006,0.0013638628,0.0013074758,0.000024827541,0.0012673268,0.00039021735,0.98113805,0.0006284205,0.0037619115,0.004632108,0.0004552552],"about_ca_topic_score_codex":0.000005494027,"about_ca_topic_score_gemma":6.274646e-8,"teacher_disagreement_score":0.91356903,"about_ca_system_score_codex":0.000043909014,"about_ca_system_score_gemma":0.00013493902,"threshold_uncertainty_score":0.9997306},"labels":[],"label_agreement":null},{"id":"W2795933029","doi":"10.1109/vppc.2017.8330908","title":"Energy Efficient Path Planning for Low Speed Autonomous Electric Vehicle","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy consumption; Motion planning; Automotive engineering; Path (computing); Context (archaeology); Energy (signal processing); Computer science; Electric vehicle; Work (physics); Minification; Efficient energy use; Aerodynamics; Engineering; Aerospace engineering; Robot; Electrical engineering; Power (physics); Mathematics; Mechanical engineering; Artificial intelligence","score_opus":0.02299981335710384,"score_gpt":0.2682311666096362,"score_spread":0.24523135325253237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795933029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03352655,0.00006854474,0.9616346,0.00033085272,0.0006731336,0.00013818355,0.0000016178755,0.00031042783,0.003316132],"genre_scores_gemma":[0.8916269,0.0000014721182,0.106659226,0.00025841457,0.00016388425,0.000016283155,0.0000027837114,0.000015930384,0.0012551151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985477,0.000024019688,0.00021659635,0.00042135507,0.00024231568,0.0005480417],"domain_scores_gemma":[0.9983919,0.00013593443,0.00018912724,0.0010668703,0.00007709139,0.00013906938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003200297,0.00017012238,0.00020566156,0.00010573858,0.0006420334,0.00050972967,0.0016418683,0.00007551698,0.0000039104916],"category_scores_gemma":[0.000052611213,0.00015213336,0.00008161707,0.0001110543,0.000028653505,0.00021943006,0.00018361138,0.00008942325,0.000035008215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005073362,0.0005806134,0.0046183593,0.000066227505,0.00012894215,0.00036873508,0.0026826523,0.58576477,0.02134226,0.0824712,0.0125498,0.2893757],"study_design_scores_gemma":[0.00048208915,0.00012055529,0.0042966194,0.000025437683,0.000004478801,0.000017128681,0.0000078718895,0.9882517,0.0054651923,0.00049107964,0.00061647,0.00022141251],"about_ca_topic_score_codex":0.00013602103,"about_ca_topic_score_gemma":6.5429055e-7,"teacher_disagreement_score":0.85810035,"about_ca_system_score_codex":0.000060644656,"about_ca_system_score_gemma":0.00010306268,"threshold_uncertainty_score":0.62038183},"labels":[],"label_agreement":null},{"id":"W2799569726","doi":"10.1139/tcsme-2016-0014","title":"DESIGN AND DEVELOPMENT OF AN AUTONOMOUS OMNIDIRECTIONAL HAZARDOUS MATERIALS HANDLING ROBOT","year":2016,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Robot; Omnidirectional antenna; Simulation; Computer science; Software; Mobile robot; Real-time computing; Engineering; Embedded system; Artificial intelligence","score_opus":0.02224660309522564,"score_gpt":0.21427810376889045,"score_spread":0.1920315006736648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799569726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043615345,0.000019181942,0.9947315,0.00023360222,0.00038982637,0.00019682778,0.000018660856,0.000048003625,8.683566e-7],"genre_scores_gemma":[0.31993514,0.0000013210858,0.6799894,0.000012315821,0.000011869583,0.000022504415,4.654683e-7,0.000008737619,0.000018265822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999237,0.00001539478,0.00023477284,0.00017418426,0.000119922435,0.00021873454],"domain_scores_gemma":[0.9994101,0.00012933048,0.000054769618,0.0002057642,0.000052740645,0.00014730301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043732757,0.00009752548,0.00015622328,0.000042886313,0.0001931433,0.000022041937,0.00032725022,0.000085406245,0.0000045146962],"category_scores_gemma":[0.000021507945,0.000073099494,0.00008610054,0.00010512394,0.000025051146,0.00014548411,0.000010292561,0.000054216536,3.3008882e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009436545,0.00004301665,0.0000012317024,0.00011645398,0.000281162,8.73267e-7,0.0017141111,0.4122558,0.520023,0.0032090286,0.000019189612,0.06232674],"study_design_scores_gemma":[0.0003691372,0.000056440156,0.00007728104,0.00009677652,0.000021280242,0.000020991009,0.000015649503,0.44060585,0.55822873,0.00019253827,0.00015530178,0.00016002201],"about_ca_topic_score_codex":0.0005857599,"about_ca_topic_score_gemma":0.0002743057,"teacher_disagreement_score":0.3155736,"about_ca_system_score_codex":0.00018507312,"about_ca_system_score_gemma":0.0004467315,"threshold_uncertainty_score":0.29809108},"labels":[],"label_agreement":null},{"id":"W2799893902","doi":"10.1117/12.2303014","title":"DRESH: DRone EnSnaring mesH","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Drone; Obstacle; Computer science; Aeronautics; Notional amount; Systems engineering; Aerospace engineering; Control engineering; Engineering; Simulation","score_opus":0.01965874536530586,"score_gpt":0.25535163666055233,"score_spread":0.23569289129524645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799893902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034879807,0.000012949215,0.9292302,0.0006575034,0.00063620677,0.000045367065,1.6055358e-7,0.00042505545,0.06550456],"genre_scores_gemma":[0.33298928,7.3221645e-7,0.66185254,0.0005092842,0.00022918374,0.0000025717575,3.9997633e-7,0.0000058076675,0.0044101886],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991009,0.000021835418,0.00011911656,0.00028576146,0.00020195049,0.00027039056],"domain_scores_gemma":[0.9992538,0.000044177472,0.000030292598,0.00053004676,0.000057100355,0.00008459817],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020643222,0.0000827337,0.00008986409,0.0000612013,0.00010959089,0.00010228602,0.00072427304,0.000036748355,0.000057782403],"category_scores_gemma":[0.000034523,0.00007200838,0.00002391762,0.0002321534,0.00005299221,0.0002641012,0.00028011584,0.00007008184,0.0009854253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000085769525,0.00024253136,0.012474502,0.000024816483,0.00011839232,0.0003434241,0.009475416,0.0006077765,0.009934892,0.6295718,0.1175594,0.21963845],"study_design_scores_gemma":[0.00043618164,0.0002529806,0.0130286645,0.000031236472,0.000005119316,0.00011482637,0.000041197123,0.95501745,0.017521165,0.0050463974,0.00806398,0.00044081887],"about_ca_topic_score_codex":0.00003469786,"about_ca_topic_score_gemma":0.0000021200951,"teacher_disagreement_score":0.95440966,"about_ca_system_score_codex":0.00002027358,"about_ca_system_score_gemma":0.000029879306,"threshold_uncertainty_score":0.9997924},"labels":[],"label_agreement":null},{"id":"W2800392593","doi":"10.1139/tcsme-2017-0008","title":"Inclined landing testing of an omni-directional unmanned aerial vehicle","year":2018,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoupling (probability); Torque; Lift (data mining); Orientation (vector space); Rotor (electric); Aerodynamics; Computer science; Position (finance); Euler angles; Traverse; Engineering; Control theory (sociology); Aerospace engineering; Simulation; Control engineering; Control (management); Mechanical engineering; Physics; Artificial intelligence","score_opus":0.02350383339198982,"score_gpt":0.2358906166057321,"score_spread":0.21238678321374227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800392593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023347154,0.000006210689,0.97505295,0.00027398896,0.0009940173,0.00016530928,0.00004252385,0.00009117632,0.00002664802],"genre_scores_gemma":[0.59067714,1.8782598e-7,0.4091421,0.000031078514,0.00011253461,0.000009252079,0.0000012755461,0.000010121117,0.000016288272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913216,0.00001402027,0.00023223837,0.00018297126,0.00016806889,0.00027053393],"domain_scores_gemma":[0.99917114,0.00016373802,0.000067811896,0.00029519707,0.00013666865,0.00016542475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036552985,0.00009871788,0.00015358598,0.000053994685,0.0002971246,0.000023378869,0.0005438858,0.00010355762,0.000005519745],"category_scores_gemma":[0.00011359926,0.000093153794,0.00021060446,0.00048260277,0.000045134904,0.00017483192,0.000015158513,0.00014277709,6.1615845e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035730332,0.00016765636,0.00006470078,0.00025410613,0.00053110573,0.0000022101415,0.0025679027,0.57236135,0.38449717,0.016980803,0.00022573296,0.02231153],"study_design_scores_gemma":[0.00029702086,0.00012245243,0.0001448404,0.000045705623,0.000021292833,0.000008019195,0.0000201584,0.9451594,0.053647067,0.00030352117,0.00013063541,0.000099883735],"about_ca_topic_score_codex":0.005068939,"about_ca_topic_score_gemma":0.0018736662,"teacher_disagreement_score":0.56733,"about_ca_system_score_codex":0.00015677008,"about_ca_system_score_gemma":0.00029076592,"threshold_uncertainty_score":0.7662754},"labels":[],"label_agreement":null},{"id":"W2800411729","doi":"10.1139/tcsme-2005-0037","title":"SUB-OBJECT PRUNING ALGORITHM: A NOVEL PRUNING STRATEGY FOR DISTANCE DETERMINATION IN COMPLEX DYNAMIC ENVIRONMENTS","year":2005,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Pruning; Algorithm; Regular polygon; Set (abstract data type); Computer science; Object (grammar); Distance transform; Mathematics; Mathematical optimization; Artificial intelligence; Geometry; Image (mathematics)","score_opus":0.01955089744360902,"score_gpt":0.23687858734520145,"score_spread":0.21732768990159243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800411729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007529902,0.000029545168,0.99779636,0.00055912026,0.00015967878,0.0005560505,0.00010054355,0.000041853833,0.000003856489],"genre_scores_gemma":[0.4584591,0.000002624937,0.541336,0.000049291993,0.000015034722,0.00008216756,0.0000075527732,0.000016579248,0.00003166338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988202,0.000009215635,0.00029487823,0.00027519302,0.00017141721,0.00042913284],"domain_scores_gemma":[0.9993349,0.00013699831,0.0000774007,0.00029188368,0.000025005489,0.00013385754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032407817,0.00015688551,0.00018935482,0.000076490775,0.00022464768,0.00004275108,0.0005704392,0.00011760894,0.0000012755827],"category_scores_gemma":[0.000025167174,0.00016394514,0.00026710168,0.000296848,0.000025230085,0.00026938567,0.00001163483,0.00020923687,3.5142705e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017284328,0.000034698798,0.0000011831771,0.000048625985,0.000037566384,3.290287e-7,0.00045377726,0.92885107,0.013592495,0.0012755226,0.000011290153,0.055691708],"study_design_scores_gemma":[0.0005339608,0.00004092737,0.00020512374,0.000058508544,0.000018740455,0.000006651227,0.00004130919,0.9952162,0.0028274406,0.00009906897,0.00078263157,0.00016944547],"about_ca_topic_score_codex":0.00077281764,"about_ca_topic_score_gemma":0.0040825433,"teacher_disagreement_score":0.45770612,"about_ca_system_score_codex":0.0007941216,"about_ca_system_score_gemma":0.0001567051,"threshold_uncertainty_score":0.6685488},"labels":[],"label_agreement":null},{"id":"W2800866650","doi":"10.1139/tcsme-2005-0011","title":"HIGH-PERFORMANCE MULTI-BODY COLLISION DETECTION FOR THE REAL-TIME CONTROL OF A CTS SYSTEM","year":2005,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collision; Collision detection; Computation; Computer science; Trajectory; Robot; Simulation; Control theory (sociology); Relative velocity; Kinematics; Real-time computing; Control (management); Algorithm; Artificial intelligence","score_opus":0.0084219496744749,"score_gpt":0.19686252875530522,"score_spread":0.1884405790808303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800866650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004201316,0.000031256623,0.993931,0.00043396628,0.00050150556,0.00073970575,0.00008340945,0.000076343844,0.0000014937409],"genre_scores_gemma":[0.7828249,0.0000053604263,0.21695475,0.000020888594,0.000042770695,0.00010320873,7.4889556e-7,0.00001336792,0.000034039385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910295,0.000011891243,0.00027717015,0.0001674267,0.0001612217,0.00027935475],"domain_scores_gemma":[0.998976,0.0003217481,0.00009400686,0.00038669814,0.000115266455,0.00010629173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046394172,0.00011735543,0.0002003716,0.0000405708,0.00032802403,0.00002091101,0.0005848397,0.00011770957,8.390922e-7],"category_scores_gemma":[0.000032178395,0.00008770362,0.00033349672,0.00027535108,0.00002326886,0.00014009033,0.0000065462737,0.0001320718,8.302175e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058641886,0.000011769102,2.8937606e-7,0.000100022924,0.000096668635,4.750243e-8,0.00014539508,0.97139275,0.023374673,0.0013112123,0.000017776732,0.0035435115],"study_design_scores_gemma":[0.0006517563,0.00007419212,0.000049474096,0.000074648575,0.000068456255,0.0000048799434,0.000021402866,0.9451828,0.05365204,0.000003978449,0.00013077642,0.000085605505],"about_ca_topic_score_codex":0.0033209736,"about_ca_topic_score_gemma":0.00064054533,"teacher_disagreement_score":0.7786235,"about_ca_system_score_codex":0.00036656225,"about_ca_system_score_gemma":0.00014750609,"threshold_uncertainty_score":0.50203407},"labels":[],"label_agreement":null},{"id":"W2801899731","doi":"10.1504/ijaac.2018.10012533","title":"Optimal path planning for an autonomous articulated vehicle with two trailers","year":2018,"lang":"en","type":"article","venue":"International Journal of Automation and Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Articulated vehicle; Motion planning; Computer science; Path (computing); Engineering; Artificial intelligence; Operations research; Transport engineering; Automotive engineering; Computer network; Robot; Truck","score_opus":0.015655825956710907,"score_gpt":0.29216470364658237,"score_spread":0.2765088776898715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801899731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28607726,0.000018991406,0.71259093,0.0008049937,0.00032807357,0.00007401731,0.0000030010553,0.00004233077,0.00006041934],"genre_scores_gemma":[0.8531214,4.1312964e-7,0.14627759,0.00032771437,0.00024931662,0.0000036323563,0.000001785246,0.000005090996,0.000013061296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911624,0.000040710103,0.00028918363,0.00012601848,0.0003029636,0.00012486357],"domain_scores_gemma":[0.99883235,0.00008769324,0.00030545765,0.000082812716,0.0005970298,0.000094645016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034811525,0.00008398379,0.00013254528,0.00013457975,0.000072162344,0.00022189335,0.00034887553,0.000027703632,0.0000045458974],"category_scores_gemma":[0.00004607239,0.00006578171,0.000035299607,0.000060849605,0.000044988188,0.0007532819,0.000014699912,0.000072308074,0.0000023969187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019577425,0.0006637406,0.009950972,0.000021119618,0.0016449856,0.00051190075,0.02122111,0.5545167,0.041192777,0.016296657,0.00204951,0.3499728],"study_design_scores_gemma":[0.0033561664,0.0008549603,0.016641326,0.000055717832,0.000016388269,0.00034839907,0.00006774706,0.97749823,0.0005453694,0.00023455425,0.00029392855,0.00008721142],"about_ca_topic_score_codex":0.000004475717,"about_ca_topic_score_gemma":3.3573028e-7,"teacher_disagreement_score":0.56704414,"about_ca_system_score_codex":0.00003917323,"about_ca_system_score_gemma":0.00009321043,"threshold_uncertainty_score":0.26825002},"labels":[],"label_agreement":null},{"id":"W2802888198","doi":"10.1139/tcsme-2015-0038","title":"DEVELOPMENT OF TRACKING SYSTEM FOR MOBILE ROBOT USING IMAGE RECOGNITION ALGORITHMS","year":2015,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Mobile robot; Computer science; Image processing; Microprocessor; Computer vision; Robot; Controller (irrigation); Artificial intelligence; PID controller; Tracking (education); Engineering; Embedded system; Control engineering; Image (mathematics)","score_opus":0.058189136404896126,"score_gpt":0.25565144190371997,"score_spread":0.19746230549882385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802888198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021855787,0.000028692377,0.9963505,0.000040048406,0.000698255,0.00055529975,0.0000723131,0.00006589552,0.0000033904778],"genre_scores_gemma":[0.15804368,3.1336617e-7,0.8418014,0.00000825658,0.000027880058,0.00009031793,0.000004206426,0.000017835646,0.000006092257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989505,0.000009761811,0.00035341567,0.00019452584,0.00019054818,0.00030124906],"domain_scores_gemma":[0.99912155,0.000084654945,0.0001033053,0.0002502215,0.0002336214,0.00020664775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060855993,0.00012372246,0.0002126856,0.00006130187,0.00019490779,0.000029954352,0.00044786083,0.00011295615,5.522904e-7],"category_scores_gemma":[0.00003482182,0.00011947435,0.0003239832,0.0002891377,0.000017809454,0.00019684079,0.00001064821,0.000114543756,3.6015265e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073994547,0.00004757516,5.0828214e-7,0.00070252805,0.0002870593,7.195122e-7,0.004209378,0.89556706,0.052494742,0.0007883438,0.000036329697,0.045858327],"study_design_scores_gemma":[0.00036609572,0.000034395867,0.000002382887,0.0001577871,0.000044088818,0.000012178799,0.0003851217,0.90098697,0.097693935,0.00005371237,0.00013254979,0.00013077693],"about_ca_topic_score_codex":0.0009898824,"about_ca_topic_score_gemma":0.0003461958,"teacher_disagreement_score":0.1558581,"about_ca_system_score_codex":0.0006334489,"about_ca_system_score_gemma":0.0006247895,"threshold_uncertainty_score":0.4872023},"labels":[],"label_agreement":null},{"id":"W2804132163","doi":"10.1155/2018/7318207","title":"Multidepot UAV Routing Problem with Weapon Configuration and Time Window","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Integer programming; Computer science; Linear programming; Routing (electronic design automation); Window (computing); Mathematical optimization; Vehicle routing problem; Operations research; Real-time computing; Engineering; Algorithm; Computer network; Mathematics","score_opus":0.007550599702386596,"score_gpt":0.23215095485995974,"score_spread":0.22460035515757315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804132163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3164313,0.00003348515,0.6828932,0.00024901796,0.00012612648,0.000109922286,0.0000010042768,0.000031326745,0.00012458347],"genre_scores_gemma":[0.5906608,0.0000048982815,0.40919653,0.000028556942,0.00007390837,0.0000010925777,0.0000027715027,0.0000057005395,0.000025727953],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989878,0.00002961185,0.00038452167,0.00015991647,0.00029252577,0.00014561026],"domain_scores_gemma":[0.99889857,0.000059086833,0.0005062945,0.00010482591,0.00035493873,0.000076282166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026280695,0.00010591969,0.00016913074,0.00009682321,0.000095582596,0.000054096898,0.0001500194,0.000037403224,0.0000027612896],"category_scores_gemma":[0.000018617005,0.0000844523,0.00002578186,0.00019113811,0.000046518904,0.0012272,0.0000021816975,0.00013050054,0.0000048465745],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084206794,0.00027384152,0.021117602,0.00013522818,0.00023235637,0.00057275256,0.044839423,0.29029214,0.37042886,0.006012434,0.00009454201,0.26515874],"study_design_scores_gemma":[0.0060078045,0.0040436634,0.84407234,0.001083323,0.00011034336,0.0005627027,0.00057704264,0.085316285,0.05507918,0.0021547112,0.00035734285,0.0006352872],"about_ca_topic_score_codex":0.0000045941565,"about_ca_topic_score_gemma":0.000004424497,"teacher_disagreement_score":0.8229547,"about_ca_system_score_codex":0.000023785993,"about_ca_system_score_gemma":0.00006158778,"threshold_uncertainty_score":0.3443865},"labels":[],"label_agreement":null},{"id":"W2804365421","doi":"10.3390/s18072185","title":"Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Flexibility (engineering); Computer science; Mathematical optimization; Constraint (computer-aided design); Motion planning; SAFER; Range (aeronautics); Convexity; Planner; Optimization problem; Convex optimization; Simulation; Robot; Regular polygon; Engineering; Algorithm; Artificial intelligence; Mathematics","score_opus":0.047076309862448346,"score_gpt":0.28813234755868594,"score_spread":0.2410560376962376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804365421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023390282,0.00003606985,0.9740448,0.000665045,0.0005215431,0.0002980516,0.0000054823463,0.00035257297,0.0006861429],"genre_scores_gemma":[0.22922553,0.000001396087,0.76982325,0.0003312144,0.00024101276,0.000005713537,0.000012332133,0.000022916842,0.00033664895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986671,0.00004216879,0.00024511304,0.00046926085,0.00018766981,0.00038864094],"domain_scores_gemma":[0.99909717,0.00018096661,0.00011823012,0.00033401183,0.00014238553,0.00012721487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023837775,0.000183047,0.00024052405,0.00014244311,0.00023489677,0.00015631063,0.00033959353,0.000085164145,0.0000066701414],"category_scores_gemma":[0.00009794052,0.00018306986,0.000039738865,0.00023392645,0.00013305871,0.00022645222,0.00014579332,0.00009808611,0.000019063837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003208633,0.000043250002,0.0031644374,0.000068679146,0.00007991738,0.00006876718,0.013013889,0.969807,0.0011932597,0.0014472887,0.0012858168,0.009795556],"study_design_scores_gemma":[0.00044452303,0.00013646584,0.0034149725,0.000055855537,0.0000099926,0.00008641707,0.000094144874,0.9941603,0.000595996,0.0002292096,0.0005374229,0.00023471616],"about_ca_topic_score_codex":0.000019835472,"about_ca_topic_score_gemma":2.9961853e-7,"teacher_disagreement_score":0.20583525,"about_ca_system_score_codex":0.000043162378,"about_ca_system_score_gemma":0.000041663083,"threshold_uncertainty_score":0.7465372},"labels":[],"label_agreement":null},{"id":"W2805063029","doi":"10.1007/s10846-018-0870-0","title":"Path Planning Generation Algorithm for a Class of UAV Multirotor Based on State of Health of Lithium Polymer Battery","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Motion planning; Battery (electricity); Path (computing); Obstacle avoidance; Computer science; Energy consumption; Mathematical optimization; State of health; Any-angle path planning; Algorithm; Control theory (sociology); Simulation; Engineering; Artificial intelligence; Control (management); Mobile robot; Mathematics; Robot; Power (physics); Electrical engineering","score_opus":0.056395147190419354,"score_gpt":0.3110947946101847,"score_spread":0.25469964741976536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805063029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059159803,0.0010442821,0.9885482,0.00017651395,0.0036589203,0.00059826864,0.000022498374,0.000015399582,0.000019928855],"genre_scores_gemma":[0.6671766,0.000016959328,0.33171484,0.0001634105,0.0007302732,0.000016696054,0.0000064715578,0.00003700086,0.0001377457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99549705,0.000431341,0.0024582953,0.0002990924,0.00092782924,0.00038637072],"domain_scores_gemma":[0.99402833,0.0005444292,0.003636829,0.0005285436,0.0010924351,0.00016942856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027454922,0.0002572474,0.0010322545,0.00056996103,0.00008042532,0.00004967716,0.0007476377,0.00009315841,0.000002253455],"category_scores_gemma":[0.00016313638,0.0002090626,0.00026598637,0.00036040085,0.00010571346,0.00022051686,0.000055636385,0.00019603023,0.0000026980156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010166163,0.0007228158,0.0016880851,0.00095442263,0.00030337318,0.000026990234,0.004711079,0.9135115,0.010313294,0.00017144244,0.0023738223,0.06512153],"study_design_scores_gemma":[0.0006161293,0.0033673316,0.00043503425,0.002133918,0.000022040947,0.000061698614,0.00019864271,0.9519058,0.041032773,0.000011260336,0.000059443573,0.00015592304],"about_ca_topic_score_codex":0.00013246149,"about_ca_topic_score_gemma":5.902143e-7,"teacher_disagreement_score":0.6612606,"about_ca_system_score_codex":0.00017929777,"about_ca_system_score_gemma":0.00048693674,"threshold_uncertainty_score":0.8525325},"labels":[],"label_agreement":null},{"id":"W2805111833","doi":"10.1109/icoa.2018.8370583","title":"Real-time UAV path-terrain collision evaluation on FPGA","year":2018,"lang":"en","type":"article","venue":"2018 4th International Conference on Optimization and Applications (ICOA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Terrain; Field-programmable gate array; Collision; Computer science; Path (computing); Real-time computing; Collision avoidance; Embedded system; Computer security; Geography; Computer network","score_opus":0.05350136287715848,"score_gpt":0.3292772157054131,"score_spread":0.27577585282825456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805111833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001079058,0.000006070794,0.9145752,0.0029079334,0.00043250338,0.00077421486,0.000042117506,0.00024324303,0.07993961],"genre_scores_gemma":[0.31405023,0.00037796106,0.6726663,0.0019565746,0.0012772949,0.0012121842,0.0008891044,0.00006773148,0.0075025973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977009,0.00013391215,0.00038840336,0.000720725,0.0008215531,0.00023450055],"domain_scores_gemma":[0.9979354,0.00011289425,0.00027619154,0.0006272508,0.0008992519,0.00014900438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006041735,0.00023491659,0.00017762407,0.00030949735,0.0003532169,0.0003871951,0.0007904677,0.00012829599,0.00038591638],"category_scores_gemma":[0.000100236415,0.00022901184,0.000042167496,0.00039906413,0.00014469925,0.0004059587,0.00014733913,0.00014128057,0.0006770617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010582223,0.00081044034,0.00013120504,0.000016322234,0.00010459149,0.0000043322552,0.00131973,0.20773982,0.0037613017,0.63600737,0.01907156,0.1309275],"study_design_scores_gemma":[0.00056522666,0.00024043107,0.0004258396,0.00007261865,0.000010552893,0.000008551121,0.00003280529,0.9914718,0.0005691201,0.0025757044,0.0037801398,0.00024719466],"about_ca_topic_score_codex":0.000023853238,"about_ca_topic_score_gemma":0.0000013660924,"teacher_disagreement_score":0.783732,"about_ca_system_score_codex":0.00016246161,"about_ca_system_score_gemma":0.00016813834,"threshold_uncertainty_score":0.9338832},"labels":[],"label_agreement":null},{"id":"W2806851394","doi":"10.1109/access.2018.2845448","title":"Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Motion planning; Mathematical optimization; Path (computing); Sampling (signal processing); State space; Control theory (sociology); Computation; Collision; Algorithm; Robot; Mathematics; Artificial intelligence; Computer vision","score_opus":0.037344186994461966,"score_gpt":0.3052642524935506,"score_spread":0.26792006549908864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806851394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23028895,0.000026512907,0.7673023,0.00010498376,0.0012814448,0.0002507992,0.000004928534,0.00012812701,0.00061200315],"genre_scores_gemma":[0.91502845,8.916988e-7,0.08424382,0.00024732278,0.00028151125,0.00005077242,0.0000035500884,0.000016551305,0.0001271502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984127,0.00004975278,0.0003104913,0.0005230918,0.00021085987,0.0004930488],"domain_scores_gemma":[0.9990767,0.000226572,0.00013122914,0.0004447534,0.00001928431,0.000101461126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036480738,0.00017975172,0.00022181081,0.00018948936,0.00014091813,0.00021955514,0.0014333983,0.000058567486,0.000005549378],"category_scores_gemma":[0.000054969176,0.00018908932,0.00004729983,0.0001589082,0.00011944401,0.00080153364,0.00014809419,0.00013506372,0.00003373071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020066912,0.0016938375,0.24886988,0.0003285483,0.00051131984,0.0029057711,0.02386899,0.22770807,0.089871675,0.0053853956,0.026802875,0.37185296],"study_design_scores_gemma":[0.0019401979,0.0003030787,0.07085912,0.00020689241,0.00001330705,0.00008232485,0.000028938308,0.87459326,0.046709314,0.0018023872,0.0027221977,0.00073899486],"about_ca_topic_score_codex":0.00002623125,"about_ca_topic_score_gemma":0.000002247505,"teacher_disagreement_score":0.6847395,"about_ca_system_score_codex":0.00011021034,"about_ca_system_score_gemma":0.000090682544,"threshold_uncertainty_score":0.7710839},"labels":[],"label_agreement":null},{"id":"W2808524442","doi":"10.1108/ijius-02-2018-0004","title":"Design and development of an aircraft type portable drone for surveillance and disaster management","year":2018,"lang":"en","type":"article","venue":"International Journal of Intelligent Unmanned Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Drone; Payload (computing); Computer science; Real-time computing; Aeronautics; Engineering; Simulation; Computer security","score_opus":0.040485643226361626,"score_gpt":0.3004398229553909,"score_spread":0.2599541797290293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808524442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036602303,0.00034521832,0.9606783,0.00009898287,0.001981273,0.00021766491,0.0000020947423,0.000011437561,0.00006275886],"genre_scores_gemma":[0.50965613,0.000033844226,0.4898969,0.000030778498,0.00018302667,0.0000048627403,0.0000025083707,0.0000088007755,0.00018319275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845207,0.00007530691,0.0006788859,0.00019695595,0.00044143948,0.00015532639],"domain_scores_gemma":[0.9983045,0.00012305583,0.00047324292,0.00015312748,0.00084834197,0.00009775105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012547538,0.0001200757,0.00023088805,0.00021693985,0.000053800326,0.00013102817,0.0006070488,0.000038534323,0.000002521533],"category_scores_gemma":[0.000039671348,0.00010037165,0.00002385467,0.00008127337,0.000053630956,0.00031967802,0.00011823668,0.000055677265,0.0000037894258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003976446,0.0025845303,0.023386078,0.0014216028,0.009129792,0.0010714872,0.06581906,0.11182226,0.014702623,0.046528608,0.009067236,0.7104903],"study_design_scores_gemma":[0.002658869,0.0033082655,0.0075591616,0.0015668586,0.000045555,0.0017424995,0.0021933066,0.93748,0.022890225,0.0014452572,0.01831113,0.0007989076],"about_ca_topic_score_codex":0.000005645574,"about_ca_topic_score_gemma":0.0000012714867,"teacher_disagreement_score":0.8256577,"about_ca_system_score_codex":0.000061320985,"about_ca_system_score_gemma":0.000067113055,"threshold_uncertainty_score":0.40930372},"labels":[],"label_agreement":null},{"id":"W2808619761","doi":"10.1108/ir-11-2017-0200","title":"Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints","year":2018,"lang":"en","type":"article","venue":"Industrial Robot the international journal of robotics research and application","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Robot; Computer science; Kinematics; Energy consumption; Trajectory; Climbing; Obstacle; Mobile robot; Control theory (sociology); Mathematical optimization; Control engineering; Simulation; Artificial intelligence; Engineering; Mathematics; Control (management)","score_opus":0.09203694439260827,"score_gpt":0.3539974542441305,"score_spread":0.2619605098515222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808619761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021008067,0.00006715839,0.9747198,0.0034263732,0.00038212066,0.00016754912,0.000004034541,0.000017570961,0.00020730926],"genre_scores_gemma":[0.9349964,0.000022751785,0.06402736,0.00004450102,0.0008375708,0.000007074893,0.000005870844,0.0000125921115,0.000045833745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703985,0.00019460329,0.0006154175,0.000274693,0.0015414264,0.00033403118],"domain_scores_gemma":[0.9963756,0.0005991206,0.0006571009,0.0003160739,0.0019016785,0.00015037706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020324204,0.00015275266,0.0002412027,0.00047678995,0.00022830753,0.00026757966,0.0016371126,0.00013233187,0.000003972803],"category_scores_gemma":[0.0002600494,0.00010849467,0.000051704512,0.000568819,0.0007148676,0.00040844662,0.00030524025,0.00061705266,0.0000037514847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009553916,0.0005007807,0.00496825,0.000013876985,0.0006007749,0.00011550373,0.0014720411,0.59314275,0.047958363,0.049202945,0.0008402069,0.3002291],"study_design_scores_gemma":[0.003679733,0.002375614,0.0031626471,0.00068816415,0.00004488714,0.0012719447,0.0005742691,0.9596968,0.021546701,0.006281697,0.0003212364,0.00035626386],"about_ca_topic_score_codex":0.0000891644,"about_ca_topic_score_gemma":0.0000039523825,"teacher_disagreement_score":0.91398835,"about_ca_system_score_codex":0.00015005207,"about_ca_system_score_gemma":0.0003585778,"threshold_uncertainty_score":0.4424284},"labels":[],"label_agreement":null},{"id":"W2809901948","doi":"10.2514/6.2018-3260","title":"Automatic Patrol Trajectory Control of UAV in A Forest Surveillance and Fires Detection Mission","year":2018,"lang":"en","type":"article","venue":"2018 Aviation Technology, Integration, and Operations Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Trajectory; Aeronautics; Computer science; Control (management); Remote sensing; Artificial intelligence; Engineering; Geography","score_opus":0.011836177664456746,"score_gpt":0.2437084337578848,"score_spread":0.23187225609342807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809901948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28138164,0.00011954367,0.7170546,0.00086073345,0.00013412611,0.0002613823,0.000006362899,0.00013509604,0.00004652348],"genre_scores_gemma":[0.95875126,0.000048866495,0.04104094,0.00004172937,0.00002646022,0.000042456228,0.0000068406453,0.000005437395,0.000035981506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988258,0.00012202071,0.00041143133,0.00034314088,0.00013525161,0.00016235487],"domain_scores_gemma":[0.99902385,0.00009368835,0.0001297249,0.00031401636,0.00039574777,0.000042965734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041365236,0.00014720391,0.00022029619,0.00045016932,0.00021852853,0.00010368373,0.00023964654,0.00018738718,0.000013632222],"category_scores_gemma":[0.0004164089,0.00013029242,0.000016694295,0.0005176398,0.00029524043,0.00047813688,0.000040878444,0.00015297135,0.0000063989232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028848943,0.00026433033,0.18551742,0.000082606,0.000054200926,0.0000045579363,0.0066905874,0.0007580045,0.08354029,0.1284869,0.0001911955,0.5943811],"study_design_scores_gemma":[0.00053397677,0.00027623476,0.11698811,0.00007925482,0.0000050233866,0.00001684135,0.00015569378,0.8720744,0.0064199255,0.0032901901,0.000024883695,0.0001354505],"about_ca_topic_score_codex":0.00022448145,"about_ca_topic_score_gemma":0.0017607715,"teacher_disagreement_score":0.87131643,"about_ca_system_score_codex":0.000039522543,"about_ca_system_score_gemma":0.0001164996,"threshold_uncertainty_score":0.53131706},"labels":[],"label_agreement":null},{"id":"W2883371381","doi":"10.1155/2018/6574183","title":"An Optimization Problem for Quadcopter Reference Flight Trajectory Generation","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Postdoctoral Program for Innovative Talents; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Quadcopter; Trajectory optimization; Trajectory; Control theory (sociology); Computer science; Optimization problem; Optimal control; Mathematical optimization; Engineering; Mathematics; Aerospace engineering; Algorithm; Control (management); Physics","score_opus":0.0274847249017842,"score_gpt":0.28905473884137184,"score_spread":0.2615700139395876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883371381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039619204,0.000041308962,0.9592696,0.0001411908,0.00065471145,0.00018677264,0.0000047150293,0.00004084186,0.000041660267],"genre_scores_gemma":[0.29561317,0.00001224141,0.7039324,0.000057897705,0.00032166883,0.000007494977,0.000034160363,0.00000840869,0.000012530717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988181,0.000039477152,0.00050979236,0.00020438219,0.00027232847,0.0001558927],"domain_scores_gemma":[0.99847674,0.000032741726,0.00048042802,0.00018302117,0.0007409908,0.00008604533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033233778,0.000111938214,0.00016032414,0.00014861922,0.00011210921,0.000059656242,0.00030990923,0.000065359935,0.000005632963],"category_scores_gemma":[0.00001536665,0.00010103382,0.000053193042,0.00019265988,0.000025730267,0.0019577474,0.0000012344839,0.00009590443,0.0000017742002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005149754,0.00006244962,0.00008551128,0.000013420435,0.000012865472,0.000004898402,0.0023786905,0.9521507,0.028323535,0.001071052,0.00006998105,0.01577542],"study_design_scores_gemma":[0.0022370527,0.003184002,0.014773456,0.00012846335,0.000059374725,0.000035257955,0.00014530588,0.9480212,0.028758125,0.0013344816,0.0009349173,0.00038836995],"about_ca_topic_score_codex":0.0000014716095,"about_ca_topic_score_gemma":0.000008477198,"teacher_disagreement_score":0.25599396,"about_ca_system_score_codex":0.000045848406,"about_ca_system_score_gemma":0.00009971877,"threshold_uncertainty_score":0.41200393},"labels":[],"label_agreement":null},{"id":"W2883949186","doi":"10.3233/jifs-169764","title":"Mathematical model analysis of an intelligent control system for open architecture robots","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"PMAC; Computer science; Inverse kinematics; Robot; Robot control; Modular design; Robot kinematics; Control engineering; Control system; Mobile robot; Artificial intelligence; Engineering","score_opus":0.04590434328322807,"score_gpt":0.3219484642589224,"score_spread":0.2760441209756943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883949186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00493505,0.00034743923,0.99180996,0.0001294464,0.0011388779,0.0009317367,0.000038212318,0.00004429368,0.0006250024],"genre_scores_gemma":[0.726589,0.0000044501485,0.2727553,0.000051979343,0.00039409928,0.000028866581,0.000004159013,0.000026682173,0.00014547179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995458,0.0003671793,0.0022685179,0.0004569451,0.00095977465,0.0004895903],"domain_scores_gemma":[0.9945173,0.0005383855,0.0019271239,0.0010487492,0.0015554464,0.00041299255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037792178,0.00033906536,0.0016365286,0.0009764606,0.00013828326,0.00046213152,0.0037593513,0.00017420338,0.000004556696],"category_scores_gemma":[0.0002500106,0.00024868926,0.0005910136,0.0009974099,0.00010951121,0.0005232883,0.0002574414,0.00028260244,0.00001690691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016705201,0.00029114957,0.0001306784,0.00026776188,0.0021264022,0.000036162484,0.0026029053,0.964993,0.00041370728,0.02505327,0.000302507,0.003615388],"study_design_scores_gemma":[0.0004692163,0.0011262916,0.000037985392,0.00063629413,0.00073673186,0.0003403756,0.00064253964,0.99190694,0.0020623493,0.0017184656,0.00007649656,0.00024630505],"about_ca_topic_score_codex":0.000045688248,"about_ca_topic_score_gemma":0.000004709225,"teacher_disagreement_score":0.72165394,"about_ca_system_score_codex":0.00025846838,"about_ca_system_score_gemma":0.00028105345,"threshold_uncertainty_score":0.99999654},"labels":[],"label_agreement":null},{"id":"W2884685495","doi":"10.1155/2018/8269698","title":"An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Mobile robot; Path (computing); Computer science; Robot; Mobile robot navigation; Artificial intelligence; Engineering; Simulation; Systems engineering; Robot control; Computer network","score_opus":0.03347143416569224,"score_gpt":0.37031094561350897,"score_spread":0.3368395114478167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884685495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40454978,0.0018487185,0.5931354,0.00004458648,0.0003377554,0.00005146886,0.000009141638,0.000011879469,0.000011251852],"genre_scores_gemma":[0.50824165,0.000042625754,0.4916454,0.000025592472,0.000034827062,4.9939956e-7,0.000003486969,0.0000043739306,0.0000015791961],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987074,0.00008677687,0.0006395153,0.00015647493,0.00029333113,0.000116489704],"domain_scores_gemma":[0.99834144,0.00010684527,0.000883725,0.00017480952,0.00041361735,0.00007953527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006540752,0.00009928757,0.00029997985,0.00014457278,0.000043523316,0.000013039963,0.00028102729,0.00005377279,0.0000029379116],"category_scores_gemma":[0.000035238958,0.00009195957,0.000075026095,0.00019595062,0.00007625654,0.0008236503,0.000004615537,0.0001808799,1.7501428e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059427205,0.00083705975,0.035168048,0.000864266,0.00039684513,0.0003640654,0.017440239,0.23803075,0.33444154,0.03644136,0.00009765712,0.3353239],"study_design_scores_gemma":[0.0022908545,0.0022859008,0.82258916,0.00093882205,0.00009471237,0.00013312098,0.00026967758,0.05841872,0.10192415,0.010301501,0.00048384993,0.0002695461],"about_ca_topic_score_codex":0.000003831584,"about_ca_topic_score_gemma":4.2389374e-7,"teacher_disagreement_score":0.7874211,"about_ca_system_score_codex":0.000018131523,"about_ca_system_score_gemma":0.00009278608,"threshold_uncertainty_score":0.37500024},"labels":[],"label_agreement":null},{"id":"W2884886191","doi":"10.2316/journal.206.2018.4.206-5337","title":"AN ONLINE PATH PLANNING METHOD BASED ON HYBRID QUANTUM ANT COLONY OPTIMIZATION FOR AUV","year":2018,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Heilongjiang Province","keywords":"Ant colony optimization algorithms; Computer science; Path (computing); ANT; Motion planning; Quantum; Mathematical optimization; Artificial intelligence; Computer network; Mathematics; Physics; Robot","score_opus":0.036161121886917495,"score_gpt":0.3452874504671935,"score_spread":0.309126328580276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884886191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009434909,0.000016587002,0.9872574,0.0016796234,0.0014397815,0.00009412391,0.000016857652,0.000044374985,0.0000162938],"genre_scores_gemma":[0.26242754,0.0000033850856,0.73675764,0.00036033615,0.0004073836,0.0000012726582,0.000029902083,0.000007863385,0.000004658293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998733,0.00008306052,0.00041709465,0.00017739079,0.00045987862,0.00012960036],"domain_scores_gemma":[0.99815243,0.0002534261,0.00052762934,0.0001359514,0.0008451715,0.000085400025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007127252,0.00011256306,0.00016023894,0.00029208892,0.00010086304,0.00023247988,0.00048166,0.00004398036,0.0000029851742],"category_scores_gemma":[0.0002105838,0.00009955669,0.000050118808,0.00009340051,0.000026881107,0.00052708964,0.000031098145,0.00010217662,8.953703e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003942747,0.00012849362,0.00016202604,0.0000049009727,0.00002611155,0.000023609458,0.00018169572,0.9770918,0.00022038042,0.0018472906,0.00025434772,0.020019898],"study_design_scores_gemma":[0.0007554885,0.0010055096,0.0022713044,0.00017106297,0.00001384934,0.00011659103,0.000018689454,0.99376446,0.00046431995,0.0012076409,0.000102583115,0.000108493725],"about_ca_topic_score_codex":0.0000029588336,"about_ca_topic_score_gemma":1.3306476e-7,"teacher_disagreement_score":0.25299263,"about_ca_system_score_codex":0.00007485235,"about_ca_system_score_gemma":0.000111145186,"threshold_uncertainty_score":0.4059804},"labels":[],"label_agreement":null},{"id":"W2885261226","doi":"10.1007/s10846-018-0899-0","title":"Investigating Human-Robot Teams for Learning-Based Semi-autonomous Control in Urban Search and Rescue Environments","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Teleoperation; Robot; Urban search and rescue; Reinforcement learning; Task (project management); Computer science; Search and rescue; Controller (irrigation); Artificial intelligence; Rescue robot; Mobile robot; Autonomous robot; Robot control; Human–computer interaction; Simulation; Engineering; Systems engineering","score_opus":0.031667573767621665,"score_gpt":0.2873618152447357,"score_spread":0.25569424147711406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885261226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0447266,0.00073476136,0.9526679,0.00024489476,0.0009997586,0.00055900327,0.0000011261296,0.000031860644,0.00003410871],"genre_scores_gemma":[0.9606229,0.000007342342,0.038340483,0.00013073493,0.0005617581,0.00001796365,0.0000016088527,0.000030994204,0.0002862013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99652505,0.0004951052,0.001317312,0.00042541488,0.00065658794,0.0005805403],"domain_scores_gemma":[0.9980436,0.00055695977,0.0005090592,0.00037728585,0.00020351796,0.000309562],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031111026,0.00028327774,0.0006788446,0.00045646206,0.00025072304,0.0002880342,0.000862039,0.00015158522,0.0000028599982],"category_scores_gemma":[0.0004288258,0.00024874683,0.00012703815,0.000278867,0.00020754623,0.00031182275,0.000103520724,0.00055344816,0.000017092245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014497444,0.0000938333,0.020731578,0.00008340424,0.00006604724,0.00004377855,0.0016618961,0.97252196,0.003400268,0.00025983053,0.00013065673,0.0009922312],"study_design_scores_gemma":[0.0012465508,0.0014133838,0.0034824193,0.00086011406,0.000027275082,0.00020316226,0.00038947625,0.9901952,0.0016363682,0.00005426625,0.00023746052,0.00025429358],"about_ca_topic_score_codex":0.0001925022,"about_ca_topic_score_gemma":0.0000066911357,"teacher_disagreement_score":0.9158963,"about_ca_system_score_codex":0.0003918818,"about_ca_system_score_gemma":0.00022618152,"threshold_uncertainty_score":0.9999965},"labels":[],"label_agreement":null},{"id":"W2885784061","doi":"10.1609/aiide.v14i1.13010","title":"Generating Paths with WFC","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Sketch; Minor (academic); USable; Path (computing); Computer science; Dynamic programming; Simple (philosophy); Motion (physics); Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Programming language","score_opus":0.04982716986534391,"score_gpt":0.284651656533928,"score_spread":0.23482448666858408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885784061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60877776,0.00006859398,0.35620713,0.003841294,0.0021900707,0.001965006,0.00010474982,0.00024184253,0.026603574],"genre_scores_gemma":[0.99024194,0.000023726501,0.008972759,0.00025316063,0.00014071165,0.0000733334,0.0000051433412,0.000023725217,0.00026546817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972113,0.000019904244,0.0006652422,0.0010028076,0.00067703484,0.00042376135],"domain_scores_gemma":[0.9976988,0.00010760079,0.0008328636,0.00044120578,0.0007828462,0.00013668084],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002555951,0.000510207,0.00047881098,0.00016466592,0.00018347523,0.0015197195,0.001839518,0.00014772873,0.000015521924],"category_scores_gemma":[0.00022675986,0.0003415703,0.00014917408,0.00020353607,0.00038877616,0.0008337222,0.0021854793,0.00068687485,0.00003316948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010057319,0.0023015526,0.003780109,0.00072881643,0.0010446622,0.000033663728,0.033434212,0.0039427113,0.012863651,0.24678469,0.0011808136,0.6928994],"study_design_scores_gemma":[0.000117465395,0.0023160737,0.00030201918,0.005252033,0.000062546416,0.00005556727,0.0032048225,0.7097701,0.21855894,0.059049692,0.00016132086,0.0011494285],"about_ca_topic_score_codex":0.00002462008,"about_ca_topic_score_gemma":0.0000013915328,"teacher_disagreement_score":0.7058274,"about_ca_system_score_codex":0.0001336674,"about_ca_system_score_gemma":0.00013161283,"threshold_uncertainty_score":0.9999036},"labels":[],"label_agreement":null},{"id":"W2887367103","doi":"10.1007/978-3-319-07153-4_40-2","title":"Parallel Meta-Heuristic Search","year":2024,"lang":"fr","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Meta heuristic; Computer science; Heuristic; Artificial intelligence; Algorithm","score_opus":0.18726326487931907,"score_gpt":0.31629999639808165,"score_spread":0.12903673151876258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887367103","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.341831e-7,0.007405917,0.4715927,0.003893463,0.0027411582,0.0002803508,0.000036914837,0.00038364957,0.5136655],"genre_scores_gemma":[0.00021641375,0.00020534947,0.3526926,0.00037635613,0.00046656292,0.00001995836,0.000016748463,0.00008388903,0.6459221],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99473196,0.000106374355,0.0008756955,0.001872439,0.00136394,0.0010495838],"domain_scores_gemma":[0.9964726,0.00076335156,0.000146449,0.0017917486,0.0002984942,0.0005273374],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010713103,0.0010126432,0.0013392238,0.0004202849,0.00023492747,0.0008796948,0.002226314,0.0006154062,0.004296582],"category_scores_gemma":[0.00006389159,0.00083762483,0.00094881625,0.0002153367,0.0005041097,0.00032469232,0.0015031185,0.0017041746,0.046744872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027754934,0.000025845651,0.0000030013098,0.00022310467,0.0025549005,0.0029823473,0.0002455114,0.009809594,0.0000025516656,0.97272563,0.0043605925,0.0070641367],"study_design_scores_gemma":[0.00017211238,0.0002266955,0.000031067677,0.00034070705,0.0026772425,0.00072667387,0.000007593744,0.5879975,0.00001069697,0.14030886,0.26637825,0.0011226246],"about_ca_topic_score_codex":0.00034804747,"about_ca_topic_score_gemma":0.0000055468768,"teacher_disagreement_score":0.8324168,"about_ca_system_score_codex":0.00016800217,"about_ca_system_score_gemma":0.00041788965,"threshold_uncertainty_score":0.9994075},"labels":[],"label_agreement":null},{"id":"W2887966089","doi":"10.1109/civemsa.2018.8439996","title":"A Hybrid Architecture for Planning and Execution of Multi-Behavior Data Acquisition Missions","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Mobile robot; Motion planning; Robot; Mobile robot navigation; Relation (database); Component (thermodynamics); Architecture; Artificial intelligence; Distributed computing; Robot control; Data mining","score_opus":0.08314294917837733,"score_gpt":0.3461681974825041,"score_spread":0.26302524830412677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887966089","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016658338,0.000072595845,0.98242646,0.00020979128,0.0001794649,0.00023986006,0.000048716403,0.00009334098,0.00007145372],"genre_scores_gemma":[0.18552487,6.220274e-7,0.8141766,0.00008072645,0.00006690827,0.000011715329,0.000048226422,0.0000050742474,0.00008525345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991738,0.00002579841,0.00015705796,0.00035571802,0.000120991586,0.0001666121],"domain_scores_gemma":[0.99906665,0.000090692105,0.00007541273,0.0006234451,0.000072321265,0.000071450166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022921334,0.00008712566,0.00011280553,0.00008329837,0.00012339359,0.00004114528,0.0005531926,0.000034598736,0.0000033938911],"category_scores_gemma":[0.00006669442,0.00007240346,0.000016644071,0.00008315214,0.00006709774,0.000241831,0.00032953412,0.00005378185,0.0000025329168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017869273,0.0012588972,0.014155926,0.00032260016,0.00015332445,0.00014313802,0.012250097,0.0034671312,0.20194252,0.0077654677,0.043495696,0.7148665],"study_design_scores_gemma":[0.0005377192,0.0001972199,0.012701617,0.00008370834,0.000018828416,0.0000971367,0.000032466196,0.977638,0.0075816046,0.00039279036,0.000579172,0.00013973261],"about_ca_topic_score_codex":0.000016442507,"about_ca_topic_score_gemma":5.227697e-7,"teacher_disagreement_score":0.97417086,"about_ca_system_score_codex":0.0000077515,"about_ca_system_score_gemma":0.000031547926,"threshold_uncertainty_score":0.29525274},"labels":[],"label_agreement":null},{"id":"W2888739182","doi":"10.1155/2018/5041401","title":"A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; National Research Foundation","keywords":"Obstacle; Obstacle avoidance; Collision avoidance; Potential field; Gaussian; Field (mathematics); Computer science; Collision; Artificial intelligence; Control theory (sociology); Mathematics; Mobile robot; Physics; Robot; Geography; Computer security","score_opus":0.01915253507176082,"score_gpt":0.3085841765788177,"score_spread":0.28943164150705686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888739182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24333286,0.00003548089,0.7554868,0.00019453996,0.00072260346,0.00015139146,0.0000088003135,0.00005374232,0.000013770159],"genre_scores_gemma":[0.2608818,0.000007407594,0.7386862,0.00006640953,0.00029737907,0.0000031463223,0.000005230968,0.000016970413,0.00003540822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820334,0.000086645116,0.00068043405,0.00031250485,0.00040052156,0.00031655934],"domain_scores_gemma":[0.99825305,0.00015999914,0.00069396716,0.00028917872,0.0004362783,0.00016755443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000588271,0.00017486911,0.00031195214,0.00016282927,0.00019866701,0.0000881027,0.000517105,0.00009553915,0.000006745132],"category_scores_gemma":[0.00005029165,0.0001690495,0.00013651878,0.00021175761,0.000031253512,0.0015801087,0.0000064931533,0.00018071177,0.0000030303345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003101076,0.00020103969,0.00015211978,0.000041005096,0.000070465176,0.00018518267,0.005811445,0.5014326,0.38821858,0.0006181341,0.000031637013,0.102927685],"study_design_scores_gemma":[0.0031589202,0.0028893535,0.028383257,0.00026128205,0.00015004481,0.00037293238,0.0004857717,0.84890413,0.10831056,0.006338385,0.00017158152,0.0005737992],"about_ca_topic_score_codex":0.000036579477,"about_ca_topic_score_gemma":0.000009770878,"teacher_disagreement_score":0.3474715,"about_ca_system_score_codex":0.00009370785,"about_ca_system_score_gemma":0.00018461533,"threshold_uncertainty_score":0.68936384},"labels":[],"label_agreement":null},{"id":"W2888975086","doi":"10.1115/1.4041332","title":"Cooperative Robot Exploration Strategy Using an Efficient Backtracking Method for Multiple Robots","year":2018,"lang":"en","type":"article","venue":"Journal of Mechanisms and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Backtracking; Robot; Workspace; Computer science; Mobile robot; Node (physics); Position (finance); Ant robotics; Tree (set theory); Artificial intelligence; Engineering; Robot control; Algorithm","score_opus":0.09455204959470602,"score_gpt":0.3454226555060254,"score_spread":0.2508706059113194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888975086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010635819,0.00007685539,0.99724734,0.00020688231,0.0011486901,0.00021741395,0.0000029598755,0.000028056296,0.000008208005],"genre_scores_gemma":[0.056513455,0.0000088286615,0.9429505,0.000100727266,0.00039115106,0.000001708957,0.0000017388119,0.00001901087,0.000012881596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828315,0.00018040156,0.0005596492,0.00029532553,0.00035942532,0.00032205967],"domain_scores_gemma":[0.9979172,0.00022140167,0.00053228653,0.00023970913,0.00087999826,0.00020941172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014577613,0.00020371465,0.00036250087,0.00018265378,0.0003936215,0.00030952558,0.00041518916,0.00011950407,0.0000019013441],"category_scores_gemma":[0.00014571106,0.0001656755,0.00007769014,0.000233951,0.000042771204,0.00086330296,0.00009044216,0.00019889501,0.0000011962179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002052672,0.00010231848,0.0000038542476,0.0000102665335,0.000030247618,0.000016868364,0.0009910029,0.9615052,0.018093083,0.0129690375,0.00001247315,0.0062451535],"study_design_scores_gemma":[0.00079182396,0.0015896067,0.000053910164,0.00009403814,0.000044435274,0.00028956364,0.00040135003,0.98506635,0.007565652,0.0038917176,0.0000070071455,0.00020456608],"about_ca_topic_score_codex":0.000009736678,"about_ca_topic_score_gemma":0.00000437186,"teacher_disagreement_score":0.055449873,"about_ca_system_score_codex":0.00006208582,"about_ca_system_score_gemma":0.0001696995,"threshold_uncertainty_score":0.67560506},"labels":[],"label_agreement":null},{"id":"W2889236971","doi":"10.1109/ccece.2018.8447767","title":"Circular ARC-Based Optimal Path Planning for Skid-Steer Rovers","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Skid (aerodynamics); Turning radius; Path (computing); Arc (geometry); Energy consumption; RADIUS; Computer science; Mathematical optimization; Control theory (sociology); Engineering; Mathematics; Robot; Aerospace engineering; Geometry; Artificial intelligence; Electrical engineering; Structural engineering","score_opus":0.029811238140973247,"score_gpt":0.2832918133677555,"score_spread":0.25348057522678225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889236971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004218363,0.00002873561,0.9908782,0.00041972788,0.0007278998,0.00023703839,0.0000051305324,0.0003658239,0.0031190517],"genre_scores_gemma":[0.18048298,1.2548873e-7,0.81760097,0.0011552417,0.00024146397,0.00002700502,0.000007902526,0.000016132106,0.0004682047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841225,0.0000356153,0.00021493544,0.00052148016,0.0003085551,0.00050714065],"domain_scores_gemma":[0.99881816,0.00019710734,0.00008195094,0.00061371626,0.00013802534,0.00015103731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040436044,0.00017975012,0.00018635063,0.00010924864,0.00020350647,0.00015679863,0.0008337934,0.00008472485,0.000020193025],"category_scores_gemma":[0.000112041234,0.00016202898,0.00009591089,0.00023575046,0.0000773333,0.00028379174,0.00010946409,0.000097206605,0.000105225314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019798796,0.00073467445,0.01489427,0.0003015152,0.00044405833,0.0007245656,0.010178672,0.5981169,0.013218568,0.055771083,0.25150973,0.053907994],"study_design_scores_gemma":[0.0006812151,0.00030487977,0.0009913095,0.00003889304,0.000007781609,0.000013612245,0.000039501763,0.9842774,0.0038223998,0.00024901354,0.009309168,0.00026481578],"about_ca_topic_score_codex":0.000016542177,"about_ca_topic_score_gemma":8.1819906e-8,"teacher_disagreement_score":0.38616052,"about_ca_system_score_codex":0.000052766234,"about_ca_system_score_gemma":0.00014302963,"threshold_uncertainty_score":0.660735},"labels":[],"label_agreement":null},{"id":"W2889612086","doi":"10.1111/cgf.13508","title":"Collision‐Aware and Online Compression of Rigid Body Simulations via Integrated Error Minimization","year":2018,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pipeline (software); Visualization; Data compression; Compression (physics); Minification; Computational science; Simulation; Computer engineering; Algorithm; Artificial intelligence","score_opus":0.02065581543110199,"score_gpt":0.2809005795100149,"score_spread":0.2602447640789129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889612086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04199367,0.000052045652,0.9560939,0.00045287394,0.00095602666,0.00021973893,0.00004056427,0.00017531168,0.000015834523],"genre_scores_gemma":[0.6828168,0.000006718162,0.316653,0.0002758564,0.000093255905,0.0000022282356,0.000117479736,0.0000136086755,0.000021017142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984133,0.00010252819,0.00042994358,0.00045133688,0.00031079372,0.00029209076],"domain_scores_gemma":[0.9983483,0.0002000582,0.00023381502,0.0005803404,0.00050887745,0.00012860175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001303755,0.00020672118,0.00027909913,0.00036701,0.00027209337,0.00008543194,0.0006225401,0.00013672489,0.0000037567677],"category_scores_gemma":[0.000027220218,0.00018330675,0.000058459773,0.000990056,0.00022670151,0.00037579995,0.00044827434,0.0001717262,0.000004079754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034713245,0.005525843,0.22332829,0.0006062883,0.00097079086,0.00019754028,0.013089058,0.2891779,0.0066854614,0.18491249,0.07975519,0.19540402],"study_design_scores_gemma":[0.00048559566,0.00032279736,0.012405797,0.0001476048,0.000011241684,0.000018301032,0.000013505919,0.98265773,0.00045029024,0.0021946009,0.0010999014,0.00019265995],"about_ca_topic_score_codex":0.000035935223,"about_ca_topic_score_gemma":0.000014603908,"teacher_disagreement_score":0.6934798,"about_ca_system_score_codex":0.000017481381,"about_ca_system_score_gemma":0.000059182275,"threshold_uncertainty_score":0.7475032},"labels":[],"label_agreement":null},{"id":"W2891189147","doi":"10.1109/icra.2018.8463196","title":"Model-Based Probabilistic Pursuit via Inverse Reinforcement Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Probabilistic logic; Machine learning; Motion planning; Graph; Robot; Model predictive control; Control (management); Theoretical computer science","score_opus":0.03261120911237201,"score_gpt":0.2590492518889098,"score_spread":0.2264380427765378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891189147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008856749,0.0000030133103,0.98326755,0.00040147005,0.0002698819,0.00018037416,1.04551326e-7,0.00048575777,0.0145062],"genre_scores_gemma":[0.44315252,1.9681035e-7,0.55255914,0.00075728755,0.00007002768,0.000017440638,0.0000026419846,0.000009221068,0.0034315463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851286,0.000048726502,0.00022804421,0.00040421533,0.0004184993,0.0003876376],"domain_scores_gemma":[0.9989873,0.00006133692,0.000083444604,0.00057618675,0.00014685978,0.00014486794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043291654,0.00015222066,0.00013594974,0.00010365846,0.00019435772,0.00013375521,0.0008654524,0.000058410176,0.00005493634],"category_scores_gemma":[0.00012566564,0.00013506763,0.00004458494,0.00030686363,0.000105045016,0.00027610146,0.00022779923,0.00015571964,0.000591338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003261781,0.000020062336,0.00012278814,0.000009052097,0.000005979202,0.000008121154,0.00033740822,0.98886395,0.00032783608,0.0044874265,0.0013390206,0.004475107],"study_design_scores_gemma":[0.00027338965,0.00023640264,0.000059510894,0.000018646922,0.000004754366,0.0000051234697,0.000005887743,0.99656475,0.00071434217,0.0014892332,0.00044076666,0.0001872039],"about_ca_topic_score_codex":0.000032585252,"about_ca_topic_score_gemma":0.000003645312,"teacher_disagreement_score":0.44226685,"about_ca_system_score_codex":0.000091591675,"about_ca_system_score_gemma":0.0001543842,"threshold_uncertainty_score":0.7600651},"labels":[],"label_agreement":null},{"id":"W2891636475","doi":"10.1007/s10846-018-0929-y","title":"Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; Université de Lorraine","keywords":"Collision avoidance; Path (computing); Computer science; Track (disk drive); Collision; Extended Kalman filter; Collision avoidance system; Simulation; Real-time computing; Engineering; Kalman filter; Artificial intelligence","score_opus":0.013374598844804847,"score_gpt":0.23686020392510831,"score_spread":0.22348560508030346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891636475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22270289,0.0017498678,0.77223796,0.00014722296,0.002859312,0.00025486696,0.0000010777943,0.000011345946,0.000035483397],"genre_scores_gemma":[0.97743386,0.000063074636,0.022073101,0.000032225584,0.00034225883,0.0000036589206,2.20194e-7,0.000012819752,0.00003878354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721366,0.0002933923,0.0012206441,0.0002674067,0.00067913916,0.0003257813],"domain_scores_gemma":[0.9981943,0.0003104628,0.00084434025,0.00035432156,0.00013897658,0.00015759502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017610029,0.0001943993,0.0006790411,0.0002456939,0.000072611656,0.00010220301,0.0006296124,0.00010065731,0.0000021381666],"category_scores_gemma":[0.00018577925,0.00016062314,0.00013593477,0.00023087597,0.000085895656,0.00031185203,0.000106764965,0.00020487756,0.000015844857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002724118,0.0005170919,0.034294914,0.0001628486,0.00032209003,0.0008186688,0.006045829,0.9337025,0.01537532,0.0015466491,0.00031393938,0.006627725],"study_design_scores_gemma":[0.0020139224,0.001631888,0.0068354616,0.001485678,0.000049332375,0.0003971358,0.00047407573,0.982013,0.004483476,0.00016176343,0.00018172509,0.0002725722],"about_ca_topic_score_codex":0.00006570738,"about_ca_topic_score_gemma":0.000001144676,"teacher_disagreement_score":0.75473094,"about_ca_system_score_codex":0.00018338069,"about_ca_system_score_gemma":0.00009844178,"threshold_uncertainty_score":0.6550022},"labels":[],"label_agreement":null},{"id":"W2891930168","doi":"10.1007/978-3-030-04651-4_2","title":"Rectilinear Shortest Paths Among Transient Obstacles","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Dijkstra's algorithm; Computer science; Obstacle; Point (geometry); Transient (computer programming); Bounded function; Point location; Algorithm; Shortest path problem; Robot; Interval (graph theory); Subdivision; Wavefront; Path (computing); Mathematics; Combinatorics; Artificial intelligence; Graph; Geometry; Theoretical computer science; Mathematical analysis; Physics; Optics","score_opus":0.020940770753114502,"score_gpt":0.24556026608638665,"score_spread":0.22461949533327213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891930168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004598209,0.00035585486,0.99025357,0.00029105367,0.004153745,0.00047244527,0.00000952618,0.00042250764,0.003581484],"genre_scores_gemma":[0.04373411,0.00003915889,0.95321184,0.00086376694,0.0012331357,0.000014748782,0.000010179528,0.00007771821,0.000815355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99412155,0.00005976273,0.00076442206,0.0024256953,0.0015423234,0.0010862728],"domain_scores_gemma":[0.99607944,0.00059127033,0.00034290645,0.0022417593,0.00039117536,0.0003534444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012819723,0.000786389,0.0007448835,0.00092553,0.00036756587,0.0007273921,0.0048012547,0.0005089176,0.000044832403],"category_scores_gemma":[0.00017107051,0.00072593964,0.00020268759,0.0008643314,0.0015554812,0.00075979927,0.001036654,0.0011312912,0.0001573967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000922223,0.000097486896,0.00055746164,0.000057397425,0.000040894123,0.0009321847,0.0058456585,0.08601086,0.00017097531,0.0019437103,0.00031792314,0.9040162],"study_design_scores_gemma":[0.00026371397,0.00038063372,0.001485563,0.00075027416,0.000015410433,0.00015822616,4.3509021e-7,0.96601427,0.0010323699,0.027006127,0.001763041,0.0011299442],"about_ca_topic_score_codex":0.00003928762,"about_ca_topic_score_gemma":0.00005350478,"teacher_disagreement_score":0.9028863,"about_ca_system_score_codex":0.00036394858,"about_ca_system_score_gemma":0.0006430229,"threshold_uncertainty_score":0.99951917},"labels":[],"label_agreement":null},{"id":"W2892018726","doi":"10.2316/journal.201.2018.4.201-2997","title":"ROBOT-TO-HUMAN HANDOVER IN A COMFORTABLE AND FRIENDLY MANNER","year":2018,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Handover; Robot; Computer science; Human–computer interaction; Artificial intelligence; Mobile robot; Computer vision; Object (grammar); Simulation; Telecommunications","score_opus":0.008171953629248763,"score_gpt":0.23454140179816568,"score_spread":0.2263694481689169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892018726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049070477,0.0016726091,0.9470528,0.00033461145,0.00065531064,0.0005721549,0.000004465674,0.00009755775,0.00054001214],"genre_scores_gemma":[0.99442804,0.0000033959882,0.004527953,0.00018759302,0.00017443247,0.000072066985,6.698917e-7,0.000011084994,0.00059474166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849284,0.000086884196,0.00028572144,0.0004665461,0.00020125204,0.00046675696],"domain_scores_gemma":[0.9992722,0.00005471782,0.000078704135,0.0003926023,0.000043906355,0.00015786107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005996629,0.0001717544,0.00035151717,0.00014505815,0.00016866249,0.0002581123,0.0003082039,0.000078608115,0.0000036960976],"category_scores_gemma":[0.000016777762,0.00014616645,0.000023985678,0.00017775672,0.00004280535,0.00024648322,0.00012244962,0.00010922782,0.00002322092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022981774,0.00045228057,0.06840846,0.0004819461,0.00053420616,0.0005411908,0.012008358,0.017022384,0.021808948,0.8110084,0.00652254,0.0609815],"study_design_scores_gemma":[0.0059037125,0.0010217367,0.014721887,0.00028608684,0.000025521958,0.00017193629,0.00024042203,0.9686051,0.00012378962,0.0012917126,0.0070475633,0.00056050695],"about_ca_topic_score_codex":0.0008503749,"about_ca_topic_score_gemma":0.00007480082,"teacher_disagreement_score":0.95158273,"about_ca_system_score_codex":0.000051197112,"about_ca_system_score_gemma":0.000036935864,"threshold_uncertainty_score":0.5960495},"labels":[],"label_agreement":null},{"id":"W2892029381","doi":"10.1007/s00453-018-0447-0","title":"Linear Search by a Pair of Distinct-Speed Robots","year":2018,"lang":"en","type":"article","venue":"Algorithmica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Bordeaux; Narodowym Centrum Nauki; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; University of Liverpool","keywords":"Computer science; Robot; Artificial intelligence","score_opus":0.02137274850842824,"score_gpt":0.2759445785898167,"score_spread":0.25457183008138845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892029381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010222341,0.000107403604,0.9855002,0.0010993103,0.00060036307,0.0001988161,0.00003343083,0.0003035466,0.001934582],"genre_scores_gemma":[0.2457964,0.0000031344773,0.7517553,0.00023714479,0.0005203886,0.0000067763185,0.00002540236,0.00003215449,0.0016233322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978574,0.00012036031,0.00037375602,0.0005483722,0.0005666915,0.00053345354],"domain_scores_gemma":[0.9983939,0.00018722606,0.00012115584,0.00088934315,0.00021195799,0.00019645042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004941553,0.0002061199,0.00030481088,0.00012004504,0.00014361716,0.000053700634,0.0013830295,0.00011026754,0.000030281823],"category_scores_gemma":[0.00009556633,0.00019113695,0.00008418862,0.0006722373,0.0002835302,0.00025019338,0.00042909296,0.0002151511,0.00030080098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008273595,0.001522896,0.005353466,0.00017053547,0.0004427736,0.00031301365,0.0078066476,0.0016662229,0.03110625,0.0071637346,0.1882578,0.75611395],"study_design_scores_gemma":[0.00057761266,0.00053201325,0.002444273,0.00005461781,0.0000114093455,0.000049863218,0.0000259963,0.94868445,0.04177514,0.00048165125,0.0050023776,0.0003605929],"about_ca_topic_score_codex":0.00016266429,"about_ca_topic_score_gemma":7.41319e-7,"teacher_disagreement_score":0.9470182,"about_ca_system_score_codex":0.000041953757,"about_ca_system_score_gemma":0.00009962185,"threshold_uncertainty_score":0.77943385},"labels":[],"label_agreement":null},{"id":"W2892094733","doi":"10.1109/icra.2018.8463199","title":"On Bisection Continuous Collision Checking Method: Spherical Joints and Minimum Distance to Obstacles","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Motion planning; Collision detection; Revolute joint; Collision; Computer science; Path (computing); Robot; Bisection method; Sampling (signal processing); Motion (physics); Collision avoidance; Trajectory; Algorithm; Computer vision; Artificial intelligence","score_opus":0.03160700400843427,"score_gpt":0.3124612559127749,"score_spread":0.28085425190434066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892094733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023661649,0.00007270588,0.9693576,0.00048914604,0.0024452216,0.00048305272,0.000008243724,0.00036957773,0.0031127739],"genre_scores_gemma":[0.03391442,0.000009929389,0.9641512,0.00044953986,0.00023140233,0.00003956196,0.000005636603,0.000025933588,0.0011723903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970581,0.00022129505,0.0004285883,0.0013433966,0.0005336505,0.0004149853],"domain_scores_gemma":[0.9979376,0.00042497538,0.00023470484,0.00097493746,0.0001698219,0.00025792533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076512183,0.00037934922,0.0005412578,0.0001452535,0.00016750259,0.00049201475,0.000845905,0.00029765954,0.000013923899],"category_scores_gemma":[0.00042726062,0.00033607212,0.00008266406,0.00032973205,0.000057749563,0.00013588906,0.0015454064,0.000448085,0.00008863679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006884316,0.0014463494,0.00601105,0.0010813082,0.0006083782,0.0010629036,0.021031072,0.12691897,0.010094859,0.03213177,0.1347253,0.6641996],"study_design_scores_gemma":[0.0005438544,0.0006590312,0.00963137,0.0011016498,0.00002848896,0.000059338774,0.00011036777,0.9657555,0.00721186,0.011279822,0.0024834145,0.0011352837],"about_ca_topic_score_codex":0.00009858955,"about_ca_topic_score_gemma":0.0000061889655,"teacher_disagreement_score":0.83883655,"about_ca_system_score_codex":0.00017193929,"about_ca_system_score_gemma":0.0000864921,"threshold_uncertainty_score":0.9999091},"labels":[],"label_agreement":null},{"id":"W2893680771","doi":"10.1109/icra.2019.8793778","title":"Decentralized collaborative transport of fabrics using micro-UAVs","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Payload (computing); Computer science; Flexibility (engineering); Drone; Task (project management); Field (mathematics); Distributed computing; Control (management); Computer network; Engineering; Systems engineering; Artificial intelligence; Network packet","score_opus":0.033159525407044,"score_gpt":0.28851031740244976,"score_spread":0.2553507919954058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893680771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031062827,0.0004871443,0.9639502,0.000122003585,0.0018479615,0.00061776023,0.000069080284,0.00016702092,0.0016759639],"genre_scores_gemma":[0.07115486,0.00007278865,0.928199,0.00009712646,0.00003653006,0.000006686466,0.00003286144,0.000023812301,0.0003763632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975248,0.00012167329,0.0006363866,0.0007870469,0.0005169044,0.00041318592],"domain_scores_gemma":[0.99763775,0.00011589625,0.0005042327,0.0012279331,0.00039810984,0.00011607184],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003750388,0.00036400455,0.0007647051,0.00020331902,0.00004384033,0.000076019416,0.0016972532,0.0003524086,0.000018681176],"category_scores_gemma":[0.000031690673,0.00034722892,0.00017284241,0.00056925253,0.00007336034,0.00017437356,0.00056542497,0.000431562,0.000021844839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052605043,0.00041429792,0.023502845,0.00094890094,0.00066347467,0.00023006533,0.010998957,0.9371502,0.013408667,0.006188945,0.0018035161,0.0046375347],"study_design_scores_gemma":[0.0010894653,0.00008259965,0.0049377177,0.00062795304,0.00010559819,0.00003165403,0.00013363379,0.9606737,0.02927609,0.0013238924,0.00070494507,0.0010127366],"about_ca_topic_score_codex":0.0002155635,"about_ca_topic_score_gemma":0.000002087558,"teacher_disagreement_score":0.040092036,"about_ca_system_score_codex":0.0001283068,"about_ca_system_score_gemma":0.001168548,"threshold_uncertainty_score":0.99989796},"labels":[],"label_agreement":null},{"id":"W2894976343","doi":"10.48550/arxiv.1810.03572","title":"Fast and In Sync: Periodic Swarm Patterns for Quadrotors","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Swarm behaviour; Computer science; Motion (physics); Trajectory; Choreography; Synchronizing; Drone; Smoothness; Artificial intelligence; Swarm robotics; Computer vision; Mathematics","score_opus":0.06680315273627947,"score_gpt":0.2030203075792196,"score_spread":0.1362171548429401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894976343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39170957,0.000022235286,0.60698277,0.00004964649,0.0005748942,0.0003953619,0.000019895619,0.00010746563,0.00013815745],"genre_scores_gemma":[0.98222107,0.000039711762,0.017019946,0.00006184317,0.00010193425,0.0000055890782,0.000014028391,0.000019285437,0.0005165951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979954,0.00009260104,0.00020306771,0.0012010968,0.00008089296,0.0004269665],"domain_scores_gemma":[0.9985299,0.00013374249,0.00018774887,0.0008924035,0.0000928934,0.00016330686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031131323,0.0003064889,0.00036394858,0.00035540844,0.00012680895,0.00015314986,0.0013707645,0.0002780118,0.0000054466564],"category_scores_gemma":[0.000044310873,0.00036184728,0.0001094755,0.00028805793,0.00012654827,0.00024922282,0.0014833126,0.00037874948,0.00002553652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015463977,0.00042442046,0.4153424,0.0011334437,0.0002763741,0.0021051045,0.0065597165,0.5493542,0.0000379099,0.018083444,0.0010905296,0.005437797],"study_design_scores_gemma":[0.0006630386,0.00010662857,0.02490394,0.00028098136,0.000027664188,0.000011695858,0.000111777415,0.9682774,0.000049138882,0.0049413294,0.00011631831,0.0005101029],"about_ca_topic_score_codex":0.00023504213,"about_ca_topic_score_gemma":0.000042971624,"teacher_disagreement_score":0.5905115,"about_ca_system_score_codex":0.00017426674,"about_ca_system_score_gemma":0.00015648916,"threshold_uncertainty_score":0.99988335},"labels":[],"label_agreement":null},{"id":"W2896802023","doi":"10.1145/3284751.3284757","title":"AI education matters","year":2018,"lang":"en","type":"article","venue":"AI Matters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Heuristic; Path (computing); Shortest path problem; Computer science; Column (typography); Artificial intelligence; Mathematical optimization; Operations research; Mathematics; Machine learning; Algorithm; Mathematics education; Theoretical computer science","score_opus":0.010165255401912826,"score_gpt":0.2750821829935356,"score_spread":0.2649169275916228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896802023","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022822878,0.000009672526,0.4994136,0.49812955,0.0017860251,0.0000674817,6.3013397e-7,0.000166744,0.00019807866],"genre_scores_gemma":[0.00848422,6.2532183e-7,0.12323498,0.8669981,0.00028050947,0.000014009909,0.000003703136,0.000013082324,0.00097080966],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990001,0.00004143767,0.00015135092,0.0003225153,0.00020344606,0.0002811935],"domain_scores_gemma":[0.99916494,0.000029469109,0.00005818219,0.000576448,0.000074734104,0.00009620236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014124576,0.00011745925,0.000099452904,0.0001173635,0.00010639573,0.00017935739,0.0007134819,0.00003889443,0.00004829764],"category_scores_gemma":[0.0000052879986,0.000116345764,0.000034029432,0.00024844756,0.000078067686,0.0004956944,0.00012365244,0.000105405445,0.0023681105],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.6962496e-7,0.00002438921,0.00012567166,0.0000062725526,0.000007087484,0.0000039306415,0.00057087437,0.000031617867,0.00038550608,0.0012417173,0.9861884,0.011413662],"study_design_scores_gemma":[0.00038304966,0.0001711716,0.0073989346,0.00014804353,0.000016157579,0.00017151429,0.00009675221,0.06193252,0.003054626,0.0057466384,0.9202192,0.0006614308],"about_ca_topic_score_codex":0.0000761921,"about_ca_topic_score_gemma":8.291025e-7,"teacher_disagreement_score":0.37617862,"about_ca_system_score_codex":0.000052543815,"about_ca_system_score_gemma":0.000104785475,"threshold_uncertainty_score":0.9984087},"labels":[],"label_agreement":null},{"id":"W2896987114","doi":"10.1109/cig.2018.8490436","title":"Multi-Agent Pathfinding with Real-Time Heuristic Search","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia; National Science Foundation","keywords":"Pathfinding; Heuristic; Computer science; Consistent heuristic; Task (project management); Incremental heuristic search; Null-move heuristic; Artificial intelligence; Algorithm; Search algorithm; Beam search; Theoretical computer science; Shortest path problem; Engineering","score_opus":0.03696175880854912,"score_gpt":0.28587723174862956,"score_spread":0.24891547294008043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896987114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021623412,0.0000057288134,0.9691119,0.00018768545,0.00019798292,0.00013951903,0.0000011597789,0.00043896813,0.008293601],"genre_scores_gemma":[0.13661095,0.0000020782072,0.8583083,0.00009658715,0.000111426634,0.0000057980888,0.0000016741475,0.000013285414,0.0048498944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984598,0.000073674746,0.00015817158,0.00046170445,0.00039148555,0.00045512212],"domain_scores_gemma":[0.9989245,0.000100744845,0.00004223137,0.00063905143,0.00012710616,0.00016636617],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040957073,0.00014285881,0.0001479273,0.00010918794,0.00019572211,0.00015291442,0.0007543608,0.000043743097,0.00006377517],"category_scores_gemma":[0.000033756496,0.00010573058,0.000024499344,0.00039849817,0.000101220954,0.00021190413,0.00024750057,0.00011093299,0.002020267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003152712,0.00440322,0.15230694,0.00047001266,0.0012039298,0.011348561,0.08860323,0.032036606,0.23249634,0.059859898,0.080411136,0.33654484],"study_design_scores_gemma":[0.00041013124,0.00035328147,0.017485637,0.00004464244,0.0000047365206,0.00008678544,0.000030714906,0.97891617,0.0022424841,0.000032988697,0.00015246996,0.0002399441],"about_ca_topic_score_codex":0.00013645609,"about_ca_topic_score_gemma":0.0000023854893,"teacher_disagreement_score":0.94687957,"about_ca_system_score_codex":0.000055965884,"about_ca_system_score_gemma":0.00009447874,"threshold_uncertainty_score":0.99875677},"labels":[],"label_agreement":null},{"id":"W2897086213","doi":"10.1109/ivs.2018.8500662","title":"Local Path Planning for Autonomous Vehicles: Crash Mitigation","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Crash; Computer science; Path (computing); Transport engineering; Engineering; Robot; Artificial intelligence; Computer network","score_opus":0.023097796669232677,"score_gpt":0.27831738670649236,"score_spread":0.2552195900372597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897086213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057279933,0.00001887821,0.9897131,0.0004560665,0.0005571993,0.00017248103,0.0000021032347,0.00043583984,0.0029163675],"genre_scores_gemma":[0.4303922,1.6546932e-7,0.5683431,0.00052307,0.00018975964,0.000019321526,0.0000044479093,0.000008291503,0.0005196467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989108,0.00002576897,0.00019686052,0.0003622776,0.0001783282,0.00032598298],"domain_scores_gemma":[0.9992224,0.00013885656,0.00006753405,0.0003538626,0.00011976826,0.00009758478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029257764,0.00011628956,0.00012194792,0.00006822698,0.00019111381,0.00012823686,0.0005289477,0.00007124144,0.000006923779],"category_scores_gemma":[0.00004921833,0.00010647106,0.000043038137,0.00017374389,0.00008971254,0.00032863126,0.00010138365,0.00006882222,0.00013551084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041040938,0.00032654556,0.010696534,0.00012641533,0.00014702183,0.00014197199,0.016752193,0.029109478,0.005850008,0.15025955,0.13911106,0.64743817],"study_design_scores_gemma":[0.00031048525,0.00023788182,0.003952685,0.000034928904,0.0000037032567,0.000019494764,0.00007279883,0.984095,0.0048199124,0.0033140779,0.0029652296,0.0001738292],"about_ca_topic_score_codex":0.000013424631,"about_ca_topic_score_gemma":4.1645677e-7,"teacher_disagreement_score":0.9549855,"about_ca_system_score_codex":0.000054439093,"about_ca_system_score_gemma":0.000088900495,"threshold_uncertainty_score":0.43417636},"labels":[],"label_agreement":null},{"id":"W2897437777","doi":"10.2316/j.2019.201-3000","title":"A HYBRID METAHEURISTIC NAVIGATION ALGORITHM FOR ROBOT PATH ROLLING PLANNING IN AN UNKNOWN ENVIRONMENT","year":2019,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Motion planning; Ant colony optimization algorithms; Robot; Path (computing); Particle swarm optimization; Computer science; Grid; Position (finance); Grid reference; Metaheuristic; Mobile robot; Local optimum; Artificial intelligence; Mathematical optimization; Algorithm; Mathematics","score_opus":0.011797228338740244,"score_gpt":0.23111898891705568,"score_spread":0.21932176057831543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897437777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019264292,0.0033289494,0.9753094,0.000048899397,0.00071120856,0.0012173731,0.00001483894,0.0000877682,0.00001727536],"genre_scores_gemma":[0.9347011,0.000007647213,0.064720444,0.000037971644,0.00014088809,0.0002601796,0.000019499434,0.000021679432,0.00009062828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979114,0.00017096287,0.00045615435,0.00065263186,0.00029946034,0.0005094245],"domain_scores_gemma":[0.9989563,0.00020031312,0.00021154125,0.00047615482,0.00003240521,0.00012324374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011082081,0.00023743753,0.00048391608,0.00012101745,0.00010498202,0.00022425159,0.0003838639,0.00007799355,0.0000011996792],"category_scores_gemma":[0.000011982235,0.00021908569,0.00006380789,0.000074707044,0.000013745922,0.0004219462,0.00005411431,0.00016317154,0.000015549793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046387853,0.00018410065,0.0031668118,0.00023335176,0.00016584618,0.00013811224,0.0013395863,0.79099244,0.00230151,0.014591936,0.000018022485,0.1868219],"study_design_scores_gemma":[0.0025096172,0.00035693706,0.0004318903,0.00018847878,0.000025489984,0.00005789428,0.00009544976,0.9951166,0.00005367612,0.0005727257,0.0003330328,0.00025824393],"about_ca_topic_score_codex":0.00007406457,"about_ca_topic_score_gemma":3.1014724e-7,"teacher_disagreement_score":0.91543674,"about_ca_system_score_codex":0.00013908719,"about_ca_system_score_gemma":0.000058970538,"threshold_uncertainty_score":0.8934055},"labels":[],"label_agreement":null},{"id":"W2898049695","doi":"10.23919/chicc.2018.8483606","title":"Line-of-Sight Path Following Control on UAV with Sideslip Estimation and Compensation","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Compensation (psychology); Computer science; Path (computing); Control theory (sociology); Line-of-sight; Control (management); Line (geometry); Sight; Control engineering; Simulation; Real-time computing; Engineering; Artificial intelligence; Aerospace engineering; Mathematics","score_opus":0.013418899879581418,"score_gpt":0.24595118677078023,"score_spread":0.23253228689119881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898049695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08632457,0.000009439022,0.91135395,0.00030873425,0.00016524435,0.0001374993,5.5183887e-7,0.00009829542,0.0016017018],"genre_scores_gemma":[0.65390176,1.884645e-7,0.34587625,0.00014490284,0.00003148732,0.0000026632008,0.000001178184,0.000003504226,0.00003808053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919343,0.00004329994,0.00015997658,0.0002305151,0.00023950107,0.0001333002],"domain_scores_gemma":[0.99933857,0.00018885477,0.00009240591,0.00025406532,0.00007579858,0.000050303726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024797663,0.00009636355,0.00014662705,0.000074063944,0.0000842133,0.000061270104,0.00016872537,0.000035345824,0.0000025920574],"category_scores_gemma":[0.00005716257,0.000069915935,0.00001922429,0.00015885384,0.000043706892,0.00027070154,0.000027269893,0.000052928444,0.00001892308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038399428,0.0007158899,0.083499774,0.00017933355,0.0005264274,0.00026813764,0.017978402,0.15704496,0.01626106,0.22255224,0.0012812624,0.49930853],"study_design_scores_gemma":[0.00073948596,0.00062029297,0.010551498,0.00007518378,0.000009731753,0.000010158178,0.000013267563,0.9847506,0.0025764587,0.00053876656,0.000013953882,0.00010058964],"about_ca_topic_score_codex":0.000025326908,"about_ca_topic_score_gemma":0.0000016801447,"teacher_disagreement_score":0.8277056,"about_ca_system_score_codex":0.000015256991,"about_ca_system_score_gemma":0.000031615884,"threshold_uncertainty_score":0.2851089},"labels":[],"label_agreement":null},{"id":"W2899003127","doi":"10.1115/detc2018-86295","title":"Robot Evidence Based Search for a Dynamic User in an Indoor Environment","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs","keywords":"Partially observable Markov decision process; Computer science; Planner; Markov decision process; Robot; Process (computing); Mobile robot; Markov process; Human–computer interaction; Plan (archaeology); Artificial intelligence; Machine learning; Markov chain; Markov model; Real-time computing","score_opus":0.07040391932045345,"score_gpt":0.32888712758183625,"score_spread":0.2584832082613828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899003127","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03351714,0.000024631685,0.9644263,0.0013859338,0.00013399005,0.00037077337,0.0000012938355,0.00008861682,0.00005131891],"genre_scores_gemma":[0.399049,0.0000017531612,0.60017765,0.00039614827,0.000030506579,0.000043442556,0.0000015638458,0.0000075974112,0.00029231948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847126,0.00010457953,0.00019454437,0.0005227531,0.00030383022,0.00040303642],"domain_scores_gemma":[0.99884236,0.00026672738,0.000033917033,0.0006985386,0.000040439172,0.000118011914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076495466,0.0001220875,0.00013313272,0.00014372235,0.000077816054,0.00010792308,0.0009540947,0.0000593006,0.000035075765],"category_scores_gemma":[0.0000430148,0.00010893904,0.00003126296,0.00021889567,0.00007563755,0.00069425337,0.00016519996,0.00009904288,0.00015266382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003130458,0.0020442025,0.17359273,0.00021533428,0.00006154407,0.00025103168,0.007197258,0.32589272,0.077082574,0.009744078,0.0019672732,0.4016382],"study_design_scores_gemma":[0.00035551944,0.0004587911,0.05079818,0.000056293724,0.0000015371587,0.000002888942,0.000013176902,0.9442843,0.0036854462,0.0001352148,0.00006140709,0.00014722385],"about_ca_topic_score_codex":0.00004907073,"about_ca_topic_score_gemma":0.000013496187,"teacher_disagreement_score":0.6183916,"about_ca_system_score_codex":0.00012861728,"about_ca_system_score_gemma":0.00010152744,"threshold_uncertainty_score":0.4442405},"labels":[],"label_agreement":null},{"id":"W2899027211","doi":"10.1115/detc2018-85395","title":"Trajectory Planning of Projectile Catching Maneuvers for Robotic Manipulators","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Trajectory; Projectile; Robot; Computer science; Heuristic; Control theory (sociology); Point (geometry); Interpolation (computer graphics); Interception; Motion planning; Simulation; Artificial intelligence; Control engineering; Engineering; Control (management); Mathematics; Motion (physics); Physics","score_opus":0.04755558444383532,"score_gpt":0.30362571775564634,"score_spread":0.256070133311811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899027211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021941038,0.000025916655,0.9733142,0.00006658904,0.00090833346,0.00029915175,7.297073e-7,0.00022671281,0.003217308],"genre_scores_gemma":[0.55825144,1.8432503e-7,0.44116464,0.000075285796,0.000110878966,0.000013128313,0.0000013546553,0.000011496239,0.00037159453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875253,0.000032180695,0.00026730067,0.00037496223,0.00022663764,0.00034635846],"domain_scores_gemma":[0.99910724,0.00017300242,0.00012547073,0.00042565766,0.00009475073,0.000073885894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003112514,0.00014334302,0.00021407785,0.00016960954,0.00013654392,0.000044912435,0.00067315705,0.00006530106,0.0000068763843],"category_scores_gemma":[0.0000611714,0.00013086498,0.000073092124,0.00026765093,0.000066508335,0.00029093152,0.00010283891,0.000086941836,0.000015184818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021738668,0.0009910975,0.07062154,0.0013173763,0.00082042033,0.00023248518,0.08904061,0.56881356,0.027211461,0.12130101,0.048894256,0.070538804],"study_design_scores_gemma":[0.00047333667,0.00044381444,0.007514052,0.00009331252,0.000016601489,0.000045044675,0.0003180674,0.9803422,0.0093855625,0.00080112234,0.00026212572,0.00030475284],"about_ca_topic_score_codex":0.000084889965,"about_ca_topic_score_gemma":0.0000025262755,"teacher_disagreement_score":0.5363104,"about_ca_system_score_codex":0.0000458513,"about_ca_system_score_gemma":0.00009224463,"threshold_uncertainty_score":0.5336519},"labels":[],"label_agreement":null},{"id":"W2899321373","doi":"10.1115/detc2018-85174","title":"Three Formulations of Sling Load Dynamics for UAV Motion Planning and Control","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Payload (computing); Control theory (sociology); Equations of motion; Elasticity (physics); Computer science; Classical mechanics; Physics; Control (management)","score_opus":0.02692590210566544,"score_gpt":0.28029957657974586,"score_spread":0.25337367447408043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899321373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065389024,0.000035464225,0.9919805,0.0001997767,0.00018652286,0.00018809634,0.000005599455,0.00008026556,0.0007849047],"genre_scores_gemma":[0.5531608,1.3971992e-7,0.44673973,0.000030470925,0.00004090354,0.0000044071726,0.0000017205207,0.0000029526918,0.000018871988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993628,0.000007265265,0.00016996884,0.00017755156,0.00012741871,0.00015499466],"domain_scores_gemma":[0.9993182,0.00018341765,0.00008230318,0.00019823125,0.00017902703,0.00003885114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027677297,0.00006822052,0.00011266477,0.00005748439,0.00011829675,0.000040660565,0.00018539072,0.000042849217,0.0000012083867],"category_scores_gemma":[0.00008743283,0.00006161796,0.000023935223,0.000103787,0.000039222414,0.00025313848,0.00004587162,0.000036209956,0.000002023758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059107588,0.00012369065,0.09221112,0.00011006587,0.00014055365,0.0000055145106,0.0042431615,0.035358187,0.0022877096,0.6036376,0.00058205565,0.2612413],"study_design_scores_gemma":[0.00049591647,0.000110762856,0.014418471,0.000022942693,0.0000070535266,0.0000056058657,0.0000150873975,0.97225696,0.00017668564,0.012406581,0.00001443319,0.00006947491],"about_ca_topic_score_codex":0.000027671997,"about_ca_topic_score_gemma":0.000016878574,"teacher_disagreement_score":0.9368988,"about_ca_system_score_codex":0.000033421697,"about_ca_system_score_gemma":0.000031092146,"threshold_uncertainty_score":0.25127074},"labels":[],"label_agreement":null},{"id":"W2901202142","doi":"10.3390/ijgi7110450","title":"Real-Time Efficient Exploration in Unknown Dynamic Environments Using MAVs","year":2018,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Trajectory; Payload (computing); Computer science; Real-time computing; Process (computing); Maximization; Time constraint; Task (project management); Path (computing); Constraint (computer-aided design); Real-time data; Dynamism; Trajectory optimization; Engineering; Mathematical optimization; Computer security; Systems engineering","score_opus":0.011025842830199676,"score_gpt":0.27078727262066,"score_spread":0.2597614297904603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901202142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11573345,0.000006368455,0.88100535,0.0005920776,0.0018875104,0.00009517441,0.0000045959728,0.000017411581,0.0006580609],"genre_scores_gemma":[0.8180729,0.00003769585,0.18134394,0.00021358122,0.00023236638,0.00000268458,0.000027083805,0.000007400825,0.0000623244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979621,0.00006181422,0.0007902739,0.00009544553,0.0009031106,0.00018724146],"domain_scores_gemma":[0.9986183,0.000047053945,0.0007670648,0.00017534498,0.00032360115,0.00006865236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070761715,0.00012275629,0.00014723417,0.0006646059,0.000069717855,0.0002255902,0.0008140801,0.00006690462,0.000020137364],"category_scores_gemma":[0.00011288699,0.000117726275,0.000060423135,0.00021698835,0.000045369346,0.004974287,0.00013689497,0.000154138,0.0002734022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018637729,0.00025202354,0.00082848105,0.000014664412,0.00014250788,0.00008653591,0.012786883,0.8809177,0.013487684,0.0029559087,0.0006653596,0.08767586],"study_design_scores_gemma":[0.00072053005,0.00011085202,0.004661614,0.00011300183,0.0000055657124,0.00015927096,0.000086267784,0.99108356,0.0011404235,0.00040698488,0.0013886876,0.00012325519],"about_ca_topic_score_codex":0.00002832092,"about_ca_topic_score_gemma":8.3229685e-7,"teacher_disagreement_score":0.7023395,"about_ca_system_score_codex":0.0005604465,"about_ca_system_score_gemma":0.00011239926,"threshold_uncertainty_score":0.4800738},"labels":[],"label_agreement":null},{"id":"W2901443535","doi":"10.1177/0959651818810621","title":"Vision-based curved lane keeping control for intelligent vehicle highway system","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lyapunov function; Intelligent transportation system; Compensation (psychology); Path (computing); Computer science; Position (finance); Control theory (sociology); Work (physics); Highway system; Function (biology); Control (management); Control engineering; Engineering; Simulation; Artificial intelligence","score_opus":0.01073737455937894,"score_gpt":0.214342385693751,"score_spread":0.20360501113437207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901443535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01099329,0.000586018,0.98568887,0.00021220416,0.0020502193,0.00039100423,0.000008023003,0.00005444611,0.000015901196],"genre_scores_gemma":[0.9850529,0.0000046557784,0.014565462,0.000019850837,0.00032763416,0.000014098112,1.3827561e-7,0.000011927421,0.000003377509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822015,0.000014930129,0.0009108377,0.00017613426,0.00043230833,0.00024564087],"domain_scores_gemma":[0.99795413,0.0002327678,0.00071381935,0.00013538562,0.00082769146,0.00013620485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016684266,0.00018205045,0.00058607216,0.0001803612,0.00008517915,0.00006370948,0.00059739535,0.000108112516,2.1862725e-7],"category_scores_gemma":[0.00046836244,0.00013215776,0.00018117987,0.00023597854,0.00005317389,0.00029600225,0.000041186937,0.00018741007,3.935813e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020351597,0.00008877636,0.00010666442,0.0018833472,0.00037040698,0.0000039174543,0.00020057838,0.7047226,0.09554705,0.19583313,0.00009742311,0.0009426247],"study_design_scores_gemma":[0.0019821888,0.00048815215,0.000062715095,0.002187328,0.0000690683,0.000066891975,0.000058116893,0.9823356,0.012270156,0.000025277948,0.00032533807,0.00012914116],"about_ca_topic_score_codex":0.000007795482,"about_ca_topic_score_gemma":5.8239078e-8,"teacher_disagreement_score":0.9740596,"about_ca_system_score_codex":0.000091169255,"about_ca_system_score_gemma":0.00007519026,"threshold_uncertainty_score":0.5389237},"labels":[],"label_agreement":null},{"id":"W2901677958","doi":"10.1145/3197517.3201284","title":"Creating and chaining camera moves for quadrotor videography","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; Videography; Computer vision; Landmark; Artificial intelligence; Chaining; Trajectory; Set (abstract data type); Drone; Task (project management); Camera auto-calibration; Motion (physics); Computer graphics (images); Camera resectioning","score_opus":0.029142999281134885,"score_gpt":0.2837672460864556,"score_spread":0.2546242468053207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901677958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009028098,0.00006661652,0.9888884,0.000866756,0.00042309423,0.00028535968,0.000013516028,0.00026360687,0.00016458698],"genre_scores_gemma":[0.3878656,0.00003369084,0.61113435,0.0006880105,0.00010001438,0.00010835852,0.0000016024483,0.000018303914,0.000050103718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879193,0.000042573974,0.00021043268,0.00043763162,0.00019985077,0.00031757812],"domain_scores_gemma":[0.9984345,0.0005851579,0.000078200486,0.0006636229,0.00012086589,0.00011768682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032332298,0.00016979148,0.00016845643,0.0003749983,0.0006978907,0.00014153867,0.00054929854,0.00009363552,0.000003044519],"category_scores_gemma":[0.00008688472,0.0001684731,0.00010459036,0.00068594806,0.00018044263,0.00031517932,0.0000126063,0.00019096184,0.000004068117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019274234,0.0008302007,0.008670594,0.00027771102,0.0009018324,0.00003768133,0.032610856,0.0033828348,0.0041742036,0.07906563,0.00078469375,0.869071],"study_design_scores_gemma":[0.0025591159,0.00326933,0.017201265,0.0004478579,0.00014029714,0.00012339458,0.0007571039,0.91067743,0.008262809,0.050299447,0.0047890055,0.0014729513],"about_ca_topic_score_codex":0.000048301914,"about_ca_topic_score_gemma":0.000010424377,"teacher_disagreement_score":0.9072946,"about_ca_system_score_codex":0.000010701858,"about_ca_system_score_gemma":0.000032024967,"threshold_uncertainty_score":0.6870133},"labels":[],"label_agreement":null},{"id":"W2901900005","doi":"10.1007/978-3-030-04651-4_33","title":"Transmitting Particles in a Polygonal Domain by Repulsion","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Point (geometry); Domain (mathematical analysis); Computer science; Time complexity; Actuator; Polynomial; Algorithm; Particle (ecology); Artificial intelligence; Mathematics; Geometry; Mathematical analysis","score_opus":0.014186107273614522,"score_gpt":0.2430706233654112,"score_spread":0.22888451609179666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901900005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017069393,0.00049024035,0.99440426,0.0010132906,0.0010861701,0.0002814941,0.0000055702894,0.0001582792,0.0008537853],"genre_scores_gemma":[0.04534763,0.000013903476,0.9529647,0.0010644797,0.0003763418,0.000008980039,0.0000051225256,0.00003479666,0.00018401543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543273,0.00007592602,0.0006983807,0.0017629026,0.0011022176,0.0009278434],"domain_scores_gemma":[0.9977648,0.00049518415,0.00027008748,0.00115816,0.00011098387,0.00020077622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017213983,0.00050052913,0.0005295096,0.00069485867,0.00020947508,0.00041308228,0.0030120478,0.0003363854,0.00002075881],"category_scores_gemma":[0.00010134616,0.0004672296,0.00010240569,0.0007843565,0.0007177412,0.0005633482,0.00073506346,0.0008247123,0.000058431473],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016292983,0.00009791225,0.00044419474,0.00005203054,0.000013740487,0.00055649976,0.0068913456,0.019616218,0.0022422944,0.0023198815,0.00038050138,0.9673691],"study_design_scores_gemma":[0.0005080719,0.00025172267,0.00040920218,0.0009404998,0.000004108603,0.00014888203,6.284657e-7,0.85402817,0.0022769037,0.13985871,0.0007406907,0.0008324285],"about_ca_topic_score_codex":0.00004073931,"about_ca_topic_score_gemma":0.000020509768,"teacher_disagreement_score":0.96653664,"about_ca_system_score_codex":0.00029728367,"about_ca_system_score_gemma":0.00037017724,"threshold_uncertainty_score":0.9997779},"labels":[],"label_agreement":null},{"id":"W2903562418","doi":"10.1161/circ.132.suppl_3.13071","title":"Abstract 13071: Computational Simulation of Virtual Ross Operation","year":2015,"lang":"en","type":"article","venue":"Circulation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"","keywords":"Medicine","score_opus":0.05934443531797733,"score_gpt":0.30487930732362667,"score_spread":0.24553487200564933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903562418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17601554,0.000025238554,0.8230376,0.00006783693,0.00033818683,0.000110632536,0.000002858029,0.00008043911,0.00032167186],"genre_scores_gemma":[0.9492074,1.4696762e-7,0.05060177,0.000035038734,0.000054154065,0.0000048878537,0.0000824555,0.000005989709,0.000008107028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988375,0.00005783951,0.00028355728,0.00020950743,0.00050430995,0.00010729637],"domain_scores_gemma":[0.9992299,0.00012430864,0.00016718051,0.00021319672,0.00020207114,0.000063316525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044475993,0.00007864866,0.00010248636,0.00010770675,0.00008538423,0.000058368718,0.00022012064,0.00004548828,0.000015480296],"category_scores_gemma":[0.000087650806,0.000088471665,0.000030623098,0.00023598132,0.000020944834,0.0007083968,0.00005222943,0.00007174814,0.000033105596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033559882,0.000032056574,0.003914593,0.0000022541558,0.000005732445,0.0000015752237,0.00078381115,0.983775,0.00027420133,0.0037689528,0.000048241975,0.007390178],"study_design_scores_gemma":[0.00030585125,0.000025671538,0.18476811,0.0000050952735,0.0000025094225,0.0000033509873,0.000023020655,0.81248754,0.000058547867,0.0022074399,0.00003592748,0.000076955075],"about_ca_topic_score_codex":0.000013898023,"about_ca_topic_score_gemma":2.1198163e-7,"teacher_disagreement_score":0.7731919,"about_ca_system_score_codex":0.00010641949,"about_ca_system_score_gemma":0.00012793453,"threshold_uncertainty_score":0.36077696},"labels":[],"label_agreement":null},{"id":"W2904904203","doi":"10.1109/itsc.2018.8569414","title":"Speed Planning for Autonomous Driving via Convex Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smoothness; Convexity; Computer science; Mathematical optimization; Motion planning; Set (abstract data type); Boundary (topology); Regular polygon; Optimization problem; Convex optimization; Vehicle dynamics; Control theory (sociology); Robot; Algorithm; Mathematics; Engineering; Artificial intelligence; Control (management)","score_opus":0.026628894444116643,"score_gpt":0.28166013243081267,"score_spread":0.255031237986696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904904203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004837081,0.0000106493435,0.99260753,0.00033203574,0.0008500339,0.00019970334,4.996953e-7,0.0004875674,0.005028288],"genre_scores_gemma":[0.07217031,1.9370391e-7,0.9263139,0.00033096818,0.00026432506,0.000006420587,0.000004696291,0.000011568814,0.0008976507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989791,0.000021956843,0.00019734354,0.00034666198,0.000144192,0.0003107518],"domain_scores_gemma":[0.9992137,0.00013341989,0.000086164626,0.00035037324,0.00013149818,0.00008482976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027764423,0.00011677006,0.00013916408,0.00009359269,0.00019547997,0.00015386997,0.00053359295,0.00006162477,0.000025774883],"category_scores_gemma":[0.000077371595,0.000110882946,0.000036183243,0.00019817566,0.000044507782,0.00038362702,0.00012252296,0.000055602577,0.00006592576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009306586,0.00006683248,0.002953802,0.00002006232,0.000052819032,0.000023596776,0.0033248058,0.949947,0.0014477219,0.008130041,0.0070710788,0.026952958],"study_design_scores_gemma":[0.00025658315,0.00012818721,0.0005459793,0.000019036326,0.000003769307,0.000020797555,0.000015207502,0.9963084,0.0016685721,0.00036809998,0.00050625554,0.00015909616],"about_ca_topic_score_codex":0.000009642194,"about_ca_topic_score_gemma":2.1927056e-7,"teacher_disagreement_score":0.0716866,"about_ca_system_score_codex":0.000041037198,"about_ca_system_score_gemma":0.000053473497,"threshold_uncertainty_score":0.4521675},"labels":[],"label_agreement":null},{"id":"W2905316674","doi":"10.1109/mits.2018.2884515","title":"Parallel Motion Planning: Learning a Deep Planning Model against Emergencies","year":2018,"lang":"en","type":"article","venue":"IEEE Intelligent Transportation Systems Magazine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Planner; Computer science; Software deployment; Motion planning; Artificial intelligence; Motion (physics); Robot; Software engineering","score_opus":0.05235142987125792,"score_gpt":0.2906668541260028,"score_spread":0.2383154242547449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905316674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03814301,0.0005280445,0.956386,0.000076953045,0.0023888901,0.000437474,0.000008285945,0.00081636955,0.001214963],"genre_scores_gemma":[0.93189704,0.000028992403,0.065953135,0.00008191762,0.0003710609,0.00008264742,0.000099544486,0.000049691705,0.0014359767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964299,0.00014447457,0.001105467,0.0008294305,0.00082602294,0.0006646889],"domain_scores_gemma":[0.9981575,0.00009869369,0.0004682434,0.00055155234,0.00047933828,0.00024469386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007161644,0.00043143582,0.00045726064,0.0004041412,0.00037984352,0.00022482696,0.0008172273,0.00019828287,0.00001003873],"category_scores_gemma":[0.00004749386,0.00043583888,0.00014836162,0.00069411146,0.000083945124,0.0007833651,0.000016469472,0.00039009622,0.00046144356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021683381,0.00005234923,0.0044877883,0.000083268045,0.000055972414,0.000083684485,0.012421904,0.9776462,0.0018979067,0.00087889784,0.0007161589,0.0016542077],"study_design_scores_gemma":[0.0002991405,0.00016798302,0.0029618568,0.00031007716,0.000023825902,0.000018818277,0.0004992131,0.9926428,0.0010640116,0.00013584441,0.0013980491,0.00047837722],"about_ca_topic_score_codex":0.000029973526,"about_ca_topic_score_gemma":0.000005081431,"teacher_disagreement_score":0.893754,"about_ca_system_score_codex":0.00010602102,"about_ca_system_score_gemma":0.00008401098,"threshold_uncertainty_score":0.9998093},"labels":[],"label_agreement":null},{"id":"W2906298103","doi":"","title":"Optimisation in the design of underground mine access","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mining engineering; Geology","score_opus":0.08584092513193244,"score_gpt":0.3115385675609133,"score_spread":0.22569764242898088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906298103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010944586,0.00001577115,0.9864089,0.0016393479,0.000064193046,0.000116606534,9.911351e-8,0.000026324122,0.00078419095],"genre_scores_gemma":[0.44851014,0.0000018525068,0.5512173,0.00022428748,0.000011891961,0.0000048866177,5.932464e-7,9.813292e-7,0.000028062695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942994,0.000058853824,0.00012381197,0.00011619279,0.00017284848,0.00009832407],"domain_scores_gemma":[0.99952805,0.00018060878,0.00004307144,0.0002146063,0.000020651545,0.000013025814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040125218,0.00004789915,0.000060715865,0.000042552227,0.000020691921,0.00007078125,0.0008582041,0.000022198916,0.0000020630334],"category_scores_gemma":[0.000028438026,0.000027726068,0.000011833076,0.00034870446,0.000014473728,0.0003519276,0.00005994082,0.000045258566,0.000004051827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016638462,0.00006975186,0.00042821383,0.000003474543,0.0000032067612,0.000013756016,0.00096409605,0.97672814,0.00036564603,0.01570029,0.000114880415,0.005606871],"study_design_scores_gemma":[0.0008603627,0.00016815493,0.041378785,0.00004700683,0.000004466285,0.000050808085,0.00024132128,0.91439515,0.0032841202,0.039368067,0.00001777065,0.00018400756],"about_ca_topic_score_codex":0.00026397346,"about_ca_topic_score_gemma":0.000008540655,"teacher_disagreement_score":0.43756554,"about_ca_system_score_codex":0.000028258266,"about_ca_system_score_gemma":0.000046223126,"threshold_uncertainty_score":0.1594771},"labels":[],"label_agreement":null},{"id":"W2908972399","doi":"10.1109/tits.2018.2873921","title":"Crash Mitigation in Motion Planning for Autonomous Vehicles","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collision avoidance; Crash; Obstacle avoidance; Model predictive control; Motion planning; Obstacle; Collision; Vehicle dynamics; Controller (irrigation); Engineering; Control theory (sociology); Computer science; Simulation; Control (management); Mobile robot; Artificial intelligence; Automotive engineering; Robot; Computer security","score_opus":0.02849456097627513,"score_gpt":0.27128378875343684,"score_spread":0.24278922777716172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908972399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09356667,0.000049141352,0.9020932,0.00009165144,0.0026865143,0.0011274097,0.000041085998,0.00026457637,0.00007979426],"genre_scores_gemma":[0.9855765,0.0000070921556,0.013562691,0.00004967477,0.0000390563,0.00030723625,0.000037247795,0.000025581925,0.00039488787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979501,0.000078720106,0.0007227203,0.00055281544,0.0003678157,0.00032787604],"domain_scores_gemma":[0.998982,0.00024716128,0.00018024708,0.0003715931,0.00012932111,0.0000896522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042639684,0.00022588189,0.00028853255,0.00045864325,0.00011151504,0.00012511073,0.00035041984,0.00015765386,0.00000905598],"category_scores_gemma":[0.0000031646264,0.0002448812,0.00013317542,0.0004663711,0.000019310348,0.0005626335,2.7665553e-7,0.00021556942,0.00011962616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021949172,0.00011075906,0.0015921624,0.00011028068,0.000025916455,0.000006534168,0.0027291887,0.98427904,0.0011271129,0.00138345,0.000021105234,0.008592507],"study_design_scores_gemma":[0.0008125144,0.00024365964,0.007566142,0.0004151413,0.000019901232,0.0000090165295,0.0006113071,0.96760297,0.021781107,0.00020118644,0.0003541771,0.00038290618],"about_ca_topic_score_codex":0.000100444566,"about_ca_topic_score_gemma":0.000014096383,"teacher_disagreement_score":0.89200985,"about_ca_system_score_codex":0.0001822947,"about_ca_system_score_gemma":0.000067436995,"threshold_uncertainty_score":0.99859655},"labels":[],"label_agreement":null},{"id":"W2909180703","doi":"10.2514/6.2019-1172","title":"Path Following Control of Multiple Quadrotors Carrying A Rigid-body Slung Payload","year":2019,"lang":"en","type":"article","venue":"AIAA Scitech 2019 Forum","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Payload (computing); Path (computing); Computer science; Control theory (sociology); Control (management); Aerospace engineering; Engineering; Artificial intelligence; Computer network","score_opus":0.005687214748376454,"score_gpt":0.22721179009399842,"score_spread":0.22152457534562198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909180703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22880313,0.00039985272,0.7651948,0.0005523196,0.0026783727,0.0011871206,0.000029075247,0.00043864315,0.0007166806],"genre_scores_gemma":[0.942996,0.000002905644,0.056218043,0.00027016128,0.000049260885,0.000035789515,0.0000071001446,0.000031003445,0.00038973536],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969062,0.000114749695,0.00057195773,0.00073508325,0.0007568092,0.0009151501],"domain_scores_gemma":[0.9976231,0.00044203422,0.00033419803,0.001306029,0.00011333596,0.00018133425],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085762056,0.00032477445,0.0005891475,0.00028432364,0.0001652234,0.00013315205,0.0015613016,0.00018247862,0.000009948856],"category_scores_gemma":[0.00018803148,0.00029686064,0.00031668023,0.00064434984,0.000048107944,0.0008384748,0.00035900093,0.00030857846,0.00032139785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004277169,0.00019441477,0.90118283,0.00013614561,0.00029763268,0.0001330803,0.0014714749,0.0068959193,0.072968684,0.004745495,0.0021042442,0.009827303],"study_design_scores_gemma":[0.0038574878,0.00044902158,0.015899459,0.00044584213,0.000042996766,0.00003540901,0.00024140684,0.9676445,0.008115856,0.00077793875,0.0016655696,0.0008245131],"about_ca_topic_score_codex":0.00036809707,"about_ca_topic_score_gemma":0.0000030452215,"teacher_disagreement_score":0.96074855,"about_ca_system_score_codex":0.00009609317,"about_ca_system_score_gemma":0.0001429872,"threshold_uncertainty_score":0.9999483},"labels":[],"label_agreement":null},{"id":"W2909187154","doi":"10.4028/www.scientific.net/amm.886.188","title":"The Development of Supervised Motion Learning and Vision System for Humanoid Robot","year":2019,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Humanoid robot; Artificial intelligence; Robot; Robot control; Computer science; Computer vision; Motion (physics); Convolutional neural network; Mechanism (biology); Social robot; Engineering; Simulation; Mobile robot","score_opus":0.012071358915405542,"score_gpt":0.24185463554364325,"score_spread":0.22978327662823772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909187154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10991034,0.00003731697,0.8891432,0.000018060795,0.0003694125,0.00042153534,0.0000012507297,0.00004367898,0.000055202127],"genre_scores_gemma":[0.728666,0.000008427127,0.27122423,0.0000053389376,0.000014913516,0.000035857753,0.000004402146,0.000007055274,0.000033795874],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99927306,0.000024699233,0.000239909,0.00021103166,0.00010760232,0.00014368763],"domain_scores_gemma":[0.9995972,0.00007953445,0.000121602025,0.00013961113,0.000033189768,0.000028844493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008973415,0.00008591561,0.00016910298,0.000027414366,0.00019843722,0.00012185672,0.000150576,0.000046685127,6.967605e-7],"category_scores_gemma":[0.000007409191,0.00006210274,0.0000091771335,0.00003625621,0.0000047545327,0.000051802766,0.000159849,0.000025600948,0.0000037989994],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015904821,0.0000071299974,0.0000016439633,0.00021825133,0.000012130021,1.5503052e-7,0.0007972553,0.000053946576,0.6744603,0.30221525,0.0000037704665,0.022214292],"study_design_scores_gemma":[0.0017785444,0.00029639772,0.0002310602,0.00024419103,0.000022590077,0.0000116594765,0.0011780742,0.099389955,0.8842804,0.01112992,0.0010345983,0.0004026009],"about_ca_topic_score_codex":0.0000012621904,"about_ca_topic_score_gemma":1.3887005e-7,"teacher_disagreement_score":0.61875564,"about_ca_system_score_codex":0.000012896361,"about_ca_system_score_gemma":0.000020858744,"threshold_uncertainty_score":0.25324762},"labels":[],"label_agreement":null},{"id":"W2909775365","doi":"10.1109/iros.2018.8594034","title":"After You: Doorway Negotiation for Human-Robot and Robot-Robot Interaction","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Human–robot interaction; Human–computer interaction; Robot control; Negotiation; Social robot; Artificial intelligence; Mobile robot; Computer vision","score_opus":0.02677772937881197,"score_gpt":0.3073490880262853,"score_spread":0.28057135864747335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909775365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00789059,0.000036786416,0.98775613,0.00089408504,0.0012272007,0.00030767138,0.0000017571753,0.00024869034,0.0016370992],"genre_scores_gemma":[0.56425375,0.0000019294916,0.43314788,0.0004563429,0.00042724068,0.000054264518,0.0000071117042,0.000014220359,0.0016372852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985654,0.000048656137,0.00028918803,0.00055002177,0.00020358665,0.00034319278],"domain_scores_gemma":[0.9990206,0.00011143453,0.00012225338,0.00045996992,0.00017535398,0.000110436435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030476696,0.0001895766,0.00018339355,0.00015023818,0.00023515154,0.00030450447,0.00036309133,0.00009934426,0.000048226688],"category_scores_gemma":[0.000056493864,0.00017025809,0.000055782617,0.0002004001,0.0000729707,0.0009447878,0.00017884739,0.00011211716,0.00012891837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053314137,0.0013479101,0.034453407,0.00048055104,0.00061755773,0.00012141356,0.028709102,0.023041364,0.12445832,0.085427925,0.040094472,0.6607148],"study_design_scores_gemma":[0.0017070195,0.0012297723,0.09787001,0.00014032445,0.00004791814,0.00010536577,0.00014981358,0.8725035,0.014455721,0.0061502103,0.004733351,0.0009069754],"about_ca_topic_score_codex":0.000075685384,"about_ca_topic_score_gemma":0.000024500509,"teacher_disagreement_score":0.84946215,"about_ca_system_score_codex":0.00006135578,"about_ca_system_score_gemma":0.00002662979,"threshold_uncertainty_score":0.69429237},"labels":[],"label_agreement":null},{"id":"W2909842744","doi":"10.1115/imece2018-88185","title":"Path Generation Algorithm Verification of Five DOF Robotic Arm With Linear Actuator Using MATLAB Sim-Mechanics","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Workspace; Inverse kinematics; Kinematics; Motion planning; Robotic arm; Degrees of freedom (physics and chemistry); Computer science; Actuator; MATLAB; Path (computing); Algorithm; Configuration space; Cartesian coordinate system; Robot kinematics; Robot; Control theory (sociology); Control engineering; Artificial intelligence; Engineering; Mathematics; Mobile robot; Geometry; Physics","score_opus":0.034408883721190886,"score_gpt":0.26707856654322665,"score_spread":0.23266968282203576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909842744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0135928625,0.000015578375,0.9854086,0.00009029612,0.00044324677,0.00021344863,0.000003569395,0.00014064298,0.000091762755],"genre_scores_gemma":[0.12684815,0.000003904015,0.87262547,0.00009876072,0.00028233934,0.00000526134,0.0000135337605,0.000015942642,0.00010663032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998461,0.000068050285,0.00031981236,0.00045886735,0.00042196203,0.00027032502],"domain_scores_gemma":[0.99851125,0.000044357108,0.0002473027,0.00069128186,0.0004130339,0.000092783506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030498527,0.00018211512,0.00022395384,0.00011965169,0.00014082826,0.00007921763,0.0004917579,0.000094582676,0.000016021546],"category_scores_gemma":[0.000039346498,0.00014963467,0.000036303558,0.00050107687,0.000052930613,0.00046056387,0.000101691345,0.00010193874,0.000044501827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058638052,0.0014955723,0.00023919616,0.00020447158,0.00043231406,0.00012649801,0.013506908,0.516732,0.19219248,0.029020134,0.0017998985,0.24419188],"study_design_scores_gemma":[0.0002936702,0.00036810487,0.00008128148,0.00004085335,0.000019081992,0.000033144308,0.000053666423,0.97278833,0.025943944,0.00016457138,0.000021684747,0.00019166547],"about_ca_topic_score_codex":0.00011539971,"about_ca_topic_score_gemma":0.000003272916,"teacher_disagreement_score":0.45605633,"about_ca_system_score_codex":0.00008115911,"about_ca_system_score_gemma":0.00017402132,"threshold_uncertainty_score":0.6101925},"labels":[],"label_agreement":null},{"id":"W2909845151","doi":"10.1109/iros.2018.8593670","title":"Motion Planning for a Small Aerobatic Fixed-Wing Unmanned Aerial Vehicle","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fixed wing; Planner; Motion planning; Computer science; Motion (physics); Cruise missile; Cruise; Flight dynamics; Vehicle dynamics; Simulation; Controller (irrigation); Mobile robot; Motion control; Robot; Trajectory; Aerodynamics; Wing; Control theory (sociology); Artificial intelligence; Engineering; Control (management); Aerospace engineering; Missile","score_opus":0.04727299315390374,"score_gpt":0.2808968375875618,"score_spread":0.23362384443365805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909845151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058188222,0.000013450846,0.93772566,0.00042550528,0.0013807646,0.00027385162,0.0000013315544,0.00043438858,0.0015567946],"genre_scores_gemma":[0.36410323,1.7170734e-7,0.6344022,0.00034225473,0.0006512345,0.000029172681,0.0000050791728,0.000013797354,0.00045287824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998624,0.000048983868,0.00025473966,0.00044010536,0.00018099687,0.00045112075],"domain_scores_gemma":[0.99905473,0.00018932227,0.000093301314,0.00043698528,0.00012046759,0.000105187304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043373593,0.00015347343,0.00017477408,0.000102150705,0.0002799175,0.00020103353,0.0006550904,0.00008172054,0.00000968265],"category_scores_gemma":[0.00016927767,0.00014243124,0.00006218981,0.00025871192,0.000047621903,0.00034472474,0.00015943045,0.00008029568,0.00010651523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000577376,0.0012367833,0.018272668,0.0005817844,0.0006420641,0.00030761247,0.053026926,0.095876984,0.111309096,0.17455947,0.053296227,0.490313],"study_design_scores_gemma":[0.0008338036,0.00034594382,0.002565001,0.00006538979,0.000009115265,0.000015997548,0.00005136747,0.9864316,0.0067316554,0.0022188856,0.00048743942,0.00024381651],"about_ca_topic_score_codex":0.000027376616,"about_ca_topic_score_gemma":0.0000027571439,"teacher_disagreement_score":0.8905546,"about_ca_system_score_codex":0.000048971157,"about_ca_system_score_gemma":0.000048511218,"threshold_uncertainty_score":0.5808177},"labels":[],"label_agreement":null},{"id":"W2910641550","doi":"10.1109/icarm.2018.8610854","title":"Sampling based Constrained Motion Planning For Floating Base Manipulators Using Constraints Driven Alternative Parameterization","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; Simon Fraser University","funders":"","keywords":"Robot end effector; Trajectory; Parametrization (atmospheric modeling); Motion planning; Control theory (sociology); Computer science; Configuration space; Planner; Mathematical optimization; Base (topology); Robot; Mathematics; Artificial intelligence; Control (management)","score_opus":0.1256279620458723,"score_gpt":0.34351522161636777,"score_spread":0.21788725957049548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910641550","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0776379,0.0000029209748,0.9205955,0.000058588616,0.0007239786,0.0004237635,0.000012636105,0.00029439977,0.00025028727],"genre_scores_gemma":[0.4828346,3.60006e-8,0.51686245,0.00014020951,0.00012589774,0.000005425045,0.000015996917,0.000011176211,0.000004224206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982208,0.00009263423,0.00040665767,0.00057037204,0.00027476595,0.00043473914],"domain_scores_gemma":[0.9985777,0.00042473222,0.00027341206,0.00032527544,0.00027324888,0.00012562556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004984228,0.00022158587,0.00022781806,0.00022116181,0.00038373304,0.00023011031,0.0004124533,0.00009141269,0.00001884156],"category_scores_gemma":[0.00038218865,0.00022733523,0.00007122317,0.00031940566,0.00020141753,0.0005066595,0.0000815748,0.00010182566,0.000008142014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002611434,0.00008726133,0.0078127,0.00006823771,0.000087234235,0.000032337342,0.0026606983,0.8984932,0.041744053,0.013360796,0.000027443743,0.035599954],"study_design_scores_gemma":[0.00070975337,0.00011339695,0.0005334502,0.00016812916,0.000013759185,0.000023546818,0.00013461581,0.9831913,0.014148687,0.00069498934,0.000004837193,0.00026352625],"about_ca_topic_score_codex":0.00002801737,"about_ca_topic_score_gemma":5.610084e-7,"teacher_disagreement_score":0.4051967,"about_ca_system_score_codex":0.00010834375,"about_ca_system_score_gemma":0.00012272857,"threshold_uncertainty_score":0.9270462},"labels":[],"label_agreement":null},{"id":"W2911442729","doi":"10.1016/j.ast.2019.01.061","title":"An iterative strategy for task assignment and path planning of distributed multiple unmanned aerial vehicles","year":2019,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Motion planning; Computer science; Computation; Task (project management); Distributed computing; Path (computing); Iterative and incremental development; Process (computing); Drone; Computational complexity theory; Stability (learning theory); Range (aeronautics); Real-time computing; Artificial intelligence; Engineering; Systems engineering; Algorithm; Machine learning; Robot; Computer network","score_opus":0.015170295287208181,"score_gpt":0.2711193924361123,"score_spread":0.2559490971489041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911442729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7321066,0.00010573787,0.2665601,0.0005911849,0.00014998147,0.0003322286,0.000025705827,0.00011306354,0.000015347134],"genre_scores_gemma":[0.95072645,0.0000042775623,0.04919057,0.000026032341,0.000010980653,0.000021300197,0.000004812351,0.0000045712836,0.00001101771],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985784,0.000021424286,0.00017256598,0.0005886378,0.00024996416,0.0003889881],"domain_scores_gemma":[0.99905807,0.00011042921,0.00013939047,0.00040600184,0.00019656535,0.000089522684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004743765,0.00013775207,0.00023050654,0.00020213598,0.00021729553,0.00013561151,0.0006424141,0.00011400904,4.3543892e-7],"category_scores_gemma":[0.00014884352,0.00011945936,0.000012336669,0.0007570526,0.00064960786,0.00062101893,0.00023346096,0.00010879175,0.000001247486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004484279,0.00015098191,0.080159724,0.00004518821,0.000024830873,0.000016800106,0.002326744,0.004937992,0.84941006,0.04036217,0.00015954055,0.022361118],"study_design_scores_gemma":[0.0020109918,0.003023073,0.017630707,0.00011015018,0.0000110345545,0.000048453996,0.0021172152,0.8374451,0.13100748,0.0059842314,0.00017362092,0.00043797222],"about_ca_topic_score_codex":0.00001370739,"about_ca_topic_score_gemma":9.304961e-7,"teacher_disagreement_score":0.8325071,"about_ca_system_score_codex":0.000031648287,"about_ca_system_score_gemma":0.00013967766,"threshold_uncertainty_score":0.48714113},"labels":[],"label_agreement":null},{"id":"W2911994444","doi":"10.1007/s11370-018-00270-7","title":"A high-performance control algorithm based on a curvature-dependent decoupled planning approach and flatness concepts for non-holonomic mobile robots","year":2019,"lang":"en","type":"article","venue":"Intelligent Service Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Holonomic; Jerk; Motion planning; Flatness (cosmology); Control theory (sociology); Robot; Acceleration; Mobile robot; Curvature; Path (computing); Algorithm; Boundary (topology); Artificial intelligence; Mathematics; Control (management)","score_opus":0.01420666792579336,"score_gpt":0.2647870593941045,"score_spread":0.2505803914683112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911994444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043501683,0.00018171639,0.95196944,0.00026566692,0.0015879,0.0021395597,0.000028426659,0.00026019147,0.00006542824],"genre_scores_gemma":[0.51887786,0.0000108825225,0.47906315,0.0014720157,0.00014819756,0.00023368796,0.00005036822,0.000058860358,0.000084950836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99665576,0.00009390303,0.0006703795,0.001161304,0.0005389642,0.0008797031],"domain_scores_gemma":[0.9971877,0.0007293547,0.00036775833,0.0011347097,0.00030116455,0.00027935626],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000733775,0.0005790221,0.00077467586,0.00025814152,0.0002229235,0.00029243814,0.0013824442,0.0003086463,0.000006049756],"category_scores_gemma":[0.000029434135,0.0005544867,0.00011005961,0.0004347061,0.000050592374,0.00042910376,0.00022304448,0.00049738976,0.00013792378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085165506,0.00018827352,0.0014889676,0.0002318753,0.000074398275,0.000012326471,0.0006219062,0.9897693,0.00015475038,0.00022938722,0.000041115218,0.0071025654],"study_design_scores_gemma":[0.0023845446,0.0007204822,0.0016599735,0.00029700706,0.000055385048,0.00003095618,0.00010471287,0.9923901,0.0015720705,0.00007728055,0.0000610397,0.00064643146],"about_ca_topic_score_codex":0.00004073066,"about_ca_topic_score_gemma":0.0000012453116,"teacher_disagreement_score":0.4753762,"about_ca_system_score_codex":0.00019189541,"about_ca_system_score_gemma":0.00017657189,"threshold_uncertainty_score":0.99969065},"labels":[],"label_agreement":null},{"id":"W2916095269","doi":"10.2316/j.2019.206-0088","title":"MULTI-OBJECTIVE TRAJECTORY PLANNING OF ROBOT MANIPULATOR IN A MOVING OBSTACLE ENVIRONMENT","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstacle; Trajectory; Robot; Computer science; Obstacle avoidance; Manipulator (device); Robot manipulator; Motion planning; Artificial intelligence; Mobile robot; Geography; Physics","score_opus":0.018880587133343268,"score_gpt":0.26148948268463595,"score_spread":0.2426088955512927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916095269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31945628,0.0001646787,0.67954814,0.00014841148,0.0005757173,0.00006556124,0.0000010229766,0.000009277929,0.00003090375],"genre_scores_gemma":[0.70126885,0.000010590377,0.2986517,0.000019240502,0.000028187325,4.794391e-7,8.834301e-7,0.0000041068365,0.000015999378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887025,0.000044667202,0.00044664834,0.00012700827,0.00041028037,0.00010111518],"domain_scores_gemma":[0.9991873,0.000106494466,0.00046731462,0.0000957462,0.000103840124,0.000039334347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003425118,0.00008568497,0.0001759114,0.0002949006,0.000015222422,0.000051756546,0.00034931596,0.000044051485,0.0000038569483],"category_scores_gemma":[0.00004504261,0.000081559694,0.00004602438,0.00007319348,0.000016820224,0.00047385055,0.00008308207,0.00013732156,0.0000035116739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008649503,0.000079416655,0.030040177,0.00001035642,0.0000422821,0.000041057796,0.0015324659,0.95656115,0.0062748254,0.0005393057,0.0000036072142,0.004866706],"study_design_scores_gemma":[0.00060815684,0.000073083735,0.27981713,0.00017938025,0.0000039097354,0.000084154344,0.00010278051,0.7182754,0.00061039464,0.00017589307,0.0000050353783,0.000064636886],"about_ca_topic_score_codex":0.000010885136,"about_ca_topic_score_gemma":3.5498763e-7,"teacher_disagreement_score":0.38181254,"about_ca_system_score_codex":0.00012034729,"about_ca_system_score_gemma":0.000047985624,"threshold_uncertainty_score":0.3325908},"labels":[],"label_agreement":null},{"id":"W2916743076","doi":"10.24868/issn.2515-818x.2018.049","title":"The High Capacity Expanding Lifeboat HiCEL – Meeting the Modern SAR Challenge","year":2018,"lang":"en","type":"article","venue":"Conference Proceedings of INEC","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Delaware Sea Grant, University of Delaware; Queen's University; Queen's University Belfast; University of Southampton","keywords":"Inflatable; Search and rescue; Software deployment; Population; Process (computing); Computer science; Retrofitting; Marine engineering; Engineering; Mechanical engineering","score_opus":0.043230517773439184,"score_gpt":0.2543996303084138,"score_spread":0.2111691125349746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916743076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7367452,0.00047582138,0.20650747,0.013993969,0.001622253,0.00075921934,0.0000062603467,0.0005862805,0.039303537],"genre_scores_gemma":[0.9643357,0.00005129892,0.034951173,0.00011149102,0.00032987195,0.00001898993,2.9817065e-7,0.0000132418,0.00018794357],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980649,0.000020947942,0.00039328999,0.00046621962,0.00054202916,0.0005126116],"domain_scores_gemma":[0.9977875,0.00031018275,0.00040054173,0.000360089,0.0010414944,0.00010020026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012599451,0.0002303081,0.0002511933,0.00006816075,0.00072661054,0.00042109008,0.0024686495,0.0000989555,0.0000034369286],"category_scores_gemma":[0.0006618866,0.00013743265,0.000061581304,0.00034069875,0.0005117511,0.0005710711,0.0005838801,0.00033140782,0.000024866493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006294267,0.00020435278,0.0061844657,0.00024119813,0.00021689483,0.0000063375483,0.10260553,0.000083950086,0.07128508,0.67646354,0.0042062746,0.13843945],"study_design_scores_gemma":[0.00083995727,0.0006909455,0.0053235535,0.0008316159,0.00004924304,0.00005629716,0.0029361516,0.78126264,0.066589944,0.13885354,0.0016931113,0.00087298144],"about_ca_topic_score_codex":0.00010238796,"about_ca_topic_score_gemma":0.000004177204,"teacher_disagreement_score":0.7811787,"about_ca_system_score_codex":0.000045171793,"about_ca_system_score_gemma":0.00009401558,"threshold_uncertainty_score":0.5604341},"labels":[],"label_agreement":null},{"id":"W2917495073","doi":"10.2316/j.2019.206-5291","title":"RELIABILITY-BASED MULTI-ROBOT ROUTE PLANNING","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Computer science; Reliability engineering; Robot; Artificial intelligence; Engineering","score_opus":0.018482605562598575,"score_gpt":0.286169673298369,"score_spread":0.26768706773577045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917495073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044553317,0.00007154979,0.9519468,0.001375514,0.001857926,0.00006401364,0.0000015942946,0.00004369283,0.00008560037],"genre_scores_gemma":[0.5119143,0.0000035233154,0.48780963,0.0001543906,0.00006788255,4.0443518e-7,0.0000026379987,0.000004460679,0.00004280077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987266,0.00005118136,0.00042401926,0.0001553979,0.00052126026,0.000121534125],"domain_scores_gemma":[0.998716,0.00014825612,0.000442428,0.00016021129,0.00046291907,0.00007017085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050166086,0.00010220108,0.00016296879,0.000232312,0.000036094523,0.0002159866,0.000589759,0.000054801123,0.0000043129135],"category_scores_gemma":[0.00010263268,0.00008923073,0.00006488876,0.0001045666,0.000019913627,0.00064757,0.00008007933,0.00016917645,0.000016862728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008524952,0.000060765167,0.0110085355,0.000010114285,0.00003363532,0.000043453434,0.00027431062,0.9781547,0.0012706472,0.0017864362,0.000059314316,0.007289556],"study_design_scores_gemma":[0.0007978267,0.00008994511,0.04966385,0.0001627453,0.000006625878,0.0001113593,0.000017239241,0.9480252,0.00037386443,0.0005091332,0.00014485588,0.000097349715],"about_ca_topic_score_codex":0.000004534311,"about_ca_topic_score_gemma":9.8875255e-8,"teacher_disagreement_score":0.46736097,"about_ca_system_score_codex":0.000083983614,"about_ca_system_score_gemma":0.00010577996,"threshold_uncertainty_score":0.36387235},"labels":[],"label_agreement":null},{"id":"W2918647773","doi":"","title":"Structural Integrity Management with Unmanned Aerial Vehicles: State- of-the-Art Review and Outlook","year":2019,"lang":"en","type":"article","venue":"Lund University Publications (Lund University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SNC-Lavalin (Canada)","funders":"","keywords":"State (computer science); Structural integrity; State of art; Aeronautics; Cartography; Engineering; Computer science; Geography; Data science; Programming language","score_opus":0.009605613205827434,"score_gpt":0.18932720800397912,"score_spread":0.1797215947981517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918647773","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06871072,0.00032099578,0.11997308,0.014889858,0.00062163646,0.0026475436,0.00011542614,0.0005354984,0.79218525],"genre_scores_gemma":[0.011471839,0.00066191296,0.08307224,0.00048315703,0.000024308652,7.753294e-7,0.000052085117,0.00002317891,0.9042105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99875987,0.00014201808,0.00012813724,0.00045876022,0.00026117841,0.0002500446],"domain_scores_gemma":[0.9984947,0.000060959817,0.00020385969,0.0008794613,0.00023069006,0.00013035205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015003877,0.00016424907,0.00024128742,0.00036185217,0.00033530875,0.000068338835,0.0014843167,0.000054257936,0.000020329453],"category_scores_gemma":[0.000011317518,0.00015008761,0.00006882841,0.0016965449,0.00018879301,0.0010744534,0.00074000587,0.0002345126,0.000022882952],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034854416,0.00007083922,0.0057683587,0.00030831975,0.00020207613,0.000021878172,0.0004345673,0.0003394777,0.000018651996,0.987595,0.0030262584,0.0021797102],"study_design_scores_gemma":[0.001719407,0.00013634168,0.028894478,0.0003563179,0.00028400097,0.0000287352,0.00054539996,0.0056066546,0.000023653709,0.00043848428,0.9615208,0.00044570846],"about_ca_topic_score_codex":0.000059000897,"about_ca_topic_score_gemma":0.000026108803,"teacher_disagreement_score":0.9871565,"about_ca_system_score_codex":0.00019417619,"about_ca_system_score_gemma":0.00015169091,"threshold_uncertainty_score":0.6120395},"labels":[],"label_agreement":null},{"id":"W2921191538","doi":"10.23919/acc.2019.8814485","title":"Multi-Robot Routing for Persistent Monitoring with Latency Constraints","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vertex (graph theory); Latency (audio); Solver; Computer science; Robot; Patrolling; Heuristic; Approximation algorithm; Mathematical optimization; Graph; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence","score_opus":0.0716275427209212,"score_gpt":0.2947560714199437,"score_spread":0.2231285286990225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921191538","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019778474,0.00011183753,0.9919942,0.00025914403,0.0029590167,0.0010314243,0.00001187436,0.000477499,0.001177158],"genre_scores_gemma":[0.26870045,0.0000042115016,0.7298569,0.000031533076,0.00015313637,0.00006871844,0.000009100908,0.000027136975,0.0011488067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733067,0.000050349005,0.00040376472,0.0011591815,0.0004114406,0.00064460153],"domain_scores_gemma":[0.9978918,0.00020970925,0.0003185353,0.0011587437,0.00026469177,0.00015656455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048499042,0.00043342335,0.0005073413,0.00013909083,0.00015316409,0.00038433954,0.0016047625,0.00027833562,0.0000045225324],"category_scores_gemma":[0.00007602052,0.00035858393,0.00024985507,0.000121156,0.000079741156,0.00018830053,0.0011290703,0.00057728897,0.000050383605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017928214,0.00021399684,0.033868432,0.0005768653,0.00062217144,0.00009806845,0.0044513657,0.9253186,0.00043725575,0.0011375647,0.0001479167,0.033109818],"study_design_scores_gemma":[0.000817937,0.00014198512,0.005148534,0.0008031987,0.00004747795,0.000051529605,0.00025075383,0.9916951,0.00035918975,0.000051212905,0.000016758384,0.0006162907],"about_ca_topic_score_codex":0.00006822071,"about_ca_topic_score_gemma":6.1735676e-7,"teacher_disagreement_score":0.26672262,"about_ca_system_score_codex":0.00017108404,"about_ca_system_score_gemma":0.00037520533,"threshold_uncertainty_score":0.99988663},"labels":[],"label_agreement":null},{"id":"W2921893704","doi":"10.1109/robio.2018.8664901","title":"On Safe Robot Navigation Among Humans as Dynamic Obstacles in Unknown Indoor Environments","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Planner; Robot; Computer science; Mobile robot; Motion planning; Path (computing); Simple (philosophy); Time horizon; Real-time computing; Simulation; Artificial intelligence; Mathematical optimization; Mathematics; Computer network","score_opus":0.00979741232537512,"score_gpt":0.257983312906483,"score_spread":0.2481859005811079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921893704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56234026,0.0000110541905,0.4288296,0.00017461914,0.0004054918,0.00019720609,8.4256243e-7,0.000110740926,0.007930191],"genre_scores_gemma":[0.93236846,0.0000022464321,0.06530736,0.00021649493,0.000033647484,0.000013086809,0.000010000852,0.000009859697,0.0020388486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984685,0.00008160005,0.00026146893,0.00048711392,0.0003669564,0.00033434702],"domain_scores_gemma":[0.9991747,0.00009489091,0.00009032863,0.00054095517,0.000013447459,0.00008563881],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002494876,0.00016546041,0.00014768243,0.00014527515,0.000118285185,0.00008983689,0.0006634842,0.00009155064,0.000045363282],"category_scores_gemma":[0.00004023592,0.0001534263,0.000031660387,0.00026721298,0.000121489174,0.00041925002,0.00016289797,0.00017629088,0.0010441452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001624299,0.002962887,0.20358804,0.000088833825,0.00022543797,0.001258588,0.027695697,0.14361109,0.028444365,0.23474959,0.0023714404,0.35484162],"study_design_scores_gemma":[0.0006963932,0.00052379444,0.42743096,0.00014759124,0.000003902766,0.000009296146,0.000046464243,0.5613626,0.002601773,0.006537402,0.00026680963,0.00037301565],"about_ca_topic_score_codex":0.00016334534,"about_ca_topic_score_gemma":0.000039746683,"teacher_disagreement_score":0.41775152,"about_ca_system_score_codex":0.00017450954,"about_ca_system_score_gemma":0.00002707871,"threshold_uncertainty_score":0.9997337},"labels":[],"label_agreement":null},{"id":"W2921962626","doi":"10.1109/ivs.2019.8813797","title":"Learning a Lattice Planner Control Set for Autonomous Vehicles","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Planner; Set (abstract data type); Speedup; Set function; Measure (data warehouse); Computation; Motion planning; Task (project management); Lattice (music); Control (management); Algorithm; Artificial intelligence; Robot; Data mining; Engineering","score_opus":0.030410367927079712,"score_gpt":0.27804057898717444,"score_spread":0.24763021106009472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921962626","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046262196,0.00013586288,0.9860831,0.0010001761,0.0019003177,0.00091531436,0.000026990703,0.00086051045,0.004451506],"genre_scores_gemma":[0.3438267,0.0000038912685,0.6400312,0.0007253034,0.000329506,0.00020401711,0.00007739352,0.000051668478,0.014750273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768114,0.00012780505,0.00038622195,0.0009547233,0.000303288,0.00054684095],"domain_scores_gemma":[0.9976916,0.0007772552,0.00029619463,0.00095701666,0.00015229604,0.00012569444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007369655,0.00035892578,0.0005481005,0.00014731141,0.00012947235,0.00042830268,0.0016602329,0.00037460914,0.000009888612],"category_scores_gemma":[0.0001851625,0.00032852977,0.00019155067,0.00008578776,0.000023685387,0.00017178462,0.0006755176,0.0008259883,0.0002761095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038108978,0.000055446566,0.0017125576,0.00029606058,0.00024582946,0.000047987458,0.0022418608,0.9601319,0.000079467725,0.008176107,0.0042314585,0.02274326],"study_design_scores_gemma":[0.0008025304,0.00014281973,0.0014511326,0.000102843514,0.000034258574,0.000017735103,0.000042062795,0.9907659,0.00007608927,0.0023370176,0.0037732285,0.00045433213],"about_ca_topic_score_codex":0.00005014205,"about_ca_topic_score_gemma":0.0000011083089,"teacher_disagreement_score":0.34605187,"about_ca_system_score_codex":0.00009326734,"about_ca_system_score_gemma":0.0003285559,"threshold_uncertainty_score":0.9999167},"labels":[],"label_agreement":null},{"id":"W2921991632","doi":"10.1109/robio.2018.8665075","title":"Autonomous Navigation by Mobile Robots in Human Environments: A Survey","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mobile robot navigation; Computer science; Mobile robot; Robot; Human–computer interaction; Motion planning; Process (computing); Artificial intelligence; Service (business); Shortest path problem; Robot control","score_opus":0.017938278293778913,"score_gpt":0.2747268083205047,"score_spread":0.2567885300267258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921991632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16132891,0.000027692306,0.83632845,0.0000407211,0.0002187226,0.00019318792,0.000003648931,0.00012622574,0.0017324408],"genre_scores_gemma":[0.91879845,0.000001050851,0.07874188,0.00015128535,0.000039937247,0.000034391585,0.00004936216,0.00000987377,0.0021737802],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881494,0.00012875641,0.00021940116,0.00037793053,0.00019607681,0.00026291868],"domain_scores_gemma":[0.99936205,0.000054304324,0.00006226274,0.0004409692,0.000013884399,0.000066533496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053364405,0.00010965884,0.00011904919,0.000060236358,0.00008709087,0.0000742044,0.0005648228,0.00006096742,0.0000328999],"category_scores_gemma":[0.000014481483,0.00010751763,0.00001620568,0.00024051552,0.00006450296,0.0003342072,0.00015204608,0.00009289216,0.0002866005],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028659666,0.0037094727,0.4267577,0.00004819766,0.00014084768,0.00037045675,0.016308824,0.07218597,0.13111427,0.0069359276,0.07960406,0.2627956],"study_design_scores_gemma":[0.0010277774,0.00076901686,0.33983302,0.0000510432,0.0000030399483,0.000027435619,0.000033354292,0.6377309,0.01705246,0.0007077287,0.0020954579,0.00066879054],"about_ca_topic_score_codex":0.0007530513,"about_ca_topic_score_gemma":0.000022128295,"teacher_disagreement_score":0.7575866,"about_ca_system_score_codex":0.000102438986,"about_ca_system_score_gemma":0.000020325479,"threshold_uncertainty_score":0.4384442},"labels":[],"label_agreement":null},{"id":"W2923018352","doi":"10.1109/iros40897.2019.8968150","title":"Computing a Minimal Set of t-Spanning Motion Primitives for Lattice Planners","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integer programming; Motion planning; Heuristics; Lattice (music); Integer lattice; Configuration space; Mathematics; Computer science; Algorithm; Linear programming; Tree traversal; Discrete mathematics; Theoretical computer science; Combinatorics; Topology (electrical circuits); Mathematical optimization; Robot; Artificial intelligence","score_opus":0.06101737584296183,"score_gpt":0.31701344307207174,"score_spread":0.2559960672291099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923018352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016056601,0.000086672815,0.9784265,0.00031463357,0.0014878865,0.0007396754,0.000046857895,0.00026777986,0.0025733497],"genre_scores_gemma":[0.19485517,0.0000019110444,0.8044405,0.00008099741,0.00018056718,0.000014430003,0.00007318966,0.000021766025,0.00033146402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976426,0.000108542416,0.00056198385,0.00086929556,0.00038081018,0.00043681054],"domain_scores_gemma":[0.9974548,0.00080556143,0.00061889895,0.0007920092,0.0002464541,0.00008226568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081843534,0.0003241369,0.00058163167,0.00024008959,0.000076466284,0.00014129787,0.0013560298,0.00029053938,0.0000020365078],"category_scores_gemma":[0.0002109829,0.00032127867,0.00019282356,0.00015426468,0.000053551994,0.00019496717,0.0014539297,0.00040415267,0.000013735077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005638436,0.00015221392,0.005893242,0.002508633,0.00044429995,0.000029222749,0.017957756,0.906262,0.00054266996,0.021574356,0.0030759852,0.041503232],"study_design_scores_gemma":[0.0003956696,0.000117447344,0.0048878314,0.0005420584,0.000029939356,0.000012718375,0.00017994994,0.9916848,0.0009062647,0.0007924717,0.000093327675,0.00035754102],"about_ca_topic_score_codex":0.00005470836,"about_ca_topic_score_gemma":3.4738352e-7,"teacher_disagreement_score":0.17879856,"about_ca_system_score_codex":0.000069702226,"about_ca_system_score_gemma":0.00021922724,"threshold_uncertainty_score":0.99992394},"labels":[],"label_agreement":null},{"id":"W2924923201","doi":"10.1155/2019/4762658","title":"Research on Local Dynamic Path Planning Method for Intelligent Vehicle Lane-Changing","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"CarSim; Path (computing); Computer science; Track (disk drive); Simulation; Stability (learning theory); Motion planning; State (computer science); Automotive engineering; Vehicle dynamics; Real-time computing; Engineering; Artificial intelligence; Algorithm","score_opus":0.040576382905095444,"score_gpt":0.38682754439646755,"score_spread":0.3462511614913721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924923201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093581714,0.00012067639,0.9051507,0.00021291556,0.00065723795,0.00020907815,0.0000036719482,0.000025152844,0.000038841652],"genre_scores_gemma":[0.4948655,0.000008366403,0.504962,0.000050680435,0.000042257638,0.000004670627,0.0000064120586,0.000010936483,0.000049133054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832267,0.00008971027,0.0004322918,0.00022882137,0.0005853503,0.00034114788],"domain_scores_gemma":[0.99850935,0.0006034803,0.00026994554,0.00019618118,0.00033009588,0.00009095468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001660158,0.000109880624,0.00022324006,0.00046470927,0.00009445545,0.000039177132,0.000398457,0.000060097624,0.00000254554],"category_scores_gemma":[0.000031332835,0.000097608005,0.00009775623,0.00045237856,0.000015740012,0.0004510705,0.0000062763597,0.0003864296,0.000010864703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012002678,0.000041265404,0.00022627412,0.000040802774,0.000019155244,0.000059482794,0.0035099036,0.9241623,0.0025276423,0.0022657271,0.000019605404,0.06700781],"study_design_scores_gemma":[0.0020059752,0.0024601656,0.026372662,0.0009793566,0.000022466618,0.000049379738,0.0055397484,0.94789743,0.0061140684,0.007237473,0.0010099534,0.00031129597],"about_ca_topic_score_codex":0.0000024804526,"about_ca_topic_score_gemma":6.999956e-7,"teacher_disagreement_score":0.40128377,"about_ca_system_score_codex":0.00014141046,"about_ca_system_score_gemma":0.00007956454,"threshold_uncertainty_score":0.3980339},"labels":[],"label_agreement":null},{"id":"W2929206366","doi":"10.1109/tase.2018.2857630","title":"Vehicle Routing for Resource Management in Time-Phased Deployment of Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Research Foundation of Korea; Canada Research Chairs","keywords":"Software deployment; Wireless sensor network; Routing (electronic design automation); Resource management (computing); Computer science; Computer network; Resource (disambiguation); Engineering","score_opus":0.013794466131825242,"score_gpt":0.24649513301324139,"score_spread":0.23270066688141613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929206366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037548263,0.0000043359296,0.9618083,0.00007468583,0.00016544206,0.00018538968,7.793303e-7,0.00013707022,0.000075696],"genre_scores_gemma":[0.82225853,0.0000012774351,0.17764331,0.000032074724,0.000013348734,0.000019899628,1.2751684e-7,0.000004856622,0.000026586074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906534,0.000009533831,0.00019099498,0.00023258521,0.00028487924,0.00021668074],"domain_scores_gemma":[0.99958485,0.00007673803,0.000042379477,0.000184514,0.000059776426,0.000051731877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007577749,0.000075565615,0.00008857792,0.00031479786,0.00013305739,0.00007369379,0.0002659363,0.000025491558,9.3963286e-7],"category_scores_gemma":[0.000009808708,0.00007804518,0.000017639424,0.0008016377,0.000056918954,0.00030649712,0.0000033721717,0.000053800995,0.000003099473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042197003,0.00003132451,0.000008991288,0.000014220292,0.0000060093466,0.0000010639421,0.00048763608,0.93919855,0.011560834,0.00016086506,0.000009005594,0.048517294],"study_design_scores_gemma":[0.0002802478,0.0000637045,0.0010619849,0.00006442832,0.0000035022774,0.0000029589414,0.000024312352,0.9780099,0.020360766,0.000005352598,0.000044718246,0.00007813129],"about_ca_topic_score_codex":0.000003551092,"about_ca_topic_score_gemma":2.215091e-7,"teacher_disagreement_score":0.7847102,"about_ca_system_score_codex":0.00006663844,"about_ca_system_score_gemma":0.000018083503,"threshold_uncertainty_score":0.318259},"labels":[],"label_agreement":null},{"id":"W2930266512","doi":"10.1109/tro.2019.2896401","title":"An SMT-Based Approach to Motion Planning for Multiple Robots With Complex Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Robot; Mathematical optimization; Travelling salesman problem; Routing (electronic design automation); Correctness; Boolean satisfiability problem; Path (computing); Task (project management); Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","score_opus":0.049457559597819896,"score_gpt":0.2779375566126992,"score_spread":0.2284799970148793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930266512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014296737,0.0000021193453,0.99581647,0.0003064355,0.0006100348,0.0011364875,0.0000488515,0.00043050395,0.00021941913],"genre_scores_gemma":[0.4664736,7.731791e-8,0.53293157,0.0004424544,0.000021343703,0.00005055544,0.000018930428,0.00002403458,0.000037444228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979199,0.00008288762,0.0003067813,0.0007457645,0.00041505243,0.0005296041],"domain_scores_gemma":[0.9982739,0.00029219608,0.0001065606,0.00087152194,0.00016925433,0.0002865495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025681243,0.0003095041,0.0003304977,0.00029827602,0.0002759825,0.00017975547,0.0007112196,0.00011058598,0.0000060773364],"category_scores_gemma":[0.000007532698,0.00029417494,0.00009171781,0.00048912194,0.00007430263,0.0003516572,0.0000023796588,0.00026199798,0.00005694802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058612222,0.00045257635,0.00020615433,0.000036060195,0.00003109685,0.000004230345,0.0004258182,0.99211365,0.0008079727,0.00024118039,0.000056888926,0.0055657416],"study_design_scores_gemma":[0.0015605381,0.00081247394,0.0009721899,0.00007513907,0.000026463731,0.000027164006,0.00010588824,0.9938141,0.002164458,0.000023516042,0.00002051485,0.0003975143],"about_ca_topic_score_codex":0.00001667768,"about_ca_topic_score_gemma":0.0000022705906,"teacher_disagreement_score":0.46504393,"about_ca_system_score_codex":0.00012504144,"about_ca_system_score_gemma":0.00013172011,"threshold_uncertainty_score":0.99995106},"labels":[],"label_agreement":null},{"id":"W2936998035","doi":"10.5772/intechopen.85668","title":"Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion","year":2019,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"King Fahd University of Petroleum and Minerals","keywords":"Subgradient method; Robot; Mobile robot; Trajectory; Computer science; Mathematical optimization; Motion planning; Set (abstract data type); Optimization problem; Decomposition; Energy (signal processing); Mathematics; Artificial intelligence","score_opus":0.020058352221293967,"score_gpt":0.22985652156984643,"score_spread":0.20979816934855247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936998035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010008966,0.00004268785,0.88217807,0.0000406489,0.0002812015,0.00034968092,0.00005849616,0.00020687735,0.116841316],"genre_scores_gemma":[0.0018155717,0.000010463626,0.58446324,0.000121747755,0.00007855434,0.000022762848,0.00040790017,0.000113206115,0.41296658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979534,0.000044282242,0.00053406926,0.00070884783,0.00048458466,0.00027477473],"domain_scores_gemma":[0.9976844,0.00007115308,0.0006320292,0.001107004,0.00041120086,0.00009416527],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020176459,0.00044176145,0.0005892953,0.0002890281,0.00006506693,0.00013693327,0.001192125,0.000398481,0.000050835126],"category_scores_gemma":[0.000023549637,0.0003952609,0.000118787604,0.000049633334,0.000120139935,0.00023308996,0.0004100767,0.00034016446,0.000112160036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020700856,0.00019851253,0.000023775698,0.00032500163,0.00072327314,0.00033795263,0.0008693071,0.65951604,0.0027692318,0.09453048,0.0025992899,0.23790015],"study_design_scores_gemma":[0.0009168782,0.0004529026,0.000016228401,0.0015138444,0.00007234757,0.00010476211,0.000004770444,0.98715204,0.0022604815,0.0007600057,0.0059356564,0.00081009266],"about_ca_topic_score_codex":0.00005584954,"about_ca_topic_score_gemma":0.0000029135495,"teacher_disagreement_score":0.327636,"about_ca_system_score_codex":0.00014232598,"about_ca_system_score_gemma":0.00018319717,"threshold_uncertainty_score":0.9998499},"labels":[],"label_agreement":null},{"id":"W2942612655","doi":"10.48550/arxiv.1905.00500","title":"Hierarchically Consistent Motion Primitives for Quadrotor Coordination","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Workspace; Scalability; Computer science; Motion (physics); Motion planning; Set (abstract data type); Distributed computing; Ideal (ethics); Motion capture; Artificial intelligence; Robot; Database; Programming language","score_opus":0.0873617886579726,"score_gpt":0.20729690364945969,"score_spread":0.11993511499148708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942612655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022374593,0.000024649575,0.97318035,0.00034724877,0.0012940945,0.0012881315,0.000041486386,0.0003084249,0.0011410213],"genre_scores_gemma":[0.9068367,0.000014474214,0.0902504,0.00009722271,0.00009314136,0.000008715231,0.000073292176,0.000021391901,0.0026046534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978216,0.00018365239,0.00024871042,0.0012390289,0.00013185556,0.00037514084],"domain_scores_gemma":[0.9977334,0.00036497676,0.00036037518,0.0010475005,0.00034857407,0.00014517414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040913714,0.00031108983,0.00038166536,0.00029895667,0.00015251542,0.00014938547,0.0013880635,0.00033148195,0.000004965084],"category_scores_gemma":[0.00014870323,0.00036623114,0.00026048135,0.00028420554,0.00009992946,0.0003403727,0.0009797331,0.00044975773,0.00007158566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007425062,0.0002661721,0.003916992,0.00038545413,0.00023164958,0.00012674525,0.00056095567,0.5022842,0.00016082858,0.48658714,0.0009012135,0.0045043766],"study_design_scores_gemma":[0.0007217344,0.00012282614,0.00517726,0.00014214354,0.000055427823,0.0000053972035,0.000021348282,0.957293,0.00010893053,0.035642873,0.00028545683,0.00042359953],"about_ca_topic_score_codex":0.000028493305,"about_ca_topic_score_gemma":0.0000015890832,"teacher_disagreement_score":0.8844621,"about_ca_system_score_codex":0.0003115422,"about_ca_system_score_gemma":0.000296028,"threshold_uncertainty_score":0.99987894},"labels":[],"label_agreement":null},{"id":"W2944974408","doi":"10.1007/s10878-019-00413-1","title":"An evolutionary approach for the target search problem in uncertain environment","year":2019,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Université Laval","funders":"","keywords":"Theory of computation; Evolutionary algorithm; Mathematical optimization; Computer science; Mathematical economics; Mathematics; Theoretical computer science; Algorithm","score_opus":0.014416983642847468,"score_gpt":0.24734171364396731,"score_spread":0.23292473000111985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944974408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001692688,0.00012745573,0.9974348,0.0004200518,0.0012277595,0.00048828006,0.0000011271331,0.000011408476,0.00011983357],"genre_scores_gemma":[0.019574955,0.000020727291,0.9801162,0.000026805255,0.00020623683,0.000012015762,0.000007511167,0.000009267882,0.000026269978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858636,0.00017873028,0.00038353942,0.00016997696,0.00048654404,0.000194877],"domain_scores_gemma":[0.9990829,0.00018995586,0.00024731708,0.0002721542,0.000144382,0.00006327804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001390696,0.0000967261,0.00017735652,0.00013632461,0.00007961986,0.000084318264,0.00074460846,0.00007207509,0.00000696853],"category_scores_gemma":[0.000035221696,0.00007086279,0.000057725123,0.0002404856,0.00002246658,0.00067269156,0.00005618953,0.00020813271,0.00000228283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004107434,0.00019592278,0.00091866433,0.000009236736,0.0000124830985,0.0000020473449,0.0003476227,0.992761,0.000036437657,0.005191971,0.00013169071,0.00035181793],"study_design_scores_gemma":[0.0013218392,0.00048048468,0.00058621395,0.00001556272,0.0000055969113,0.000019831932,0.000058284753,0.9945028,0.00003899649,0.0027219725,0.00016289503,0.00008556736],"about_ca_topic_score_codex":0.0000058691908,"about_ca_topic_score_gemma":1.0363208e-9,"teacher_disagreement_score":0.019405687,"about_ca_system_score_codex":0.00019219944,"about_ca_system_score_gemma":0.00017449739,"threshold_uncertainty_score":0.28897008},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W2945338643","doi":"10.1007/s10514-019-09863-2","title":"Real-time motion planning with a fixed-wing UAV using an agile maneuver space","year":2019,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Fixed wing; Agile software development; Computer science; Planner; Motion planning; Wing; Transient (computer programming); Simulation; Motion (physics); Real-time computing; Aerospace engineering; Artificial intelligence; Robot; Engineering","score_opus":0.02164260124443779,"score_gpt":0.25546425248654575,"score_spread":0.23382165124210796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945338643","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5132452,0.000023468043,0.48178354,0.000100006,0.00043306267,0.00032952518,0.0000019738923,0.00068557734,0.003397616],"genre_scores_gemma":[0.42054585,0.0000010576123,0.5774114,0.000108737215,0.00013497239,0.000008746257,0.000017315799,0.00005620466,0.0017157301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762857,0.0001275939,0.00029568028,0.0008546311,0.00041777434,0.0006757593],"domain_scores_gemma":[0.99832445,0.00009651591,0.00023291958,0.0010595915,0.00008278933,0.00020371807],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033438767,0.00032394475,0.00036746985,0.00021321051,0.0002266146,0.00030225128,0.0008175901,0.00013681468,0.0000316464],"category_scores_gemma":[0.000015223533,0.00030577704,0.000058626814,0.00042736134,0.00004014191,0.0013913837,0.00022473524,0.00025833424,0.00043113332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009232173,0.0000754264,0.0065699914,0.000020368747,0.00003135601,0.00021453474,0.0020555,0.9703809,0.018005898,0.00032660153,0.00009766626,0.0022125468],"study_design_scores_gemma":[0.00049774477,0.00025411142,0.016649142,0.0001723382,0.000018535553,0.0002844022,0.00006944249,0.98002493,0.0013906113,0.00008244978,0.00007524425,0.00048106248],"about_ca_topic_score_codex":0.00031882263,"about_ca_topic_score_gemma":0.0000014927621,"teacher_disagreement_score":0.09562787,"about_ca_system_score_codex":0.00022938156,"about_ca_system_score_gemma":0.00017740365,"threshold_uncertainty_score":0.99993944},"labels":[],"label_agreement":null},{"id":"W2946412725","doi":"10.65109/fisz6997","title":"Tangible Robotic Fleet Control","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Robot; Computer science; Software deployment; Command and control; Testbed; Swarm behaviour; Human–computer interaction; Leverage (statistics); Operator (biology); Robustness (evolution); Automation; Simulation; Artificial intelligence; Software engineering; Engineering; Telecommunications","score_opus":0.009057899811261613,"score_gpt":0.22119011930809895,"score_spread":0.21213221949683733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946412725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009846266,0.000036805854,0.9601367,0.0009347124,0.00084442156,0.00016075524,3.5928704e-7,0.00035949994,0.036542136],"genre_scores_gemma":[0.59499675,8.774796e-7,0.39080328,0.0012188948,0.00005429343,0.0000061261617,9.940695e-7,0.000008874768,0.012909906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989929,0.00003547033,0.00013840983,0.00030197416,0.00022837067,0.00030287795],"domain_scores_gemma":[0.99910593,0.000106016654,0.0000389799,0.00062343676,0.000039414434,0.00008619679],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020436618,0.000101297956,0.00016306246,0.00007064564,0.000035522637,0.00010162713,0.00073146395,0.000044476194,0.00011535603],"category_scores_gemma":[0.000020330812,0.000083339095,0.000046111094,0.00023715105,0.0000116337405,0.00031699144,0.0000939759,0.00008680792,0.004247053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012569435,0.00023828202,0.04497191,0.00005233684,0.00015447823,0.0002003794,0.0008558604,0.48049596,0.0064912722,0.4114507,0.032416105,0.022660132],"study_design_scores_gemma":[0.000594272,0.00007666144,0.0046582664,0.000011055742,0.0000034412426,0.000029727133,0.00000826588,0.9921094,0.0003120052,0.00095593353,0.001075289,0.00016565074],"about_ca_topic_score_codex":0.000020092177,"about_ca_topic_score_gemma":2.0459251e-7,"teacher_disagreement_score":0.59401214,"about_ca_system_score_codex":0.000024054774,"about_ca_system_score_gemma":0.00004161699,"threshold_uncertainty_score":0.99652827},"labels":[],"label_agreement":null},{"id":"W2947082796","doi":"10.3390/app9112230","title":"Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Technology Planning Project of Guangdong Province; National Key Research and Development Program of China","keywords":"Control theory (sociology); Manipulator (device); Controller (irrigation); Control engineering; Computer science; Engineering; Robot; Simulation; Control (management); Artificial intelligence","score_opus":0.030234358753490804,"score_gpt":0.2617553738514805,"score_spread":0.2315210150979897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947082796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95247877,0.000012987507,0.0444078,0.000042908894,0.00071396073,0.00043685554,0.0000028628965,0.000033614004,0.0018702564],"genre_scores_gemma":[0.9595183,2.3850677e-7,0.040281877,0.000059886148,0.00012251422,0.0000074717686,0.0000012571238,0.000004277393,0.000004148631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798906,0.00014518021,0.00036070772,0.00046523614,0.00068190566,0.00035789591],"domain_scores_gemma":[0.99894893,0.00016649914,0.0002421873,0.000555373,0.000030813873,0.00005617005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019838365,0.00013542568,0.00029408775,0.00014808514,0.00010837498,0.00012725173,0.0022134378,0.000060174345,0.000009082719],"category_scores_gemma":[0.000026541109,0.00009637896,0.000036972484,0.00063123525,0.000268511,0.0005517266,0.00009835617,0.0001177102,0.000009432519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095012874,0.00033565334,0.0049215388,0.00003606806,0.000020509464,0.000014108079,0.008140386,0.7954751,0.07611131,0.10869122,0.000021617467,0.0061374386],"study_design_scores_gemma":[0.0020491886,0.0008982902,0.032764547,0.00004538111,0.00001134598,0.000018090343,0.0020164934,0.9517234,0.008464375,0.0016693973,0.000012118915,0.00032739126],"about_ca_topic_score_codex":0.00021386723,"about_ca_topic_score_gemma":0.000017631915,"teacher_disagreement_score":0.15624824,"about_ca_system_score_codex":0.000016413303,"about_ca_system_score_gemma":0.0001707994,"threshold_uncertainty_score":0.41131547},"labels":[],"label_agreement":null},{"id":"W2948622461","doi":"10.2316/j.2019.206-0172","title":"HYBRID MOTION CONTROL OF CABLE-DRIVEN HYPER REDUNDANT ROBOT CONSIDERING KINEMATIC AND TENSION OPTIMIZATION","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kinematics; Tension (geology); Control theory (sociology); Motion control; Computer science; Robot; Motion (physics); Control (management); Control engineering; Engineering; Physics; Artificial intelligence; Classical mechanics","score_opus":0.009781502227999173,"score_gpt":0.23047620071094335,"score_spread":0.22069469848294418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948622461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04748175,0.00011088162,0.9503173,0.0011758029,0.0007525002,0.00011044428,0.00000217452,0.00001930746,0.000029789175],"genre_scores_gemma":[0.6322895,0.000038614282,0.36757237,0.000045709996,0.00003749723,3.9670897e-7,0.0000026087653,0.0000047179374,0.000008559634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875057,0.000056303594,0.0005257328,0.00013610345,0.0004366215,0.00009466373],"domain_scores_gemma":[0.99842733,0.0001511112,0.0006743973,0.00011065605,0.00058126927,0.00005523389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035484988,0.000100602054,0.00023664777,0.00024949966,0.000034537003,0.00012238616,0.00021212881,0.0000388736,0.0000053180315],"category_scores_gemma":[0.00013879265,0.00008835676,0.00004132158,0.00006360138,0.00003127943,0.0006415517,0.00006780728,0.00009906475,0.0000017639936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008607118,0.000029407862,0.0021783256,0.000024886833,0.000049221908,0.000013487136,0.00015134494,0.9875532,0.0045523797,0.001077755,0.000016439782,0.0043449453],"study_design_scores_gemma":[0.0009583177,0.00011066827,0.0061475933,0.00030814752,0.000023457807,0.00066137174,0.000021717151,0.99069136,0.00043290236,0.00055521773,0.000005762311,0.00008346945],"about_ca_topic_score_codex":0.000007549059,"about_ca_topic_score_gemma":1.1088736e-7,"teacher_disagreement_score":0.58480775,"about_ca_system_score_codex":0.000042520707,"about_ca_system_score_gemma":0.000051775976,"threshold_uncertainty_score":0.3603084},"labels":[],"label_agreement":null},{"id":"W2948900487","doi":"10.2316/j.2019.206-0231","title":"VISION-BASED MOBILE ROBOT LEADER–FOLLOWER CONTROL USING MODEL PREDICTIVE CONTROL","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Model predictive control; Mobile robot; Control (management); Computer science; Robot control; Robot; Control theory (sociology); Control engineering; Artificial intelligence; Engineering","score_opus":0.010610768715944656,"score_gpt":0.27172346598702407,"score_spread":0.26111269727107944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948900487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02190816,0.000108290486,0.97531706,0.00102577,0.0013563945,0.00018599829,0.000009713746,0.000039040577,0.000049580645],"genre_scores_gemma":[0.7860127,0.0000042928064,0.21352097,0.0003236893,0.00010269122,0.0000017638039,0.0000020704838,0.000008510848,0.000023273442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983247,0.000068123714,0.0005068245,0.00019021115,0.00074607285,0.00016405222],"domain_scores_gemma":[0.9982336,0.00021836099,0.000585692,0.00015730796,0.0007119045,0.00009312893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048013282,0.0001426501,0.00026061974,0.00027185303,0.0000546305,0.00023528344,0.00054073014,0.00007844384,0.00000570761],"category_scores_gemma":[0.00006638039,0.00012457184,0.000106777086,0.000097418706,0.000033374672,0.0008184638,0.00004245869,0.00018651785,0.000008272971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034971526,0.000073924595,0.001310648,0.0000052834366,0.00009514855,0.00002140188,0.00015067212,0.99248517,0.0032518967,0.0006888168,0.000039338345,0.0018427595],"study_design_scores_gemma":[0.002503017,0.0002757384,0.0022208379,0.0001588165,0.000031790005,0.00009371062,0.000023231996,0.9937094,0.00016451658,0.0006843393,0.000011468343,0.00012313938],"about_ca_topic_score_codex":0.0000037465209,"about_ca_topic_score_gemma":1.1713238e-7,"teacher_disagreement_score":0.76410455,"about_ca_system_score_codex":0.0001294255,"about_ca_system_score_gemma":0.00020639123,"threshold_uncertainty_score":0.5079892},"labels":[],"label_agreement":null},{"id":"W2949045728","doi":"10.13140/2.1.1952.0965","title":"Towards Development of a Robotic Guide Dog","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.02000702333657314,"score_gpt":0.2786190794371979,"score_spread":0.25861205610062477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949045728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007110204,0.000007346333,0.9695634,0.00029553135,0.0007163403,0.00006281877,1.1801483e-7,0.0001419171,0.02210232],"genre_scores_gemma":[0.07005382,2.3397791e-7,0.9283991,0.00009364137,0.000025692625,0.000004500816,5.9579776e-7,0.000004315906,0.0014181138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989829,0.000013287603,0.00028758062,0.00022595168,0.00027933155,0.00021093215],"domain_scores_gemma":[0.99923426,0.000037136175,0.000068187124,0.0004889467,0.00008419066,0.00008730412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003641136,0.000092824186,0.00014084288,0.00007967419,0.00004992317,0.000036132682,0.0008656892,0.000050587158,0.000034412777],"category_scores_gemma":[0.000076075354,0.000075607924,0.000029947521,0.00021893613,0.000029491497,0.00014634461,0.00024709228,0.00012298222,0.00010545663],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025079014,0.00036283486,0.0046380325,0.00007172277,0.000104907645,0.0000866286,0.009032056,0.0058996766,0.06361298,0.112844214,0.0097710695,0.7935734],"study_design_scores_gemma":[0.00087703794,0.000119570956,0.11696252,0.00007151151,0.0000125254555,0.00016241317,0.00007158338,0.6733029,0.1815247,0.003305683,0.02271341,0.0008761742],"about_ca_topic_score_codex":0.000030939656,"about_ca_topic_score_gemma":0.0000057493085,"teacher_disagreement_score":0.7926972,"about_ca_system_score_codex":0.0000147409555,"about_ca_system_score_gemma":0.0003006361,"threshold_uncertainty_score":0.30832016},"labels":[],"label_agreement":null},{"id":"W2949138173","doi":"10.1109/lra.2019.2923368","title":"Efficient Autonomous Robotic Exploration With Semantic Road Map in Indoor Environments","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Construct (python library); Graph; Semantic mapping; Process (computing); Robot; Road map; Artificial intelligence; Theoretical computer science; Geography","score_opus":0.01045855610812295,"score_gpt":0.20779849621965393,"score_spread":0.19733994011153097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949138173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24525867,0.000019828809,0.75025403,0.003534771,0.0004766051,0.0003371806,5.8186197e-7,0.000101555815,0.000016785722],"genre_scores_gemma":[0.8842375,0.0000025338995,0.115053706,0.00057389395,0.000029451903,0.000016808046,0.000008475404,0.000016265283,0.00006134909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984512,0.00007795935,0.00031961224,0.00045903467,0.00036575718,0.00032643363],"domain_scores_gemma":[0.99929243,0.000052725594,0.00017054577,0.00039328123,0.000012459132,0.00007857973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002389702,0.00020429972,0.00023451896,0.0002328555,0.00007333503,0.00017567286,0.00027376768,0.0000629903,0.0000025245126],"category_scores_gemma":[0.0000061084993,0.00018610577,0.000025819156,0.0002564314,0.00004105772,0.00038112194,0.00005812522,0.00015487421,0.00017719196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002993855,0.000050356884,0.0025861203,0.000026493703,0.000010620579,0.000027279084,0.00071030384,0.9924568,0.0022108601,0.00029045186,0.000036812085,0.0015909072],"study_design_scores_gemma":[0.0006645141,0.000093312796,0.02932518,0.0001060769,0.000008197008,0.000019641015,0.000027055567,0.9692385,0.00022616389,0.00003635592,0.000014648857,0.0002403529],"about_ca_topic_score_codex":0.000019648687,"about_ca_topic_score_gemma":0.0000012636254,"teacher_disagreement_score":0.63897884,"about_ca_system_score_codex":0.00011493224,"about_ca_system_score_gemma":0.00003069361,"threshold_uncertainty_score":0.75891733},"labels":[],"label_agreement":null},{"id":"W2949992533","doi":"10.1609/socs.v10i1.18510","title":"Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks","year":2021,"lang":"en","type":"preprint","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":276,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation; National Science Foundation","keywords":"Computer science; Pathfinding; Traverse; Benchmark (surveying); Terminology; Constraint (computer-aided design); Key (lock); Operations research; Theoretical computer science; Shortest path problem; Mathematics; Computer security","score_opus":0.04275484513429419,"score_gpt":0.2752149483165113,"score_spread":0.2324601031822171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949992533","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85634047,0.00054041337,0.010400012,0.028767679,0.07544325,0.002596935,0.00022637048,0.00040265478,0.025282191],"genre_scores_gemma":[0.9532281,0.00026423513,0.04550761,0.0001658842,0.00052984053,0.000084121035,0.0000419578,0.00003368742,0.00014452908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638784,0.00005128188,0.00056659576,0.00095504796,0.0016633357,0.00037590618],"domain_scores_gemma":[0.9977111,0.00023625018,0.00043447988,0.00042542393,0.0010383513,0.00015440227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001081765,0.00035391032,0.0004022031,0.00020080815,0.00024406218,0.00094932783,0.003695951,0.00032637062,0.000016680155],"category_scores_gemma":[0.00043359058,0.0003080135,0.00020865205,0.0003018849,0.00012918908,0.00029980633,0.0060504493,0.0011999295,0.0000056455365],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014962968,0.0028167109,0.019291358,0.0008169729,0.0011469335,0.00007016418,0.006998152,0.0030262736,0.06127611,0.8973351,0.005068517,0.0020040432],"study_design_scores_gemma":[0.009739325,0.0011295032,0.07735433,0.009245012,0.0003151605,0.00045240164,0.0010920509,0.641916,0.13982221,0.1129097,0.0022355965,0.003788735],"about_ca_topic_score_codex":0.00016811218,"about_ca_topic_score_gemma":5.6026306e-7,"teacher_disagreement_score":0.78442544,"about_ca_system_score_codex":0.00034765754,"about_ca_system_score_gemma":0.0002708383,"threshold_uncertainty_score":0.9999372},"labels":[],"label_agreement":null},{"id":"W2952787620","doi":"10.48550/arxiv.1809.08898","title":"Rectilinear Shortest Paths Among Transient Obstacles","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Obstacle; Dijkstra's algorithm; Point (geometry); Transient (computer programming); Bounded function; Point location; Algorithm; Interval (graph theory); Shortest path problem; Computer science; Subdivision; Path (computing); Robot; Wavefront; Mathematics; Combinatorics; Geometry; Graph; Mathematical analysis; Artificial intelligence; Physics; Geography; Optics","score_opus":0.07599325619963257,"score_gpt":0.1918592953523858,"score_spread":0.11586603915275323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952787620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2927299,0.000054848097,0.70176125,0.000048354457,0.0016963468,0.00029973293,0.000021066024,0.0005820638,0.0028064104],"genre_scores_gemma":[0.9707013,0.00007069006,0.027594991,0.000062758714,0.00022633966,0.0000015331348,0.000023124381,0.000033495406,0.0012857922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996954,0.00020352154,0.0003076584,0.0017416571,0.00019245523,0.0006006628],"domain_scores_gemma":[0.9970011,0.00015734698,0.0002743036,0.001993426,0.00025497942,0.00031883593],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040891833,0.00047230034,0.00046913317,0.00031482158,0.00022738078,0.0001980719,0.0027654644,0.00045019915,0.00002632525],"category_scores_gemma":[0.00006944913,0.0005446896,0.00027948743,0.0006963001,0.00029176445,0.00041953736,0.0014253233,0.0008110079,0.00020080354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048830792,0.0005102876,0.043518513,0.00017327516,0.00041805868,0.0050234087,0.0032889696,0.9243172,0.0000790868,0.013034031,0.004631851,0.004956488],"study_design_scores_gemma":[0.00031948168,0.00010852503,0.021127736,0.0002122927,0.000074575786,0.000013189152,0.00006508341,0.9719521,0.00017348504,0.004721614,0.00053213234,0.0006997859],"about_ca_topic_score_codex":0.00023466357,"about_ca_topic_score_gemma":0.000027042612,"teacher_disagreement_score":0.67797136,"about_ca_system_score_codex":0.0002427955,"about_ca_system_score_gemma":0.00026363652,"threshold_uncertainty_score":0.9997005},"labels":[],"label_agreement":null},{"id":"W2953216510","doi":"10.48550/arxiv.1702.08410","title":"Clustering in Discrete Path Planning for Approximating Minimum Length Paths","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Travelling salesman problem; Mathematical optimization; Reduction (mathematics); Mathematics; Computation; Graph; Metric (unit); Constant (computer programming); Time complexity; Path (computing); Computer science; Algorithm; Combinatorics; Statistics","score_opus":0.11293225696221194,"score_gpt":0.23668894794627543,"score_spread":0.12375669098406349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953216510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04913838,0.00007476261,0.9473179,0.00007148165,0.0010753929,0.0006353571,0.000029725314,0.0002645283,0.0013924864],"genre_scores_gemma":[0.8277619,0.000016782324,0.17137833,0.00003145161,0.0001387154,0.000008090118,0.00002875801,0.000035534234,0.00060045766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701303,0.00013196189,0.0004104131,0.0015681002,0.00013701148,0.0007394523],"domain_scores_gemma":[0.9968824,0.00031259248,0.00071656826,0.0018158379,0.0000955222,0.00017711516],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008391912,0.00047002066,0.00062292675,0.00036516276,0.0003846533,0.00037699632,0.0032126214,0.00037953653,0.0000012796755],"category_scores_gemma":[0.0002491072,0.0005560974,0.00022565186,0.00021507869,0.00009040659,0.00061984756,0.003174951,0.0008006001,0.000011532678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030866086,0.00005008453,0.005731543,0.00034332366,0.000061384424,0.00095667224,0.0019862936,0.98375905,0.0000273684,0.0047285967,0.00010861387,0.0022162008],"study_design_scores_gemma":[0.0007787658,0.00005738553,0.001435958,0.0009770158,0.000028998105,0.00000920522,0.00018320706,0.98719764,0.00002113411,0.00861905,0.00007138441,0.0006202564],"about_ca_topic_score_codex":0.000121088015,"about_ca_topic_score_gemma":0.0000063975554,"teacher_disagreement_score":0.7786235,"about_ca_system_score_codex":0.00026742127,"about_ca_system_score_gemma":0.00021791598,"threshold_uncertainty_score":0.99968904},"labels":[],"label_agreement":null},{"id":"W2955512605","doi":"10.1142/s2301385019500109","title":"Failsafe Mechanism Design for Autonomous Aerial Refueling using State Tree Structures","year":2019,"lang":"en","type":"article","venue":"Unmanned Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Correctness; Supervisor; State (computer science); Tree (set theory); Mechanism (biology); Computer science; Control (management); Supervisory control; Software; Controller (irrigation); Reliability engineering; Real-time computing; Engineering; Control engineering; Operating system; Artificial intelligence","score_opus":0.04612640294527244,"score_gpt":0.2678433631190491,"score_spread":0.22171696017377668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955512605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028978031,0.00012687003,0.9620179,0.000041951134,0.0069698473,0.0013654591,0.000015631254,0.00036864137,0.0001156948],"genre_scores_gemma":[0.5025633,0.0000010074673,0.49631327,0.00004746946,0.0003946324,0.000041187508,0.000007218201,0.000042821663,0.00058908213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997458,0.00023206756,0.00054973573,0.0007009658,0.00039490027,0.0006643703],"domain_scores_gemma":[0.9982991,0.00025946973,0.00033381852,0.00082066614,0.0001449037,0.0001420282],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009962434,0.0002981905,0.00047768521,0.00021001363,0.00020704982,0.00048089286,0.0010105908,0.00014629435,0.0000029270525],"category_scores_gemma":[0.000067523564,0.00027551883,0.00010339545,0.00023227093,0.000017645058,0.0004683467,0.00014831586,0.00014290267,0.000065289685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063731975,0.000025576257,0.000031472544,0.00019909516,0.00010983805,0.000043206936,0.0020791027,0.91745085,0.034924638,0.0426928,0.00034130394,0.0020384018],"study_design_scores_gemma":[0.0008648678,0.00022386163,0.000019400413,0.00010457978,0.000013291213,0.000074989824,0.00009581652,0.9867907,0.0046862704,0.0064329803,0.00032960004,0.00036364642],"about_ca_topic_score_codex":0.00011808084,"about_ca_topic_score_gemma":0.000001015844,"teacher_disagreement_score":0.47358528,"about_ca_system_score_codex":0.00018790583,"about_ca_system_score_gemma":0.00020375616,"threshold_uncertainty_score":0.9999697},"labels":[],"label_agreement":null},{"id":"W2960879114","doi":"10.48550/arxiv.1905.00495","title":"A Modular Framework for Motion Planning using Safe-by-Design Motion Primitives","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Workspace; Modular design; Motion planning; Motion (physics); Computer science; Modularity (biology); Robot; Set (abstract data type); Automaton; Motion control; Motion capture; Finite-state machine; Distributed computing; Artificial intelligence; Programming language","score_opus":0.15652491715915284,"score_gpt":0.23877964096663368,"score_spread":0.08225472380748083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2960879114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02307168,0.00012756036,0.97383547,0.000052809446,0.0013796464,0.0010430098,0.000035754783,0.0003360781,0.000117997944],"genre_scores_gemma":[0.5902747,0.000014340025,0.4092982,0.000044521254,0.00010234957,0.000002207444,0.00003705373,0.00003089744,0.0001957235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997042,0.00030179796,0.00029062957,0.0016140239,0.00017534215,0.0005762101],"domain_scores_gemma":[0.99736273,0.00046945427,0.0005272441,0.0012618316,0.00021748253,0.00016127055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005904755,0.00046582302,0.00050247426,0.00036222488,0.00027143216,0.00022067399,0.0015790565,0.0006662942,0.00000438968],"category_scores_gemma":[0.00017197269,0.0005830867,0.00024210838,0.00047280322,0.00007565987,0.0006377983,0.0009421865,0.0007539258,0.000031990785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002752627,0.00006727702,0.00064841914,0.00010456643,0.000089333415,0.000062575025,0.00043402065,0.9775992,0.00023628035,0.019853735,0.0000785955,0.0007984759],"study_design_scores_gemma":[0.00040648758,0.00007769879,0.00073026406,0.00051620963,0.00008375518,0.0000067356914,0.000059362428,0.9298285,0.0004164489,0.067299314,0.000024983035,0.00055022445],"about_ca_topic_score_codex":0.00003676246,"about_ca_topic_score_gemma":7.687369e-8,"teacher_disagreement_score":0.567203,"about_ca_system_score_codex":0.00047581186,"about_ca_system_score_gemma":0.00019925782,"threshold_uncertainty_score":0.99966204},"labels":[],"label_agreement":null},{"id":"W2962865729","doi":"10.1139/tcsme-2019-0083","title":"Motion modeling of a non-holonomic wheeled mobile robot based on trajectory tracking control","year":2019,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trajectory; Tracking (education); Kinematics; Control theory (sociology); Mobile robot; Computer science; Artificial neural network; Transformation (genetics); Robot; Coordinate system; Tracking error; Motion control; Simulation; Control (management); Artificial intelligence; Physics","score_opus":0.010073158625571118,"score_gpt":0.20420664136639924,"score_spread":0.19413348274082812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962865729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013034835,0.000014781253,0.98563343,0.0001375041,0.0005114142,0.0005735992,0.00003645022,0.000048622776,0.000009371731],"genre_scores_gemma":[0.8759575,6.0377687e-7,0.12388063,0.00006740687,0.00001588033,0.000048216927,0.0000013973779,0.00001863688,0.000009750638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900985,0.000014281139,0.0002768287,0.00023574966,0.00017001029,0.0002932895],"domain_scores_gemma":[0.99914324,0.00014624014,0.00007231953,0.00044593244,0.00006595307,0.00012632806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003536328,0.00013628424,0.00025123966,0.0000835339,0.00010106661,0.000019793091,0.0005369869,0.00013371414,0.0000052208993],"category_scores_gemma":[0.00001652289,0.0001305454,0.0005353374,0.00022959024,0.000012554853,0.00012935669,0.000004031946,0.00023870534,0.0000013020898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003957648,0.000017754644,0.0000021372239,0.000054112497,0.000039870396,1.0655526e-7,0.00016040888,0.9881812,0.010298756,0.00017266598,0.00000247927,0.0010665639],"study_design_scores_gemma":[0.000747352,0.00011103453,0.000038754075,0.00009502035,0.0000321859,0.0000014049796,0.000026946136,0.99361235,0.00518296,0.000024564646,0.000006620827,0.00012081718],"about_ca_topic_score_codex":0.0015435276,"about_ca_topic_score_gemma":0.00022929553,"teacher_disagreement_score":0.8629226,"about_ca_system_score_codex":0.00029488982,"about_ca_system_score_gemma":0.00024174103,"threshold_uncertainty_score":0.53234875},"labels":[],"label_agreement":null},{"id":"W2964187430","doi":"10.1109/iwcmc.2019.8766530","title":"RADR: Routing for Autonomous Drones","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Drone; Computer science; Routing (electronic design automation); Computer network; Computer security","score_opus":0.014885723624044564,"score_gpt":0.2458584712103585,"score_spread":0.23097274758631395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964187430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00465342,0.000018231194,0.9834586,0.00064129935,0.0008070536,0.00021654987,6.037239e-7,0.00035833838,0.0098459115],"genre_scores_gemma":[0.23721275,2.5879325e-7,0.7546072,0.0002791027,0.00006136216,0.000010518576,0.0000013328589,0.00000686292,0.007820625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992209,0.0000125454935,0.00013040849,0.00027458917,0.00010647043,0.0002550572],"domain_scores_gemma":[0.9993489,0.00014693176,0.00004523225,0.0003810778,0.000031689182,0.000046183453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023723845,0.000079320045,0.00011361858,0.000039968712,0.00005519742,0.00009129007,0.00053547404,0.000035564008,0.00001444869],"category_scores_gemma":[0.000029676494,0.000068641886,0.00004386274,0.00010089751,0.000007272788,0.00023653224,0.00011468351,0.0000467753,0.0003689434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009606088,0.00013740601,0.016145283,0.00008052488,0.00007787046,0.000024485189,0.0032897044,0.026133291,0.0040774914,0.7067536,0.010814419,0.2324563],"study_design_scores_gemma":[0.00028263155,0.00006537943,0.0014257546,0.000009531615,0.0000016622258,0.000013554947,0.000020934429,0.993545,0.0011167972,0.0013382076,0.0020473106,0.00013324742],"about_ca_topic_score_codex":0.000016330305,"about_ca_topic_score_gemma":2.318467e-7,"teacher_disagreement_score":0.9674117,"about_ca_system_score_codex":0.000025914103,"about_ca_system_score_gemma":0.000046616697,"threshold_uncertainty_score":0.47421443},"labels":[],"label_agreement":null},{"id":"W2964263445","doi":"","title":"Parallel Implementation and Comparison of Two UAV Path Planning Algorithms.","year":2011,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Path (computing); Algorithm; Artificial intelligence; Programming language","score_opus":0.09512975310202852,"score_gpt":0.3657178002001756,"score_spread":0.27058804709814704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964263445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019746367,0.00013757969,0.97648877,0.00003885884,0.00016189727,0.00012087644,0.0000019037434,0.00011248623,0.0031912534],"genre_scores_gemma":[0.3859107,0.0000017372762,0.61399317,0.00003901275,0.000014150555,0.0000050165436,0.0000029262749,0.000004109959,0.000029209832],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891067,0.000048225716,0.00032215627,0.000276081,0.00021479861,0.00022804357],"domain_scores_gemma":[0.9993832,0.000054963468,0.00015713392,0.00027363925,0.00004907179,0.000081988306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002559867,0.00011651798,0.00020260873,0.000089250185,0.00006797061,0.00003383596,0.000353056,0.00003148372,0.000019588986],"category_scores_gemma":[0.000007285581,0.000104579136,0.000024883408,0.00015533526,0.000038317536,0.00031901008,0.00016668455,0.00008145094,0.000009035653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016764414,0.00028107778,0.62051404,0.000049525333,0.00010352317,0.00006262472,0.04756846,0.002204359,0.0006390885,0.06594427,0.0038026501,0.25881365],"study_design_scores_gemma":[0.0016150991,0.00042825672,0.16927111,0.0000486887,0.00001965771,0.000037201364,0.0021173176,0.815551,0.006189745,0.004226981,0.00011812073,0.0003768347],"about_ca_topic_score_codex":0.00031368659,"about_ca_topic_score_gemma":0.0000021871945,"teacher_disagreement_score":0.8133466,"about_ca_system_score_codex":0.000012069623,"about_ca_system_score_gemma":0.00002830095,"threshold_uncertainty_score":0.42646134},"labels":[],"label_agreement":null},{"id":"W2965387939","doi":"10.1609/socs.v10i1.18498","title":"Revisiting Suboptimal Search","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bounded function; Search algorithm; Beam search; Iterative deepening depth-first search; Best-first search; Computer science; Mathematical optimization; Upper and lower bounds; Incremental heuristic search; Beam stack search; Bidirectional search; Node (physics); Search problem; Mathematics; Algorithm","score_opus":0.02214453960801229,"score_gpt":0.2754653915602443,"score_spread":0.25332085195223203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965387939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7619923,0.000109656175,0.0050308127,0.06607684,0.016236138,0.00067763426,0.000021173533,0.00036422873,0.14949122],"genre_scores_gemma":[0.9862417,0.000016545673,0.011901554,0.00018605261,0.00076166546,0.00001052492,0.0000025055808,0.000018852657,0.00086057425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969089,0.0000445183,0.00035418698,0.0005031929,0.0018239116,0.00036530435],"domain_scores_gemma":[0.9977738,0.0002755331,0.00013276987,0.00027259646,0.0014405575,0.00010473095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013534077,0.00015920671,0.00020422367,0.00009183673,0.00020398096,0.00039439666,0.0028440983,0.000085046064,0.000021428703],"category_scores_gemma":[0.00044940857,0.00013354681,0.00014850526,0.00063522364,0.000099210796,0.00039188494,0.001480518,0.00047920007,0.000039742685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051868865,0.00022790158,0.007418774,0.000060311853,0.000096009964,0.000019489227,0.0005918851,0.0011243358,0.11766253,0.86889476,0.0009349481,0.0029172034],"study_design_scores_gemma":[0.0016745309,0.00018732424,0.0059904833,0.000580999,0.000015779207,0.00015664948,0.00017005786,0.09403087,0.88316983,0.010590991,0.0029844616,0.00044802285],"about_ca_topic_score_codex":0.000032956345,"about_ca_topic_score_gemma":5.0863314e-8,"teacher_disagreement_score":0.8583037,"about_ca_system_score_codex":0.00021966129,"about_ca_system_score_gemma":0.00017740019,"threshold_uncertainty_score":0.5445881},"labels":[],"label_agreement":null},{"id":"W2965434695","doi":"10.1609/socs.v10i1.18492","title":"Unbounded Sub-Optimal Conflict-Based Search in Complex Domains","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Constraint (computer-aided design); Computer science; Mathematical optimization; Exploit; Axiom; Action (physics); Mathematics","score_opus":0.03299882872112784,"score_gpt":0.2861712336286616,"score_spread":0.25317240490753373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965434695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9560428,0.000019969499,0.002208418,0.022491535,0.0043316972,0.00050494453,0.000020973723,0.0001256839,0.0142539255],"genre_scores_gemma":[0.99179393,0.00000861618,0.007410068,0.00031131448,0.0002454335,0.0000224061,0.000009796829,0.000020525109,0.00017793433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964143,0.00006442237,0.0004624643,0.00059834286,0.001987501,0.00047299403],"domain_scores_gemma":[0.99793136,0.00033338988,0.00014190162,0.00031212764,0.0011553363,0.00012590259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011091233,0.00021195559,0.00028749838,0.00020441748,0.00016683458,0.0003882357,0.0029404198,0.000121018726,0.000021160948],"category_scores_gemma":[0.00024697313,0.00018877967,0.00014671608,0.0009239774,0.00016769572,0.00029485818,0.0009497933,0.0005681875,0.000027995713],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025140066,0.0011165788,0.014386093,0.00007415406,0.000103074206,0.00004267355,0.0011532891,0.009821972,0.37846595,0.59223634,0.0018015385,0.0005469504],"study_design_scores_gemma":[0.004355362,0.0003470737,0.016392516,0.00025632323,0.0000091596075,0.00004565194,0.00012155169,0.27913886,0.69121766,0.005151106,0.0025455006,0.0004192362],"about_ca_topic_score_codex":0.00013603624,"about_ca_topic_score_gemma":0.000002019993,"teacher_disagreement_score":0.58708525,"about_ca_system_score_codex":0.00046107773,"about_ca_system_score_gemma":0.0003670368,"threshold_uncertainty_score":0.76982117},"labels":[],"label_agreement":null},{"id":"W2966291835","doi":"10.1609/icaps.v29i1.3474","title":"A Multi-Label A* Algorithm for Multi-Agent Pathfinding","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pathfinding; Computer science; Computation; Pickup; Set (abstract data type); Heuristic; Sequence (biology); Mathematical optimization; Path (computing); Job shop scheduling; Algorithm; Theoretical computer science; Artificial intelligence; Shortest path problem; Mathematics; Schedule; Graph","score_opus":0.08975077837246882,"score_gpt":0.3315500664996794,"score_spread":0.24179928812721058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966291835","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51446766,0.00010633404,0.47996277,0.0010141128,0.0020241074,0.0007325503,0.00003797234,0.00080179196,0.00085270725],"genre_scores_gemma":[0.3532882,0.0000040882187,0.6462588,0.000112900256,0.000029635863,0.000021285789,0.0000033314952,0.000011854704,0.00026993785],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984575,0.00000909221,0.00034816164,0.00047478368,0.00042084616,0.0002896246],"domain_scores_gemma":[0.9988872,0.000104441526,0.0003694637,0.0001358514,0.0004276138,0.00007545287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005456024,0.0002114858,0.00024213642,0.0001752242,0.000161829,0.0003152151,0.0012256928,0.0001040571,0.0000038715134],"category_scores_gemma":[0.0002073517,0.00016699199,0.00006796462,0.00016209258,0.00004092362,0.00034404255,0.000306988,0.0002494476,0.000012298493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022939064,0.0016209322,0.11213258,0.0008227231,0.0013309959,0.000029660227,0.020979505,0.02349365,0.5235699,0.14779861,0.0011036936,0.16688836],"study_design_scores_gemma":[0.0011479525,0.00008843892,0.0027815062,0.00076750305,0.000009450765,0.000024527248,0.00034004572,0.9891583,0.005277244,0.00018652495,0.00001985158,0.00019867416],"about_ca_topic_score_codex":0.000008790404,"about_ca_topic_score_gemma":5.5049853e-8,"teacher_disagreement_score":0.9656646,"about_ca_system_score_codex":0.000056134773,"about_ca_system_score_gemma":0.00006496481,"threshold_uncertainty_score":0.6809736},"labels":[],"label_agreement":null},{"id":"W2966782683","doi":"10.1609/icaps.v29i1.3496","title":"Lagrangian Decomposition for Optimal Cost Partitioning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"European Commission","keywords":"Lagrangian relaxation; Mathematical optimization; Heuristics; Solver; Computation; Computer science; Context (archaeology); Heuristic; Lagrange multiplier; Decomposition; Mathematics; Algorithm","score_opus":0.046115230530977894,"score_gpt":0.323265699325086,"score_spread":0.2771504687941081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966782683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7654353,0.00010357557,0.21796378,0.007923576,0.001178162,0.00034564428,0.000030807765,0.00070959196,0.0063095745],"genre_scores_gemma":[0.78059715,0.000004029566,0.2190872,0.00016344381,0.000044617853,0.00002017227,0.00001047397,0.0000062772287,0.00006666305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900204,0.000008368741,0.00023492517,0.00029420687,0.00028227316,0.00017816192],"domain_scores_gemma":[0.9990083,0.00009759625,0.00020209057,0.000081648635,0.00055719644,0.000053185548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029789915,0.000117607764,0.0001404788,0.00008241752,0.00020801183,0.00036276443,0.0005230018,0.00006256599,0.000003925227],"category_scores_gemma":[0.00023798463,0.000101335245,0.000055600663,0.00015726227,0.000036127723,0.00029564035,0.00014949068,0.0001478014,0.0000019662327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014843741,0.00024504177,0.020099191,0.00021866361,0.00035782112,0.000024923926,0.004606542,0.118666,0.2423797,0.6040395,0.0016336145,0.0075805755],"study_design_scores_gemma":[0.00033098864,0.000037381167,0.0028562632,0.00064114,0.000010218968,0.00006412796,0.00023910154,0.9524232,0.04187812,0.0013421115,0.000060404127,0.000116933894],"about_ca_topic_score_codex":0.000003269836,"about_ca_topic_score_gemma":9.703305e-8,"teacher_disagreement_score":0.8337572,"about_ca_system_score_codex":0.0000321605,"about_ca_system_score_gemma":0.00006911793,"threshold_uncertainty_score":0.41323313},"labels":[],"label_agreement":null},{"id":"W2967124348","doi":"10.1109/rose.2019.8790420","title":"Probabilistic Task Assignment for Specialized Multi-Agent Robotic Systems","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Probabilistic logic; Computer science; Robustness (evolution); Task (project management); Robot; Artificial intelligence; Matching (statistics); Swarm behaviour; Scheme (mathematics); Task analysis; Multi-agent system; Distributed computing; Machine learning; Engineering; Systems engineering","score_opus":0.04000534667909714,"score_gpt":0.2728997666755837,"score_spread":0.23289441999648655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967124348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004345644,0.00008611409,0.9916687,0.00024538866,0.0037631695,0.0020528147,0.000003663027,0.00031692805,0.0014286793],"genre_scores_gemma":[0.1943406,0.0000025698546,0.78521687,0.00022043499,0.00025160387,0.0003299303,0.000012511364,0.000033670058,0.019591803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979676,0.00009168193,0.00039851505,0.00064164476,0.00040671887,0.0004938193],"domain_scores_gemma":[0.9984956,0.0002588532,0.00014004225,0.000845408,0.000105275634,0.00015484104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053590554,0.00021643093,0.00035848553,0.00008817701,0.000085754895,0.00026299522,0.0008896635,0.00007930698,0.000022815],"category_scores_gemma":[0.000100900805,0.0001769701,0.00010082725,0.00019991778,0.000023130793,0.00021942442,0.0001787151,0.00008977833,0.0007047833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014401911,0.0004040272,0.0008120535,0.00029710995,0.00009848506,0.000027130232,0.0006820191,0.902748,0.0019511818,0.08385071,0.0063911946,0.0027236766],"study_design_scores_gemma":[0.0011995607,0.00016373085,0.0006691333,0.00005984626,0.000012789259,0.000016771759,0.00003470523,0.99459547,0.00008626632,0.0002561441,0.002631443,0.00027415252],"about_ca_topic_score_codex":0.000038773982,"about_ca_topic_score_gemma":8.603391e-7,"teacher_disagreement_score":0.2064518,"about_ca_system_score_codex":0.00020718921,"about_ca_system_score_gemma":0.00010665686,"threshold_uncertainty_score":0.90587986},"labels":[],"label_agreement":null},{"id":"W2967449243","doi":"10.23919/ecc.2019.8796195","title":"Optimal Longitudinal Trajectory Planning for a Quadrotor UAV Including Linear Drag Effects","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Optimal control; Drag; Smoothness; Trajectory; Control theory (sociology); Parameterized complexity; Mathematical optimization; Computer science; Pontryagin's minimum principle; Pareto principle; Control (management); Mathematics; Engineering; Algorithm; Aerospace engineering; Physics","score_opus":0.040978255745365774,"score_gpt":0.31052976124931275,"score_spread":0.26955150550394696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967449243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14778416,0.00009351165,0.84851444,0.00010423107,0.0011575229,0.00079549686,0.0000017941892,0.00043276782,0.0011160728],"genre_scores_gemma":[0.31826538,4.162125e-7,0.6803918,0.00014575738,0.00022352902,0.000083564155,0.000004091178,0.000022564134,0.0008629191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980035,0.000060868428,0.00028747862,0.00068175606,0.00035347088,0.00061294826],"domain_scores_gemma":[0.9982693,0.0008072597,0.000116476745,0.00057015714,0.000076721306,0.00016005735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000561375,0.00025637302,0.0003511742,0.00018338558,0.00018306545,0.00014295733,0.0009137804,0.00011783582,0.000013134678],"category_scores_gemma":[0.0001307204,0.0002326085,0.00013665785,0.00028240305,0.000027863907,0.0005268576,0.00028627733,0.00022103036,0.00020271265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002948612,0.00065473485,0.12768492,0.0016208801,0.00056764757,0.0007424328,0.01186078,0.7764275,0.026590765,0.020756591,0.010328358,0.022470493],"study_design_scores_gemma":[0.0011213667,0.00054870016,0.009715304,0.00017400015,0.000012320195,0.0000547391,0.000040976967,0.9828621,0.0043498785,0.000078616256,0.0006336229,0.00040835346],"about_ca_topic_score_codex":0.000020271711,"about_ca_topic_score_gemma":2.8210212e-7,"teacher_disagreement_score":0.2064346,"about_ca_system_score_codex":0.000085080006,"about_ca_system_score_gemma":0.00010818393,"threshold_uncertainty_score":0.9485499},"labels":[],"label_agreement":null},{"id":"W2967595637","doi":"10.1109/icra.2019.8793961","title":"Learning Motion Planning Policies in Uncertain Environments through Repeated Task Executions","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Robot; Motion planning; Motion (physics); Work (physics); Artificial intelligence; Collision avoidance; Task analysis; Human–computer interaction; Machine learning; Computer security; Collision; Engineering","score_opus":0.023323152866232943,"score_gpt":0.27144709731325717,"score_spread":0.24812394444702424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967595637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1915333,0.00004881308,0.79520744,0.0004137274,0.00026490272,0.00020104396,7.5441534e-7,0.00025689942,0.012073115],"genre_scores_gemma":[0.8877703,0.0000068105387,0.10760067,0.00020801234,0.000024250447,0.000011054921,0.000014979159,0.000010830698,0.004353119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854267,0.000123863,0.0002574691,0.00042273762,0.00027344143,0.00037980857],"domain_scores_gemma":[0.9993518,0.000091964015,0.00009154339,0.00040790258,0.000009647355,0.000047156987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027709964,0.00014430162,0.00016491147,0.00013673853,0.00010563709,0.00008343632,0.00043932121,0.00007959564,0.000020648904],"category_scores_gemma":[0.000052660034,0.00013888783,0.00003500116,0.00042204573,0.000027837641,0.0006013763,0.00019117001,0.0002580805,0.00033716304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031344434,0.00006594139,0.1137289,0.0000076006354,0.000014055372,0.00004547702,0.008703405,0.86575735,0.0048240214,0.004464798,0.00022062594,0.0021647166],"study_design_scores_gemma":[0.0005973541,0.00014114573,0.12648655,0.00010308267,0.0000036220642,0.000033276654,0.0008823054,0.86778665,0.0010199992,0.0009060919,0.0017039544,0.00033595454],"about_ca_topic_score_codex":0.00043229613,"about_ca_topic_score_gemma":8.0323264e-7,"teacher_disagreement_score":0.69623697,"about_ca_system_score_codex":0.00013129287,"about_ca_system_score_gemma":0.000023648465,"threshold_uncertainty_score":0.5663681},"labels":[],"label_agreement":null},{"id":"W2967682328","doi":"10.1109/icuas.2019.8798130","title":"Onboard Generation of Optimal Flight Trajectory for Delivery of Fragile Packages","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trajectory; Trajectory optimization; Computer science; Drone; Constraint (computer-aided design); Point (geometry); Optimal control; Dynamic programming; Boundary (topology); Mathematical optimization; Control theory (sociology); Simulation; Engineering; Control (management); Algorithm; Mathematics; Artificial intelligence","score_opus":0.029606266709125428,"score_gpt":0.24591127520549794,"score_spread":0.21630500849637252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967682328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3200072,0.000057930763,0.6788646,0.00004662282,0.00032567876,0.00015694178,0.0000062165495,0.000034002853,0.00050078443],"genre_scores_gemma":[0.3561135,0.0000027642166,0.64323807,0.00003716446,0.00004289182,0.0000060027583,0.000005928502,0.0000047694657,0.0005489256],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922836,0.00002514054,0.00021974185,0.00021027858,0.00018166071,0.00013484937],"domain_scores_gemma":[0.9992877,0.00011179023,0.00010480777,0.0003403957,0.00012420828,0.00003106479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019111439,0.00007828743,0.00017713166,0.000075032665,0.00001920382,0.00001387136,0.00034792,0.000052542524,0.000023188582],"category_scores_gemma":[0.000016347069,0.000068015266,0.00006820262,0.00010559646,0.000019707002,0.00021663091,0.000053506483,0.000037715803,0.000015346292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024709678,0.00023128405,0.0028818164,0.00018560262,0.000101888356,0.000004071522,0.0024396807,0.14305256,0.80097604,0.012995151,0.011432261,0.02567495],"study_design_scores_gemma":[0.00031965986,0.00024254645,0.0011658104,0.00001604427,0.0000055589676,0.000002020492,0.000025834182,0.64301854,0.35485038,0.00003621115,0.00022162474,0.00009577621],"about_ca_topic_score_codex":0.000015497197,"about_ca_topic_score_gemma":4.7556904e-7,"teacher_disagreement_score":0.49996597,"about_ca_system_score_codex":0.000013650756,"about_ca_system_score_gemma":0.000060806906,"threshold_uncertainty_score":0.2773582},"labels":[],"label_agreement":null},{"id":"W2967690842","doi":"10.1109/icra.2019.8794017","title":"Fast and In Sync: Periodic Swarm Patterns for Quadrotors","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Swarm behaviour; Computer science; Trajectory; Motion (physics); Synchronizing; Choreography; Drone; Smoothness; Artificial intelligence; Computer vision; Motion control; Robot; Mathematics","score_opus":0.011190771166269384,"score_gpt":0.24168140176249417,"score_spread":0.2304906305962248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967690842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39502174,0.000018387875,0.60343444,0.0002949302,0.00031283556,0.000335345,0.000001648196,0.00007036531,0.00051034463],"genre_scores_gemma":[0.86196494,0.0000022184533,0.13653004,0.00024533964,0.000025265752,0.000038855436,0.0000016571211,0.000007198741,0.0011844623],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992215,0.00001779521,0.00012968999,0.00029859913,0.000108507746,0.00022389134],"domain_scores_gemma":[0.99953383,0.00009066388,0.000029611982,0.00027362557,0.000016454029,0.000055816217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018160457,0.00008741961,0.00012913857,0.000085384134,0.000027990929,0.00009360986,0.0003197546,0.00003989243,0.00001020258],"category_scores_gemma":[0.000015537551,0.00007568336,0.000022120126,0.00010119897,0.000011828905,0.00020097235,0.00010963845,0.00006279796,0.000052808864],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013878528,0.00010548036,0.9351095,0.00016319781,0.000019179693,0.000039624934,0.0047597717,0.005227705,0.0005171806,0.005603964,0.00064365345,0.047796838],"study_design_scores_gemma":[0.00076513976,0.00015486925,0.16626084,0.000060576334,0.0000017222667,0.000024966996,0.00017538885,0.8312569,0.0004351517,0.0002459864,0.00037633718,0.00024210183],"about_ca_topic_score_codex":0.00008348929,"about_ca_topic_score_gemma":0.000009032704,"teacher_disagreement_score":0.82602924,"about_ca_system_score_codex":0.00002079827,"about_ca_system_score_gemma":0.000026178124,"threshold_uncertainty_score":0.30862778},"labels":[],"label_agreement":null},{"id":"W2968065474","doi":"10.2140/memocs.2019.7.189","title":"The object detection by autonomous apparatus as a solution of the Buffon needle problem","year":2019,"lang":"en","type":"article","venue":"Mathematics and Mechanics of Complex Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Otto von Guericke University Magdeburg; College of Engineering, Michigan State University; Universität Duisburg-Essen; Freie Universität Berlin; Bilkent Üniversitesi; Centre National de la Recherche Scientifique; University of North Carolina at Chapel Hill; Universität zu Köln; Università degli Studi di Pavia; Université de Lyon; Universität Wien; Akademie Věd České Republiky; Russian Academy of Sciences; Russian Foundation for Basic Research; McGill University; Universidad Rey Juan Carlos; Louisiana State University; University of Pittsburgh; Indian National Science Academy; Michigan State University; Carnegie Mellon University; Vanderbilt University; Wayne State University","keywords":"Object (grammar); Artificial intelligence; Computer vision; Computer science","score_opus":0.014963689586798461,"score_gpt":0.22207375321353026,"score_spread":0.2071100636267318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968065474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020249855,0.0005695111,0.9756921,0.000102921564,0.0007972075,0.0010149502,0.000007288688,0.00005590263,0.0015102429],"genre_scores_gemma":[0.98112065,0.0000093775125,0.018500883,0.000008414259,0.000015303354,0.000025042347,7.47838e-7,0.000010763691,0.00030881225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987248,0.00007351003,0.00045351952,0.00018320636,0.00036144923,0.00020352402],"domain_scores_gemma":[0.9985029,0.00017984142,0.00053468236,0.0006287309,0.000115751085,0.00003810238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000814864,0.00012934732,0.00027880425,0.000038020396,0.00015760065,0.00009635903,0.00062242756,0.000067489666,6.727881e-7],"category_scores_gemma":[0.000035345052,0.00007882897,0.00006332482,0.00020846042,0.000025943567,0.0000847229,0.00020802648,0.00009696492,0.000008297829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007771135,0.000142315,0.000010842575,0.0009043863,0.000093806986,5.2415044e-7,0.0033206895,0.00340312,0.22055048,0.7670569,0.0005419437,0.003967233],"study_design_scores_gemma":[0.00019491244,0.00016589694,0.0000096847325,0.00014200296,0.000012218093,0.000053401327,0.00030170984,0.9774329,0.006597317,0.014553827,0.00044145834,0.000094674586],"about_ca_topic_score_codex":0.00014307168,"about_ca_topic_score_gemma":0.0000026231157,"teacher_disagreement_score":0.9740298,"about_ca_system_score_codex":0.00003346323,"about_ca_system_score_gemma":0.000048549595,"threshold_uncertainty_score":0.3214552},"labels":[],"label_agreement":null},{"id":"W2968380456","doi":"10.1109/icuas.2019.8797720","title":"High-Speed Obstacle-Avoidance with Agile Fixed-Wing Aircraft","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Obstacle avoidance; Payload (computing); Collision avoidance; Fixed wing; Obstacle; Computer science; Trajectory; Wing; Agile software development; Aerospace engineering; Simulation; Aeronautics; Engineering; Collision; Robot; Artificial intelligence; Mobile robot","score_opus":0.008821675830410908,"score_gpt":0.20840673641988483,"score_spread":0.19958506058947392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968380456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17925681,0.000059317277,0.8084286,0.0010209008,0.0009795353,0.00027727068,0.0000019512295,0.00080280035,0.009172857],"genre_scores_gemma":[0.46096572,0.0000011910656,0.53079855,0.0005407365,0.00006407883,0.000004064363,0.0000027254114,0.000016960725,0.007605974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822557,0.000047386173,0.00020841157,0.0005917803,0.00045111714,0.00047576148],"domain_scores_gemma":[0.99856555,0.00016035746,0.00008907797,0.0009703938,0.00008488538,0.00012974624],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002258858,0.00020316629,0.00024919098,0.0000868283,0.0000966431,0.00017771666,0.001031261,0.00006450115,0.00006948971],"category_scores_gemma":[0.000023882734,0.0001567015,0.00004240892,0.0004451756,0.000035315767,0.00069200713,0.00021337815,0.00020386296,0.001385185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009758037,0.0006690761,0.12927282,0.0002680433,0.00039053685,0.001278877,0.007932675,0.5463894,0.04503832,0.16584577,0.022668596,0.08014832],"study_design_scores_gemma":[0.0024683583,0.0006413136,0.05898049,0.00028581603,0.000016321132,0.00023997048,0.0001662593,0.90881217,0.021247352,0.0011766105,0.0046457443,0.0013196046],"about_ca_topic_score_codex":0.00012467617,"about_ca_topic_score_gemma":0.000002878382,"teacher_disagreement_score":0.36242276,"about_ca_system_score_codex":0.000055356493,"about_ca_system_score_gemma":0.00008221286,"threshold_uncertainty_score":0.99939233},"labels":[],"label_agreement":null},{"id":"W2968573295","doi":"10.1109/icra.2019.8793549","title":"Energy Optimal Control Allocation in a Redundantly Actuated Omnidirectional UAV","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Torque; Control theory (sociology); Thrust; Redundancy (engineering); Actuator; Underactuation; Propeller; Multirotor; Computer science; Engineering; Control engineering; Control (management); Aerospace engineering","score_opus":0.007990774785690637,"score_gpt":0.21977012447580077,"score_spread":0.21177934969011014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968573295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020633735,0.000026860223,0.9717173,0.000931667,0.0005532775,0.00010459149,8.3285533e-7,0.00021655111,0.005815195],"genre_scores_gemma":[0.87102085,0.000002194804,0.1260471,0.0004149415,0.00003832821,0.000015069991,0.00000562914,0.0000063584753,0.0024494892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887925,0.00007049263,0.00020501536,0.0003481248,0.00025061838,0.0002465202],"domain_scores_gemma":[0.99938416,0.00012044563,0.000058817477,0.0003111277,0.00006578837,0.000059685768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025742053,0.00010761469,0.00014735012,0.00015712313,0.000032539225,0.000075937765,0.00040835762,0.0000667899,0.000053297947],"category_scores_gemma":[0.00002593242,0.00009981105,0.000032520482,0.00037731664,0.000015122548,0.0003782764,0.000055088352,0.000093966875,0.00020303653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013408434,0.00067233894,0.019971078,0.000022904751,0.00015817976,0.00014023691,0.0017060363,0.6447554,0.03537905,0.17222725,0.0062788012,0.11855465],"study_design_scores_gemma":[0.00080883096,0.000069059264,0.010917865,0.000014320243,0.0000015788816,0.000031546202,0.000009438621,0.9857991,0.0011716058,0.0002630195,0.0007742316,0.00013943508],"about_ca_topic_score_codex":0.00030970713,"about_ca_topic_score_gemma":0.000007748279,"teacher_disagreement_score":0.85038716,"about_ca_system_score_codex":0.000078823694,"about_ca_system_score_gemma":0.000120020166,"threshold_uncertainty_score":0.40701765},"labels":[],"label_agreement":null},{"id":"W2968982573","doi":"10.1109/tase.2019.2928774","title":"Person Finding: An Autonomous Robot Search Method for Finding Multiple Dynamic Users in Human-Centered Environments","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Consortium canadien en neurodégénérescence associée au vieillissement; Ontario Centres of Excellence","keywords":"Robot; Knapsack problem; Computer science; Mobile robot; Markov decision process; Planner; Search algorithm; Travelling salesman problem; Search problem; Artificial intelligence; Markov process; Algorithm; Mathematics","score_opus":0.04572013748095064,"score_gpt":0.31111525681184693,"score_spread":0.26539511933089627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968982573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2679303,0.000003070541,0.7312937,0.000049399412,0.00031633663,0.00027414953,0.0000032666705,0.00012143726,0.00000840253],"genre_scores_gemma":[0.72368324,0.000001291328,0.2761997,0.00001649733,0.000004194798,0.000029045104,0.0000014048454,0.000009242473,0.00005534438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984837,0.000028302451,0.00019733484,0.0005132968,0.0003843088,0.00039304717],"domain_scores_gemma":[0.9993818,0.00013875289,0.000044450047,0.0002976185,0.000022893973,0.000114477145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010160501,0.00014550581,0.00015212492,0.0006846839,0.00026229428,0.00021193747,0.00044145097,0.00005929705,0.000004007955],"category_scores_gemma":[0.00001495246,0.00015962306,0.000033404915,0.00059052097,0.00003381154,0.001346038,0.000004872514,0.00016902645,0.000012721126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001719964,0.00003404232,0.00008419581,0.000016249285,0.0000036888478,0.0000013480993,0.0015989373,0.8582099,0.12826091,0.000035269244,4.4592957e-7,0.011753257],"study_design_scores_gemma":[0.0004961454,0.00010609011,0.008873548,0.000049846865,0.0000029170392,0.000011021916,0.00016525919,0.9771059,0.013006146,0.0000040373366,0.000008731722,0.00017034792],"about_ca_topic_score_codex":0.000025821615,"about_ca_topic_score_gemma":0.000002644133,"teacher_disagreement_score":0.45575297,"about_ca_system_score_codex":0.00034615505,"about_ca_system_score_gemma":0.000056317494,"threshold_uncertainty_score":0.65092397},"labels":[],"label_agreement":null},{"id":"W2969823375","doi":"10.4050/f-0075-2019-14482","title":"Experimental Validation of Vertical Lifting Capabilities of Circling Tethered Fixed Wing UAVs","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Payload (computing); Airframe; Lift (data mining); Aerospace engineering; Airplane; Fixed wing; Wing; Engineering; Elevator; Marine engineering; Control theory (sociology); Computer science","score_opus":0.022305024114884976,"score_gpt":0.2667370531233199,"score_spread":0.24443202900843494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969823375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8474363,0.00003485137,0.14980578,0.00003515951,0.00025464076,0.0000968112,4.5656626e-7,0.00006127942,0.0022746858],"genre_scores_gemma":[0.8491135,1.5168493e-7,0.15078175,0.000014286998,0.000017245367,0.0000019412394,0.000001038323,0.0000051899765,0.00006488024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989172,0.000064824344,0.00034370122,0.00022395968,0.0002805989,0.00016967038],"domain_scores_gemma":[0.9991766,0.00029406213,0.00007610469,0.0003508424,0.000065297645,0.000037050006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034009336,0.000082678984,0.00019990718,0.00006357284,0.000026352545,0.000023054161,0.00034964233,0.000044974437,0.0000391895],"category_scores_gemma":[0.00011354559,0.0000786098,0.00005340179,0.00014729208,0.000042227537,0.00027835593,0.00013534275,0.00006445507,0.000017920014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009410125,0.00014226155,0.024703825,0.00009488179,0.00003316776,0.0000022527659,0.011191687,0.017599765,0.94004714,0.004749633,0.00002341036,0.0014025528],"study_design_scores_gemma":[0.0002539346,0.000097702905,0.0023181643,0.00007140728,0.0000025152108,0.0000032243968,0.0009689401,0.2142943,0.7817996,0.000100037956,0.0000026044042,0.00008756694],"about_ca_topic_score_codex":0.00006769929,"about_ca_topic_score_gemma":6.443329e-8,"teacher_disagreement_score":0.19669452,"about_ca_system_score_codex":0.000029837687,"about_ca_system_score_gemma":0.000043736363,"threshold_uncertainty_score":0.32056147},"labels":[],"label_agreement":null},{"id":"W2971925930","doi":"10.1109/cdc40024.2019.9029915","title":"Robotic Coverage for Continuous Mapping Ahead of a Moving Vehicle","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Curvature; Plan (archaeology); Path (computing); Computer science; Cover (algebra); Set (abstract data type); Simple (philosophy); Class (philosophy); Simulation; Engineering; Artificial intelligence; Mathematics; Geometry; Geography","score_opus":0.03306253037631292,"score_gpt":0.26498441246995696,"score_spread":0.23192188209364406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971925930","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049628667,0.00020922972,0.9880043,0.0003071678,0.0019891092,0.0009373155,0.000012486829,0.0002522642,0.0033252102],"genre_scores_gemma":[0.31039932,0.000007078968,0.6869197,0.00018709042,0.00010435078,0.000050445014,0.000014698363,0.000026944428,0.0022903802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778855,0.000067004046,0.0005629662,0.0007812492,0.000351427,0.0004488223],"domain_scores_gemma":[0.99746937,0.00048627943,0.00044742585,0.001305894,0.00020690654,0.000084115294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005801074,0.00029358725,0.00074363465,0.00019794141,0.000055400687,0.00017393906,0.0016622314,0.00026240645,0.000005989173],"category_scores_gemma":[0.00017357874,0.00028975596,0.00023199835,0.00016252117,0.00003197546,0.00016837406,0.0015916462,0.00037217943,0.000052117706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008995714,0.00011731181,0.0026444993,0.0012351465,0.0001726405,0.000028240584,0.0017000369,0.964069,0.0015357317,0.0067125717,0.0019344506,0.019841338],"study_design_scores_gemma":[0.00047080716,0.000076289994,0.0023145585,0.0005076444,0.000015985212,0.0000089061905,0.000027455348,0.99218017,0.0006028631,0.003286598,0.00015752034,0.00035119086],"about_ca_topic_score_codex":0.00013483306,"about_ca_topic_score_gemma":7.180325e-7,"teacher_disagreement_score":0.30543646,"about_ca_system_score_codex":0.0000852526,"about_ca_system_score_gemma":0.0002775683,"threshold_uncertainty_score":0.9999555},"labels":[],"label_agreement":null},{"id":"W2972456753","doi":"10.1049/iet-csr.2019.0001","title":"FreeD∗: a mechanism for finding a short and collision free path","year":2019,"lang":"en","type":"article","venue":"IET Cyber-Systems and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"King Abdulaziz City for Science and Technology","keywords":"Motion planning; Shortest path problem; Dijkstra's algorithm; Path (computing); Obstacle avoidance; Any-angle path planning; Mobile robot; Computer science; Pathfinding; Obstacle; Fast path; Robot; Robotics; Artificial intelligence; Diagonal; Position (finance); Yen's algorithm; Mathematics; Graph; Theoretical computer science; Geography","score_opus":0.021880756744325474,"score_gpt":0.24539440525437534,"score_spread":0.22351364851004987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972456753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036788438,0.0006822958,0.957884,0.00047269315,0.0026004205,0.001069706,0.0000382959,0.000169032,0.00029511662],"genre_scores_gemma":[0.5363962,0.00007271772,0.46167788,0.00020823014,0.00028467202,0.000059543643,0.000010878023,0.00004581461,0.0012440346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826825,0.000051052037,0.00034983712,0.00057807285,0.00032497526,0.00042781731],"domain_scores_gemma":[0.9986272,0.00026299205,0.00011548035,0.00068482064,0.00012865539,0.00018089415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052722794,0.00024149459,0.00040599625,0.000109985034,0.00022148197,0.00036831223,0.0005127139,0.0001871682,6.5869665e-7],"category_scores_gemma":[0.0000744828,0.00020626132,0.000055000615,0.00018457092,0.000030355592,0.00029362043,0.00037966028,0.00015050115,0.000008511603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050834093,0.00021029016,0.011686684,0.0010905126,0.0002552841,0.00015915188,0.0044285664,0.14611425,0.0053639114,0.81614953,0.008036111,0.006454872],"study_design_scores_gemma":[0.0007680648,0.00038510357,0.00093835965,0.0002856137,0.00002337585,0.00014265825,0.0001270004,0.993593,0.000070751856,0.002761006,0.00056509936,0.00033997916],"about_ca_topic_score_codex":0.00007336288,"about_ca_topic_score_gemma":0.000003417233,"teacher_disagreement_score":0.84747875,"about_ca_system_score_codex":0.00003895794,"about_ca_system_score_gemma":0.00004989365,"threshold_uncertainty_score":0.8411092},"labels":[],"label_agreement":null},{"id":"W2972941110","doi":"10.1109/lra.2020.2964159","title":"Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"","keywords":"Trajectory; Model predictive control; Computer science; Motion planning; Motion (physics); Control (management); Control theory (sociology); Robot; Artificial intelligence; Physics","score_opus":0.05669042353869146,"score_gpt":0.2668804501090223,"score_spread":0.2101900265703308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972941110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01024153,0.000022010498,0.9819596,0.00671391,0.00018111759,0.00045358686,0.000108905064,0.0003184331,9.428289e-7],"genre_scores_gemma":[0.46919176,9.2116375e-7,0.52880794,0.0016884259,0.00014203292,0.000022085786,0.00013313898,0.000011809797,0.0000018683711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883586,0.000049354163,0.0002626168,0.000400952,0.0002209198,0.0002303],"domain_scores_gemma":[0.99938434,0.00006501725,0.00017608734,0.00015152314,0.000097175995,0.00012582494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001201398,0.0001839236,0.00020725731,0.00006429468,0.00017866193,0.00014546546,0.00018472274,0.0000659561,1.6437625e-7],"category_scores_gemma":[0.00003873947,0.00016794675,0.0000378987,0.00013304119,0.000038096012,0.00045601226,0.000016532416,0.00012299634,0.0000010558554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000115847815,0.000041773663,0.00015763707,0.000022619399,0.000033407254,0.0000061343308,0.00081247126,0.98480606,0.013065102,0.000113097056,0.00034440245,0.0005857313],"study_design_scores_gemma":[0.0016143889,0.00014715883,0.0025263815,0.000033678625,0.000036063488,0.000009304936,0.000019521507,0.9949484,0.00045305473,0.000010540805,0.0000025798868,0.00019895252],"about_ca_topic_score_codex":0.0000017592303,"about_ca_topic_score_gemma":3.928038e-7,"teacher_disagreement_score":0.45895025,"about_ca_system_score_codex":0.000049707087,"about_ca_system_score_gemma":0.0000401708,"threshold_uncertainty_score":0.68486696},"labels":[],"label_agreement":null},{"id":"W2975877068","doi":"10.1109/infcomw.2019.8845318","title":"Geocaching-inspired Resilient Path Planning for Drone Swarms","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Computer science; Motion planning; Spoofing attack; Path (computing); Global Positioning System; Dimension (graph theory); Real-time computing; Artificial intelligence; Computer security; Robot; Computer network; Mathematics; Telecommunications","score_opus":0.018432725801626543,"score_gpt":0.26555358730967915,"score_spread":0.2471208615080526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975877068","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045266498,0.00009677368,0.94658303,0.0004964024,0.0010360955,0.0004564667,0.0000029029093,0.00048385074,0.0055779926],"genre_scores_gemma":[0.38116416,0.0000010366684,0.61369294,0.0006043064,0.00010220906,0.000034527886,0.000010162256,0.000019853176,0.0043708426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983158,0.00003831803,0.00026353888,0.000565302,0.00032377764,0.00049327384],"domain_scores_gemma":[0.99874765,0.00021975304,0.00010558447,0.0007461664,0.0000573807,0.00012349174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081536,0.00017714786,0.0002297516,0.00010810585,0.00012522103,0.00014042517,0.00089009275,0.000076952114,0.000011536221],"category_scores_gemma":[0.00006631149,0.00015142608,0.00007985733,0.00020223064,0.000016591765,0.00035038762,0.00021756973,0.00013640572,0.00028107333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017727698,0.0008942557,0.08926274,0.0004676692,0.0003283554,0.00032897762,0.017768934,0.46768332,0.018847665,0.18300869,0.10188105,0.11935109],"study_design_scores_gemma":[0.0008616998,0.00032582157,0.0060492586,0.00007724745,0.0000053456433,0.000020963036,0.00007563204,0.98442185,0.0013493386,0.0014484676,0.0050315848,0.00033280882],"about_ca_topic_score_codex":0.000033313678,"about_ca_topic_score_gemma":2.3503155e-7,"teacher_disagreement_score":0.51673853,"about_ca_system_score_codex":0.000053510612,"about_ca_system_score_gemma":0.00007358383,"threshold_uncertainty_score":0.6174976},"labels":[],"label_agreement":null},{"id":"W2976416243","doi":"10.1109/lra.2019.2944060","title":"Homotopic Approach for Robot Allocation Optimization Coupled With Path Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Path (computing); Task (project management); Computer science; Mathematical optimization; Motion planning; Robot; Process (computing); Transformation (genetics); Distributed computing; Homotopy; Artificial intelligence; Mathematics; Engineering; Computer network","score_opus":0.010789557475473064,"score_gpt":0.2124348639474545,"score_spread":0.20164530647198145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976416243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007003454,0.000010593276,0.99019897,0.0015806081,0.00028649892,0.0006677837,0.0000022685851,0.00019283463,0.000057007648],"genre_scores_gemma":[0.21737403,0.0000027162682,0.7819037,0.00055944937,0.00003918882,0.00003201413,0.000043239786,0.000011809398,0.000033857636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893445,0.00003414746,0.00021857746,0.0003669472,0.00022400651,0.0002218516],"domain_scores_gemma":[0.9993245,0.00006452764,0.00016514125,0.00029025032,0.00009188963,0.00006367054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002124613,0.0001540145,0.00017856438,0.00010083347,0.00010890732,0.0002041856,0.00021935887,0.00006157099,0.0000020041857],"category_scores_gemma":[0.000012260211,0.00013749764,0.000027783983,0.00018870636,0.000056022367,0.0004062647,0.000020758747,0.00007426554,0.0000054634484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002495729,0.000025998504,0.0002456936,0.000049593673,0.000018491795,0.0000011861173,0.00019535207,0.994788,0.0013314002,0.0015745154,0.00006701564,0.001700293],"study_design_scores_gemma":[0.0007717227,0.000080908365,0.0016439953,0.000038723323,0.0000138735895,0.000023185636,0.000018293538,0.99707216,0.00011573309,0.00002474918,0.0000048205957,0.00019185235],"about_ca_topic_score_codex":0.0000046682308,"about_ca_topic_score_gemma":7.912613e-8,"teacher_disagreement_score":0.21037059,"about_ca_system_score_codex":0.000039359456,"about_ca_system_score_gemma":0.000043183947,"threshold_uncertainty_score":0.5606991},"labels":[],"label_agreement":null},{"id":"W2976599095","doi":"10.18280/isi.240313","title":"Design and Simulation of a Collision-free Path Planning Algorithm for Mobile Robots Based on Improved Ant Colony Optimization","year":2019,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Motion planning; Computer science; Mobile robot; Path (computing); Robot; Collision; Collision avoidance; Simulation; Artificial intelligence; Mathematical optimization; Algorithm; Computer network; Mathematics; Computer security","score_opus":0.01467092682537847,"score_gpt":0.24584284291587302,"score_spread":0.23117191609049456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976599095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045737927,0.000032580345,0.9929917,0.000012643946,0.00031512612,0.0018012046,0.000026739852,0.00014301715,0.000103190214],"genre_scores_gemma":[0.21064223,0.0000020849004,0.7890251,0.00008086958,0.000017923241,0.00013897767,0.000071801034,0.000010843283,0.000010135813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998536,0.00008575091,0.0005803117,0.00022455743,0.00031456692,0.0002588202],"domain_scores_gemma":[0.99782246,0.0007201628,0.00054166454,0.0004595286,0.00038609424,0.000070081944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007932804,0.00019227465,0.00027733846,0.00031299941,0.00016893062,0.00020917534,0.00036624816,0.00013784482,0.000002184955],"category_scores_gemma":[0.0002949167,0.00018574526,0.000045766545,0.0003882839,0.00004233069,0.002056699,0.00008630841,0.00008314769,0.000004283663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048148577,0.000023452545,0.000072138835,0.000117247364,0.000009986754,5.555581e-7,0.0016802246,0.96044725,0.000074497104,0.000051984553,0.00004270896,0.037431795],"study_design_scores_gemma":[0.001418597,0.0009965212,0.0003047404,0.0002896788,0.000009820086,0.0000043451128,0.000109872606,0.9956874,0.0006890444,0.0002516363,0.00003494121,0.00020339321],"about_ca_topic_score_codex":0.000010531248,"about_ca_topic_score_gemma":3.1819372e-8,"teacher_disagreement_score":0.20606844,"about_ca_system_score_codex":0.0001592203,"about_ca_system_score_gemma":0.00013930508,"threshold_uncertainty_score":0.7574472},"labels":[],"label_agreement":null},{"id":"W2979970169","doi":"10.1109/ccece.2019.8861921","title":"A GPU Accelerated Path Planner for Multiple Unmanned Aerial Vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Motion planning; Real-time computing; Path (computing); Terrain; Point of interest; Planner; Acceleration; Task (project management); Process (computing); Shortest path problem; Simulation; Artificial intelligence; Robot; Operating system; Graph; Engineering","score_opus":0.03190223287727267,"score_gpt":0.2591881556539167,"score_spread":0.227285922776644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979970169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20692734,0.000029520474,0.7865832,0.0007120203,0.002250993,0.0008792424,0.00002122674,0.00071882864,0.0018776169],"genre_scores_gemma":[0.62966543,9.94516e-7,0.36707488,0.00046946987,0.00018711691,0.000048654238,0.00002664668,0.000018377104,0.002508459],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986074,0.000040245443,0.00022877139,0.0004774769,0.00021668193,0.00042945202],"domain_scores_gemma":[0.99892056,0.00027328814,0.0000764317,0.000526368,0.00009856358,0.00010480119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024152224,0.00016983536,0.00021941971,0.00007801395,0.00009259315,0.00019115659,0.0008001759,0.000099383185,0.00002886697],"category_scores_gemma":[0.00007803244,0.00013991135,0.0000689059,0.00022376775,0.000016859267,0.00039036202,0.0001633596,0.00009201767,0.0004052957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014782813,0.0020847379,0.09152082,0.0005081617,0.00077500346,0.00033331936,0.012240859,0.11638415,0.21244876,0.10224525,0.30526146,0.15471919],"study_design_scores_gemma":[0.0020318949,0.00023829061,0.0038810617,0.000023215498,0.0000041431454,0.000011320375,0.0000347124,0.9819961,0.0063724048,0.00049007824,0.0046180063,0.0002987738],"about_ca_topic_score_codex":0.000046066296,"about_ca_topic_score_gemma":0.0000017878185,"teacher_disagreement_score":0.865612,"about_ca_system_score_codex":0.000029134737,"about_ca_system_score_gemma":0.0000741968,"threshold_uncertainty_score":0.570542},"labels":[],"label_agreement":null},{"id":"W2980721454","doi":"10.1109/aim.2019.8868423","title":"A New Global Path Planning Strategy for Mobile Robots Using Hierarchical Topology Map and Safety-Aware Navigation Speed","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motion planning; Shortest path problem; Computer science; Mobile robot; Topology (electrical circuits); Any-angle path planning; Representation (politics); Mathematical optimization; Dijkstra's algorithm; Path (computing); Metric (unit); Graph; Robot; Configuration space; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Engineering","score_opus":0.03130927298173053,"score_gpt":0.3191535005287226,"score_spread":0.28784422754699207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980721454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055046037,0.00011461544,0.94291914,0.0002592479,0.00057703524,0.00051596935,0.000009645817,0.00015373807,0.00040458326],"genre_scores_gemma":[0.23139273,0.0000011112251,0.7677688,0.0001595775,0.0001599409,0.0000053977783,0.000028825347,0.000011804528,0.00047181593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852103,0.00006001108,0.00027189273,0.0005317619,0.00019523529,0.00042004028],"domain_scores_gemma":[0.9992048,0.0001504053,0.000092841554,0.0003209455,0.00005200534,0.0001789841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022174121,0.00017858112,0.00025724535,0.00004675519,0.000119492026,0.00015579388,0.00037527905,0.0001380951,0.000016183933],"category_scores_gemma":[0.000012020871,0.00016539292,0.000046010733,0.00018160493,0.000034576096,0.000379085,0.00018491087,0.00015332659,0.000021565113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011217298,0.00006447183,0.019432921,0.00013092998,0.00006626686,0.000102849466,0.00113721,0.8597423,0.0011313525,0.04972424,0.0013738121,0.06698147],"study_design_scores_gemma":[0.00090256194,0.00036363286,0.0036208672,0.000086339656,0.000008095316,0.00017102434,0.00011905203,0.98755103,0.00007309574,0.006707741,0.00016206612,0.00023446277],"about_ca_topic_score_codex":0.00018287972,"about_ca_topic_score_gemma":9.0395673e-7,"teacher_disagreement_score":0.17634669,"about_ca_system_score_codex":0.00008834863,"about_ca_system_score_gemma":0.00021970905,"threshold_uncertainty_score":0.6744528},"labels":[],"label_agreement":null},{"id":"W2984588480","doi":"10.1109/tim.2003.816846","title":"Behavior-based neuro-fuzzy controller for mobile robot navigation","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Mobile robot; Computer science; Mobile robot navigation; Neuro-fuzzy; Controller (irrigation); Control engineering; Artificial intelligence; Robot; Fuzzy logic; Robot control; Fuzzy control system; Control theory (sociology); Engineering; Control (management)","score_opus":0.04561478188813609,"score_gpt":0.2758882335146639,"score_spread":0.23027345162652782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984588480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067072846,0.000019481362,0.9908745,0.00014249397,0.0009520635,0.0010796066,0.000009729857,0.00011340936,0.000101399404],"genre_scores_gemma":[0.89774376,0.000004026714,0.10082264,0.00036726845,0.000010150652,0.001007355,0.000003532969,0.000011322855,0.00002995018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986088,0.00009920955,0.00026920473,0.00034687237,0.00046949112,0.00020636701],"domain_scores_gemma":[0.999375,0.00006341436,0.000092914495,0.00019398818,0.0001664068,0.00010826405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004248127,0.00015758144,0.00013954019,0.0001266013,0.0002899143,0.00010930911,0.00011812514,0.000053457185,0.0000077161685],"category_scores_gemma":[0.0000087082435,0.00015919184,0.00007136843,0.0001663692,0.000028617846,0.00023504357,4.7127284e-7,0.000102211205,0.000008836267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018156618,0.0015144106,0.00028589342,0.000093618146,0.00012398034,0.000007840886,0.0011622349,0.32086867,0.061190777,0.002840325,0.00022121874,0.61150944],"study_design_scores_gemma":[0.020123264,0.0038162598,0.0025312062,0.00023764532,0.00032100544,0.00006890896,0.0004961536,0.50472796,0.46269843,0.0010173979,0.0027517811,0.0012099723],"about_ca_topic_score_codex":0.000012335325,"about_ca_topic_score_gemma":0.00000221517,"teacher_disagreement_score":0.89103645,"about_ca_system_score_codex":0.00013763714,"about_ca_system_score_gemma":0.00008747196,"threshold_uncertainty_score":0.64916545},"labels":[],"label_agreement":null},{"id":"W2985525587","doi":"10.1139/juvs-2017-0004","title":"Recent advances in unmanned aerial vehicles real-time trajectory planning","year":2019,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Trajectory; Class (philosophy); Fixed wing; Computer science; Autonomy; Aeronautics; Drone; Work (physics); Motion planning; Operations research; Real-time computing; Aerospace engineering; Artificial intelligence; Engineering; Political science; Wing; Robot","score_opus":0.015198663685044127,"score_gpt":0.26126028163772314,"score_spread":0.246061617952679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985525587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96910864,0.013138858,0.006381007,0.00051953056,0.0070355046,0.0005933088,0.0000056253716,0.0001721703,0.0030453478],"genre_scores_gemma":[0.9827638,0.001542474,0.013705387,0.00008996766,0.0012307245,0.000012059716,0.0000036293825,0.00006243466,0.000589522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960844,0.0005168566,0.0013584149,0.000442692,0.0009409487,0.00065668847],"domain_scores_gemma":[0.9973829,0.00038727713,0.0011211255,0.0005657377,0.00029839113,0.00024459074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021021734,0.00031330457,0.00084815954,0.0005766053,0.00009090247,0.00023181771,0.0013689399,0.00018939014,0.000012246061],"category_scores_gemma":[0.00012045555,0.0002762008,0.00015614516,0.00074738794,0.000043166034,0.0017330939,0.0001314425,0.0005214105,0.0001613446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009035015,0.0006638526,0.046036154,0.00045357132,0.00025132383,0.002221593,0.007222584,0.8145324,0.07103898,0.001632408,0.0041290163,0.0509146],"study_design_scores_gemma":[0.010506785,0.0033147514,0.042801317,0.0046936492,0.000055112872,0.0016595111,0.0019466663,0.89292485,0.003124787,0.00061760884,0.03667035,0.0016846072],"about_ca_topic_score_codex":0.000025435233,"about_ca_topic_score_gemma":0.0000010373561,"teacher_disagreement_score":0.07839243,"about_ca_system_score_codex":0.00029978185,"about_ca_system_score_gemma":0.0002681205,"threshold_uncertainty_score":0.999969},"labels":[],"label_agreement":null},{"id":"W2989164579","doi":"10.1007/s10846-019-01110-1","title":"Bi-objective Motion Planning Approach for Safe Motions: Application to a Collaborative Robot","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Workspace; Motion planning; Task (project management); Computer science; Robot; Path (computing); Motion (physics); Field (mathematics); Human–robot interaction; Artificial intelligence; Human–computer interaction; Simulation; Control engineering; Systems engineering; Engineering; Mathematics","score_opus":0.025009037311649347,"score_gpt":0.2877288817725395,"score_spread":0.2627198444608902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989164579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084186153,0.0007708189,0.99284524,0.00023337081,0.002560366,0.002149466,0.000005138688,0.0000611386,0.0005325958],"genre_scores_gemma":[0.46392685,0.000010222705,0.5348989,0.00008843138,0.0004996974,0.00014285023,0.000009512245,0.000031674983,0.0003919028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693584,0.00024597076,0.0011704225,0.00050416216,0.0007197864,0.0004237859],"domain_scores_gemma":[0.9962675,0.00036368606,0.0011275952,0.0005627808,0.0014231451,0.00025527313],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001688612,0.00029370908,0.00069066574,0.00062763144,0.00014729769,0.00029638235,0.0010315145,0.0001467349,0.0000019705064],"category_scores_gemma":[0.00029350907,0.0002531054,0.00018948528,0.0011429567,0.00002413021,0.0006142494,0.000103358056,0.0002799595,0.00008751914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032229742,0.00015328509,0.0007133871,0.00009395238,0.00013235815,0.0000055933583,0.0027910334,0.9882732,0.0009729604,0.0034953996,0.0005557204,0.002780901],"study_design_scores_gemma":[0.00047922425,0.0008499044,0.0012449473,0.00044038528,0.000043738382,0.00027734158,0.0026315998,0.99234974,0.00084008713,0.00022356876,0.0003176458,0.00030182503],"about_ca_topic_score_codex":0.000024472012,"about_ca_topic_score_gemma":2.2181544e-7,"teacher_disagreement_score":0.463085,"about_ca_system_score_codex":0.0005114328,"about_ca_system_score_gemma":0.00023183078,"threshold_uncertainty_score":0.99999213},"labels":[],"label_agreement":null},{"id":"W2989943660","doi":"10.1609/aaai.v34i06.6547","title":"Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"University of Southern California; National Science Foundation","keywords":"Computer science; Queue; Sorting; Idle; Path (computing); Set (abstract data type); Task (project management); Real-time computing; Bin; Queueing theory; Throughput; Mathematical optimization; Operations research; Distributed computing; Simulation; Algorithm; Computer network; Engineering; Mathematics; Operating system","score_opus":0.09084846226161183,"score_gpt":0.3010939958970604,"score_spread":0.21024553363544854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989943660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008763066,0.000023621373,0.9846414,0.004031447,0.0006177685,0.0012382864,0.000027358285,0.00008598296,0.0005710805],"genre_scores_gemma":[0.73416096,0.000029798472,0.265427,0.00013074678,0.00006533396,0.00011209013,0.000010863543,0.000019218944,0.000043974822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978335,0.000024965942,0.0006422551,0.00077052426,0.00041893352,0.00030980454],"domain_scores_gemma":[0.9986025,0.00012159966,0.0006681137,0.00021607595,0.00030529022,0.00008642852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057655305,0.00028835694,0.00038342597,0.00016968894,0.000113139344,0.00034599085,0.0013339536,0.00017514682,0.000010848878],"category_scores_gemma":[0.00051022356,0.0002548697,0.00008994565,0.00030577,0.000099715886,0.00025918684,0.0007224333,0.00041722492,0.0000091383645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023925466,0.00044322707,0.0014396993,0.0010144798,0.000097705546,0.0000040883265,0.015171855,0.6205302,0.0229878,0.2860368,0.0005905544,0.05144435],"study_design_scores_gemma":[0.000038033635,0.00012532517,0.00012584381,0.00067356776,0.000011224548,9.013789e-7,0.00015734341,0.90135854,0.029323725,0.067958504,0.0000042057327,0.00022280632],"about_ca_topic_score_codex":0.00002231739,"about_ca_topic_score_gemma":5.2936605e-7,"teacher_disagreement_score":0.7253979,"about_ca_system_score_codex":0.00010802993,"about_ca_system_score_gemma":0.00014298926,"threshold_uncertainty_score":0.99999034},"labels":[],"label_agreement":null},{"id":"W2991543276","doi":"10.1115/detc2019-97163","title":"A Novel Autonomous Scaled Electric Combat Vehicle","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Chassis; Modular design; Obstacle avoidance; Mobile robot; Obstacle; Software; Motion planning; Computer science; Robot; Engineering; Embedded system; Control engineering; Systems engineering; Artificial intelligence","score_opus":0.011953725474430759,"score_gpt":0.2236960160596809,"score_spread":0.21174229058525013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991543276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06681464,0.000047134512,0.91685224,0.0005030219,0.00046814763,0.00017737411,4.2552944e-7,0.0005188927,0.014618116],"genre_scores_gemma":[0.6481723,0.0000011976161,0.3439406,0.00075942394,0.000040853683,0.000007295055,0.0000011433035,0.00001136688,0.0070658075],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998834,0.000021979586,0.00016623759,0.00037381,0.00023949562,0.00036445956],"domain_scores_gemma":[0.99908996,0.000097613214,0.00005132001,0.0006189293,0.000044445387,0.00009773533],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019482769,0.00011920438,0.00016281243,0.00010528292,0.00005306527,0.000103278035,0.00086431066,0.00005753364,0.00003374911],"category_scores_gemma":[0.00001926549,0.00010468823,0.00004768314,0.00052459346,0.0000101588075,0.00028846267,0.00017652629,0.00013693376,0.0018014264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022446402,0.0011219324,0.028405752,0.00006443112,0.00014556604,0.00012797314,0.0014221364,0.028370267,0.55125463,0.207565,0.00996265,0.1715372],"study_design_scores_gemma":[0.00052752584,0.00010101697,0.023763169,0.0000072468147,0.0000021032479,0.000050518684,0.000003734334,0.9712785,0.0035637964,0.00013979281,0.00038741564,0.00017520272],"about_ca_topic_score_codex":0.000053630978,"about_ca_topic_score_gemma":4.3318587e-7,"teacher_disagreement_score":0.9429082,"about_ca_system_score_codex":0.000054231135,"about_ca_system_score_gemma":0.00008724133,"threshold_uncertainty_score":0.9989758},"labels":[],"label_agreement":null},{"id":"W2993467133","doi":"10.1109/wisee.2019.8920360","title":"Effective Learning Algorithms for Search and Rescue Missions in Unknown Environments","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Search and rescue; Artificial intelligence; Algorithm; Robot","score_opus":0.0189603245578892,"score_gpt":0.2828100550494208,"score_spread":0.2638497304915316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993467133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15067317,0.00007313513,0.8468688,0.00031874602,0.00018596492,0.0008218937,9.461937e-7,0.00007085809,0.0009864889],"genre_scores_gemma":[0.4492741,0.000015200639,0.54070807,0.00010300538,0.00004107843,0.00007969965,0.000004891832,0.000018584211,0.009755355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987804,0.000119076096,0.00013516429,0.00044650407,0.00019137315,0.00032747985],"domain_scores_gemma":[0.99925476,0.0003677027,0.000015057076,0.00025089507,0.000011166005,0.00010043585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005616253,0.00011198789,0.00015891636,0.000120857556,0.000083686326,0.000059833666,0.00029786475,0.000066920824,0.0000082128045],"category_scores_gemma":[0.00007124496,0.000096531134,0.000026807507,0.00016501812,0.0000261333,0.00022121471,0.0002448095,0.0002162714,0.000072175935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041993342,0.000288565,0.06004043,0.00011111205,0.00006858894,0.0000664675,0.0065287896,0.21997073,0.014525572,0.008803366,0.0002137282,0.68934065],"study_design_scores_gemma":[0.00078625453,0.00023447389,0.044108182,0.000043733966,0.0000017885641,0.000009237716,0.00006607651,0.9514092,0.0016978903,0.00014999321,0.0013472729,0.00014585289],"about_ca_topic_score_codex":0.000078558725,"about_ca_topic_score_gemma":0.0000018637081,"teacher_disagreement_score":0.7314385,"about_ca_system_score_codex":0.000054598902,"about_ca_system_score_gemma":0.000024468258,"threshold_uncertainty_score":0.39364254},"labels":[],"label_agreement":null},{"id":"W2994880973","doi":"10.1109/iecon.2019.8926856","title":"Optimization-based Path Planning for an Autonomous Vehicle in a Racing Track","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Track (disk drive); Path (computing); Computer science; Point (geometry); Mathematical optimization; Trajectory; Time horizon; Nonlinear system; Optimization problem; Control theory (sociology); Optimal control; Control (management); Mathematics; Algorithm; Artificial intelligence; Robot","score_opus":0.025998919589197065,"score_gpt":0.2772808388389634,"score_spread":0.2512819192497663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994880973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02674472,0.000015572074,0.97076416,0.0001628023,0.00022744702,0.00036360556,0.000001571402,0.0002597394,0.0014603868],"genre_scores_gemma":[0.4159645,6.64838e-8,0.58357745,0.00027596988,0.000018899691,0.000019665154,0.000007439873,0.000008989551,0.00012702827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988559,0.000044659144,0.00022864933,0.0004107069,0.00014583659,0.00031424317],"domain_scores_gemma":[0.99916565,0.00022577641,0.00007028404,0.0004151479,0.00004517147,0.00007794145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004109534,0.00011731771,0.00016399739,0.0001381443,0.0000580399,0.00013584453,0.0004845015,0.00006362953,0.000011298433],"category_scores_gemma":[0.000041190415,0.00011451477,0.000034095767,0.00026337054,0.000008847424,0.0005350111,0.000037517948,0.00009290884,0.00002331118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000509641,0.00004408926,0.0048093456,0.000010558701,0.0000019515335,0.0000064213123,0.0006425307,0.99144304,0.00013415478,0.00043980678,0.00005337007,0.0024096088],"study_design_scores_gemma":[0.00080740947,0.00017411182,0.0025447207,0.000043562337,0.0000013975769,0.0000027205026,0.00005539724,0.9958377,0.00023627153,0.000083733445,0.000042611162,0.00017038544],"about_ca_topic_score_codex":0.000034616325,"about_ca_topic_score_gemma":8.893665e-7,"teacher_disagreement_score":0.3892198,"about_ca_system_score_codex":0.000059307826,"about_ca_system_score_gemma":0.00013533243,"threshold_uncertainty_score":0.46697766},"labels":[],"label_agreement":null},{"id":"W2996100267","doi":"10.1049/iet-cta.2019.0281","title":"Generalised formulations for minimum distance trajectory in patrolling problems","year":2019,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Patrolling; Trajectory; Control theory (sociology); Computer science; Mathematics; Mathematical optimization; Artificial intelligence; Control (management); Physics; Law; Political science","score_opus":0.009993605877981357,"score_gpt":0.23957791572134948,"score_spread":0.22958430984336814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996100267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077029113,0.00031155904,0.9896081,0.00030803858,0.00006502647,0.0014833082,0.00003529512,0.000082823084,0.00040290123],"genre_scores_gemma":[0.95138305,0.0000068509094,0.046614062,0.00024191206,0.00007279126,0.0013738174,0.000013336978,0.000009669475,0.0002845294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906754,0.00007267501,0.0002294858,0.00032987972,0.00007903905,0.0002213921],"domain_scores_gemma":[0.9987647,0.0006809407,0.00008505432,0.0003698054,0.00004389364,0.000055604287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060562126,0.00010742003,0.00016960518,0.00006934482,0.0001271851,0.000063938816,0.00030338348,0.000051743784,0.0000025399693],"category_scores_gemma":[0.000021880118,0.00010174335,0.00004734406,0.00018511474,0.000030417727,0.00021344036,0.000020221263,0.0000815428,0.000010502595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031187144,0.00007021321,0.0015314711,0.0000337378,0.000018527271,3.4537996e-7,0.0005214723,0.0448584,0.0042428337,0.93751186,0.000025953663,0.011153998],"study_design_scores_gemma":[0.0025953373,0.000057763773,0.0031646404,0.000030920368,0.000014837112,0.0000032196413,0.000039258313,0.79200685,0.000088251356,0.19644679,0.005313108,0.00023904597],"about_ca_topic_score_codex":0.0000024635242,"about_ca_topic_score_gemma":0.0000023898592,"teacher_disagreement_score":0.9436801,"about_ca_system_score_codex":0.000020878511,"about_ca_system_score_gemma":0.000033370256,"threshold_uncertainty_score":0.41489735},"labels":[],"label_agreement":null},{"id":"W2997531191","doi":"10.1609/aaai.v34i06.6547","title":"Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"University of Southern California; National Science Foundation","keywords":"Computer science; Queue; Sorting; Idle; Path (computing); Set (abstract data type); Task (project management); Real-time computing; Queueing theory; Bin; Throughput; Mathematical optimization; Operations research; Distributed computing; Simulation; Algorithm; Computer network; Engineering; Mathematics; Operating system","score_opus":0.023121945764156614,"score_gpt":0.23937153423838672,"score_spread":0.21624958847423012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997531191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013254973,0.000010942005,0.9966272,0.0015089621,0.000066919725,0.0001925345,0.0000020348111,0.000065735796,0.00020018508],"genre_scores_gemma":[0.06873195,0.0000016537821,0.93070596,0.0004488889,0.000020271873,0.000013729139,0.000016377671,0.000004643648,0.00005649451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994143,0.000020577556,0.00012443057,0.00021441533,0.00009836907,0.0001278848],"domain_scores_gemma":[0.99977106,0.000050468003,0.00004230293,0.00006612853,0.000014136645,0.000055933255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012615405,0.000060136896,0.0000777507,0.00003765772,0.000036507332,0.000064489825,0.00012312684,0.000024223093,0.000009174369],"category_scores_gemma":[0.00004334927,0.00005809654,0.000011935845,0.00012487483,0.0000057184834,0.00025151062,0.00004065163,0.000031861666,0.000006538842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005477598,0.000021875212,0.0027674455,0.000016848204,0.0000054452144,0.000005478918,0.0018704156,0.98969805,0.00051927206,0.0008631319,0.0011198589,0.0031067063],"study_design_scores_gemma":[0.0003249927,0.00006901506,0.0008828016,0.000011397954,0.0000011808942,6.799758e-7,0.0000289147,0.9981626,0.00030809423,0.00010285025,0.00003736125,0.0000700719],"about_ca_topic_score_codex":0.0000039462293,"about_ca_topic_score_gemma":8.113363e-8,"teacher_disagreement_score":0.06740645,"about_ca_system_score_codex":0.00002568126,"about_ca_system_score_gemma":0.000016200438,"threshold_uncertainty_score":0.23691082},"labels":[],"label_agreement":null},{"id":"W2997591247","doi":"10.1109/robio49542.2019.8961836","title":"A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"University of California, Irvine; Killam Trusts","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Robot; Mobile robot; Decision tree; Algorithm; Decision tree learning; Data mining","score_opus":0.04560288475446919,"score_gpt":0.3127595408307475,"score_spread":0.26715665607627825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997591247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019071972,0.00009413267,0.9775665,0.00045154273,0.00045665586,0.0015687079,0.0000021319486,0.00012155603,0.0006668067],"genre_scores_gemma":[0.92933184,3.6704142e-7,0.06946981,0.00017370585,0.000024858615,0.00006776247,0.0000013177718,0.0000096341555,0.00092067744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984537,0.00016937002,0.00029590024,0.00039294222,0.00039604705,0.00029204861],"domain_scores_gemma":[0.99745816,0.0017232451,0.00015816961,0.000530651,0.00008006289,0.000049738705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008968374,0.00017992649,0.0004351448,0.00008063494,0.00015242334,0.00007532587,0.00089460757,0.000035033358,0.000006274602],"category_scores_gemma":[0.000067080204,0.000111126494,0.00015544743,0.00024440882,0.00001878577,0.00015782508,0.000107087566,0.00021980226,0.000050739007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001869427,0.0014585041,0.05834201,0.000031092317,0.0015725131,0.00003640972,0.032511797,0.8779742,0.0022713335,0.0062911888,0.0002875176,0.01903649],"study_design_scores_gemma":[0.0024819917,0.0015658451,0.008793375,0.000022598055,0.00003127203,0.0000025472546,0.0007380733,0.9857599,0.000266517,0.00006069748,0.00013356928,0.0001435815],"about_ca_topic_score_codex":0.00011699906,"about_ca_topic_score_gemma":0.0000033497488,"teacher_disagreement_score":0.9102599,"about_ca_system_score_codex":0.00002829991,"about_ca_system_score_gemma":0.0000402445,"threshold_uncertainty_score":0.4531607},"labels":[],"label_agreement":null},{"id":"W2998030398","doi":"10.1609/aaai.v34i05.6219","title":"Generalized and Sub-Optimal Bipartite Constraints for Conflict-Based Search","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Lockheed Martin; National Science Foundation","keywords":"Bipartite graph; Constraint (computer-aided design); Mathematical optimization; Computer science; Reduction (mathematics); Graph; Pathfinding; Search algorithm; State (computer science); Theoretical computer science; Algorithm; Mathematics; Shortest path problem","score_opus":0.15937628050591027,"score_gpt":0.32233591005917805,"score_spread":0.1629596295532678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998030398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24261178,0.000041886433,0.7339831,0.020910218,0.00030828113,0.0010830485,0.00003282682,0.00019775369,0.00083107],"genre_scores_gemma":[0.92104554,0.000013813689,0.077816434,0.0009700486,0.0000743319,0.000039654034,0.0000010760491,0.00001389655,0.000025202271],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804735,0.000023801027,0.00047247013,0.0006024726,0.00043085698,0.00042302904],"domain_scores_gemma":[0.9985587,0.00019549699,0.00023331947,0.00020306365,0.00059275347,0.00021665722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051321706,0.00023247015,0.0003218141,0.000085236934,0.00019804,0.0002922964,0.001363092,0.00009354241,0.000016718612],"category_scores_gemma":[0.0005235474,0.00018624119,0.000103170656,0.0004555607,0.0005734224,0.00023208161,0.00025462045,0.00024700025,0.000029382247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025317253,0.00011037671,0.0003117731,0.00015387431,0.000037658625,0.0000016924009,0.0033481214,0.0028115886,0.18542899,0.73438805,0.00043567587,0.07271901],"study_design_scores_gemma":[0.00007206715,0.00024159101,0.000058372905,0.00006969643,0.000008472745,0.0000020974906,0.00011514047,0.5405444,0.45590565,0.0027619025,0.00007331885,0.00014730153],"about_ca_topic_score_codex":0.000011215089,"about_ca_topic_score_gemma":4.0933003e-7,"teacher_disagreement_score":0.73162615,"about_ca_system_score_codex":0.000020603175,"about_ca_system_score_gemma":0.00017296585,"threshold_uncertainty_score":0.7594695},"labels":[],"label_agreement":null},{"id":"W3001531785","doi":"10.1109/robio49542.2019.8961498","title":"A Survey on Robot Path Planning using Bio-inspired Algorithms","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Computer science; Artificial intelligence; Path (computing); Robot; Robotics; Swarm intelligence; Any-angle path planning; Mobile robot; Machine learning; Algorithm; Particle swarm optimization","score_opus":0.06704121502873085,"score_gpt":0.3000841284020864,"score_spread":0.2330429133733556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001531785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07459363,0.00006841335,0.9197841,0.00009784706,0.0016129033,0.0002797632,0.000009007852,0.00055007805,0.0030042555],"genre_scores_gemma":[0.4152944,0.0000013626016,0.58280927,0.000657803,0.00010854409,0.000004609687,0.000020398851,0.000034386252,0.0010692194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746215,0.00020998482,0.00034436435,0.0007901758,0.0005689282,0.0006244001],"domain_scores_gemma":[0.99820673,0.00034691542,0.00014794942,0.0010197727,0.000097406126,0.00018121798],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009258784,0.0002913822,0.00035013392,0.00023111702,0.00014380895,0.0002502501,0.0011198815,0.00012890357,0.00002940852],"category_scores_gemma":[0.00008996469,0.0002550974,0.00007885873,0.0007008732,0.000029151777,0.0004247689,0.00029905472,0.0002572094,0.00065131026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048036116,0.00046413124,0.2947985,0.000050776885,0.00017408849,0.00057248614,0.0022706562,0.63348645,0.005235658,0.0032729956,0.005088116,0.054538116],"study_design_scores_gemma":[0.00042163656,0.00018267782,0.13907848,0.00009388183,0.0000031380966,0.000021280342,0.000017311168,0.85914934,0.0005099266,0.00006163992,0.00010961349,0.00035108064],"about_ca_topic_score_codex":0.00053371146,"about_ca_topic_score_gemma":0.0000012321871,"teacher_disagreement_score":0.34070078,"about_ca_system_score_codex":0.00010366557,"about_ca_system_score_gemma":0.00012849736,"threshold_uncertainty_score":0.9999901},"labels":[],"label_agreement":null},{"id":"W3002166437","doi":"10.1080/01605682.2019.1685362","title":"Near-optimal search-and-rescue path planning for a moving target","year":2020,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Mathematical optimization; Computer science; Motion planning; Heuristic; Integer programming; Path (computing); Computational complexity theory; Mathematics; Algorithm; Artificial intelligence","score_opus":0.11041881033095456,"score_gpt":0.375413378670801,"score_spread":0.2649945683398464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002166437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08429995,0.00044670943,0.86734796,0.047342565,0.00018920645,0.00030464408,0.000011936137,0.000016801421,0.00004024551],"genre_scores_gemma":[0.24619117,0.000017000197,0.75182295,0.0012585983,0.00055422646,0.000007844683,0.0000012455201,0.000011356232,0.00013561123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974261,0.00028469483,0.00031450563,0.00021396251,0.0013817411,0.00037900082],"domain_scores_gemma":[0.99808955,0.00062987633,0.000059222162,0.00017234335,0.0007977927,0.00025123818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038041167,0.00009757754,0.00017546913,0.000027946975,0.0009171127,0.00061540114,0.0011683992,0.000063441905,0.0000054049756],"category_scores_gemma":[0.0013401224,0.00006472599,0.0002067431,0.0003592434,0.0001695972,0.0005825552,0.0005671136,0.0008547414,0.000003528155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050725055,0.00004939371,0.0012084701,0.000052747084,0.00012747856,0.000024592246,0.01756643,0.93031454,0.0063143633,0.0038278338,0.03937101,0.0010923964],"study_design_scores_gemma":[0.00045981637,0.00024217041,0.0020883514,0.00006634546,0.0000034048496,0.000054123353,0.00045538502,0.9936247,0.00063688896,0.00027784298,0.0020161918,0.00007478273],"about_ca_topic_score_codex":0.000009691578,"about_ca_topic_score_gemma":6.359024e-8,"teacher_disagreement_score":0.1618912,"about_ca_system_score_codex":0.0001020335,"about_ca_system_score_gemma":0.001021589,"threshold_uncertainty_score":0.70537823},"labels":[],"label_agreement":null},{"id":"W3002291166","doi":"10.1145/3369114.3369140","title":"Any Angle Path Finding in Stochastic Obstacle Scenes","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Obstacle; Shortest path problem; Dijkstra's algorithm; Discretization; Computer science; Traverse; Euclidean shortest path; Path (computing); Motion planning; Pathfinding; Algorithm; Mathematical optimization; Graph; K shortest path routing; Artificial intelligence; Mathematics; Theoretical computer science; Robot; Geography","score_opus":0.018989252505137447,"score_gpt":0.24797934288349668,"score_spread":0.22899009037835924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002291166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23052871,0.000067642955,0.7655432,0.0001839507,0.0006250006,0.00015913775,7.856591e-7,0.00018563512,0.0027059116],"genre_scores_gemma":[0.86069703,4.5506286e-7,0.1380181,0.00013246034,0.000023625558,0.000004707531,0.000001007368,0.000006649215,0.0011159402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998907,0.000034373577,0.00016232829,0.0003392646,0.00021284084,0.00034414433],"domain_scores_gemma":[0.99930286,0.00013373127,0.00004245157,0.00043201537,0.000022240269,0.000066716835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025255137,0.00010490278,0.00014552931,0.00013258871,0.000034587083,0.00008883405,0.00057320064,0.000046363904,0.000034376077],"category_scores_gemma":[0.000046650934,0.00009468567,0.000027066562,0.00040865288,0.000012063232,0.00035974092,0.0001749156,0.0001096186,0.000560687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018211349,0.0005494198,0.3252413,0.00009667464,0.000045022916,0.00049107766,0.0097021,0.51647604,0.0076213884,0.0762901,0.0027753618,0.060693312],"study_design_scores_gemma":[0.0003734963,0.00006518627,0.057530064,0.00006656497,9.916057e-7,0.000020703204,0.00007337722,0.94068646,0.00021076493,0.0007383,0.000043847507,0.00019025418],"about_ca_topic_score_codex":0.000063104264,"about_ca_topic_score_gemma":0.0000025871573,"teacher_disagreement_score":0.6301683,"about_ca_system_score_codex":0.00005180217,"about_ca_system_score_gemma":0.000051100767,"threshold_uncertainty_score":0.7206684},"labels":[],"label_agreement":null},{"id":"W3002465036","doi":"10.3390/app10020676","title":"Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; McGill University","funders":"National Defense Basic Scientific Research Program of China; Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; McGill University","keywords":"Mobile robot; Trajectory; Control theory (sociology); Kinematics; Computer science; Path (computing); Robot; Angular velocity; Inverse kinematics; Torque; Artificial intelligence; Computer vision; Control (management); Physics","score_opus":0.025429101234299794,"score_gpt":0.2920672194524547,"score_spread":0.26663811821815486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002465036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25042504,0.0010845312,0.7244574,0.0033578533,0.00034506214,0.00097111386,0.0000022066504,0.00012938134,0.019227436],"genre_scores_gemma":[0.88860095,0.000015070654,0.11103596,0.00029741248,0.00002410847,0.000021251532,1.8736858e-7,0.0000029735313,0.000002112992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984748,0.00005773733,0.00029898234,0.00038412743,0.0005599707,0.00022440786],"domain_scores_gemma":[0.99895275,0.0005133394,0.0001987317,0.00026638297,0.00002659041,0.000042211403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010596772,0.00010803479,0.00022547663,0.000082087055,0.0000922393,0.000046538054,0.0015704561,0.00003584461,0.000003757867],"category_scores_gemma":[0.00005937922,0.0000673702,0.000026520558,0.0015561061,0.00019980376,0.00021852362,0.000113155016,0.00013177659,0.0000027125484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007708197,0.0003944686,0.012048104,0.00050928607,0.000032393255,0.00004477862,0.045008637,0.33954886,0.11796848,0.07427997,0.0027820226,0.40730593],"study_design_scores_gemma":[0.0027719294,0.01770797,0.0344625,0.0022148313,0.000053533553,0.000040787596,0.009877838,0.6603004,0.26326376,0.0044976613,0.0029590044,0.0018498413],"about_ca_topic_score_codex":0.000026613134,"about_ca_topic_score_gemma":0.000003314164,"teacher_disagreement_score":0.6381759,"about_ca_system_score_codex":0.000009538472,"about_ca_system_score_gemma":0.00008973067,"threshold_uncertainty_score":0.29183242},"labels":[],"label_agreement":null},{"id":"W3004407706","doi":"10.1177/0278364919890396","title":"Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search","year":2020,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Heuristics; A priori and a posteriori; Motion planning; Domain (mathematical analysis); Path (computing); Asymptotically optimal algorithm; Random search; Approximation algorithm; Robotics","score_opus":0.12546302209377705,"score_gpt":0.39194445326996863,"score_spread":0.2664814311761916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004407706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018472694,0.0001640404,0.8207474,0.1545146,0.0012139274,0.0003349486,0.000008918122,0.00008047342,0.0044630347],"genre_scores_gemma":[0.55101275,0.00024554576,0.44461507,0.0018877184,0.0015643751,0.0000053330336,0.0000068297895,0.000035130855,0.0006272494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931292,0.00031497213,0.0008719677,0.00024955306,0.0047532977,0.0006809961],"domain_scores_gemma":[0.9932074,0.0025445328,0.0002622014,0.00043174293,0.0030596952,0.0004944039],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.003204623,0.00019273047,0.00031145924,0.0004721135,0.00022458012,0.0009015346,0.0073791654,0.00010807257,0.00005538311],"category_scores_gemma":[0.0035290231,0.0001325972,0.00018131669,0.0008924152,0.00032696716,0.0010253004,0.0015868368,0.0016498711,0.0002716206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038696898,0.00018897692,0.0012128381,0.00002998723,0.000557891,0.0008970554,0.00956296,0.91698056,0.0015666217,0.02342556,0.023610417,0.021580176],"study_design_scores_gemma":[0.0014773095,0.0010177294,0.0036940384,0.00016443444,0.000013556192,0.0007096446,0.0006461684,0.9852015,0.0019857239,0.001087693,0.0037513657,0.00025084068],"about_ca_topic_score_codex":0.000023415347,"about_ca_topic_score_gemma":0.0000032967744,"teacher_disagreement_score":0.53254,"about_ca_system_score_codex":0.00034746554,"about_ca_system_score_gemma":0.0023728937,"threshold_uncertainty_score":0.9979914},"labels":[],"label_agreement":null},{"id":"W3006525715","doi":"10.2316/j.2020.206-0221","title":"EAGLE-VISION-INSPIRED VISUAL MEASUREMENT ALGORITHM FOR UAV’S AUTONOMOUS LANDING","year":2020,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eagle; Computer vision; Computer science; Artificial intelligence; Geology","score_opus":0.034473471863695086,"score_gpt":0.2958516896118185,"score_spread":0.2613782177481234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006525715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00100881,0.000109289096,0.9891994,0.008225639,0.0012553631,0.00010490889,0.000004929196,0.000055613138,0.000036018344],"genre_scores_gemma":[0.29670995,0.000019802357,0.7022978,0.00043330222,0.0005125751,0.0000022988322,0.0000047825997,0.000009257115,0.000010236135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998408,0.000036065834,0.0004920936,0.00016847983,0.0007600022,0.00013531979],"domain_scores_gemma":[0.9984889,0.00009189455,0.00046168666,0.00006570342,0.0007590201,0.00013281798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055867847,0.00011418242,0.0001870745,0.00014672415,0.00007509421,0.00029541482,0.0004840588,0.000049125607,0.0000023790515],"category_scores_gemma":[0.00020851077,0.00010236079,0.00008474945,0.00009997496,0.000018431378,0.0005608539,0.00009602799,0.00011113834,0.0000050567955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002612648,0.00014499974,0.00037580312,0.000026826003,0.00027427316,0.00008761153,0.0014028348,0.10123539,0.0029918829,0.0036780895,0.0014624622,0.8882937],"study_design_scores_gemma":[0.0008842788,0.00041409928,0.0019416834,0.00009616505,0.000016968246,0.00010163071,0.000025208159,0.9946056,0.0004369205,0.0006605865,0.00070564466,0.00011120764],"about_ca_topic_score_codex":0.0000023605148,"about_ca_topic_score_gemma":1.4275055e-7,"teacher_disagreement_score":0.8933702,"about_ca_system_score_codex":0.00011464532,"about_ca_system_score_gemma":0.000121689336,"threshold_uncertainty_score":0.4174152},"labels":[],"label_agreement":null},{"id":"W3007075897","doi":"10.48550/arxiv.2002.07890","title":"Informative Path Planning for Mobile Sensing with Reinforcement Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Computer science; Motion planning; Mobile robot; Variety (cybernetics); Robot; Plan (archaeology); Convergence (economics); Artificial intelligence; Path (computing); Machine learning","score_opus":0.076831269589066,"score_gpt":0.20528204313446127,"score_spread":0.12845077354539527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007075897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016394537,0.00002319824,0.98017824,0.000045910252,0.0002916388,0.00076463795,0.0000057369357,0.00050769467,0.0017884182],"genre_scores_gemma":[0.8801828,0.000010720183,0.11903861,0.00012368208,0.00007999547,0.0000036785448,0.000060100665,0.00002632672,0.00047410413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805,0.000087303284,0.00027862986,0.00091575744,0.0001635868,0.00050474686],"domain_scores_gemma":[0.99813634,0.00022897274,0.0005289765,0.00066194637,0.00022290641,0.00022086229],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027583048,0.00040937436,0.0004518855,0.00021654287,0.00033536056,0.00021223252,0.0010748103,0.00020575477,0.0000024434742],"category_scores_gemma":[0.000059493,0.0004293219,0.00014611674,0.00046833453,0.00007512123,0.0005533408,0.0014110391,0.00088136666,0.000029344388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049098962,0.0000091215925,0.00034375873,0.00012342031,0.00011293016,0.00033750784,0.004148035,0.98996145,0.000008607246,0.004041089,0.00016445588,0.00070052204],"study_design_scores_gemma":[0.00068915193,0.00041318385,0.00010708681,0.00042662714,0.000059007256,0.000013931342,0.00082613475,0.9947001,0.00010743772,0.0014080364,0.0007085183,0.0005407809],"about_ca_topic_score_codex":0.000036998434,"about_ca_topic_score_gemma":3.9448776e-7,"teacher_disagreement_score":0.86378825,"about_ca_system_score_codex":0.0002497724,"about_ca_system_score_gemma":0.0003136025,"threshold_uncertainty_score":0.9998159},"labels":[],"label_agreement":null},{"id":"W3009043760","doi":"10.1109/gncc42960.2018.9018877","title":"Intelligent Path Planning and Following for UAVs in Forest Surveillance and Fire Fighting Missions","year":2018,"lang":"en","type":"article","venue":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Terrain; Computer science; Path (computing); Task (project management); Field (mathematics); Firefighting; Real-time computing; Plan (archaeology); Curse of dimensionality; Simulation; Artificial intelligence; Robot; Engineering; Systems engineering","score_opus":0.03172260241822751,"score_gpt":0.29047688730140436,"score_spread":0.25875428488317687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009043760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25336713,0.0012711196,0.7430338,0.00065646763,0.0007474906,0.0005561091,0.000020856163,0.00011015123,0.00023685103],"genre_scores_gemma":[0.9744867,0.000044464552,0.024752658,0.00029216288,0.00019520964,0.00007366294,0.000011112622,0.000015158875,0.00012884846],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803114,0.00011437557,0.00048161673,0.0006738396,0.00022535746,0.00047365736],"domain_scores_gemma":[0.99855286,0.00048501647,0.00022543513,0.00031110988,0.00021822838,0.00020735484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070659356,0.00027060744,0.0004070236,0.00010015155,0.00040452683,0.0003321409,0.00031371074,0.000139178,0.000002588342],"category_scores_gemma":[0.00031273792,0.00025136065,0.000047033052,0.00022032308,0.0001670382,0.00046439547,0.00008642233,0.00018353657,0.000004891316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003791647,0.00019160936,0.6030265,0.00043474886,0.00020362591,0.00015157086,0.015736444,0.0016699377,0.007773706,0.0143585345,0.0051389043,0.35093525],"study_design_scores_gemma":[0.0019055853,0.0003154489,0.049569346,0.00085040985,0.000012791523,0.000022749668,0.00015992262,0.94283724,0.0002449373,0.0020494147,0.0016366858,0.00039544742],"about_ca_topic_score_codex":0.0000657336,"about_ca_topic_score_gemma":0.00001451314,"teacher_disagreement_score":0.9411673,"about_ca_system_score_codex":0.00003661417,"about_ca_system_score_gemma":0.000102625,"threshold_uncertainty_score":0.99999386},"labels":[],"label_agreement":null},{"id":"W3010468427","doi":"10.4271/2020-01-0106","title":"Toward High Automatic Driving by a Dynamic Optimal Trajectory Planning Method Based on High-Order Polynomials","year":2020,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Computer science; Order (exchange); Mathematical optimization; Mathematics","score_opus":0.01794997677264548,"score_gpt":0.272171980075781,"score_spread":0.2542220033031355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010468427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5446949,0.001922439,0.14756076,0.19207978,0.0051594907,0.011179901,0.0007522619,0.071325906,0.025324568],"genre_scores_gemma":[0.5935685,0.000012071356,0.40012473,0.0057496238,0.00010780454,0.00020604146,0.00004698105,0.000120276905,0.000063950094],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99131846,0.00074191124,0.0016638289,0.0026834193,0.0018684936,0.0017238659],"domain_scores_gemma":[0.9938709,0.0022817582,0.00054958457,0.002178937,0.00012901663,0.0009897805],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0013743273,0.0013586159,0.0018068796,0.00038186947,0.00056670385,0.00044095615,0.003811938,0.0010634465,0.0002169393],"category_scores_gemma":[0.0019434472,0.0012399178,0.00054135616,0.0018561282,0.0006033271,0.0008659585,0.00091320026,0.002304688,0.0001968511],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014530284,0.00044370437,0.000044881494,0.000105498846,0.00009125355,0.00037971462,0.00020090905,0.01658896,0.9631552,0.002379954,0.0062068957,0.010257715],"study_design_scores_gemma":[0.002999135,0.0059033567,0.9744851,0.0011004547,0.0001989554,0.00017955004,0.00014175796,0.0053134602,0.00076882605,0.0007507478,0.0051926915,0.0029659611],"about_ca_topic_score_codex":0.00005659826,"about_ca_topic_score_gemma":0.0002980634,"teacher_disagreement_score":0.9744402,"about_ca_system_score_codex":0.0005686941,"about_ca_system_score_gemma":0.0003730149,"threshold_uncertainty_score":0.999997},"labels":[],"label_agreement":null},{"id":"W3010663890","doi":"10.1109/gcwkshps45667.2019.9024394","title":"Quality Amplification of Error Prone Navigation for Swarms of Micro Aerial Vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; A priori and a posteriori; Computer vision; Terrain; Landmark; Artificial intelligence; Confusion; Global Positioning System; Path (computing); Real-time computing; Error detection and correction; Algorithm; Geography; Telecommunications","score_opus":0.06239411534457722,"score_gpt":0.33900074050210405,"score_spread":0.2766066251575268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010663890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4257526,0.000007274877,0.5734185,0.00013161462,0.00021153287,0.0003153015,0.000006767452,0.000031801836,0.00012458304],"genre_scores_gemma":[0.5734131,2.1526941e-7,0.4264016,0.000011470831,0.000019905712,0.000010306302,0.000013073278,0.0000029280968,0.0001274492],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909824,0.000045362045,0.00034905545,0.00021222208,0.00018187874,0.000113232556],"domain_scores_gemma":[0.9989984,0.00013368798,0.00027410153,0.0003938863,0.00017558997,0.000024302997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005500312,0.00006450262,0.00016989493,0.000039417686,0.000018560288,0.0000139499225,0.0003543982,0.000050097024,0.0000031304808],"category_scores_gemma":[0.00004993784,0.000057017347,0.00004513829,0.0001390412,0.000028974091,0.00018766933,0.000047110818,0.000032404216,0.000012644084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003805889,0.00012379927,0.0041435747,0.000209206,0.000016930428,9.156038e-8,0.0011183799,0.0013558918,0.9608382,0.021788754,0.00016129068,0.010205828],"study_design_scores_gemma":[0.0012411543,0.00028208914,0.08853485,0.000085051375,0.000007674901,0.0000027272838,0.000122954,0.15220413,0.7526603,0.0045302785,0.0001176608,0.00021115075],"about_ca_topic_score_codex":0.00007576028,"about_ca_topic_score_gemma":3.7960547e-7,"teacher_disagreement_score":0.2081779,"about_ca_system_score_codex":0.000017375829,"about_ca_system_score_gemma":0.00005424522,"threshold_uncertainty_score":0.23250999},"labels":[],"label_agreement":null},{"id":"W3011188459","doi":"10.11591/ijra.v9i2.pp94-112","title":"Particle swarm optimization algorithms with selective differential evolution for AUV path planning","year":2020,"lang":"en","type":"article","venue":"IAES International Journal of Robotics and Automation (IJRA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Differential evolution; Motion planning; Particle swarm optimization; Path (computing); Algorithm; Computer science; Mathematical optimization; Computational complexity theory; Any-angle path planning; A priori and a posteriori; Mathematics; Robot; Artificial intelligence","score_opus":0.019987568260515877,"score_gpt":0.2663851694009345,"score_spread":0.24639760114041862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011188459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008523292,0.000098206416,0.9869975,0.0035475,0.00059754157,0.00013429631,0.0000074002587,0.0000676804,0.00002658959],"genre_scores_gemma":[0.55880135,0.000007627469,0.44076157,0.00013386387,0.00026749325,0.000003532392,0.000007004165,0.000008883872,0.000008673718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998601,0.000048512244,0.0004170307,0.00020924021,0.0005548223,0.00016934617],"domain_scores_gemma":[0.99841714,0.00012559611,0.00051594625,0.00007896014,0.00072950707,0.00013286734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019373988,0.00013964663,0.00019616596,0.000110683686,0.000114542905,0.00027939942,0.000414726,0.000055393237,0.0000025116017],"category_scores_gemma":[0.00014898107,0.000118413445,0.000060017002,0.00018282104,0.000035933805,0.0007483975,0.000060917737,0.0001428741,0.0000013120376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058014673,0.00005320017,0.0011608826,0.000010305903,0.00011678925,0.000027912005,0.0012264143,0.98853177,0.00023854019,0.0042682416,0.00019765072,0.004110254],"study_design_scores_gemma":[0.0012232194,0.00048688147,0.0048103062,0.000102658196,0.000032165466,0.00015043463,0.0001005795,0.991848,0.0006010777,0.00046887287,0.000034374953,0.00014143436],"about_ca_topic_score_codex":0.0000020051334,"about_ca_topic_score_gemma":1.1030774e-7,"teacher_disagreement_score":0.55027807,"about_ca_system_score_codex":0.000094529525,"about_ca_system_score_gemma":0.00011191797,"threshold_uncertainty_score":0.48287603},"labels":[],"label_agreement":null},{"id":"W3011228281","doi":"10.2514/6.iac-06-b4.4.03","title":"Feature Extraction Through Time","year":2006,"lang":"en","type":"article","venue":"57th International Astronautical Congress","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Guelph","funders":"","keywords":"Computer science; Feature extraction; Extraction (chemistry); Artificial intelligence; Chromatography; Chemistry","score_opus":0.010169324767205085,"score_gpt":0.2726398619448606,"score_spread":0.26247053717765556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011228281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035829155,0.000055674947,0.9487091,0.014682697,0.0031054046,0.00012716782,0.000024117156,0.0003659645,0.02934699],"genre_scores_gemma":[0.3221921,0.0000014681115,0.64679337,0.00052110606,0.0011317807,0.000025547062,0.00012427487,0.000022695696,0.029187635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840033,0.0000488728,0.00020152936,0.0004148728,0.0006187876,0.00031559033],"domain_scores_gemma":[0.9992383,0.00014028174,0.000099563484,0.000291737,0.00016203347,0.00006809302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014818806,0.00016773125,0.00014730524,0.000069618654,0.00009067569,0.00022977548,0.0009028816,0.00009879783,0.0001400347],"category_scores_gemma":[0.00005880282,0.00015501358,0.00007429221,0.00014952674,0.00007791446,0.000746363,0.00019251683,0.00027983345,0.0006815469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061524785,0.0005896789,0.00779822,0.0000133975045,0.00016610249,0.00046041573,0.00010894586,0.011409747,0.0033485412,0.79163295,0.15416378,0.030246668],"study_design_scores_gemma":[0.000978918,0.00010885502,0.060506333,0.000066592555,0.000015625048,0.00028774733,0.000007295567,0.724644,0.0024430521,0.014723517,0.19575219,0.00046585983],"about_ca_topic_score_codex":0.000024260586,"about_ca_topic_score_gemma":7.566342e-7,"teacher_disagreement_score":0.7769095,"about_ca_system_score_codex":0.000106661595,"about_ca_system_score_gemma":0.00004388455,"threshold_uncertainty_score":0.8760134},"labels":[],"label_agreement":null},{"id":"W3012098316","doi":"10.1139/tcsme-2019-0179","title":"Trajectory via-point generation for autonomous mobile manipulation using 3D LiDAR data","year":2020,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Trajectory; Computer science; Lidar; Position (finance); Task (project management); Point cloud; Orientation (vector space); Ranging; Set (abstract data type); Computer vision; Motion planning; Point (geometry); Mobile robot; Artificial intelligence; Robot; Simulation; Engineering; Remote sensing; Mathematics; Geology","score_opus":0.06912547519556424,"score_gpt":0.2525958987984515,"score_spread":0.1834704236028873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012098316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084894965,0.000046187164,0.997173,0.00056789117,0.0004899676,0.000631898,0.00015598541,0.0000848181,0.0000013133644],"genre_scores_gemma":[0.33313656,9.4991816e-7,0.66648656,0.00016207022,0.000119003555,0.000044944496,0.000024881205,0.000018890567,0.0000061302994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990606,0.000009920779,0.00024007508,0.0003078693,0.00012737636,0.0002541755],"domain_scores_gemma":[0.99913764,0.00006699045,0.000059842463,0.0004925616,0.000056586563,0.00018635734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002509466,0.00011852035,0.00015333864,0.000029279197,0.00026076954,0.00004519346,0.0007590164,0.00010965327,0.0000031093095],"category_scores_gemma":[0.000033993165,0.000119659526,0.00023399536,0.00022858083,0.00001230095,0.000298332,0.000020195068,0.00014002537,4.4232155e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011490148,0.000007390976,2.8563912e-7,0.000053769756,0.00005592858,1.7118887e-7,0.0003470811,0.97132635,0.024582295,0.000449865,0.00007407759,0.0031016585],"study_design_scores_gemma":[0.00020871701,0.000053599735,0.0000036127078,0.0000134226475,0.000052762647,0.0000050091676,0.00001799728,0.99285394,0.0059845196,0.000034397188,0.0006449899,0.00012705989],"about_ca_topic_score_codex":0.0015045982,"about_ca_topic_score_gemma":0.00054263783,"teacher_disagreement_score":0.3322876,"about_ca_system_score_codex":0.0002602425,"about_ca_system_score_gemma":0.00028616915,"threshold_uncertainty_score":0.4879574},"labels":[],"label_agreement":null},{"id":"W3014780816","doi":"10.1061/(asce)as.1943-5525.0001143","title":"Real-Time Autonomous Obstacle Avoidance for Fixed-Wing UAVs Using a Dynamic Model","year":2020,"lang":"en","type":"article","venue":"Journal of Aerospace Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Obstacle avoidance; Trajectory; Kinematics; Solver; Control theory (sociology); Model predictive control; Computer science; Fidelity; Obstacle; Collision avoidance; Nonlinear system; Key (lock); Fixed wing; Control engineering; Wing; Engineering; Aerospace engineering; Mobile robot; Artificial intelligence; Collision; Control (management); Robot","score_opus":0.020012345080729656,"score_gpt":0.24451575888954238,"score_spread":0.22450341380881272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014780816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09310412,0.0001550394,0.90530705,0.00081747543,0.00035708738,0.000105040264,0.0000033089752,0.00013733203,0.000013540793],"genre_scores_gemma":[0.18460684,0.000009770884,0.8150945,0.000052240764,0.00015461718,0.0000018233989,3.9574294e-7,0.00003208782,0.00004773902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860185,0.000015969394,0.0004405459,0.0002411395,0.00030008433,0.0004004158],"domain_scores_gemma":[0.9989059,0.00015778144,0.00031946372,0.00022955694,0.00013649794,0.0002508035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035887555,0.00020476108,0.0003853854,0.00010566705,0.000086046384,0.00014548692,0.0007003304,0.0000735257,8.5555934e-7],"category_scores_gemma":[0.000247964,0.00021050894,0.00016394566,0.0002972256,0.0000113147935,0.0007276568,0.00011284199,0.00030638112,0.0000043703626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005586094,0.000008750024,0.00001396169,0.000035939513,0.00002572731,0.000038471353,0.00097026495,0.86243904,0.13595977,0.000092818715,0.00008686786,0.00032278534],"study_design_scores_gemma":[0.00043481003,0.00012098725,0.00009880075,0.00015276995,0.000018706387,0.00013449168,0.000020475254,0.9969499,0.0017811363,0.00003391621,0.000035790395,0.00021822783],"about_ca_topic_score_codex":0.0000026603975,"about_ca_topic_score_gemma":6.4631976e-8,"teacher_disagreement_score":0.13451083,"about_ca_system_score_codex":0.00022683691,"about_ca_system_score_gemma":0.00017765295,"threshold_uncertainty_score":0.85843056},"labels":[],"label_agreement":null},{"id":"W3014882487","doi":"10.3390/infrastructures5050044","title":"Optimal Speed Plan for the Overtaking of Autonomous Vehicles on Two-Lane Highways","year":2020,"lang":"en","type":"article","venue":"Infrastructures","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overtaking; Sensitivity (control systems); Heuristic; Task (project management); Position (finance); Simulation; Computer science; Power (physics); Mode (computer interface); Control (management); Control theory (sociology); Automotive engineering; Engineering; Transport engineering; Artificial intelligence","score_opus":0.03463389540474962,"score_gpt":0.26520107765238443,"score_spread":0.2305671822476348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014882487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100954674,0.00015426236,0.893307,0.0027532133,0.0009698244,0.0004689508,0.00007348409,0.0004479158,0.0008707268],"genre_scores_gemma":[0.7878003,8.5635907e-7,0.21102063,0.0008815936,0.00026847044,0.0000043777663,0.0000048489414,0.000011285912,0.000007646072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989083,0.00003124838,0.00023997863,0.00030772807,0.000251279,0.00026148264],"domain_scores_gemma":[0.99882513,0.00051327,0.00016711125,0.0003719047,0.000042597003,0.00007998726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116589945,0.00016291637,0.00020933863,0.000044567903,0.00012990493,0.00009344625,0.00105734,0.000053034993,0.000007969898],"category_scores_gemma":[0.00013970418,0.00011150515,0.00007460434,0.00015315083,0.00006705556,0.000103882485,0.00014625966,0.00017082959,0.0000073577626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008425806,0.000016263593,0.0011129634,0.000038285623,0.000089618086,0.000026680229,0.0039046379,0.93441373,0.008437774,0.020500401,0.008235799,0.023139589],"study_design_scores_gemma":[0.00065539737,0.00031635852,0.023177106,0.000026659816,0.000015072766,0.000018617782,0.0000516778,0.9595396,0.013646004,0.0013247388,0.0010457783,0.00018300398],"about_ca_topic_score_codex":0.00004021773,"about_ca_topic_score_gemma":5.4535207e-7,"teacher_disagreement_score":0.6868456,"about_ca_system_score_codex":0.000019537767,"about_ca_system_score_gemma":0.00006415258,"threshold_uncertainty_score":0.45470482},"labels":[],"label_agreement":null},{"id":"W3014935988","doi":"10.3390/infrastructures5050042","title":"Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed","year":2020,"lang":"en","type":"article","venue":"Infrastructures","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kinematics; Control theory (sociology); Nonlinear system; Jerk; Linearization; Motion planning; Computer science; Model predictive control; Engineering; Control engineering; Control (management); Artificial intelligence; Robot; Acceleration","score_opus":0.013399690478390118,"score_gpt":0.23956122744657157,"score_spread":0.22616153696818145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014935988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6272396,0.00065019756,0.368694,0.0024925189,0.00017626854,0.00042855286,0.000027994014,0.00019940418,0.00009149981],"genre_scores_gemma":[0.88923854,0.0000033704714,0.10999036,0.00066013326,0.000086099724,0.0000054075126,0.0000026522298,0.0000093499675,0.00000409277],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989969,0.00004221754,0.00019240277,0.00037849514,0.000122429,0.00026753425],"domain_scores_gemma":[0.9993351,0.00026045437,0.00008446321,0.00015094155,0.000027115184,0.00014192626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000842167,0.00015345734,0.00023156572,0.000082959705,0.00007402442,0.00015566837,0.00026444526,0.00008089523,8.901219e-7],"category_scores_gemma":[0.00023912163,0.00014066642,0.00001828647,0.00018360419,0.00005543303,0.00018650753,0.00010263899,0.00016248498,6.8058506e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058059586,0.000074115545,0.49580675,0.00075549644,0.00021124902,0.0012316433,0.045257684,0.2124634,0.103364825,0.017774012,0.0110629555,0.11141729],"study_design_scores_gemma":[0.0013666645,0.00012988943,0.3383682,0.00004080663,0.0000051110687,0.000026012867,0.00005400171,0.6562584,0.00048756652,0.0029847205,0.00011743942,0.00016117522],"about_ca_topic_score_codex":0.000029109382,"about_ca_topic_score_gemma":4.1151864e-7,"teacher_disagreement_score":0.44379503,"about_ca_system_score_codex":0.000016418324,"about_ca_system_score_gemma":0.0000529521,"threshold_uncertainty_score":0.57362103},"labels":[],"label_agreement":null},{"id":"W3015784855","doi":"10.36288/roscon2017-900255","title":"From simulation to the field: Learning to swim with the AQUA robot","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robot; Field (mathematics); Artificial intelligence; Simulation; Human–computer interaction; Mathematics","score_opus":0.023472781327984908,"score_gpt":0.30580494586079976,"score_spread":0.28233216453281484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015784855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009228878,0.0000047443295,0.92738473,0.060196117,0.00023952105,0.00017160983,2.5797866e-7,0.000091098416,0.0026830602],"genre_scores_gemma":[0.81324536,1.4991663e-7,0.18072739,0.003713666,0.00021687661,0.000010153534,4.6171627e-7,0.0000057041607,0.0020802643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991404,0.00005763774,0.00008450069,0.00026708507,0.00025835697,0.00019202003],"domain_scores_gemma":[0.9980546,0.00064615556,0.000066827364,0.0011141978,0.000047190966,0.00007098154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000293562,0.00009097351,0.000082458006,0.000023471399,0.0007546354,0.0006467781,0.0016968132,0.000028910845,0.000014702076],"category_scores_gemma":[0.0003601961,0.000043105305,0.00002038854,0.000096704905,0.000014403106,0.0002219018,0.00038987957,0.00016029112,0.00032557014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004469931,0.0000039631122,0.00090529723,3.1690726e-7,0.000007728344,0.000004846354,0.0029871361,0.95465994,0.00003488377,0.00023507298,0.0031645051,0.037991848],"study_design_scores_gemma":[0.000097912955,0.00013080031,0.031829868,0.000018317021,0.0000043238388,0.0000012246156,0.00015008684,0.9553271,0.00024734435,0.00012083706,0.011958726,0.000113441376],"about_ca_topic_score_codex":0.0008748707,"about_ca_topic_score_gemma":0.0000702141,"teacher_disagreement_score":0.8040165,"about_ca_system_score_codex":0.0000127143185,"about_ca_system_score_gemma":0.000022540682,"threshold_uncertainty_score":0.6236896},"labels":[],"label_agreement":null},{"id":"W3018404578","doi":"10.2514/1.g004460","title":"Online Feasible Trajectory Generation for Collision Avoidance in Fixed-Wing Unmanned Aerial Vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fixed wing; Aeronautics; Obstacle avoidance; Trajectory; Collision avoidance; Computer science; Aerospace; Operations research; Artificial intelligence; Engineering; Aerospace engineering; Wing; Collision; Mobile robot; Physics; Robot; Computer security","score_opus":0.027423359904801544,"score_gpt":0.26101033797805845,"score_spread":0.2335869780732569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018404578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28589764,0.0007559834,0.70930046,0.0034019388,0.000461989,0.0001432504,0.000018763338,0.00001478804,0.0000051884517],"genre_scores_gemma":[0.79228806,0.00006394461,0.20614244,0.00074000476,0.0007359855,0.000003844956,0.0000042458855,0.000009366359,0.00001208277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987489,0.00008264771,0.00053777045,0.00020665326,0.0002207741,0.00020327423],"domain_scores_gemma":[0.99903023,0.00018211559,0.00036736822,0.00011161228,0.00018885972,0.0001197967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052865985,0.00012608558,0.00033938218,0.000088385685,0.000088799825,0.00010621596,0.0003393233,0.00007240025,2.025977e-7],"category_scores_gemma":[0.00035297184,0.000112436326,0.000074141455,0.00019238483,0.000025744976,0.00047655255,0.00003306195,0.00018482137,3.5806377e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000946648,0.00035084516,0.0114216525,0.00014859994,0.00009178314,0.00020987939,0.0021412114,0.83760023,0.093475536,0.0035019573,0.0013991545,0.048712473],"study_design_scores_gemma":[0.0032804434,0.00041921102,0.008203493,0.00008852662,0.000011451952,0.000029346636,0.000038598933,0.9873477,0.00013433678,0.00023509847,0.00009250389,0.000119251294],"about_ca_topic_score_codex":0.0000048725115,"about_ca_topic_score_gemma":0.000019956904,"teacher_disagreement_score":0.50639045,"about_ca_system_score_codex":0.00009091968,"about_ca_system_score_gemma":0.00014072572,"threshold_uncertainty_score":0.45850202},"labels":[],"label_agreement":null},{"id":"W3018466250","doi":"10.15837/ijccc.2020.3.3844","title":"Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots","year":2020,"lang":"en","type":"article","venue":"International Journal of Computers Communications & Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Computer science; Trajectory; Fuzzy logic; Motion planning; Nonholonomic system; Robot; Controller (irrigation); Path (computing); Tracking (education); Mathematical optimization; Control theory (sociology); Control (management); Artificial intelligence; Mathematics","score_opus":0.13160619964316364,"score_gpt":0.3115582995587154,"score_spread":0.17995209991555175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018466250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006259038,0.0008917623,0.95380217,0.043386333,0.0004867242,0.00063235353,0.000035494562,0.00008745963,0.000051794184],"genre_scores_gemma":[0.5223676,0.0000036484844,0.47268733,0.004734921,0.0001354617,0.00004959979,0.000008116769,0.000012223705,0.0000011037629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766684,0.00029754086,0.00084914785,0.00033220617,0.0005599014,0.0002943881],"domain_scores_gemma":[0.9953618,0.0020826599,0.00073690066,0.00067081215,0.00081671425,0.0003310823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008576961,0.00025696898,0.00051364827,0.00033367105,0.00021318634,0.0005378438,0.0041137505,0.00008335409,0.0000013610662],"category_scores_gemma":[0.00034912705,0.00024617935,0.00022884607,0.00023361904,0.00009822134,0.00052193477,0.00024223162,0.00038853573,0.0000033791887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023655625,0.00016675466,0.0009105442,0.00001034784,0.00023329194,0.000031958094,0.00080534257,0.96844167,0.00024427086,0.0031892709,0.00048347874,0.025246514],"study_design_scores_gemma":[0.005925149,0.0005929963,0.0010774817,0.00019226571,0.00005015222,0.000050492356,0.00005461142,0.9898181,0.000069997965,0.00050335913,0.0014247898,0.00024063239],"about_ca_topic_score_codex":0.0000043638383,"about_ca_topic_score_gemma":2.6641803e-7,"teacher_disagreement_score":0.5217417,"about_ca_system_score_codex":0.000113080045,"about_ca_system_score_gemma":0.00025718598,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W3020093868","doi":"10.1007/978-3-030-43929-3_15","title":"Optimization and Design of a Gripper Mechanism for Autonomous Unmanned Aerial Vehicle Perching","year":2020,"lang":"en","type":"book-chapter","venue":"Mechanisms and machine science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Grippers; Mechanism (biology); GRASP; Engineering; Rotor (electric); Mechanism design; Computer science; Simulation; Automotive engineering; Control engineering; Aerospace engineering; Mechanical engineering","score_opus":0.028957159963076125,"score_gpt":0.23510056543503777,"score_spread":0.20614340547196164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020093868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007630904,0.000103574435,0.9970027,0.0004490663,0.0005401783,0.00066742604,0.000032098626,0.00011151384,0.0010857655],"genre_scores_gemma":[0.004814427,0.000053655505,0.992852,0.0002851091,0.000065545304,0.000019112837,0.0000066845514,0.000031127594,0.0018723604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979231,0.000026372505,0.00035024967,0.0009276327,0.00044742218,0.00032526476],"domain_scores_gemma":[0.99887043,0.00010794026,0.0002796708,0.00035988772,0.00016599832,0.00021604972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090354576,0.0003212584,0.0004365151,0.00023929488,0.00039484104,0.0002442755,0.00078895723,0.00014729281,0.000005816552],"category_scores_gemma":[0.0001170992,0.0002955563,0.000054709617,0.000118561366,0.00019010235,0.00043153446,0.00056297786,0.00019386725,0.0000017114752],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013868244,0.000008261842,9.62454e-8,0.00004722434,0.000011516144,0.000008514648,0.00047546922,0.015303737,0.012715663,0.9651125,0.000010211104,0.006292924],"study_design_scores_gemma":[0.00036487696,0.00048157704,0.0000010346798,0.000072511284,0.000025358988,0.000033695742,0.0000068627874,0.82593155,0.002294599,0.1704814,0.000033932352,0.0002725986],"about_ca_topic_score_codex":0.00001799409,"about_ca_topic_score_gemma":5.244966e-7,"teacher_disagreement_score":0.8106278,"about_ca_system_score_codex":0.000042486416,"about_ca_system_score_gemma":0.00025180823,"threshold_uncertainty_score":0.99994963},"labels":[],"label_agreement":null},{"id":"W3021224865","doi":"10.2316/j.2020.206-0347","title":"INVERSE KINEMATICS AND TRAJECTORY PLANNING FOR A HYPER-REDUNDANT BIONIC TRUNK-LIKE ROBOT","year":2020,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inverse kinematics; Trajectory; Kinematics; Trunk; Computer science; Robot; Inverse; Control theory (sociology); Artificial intelligence; Mathematics; Physics; Classical mechanics; Geometry; Biology","score_opus":0.03690181996563711,"score_gpt":0.2818164516539834,"score_spread":0.2449146316883463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021224865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031147981,0.00022819688,0.9609936,0.0066180932,0.00085565035,0.000096437434,0.000004202354,0.000037117647,0.000018742789],"genre_scores_gemma":[0.27065274,0.00004153016,0.728368,0.0006468463,0.0002618953,0.0000017159791,0.0000051539446,0.000009393943,0.000012719824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890965,0.00003181062,0.00043458838,0.0001508644,0.00035270292,0.000120360586],"domain_scores_gemma":[0.9989399,0.00016911382,0.0004109467,0.00006978328,0.00028089577,0.00012940165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027967396,0.00011426528,0.00019426756,0.00015535182,0.00006704139,0.0002288228,0.00035770558,0.000050722174,0.0000014895746],"category_scores_gemma":[0.00018614397,0.00010209706,0.000057857855,0.00009403565,0.00003528572,0.00047086747,0.000083063474,0.00012569435,0.000001368911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048215043,0.00008135921,0.001118001,0.00009761157,0.00021386087,0.00009394697,0.0057034553,0.95138955,0.00560713,0.005248335,0.0016786674,0.028719863],"study_design_scores_gemma":[0.0008454777,0.00022666142,0.0031078095,0.00014478348,0.000025113197,0.00029703174,0.0001121085,0.99375397,0.0001777335,0.0008669335,0.00032773023,0.0001146661],"about_ca_topic_score_codex":0.0000017693131,"about_ca_topic_score_gemma":2.1172721e-7,"teacher_disagreement_score":0.23950477,"about_ca_system_score_codex":0.00003780489,"about_ca_system_score_gemma":0.00008459228,"threshold_uncertainty_score":0.41633973},"labels":[],"label_agreement":null},{"id":"W3022695419","doi":"10.2316/journal.206.2006.2.206-2798","title":"AN AGENT-BASED APPROACH TO INTRODUCTORY ROBOTICS USING ROBOTIC SOCCER","year":2006,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Robot","score_opus":0.022146395300181185,"score_gpt":0.279397791447362,"score_spread":0.2572513961471808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022695419","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0117437085,0.00007130071,0.9848418,0.0015395764,0.0015698704,0.000091670656,0.0000017388668,0.000055855948,0.000084442116],"genre_scores_gemma":[0.40766993,0.0000018198854,0.5916436,0.00018929089,0.00046325146,6.063653e-7,0.000007658433,0.0000100058905,0.000013891676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998085,0.00009379226,0.0005940465,0.00025237264,0.0007667087,0.0002080666],"domain_scores_gemma":[0.9984434,0.00006284946,0.00046495313,0.00023631101,0.0006511701,0.00014128452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052140234,0.00016826809,0.00023062,0.0004813896,0.000092150985,0.0004401195,0.00081832753,0.00007238708,0.000002439007],"category_scores_gemma":[0.00006315086,0.0001585697,0.00007345173,0.00023048681,0.000037533257,0.00073153595,0.00008147521,0.00017194467,0.000005546815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061028986,0.000194459,0.0008079913,0.0000075712755,0.00003119617,0.000025173167,0.00011836979,0.9838836,0.0020607489,0.009756674,0.0002030767,0.0029050354],"study_design_scores_gemma":[0.00043004582,0.00010214146,0.007742625,0.000059828122,0.000024761852,0.00022577557,0.000019986303,0.98954314,0.0003047969,0.001333572,0.000040562914,0.0001727929],"about_ca_topic_score_codex":0.00002565652,"about_ca_topic_score_gemma":6.3549146e-7,"teacher_disagreement_score":0.39592624,"about_ca_system_score_codex":0.0001872225,"about_ca_system_score_gemma":0.00015389117,"threshold_uncertainty_score":0.6466285},"labels":[],"label_agreement":null},{"id":"W3022744233","doi":"10.1007/978-3-030-43089-4_17","title":"Reactive Motion Planning in Uncertain Environments via Mutual Information Policies","year":2020,"lang":"en","type":"book-chapter","venue":"Springer proceedings in advanced robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motion planning; Scalability; A priori and a posteriori; Robot; Path (computing); Computer science; Enhanced Data Rates for GSM Evolution; Graph; Mathematical optimization; Vertex (graph theory); Distributed computing; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.020691895641749537,"score_gpt":0.24621993964393782,"score_spread":0.2255280440021883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022744233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002517685,0.00020515188,0.91003877,0.00082297943,0.000955019,0.0011358899,0.000010625087,0.00036820731,0.08621159],"genre_scores_gemma":[0.029605096,0.00012834663,0.9616113,0.0009878512,0.00036709473,0.000086189066,0.00008902704,0.00016430442,0.006960791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692714,0.000010413222,0.000938481,0.00075961865,0.0007479213,0.0006164235],"domain_scores_gemma":[0.99859285,0.00008254662,0.0007519721,0.0003146626,0.000094406634,0.00016357157],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031618812,0.0006221331,0.0006514979,0.00080995285,0.000092556395,0.00016948863,0.0009948857,0.0004635146,0.0000025615875],"category_scores_gemma":[0.00021580516,0.0007228775,0.0000899673,0.00029786752,0.000097702745,0.0023061293,0.00061759405,0.001319624,0.00012295268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082325714,0.000079369194,0.0024288562,0.00034143683,0.00007047506,0.00017893722,0.013875265,0.7955361,0.0008200186,0.1476683,0.00011653993,0.038802393],"study_design_scores_gemma":[0.0032218827,0.0006284494,0.008534158,0.004320061,0.000066350425,0.00013801026,0.00076046714,0.8922237,0.0013198241,0.06972718,0.015330299,0.003729597],"about_ca_topic_score_codex":0.000013833479,"about_ca_topic_score_gemma":8.0291665e-7,"teacher_disagreement_score":0.09668764,"about_ca_system_score_codex":0.0009627954,"about_ca_system_score_gemma":0.00007422132,"threshold_uncertainty_score":0.9995222},"labels":[],"label_agreement":null},{"id":"W3023999630","doi":"","title":"Minimizing the trace length of a rod endpoint amidst polygonal obstacles is NP-hard","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"TRACE (psycholinguistics); Point (geometry); Computer science; Geology; Combinatorics; Mathematics; Geometry; Philosophy","score_opus":0.03091236979992319,"score_gpt":0.24431872980098707,"score_spread":0.2134063600010639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023999630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04652084,0.00092934247,0.9293905,0.0045328476,0.00077270635,0.0002671768,0.000012091571,0.00030666785,0.017267857],"genre_scores_gemma":[0.44102803,0.000014017732,0.5561582,0.000838529,0.000043485736,0.000008311681,6.9612673e-7,0.000012784453,0.001895955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983549,0.00013585419,0.00033047955,0.0003782401,0.00043948312,0.00036103078],"domain_scores_gemma":[0.998642,0.00033813153,0.00013105471,0.00073386217,0.000069057736,0.000085873005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000565206,0.00016840725,0.00021246546,0.000073375166,0.00013103013,0.00009357087,0.0009283786,0.000057512974,0.000060350758],"category_scores_gemma":[0.00013966585,0.000114257964,0.0001065722,0.00034561515,0.00009179302,0.0002445907,0.00011490827,0.00017610045,0.000050392176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005791599,0.0013093563,0.008638625,0.00017213135,0.00065189885,0.00029085483,0.079002105,0.010019038,0.022888552,0.6824532,0.07539471,0.11912158],"study_design_scores_gemma":[0.0030915102,0.00070725696,0.0550321,0.00021811193,0.00010938574,0.00080813264,0.0041824654,0.6187228,0.23583499,0.012107919,0.067060106,0.0021252306],"about_ca_topic_score_codex":0.000052087664,"about_ca_topic_score_gemma":0.0000017556625,"teacher_disagreement_score":0.6703453,"about_ca_system_score_codex":0.00003540977,"about_ca_system_score_gemma":0.00010050871,"threshold_uncertainty_score":0.46593046},"labels":[],"label_agreement":null},{"id":"W3025819880","doi":"10.1007/978-3-030-45402-9_7","title":"An Improved Robot Path Planning Algorithm for a Novel Self-adapting Intelligent Machine Tending Robotic System","year":2020,"lang":"en","type":"book-chapter","venue":"Mechanisms and machine science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Workspace; Automation; Path (computing); Random tree; Computer science; Mobile robot; Robot; Algorithm; Engineering; Real-time computing; Artificial intelligence; Distributed computing; Control engineering","score_opus":0.03057357454846743,"score_gpt":0.25584864656886686,"score_spread":0.22527507202039943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025819880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000014900938,0.00059030775,0.99306715,0.00014486494,0.0018229068,0.0011798717,0.000105040635,0.0010135267,0.002074839],"genre_scores_gemma":[0.005523779,0.000018091201,0.9920325,0.00026458962,0.00028290902,0.000058128742,0.000046139132,0.000113665046,0.001660175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99440086,0.00003698691,0.0008939394,0.0024992572,0.0010722078,0.001096742],"domain_scores_gemma":[0.99685395,0.00018319742,0.00076655555,0.001097322,0.00026711693,0.0008318628],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0022546593,0.0009551496,0.0010431604,0.0006009661,0.0012667828,0.001053625,0.0028157164,0.0002923529,0.0000034235545],"category_scores_gemma":[0.00007964608,0.00086182886,0.00019696724,0.00036870877,0.00016961772,0.00091968366,0.001200722,0.0007879737,0.000010829518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001759055,0.00012059181,0.0000030267202,0.0005592319,0.00014432771,0.00029259015,0.0021545393,0.017701875,0.022043375,0.730153,0.000020533305,0.22678931],"study_design_scores_gemma":[0.00050305715,0.00090810994,0.000004636111,0.0006266364,0.00009429807,0.00041813063,0.00012334686,0.99191517,0.00069297943,0.0034333745,0.00028038296,0.0009998955],"about_ca_topic_score_codex":0.00010278645,"about_ca_topic_score_gemma":0.0000017918508,"teacher_disagreement_score":0.9742133,"about_ca_system_score_codex":0.00036781834,"about_ca_system_score_gemma":0.00042869622,"threshold_uncertainty_score":0.9999834},"labels":[],"label_agreement":null},{"id":"W3027296912","doi":"10.1002/nav.21906","title":"Clustered coverage orienteering problem of unmanned surface vehicles for water sampling","year":2020,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"National Natural Science Foundation of China","keywords":"Orienteering; Computer science; Sampling (signal processing); Generalization; Linearization; Mathematical optimization; Node (physics); Drone; Integer programming; Algorithm; Real-time computing; Mathematics; Engineering; Computer vision","score_opus":0.26518561080921804,"score_gpt":0.3982246204879279,"score_spread":0.13303900967870985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027296912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02298948,0.00008590293,0.97365856,0.0017532584,0.00018166594,0.0006879825,0.00005063911,0.00014332862,0.0004492059],"genre_scores_gemma":[0.6291931,0.000013762685,0.37040064,0.00007078808,0.00011560057,0.000018660492,0.000020449774,0.000023623339,0.00014334689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654555,0.00020867396,0.00048619192,0.0006554299,0.0011087491,0.000995393],"domain_scores_gemma":[0.9969968,0.0013047401,0.000089996596,0.00052887923,0.00077889964,0.00030066344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022736148,0.00019598931,0.00035532415,0.000112735586,0.00027168146,0.00022081149,0.0014866529,0.00013256303,0.000004464559],"category_scores_gemma":[0.0017028336,0.00016131352,0.00008985671,0.00049827935,0.00019418787,0.00022120857,0.0010201974,0.00052102003,0.000051288698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048772185,0.00029900367,0.0015024815,0.0015490396,0.00016618612,0.00019281419,0.009292321,0.75425875,0.1810616,0.03282248,0.0034503525,0.014917248],"study_design_scores_gemma":[0.0010562649,0.0009081828,0.00018359421,0.00012411262,0.0000090210115,0.0000064476094,0.00009951176,0.92578596,0.058916576,0.009281852,0.0033345092,0.00029397022],"about_ca_topic_score_codex":0.000068429035,"about_ca_topic_score_gemma":0.0000015697685,"teacher_disagreement_score":0.6062037,"about_ca_system_score_codex":0.00011247823,"about_ca_system_score_gemma":0.00019778407,"threshold_uncertainty_score":0.6578175},"labels":[],"label_agreement":null},{"id":"W3028133957","doi":"10.3390/electronics9050859","title":"A Multi-Objective Trajectory Planning Method for Collaborative Robot","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Information Science and Technology University; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Jerk; Trajectory; Workspace; Particle swarm optimization; Inverse kinematics; Control theory (sociology); Robot; Kinematics; Computer science; Trajectory optimization; Acceleration; Mathematical optimization; Optimal control; Mathematics; Artificial intelligence; Algorithm; Control (management)","score_opus":0.044958097080281624,"score_gpt":0.32001241032443634,"score_spread":0.2750543132441547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028133957","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000121220626,0.0036385006,0.99348605,0.0014342493,0.00020615068,0.0005280948,0.000011935798,0.00036394782,0.00020987148],"genre_scores_gemma":[0.004199139,0.000015840043,0.9942124,0.0011916277,0.00015340332,0.000101536396,0.000008545123,0.00002688281,0.00009065556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810916,0.00016898353,0.00023545935,0.0006342524,0.00022851054,0.0006236451],"domain_scores_gemma":[0.9987912,0.00043091943,0.0001413902,0.00028180616,0.00018183704,0.0001728216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039497655,0.00022533124,0.00031210948,0.00006955324,0.00019393508,0.00010752357,0.0007424504,0.00010126044,0.0000019452461],"category_scores_gemma":[0.0003067025,0.00022906945,0.00008843584,0.00076479197,0.000023161052,0.00028706028,0.0001004268,0.0003488782,0.000017653641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036098767,0.00044072155,0.00045361454,0.0001736958,0.00088383886,0.00020113234,0.100204885,0.6581271,0.07957969,0.023630181,0.016960567,0.118983544],"study_design_scores_gemma":[0.0010171684,0.00074233586,0.00025171868,0.000016680333,0.00001870486,0.000012563567,0.00023262846,0.97693336,0.010508755,0.00037937952,0.009564721,0.0003219682],"about_ca_topic_score_codex":0.000002379693,"about_ca_topic_score_gemma":0.0000018543499,"teacher_disagreement_score":0.31880623,"about_ca_system_score_codex":0.00016681195,"about_ca_system_score_gemma":0.0006141233,"threshold_uncertainty_score":0.93411815},"labels":[],"label_agreement":null},{"id":"W3033320836","doi":"10.1109/crv50864.2020.00032","title":"Real-time Motion Planning for Robotic Teleoperation Using Dynamic-goal Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Robot; Trajectory; Teleoperation; Artificial intelligence; Kinematics; Motion planning; Artificial neural network; Virtual reality; Controller (irrigation); Robot control; Deep learning; Simulation; Computer vision; Mobile robot","score_opus":0.03110097051430656,"score_gpt":0.2794648738796858,"score_spread":0.24836390336537928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033320836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042301384,0.000014417761,0.99341106,0.00054066983,0.00019859892,0.0003830598,1.39818e-7,0.0005014947,0.00072043674],"genre_scores_gemma":[0.36441657,0.0000018389574,0.63493526,0.00019591274,0.000102595506,0.000013255466,0.0000320395,0.000018228553,0.00028431756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983978,0.00006873497,0.00035883035,0.00048432927,0.00030099516,0.0003893065],"domain_scores_gemma":[0.99926686,0.000109249435,0.00014572246,0.0002218284,0.000103761886,0.00015256734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002948634,0.00018976507,0.00022518645,0.000088096764,0.00032034272,0.00024187958,0.00039456415,0.0000807899,0.000015772779],"category_scores_gemma":[0.00016702266,0.00018916468,0.00006683643,0.00029657476,0.000016319871,0.0006666724,0.00014411048,0.00015756807,0.00005996579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006931105,0.000007414449,0.00013865586,0.000018996612,0.000014244935,0.0000083076275,0.0013490675,0.9819972,0.013134973,0.0005466467,0.000039576113,0.0027380143],"study_design_scores_gemma":[0.0003884356,0.000226661,0.00029462343,0.000033203472,0.000014423712,0.0000148838435,0.000088422275,0.99797326,0.0006708222,0.000046139638,0.000017756509,0.00023137535],"about_ca_topic_score_codex":0.000026348265,"about_ca_topic_score_gemma":2.4464242e-7,"teacher_disagreement_score":0.36018643,"about_ca_system_score_codex":0.0001321446,"about_ca_system_score_gemma":0.00006104017,"threshold_uncertainty_score":0.77139115},"labels":[],"label_agreement":null},{"id":"W3033434007","doi":"10.1109/tmech.2020.2998718","title":"Guidance and Control Law Design for a Slung Payload in Autonomous Landing: A Drone Delivery Case Study","year":2020,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Payload (computing); Drone; Aerospace engineering; Control (management); Aeronautics; Swing; Computer science; Path (computing); Engineering; Control engineering; Simulation; Computer security; Mechanical engineering; Artificial intelligence","score_opus":0.04491093442629015,"score_gpt":0.26726644159102925,"score_spread":0.2223555071647391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033434007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019034164,0.00021726155,0.9778546,0.000897593,0.00030541184,0.0013959056,0.000033448407,0.00025082368,0.000010775214],"genre_scores_gemma":[0.8353559,0.000009809982,0.16388556,0.00043643735,0.00003373396,0.0002376187,6.6339265e-7,0.000023162656,0.00001714565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979596,0.00020969928,0.00041073497,0.0007010838,0.00023508185,0.0004838088],"domain_scores_gemma":[0.99876297,0.00045952963,0.00010339452,0.00040439243,0.00006175376,0.00020795838],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005994226,0.00027209657,0.00038517057,0.00013138003,0.00029868187,0.00014919005,0.00041107804,0.00010865324,0.0000024506246],"category_scores_gemma":[0.000016467407,0.00028099067,0.0000835919,0.0003549367,0.000038240425,0.00037946022,0.0000084787525,0.00036718822,0.000011579565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035510404,0.0011174442,0.00012071266,0.00009851603,0.00027287554,0.0031401706,0.012759388,0.95245856,0.0021280658,0.0026523496,0.00007368495,0.024823122],"study_design_scores_gemma":[0.0038827565,0.001460638,0.000013061989,0.000030453584,0.000065602166,0.0003296527,0.00055945705,0.99222594,0.0007683168,0.00025385566,0.00009566695,0.00031458525],"about_ca_topic_score_codex":0.00026258163,"about_ca_topic_score_gemma":0.00011332775,"teacher_disagreement_score":0.8163217,"about_ca_system_score_codex":0.00017066351,"about_ca_system_score_gemma":0.00014922784,"threshold_uncertainty_score":0.99996424},"labels":[],"label_agreement":null},{"id":"W3034159639","doi":"10.1109/tcyb.2020.2994122","title":"Force-Based Algorithm for Motion Planning of Large Agent","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Benchmark (surveying); Collision avoidance; Position (finance); Scalability; Motion planning; Computer science; Overhead (engineering); Motion (physics); Collision; Algorithm; Term (time); State (computer science); Relative velocity; Distributed computing; Simulation; Artificial intelligence; Robot","score_opus":0.04034396080921998,"score_gpt":0.27929835182852136,"score_spread":0.23895439101930138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034159639","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003275686,0.000044600118,0.9977054,0.0005996717,0.0006076429,0.0003348237,0.00009762224,0.00019442318,0.0000882421],"genre_scores_gemma":[0.32018295,0.00000391739,0.6790287,0.00056536664,0.000058053727,0.000037563208,0.000006731855,0.00002125503,0.00009546025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863183,0.000045873374,0.00032245487,0.00036572374,0.00032215935,0.00031195415],"domain_scores_gemma":[0.9990957,0.00019407485,0.00012517035,0.00032151557,0.00010782151,0.00015572543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016766622,0.00017314017,0.00022063547,0.000105663516,0.00012231275,0.000040451127,0.00045035649,0.00009926227,0.000007892305],"category_scores_gemma":[0.000009795721,0.00018362475,0.00014872376,0.00027126638,0.00003058257,0.000117058,0.0000027185436,0.00018241239,0.000018257604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021862044,0.0002925597,0.000013177794,0.00006367791,0.000055472683,0.000014703685,0.0019203934,0.6819821,0.0009110573,0.00027146452,0.00038248405,0.31407103],"study_design_scores_gemma":[0.0009384035,0.00044365416,0.000055791905,0.000045032604,0.000028645925,0.0000027960584,0.000030651914,0.95400316,0.043642282,0.000105545354,0.0005339092,0.00017012988],"about_ca_topic_score_codex":0.0000054679763,"about_ca_topic_score_gemma":4.844093e-7,"teacher_disagreement_score":0.31985536,"about_ca_system_score_codex":0.000042062366,"about_ca_system_score_gemma":0.00006514214,"threshold_uncertainty_score":0.7488},"labels":[],"label_agreement":null},{"id":"W3035205004","doi":"10.2316/j.2020.206-0316","title":"ORIENTATION OPTIMIZATION OF CABLE-DRIVEN PARALLEL MANIPULATOR FOR CLEANING THE DEEP SEA FISHING GROUND","year":2020,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Orientation (vector space); Fishing; Marine engineering; Environmental science; Computer science; Fishery; Engineering; Mathematics; Biology; Geometry","score_opus":0.029775508157280885,"score_gpt":0.27285951448124535,"score_spread":0.24308400632396446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035205004","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036289913,0.000051276118,0.98953146,0.0060820007,0.00056183396,0.00010489855,0.0000026838138,0.000018247076,0.000018609537],"genre_scores_gemma":[0.3808114,0.000019792815,0.6187998,0.00018984324,0.00016107244,0.0000012863463,0.000008928455,0.000005167679,0.00000272835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895424,0.000040170973,0.00042570897,0.00010646675,0.00039211207,0.00008130379],"domain_scores_gemma":[0.99855405,0.00017639704,0.000638003,0.000066727844,0.0005158311,0.00004896574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002824222,0.00007374886,0.00012369856,0.000076819815,0.00007497888,0.00019759609,0.0004329153,0.000035266068,0.0000018692622],"category_scores_gemma":[0.00020622584,0.00005888115,0.000053466556,0.000109915585,0.000019874999,0.00074424775,0.00006257364,0.000085451466,4.5350572e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013793174,0.000013271771,0.00068202155,0.000011975022,0.000048583406,0.0000038371886,0.0014042157,0.9873338,0.000097764365,0.005486663,0.00009772584,0.0048063453],"study_design_scores_gemma":[0.0004991589,0.00010962125,0.003569191,0.00005921583,0.000019843566,0.000045851375,0.00013088765,0.9948056,0.000057744848,0.00061399327,0.000030433232,0.000058478803],"about_ca_topic_score_codex":0.0000075235453,"about_ca_topic_score_gemma":5.510121e-7,"teacher_disagreement_score":0.3771824,"about_ca_system_score_codex":0.000040762327,"about_ca_system_score_gemma":0.000048038168,"threshold_uncertainty_score":0.24011037},"labels":[],"label_agreement":null},{"id":"W3037357983","doi":"10.1609/icaps.v30i1.6642","title":"Probabilistic Robust Multi-Agent Path Finding","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation; National Science Foundation","keywords":"Probabilistic logic; Path (computing); Set (abstract data type); Computer science; Task (project management); Mathematical optimization; Algorithm; Robustness (evolution); Mathematics; Artificial intelligence; Engineering","score_opus":0.11003610080036016,"score_gpt":0.2997737098215403,"score_spread":0.18973760902118011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037357983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8955045,0.0001115355,0.08442745,0.012128721,0.0010659358,0.0004597089,0.000019164583,0.001537177,0.0047458513],"genre_scores_gemma":[0.85604054,0.0000042258507,0.14350456,0.00034035882,0.000048576887,0.000008431319,0.0000019069138,0.0000094853285,0.000041896677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985362,0.0000117496265,0.00032642655,0.00042821933,0.0004747273,0.00022264956],"domain_scores_gemma":[0.9991184,0.00007641086,0.00030931694,0.0000958517,0.00028367597,0.000116362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031320727,0.00018501254,0.00020089402,0.00008694497,0.00016215604,0.00032707487,0.00126333,0.000073660616,0.0000048569145],"category_scores_gemma":[0.0006678925,0.00014300246,0.00005268632,0.00024129546,0.00005854556,0.00028399972,0.00038391264,0.00031193692,0.000008175066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018562676,0.00040898318,0.06775259,0.00068709254,0.00050128356,0.0000702962,0.030432252,0.5243023,0.1053153,0.26229528,0.0023604203,0.0056885807],"study_design_scores_gemma":[0.00031395673,0.000067254194,0.0036926232,0.0006063921,0.000008805446,0.0000210786,0.0003318003,0.99226516,0.0022002952,0.00031395373,0.000018869723,0.00015981407],"about_ca_topic_score_codex":0.0000061340925,"about_ca_topic_score_gemma":3.8408906e-8,"teacher_disagreement_score":0.46796286,"about_ca_system_score_codex":0.000039506824,"about_ca_system_score_gemma":0.000064748936,"threshold_uncertainty_score":0.58314705},"labels":[],"label_agreement":null},{"id":"W3037524112","doi":"10.1609/socs.v11i1.18524","title":"New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"University of Southern California; National Science Foundation","keywords":"Pairwise comparison; Homogeneous space; Path (computing); Context (archaeology); Computer science; Symmetry (geometry); Timeout; Symmetry breaking; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Physics; Geometry","score_opus":0.03613716419832629,"score_gpt":0.3066897939320542,"score_spread":0.2705526297337279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037524112","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48029265,0.0003120477,0.36498362,0.06386807,0.053976636,0.0056139557,0.000094849325,0.0012643714,0.02959383],"genre_scores_gemma":[0.8391007,0.000014968709,0.1594521,0.00016018386,0.0005632378,0.000061018345,0.0000036375247,0.000027078157,0.0006170421],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978748,0.000021559015,0.00038480374,0.00047879445,0.0009132945,0.00032675144],"domain_scores_gemma":[0.99867606,0.00026269766,0.00018154243,0.00019310856,0.00059951004,0.00008709246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009184407,0.00016581424,0.00021523103,0.00016613393,0.00011288703,0.00024656078,0.0019606147,0.000105878455,0.0000040427926],"category_scores_gemma":[0.00047744712,0.00014497539,0.0001394703,0.0005474547,0.000028316033,0.00032500594,0.00080296013,0.00031287098,0.0000036602312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017115641,0.0010621018,0.028271737,0.0001940625,0.00015102352,0.000026019936,0.0021929052,0.00045004976,0.17369881,0.76034594,0.004564571,0.028871596],"study_design_scores_gemma":[0.0029206767,0.0004075285,0.0044790357,0.0011622043,0.000014489626,0.000047409998,0.00013636262,0.0851998,0.8795759,0.022236949,0.003354076,0.00046554033],"about_ca_topic_score_codex":0.00012515622,"about_ca_topic_score_gemma":0.0000010030448,"teacher_disagreement_score":0.738109,"about_ca_system_score_codex":0.00038741154,"about_ca_system_score_gemma":0.00019821215,"threshold_uncertainty_score":0.5911925},"labels":[],"label_agreement":null},{"id":"W3038024331","doi":"10.1609/icaps.v30i1.6661","title":"New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"University of Southern California; National Science Foundation","keywords":"Pairwise comparison; Homogeneous space; Path (computing); Symmetry (geometry); Context (archaeology); Rotational symmetry; Computer science; Symmetry breaking; Class (philosophy); Local symmetry; Theoretical computer science; Algorithm; Theoretical physics; Mathematics; Artificial intelligence; Physics; Geometry; Quantum mechanics","score_opus":0.09075200892442147,"score_gpt":0.32523314933394004,"score_spread":0.23448114040951856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038024331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38899943,0.0001678697,0.5929308,0.012135633,0.0007876237,0.00083564036,0.000021664518,0.0017207933,0.0024005445],"genre_scores_gemma":[0.6392721,0.0000037581028,0.36041272,0.00022286078,0.000047415775,0.000010855572,0.0000015154645,0.0000082631295,0.000020511212],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870497,0.00000883238,0.00034608276,0.0003914073,0.000325242,0.00022348815],"domain_scores_gemma":[0.99924767,0.00011048544,0.00029340995,0.000074884345,0.00017836815,0.00009515846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040694175,0.00017181208,0.00021299471,0.00016581679,0.00009767483,0.00025043843,0.0010184664,0.00008786319,0.00000170067],"category_scores_gemma":[0.00052322994,0.00014242179,0.000055518496,0.0002674311,0.000023644949,0.0002995337,0.00026809896,0.00026044316,0.0000012456211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005176116,0.0004158381,0.22049841,0.0010981392,0.00048869284,0.000067654946,0.03906827,0.028301412,0.2692081,0.30284005,0.0041703586,0.13332543],"study_design_scores_gemma":[0.00041083246,0.00009469302,0.0029369376,0.0011851343,0.0000058842934,0.000012192601,0.00031280413,0.97949815,0.014707784,0.00063367514,0.000041309428,0.00016062942],"about_ca_topic_score_codex":0.000022991338,"about_ca_topic_score_gemma":1.3949818e-7,"teacher_disagreement_score":0.95119673,"about_ca_system_score_codex":0.00005000848,"about_ca_system_score_gemma":0.00008302837,"threshold_uncertainty_score":0.5807792},"labels":[],"label_agreement":null},{"id":"W3039447221","doi":"10.1109/vtc2020-spring48590.2020.9128387","title":"Towards the Development of a Robust Path Planner for Autonomous Drones","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Drone; Motion planning; Path (computing); Planner; Convergence (economics); Computer science; Mathematical optimization; Artificial intelligence; Mathematics; Robot; Computer network","score_opus":0.08104637462468199,"score_gpt":0.257193825400237,"score_spread":0.176147450775555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039447221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011254221,0.000031372976,0.99249935,0.00448515,0.00016541094,0.00020462777,0.0000027733665,0.00012720605,0.0013586561],"genre_scores_gemma":[0.06594423,3.4202557e-7,0.93317604,0.0006082301,0.000049856517,0.00003116472,0.0000025736688,0.0000059000463,0.00018167426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913526,0.00001911782,0.00024080715,0.00023015804,0.00018372595,0.00019094792],"domain_scores_gemma":[0.99948794,0.000086859676,0.00007371449,0.0002273457,0.00005391898,0.000070223345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024014777,0.00009596004,0.00013905363,0.000019770308,0.000093542236,0.000042307667,0.0008130281,0.00003147878,0.000006262119],"category_scores_gemma":[0.00005615473,0.000059043086,0.00004041857,0.00015399828,0.000022961187,0.00009382012,0.00019423239,0.000054829332,0.000017484015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059298483,0.0002931684,0.0011002006,0.00029784624,0.0003059123,0.000035879268,0.12303884,0.12681077,0.0014145112,0.07889217,0.041824088,0.6259273],"study_design_scores_gemma":[0.00029358754,0.00010602444,0.0019871113,0.000014132097,0.000005115596,0.00000510841,0.0002003343,0.98648775,0.003560187,0.0002867441,0.0068970793,0.00015685093],"about_ca_topic_score_codex":0.0000066717657,"about_ca_topic_score_gemma":9.068957e-7,"teacher_disagreement_score":0.85967696,"about_ca_system_score_codex":0.000017826427,"about_ca_system_score_gemma":0.00024879622,"threshold_uncertainty_score":0.24077073},"labels":[],"label_agreement":null},{"id":"W3041287753","doi":"10.1007/s10878-020-00604-1","title":"Shortest paths among transient obstacles","year":2020,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Theory of computation; Transient (computer programming); Computer science; Mathematics; Combinatorics; Mathematical optimization; Algorithm","score_opus":0.015981058834448825,"score_gpt":0.22437255145828916,"score_spread":0.20839149262384032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041287753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031512922,0.00009238443,0.99103796,0.0014672289,0.0038090886,0.00009468326,8.7084584e-7,0.000059157708,0.00028729963],"genre_scores_gemma":[0.49171814,0.000028361472,0.5074473,0.00018790118,0.0005967358,0.0000011190508,0.0000020323337,0.000013424254,0.0000049501978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984598,0.000104531995,0.0005442947,0.000163901,0.0005538852,0.00017356782],"domain_scores_gemma":[0.9986992,0.0000932668,0.0004431998,0.00015785235,0.00036814954,0.00023835807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032757164,0.00012753138,0.00026585476,0.000093027884,0.00007342733,0.00016311515,0.00065234123,0.00007947612,0.000008173063],"category_scores_gemma":[0.00026801793,0.00011619891,0.00010651887,0.00046236947,0.000029592475,0.00091718766,0.00005026023,0.00023499102,0.000004060231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020499288,0.00007999771,0.00074301823,0.000006360892,0.000025450041,0.000095276424,0.0013679452,0.99231845,0.00010452541,0.0029310307,0.0009773003,0.0013301591],"study_design_scores_gemma":[0.001170247,0.00053639075,0.0014011955,0.000044863158,0.000024060635,0.000031666932,0.000036111105,0.9951759,0.00039726583,0.00063083717,0.00039472108,0.00015673994],"about_ca_topic_score_codex":0.0000019006516,"about_ca_topic_score_gemma":1.30726905e-8,"teacher_disagreement_score":0.48856685,"about_ca_system_score_codex":0.000050968447,"about_ca_system_score_gemma":0.00013156193,"threshold_uncertainty_score":0.47384542},"labels":[],"label_agreement":null},{"id":"W3043384677","doi":"","title":"Modélisation et asservissement du décollage vertical d’un drone aquatique à voilure fixe","year":2020,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Office of Naval Research; National Aeronautics and Space Administration; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; U.S. Department of Defense","keywords":"Drone; Geology; Geography","score_opus":0.02645317555919863,"score_gpt":0.21999632373333913,"score_spread":0.1935431481741405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043384677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069696695,0.004090476,0.9044552,0.016068578,0.0010199349,0.00038438858,0.00001839802,0.0000622832,0.004204033],"genre_scores_gemma":[0.9113905,0.00026888575,0.08723757,0.0002707329,0.00015176523,6.468894e-7,0.000013240107,0.000012435827,0.0006542167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976346,0.00043992742,0.000421276,0.0005191343,0.0006507557,0.00033433078],"domain_scores_gemma":[0.9986093,0.00016845753,0.00014512915,0.0004785937,0.00033343423,0.00026510155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041222706,0.00029629577,0.00042252953,0.00008490902,0.00050754927,0.000053666958,0.0016313565,0.0002486478,0.000052356863],"category_scores_gemma":[0.0002231625,0.00030843372,0.0002995601,0.0006528869,0.0006733769,0.00071768364,0.0010051879,0.00038321238,0.00011252692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015266945,0.0010301579,0.0031770505,0.0027228955,0.00041139848,0.00015243895,0.03701906,0.7219812,0.025927171,0.19946618,0.0044556083,0.0035041256],"study_design_scores_gemma":[0.0010583377,0.00023140339,0.029552905,0.0019432707,0.0001902516,0.00005402349,0.00017998574,0.93491876,0.024874162,0.00055779505,0.006143497,0.0002955879],"about_ca_topic_score_codex":0.000971814,"about_ca_topic_score_gemma":0.00054653647,"teacher_disagreement_score":0.8416938,"about_ca_system_score_codex":0.0007830249,"about_ca_system_score_gemma":0.0006398956,"threshold_uncertainty_score":0.99993676},"labels":[],"label_agreement":null},{"id":"W3044888263","doi":"10.48550/arxiv.2007.12045","title":"High Precision Real Time Collision Detection","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Collision; Collision detection; Computer science; Robot; Collision avoidance; Robotics; Artificial intelligence; Real-time computing; Ideal (ethics); Simulation","score_opus":0.057787337991314976,"score_gpt":0.18607289861145918,"score_spread":0.1282855606201442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044888263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12995143,0.000010496644,0.8664316,0.0001271336,0.0012715097,0.0003231162,0.000015100127,0.00074752234,0.00112207],"genre_scores_gemma":[0.9512633,0.00008994699,0.046955053,0.00004376884,0.00019143353,0.000001027043,0.000030945903,0.000026221294,0.0013983047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746,0.00023655998,0.0002509187,0.0015062866,0.00020033939,0.00034587306],"domain_scores_gemma":[0.9978132,0.0001849002,0.00032112264,0.0012649144,0.00016076364,0.0002550579],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028802856,0.00035053203,0.0004126376,0.0002595323,0.00020101039,0.00017160045,0.0019952913,0.00043148932,0.000018014718],"category_scores_gemma":[0.00008533484,0.00040877235,0.00018241149,0.0008235324,0.000056584417,0.00039922752,0.0026024377,0.00068255974,0.0007818382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007151595,0.00007734174,0.00014171637,0.00004674252,0.0000744398,0.0009684349,0.00027436137,0.9836757,0.0014982896,0.0075486293,0.0010851984,0.004537593],"study_design_scores_gemma":[0.00038294157,0.00013896443,0.001653154,0.00010754612,0.000045204037,0.000008797991,0.00000826332,0.9773775,0.0014122002,0.018303588,0.00013647413,0.00042538284],"about_ca_topic_score_codex":0.00037617245,"about_ca_topic_score_gemma":0.000003498856,"teacher_disagreement_score":0.8213119,"about_ca_system_score_codex":0.00035081524,"about_ca_system_score_gemma":0.00018147823,"threshold_uncertainty_score":0.9999962},"labels":[],"label_agreement":null},{"id":"W3045616389","doi":"10.23919/acc45564.2020.9148022","title":"ROS Based Real-Time Motion Control for Robotic Visual Arts Exhibit Using Decawave Local Positioning System","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Computer science; Motion (physics); Artificial intelligence; Motion control; Computer graphics (images); Robot","score_opus":0.029235040246745,"score_gpt":0.26593909048258535,"score_spread":0.23670405023584035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045616389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014607242,0.000010825032,0.99586076,0.00074204785,0.00026058315,0.0006081987,0.000003952928,0.000739716,0.00031321432],"genre_scores_gemma":[0.6049852,5.5024238e-8,0.39437288,0.0004430947,0.00013629658,0.000016594302,0.0000117856425,0.000017826447,0.00001629868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799913,0.00012627104,0.000421205,0.0006135354,0.00037416376,0.00046570826],"domain_scores_gemma":[0.99879926,0.00030159438,0.00017221241,0.0002810846,0.00017495203,0.00027090893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003683881,0.0002307739,0.00037087634,0.00009741475,0.00026697398,0.0002593968,0.0003683595,0.000109668814,0.000006496184],"category_scores_gemma":[0.000065880886,0.00022841302,0.00012533099,0.00032367042,0.000036384066,0.0004177881,0.00006788794,0.00010813765,0.00008344915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025174126,0.000036351008,0.00008161876,0.000083045066,0.00002590915,0.000059389167,0.00014167801,0.9842617,0.010933696,0.002677445,0.00013718182,0.001536811],"study_design_scores_gemma":[0.0012755023,0.00024634198,0.00017735169,0.0001423409,0.00004044467,0.00004897243,0.000050678897,0.99440956,0.0033233163,0.00002487994,0.0000026107157,0.00025798182],"about_ca_topic_score_codex":0.000060598217,"about_ca_topic_score_gemma":3.9261528e-7,"teacher_disagreement_score":0.60352445,"about_ca_system_score_codex":0.00022115037,"about_ca_system_score_gemma":0.00013172347,"threshold_uncertainty_score":0.93144125},"labels":[],"label_agreement":null},{"id":"W3045888462","doi":"10.23919/acc45564.2020.9147473","title":"Piecewise-Linear Path Following for a Unicycle using Transverse Feedback Linearization","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tree traversal; Path (computing); Control theory (sociology); Motion planning; Controller (irrigation); Linearization; Piecewise linear function; Computer science; Feedback linearization; Trajectory; Kinematics; Mathematics; Robot; Algorithm; Nonlinear system; Artificial intelligence; Control (management); Geometry","score_opus":0.06982923268441546,"score_gpt":0.2896312966219213,"score_spread":0.21980206393750581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045888462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010352816,0.000023898929,0.9868701,0.0015390204,0.00033012396,0.00028984243,0.000004675694,0.00036333263,0.00022616926],"genre_scores_gemma":[0.15490605,0.0000011022421,0.8439684,0.00087700743,0.00015055866,0.000005823492,0.000009165074,0.000017375529,0.000064497326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998786,0.000035804966,0.00024232401,0.00041066526,0.00022627634,0.00029896334],"domain_scores_gemma":[0.9993684,0.000084331674,0.00006156556,0.00026205546,0.00005806589,0.0001655662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018346008,0.00014424653,0.00018986943,0.000045328037,0.00014831728,0.00010046196,0.0005063255,0.00006976868,0.0000054318803],"category_scores_gemma":[0.00010795429,0.00014021332,0.000133772,0.00048363674,0.000013206318,0.00048734364,0.00007539353,0.0000913434,0.000030678395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010282993,0.00025690193,0.0025109262,0.00021828913,0.00025132758,0.00019347877,0.014485776,0.8694217,0.058429997,0.013839454,0.001680352,0.038608946],"study_design_scores_gemma":[0.00072446227,0.00008731732,0.000074403964,0.00002119264,0.000018262239,0.0000036914662,0.000060731094,0.9960033,0.0020430323,0.000121248864,0.00066084415,0.00018150984],"about_ca_topic_score_codex":0.000015652724,"about_ca_topic_score_gemma":2.7144878e-7,"teacher_disagreement_score":0.14455323,"about_ca_system_score_codex":0.000030824613,"about_ca_system_score_gemma":0.000098775505,"threshold_uncertainty_score":0.57177335},"labels":[],"label_agreement":null},{"id":"W3046111609","doi":"","title":"Planning optimal paths for industrial robots using a 3D surface model","year":2003,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Computer science; Surface (topology); Artificial intelligence; Mathematics; Geometry","score_opus":0.07325877953710733,"score_gpt":0.318696359459898,"score_spread":0.24543757992279067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046111609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052233536,0.00009567107,0.9459025,0.0003208891,0.0012966497,0.000083120955,0.0000037006635,0.000022727232,0.00004117341],"genre_scores_gemma":[0.2456832,0.000005267574,0.7541044,0.000046484653,0.00013283291,5.089834e-7,0.0000018844858,0.000007213511,0.000018230345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876684,0.000047705853,0.00044833054,0.00014486242,0.0004347023,0.00015754491],"domain_scores_gemma":[0.99874586,0.00014069251,0.0004929599,0.00009008809,0.00045165757,0.00007875818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064048154,0.00011447409,0.00017461038,0.0001587627,0.00008454296,0.000253412,0.00037093277,0.00008329914,8.720754e-7],"category_scores_gemma":[0.00024878504,0.00010814226,0.00006364032,0.000092956565,0.000019545085,0.0006704477,0.000050004794,0.00015212451,5.2843245e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009640317,0.000030133464,0.00041485115,0.000002681252,0.000047080022,0.000019400864,0.0003346856,0.9901643,0.0006272567,0.0056158714,0.00008596609,0.0026481769],"study_design_scores_gemma":[0.0009158606,0.0000784195,0.00013226387,0.00011981291,0.000017416694,0.00032371888,0.000024753906,0.9962456,0.00035069668,0.0016366576,0.00004147859,0.00011332538],"about_ca_topic_score_codex":0.0000019156034,"about_ca_topic_score_gemma":4.326153e-8,"teacher_disagreement_score":0.19344966,"about_ca_system_score_codex":0.00009087768,"about_ca_system_score_gemma":0.00020889172,"threshold_uncertainty_score":0.4409913},"labels":[],"label_agreement":null},{"id":"W3046401141","doi":"10.1109/access.2020.3012886","title":"An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Motion planning; Computer science; Robot; Path (computing); Grid; Field (mathematics); Real-time computing; Artificial neural network; Grid reference; Artificial intelligence; Mobile robot","score_opus":0.06097009512718979,"score_gpt":0.3528320310184035,"score_spread":0.2918619358912137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046401141","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073483906,0.000019814883,0.99578345,0.00025194255,0.0009643891,0.00087303255,0.00007759373,0.0012282132,0.00006673854],"genre_scores_gemma":[0.05409323,5.862046e-7,0.9439364,0.0012002575,0.00046346357,0.00017635926,0.000033674845,0.000072138086,0.000023905584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966478,0.00033563277,0.00054160185,0.0012868574,0.0004557642,0.0007323574],"domain_scores_gemma":[0.9974053,0.0005007166,0.00034010908,0.0010618272,0.0001848744,0.0005071775],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006937051,0.00044389517,0.0005881805,0.00016248265,0.00029902603,0.0007364791,0.0028768482,0.00019369314,0.0000025232691],"category_scores_gemma":[0.00007033467,0.0004226345,0.00017385391,0.00048307082,0.000031373722,0.0007686646,0.00015535417,0.00025186807,0.00003938829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059804723,0.00017666053,0.00006796558,0.00016004122,0.000061615996,0.00042254233,0.00095453975,0.9124254,0.014887145,0.00001804522,0.0004131968,0.07035306],"study_design_scores_gemma":[0.0013503466,0.00071009184,0.00016185068,0.00012433679,0.000027324013,0.00003180692,0.000019639034,0.98000896,0.017002866,0.00000888012,0.000054593213,0.0004993332],"about_ca_topic_score_codex":0.0001039764,"about_ca_topic_score_gemma":2.5519358e-7,"teacher_disagreement_score":0.06985373,"about_ca_system_score_codex":0.0001262248,"about_ca_system_score_gemma":0.00016677869,"threshold_uncertainty_score":0.99982256},"labels":[],"label_agreement":null},{"id":"W3046881201","doi":"10.23919/ecc51009.2020.9143947","title":"A Gaussian-Biased Heuristic for Stochastic Sampling-Based 2D Trajectory Planning Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trajectory; Trajectory optimization; Sampling (signal processing); Gaussian; Mathematical optimization; Motion planning; Computer science; Heuristic; Algorithm; Gaussian process; Importance sampling; Mathematics; Control theory (sociology); Optimal control; Robot; Statistics; Monte Carlo method; Artificial intelligence","score_opus":0.09686184810358756,"score_gpt":0.31216275587028625,"score_spread":0.2153009077666987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046881201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033459123,0.00013220227,0.994618,0.0024704002,0.0005630187,0.000585498,0.000027866956,0.0010144669,0.00025397356],"genre_scores_gemma":[0.21289456,1.6822727e-7,0.7830047,0.003530081,0.00032959747,0.00010063127,0.00002883159,0.00004097499,0.00007046427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736875,0.000070545946,0.0004664745,0.0009167311,0.0004608586,0.0007166626],"domain_scores_gemma":[0.9976406,0.001062339,0.00016528644,0.0005477824,0.00010862239,0.00047538974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034006586,0.00035544086,0.0004308326,0.00015057059,0.0002476741,0.00023054896,0.0011801929,0.00017375122,0.000020533997],"category_scores_gemma":[0.0006030969,0.0003382053,0.00017142216,0.00053780485,0.00006262144,0.00023490078,0.00010778993,0.0003811447,0.00007817572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007297547,0.0001339343,0.00012801096,0.00015093695,0.00006876761,0.00012714908,0.002407679,0.97975844,0.0007549725,0.0015512308,0.0043006167,0.010545285],"study_design_scores_gemma":[0.0013035232,0.0004098274,0.00045522343,0.0000785406,0.000025211333,0.000013182739,0.00007014117,0.9961738,0.00036176763,0.00028854443,0.0003691906,0.0004510652],"about_ca_topic_score_codex":0.000013557439,"about_ca_topic_score_gemma":2.8520773e-7,"teacher_disagreement_score":0.21255997,"about_ca_system_score_codex":0.00006664185,"about_ca_system_score_gemma":0.00031114867,"threshold_uncertainty_score":0.999907},"labels":[],"label_agreement":null},{"id":"W3047400633","doi":"10.1109/infocom41043.2020.9155528","title":"Informative Path Planning for Mobile Sensing with Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Computer science; Motion planning; Mobile robot; Robot; Convergence (economics); Variety (cybernetics); Plan (archaeology); Artificial intelligence; Path (computing); Machine learning; Distributed computing","score_opus":0.026643961561059987,"score_gpt":0.2557575247277681,"score_spread":0.22911356316670808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047400633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018612256,0.000015116733,0.99205256,0.00030682184,0.00006326892,0.0003707362,3.929668e-7,0.00041945337,0.0049104183],"genre_scores_gemma":[0.31733942,6.3598e-7,0.68133533,0.0010281946,0.00005670007,0.000018107792,0.000007849987,0.000009943006,0.00020384147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989252,0.000022155551,0.00022382336,0.00025169534,0.0002542637,0.00032285007],"domain_scores_gemma":[0.9993194,0.00014605655,0.00013072065,0.00016915365,0.00009286658,0.00014182902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018111215,0.0001481491,0.00017334853,0.000046618075,0.00019942825,0.00015523712,0.0003003898,0.00003518068,0.000003548194],"category_scores_gemma":[0.000062185194,0.000115279276,0.00003457317,0.00025268606,0.000020841213,0.00058297545,0.00014400935,0.00017104235,0.000027846298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001833224,0.0000033855986,0.00020665099,0.000029108263,0.000024599976,0.000022324493,0.017387737,0.96929175,0.00008545679,0.0009796565,0.00081943034,0.011131549],"study_design_scores_gemma":[0.0004821431,0.0007895926,0.000058263693,0.000056788227,0.000004779458,0.000017489197,0.001224405,0.9923075,0.001080997,0.00002235492,0.00376227,0.00019345059],"about_ca_topic_score_codex":0.0000062170366,"about_ca_topic_score_gemma":3.8736683e-8,"teacher_disagreement_score":0.3154782,"about_ca_system_score_codex":0.000030751253,"about_ca_system_score_gemma":0.000068498775,"threshold_uncertainty_score":0.47009525},"labels":[],"label_agreement":null},{"id":"W3081526557","doi":"10.1007/s42401-020-00059-6","title":"Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight","year":2020,"lang":"en","type":"article","venue":"Aerospace Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Collision avoidance; Flexibility (engineering); GNSS applications; Computer science; Scalability; SAFER; Real-time computing; Field (mathematics); Collision; Global Positioning System; Simulation; Algorithm; Computer security","score_opus":0.016130782145383157,"score_gpt":0.23901252726593875,"score_spread":0.22288174512055559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081526557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006435362,0.00005307966,0.9879038,0.0028421364,0.0014254205,0.0010511135,0.000018372233,0.00020341198,0.00006733125],"genre_scores_gemma":[0.7948368,0.0000075519997,0.20337045,0.0006245153,0.0005274606,0.00028736316,0.00025582977,0.00001825988,0.00007176426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830335,0.00009355826,0.0005891495,0.0002682447,0.0004341772,0.00031153366],"domain_scores_gemma":[0.9990694,0.000098245066,0.00033095994,0.00025557954,0.00013968971,0.00010615607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004470155,0.00016887851,0.00023387959,0.000101718666,0.00017537034,0.00037466377,0.00040414356,0.00014434199,6.106504e-7],"category_scores_gemma":[0.00011358755,0.00017146638,0.00005848927,0.00055644393,0.00001275356,0.0025233116,0.000077333745,0.00015770421,0.000080591766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029200703,0.00030296593,0.0005020759,0.0014167321,0.00010712428,0.00011222819,0.041765235,0.513318,0.024343792,0.025561161,0.04811595,0.34416273],"study_design_scores_gemma":[0.00060542347,0.00020360951,0.00009204509,0.00014603807,0.000004734459,0.000017485509,0.00034117044,0.9904428,0.006795461,0.00007341199,0.0010894734,0.00018833447],"about_ca_topic_score_codex":0.00009121854,"about_ca_topic_score_gemma":0.0000020913471,"teacher_disagreement_score":0.7884014,"about_ca_system_score_codex":0.00011984601,"about_ca_system_score_gemma":0.000055376433,"threshold_uncertainty_score":0.6992196},"labels":[],"label_agreement":null},{"id":"W3081950458","doi":"","title":"Autonomous Navigation of Quadrotor Swarms","year":2020,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Consortium de Recherche et d’innovation en Aérospatiale au Québec; Polytechnique Montréal","keywords":"Humanities; Philosophy","score_opus":0.01618180044224371,"score_gpt":0.237499406514287,"score_spread":0.2213176060720433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081950458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02496086,0.00029948773,0.96589625,0.0067072515,0.0001498213,0.00054146396,0.000016772243,0.0011418074,0.00028626042],"genre_scores_gemma":[0.4787882,0.000009298504,0.51942486,0.0014556419,0.00009213217,0.00011424762,0.000010795965,0.000025155885,0.00007965542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977914,0.00012292646,0.00056179875,0.0005386875,0.00046067737,0.0005245095],"domain_scores_gemma":[0.9981757,0.000110143665,0.00034330747,0.00085631845,0.00012848145,0.0003860939],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044799616,0.00026855554,0.00038565436,0.00018151569,0.0001165385,0.00013005733,0.0014020661,0.00019482743,0.000008345037],"category_scores_gemma":[0.0002063199,0.00027740022,0.00014756975,0.00085179857,0.00007062615,0.0005520619,0.00036137487,0.00035318264,0.000045580346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000200135,0.0010712846,0.07281904,0.00059573725,0.00032709632,0.0010280078,0.014780637,0.17335278,0.16313009,0.24941358,0.011446453,0.31183517],"study_design_scores_gemma":[0.00035997882,0.0002714394,0.010839593,0.00005692344,0.0000123119735,0.00007363266,0.000037231555,0.9488016,0.037047993,0.0013380338,0.00082853896,0.00033275664],"about_ca_topic_score_codex":0.0015036645,"about_ca_topic_score_gemma":0.000010088108,"teacher_disagreement_score":0.7754488,"about_ca_system_score_codex":0.00018231983,"about_ca_system_score_gemma":0.0002822834,"threshold_uncertainty_score":0.9999678},"labels":[],"label_agreement":null},{"id":"W3082883870","doi":"10.1007/978-3-030-57802-2_56","title":"Synthesized A* Multi-robot Path Planning in an Indoor Smart Lab Using Distributed Cloud Computing","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Motion planning; Computer science; Path (computing); Cloud computing; Distributed computing; Planner; Cover (algebra); Limiting; Robot; Real-time computing; Artificial intelligence; Computer network; Engineering","score_opus":0.057610925090668896,"score_gpt":0.3092866275538539,"score_spread":0.251675702463185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082883870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022395991,0.013142081,0.9797633,0.000024334711,0.0027395675,0.00080867024,0.000026245893,0.00030854953,0.00094760105],"genre_scores_gemma":[0.7024323,0.0003244481,0.29528344,0.00016038876,0.0011068211,0.000013219639,0.00011240852,0.00019578195,0.00037123624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99476856,0.0003023684,0.0018420911,0.0016481819,0.000584932,0.00085388374],"domain_scores_gemma":[0.99689645,0.00086562795,0.0011226182,0.0006936518,0.00013323569,0.00028841017],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012293196,0.00085791707,0.0015357335,0.00046520328,0.00028845214,0.0004664947,0.0012822974,0.0004175552,0.0000014778745],"category_scores_gemma":[0.00015258742,0.00089958246,0.00012899413,0.00033919854,0.00012328538,0.0006055985,0.0009201957,0.0012364496,0.000011790833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035508747,0.00009563458,0.011916512,0.0007638226,0.00007526321,0.001462664,0.004076691,0.92228025,0.000047948706,0.018558854,0.0000111670015,0.040675685],"study_design_scores_gemma":[0.0005306068,0.00011842612,0.0002967961,0.008054122,0.000018884139,0.00015435496,0.00042027092,0.98744714,0.00002101075,0.0004287683,0.0015640435,0.0009455934],"about_ca_topic_score_codex":0.00017578584,"about_ca_topic_score_gemma":0.0000106321995,"teacher_disagreement_score":0.70019263,"about_ca_system_score_codex":0.000386454,"about_ca_system_score_gemma":0.00013201324,"threshold_uncertainty_score":0.9993455},"labels":[],"label_agreement":null},{"id":"W3083023308","doi":"10.1109/lra.2020.3021382","title":"Path Planning Under Malicious Injections and Removals of Perceived Obstacles: A Probabilistic Programming Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Aeronautics and Space Administration","keywords":"Probabilistic logic; Path (computing); Computer science; Computer security; Motion planning; Artificial intelligence; Computer network","score_opus":0.03390529083658966,"score_gpt":0.25031610608732957,"score_spread":0.2164108152507399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083023308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13645947,0.00006177871,0.8598371,0.0030432257,0.00010526932,0.00026165543,0.0000021518858,0.00020852778,0.000020779078],"genre_scores_gemma":[0.54466397,0.000003189757,0.45455584,0.00071285735,0.000038454575,0.000008931332,0.0000038955227,0.000009683421,0.0000032050414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877846,0.00007833676,0.00031666475,0.0003745379,0.00023017748,0.00022180354],"domain_scores_gemma":[0.9993571,0.00010796893,0.00017553856,0.0001872283,0.000050299055,0.0001218538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017373169,0.00015807002,0.00023327829,0.000091139234,0.00014811654,0.0001687624,0.00019294799,0.000059459282,2.8442565e-7],"category_scores_gemma":[0.00006230383,0.00015243098,0.000033709686,0.0003078885,0.00009802241,0.00025349346,0.00006582935,0.00014119345,0.0000011874752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002695458,0.000040878153,0.00077522715,0.00024887305,0.00003538832,0.000014220062,0.008369741,0.9771964,0.008607945,0.0015528867,0.00016306793,0.0029926777],"study_design_scores_gemma":[0.00027484898,0.00007572529,0.012759129,0.00009302255,0.000022437533,0.000058602564,0.0002804809,0.98608565,0.000034359535,0.00012387302,0.000015317482,0.00017654726],"about_ca_topic_score_codex":0.000012827217,"about_ca_topic_score_gemma":8.287892e-8,"teacher_disagreement_score":0.40820447,"about_ca_system_score_codex":0.00002541158,"about_ca_system_score_gemma":0.00003069976,"threshold_uncertainty_score":0.6215955},"labels":[],"label_agreement":null},{"id":"W3083314235","doi":"10.20944/preprints202009.0128.v1","title":"Human-Rover Interactions and Swarm Algorithms of Mobile Robots in an Open and Crowded Environment: A Survey","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université du Québec à Chicoutimi","keywords":"Swarm behaviour; Mobile robot; Robot; Computer science; Field (mathematics); Human–computer interaction; Open research; Focus (optics); Swarm robotics; Architecture; Artificial intelligence; Systems engineering; Engineering; Geography; World Wide Web","score_opus":0.2383439065448661,"score_gpt":0.4010564761282223,"score_spread":0.16271256958335623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083314235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9672841,0.00014128003,0.029931186,0.00017711728,0.00037873082,0.0013673384,0.00006828278,0.00010719434,0.00054479967],"genre_scores_gemma":[0.95968705,0.00007013294,0.03959527,0.000069411486,0.00004467965,0.00023902941,0.000060651302,0.00003566868,0.00019808531],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962356,0.0005558164,0.00075130735,0.0017664701,0.0003417722,0.00034905082],"domain_scores_gemma":[0.997327,0.00019114146,0.0004792761,0.001671335,0.00006292093,0.00026830335],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0013609551,0.00040610586,0.0007672036,0.00022196113,0.00010907628,0.00019541034,0.0023314615,0.0002281807,0.000055894314],"category_scores_gemma":[0.00014477878,0.00045711198,0.00005332463,0.00020272726,0.00016693985,0.0006755661,0.012781335,0.00096127053,0.000058829468],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035841847,0.0007385415,0.9253719,0.00020946296,0.00016897747,0.00010856526,0.010549637,0.048724927,0.009401153,0.00026242642,0.000036571237,0.0043919897],"study_design_scores_gemma":[0.0005869964,0.00009826294,0.93270504,0.00018572256,0.000018788476,0.000016871554,0.00005983857,0.061864186,0.0025695604,0.0013868453,0.000067577275,0.0004403301],"about_ca_topic_score_codex":0.0029797521,"about_ca_topic_score_gemma":0.00008852995,"teacher_disagreement_score":0.01313926,"about_ca_system_score_codex":0.00011409659,"about_ca_system_score_gemma":0.000116885225,"threshold_uncertainty_score":0.99978805},"labels":[],"label_agreement":null},{"id":"W3089354495","doi":"10.1145/3460775","title":"A Large-scale Benchmark and an Inclusion-based Algorithm for Continuous Collision Detection","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; York University","funders":"National Key Research and Development Program of China; Advanced Micro Devices; National Science Foundation","keywords":"False positive paradox; Benchmark (surveying); False positives and false negatives; Collision; Collision detection; Interval (graph theory)","score_opus":0.013859024504019426,"score_gpt":0.2677558363569493,"score_spread":0.25389681185292984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089354495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007592074,0.00013316811,0.9902832,0.0007234789,0.00065446907,0.00029300048,0.000084816136,0.00022012778,0.000015645699],"genre_scores_gemma":[0.16087882,0.000048775637,0.8379713,0.0008574741,0.000051569335,0.00009353403,0.000032377742,0.000021601341,0.000044539916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834824,0.0001271396,0.00024710209,0.0006070925,0.00034293166,0.0003274695],"domain_scores_gemma":[0.998136,0.0004810096,0.00007647592,0.0008579659,0.00027269346,0.00017586017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046168122,0.00019118318,0.00022044317,0.0002564412,0.0012322362,0.00014046834,0.00046586196,0.0001816802,0.000005884951],"category_scores_gemma":[0.000053803287,0.00020088257,0.00011533482,0.00087084423,0.000052747764,0.00034477533,0.000049289196,0.00026955036,0.0000018196203],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003517627,0.0009883668,0.000109677974,0.000040845956,0.000056424084,0.00005842723,0.0020615407,0.005325666,0.0018266616,0.00028596152,0.00006748917,0.9891438],"study_design_scores_gemma":[0.001165061,0.00050418836,0.0006024903,0.000047707636,0.00003642317,0.000040576182,0.00014255843,0.9774527,0.014490753,0.0039494834,0.001303448,0.00026462047],"about_ca_topic_score_codex":0.00002200308,"about_ca_topic_score_gemma":0.00009635574,"teacher_disagreement_score":0.98887914,"about_ca_system_score_codex":0.00003941066,"about_ca_system_score_gemma":0.0001221499,"threshold_uncertainty_score":0.94774884},"labels":[],"label_agreement":null},{"id":"W3090081817","doi":"10.1109/ccta41146.2020.9206268","title":"Dynamic planning of redundant robots within a set-based task-priority inverse kinematics framework","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Control Technology and Applications (CCTA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Kinova (Canada)","funders":"","keywords":"Inverse kinematics; Computer science; Motion planning; Robot; Kinematics; Workspace; Task (project management); Trajectory; Robot kinematics; Set (abstract data type); Artificial intelligence; Mobile robot; Engineering","score_opus":0.02647058629094817,"score_gpt":0.2830837348054746,"score_spread":0.25661314851452643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090081817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01081279,0.00009368513,0.9732462,0.0144161405,0.000095609976,0.00066080125,0.000038181603,0.00050791766,0.0001286715],"genre_scores_gemma":[0.7300329,0.000010132154,0.26896358,0.0007561761,0.000023791637,0.0001865136,0.000006340808,0.000011436977,0.000009105853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981651,0.000073793875,0.0005044558,0.0006947904,0.00024708643,0.00031482332],"domain_scores_gemma":[0.9981206,0.0002885076,0.000421031,0.0008357079,0.00017072866,0.00016344847],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023194813,0.0002705678,0.0005112195,0.000244678,0.0002093971,0.00006538737,0.0011286789,0.00038357507,0.0000048895204],"category_scores_gemma":[0.0002551068,0.00026663754,0.000056748042,0.001220427,0.00037881394,0.00010733075,0.00011505281,0.0007357125,0.00003786998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016064911,0.00044677051,0.0039679017,0.0004137935,0.00028700742,0.00014778938,0.0023908282,0.16722879,0.057914298,0.737721,0.0007802165,0.02854094],"study_design_scores_gemma":[0.00078546786,0.0002588692,0.00027765203,0.00015080502,0.00003945433,0.0000151976365,0.00013487226,0.97491384,0.0013239294,0.021744868,0.00009011119,0.000264936],"about_ca_topic_score_codex":0.0000027157898,"about_ca_topic_score_gemma":0.0000010675922,"teacher_disagreement_score":0.807685,"about_ca_system_score_codex":0.000030480687,"about_ca_system_score_gemma":0.00023500175,"threshold_uncertainty_score":0.9999786},"labels":[],"label_agreement":null},{"id":"W3090470622","doi":"10.1109/icra40945.2020.9197040","title":"Learning User Preferences from Corrections on State Lattices","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robot; Computer science; Motion planning; Mobile robot; Human–computer interaction; Task (project management); Set (abstract data type); Planner; Path (computing); Motion (physics); Artificial intelligence; Range (aeronautics); Engineering","score_opus":0.04571493813754668,"score_gpt":0.25706023545793183,"score_spread":0.21134529732038515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090470622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09158611,0.000019215964,0.8941115,0.0017641431,0.0005736197,0.00007306769,0.0000029728842,0.00075665326,0.011112675],"genre_scores_gemma":[0.69067705,0.000008864371,0.30484197,0.0014931516,0.00012922044,0.000007761163,0.0000059274657,0.0000096132735,0.0028264264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908686,0.000076232405,0.00012243984,0.00033830456,0.00020835172,0.00016780212],"domain_scores_gemma":[0.99940807,0.00021862474,0.000056395602,0.00017114746,0.000029548959,0.00011622931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006645617,0.0000950105,0.00010562668,0.00003136451,0.00012143758,0.00018688616,0.0004783087,0.000026771795,0.000069725436],"category_scores_gemma":[0.000100947706,0.00007793218,0.00002514861,0.00023653226,0.000016443813,0.00028611268,0.00011534943,0.00021811618,0.0007041422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003594016,0.00019364274,0.12586097,0.00001976603,0.00019051034,0.00012656012,0.036106136,0.56725585,0.0010654805,0.006525619,0.01711826,0.24550126],"study_design_scores_gemma":[0.0001698079,0.00033389937,0.023899721,0.000022221242,0.0000049207556,7.771351e-7,0.00020692618,0.96825194,0.0010848148,0.0005482288,0.0052832547,0.00019346112],"about_ca_topic_score_codex":0.0001937056,"about_ca_topic_score_gemma":0.000005341702,"teacher_disagreement_score":0.59909093,"about_ca_system_score_codex":0.000010027622,"about_ca_system_score_gemma":0.000031040454,"threshold_uncertainty_score":0.9050558},"labels":[],"label_agreement":null},{"id":"W3090901509","doi":"","title":"BIT*: Sampling-based optimal planning via Batch Informed Trees","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristic; Computer science; Sampling (signal processing); Bit (key); Mathematical optimization; Fast marching method; Tree (set theory); Work (physics); Algorithm; Artificial intelligence; Mathematics; Engineering; Computer vision","score_opus":0.034220385048466126,"score_gpt":0.2903183676406001,"score_spread":0.25609798259213395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090901509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005909036,0.000015841657,0.98626494,0.00059794215,0.00044214554,0.00009883866,7.546095e-7,0.00067271886,0.005997754],"genre_scores_gemma":[0.26047838,2.1264432e-7,0.7383414,0.0008088972,0.00010157802,0.000010226482,0.0000069243524,0.000011286233,0.00024112216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998335,0.000052285868,0.00031024124,0.00040552177,0.0003903215,0.0005066391],"domain_scores_gemma":[0.9984083,0.00056262536,0.00010636335,0.0006716563,0.00006638353,0.00018466984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044790324,0.0002113201,0.00023232936,0.00018653958,0.00016281866,0.00021843937,0.0010505888,0.00009662237,0.00002234084],"category_scores_gemma":[0.00018716394,0.00018128856,0.000071113835,0.00037317094,0.000046839916,0.00042057675,0.00017304129,0.0001790177,0.00020829424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013673638,0.00007494958,0.007779922,0.000025928783,0.000027397398,0.000024507188,0.001046531,0.9175833,0.0005261641,0.0041487818,0.0028512091,0.065897666],"study_design_scores_gemma":[0.00048857345,0.00015850457,0.009157416,0.000041897325,0.000003991529,0.000015759333,0.000015497126,0.98482895,0.0010357529,0.00022557376,0.0037544922,0.00027358465],"about_ca_topic_score_codex":0.000048516693,"about_ca_topic_score_gemma":0.0000025845075,"teacher_disagreement_score":0.25456932,"about_ca_system_score_codex":0.00004332738,"about_ca_system_score_gemma":0.00012437152,"threshold_uncertainty_score":0.7392733},"labels":[],"label_agreement":null},{"id":"W3091663946","doi":"10.1109/icra40945.2020.9196836","title":"Time Optimal Motion Planning with ZMP Stability Constraint for Timber Manipulation","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Constraint (computer-aided design); Computer science; Motion planning; Stability (learning theory); Motion (physics); Control theory (sociology); Artificial intelligence; Robot; Engineering; Control (management)","score_opus":0.06367583330497567,"score_gpt":0.26371737253901,"score_spread":0.20004153923403434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091663946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020300824,0.0000046360688,0.97598577,0.0013975501,0.00005518297,0.0003359212,0.000004258811,0.00034571902,0.0015701207],"genre_scores_gemma":[0.33624223,3.3409663e-8,0.66338396,0.00023901573,0.000050674666,0.00001194351,0.00001518217,0.0000068118748,0.0000501225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895686,0.00003451066,0.00018218736,0.00039929355,0.00020450425,0.00022267256],"domain_scores_gemma":[0.99939555,0.000111519774,0.000071944996,0.00021698354,0.00008551144,0.000118464246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022494893,0.000121540084,0.00014367631,0.000025940195,0.00008517303,0.00009090716,0.00025453945,0.00004799045,0.000043112243],"category_scores_gemma":[0.000055248893,0.0001002507,0.000033388158,0.00015700761,0.00004815187,0.00036356063,0.00006249451,0.00008395322,0.00006255753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000793111,0.000078468554,0.004804666,0.0000610366,0.000049721373,0.00003571618,0.0044291415,0.9739116,0.0014979802,0.005254314,0.0014635344,0.008334498],"study_design_scores_gemma":[0.00039021854,0.00021722134,0.0025379716,0.0000128190295,0.000006216959,0.000015974023,0.00007566851,0.9955774,0.00087067985,0.000074825475,0.000075861215,0.00014514025],"about_ca_topic_score_codex":0.0000036056233,"about_ca_topic_score_gemma":7.8812946e-8,"teacher_disagreement_score":0.31594142,"about_ca_system_score_codex":0.000035325436,"about_ca_system_score_gemma":0.000047714308,"threshold_uncertainty_score":0.4088105},"labels":[],"label_agreement":null},{"id":"W3091964200","doi":"10.1109/case48305.2020.9217044","title":"On-road Trajectory Planning with Spatio-temporal RRT* and Always-feasible Quadratic Program","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trajectory; Mathematical optimization; Computer science; Quadratic programming; Sampling (signal processing); Flexibility (engineering); Randomness; Curse of dimensionality; Benchmark (surveying); Decomposition; Motion planning; Planner; Quadratic equation; Set (abstract data type); Trajectory optimization; Mathematics; Artificial intelligence; Robot; Detector","score_opus":0.0413408031985799,"score_gpt":0.27413423676519316,"score_spread":0.23279343356661325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091964200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2034952,0.00015556031,0.78250366,0.0029330933,0.00019280946,0.0008161055,0.0000024885583,0.0016747271,0.008226369],"genre_scores_gemma":[0.5841685,7.0336336e-7,0.41470483,0.0009148206,0.00005665347,0.000030841245,0.000006252331,0.000014477475,0.00010292076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843943,0.00006290371,0.00020786772,0.0005605296,0.00038032103,0.00034895027],"domain_scores_gemma":[0.9991725,0.00010075393,0.000083254796,0.00032142314,0.000039283954,0.00028274645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014561159,0.00021971334,0.00023824812,0.000074636126,0.00013230831,0.00025484458,0.0004472672,0.000059162714,0.000012906485],"category_scores_gemma":[0.00004572682,0.00016808898,0.000028726034,0.00039963113,0.000065197484,0.00036305125,0.000096340045,0.00021981864,0.000049865586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083747105,0.0017356629,0.22395766,0.000805659,0.000594316,0.004369975,0.054246012,0.15271822,0.00043507264,0.04703096,0.036871802,0.4763972],"study_design_scores_gemma":[0.0009602414,0.0029010857,0.015955508,0.00010999633,0.0000120963905,0.000059081274,0.00016113577,0.97841513,0.00022589706,0.00022056272,0.0005437759,0.00043550736],"about_ca_topic_score_codex":0.00005407426,"about_ca_topic_score_gemma":0.000003992752,"teacher_disagreement_score":0.8256969,"about_ca_system_score_codex":0.000022302725,"about_ca_system_score_gemma":0.0001220973,"threshold_uncertainty_score":0.685447},"labels":[],"label_agreement":null},{"id":"W3092028814","doi":"10.1139/juvs-2019-0022","title":"Point de référence pour la planification de trajectoires d’UAV à voilure fixe","year":2020,"lang":"fr","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Humanities; Political science; Philosophy","score_opus":0.03757363995305624,"score_gpt":0.2606333425055771,"score_spread":0.2230597025525209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092028814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07802007,0.009885245,0.83974767,0.06853659,0.0025948915,0.00028938788,0.000031148313,0.00010833534,0.00078667793],"genre_scores_gemma":[0.9359443,0.00019311998,0.059315126,0.0006154777,0.0024397029,0.00000592941,0.0000018755,0.000043267843,0.0014411627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556214,0.0013198911,0.0012046326,0.00040060698,0.0007671528,0.0007455611],"domain_scores_gemma":[0.9963997,0.00065369316,0.0013152893,0.0003851163,0.00049827225,0.00074790115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025295753,0.00033498398,0.00063737796,0.00016755059,0.00018465269,0.0006248361,0.0014148793,0.00034269964,0.000014049108],"category_scores_gemma":[0.000703653,0.00032892823,0.00024612842,0.00059292867,0.00010842784,0.0009479674,0.0001139573,0.000969861,0.00012677918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038882866,0.0009591029,0.013617704,0.002334578,0.00081876497,0.011227285,0.07243191,0.6245554,0.07672954,0.036329184,0.12934004,0.031267628],"study_design_scores_gemma":[0.0012313825,0.0006944619,0.010701836,0.0019807494,0.000100289995,0.00722179,0.0015798329,0.95959485,0.0017654005,0.00050815055,0.014223306,0.0003979464],"about_ca_topic_score_codex":0.00031090103,"about_ca_topic_score_gemma":0.0000030511872,"teacher_disagreement_score":0.8579243,"about_ca_system_score_codex":0.00035713258,"about_ca_system_score_gemma":0.0012198248,"threshold_uncertainty_score":0.99991626},"labels":[],"label_agreement":null},{"id":"W3092171610","doi":"10.1109/case48305.2020.9216998","title":"Aerial Coverage Planning for Areas Hidden from the View of a Moving Ground Vehicle","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scalability; Computer science; Motion planning; Integer programming; Linear programming; Real-time computing; Orienteering; Bounded function; Mathematical optimization; Set (abstract data type); Path (computing); Integer (computer science); Planner; Route planning; Algorithm; Robot; Mathematics; Artificial intelligence; Computer network","score_opus":0.047712862652593724,"score_gpt":0.2671537111170245,"score_spread":0.2194408484644308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092171610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052560546,0.00025260504,0.94292635,0.002744643,0.00035502441,0.0002538578,0.000018168521,0.000132037,0.00075674854],"genre_scores_gemma":[0.7336768,0.0000037902803,0.26274103,0.0029159614,0.00055619964,0.0000212807,0.0000107647,0.000014770908,0.00005940674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989784,0.000055972454,0.0002361653,0.00029842253,0.00022258492,0.00020847589],"domain_scores_gemma":[0.99876803,0.0006773831,0.00010850437,0.00032408437,0.000042523985,0.00007948398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019315013,0.00011169632,0.00020455322,0.000012934026,0.000105579595,0.00013107112,0.00091920624,0.000041211104,0.000013482865],"category_scores_gemma":[0.00017696319,0.000079493155,0.00006737024,0.00017769463,0.000026495292,0.0002515717,0.00024744213,0.00009199419,0.000020361667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067026156,0.00048234322,0.042136192,0.00052625086,0.0011289788,0.00041973492,0.13565287,0.06304754,0.049750887,0.104050666,0.08044637,0.5216879],"study_design_scores_gemma":[0.0011466735,0.00019979815,0.013827889,0.00011554618,0.00002264065,0.0000053624967,0.00020803594,0.9752672,0.0017365426,0.003590203,0.0036206604,0.000259464],"about_ca_topic_score_codex":0.00018877944,"about_ca_topic_score_gemma":9.111539e-7,"teacher_disagreement_score":0.91221964,"about_ca_system_score_codex":0.000014641265,"about_ca_system_score_gemma":0.000060328828,"threshold_uncertainty_score":0.32416368},"labels":[],"label_agreement":null},{"id":"W3092643410","doi":"10.1109/icuas48674.2020.9213911","title":"UAS Flight Path Planning for Dynamic, Multi-Vehicle Environment","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Concordia University","funders":"","keywords":"Autopilot; Motion planning; Computer science; Patrolling; Path (computing); Real-time computing; Markov decision process; Kalman filter; Simulation; Artificial intelligence; Algorithm; Markov process; Computer vision; Aerospace engineering; Robot; Engineering; Mathematics; Geography","score_opus":0.0442436449413525,"score_gpt":0.2665562727484088,"score_spread":0.22231262780705632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092643410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010111574,0.00012059941,0.99296063,0.004860112,0.0001576393,0.00027192812,0.0000064244214,0.00032994858,0.00028154676],"genre_scores_gemma":[0.065930955,0.0000035486214,0.93115795,0.002248428,0.000060021186,0.000034808127,0.00000839075,0.00001611417,0.00053981156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866474,0.000028290557,0.00021502348,0.0005126218,0.00022326571,0.00035606005],"domain_scores_gemma":[0.99927026,0.000100225,0.000067060464,0.00034090184,0.00001467755,0.00020686247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014462076,0.00015880581,0.00017267314,0.000030473208,0.00012676064,0.00009060821,0.0007110892,0.000058495563,0.000015742959],"category_scores_gemma":[0.000039412243,0.00014161906,0.00006763363,0.00010439851,0.00002242206,0.0002332668,0.00024276366,0.0001079962,0.00021219676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012970793,0.0012515226,0.01377826,0.00038666857,0.0004533362,0.0010818287,0.04369509,0.5463287,0.071194775,0.02400215,0.07091654,0.2267814],"study_design_scores_gemma":[0.00058056944,0.00013452415,0.001972455,0.000011165963,0.0000047207495,0.0000047948806,0.000042225238,0.987729,0.0011057204,0.00008677368,0.008123467,0.00020455473],"about_ca_topic_score_codex":0.0000055366713,"about_ca_topic_score_gemma":6.7065336e-8,"teacher_disagreement_score":0.44140032,"about_ca_system_score_codex":0.000043891414,"about_ca_system_score_gemma":0.000025510348,"threshold_uncertainty_score":0.5775058},"labels":[],"label_agreement":null},{"id":"W3093297645","doi":"10.3390/su12208541","title":"Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Battery (electricity); Context (archaeology); State of charge; Trajectory; Path (computing); Automotive engineering; Energy (signal processing); Motion planning; Simulation; Engineering; Computer science; Transport engineering; Power (physics); Artificial intelligence; Robot; Mathematics","score_opus":0.053239456873622346,"score_gpt":0.2864486320426742,"score_spread":0.23320917516905182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093297645","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9690853,0.000053138294,0.013589276,0.016247015,0.00005625322,0.00047359598,0.0000038114165,0.00011772525,0.00037388442],"genre_scores_gemma":[0.9983147,3.8245832e-7,0.00057175127,0.00097390445,0.00007275043,0.00004107366,7.1918726e-7,0.0000078963885,0.000016849715],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972674,0.0011263037,0.00041856302,0.00034651542,0.00044627127,0.00039490275],"domain_scores_gemma":[0.9972699,0.0011634621,0.00023368307,0.0009702123,0.0002759177,0.00008682359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025391022,0.00018675387,0.0002277885,0.00003361446,0.00028969327,0.000120790566,0.0019255746,0.00012016589,0.0000016110208],"category_scores_gemma":[0.0016714513,0.00008532137,0.0001085731,0.0009372449,0.00024647277,0.00019017371,0.00030055875,0.00074282475,0.0000023537393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035295878,0.00031134355,0.43009457,0.00057968934,0.00013661868,0.00005241172,0.12507734,0.31025162,0.000087938635,0.10262635,0.00557744,0.02516937],"study_design_scores_gemma":[0.00027213528,0.00021396736,0.16292788,0.000051967276,0.0000062434374,0.000014868447,0.013321062,0.8216832,0.0002584708,0.0009958023,0.00011840285,0.00013599596],"about_ca_topic_score_codex":0.0021455218,"about_ca_topic_score_gemma":0.0000055669548,"teacher_disagreement_score":0.5114316,"about_ca_system_score_codex":0.00061982765,"about_ca_system_score_gemma":0.0009845103,"threshold_uncertainty_score":0.35782287},"labels":[],"label_agreement":null},{"id":"W3093947831","doi":"10.1109/cog47356.2020.9231637","title":"Evolving Initial Heuristic Functions for Agent-Centered Heuristic Search","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Games (CoG)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Incremental heuristic search; Pathfinding; Computer science; Heuristic; Beam search; Artificial intelligence; Hyper-heuristic; Context (archaeology); Domain (mathematical analysis); Class (philosophy); Consistent heuristic; Mathematical optimization; Theoretical computer science; Machine learning; Graph; Search algorithm; Mathematics; Algorithm; Robot; Mobile robot","score_opus":0.13136062393839582,"score_gpt":0.32366120807018145,"score_spread":0.19230058413178563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093947831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00151626,0.000102675316,0.9882207,0.0042743986,0.0019890012,0.0007729955,0.00011580538,0.00049277075,0.002515412],"genre_scores_gemma":[0.9584839,0.000017355604,0.038016513,0.0017061657,0.00074609445,0.00015164532,0.000049556893,0.00004446178,0.000784348],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694496,0.00018969495,0.0004847468,0.0010564894,0.0005803914,0.00074373884],"domain_scores_gemma":[0.9978253,0.00047833426,0.00015258629,0.0006831264,0.00035730674,0.00050334795],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026088316,0.00037038405,0.00044269362,0.00012391548,0.00028797425,0.00051864993,0.0013577781,0.00012812593,0.0001400664],"category_scores_gemma":[0.000679327,0.0003778687,0.00017163114,0.00052667374,0.00011413137,0.0003303787,0.00022936426,0.00048835995,0.000878707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012909243,0.0021831666,0.0029006023,0.002070504,0.0017309663,0.003141707,0.027406454,0.05348403,0.021815589,0.0516913,0.48821464,0.3440701],"study_design_scores_gemma":[0.0011157792,0.00091526867,0.0012194164,0.00016464185,0.0000699499,0.000025562671,0.00015292379,0.9928009,0.00043821044,0.0006592986,0.001937994,0.0005000567],"about_ca_topic_score_codex":0.000019883812,"about_ca_topic_score_gemma":0.0000021027329,"teacher_disagreement_score":0.9569676,"about_ca_system_score_codex":0.000061329025,"about_ca_system_score_gemma":0.00039721324,"threshold_uncertainty_score":0.9998992},"labels":[],"label_agreement":null},{"id":"W3095136631","doi":"10.1007/978-3-030-61746-2_5","title":"Geocaching-Inspired Navigation for Micro Aerial Vehicles with Fallible Place Recognition","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Global Positioning System; A priori and a posteriori; Artificial intelligence; Trajectory; Swarm behaviour; Computer vision; Machine learning; Human–computer interaction","score_opus":0.0253881843898928,"score_gpt":0.24211193241087658,"score_spread":0.2167237480209838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095136631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044285445,0.000116962714,0.995187,0.0011945915,0.0015113397,0.0008348062,0.00003160775,0.0003363595,0.0003445001],"genre_scores_gemma":[0.011669891,0.000005840748,0.9861988,0.0010274395,0.0008486815,0.00003299556,0.00008531396,0.000051262927,0.000079773985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635804,0.000042581665,0.00047746795,0.0016899235,0.00082154665,0.00061044376],"domain_scores_gemma":[0.9977608,0.0005781422,0.0004051454,0.0007683361,0.00029115216,0.00019647437],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066829135,0.0005416823,0.0005351742,0.0004001157,0.000354891,0.000692015,0.0021255012,0.00031881614,0.0000021316348],"category_scores_gemma":[0.0001066525,0.0004939924,0.00010625202,0.000512452,0.00037007302,0.0007396623,0.00048707429,0.0006747471,0.00003743184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001154395,0.000052779866,0.00004625144,0.00019548858,0.000044213783,0.00020513855,0.0020262736,0.15598674,0.0024111327,0.0020879263,0.00010918565,0.83671945],"study_design_scores_gemma":[0.0009598308,0.0007633179,0.00004388048,0.0010626748,0.000023246494,0.00014090413,3.229721e-7,0.9374961,0.006852712,0.050959896,0.0007782654,0.0009188428],"about_ca_topic_score_codex":0.000025352037,"about_ca_topic_score_gemma":0.000010552881,"teacher_disagreement_score":0.8358006,"about_ca_system_score_codex":0.00026331845,"about_ca_system_score_gemma":0.0006686711,"threshold_uncertainty_score":0.99975115},"labels":[],"label_agreement":null},{"id":"W3095424771","doi":"10.11159/cdsr20.134","title":"Trajectory Planning for a Human-Robot Interaction Rehabilitation Systemusing Direct-Collocation Optimization","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collocation (remote sensing); Trajectory; Human–robot interaction; Computer science; Robot; Motion planning; Human–computer interaction; Artificial intelligence; Machine learning; Physics","score_opus":0.04052444292335731,"score_gpt":0.28282047651626274,"score_spread":0.24229603359290544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095424771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009797555,0.00013599922,0.98531145,0.0022226842,0.001009024,0.00081410434,0.00001675242,0.000071325434,0.0006211322],"genre_scores_gemma":[0.9272032,0.0000059079375,0.07256313,0.00004279991,0.000084157116,0.00004111633,0.000007001846,0.000012443441,0.000040272655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983666,0.00003250017,0.0007003132,0.00033097184,0.00042346373,0.00014614797],"domain_scores_gemma":[0.9971849,0.00030466125,0.0009876312,0.000109151784,0.001357605,0.000056064295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051857723,0.00016612011,0.00037319225,0.00014052264,0.00013286118,0.00019531208,0.0006821467,0.00008876919,6.230226e-7],"category_scores_gemma":[0.00063930015,0.00014142654,0.000087577355,0.00018158778,0.00007420089,0.0006168292,0.00009177017,0.00011219843,2.4140223e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006215684,0.00003415607,0.001815846,0.00058913324,0.00010209049,1.5437473e-7,0.0020775576,0.94728047,0.018532898,0.029219879,0.000049898907,0.00023578326],"study_design_scores_gemma":[0.00076290383,0.00019327374,0.00089792773,0.0009121752,0.000051018138,0.000010009514,0.0009680518,0.99561495,0.00017782945,0.00026707508,0.000013434774,0.00013133508],"about_ca_topic_score_codex":0.000032849253,"about_ca_topic_score_gemma":8.105556e-7,"teacher_disagreement_score":0.9174056,"about_ca_system_score_codex":0.000106893945,"about_ca_system_score_gemma":0.00007919081,"threshold_uncertainty_score":0.5767207},"labels":[],"label_agreement":null},{"id":"W3096082393","doi":"10.1115/detc2020-22297","title":"Task Taxonomy for Autonomous Unmanned Aerial Manipulator: A Review","year":2020,"lang":"en","type":"review","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Taxonomy (biology); Task (project management); Categorization; Artificial intelligence; Robot; Human–computer interaction; Robotics; Abstraction; Systems engineering; Engineering","score_opus":0.1304793483815822,"score_gpt":0.33705477554546387,"score_spread":0.20657542716388166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096082393","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.0343404e-11,0.6080783,0.38515702,0.00020474002,0.0012744383,0.0031052704,0.000037017417,0.00042280738,0.0017203942],"genre_scores_gemma":[1.7834855e-9,0.67599034,0.319853,0.0006578371,0.0005309764,0.0015122679,0.00008002306,0.000046251345,0.0013292636],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962097,0.00020639175,0.0012443656,0.0013638932,0.00032779662,0.00064785936],"domain_scores_gemma":[0.99708277,0.0003266968,0.00072025013,0.001455477,0.000084870015,0.00032991782],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00054386345,0.0007774284,0.0033606023,0.00014361755,0.000121833524,0.00023898232,0.003084894,0.0003171453,0.000055860935],"category_scores_gemma":[0.00020052314,0.0006264991,0.0010630812,0.00063995,0.000033504755,0.00025463893,0.00065010815,0.0003948636,0.0015799335],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.881198e-7,0.000015757058,1.215907e-8,0.031214325,0.00010027067,0.000045420038,0.000006551677,0.000002134319,9.081128e-9,0.003420211,0.09321866,0.87197614],"study_design_scores_gemma":[0.00016973513,0.00009874182,2.1070125e-8,0.024892645,0.00045370596,0.00017124297,3.8939203e-7,0.00449275,1.0308301e-7,0.000047077036,0.96899164,0.00068195345],"about_ca_topic_score_codex":0.000007724772,"about_ca_topic_score_gemma":2.4996942e-7,"teacher_disagreement_score":0.87577295,"about_ca_system_score_codex":0.00023780066,"about_ca_system_score_gemma":0.0011087114,"threshold_uncertainty_score":0.99961865},"labels":[],"label_agreement":null},{"id":"W3098729201","doi":"10.1155/2020/8867937","title":"2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mobile robot; Lidar; Motion planning; Computer science; Path (computing); Robot; Artificial intelligence; Rescue robot; Real-time computing; Computer vision; Simulation; Remote sensing; Geography; Computer network","score_opus":0.03069680157505414,"score_gpt":0.29213547781762184,"score_spread":0.26143867624256767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098729201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3216395,0.00023978428,0.6775369,0.00020409118,0.00019558288,0.00015213467,0.000006051766,0.000024177609,0.0000017590266],"genre_scores_gemma":[0.476234,0.0000048133384,0.52352095,0.00013987545,0.0000814908,0.000003941375,0.0000051895636,0.000008805029,9.672401e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988575,0.00002610687,0.00047051566,0.00020195374,0.0002645418,0.0001793679],"domain_scores_gemma":[0.9990433,0.00014198938,0.0003778321,0.00009570425,0.00019535753,0.00014580754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020935725,0.00012548071,0.00024967975,0.00009428173,0.0000901825,0.0000514698,0.00022183303,0.000052693766,6.8334555e-7],"category_scores_gemma":[0.00006783702,0.00011823547,0.000078166406,0.00022292873,0.000019174478,0.0007294724,0.0000035023702,0.00017381317,2.6347712e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010381221,0.000020847,0.0015313954,0.000051425817,0.000014555994,0.00008621954,0.0030241893,0.9774322,0.010266251,0.000055829736,0.000011561844,0.00740173],"study_design_scores_gemma":[0.0051485184,0.002119319,0.06766178,0.00081221055,0.000102972284,0.000055572087,0.0007159338,0.9102345,0.011699819,0.0005802846,0.00042206858,0.00044701144],"about_ca_topic_score_codex":0.0000016072311,"about_ca_topic_score_gemma":2.992503e-7,"teacher_disagreement_score":0.1545945,"about_ca_system_score_codex":0.000028203669,"about_ca_system_score_gemma":0.000121056815,"threshold_uncertainty_score":0.48215026},"labels":[],"label_agreement":null},{"id":"W3103346840","doi":"","title":"Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":417,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bit (key); Computer science; Sampling (signal processing); Random graph; Algorithm; Theoretical computer science; Artificial intelligence; Graph; Computer vision","score_opus":0.08597512149257881,"score_gpt":0.3449501781571991,"score_spread":0.2589750566646203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103346840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011556218,0.00034925237,0.98226905,0.00064196525,0.00091453904,0.0010705825,0.000043882745,0.00043448203,0.0027200521],"genre_scores_gemma":[0.28379625,0.000012314399,0.71530104,0.0003134506,0.0001532721,0.00011138728,0.00010535829,0.00005140928,0.00015553794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99387795,0.00035511094,0.0015485623,0.0011496999,0.0019735561,0.0010951508],"domain_scores_gemma":[0.99147457,0.0036894884,0.0006439844,0.0026274235,0.0011220553,0.00044245226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003894864,0.00071608095,0.0012683873,0.0014419393,0.00024398869,0.00051316,0.005337812,0.00052795134,0.000025859228],"category_scores_gemma":[0.0018387962,0.0005007166,0.000440552,0.0022290654,0.00031796098,0.00022012874,0.003018928,0.0014660504,0.000037279086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011101187,0.000106778105,0.0020677855,0.00016510165,0.00018276255,0.000043813878,0.00082327035,0.98540956,0.00005680954,0.00079446274,0.004314506,0.005924162],"study_design_scores_gemma":[0.002580882,0.00025260332,0.00843444,0.00028425446,0.00007025959,0.000052484003,0.00005982564,0.98379344,0.0005558972,0.0031261588,0.000120622295,0.0006691614],"about_ca_topic_score_codex":0.0007949866,"about_ca_topic_score_gemma":0.0000055904175,"teacher_disagreement_score":0.27224004,"about_ca_system_score_codex":0.00020013312,"about_ca_system_score_gemma":0.002008501,"threshold_uncertainty_score":0.9997445},"labels":[],"label_agreement":null},{"id":"W3107693114","doi":"10.1109/lra.2021.3062338","title":"Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Computer science; Visibility; Probabilistic roadmap; Artificial intelligence; Motion planning; Nonholonomic system; Feature (linguistics); Mathematical optimization; Modular design; Trajectory; Robot; Mobile robot; Computer vision; Mathematics","score_opus":0.012754305684298407,"score_gpt":0.2675171638974879,"score_spread":0.2547628582131895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107693114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029729811,0.0000627974,0.95453924,0.012574926,0.0018941974,0.0008933766,0.000013074986,0.00028872959,0.000003846756],"genre_scores_gemma":[0.2631767,0.0000074453096,0.73593986,0.00043727222,0.00008088604,0.000102747705,0.00021136343,0.000029413864,0.000014279033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973456,0.00015587526,0.00059801136,0.0009708798,0.00040786143,0.0005217702],"domain_scores_gemma":[0.99847287,0.00028302064,0.0003897518,0.0006165371,0.00011042931,0.00012736738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039379153,0.00043091466,0.00055016036,0.00038833037,0.00015508481,0.00091009535,0.0005753283,0.0002965819,0.0000011962283],"category_scores_gemma":[0.00007231485,0.00045752144,0.00016585822,0.0002826383,0.00008531447,0.00023843722,0.0002769973,0.00048711526,0.0000014237789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043138525,0.000086668806,0.000072786184,0.00029960962,0.0000293563,0.000041993175,0.0003750678,0.977564,0.002513965,0.00020548384,0.00008291426,0.018723866],"study_design_scores_gemma":[0.00091390085,0.000056604003,0.0026926438,0.00084341306,0.000031475378,0.000009956967,0.00002068708,0.99464107,0.00012976548,0.0001678875,0.000017112223,0.00047546296],"about_ca_topic_score_codex":0.00005586894,"about_ca_topic_score_gemma":0.000014925854,"teacher_disagreement_score":0.23344691,"about_ca_system_score_codex":0.00020032258,"about_ca_system_score_gemma":0.0002516194,"threshold_uncertainty_score":0.9997876},"labels":[],"label_agreement":null},{"id":"W3107835697","doi":"10.1109/ccece47787.2020.9255754","title":"Navigation and Obstacle Avoidance System in Unknown Environment","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Western University; Qatar University","keywords":"Obstacle avoidance; Drone; Obstacle; Computer science; Smoothing; Sonar; Real-time computing; Collision avoidance; Artificial intelligence; Computer vision; Mobile robot; Computer security; Geography","score_opus":0.016027482480295768,"score_gpt":0.19989441877722514,"score_spread":0.18386693629692938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107835697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050385986,0.00015408175,0.946724,0.0013050789,0.00007776517,0.00011351155,5.2699033e-7,0.00014875198,0.0010903307],"genre_scores_gemma":[0.81808114,0.0000028208701,0.1816402,0.00018457102,0.000020361356,0.000005818982,9.182317e-7,0.0000030620347,0.00006109509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993342,0.00003434729,0.00012580887,0.00025177945,0.00013321654,0.00012065614],"domain_scores_gemma":[0.9997086,0.00003420425,0.0000305187,0.00014723225,0.000004202481,0.0000752422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010907149,0.00006263677,0.0000837101,0.000016334767,0.00002883458,0.0000440369,0.00019871048,0.000024655339,0.0000015067575],"category_scores_gemma":[0.000009114835,0.00005823145,0.000008407744,0.00011292345,0.000014079709,0.00018752883,0.000108338,0.00006960599,0.00007247391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027067485,0.00023864061,0.068604626,0.00091181276,0.000053037038,0.0014102553,0.03560459,0.21866634,0.016879996,0.3450902,0.0024279226,0.3100855],"study_design_scores_gemma":[0.00019762666,0.000035535282,0.009376867,0.000039065933,8.358382e-7,0.000015786109,0.0000813673,0.98868275,0.0007322985,0.000058752088,0.0006885702,0.0000905762],"about_ca_topic_score_codex":0.000016086553,"about_ca_topic_score_gemma":1.340556e-7,"teacher_disagreement_score":0.7700164,"about_ca_system_score_codex":0.00004247841,"about_ca_system_score_gemma":0.000009457069,"threshold_uncertainty_score":0.23746096},"labels":[],"label_agreement":null},{"id":"W3109084730","doi":"10.1109/icds50568.2020.9268775","title":"Parallel Algorithm for the Path Planning of Multiple Unmanned Aerial Vehicles","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Motion planning; Graphics processing unit; Set (abstract data type); Context (archaeology); Point of interest; Path (computing); Multi-core processor; Algorithm; Speedup; Graphics; Real-time computing; Parallel computing; Artificial intelligence; Computer graphics (images); Robot","score_opus":0.04662408729808612,"score_gpt":0.2696807814989571,"score_spread":0.22305669420087096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109084730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006959373,0.00014992709,0.9956244,0.0024617312,0.00039498426,0.0003429143,0.0000138854375,0.00018568152,0.00013052486],"genre_scores_gemma":[0.081255354,0.0000033189683,0.9175902,0.00075369666,0.0002846943,0.000036072124,0.0000049051105,0.000011751628,0.00005998878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988094,0.00004270309,0.00027991293,0.00032493004,0.00025216874,0.0002909098],"domain_scores_gemma":[0.99862003,0.0007380614,0.00012339216,0.00034293777,0.00007375703,0.000101811434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025538797,0.00013944866,0.00021419875,0.000026471444,0.00014380662,0.00007065218,0.0010515683,0.00005650893,0.0000035325438],"category_scores_gemma":[0.00017536314,0.000094435636,0.000093885115,0.00021748965,0.00005115504,0.00018014018,0.00021749087,0.00010170471,0.000012282488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019870754,0.0002605452,0.0037546565,0.00013219612,0.0004022076,0.00009809632,0.019689605,0.27953345,0.005568243,0.014302358,0.05447733,0.6215826],"study_design_scores_gemma":[0.00090426556,0.00017472706,0.0010438937,0.000015108921,0.000008820057,0.000004028916,0.00015569854,0.9938633,0.0016885158,0.00025162363,0.0017586214,0.00013140556],"about_ca_topic_score_codex":0.000032137752,"about_ca_topic_score_gemma":1.8985374e-7,"teacher_disagreement_score":0.71432984,"about_ca_system_score_codex":0.0000094294355,"about_ca_system_score_gemma":0.000056839857,"threshold_uncertainty_score":0.38509735},"labels":[],"label_agreement":null},{"id":"W3110248795","doi":"10.1109/auv50043.2020.9267929","title":"A Hybrid Path Planning Strategy of Autonomous Underwater Vehicles","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Motion planning; Underwater; Computer science; Path (computing); Mobile robot; Marine engineering; Computer network; Robot; Engineering; Artificial intelligence; Geology","score_opus":0.04599856478629069,"score_gpt":0.25929260038507185,"score_spread":0.21329403559878116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110248795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045496993,0.00007258166,0.94916713,0.0011369777,0.00008756,0.00008019744,0.0000029193366,0.00031944158,0.0036361953],"genre_scores_gemma":[0.8111375,7.248417e-7,0.1881854,0.0005241579,0.00004063267,0.0000021933486,0.000002076181,0.000007204896,0.000100093835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892426,0.00004017707,0.0002495594,0.00032145946,0.00021422483,0.00025031847],"domain_scores_gemma":[0.9994142,0.00006173086,0.00008228287,0.00026822468,0.000037742924,0.00013579511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116677664,0.00012250205,0.00019188918,0.000041891166,0.000043155404,0.000074620795,0.0006613345,0.00003108244,0.000014791114],"category_scores_gemma":[0.000018697718,0.000103105805,0.000046542962,0.00014399676,0.000032377113,0.00025106157,0.00018912605,0.000115546565,0.0000654453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059050137,0.0003925366,0.012418614,0.00035174465,0.00031593585,0.0027755466,0.019340754,0.6354323,0.039420504,0.059286155,0.0320435,0.19816336],"study_design_scores_gemma":[0.00028268844,0.00020243584,0.0028468417,0.000026025342,0.0000047660546,0.000033210683,0.00008423728,0.9784364,0.016087402,0.0014358505,0.00036631056,0.0001937958],"about_ca_topic_score_codex":0.000026576086,"about_ca_topic_score_gemma":4.2083432e-8,"teacher_disagreement_score":0.7656405,"about_ca_system_score_codex":0.000014413481,"about_ca_system_score_gemma":0.00009011286,"threshold_uncertainty_score":0.42045328},"labels":[],"label_agreement":null},{"id":"W3110627065","doi":"10.1155/2020/6649867","title":"Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hefei University of Technology; Hefei University; National Natural Science Foundation of China","keywords":"Motion planning; Nonholonomic system; Smoothness; Path (computing); Computer science; Obstacle avoidance; Mathematical optimization; Any-angle path planning; Simulation; Control theory (sociology); Artificial intelligence; Robot; Mathematics; Mobile robot; Control (management)","score_opus":0.049092394180622084,"score_gpt":0.2873262511091034,"score_spread":0.23823385692848134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110627065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099318944,0.000046222216,0.8994584,0.0008332394,0.000083307554,0.00019506237,0.000012434026,0.000032810512,0.000019557783],"genre_scores_gemma":[0.57599974,0.0000010964261,0.42354655,0.00038729954,0.00003454518,0.0000067046003,0.000013280592,0.000009612175,0.0000011867809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851835,0.00003074694,0.00059830176,0.0002570671,0.00035736413,0.00023814898],"domain_scores_gemma":[0.99911046,0.00013247642,0.00043786538,0.00013439837,0.000045866993,0.00013890822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023676851,0.00015613533,0.0002880423,0.00013635104,0.000062562816,0.000030124529,0.00033382105,0.000042961823,0.0000015227141],"category_scores_gemma":[0.000026173822,0.00015590168,0.00009566159,0.00015690632,0.000013773794,0.0004486545,0.000003836377,0.00023370737,0.0000013241365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022447816,0.0000641476,0.0010942675,0.000027286136,0.000008140531,0.00013655526,0.0044377777,0.98437214,0.0065906974,0.00012980997,0.000027816795,0.0028868874],"study_design_scores_gemma":[0.0030942208,0.00058583583,0.03342324,0.00014738567,0.000010166814,0.0000029535015,0.0001339258,0.96141887,0.00055320945,0.00040213225,0.00007419678,0.00015385586],"about_ca_topic_score_codex":0.0000011689096,"about_ca_topic_score_gemma":1.6148844e-7,"teacher_disagreement_score":0.4766808,"about_ca_system_score_codex":0.000095785035,"about_ca_system_score_gemma":0.00009302844,"threshold_uncertainty_score":0.6357486},"labels":[],"label_agreement":null},{"id":"W3110660569","doi":"10.3390/s20247197","title":"Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Motion planning; Potential field; Sigmoid function; Path (computing); Trajectory; Stability (learning theory); Collision avoidance; Field (mathematics); Computer science; Simulation; Control theory (sociology); Engineering; Collision; Artificial intelligence; Mathematics; Control (management); Robot; Artificial neural network","score_opus":0.01892239436582381,"score_gpt":0.2408737988330617,"score_spread":0.22195140446723788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110660569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15179686,0.00003432822,0.8446387,0.0020505611,0.00037717237,0.00023896752,0.0000044876397,0.00044282753,0.0004160866],"genre_scores_gemma":[0.7420609,6.4120854e-7,0.25680536,0.0008254349,0.00019370926,0.000010878772,0.0000071643,0.000022991715,0.00007294005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983523,0.000057803638,0.00025086806,0.0005718727,0.00027914226,0.0004879997],"domain_scores_gemma":[0.9989238,0.00031580494,0.00014742208,0.00033577828,0.00006824739,0.00020896725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019223982,0.0002175795,0.00027341148,0.0000625073,0.0002277359,0.00019102397,0.00060728745,0.000055727265,0.0000037362129],"category_scores_gemma":[0.00018474249,0.0002015874,0.00007730144,0.00017340342,0.000029914785,0.00022978875,0.00016030997,0.00025246176,0.000022035741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012108848,0.00009393747,0.0076617724,0.000120510915,0.00019298591,0.0023832321,0.012730126,0.94302803,0.0021461241,0.0024351252,0.008212318,0.020874742],"study_design_scores_gemma":[0.0006616991,0.00061387697,0.0006486819,0.00008511388,0.000015461548,0.000107755004,0.00014412974,0.9937873,0.00269345,0.00015479767,0.0007586805,0.00032905673],"about_ca_topic_score_codex":0.00001367082,"about_ca_topic_score_gemma":1.394981e-7,"teacher_disagreement_score":0.590264,"about_ca_system_score_codex":0.000026760257,"about_ca_system_score_gemma":0.00008475398,"threshold_uncertainty_score":0.8220496},"labels":[],"label_agreement":null},{"id":"W3111515510","doi":"10.1109/smc42975.2020.9282945","title":"Constraint-Free Discretized Manifolds for Robotic Path Planning","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Motion planning; Constraint (computer-aided design); Any-angle path planning; Path (computing); Mathematical optimization; Manifold (fluid mechanics); Discretization; Gravitational singularity; Obstacle; Computation; Computer science; Configuration space; Obstacle avoidance; Topology (electrical circuits); Mathematics; Robot; Algorithm; Artificial intelligence; Mobile robot; Geometry; Engineering; Mathematical analysis; Combinatorics","score_opus":0.04602288099158324,"score_gpt":0.2698890060469103,"score_spread":0.22386612505532705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111515510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020293651,0.00006466028,0.98424417,0.009328227,0.0003540128,0.00034910356,0.0000063613247,0.00063920807,0.0048113004],"genre_scores_gemma":[0.13660665,9.770504e-7,0.86049354,0.0023971966,0.00017767849,0.000031150812,0.00000999621,0.00001838486,0.00026439238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983694,0.000042943317,0.00029867745,0.0005513513,0.0002791644,0.00045845806],"domain_scores_gemma":[0.99867284,0.00027651008,0.00009356415,0.00062368275,0.000058049915,0.00027534855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022474359,0.00020255074,0.00028986187,0.000047358633,0.000132548,0.00019929545,0.0016453464,0.000075785196,0.000021021086],"category_scores_gemma":[0.0003544858,0.00017678045,0.00010624922,0.0002484583,0.000052656218,0.00029535353,0.0004036713,0.00013753382,0.00005986315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000984227,0.00016840875,0.0034467892,0.00030128902,0.0002633442,0.0008296256,0.00952874,0.13030313,0.003655307,0.6564646,0.17517726,0.019763095],"study_design_scores_gemma":[0.0012587985,0.00022581362,0.0005833944,0.000040709634,0.00001344454,0.00003744161,0.00014490583,0.991885,0.00030399754,0.0044604423,0.00073719095,0.00030884854],"about_ca_topic_score_codex":0.0000054070697,"about_ca_topic_score_gemma":1.18680525e-7,"teacher_disagreement_score":0.86158186,"about_ca_system_score_codex":0.000022557053,"about_ca_system_score_gemma":0.00008391209,"threshold_uncertainty_score":0.72088975},"labels":[],"label_agreement":null},{"id":"W3112118598","doi":"10.1109/tro.2021.3109414","title":"LAMP: Learning a Motion Policy to Repeatedly Navigate in an Uncertain Environment","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Robot; Task (project management); Motion planning; Artificial intelligence; Mobile robot; Set (abstract data type); Motion (physics); Computer vision; Machine learning; Engineering","score_opus":0.03309156889803917,"score_gpt":0.2911656711427663,"score_spread":0.25807410224472715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112118598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007091477,0.00002915053,0.9886483,0.0020587535,0.0012169793,0.00050762744,0.000013457134,0.00036708792,0.00006715482],"genre_scores_gemma":[0.33677825,0.000097336044,0.6618257,0.000436017,0.00013413768,0.00012771047,0.00004532691,0.00007069541,0.00048477532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613684,0.000515232,0.0006461756,0.0013874754,0.00065621483,0.00065803475],"domain_scores_gemma":[0.9977233,0.00011102099,0.00018382608,0.0015453042,0.000084034065,0.0003525531],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005053752,0.00051075843,0.00053117104,0.000738658,0.00022471503,0.00042380273,0.0010954841,0.0004476018,0.00001178431],"category_scores_gemma":[0.000025263133,0.0006049744,0.00018088907,0.00078405335,0.000045621957,0.00030494478,0.000050743954,0.0020586608,0.00008864101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008526965,0.0003216351,0.00005675378,0.000038537204,0.00003085085,0.00020662432,0.002618392,0.97031194,0.00029639874,0.000044906865,0.0000056962626,0.026059752],"study_design_scores_gemma":[0.0003384396,0.00026438772,0.00076227495,0.000486336,0.00003069794,0.000042292617,0.00016351938,0.99555695,0.0014650361,0.00021155873,0.000025259013,0.00065325416],"about_ca_topic_score_codex":0.00049380667,"about_ca_topic_score_gemma":0.000037244135,"teacher_disagreement_score":0.3296868,"about_ca_system_score_codex":0.0009435951,"about_ca_system_score_gemma":0.00036432588,"threshold_uncertainty_score":0.99964017},"labels":[],"label_agreement":null},{"id":"W3112691945","doi":"10.3390/jsan9040056","title":"Toward Campus Mail Delivery Using BDI","year":2020,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Action (physics); Human–computer interaction; Architecture; Robot; Perception; Systems architecture; Embedded system; Software; Artificial intelligence; Operating system","score_opus":0.040124494747146765,"score_gpt":0.23953188333637665,"score_spread":0.19940738858922988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112691945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16471131,0.0010939969,0.8322265,0.0011741337,0.0006685777,0.000042873326,7.9389645e-7,0.000030374555,0.000051404262],"genre_scores_gemma":[0.7320659,0.00014663427,0.26552162,0.001235725,0.0010098678,1.2078581e-7,2.1115089e-7,0.00001078153,0.000009116536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988908,0.00005922119,0.00035397115,0.00017379342,0.00026879474,0.0002533874],"domain_scores_gemma":[0.99899596,0.0001242667,0.0003206764,0.000121182755,0.00011794794,0.00031998844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022355947,0.00013810997,0.00028747987,0.0000576188,0.00008515702,0.00015712343,0.00037431598,0.000083675724,0.00000500265],"category_scores_gemma":[0.000060615956,0.000112759124,0.00008807017,0.00020387062,0.00003539241,0.00040182073,0.000116134834,0.00037441484,0.000003384054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022775958,0.00014003585,0.007901416,0.00010420748,0.0004951808,0.0068332055,0.00876639,0.69286907,0.0056479746,0.0003971172,0.013687278,0.2629304],"study_design_scores_gemma":[0.00045826944,0.00016653637,0.0010057513,0.00006961601,0.000030800602,0.0010734422,0.00011467259,0.9960945,0.00011641493,0.000061148254,0.0006470605,0.00016175231],"about_ca_topic_score_codex":0.0000037626896,"about_ca_topic_score_gemma":4.31184e-8,"teacher_disagreement_score":0.5673546,"about_ca_system_score_codex":0.000027730352,"about_ca_system_score_gemma":0.000062598025,"threshold_uncertainty_score":0.45981836},"labels":[],"label_agreement":null},{"id":"W3113075130","doi":"10.1109/tits.2020.3042670","title":"An Iterative Two-Phase Optimization Method Based on Divide and Conquer Framework for Integrated Scheduling of Multiple UAVs","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Hunan Province; Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Divide and conquer algorithms; Scheduling (production processes); Fair-share scheduling; Dynamic priority scheduling; Simulated annealing; Fixed-priority pre-emptive scheduling; Rate-monotonic scheduling; Round-robin scheduling; Two-level scheduling; Distributed computing; Task analysis; Mathematical optimization; Algorithm; Task (project management); Quality of service; Computer network; Mathematics; Engineering","score_opus":0.053739840374272825,"score_gpt":0.34369755264969637,"score_spread":0.28995771227542355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113075130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023812728,0.00002464329,0.9950935,0.00023336418,0.0006565705,0.0010406398,0.00034435873,0.00022057707,0.000005033877],"genre_scores_gemma":[0.53696364,0.0000035247433,0.46268773,0.00012552777,0.000025411036,0.00011574921,0.000057743957,0.000017695977,0.0000029900782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978189,0.00024804674,0.00074153376,0.00061967067,0.00035523984,0.00021664384],"domain_scores_gemma":[0.99781626,0.0010078923,0.00027437956,0.00032399932,0.0003611047,0.00021635028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040591366,0.00027503664,0.00040188222,0.00024499523,0.0001679536,0.00012787606,0.00029054054,0.00014635232,0.000009979914],"category_scores_gemma":[0.000038495724,0.0002643291,0.000114498245,0.0005483094,0.000044708067,0.000405598,2.9516843e-7,0.00027334542,0.0000032872222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023602255,0.00026002948,0.000042968746,0.000104600775,0.00005660991,0.0000041763833,0.004406356,0.98585534,0.0009690158,0.0017021862,0.0000022448364,0.0063604293],"study_design_scores_gemma":[0.0012457985,0.0010204386,0.000010972245,0.000322801,0.000051382656,0.0000013669579,0.00055119046,0.95297295,0.04351332,0.000049428378,0.000025790669,0.00023457993],"about_ca_topic_score_codex":0.00006566361,"about_ca_topic_score_gemma":0.000005060986,"teacher_disagreement_score":0.5345824,"about_ca_system_score_codex":0.000046002613,"about_ca_system_score_gemma":0.00008334877,"threshold_uncertainty_score":0.99998087},"labels":[],"label_agreement":null},{"id":"W3113323577","doi":"10.1155/2020/8865608","title":"Stabilizing Controllers for Landmark Navigation of Planar Robots in an Obstacle-Ridden Workspace","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Landmark; Workspace; Robot; Control theory (sociology); Mobile robot; Motion planning; Obstacle avoidance; Computer science; Nonlinear system; Planar; MATLAB; Point (geometry); Artificial intelligence; Mathematics; Control (management); Geometry","score_opus":0.025512514273634334,"score_gpt":0.2787355762915044,"score_spread":0.2532230620178701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113323577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3464471,0.000115130206,0.65251875,0.00057064684,0.00016404975,0.00016265016,0.0000063659654,0.000012943034,0.0000023227742],"genre_scores_gemma":[0.5508156,0.000007777899,0.44906875,0.000043070682,0.000041207084,0.0000029708483,0.000013685542,0.000006099095,8.219336e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987993,0.000041968884,0.000591659,0.00015610237,0.00026724167,0.00014371864],"domain_scores_gemma":[0.9988178,0.00017715362,0.00058858836,0.00009988821,0.00021509109,0.00010146062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886628,0.000096144475,0.00029177425,0.000093690134,0.000027064396,0.000020413538,0.0002689874,0.000048367932,4.3660208e-7],"category_scores_gemma":[0.000064319094,0.00009165592,0.0000746167,0.00028728868,0.000014871178,0.0010746713,0.0000014236025,0.00014863625,1.987354e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034018667,0.00004298631,0.004884184,0.000055398654,0.000015785574,0.000034755118,0.008111004,0.9657833,0.009908336,0.00059956126,0.000007107171,0.010217378],"study_design_scores_gemma":[0.018266564,0.0038313868,0.4378807,0.0012967498,0.000115286464,0.000029838342,0.005829356,0.51890785,0.0076108384,0.0054050963,0.00015222066,0.0006741348],"about_ca_topic_score_codex":0.0000035415385,"about_ca_topic_score_gemma":0.000010370896,"teacher_disagreement_score":0.44687548,"about_ca_system_score_codex":0.000037284604,"about_ca_system_score_gemma":0.000075519965,"threshold_uncertainty_score":0.373762},"labels":[],"label_agreement":null},{"id":"W3113445109","doi":"10.2316/j.2021.201-0105","title":"GUIDING UNMANNED AERIAL VEHICLE PATH PLANNING DESIGN BASED ON IMPROVED ANT COLONY ALGORITHM","year":2020,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Ant colony optimization algorithms; Convergence (economics); Path (computing); Computer science; Rate of convergence; Real-time computing; Artificial intelligence; Algorithm; Control engineering; Engineering; Simulation; Robot; Computer security","score_opus":0.032175883153569625,"score_gpt":0.22879928977772265,"score_spread":0.19662340662415304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113445109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084838417,0.0010129499,0.9946457,0.0010726232,0.0011310006,0.0008619999,0.000015917965,0.00035521036,0.000056183042],"genre_scores_gemma":[0.9420001,0.0000032281491,0.05617203,0.001017122,0.000620139,0.00012674178,0.000003698646,0.000030646126,0.000026285259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973372,0.00038260198,0.00046729163,0.00074688415,0.00040580524,0.000660215],"domain_scores_gemma":[0.9985567,0.0003661509,0.00024853944,0.00044309773,0.000073634015,0.0003119161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094649225,0.00032351405,0.0005540342,0.00008678267,0.00028362367,0.00038290056,0.00061010127,0.00014769974,0.0000023092161],"category_scores_gemma":[0.00011828009,0.00028639875,0.00008607148,0.00023625752,0.000026754951,0.00022377152,0.000087178996,0.00018915164,0.000019245808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013797842,0.00036188547,0.0005888047,0.0003884078,0.00061462086,0.0014858781,0.0045247097,0.793656,0.08180873,0.011724097,0.0049193436,0.09854776],"study_design_scores_gemma":[0.0039970935,0.0013968458,0.000051430903,0.00013711675,0.000027605876,0.00001794679,0.00010568038,0.992797,0.00052049785,0.0000453056,0.00057811284,0.00032532692],"about_ca_topic_score_codex":0.0000788479,"about_ca_topic_score_gemma":9.691648e-8,"teacher_disagreement_score":0.94115174,"about_ca_system_score_codex":0.00011025293,"about_ca_system_score_gemma":0.00021244641,"threshold_uncertainty_score":0.9999588},"labels":[],"label_agreement":null},{"id":"W3114650135","doi":"10.1016/j.jterra.2020.12.001","title":"A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains","year":2020,"lang":"en","type":"article","venue":"Journal of Terramechanics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Robot; Terrain; Motion planning; Control theory (sociology); Robustness (evolution); Nonholonomic system; Lyapunov stability; Computer science; Path (computing); Mathematical optimization; Mobile robot; Control engineering; Artificial intelligence; Engineering; Mathematics; Control (management)","score_opus":0.042963479430806216,"score_gpt":0.2771531724551616,"score_spread":0.23418969302435536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114650135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004051868,0.00018161834,0.9929265,0.0016024872,0.0009464938,0.00018567764,0.0000074614695,0.00006460374,0.000033333297],"genre_scores_gemma":[0.046721973,0.000005669882,0.95140606,0.0014993239,0.0003120812,0.0000053901445,0.0000023609448,0.000026082042,0.00002104394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818194,0.00003388971,0.00057110895,0.00031108435,0.00047085612,0.00043114705],"domain_scores_gemma":[0.99868304,0.0001518719,0.0004994656,0.000216487,0.00011950353,0.00032964282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065814506,0.00023926342,0.00045796935,0.00015151205,0.00017437164,0.0002495564,0.00077097566,0.000118974036,0.0000010004577],"category_scores_gemma":[0.0002234307,0.00020609211,0.0002282473,0.00023405797,0.000012280222,0.00071614893,0.00019461066,0.00042896584,0.0000027782003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029633337,0.0006808425,0.00013883266,0.00022381777,0.001054008,0.00203053,0.028159475,0.30291015,0.07505342,0.0036030365,0.0073099593,0.5785396],"study_design_scores_gemma":[0.0016392035,0.0015637325,0.000033132415,0.00022562071,0.000038074293,0.00045459665,0.00021532121,0.9932263,0.0011494507,0.00030362548,0.0009137548,0.00023720397],"about_ca_topic_score_codex":0.0000019190816,"about_ca_topic_score_gemma":2.7713343e-8,"teacher_disagreement_score":0.69031614,"about_ca_system_score_codex":0.00007127699,"about_ca_system_score_gemma":0.00012565777,"threshold_uncertainty_score":0.84041923},"labels":[],"label_agreement":null},{"id":"W3115515976","doi":"10.1109/tcst.2020.3042815","title":"Provably Safe and Scalable Multivehicle Trajectory Planning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reachability; Scalability; Computer science; Trajectory; Computation; Leverage (statistics); Toolbox; Mathematical optimization; Trajectory optimization; Motion planning; Theoretical computer science; Distributed computing; Algorithm; Artificial intelligence; Mathematics; Robot; Optimal control","score_opus":0.0184781317648522,"score_gpt":0.23104671344368582,"score_spread":0.21256858167883363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115515976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066051465,0.0005012676,0.98681617,0.0033768262,0.0006103796,0.0005967921,0.00001426902,0.0012539556,0.0002252065],"genre_scores_gemma":[0.9784822,0.0000050168983,0.020873869,0.00030267355,0.000046289082,0.00015434837,2.4891878e-7,0.000019954548,0.00011540907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998265,0.00010203217,0.00034929317,0.00063850184,0.00022207633,0.00042305441],"domain_scores_gemma":[0.9990475,0.00017826025,0.00011803167,0.00041982756,0.000076782926,0.0001595771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020607322,0.00023134888,0.0004159338,0.00030558088,0.00025731584,0.0000971402,0.00057049515,0.0002728873,0.0000023291964],"category_scores_gemma":[0.000023214789,0.00022559255,0.000054374705,0.0005128979,0.000120720666,0.00026534987,0.0000047506605,0.0005108999,0.000073180185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022784357,0.00043058902,0.0012989972,0.00029969084,0.0006035438,0.0005866892,0.0034607986,0.76008016,0.10118383,0.0035713627,0.0008052591,0.12745123],"study_design_scores_gemma":[0.0019855008,0.00055694935,0.00014019318,0.00009147081,0.000025366722,0.00013945957,0.00018867369,0.990419,0.005148781,0.00004597586,0.00096992997,0.00028870243],"about_ca_topic_score_codex":0.000037175487,"about_ca_topic_score_gemma":0.0000014250884,"teacher_disagreement_score":0.97187704,"about_ca_system_score_codex":0.00005854693,"about_ca_system_score_gemma":0.000066268076,"threshold_uncertainty_score":0.91993976},"labels":[],"label_agreement":null},{"id":"W3117187989","doi":"10.1109/iemcon51383.2020.9284825","title":"Autonomous Perching of an Omni-Directional Unmanned Aerial Vehicle","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Omnidirectional antenna; Computer science; Mechanism (biology); Controller (irrigation); Simulation; Aerospace engineering; Real-time computing; Engineering; Antenna (radio); Physics","score_opus":0.028559896452249184,"score_gpt":0.25479054610945506,"score_spread":0.22623064965720588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117187989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18073569,0.000013807458,0.8105815,0.0022759861,0.0006284926,0.000095413896,0.00000321299,0.00055509695,0.0051108147],"genre_scores_gemma":[0.5428752,2.76258e-7,0.45637262,0.00045233726,0.00021612986,0.0000021663736,0.00000249268,0.000005967778,0.00007280579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990311,0.0000697824,0.00019149622,0.00030207707,0.00022746643,0.0001780419],"domain_scores_gemma":[0.99945927,0.000057468813,0.00005725718,0.00023251443,0.000044069657,0.00014939571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016998523,0.00008901084,0.00014309774,0.000040081937,0.00007516164,0.00006097533,0.0005721289,0.000041415315,0.000036638354],"category_scores_gemma":[0.00006245818,0.00008460345,0.000041753487,0.00022628294,0.000027244258,0.00040815945,0.00015226955,0.000104282495,0.00004766774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021554463,0.0009981382,0.005275297,0.00013776586,0.00024083353,0.00038758168,0.037096173,0.33016536,0.28151748,0.07696211,0.0061882795,0.2608154],"study_design_scores_gemma":[0.00030800403,0.00021184326,0.0039442475,0.0000051810684,0.000002751319,0.000016014183,0.000038840426,0.98467386,0.0100554535,0.00031602758,0.00030633455,0.00012144826],"about_ca_topic_score_codex":0.000080153535,"about_ca_topic_score_gemma":9.759356e-7,"teacher_disagreement_score":0.6545085,"about_ca_system_score_codex":0.000020334322,"about_ca_system_score_gemma":0.000095175514,"threshold_uncertainty_score":0.3450029},"labels":[],"label_agreement":null},{"id":"W3117690364","doi":"10.1109/irc.2020.00010","title":"Catching a Robot Intruder with Limited Information","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Snapshot (computer storage); Computer science; Guard (computer science); Soar; Artificial intelligence; Theoretical computer science; Information retrieval; Programming language; Database","score_opus":0.015479366096586247,"score_gpt":0.19752675170374454,"score_spread":0.18204738560715827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117690364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010943973,0.0000046916202,0.9857279,0.008387107,0.00006320585,0.000079250894,3.1949017e-7,0.00041222593,0.0042309184],"genre_scores_gemma":[0.22284885,5.0588505e-7,0.7715476,0.005522537,0.00003789842,0.0000044589574,0.0000045342167,0.0000033846432,0.000030187115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993524,0.000017983924,0.00013210565,0.00013302514,0.00021046157,0.00015400666],"domain_scores_gemma":[0.99955714,0.000033415,0.000053096126,0.00019314347,0.00005314057,0.00011003914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007024824,0.00008126561,0.000083611674,0.000044294873,0.000056207806,0.0001859528,0.0004077691,0.000026295576,0.0000068548015],"category_scores_gemma":[0.000045774003,0.00006001539,0.000013280449,0.00039913814,0.000010875093,0.00138413,0.000114929986,0.00011322463,0.0002264176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058168658,0.00008349974,0.0072139967,0.00011087757,0.00013900101,0.00014841712,0.068653494,0.503509,0.00071576243,0.056880824,0.020790925,0.34169605],"study_design_scores_gemma":[0.0002652762,0.00010657217,0.0026391507,0.0000108912645,0.0000024883266,0.000022630866,0.00008608563,0.99470913,0.00031423112,0.00006481094,0.0016463618,0.00013237704],"about_ca_topic_score_codex":0.000029026773,"about_ca_topic_score_gemma":5.3921787e-7,"teacher_disagreement_score":0.49120012,"about_ca_system_score_codex":0.000014248656,"about_ca_system_score_gemma":0.0000475869,"threshold_uncertainty_score":0.2910216},"labels":[],"label_agreement":null},{"id":"W3119443023","doi":"10.1109/cdc42340.2020.9304481","title":"Explorative Probabilistic Planning with Unknown Target Locations","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Grid; Probabilistic logic; A priori and a posteriori; Automaton; Task (project management); Graph; Path (computing); State (computer science); Control (management); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Distributed computing; Robot; Mathematical optimization; Theoretical computer science; Algorithm; Engineering; Mathematics","score_opus":0.050789864875971306,"score_gpt":0.25634194333078625,"score_spread":0.20555207845481493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119443023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038146754,0.000035213074,0.9861886,0.0062100585,0.000069331865,0.0001881289,0.0000011604897,0.0004823703,0.0064436947],"genre_scores_gemma":[0.18870492,2.9872618e-7,0.8099453,0.0009942108,0.00006350774,0.000037680726,0.0000061351075,0.000009266416,0.00023868898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894994,0.000046116937,0.000150125,0.0003919938,0.00023341169,0.00022842923],"domain_scores_gemma":[0.9992789,0.00010283053,0.000058636146,0.0002915173,0.000094809526,0.00017329818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008450976,0.00013131209,0.00013970376,0.000037654867,0.000117561394,0.00011772038,0.0005580513,0.000028135244,0.000013190446],"category_scores_gemma":[0.000097743985,0.00009806796,0.000018013194,0.00052861904,0.000050127648,0.00048412464,0.00011567343,0.00013386023,0.00014393406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016579046,0.000100868085,0.00207688,0.000042991,0.00006277476,0.0002078613,0.020325921,0.7545697,0.00012360186,0.2124128,0.008296815,0.001763224],"study_design_scores_gemma":[0.0002528934,0.0002736125,0.000644466,0.00003411611,0.000005319962,0.000016258617,0.00028623987,0.9941137,0.0004198355,0.0021607315,0.0015653836,0.00022746254],"about_ca_topic_score_codex":0.000005421272,"about_ca_topic_score_gemma":3.3212288e-7,"teacher_disagreement_score":0.239544,"about_ca_system_score_codex":0.000024108402,"about_ca_system_score_gemma":0.00014003698,"threshold_uncertainty_score":0.39990953},"labels":[],"label_agreement":null},{"id":"W3121518346","doi":"10.20944/preprints202004.0042.v1","title":"Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Control theory (sociology); Nonlinear system; Motion planning; Linearization; Jerk; Computer science; Model predictive control; Engineering; Control engineering; Control (management); Artificial intelligence; Robot; Acceleration","score_opus":0.097795633617434,"score_gpt":0.32498892124316303,"score_spread":0.22719328762572905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121518346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8250406,0.00053636875,0.16856243,0.002962624,0.00052460114,0.0015376009,0.000042354775,0.00045489758,0.00033856445],"genre_scores_gemma":[0.95572907,0.000038301587,0.04368202,0.00020970184,0.0001269695,0.000092635586,0.000015445656,0.000033568966,0.0000722593],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969272,0.00017857227,0.0005327099,0.0015845798,0.00027367406,0.0005032757],"domain_scores_gemma":[0.9978909,0.00049882993,0.00035118908,0.0009157834,0.00008405571,0.00025925646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000638484,0.00041606213,0.00069223903,0.00022910662,0.000104416205,0.0001503118,0.0009629567,0.00034190514,0.0000022333904],"category_scores_gemma":[0.0007187128,0.00045485163,0.00007659969,0.00021361116,0.00009163265,0.00018219618,0.0022066021,0.0008726567,0.00002393802],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014404191,0.00012422391,0.9462539,0.000812735,0.00015856093,0.00047685285,0.007801492,0.028151749,0.011722254,0.0014496547,0.000096268086,0.0028082654],"study_design_scores_gemma":[0.0012659777,0.00004159107,0.48796943,0.00039453417,0.000021398106,0.000021112957,0.000039310387,0.5053899,0.001044965,0.0033310854,0.00009862616,0.00038204086],"about_ca_topic_score_codex":0.00015307777,"about_ca_topic_score_gemma":0.0000010001045,"teacher_disagreement_score":0.47723818,"about_ca_system_score_codex":0.00010449689,"about_ca_system_score_gemma":0.0002511052,"threshold_uncertainty_score":0.9997903},"labels":[],"label_agreement":null},{"id":"W3123929699","doi":"10.20944/preprints201805.0164.v2","title":"Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Flexibility (engineering); Mathematical optimization; Motion planning; Constraint (computer-aided design); SAFER; Convexity; Range (aeronautics); Planner; Robot; Engineering; Mathematics; Artificial intelligence","score_opus":0.1740030006361202,"score_gpt":0.3555264488754764,"score_spread":0.1815234482393562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123929699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03381791,0.00011695755,0.960142,0.0007190295,0.0016568996,0.0013558149,0.00002715633,0.0008931007,0.0012711085],"genre_scores_gemma":[0.38919064,0.000016575206,0.60916173,0.00031881078,0.00046196702,0.00015708353,0.00012927274,0.00008305274,0.00048083815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582756,0.0001474994,0.000802248,0.0020045342,0.0004809441,0.000737244],"domain_scores_gemma":[0.99658436,0.00031024383,0.0006359453,0.0017906369,0.00038333854,0.00029550915],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011047603,0.0006244626,0.0008307348,0.00035439772,0.00030800526,0.00024429182,0.0019362409,0.00048851466,0.000054756565],"category_scores_gemma":[0.00038304485,0.00069276016,0.00017851076,0.00024240573,0.000221633,0.00037904625,0.0046546645,0.0007192918,0.00012992664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041827534,0.000107025226,0.12291141,0.0005093424,0.00027623915,0.000060265513,0.008064462,0.8649897,0.00084736774,0.00040223787,0.00023268472,0.0015574233],"study_design_scores_gemma":[0.0005854667,0.00004958003,0.07130927,0.0004815331,0.00005187607,0.000072230956,0.000040056686,0.92309797,0.0015114002,0.0016923526,0.00039345102,0.0007148371],"about_ca_topic_score_codex":0.000073295334,"about_ca_topic_score_gemma":3.6514766e-7,"teacher_disagreement_score":0.35537276,"about_ca_system_score_codex":0.00021662944,"about_ca_system_score_gemma":0.00027865253,"threshold_uncertainty_score":0.99955237},"labels":[],"label_agreement":null},{"id":"W3125075417","doi":"10.22215/etd/2015-10907","title":"Moving Target Search with Environment Abstraction","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Abstraction; Path (computing); Computer science; Robotics; Artificial intelligence; Dynamic programming; Motion planning; Search algorithm; Algorithm; Theoretical computer science; Mathematical optimization; Robot; Mathematics; Programming language","score_opus":0.021319398828913174,"score_gpt":0.26950118552088054,"score_spread":0.24818178669196736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125075417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007747831,0.00019409743,0.9092459,0.00014570144,0.0010848151,0.0003927381,0.0000037353846,0.00039623026,0.080788955],"genre_scores_gemma":[0.012715587,0.000013221026,0.944211,0.00003908279,0.00014770076,0.000032187443,0.00038032635,0.000037640024,0.042423237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998128,0.00003938717,0.00019224854,0.00051424326,0.0008340577,0.00029209044],"domain_scores_gemma":[0.9991091,0.000036633643,0.00011992119,0.00052027445,0.00007308206,0.00014101695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003383937,0.00022075085,0.00019672795,0.00014159364,0.00008541732,0.0001514947,0.000582505,0.00016729992,0.000058213056],"category_scores_gemma":[0.000009923408,0.00018038439,0.000032130156,0.00012202607,0.000012677466,0.00034094433,0.000046787252,0.0003701785,0.0004804188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019934008,0.0008008487,0.0019662464,0.0005773855,0.00056196365,0.0012756846,0.021172825,0.640081,0.002797141,0.005205491,0.028050467,0.2973116],"study_design_scores_gemma":[0.0013688804,0.0009770294,0.04631835,0.000490092,0.00007529792,0.00019249927,0.0032265396,0.9231008,0.009666889,0.0028717525,0.009206478,0.002505364],"about_ca_topic_score_codex":0.000128695,"about_ca_topic_score_gemma":0.0000039132105,"teacher_disagreement_score":0.29480624,"about_ca_system_score_codex":0.00016889357,"about_ca_system_score_gemma":0.00024366326,"threshold_uncertainty_score":0.73558617},"labels":[],"label_agreement":null},{"id":"W3126409450","doi":"10.1109/cac51589.2020.9326562","title":"UAV Trajectory Generation Based on Integration of RRT and Minimum Snap Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Path (computing); Smoothness; Obstacle; Motion planning; Algorithm; Limit (mathematics); Obstacle avoidance; Simulation; Control theory (sociology); Real-time computing; Robot; Mobile robot; Artificial intelligence; Mathematics; Control (management)","score_opus":0.05667442753150732,"score_gpt":0.26008837559906867,"score_spread":0.20341394806756136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126409450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008022462,0.000023167877,0.98848087,0.0020169213,0.00020777716,0.000119017284,0.000002297598,0.000105264684,0.0010222415],"genre_scores_gemma":[0.4188866,0.0000018959369,0.5796334,0.0012864622,0.00011767797,0.000005698641,0.0000108353815,0.0000059558656,0.000051469557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904424,0.000065496926,0.00020479565,0.0003174834,0.0002497272,0.000118284275],"domain_scores_gemma":[0.9994836,0.00007814559,0.000071387265,0.00021559649,0.00005722843,0.000094038216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016771606,0.00011007218,0.00014089381,0.00007282267,0.000043661534,0.000052542364,0.00022714327,0.00005440171,0.000009735575],"category_scores_gemma":[0.00007347065,0.00009380392,0.00003074484,0.00022445382,0.00002902845,0.00019890396,0.000033783726,0.000090130656,0.000010694585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005690079,0.00038731017,0.00092100183,0.00010748944,0.00005715562,0.000068051864,0.008533312,0.10202282,0.35276237,0.014455225,0.017687805,0.50294054],"study_design_scores_gemma":[0.00027840826,0.00031373647,0.0009984225,0.000012651047,0.0000036175256,0.0000018073197,0.000025246092,0.970539,0.027638162,0.000031766423,0.00005730983,0.00009988941],"about_ca_topic_score_codex":0.000012980579,"about_ca_topic_score_gemma":0.0000010447234,"teacher_disagreement_score":0.86851615,"about_ca_system_score_codex":0.000017139228,"about_ca_system_score_gemma":0.000056332152,"threshold_uncertainty_score":0.3825213},"labels":[],"label_agreement":null},{"id":"W3127566536","doi":"10.1109/lra.2021.3056346","title":"Optimal Cooperative Maneuver Planning for Multiple Nonholonomic Robots in a Tiny Environment via Adaptive-Scaling Constrained Optimization","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Nonholonomic system; Convexity; Mathematical optimization; Computer science; Trajectory; Robot; Computation; Kinematics; Convergence (economics); Control theory (sociology); Range (aeronautics); Constraint (computer-aided design); Trajectory optimization; Motion planning; Mobile robot; Optimal control; Mathematics; Artificial intelligence; Engineering; Algorithm; Control (management)","score_opus":0.02128553484344437,"score_gpt":0.23518339533228216,"score_spread":0.2138978604888378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127566536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029064432,0.00004505318,0.96859616,0.0015912752,0.00029083865,0.0003085347,0.000007280583,0.000086135326,0.000010262579],"genre_scores_gemma":[0.2606603,0.0000040970444,0.7387404,0.0004697967,0.00004341774,0.000028465458,0.000030493853,0.000012922304,0.000010132078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986505,0.0000844778,0.00035567975,0.00046378892,0.00015332033,0.000292252],"domain_scores_gemma":[0.999293,0.00024457293,0.00014859995,0.00019899364,0.00004143154,0.000073358875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022612777,0.00018759415,0.00023455534,0.00012054546,0.00015390602,0.00015968623,0.00016135498,0.0000787115,0.0000027408926],"category_scores_gemma":[0.00003476478,0.00020892626,0.00004196865,0.00014499157,0.000056349883,0.0003640134,0.000061717816,0.00012894851,0.0000049146997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069002053,0.000035109395,0.00023871586,0.000012344245,0.00002374832,0.000053093514,0.00089830067,0.99303246,0.004627882,0.00013880288,0.00005106501,0.0008815635],"study_design_scores_gemma":[0.0010325614,0.000044360764,0.0014186074,0.00007509014,0.000011305397,0.000043146745,0.000068339825,0.9956917,0.0013578751,0.000011483269,0.000007994427,0.00023752278],"about_ca_topic_score_codex":0.000004942605,"about_ca_topic_score_gemma":4.5186323e-7,"teacher_disagreement_score":0.23159586,"about_ca_system_score_codex":0.00010572017,"about_ca_system_score_gemma":0.00004735013,"threshold_uncertainty_score":0.8519766},"labels":[],"label_agreement":null},{"id":"W3127820998","doi":"10.1007/978-3-030-66723-8_23","title":"Learning Control Sets for Lattice Planners from User Preferences","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in advanced robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Planner; Computer science; Lattice (music); Set (abstract data type); Mathematical optimization; Robot; Motion planning; Trajectory; Function (biology); Artificial intelligence; Mathematics","score_opus":0.025202900923239367,"score_gpt":0.26201867922414623,"score_spread":0.23681577830090686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127820998","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037425235,0.001875215,0.9361727,0.000551593,0.0028487605,0.0015977852,0.00005587158,0.00081156055,0.055712268],"genre_scores_gemma":[0.0019646464,0.00021519932,0.9465443,0.00024696684,0.00035567652,0.00009228556,0.00006204186,0.0001522691,0.050366644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99603003,0.000016084105,0.0008350025,0.0015913692,0.00067870604,0.0008488181],"domain_scores_gemma":[0.9974566,0.0006040807,0.0007434979,0.00045880963,0.00052022055,0.000216774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043698927,0.0007789608,0.0010709215,0.0003398822,0.00020290502,0.0003919355,0.0015032251,0.00063931325,0.000013971426],"category_scores_gemma":[0.0005172259,0.0008440381,0.00021122542,0.00016941561,0.00010074815,0.00070006796,0.00045430972,0.0014730927,0.000036316615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002001993,0.00016277122,0.004658372,0.00092640164,0.000747676,0.0003165316,0.0034553271,0.40532297,0.00068072823,0.5390639,0.00062175636,0.043843422],"study_design_scores_gemma":[0.009150632,0.0013047772,0.00203475,0.010492672,0.0005755134,0.000092941125,0.0006197157,0.7466194,0.0008896255,0.13289322,0.088534355,0.0067923963],"about_ca_topic_score_codex":0.000009185506,"about_ca_topic_score_gemma":0.000005451795,"teacher_disagreement_score":0.40617064,"about_ca_system_score_codex":0.00026668343,"about_ca_system_score_gemma":0.00020921585,"threshold_uncertainty_score":0.99940103},"labels":[],"label_agreement":null},{"id":"W3128530779","doi":"10.1109/tac.2021.3086329","title":"Obstacle Avoidance via Hybrid Feedback","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Vetenskapsrådet","keywords":"Obstacle avoidance; Controller (irrigation); Control theory (sociology); Position (finance); Collision avoidance; Computer science; Control (management); Mathematics; Artificial intelligence; Mobile robot; Robot","score_opus":0.011708424601764541,"score_gpt":0.23043765781284237,"score_spread":0.21872923321107782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128530779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003531435,0.00010053697,0.99151003,0.0017154326,0.0016676039,0.0002383297,0.000018835572,0.00080586376,0.00041195287],"genre_scores_gemma":[0.87609226,0.0000044038916,0.1217201,0.0009998903,0.00004089055,0.000068480644,0.0000011026227,0.000020650352,0.0010522215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795806,0.00019081909,0.00040989256,0.00053289364,0.00045929593,0.00044905636],"domain_scores_gemma":[0.99809796,0.000472498,0.000111107875,0.0010070653,0.0001322734,0.0001790759],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024129897,0.00024036119,0.00035588577,0.00011750706,0.00026534995,0.0001893009,0.00060197763,0.00006418565,0.00014598614],"category_scores_gemma":[0.000025821875,0.0002470194,0.00017900157,0.00045920437,0.000047953108,0.00036705442,0.0000029317862,0.00024867887,0.0008172811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000131360885,0.00073831755,0.000016413342,0.000077216835,0.0002703716,0.00079285476,0.0007891303,0.15727171,0.010044436,0.00030872214,0.000941542,0.8287361],"study_design_scores_gemma":[0.0014116623,0.00007185484,0.00046369762,0.00007328039,0.00003579739,0.00035378453,0.00001576849,0.9778965,0.01872964,0.00047485856,0.00020196348,0.0002711822],"about_ca_topic_score_codex":0.000013962792,"about_ca_topic_score_gemma":0.000003861442,"teacher_disagreement_score":0.87256086,"about_ca_system_score_codex":0.00010891703,"about_ca_system_score_gemma":0.0001882646,"threshold_uncertainty_score":0.9999982},"labels":[],"label_agreement":null},{"id":"W3129503641","doi":"10.1109/iros45743.2020.9341134","title":"Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Vector Institute","funders":"","keywords":"Workspace; Computer science; Sequential quadratic programming; Inverse dynamics; Quadratic programming; Trajectory; Ball (mathematics); Mobile manipulator; Mobile robot; Motion control; Control theory (sociology); Artificial intelligence; Robot; Control engineering; Mathematical optimization; Engineering; Mathematics; Control (management); Kinematics","score_opus":0.033048088405852744,"score_gpt":0.2591825047532625,"score_spread":0.22613441634740974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129503641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009729816,0.000009995231,0.9837769,0.004772287,0.00042564215,0.0004394341,0.000004601308,0.00048003774,0.000361275],"genre_scores_gemma":[0.7667652,0.0000039795354,0.23051742,0.0014047386,0.00013903933,0.000054374468,0.000030936037,0.000026138818,0.0010581997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987141,0.00007617301,0.00023633103,0.00041029355,0.00022319224,0.00033991702],"domain_scores_gemma":[0.9990859,0.00017873048,0.00010547108,0.00038621886,0.00008937208,0.00015427997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002387257,0.0001650606,0.00016798238,0.000040558258,0.00034372509,0.00017767941,0.0010120012,0.00006750465,0.000016860145],"category_scores_gemma":[0.00010923689,0.00012265709,0.00008248755,0.00046066553,0.000042200867,0.00032720534,0.00028334823,0.0002535802,0.00014684559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043309005,0.00003983164,0.00096122193,0.000022747921,0.000053148375,0.000017199895,0.0027596417,0.95879257,0.00031643346,0.025997145,0.003414716,0.00762101],"study_design_scores_gemma":[0.00024979867,0.0001226214,0.00091576664,0.0000050768613,0.000011778466,0.0000080831915,0.00027146368,0.9970464,0.00010241244,0.00044146244,0.0006606076,0.00016453824],"about_ca_topic_score_codex":0.00014315921,"about_ca_topic_score_gemma":0.000014757278,"teacher_disagreement_score":0.7570354,"about_ca_system_score_codex":0.00006920095,"about_ca_system_score_gemma":0.000055569293,"threshold_uncertainty_score":0.5001811},"labels":[],"label_agreement":null},{"id":"W3130542577","doi":"10.1109/igarss39084.2020.9324503","title":"Multi-Agents Path Planning for a Swarm of Unmanned Aerial Vehicles","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Motion planning; Swarm behaviour; Computer science; Remotely operated underwater vehicle; Path (computing); Mobile robot; Artificial intelligence; Robot; Computer network","score_opus":0.09441222198911259,"score_gpt":0.3101855919763001,"score_spread":0.2157733699871875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130542577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024810407,0.000044859185,0.97340375,0.0007598438,0.00035703878,0.0002469674,0.000010024148,0.00020612095,0.00016100773],"genre_scores_gemma":[0.27928987,8.470868e-7,0.71994454,0.0005544175,0.00011145415,0.0000132826635,0.0000044438775,0.0000092771725,0.00007186726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989077,0.000031722833,0.0002620015,0.0003365778,0.00020215099,0.00025983556],"domain_scores_gemma":[0.99933994,0.000115917595,0.00011078288,0.00023585802,0.00006413518,0.00013337305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001608246,0.00012212528,0.00021366509,0.00004111792,0.00006342772,0.000047006262,0.00069690676,0.000054673776,0.0000033519775],"category_scores_gemma":[0.00015359721,0.000109725945,0.00007153362,0.00019755607,0.000023860148,0.0001808716,0.00018741678,0.00006684737,0.00001870589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086432224,0.0018152929,0.027887821,0.0013783934,0.00086346123,0.00063245825,0.10731064,0.42728266,0.17617457,0.017555766,0.13482684,0.10340778],"study_design_scores_gemma":[0.0011972183,0.00022209933,0.0018585819,0.000031531876,0.000006374221,0.0000027062774,0.00007958282,0.9858256,0.009709387,0.000080455655,0.0008381488,0.00014831312],"about_ca_topic_score_codex":0.00001445931,"about_ca_topic_score_gemma":8.233549e-8,"teacher_disagreement_score":0.5585429,"about_ca_system_score_codex":0.0000103635,"about_ca_system_score_gemma":0.00005302178,"threshold_uncertainty_score":0.44744942},"labels":[],"label_agreement":null},{"id":"W3131580247","doi":"10.1109/tac.2021.3059838","title":"FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Office of Naval Research","keywords":"Modular design; Computer science; Tracking (education); Motion planning; Motion (physics); Real-time computing; Control engineering; Artificial intelligence; Engineering; Robot; Operating system","score_opus":0.019251701274741906,"score_gpt":0.2698079643192674,"score_spread":0.2505562630445255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131580247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011674167,0.00007320444,0.98569125,0.00073700777,0.0006468295,0.0004933455,0.000033045646,0.0005707056,0.00008045843],"genre_scores_gemma":[0.60572,0.0000035924052,0.39385253,0.0001915973,0.000044286848,0.000076157645,0.0000020508176,0.000020569098,0.00008922355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980742,0.00014728321,0.00045867628,0.00056352676,0.00033062388,0.00042569495],"domain_scores_gemma":[0.99787694,0.0011418955,0.00015297461,0.0005715355,0.00012386801,0.00013281376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041887115,0.00025784774,0.00042741623,0.00016862429,0.000361713,0.00027121356,0.00030251773,0.0001738243,0.000030086352],"category_scores_gemma":[0.00007939799,0.0002707373,0.00015330043,0.00031928052,0.00004289334,0.00035131126,0.0000024202134,0.00023401323,0.000035241566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005263888,0.00045247647,0.000052061783,0.00021087078,0.00039453362,0.00021605787,0.0037799317,0.47951043,0.029710937,0.0024716684,0.00006500732,0.48308337],"study_design_scores_gemma":[0.001447042,0.000120742974,0.0013598084,0.0003359208,0.0000815459,0.000128317,0.00007097345,0.98956525,0.0040069497,0.0026053628,0.000014648001,0.0002634079],"about_ca_topic_score_codex":0.000008013201,"about_ca_topic_score_gemma":5.8260383e-7,"teacher_disagreement_score":0.5940458,"about_ca_system_score_codex":0.000067315865,"about_ca_system_score_gemma":0.000091019814,"threshold_uncertainty_score":0.9999745},"labels":[],"label_agreement":null},{"id":"W3133564702","doi":"10.1109/lra.2021.3063992","title":"Real-Time Path Planning With Virtual Magnetic Fields","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Robot; Path (computing); Magnetic field; Virtual reality; Field (mathematics); Artificial intelligence; Real-time computing; Simulation; Control engineering; Mathematics; Engineering; Physics","score_opus":0.009880572350459716,"score_gpt":0.22026016579683558,"score_spread":0.21037959344637586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133564702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10616952,0.000030137286,0.88835126,0.004549643,0.00030439466,0.000067212975,0.0000014897197,0.0002524426,0.00027389222],"genre_scores_gemma":[0.22112896,0.000016675436,0.7760604,0.0021834527,0.00014476608,0.000007994553,0.000015018774,0.000021234791,0.0004214978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988871,0.00006456604,0.00019308653,0.00033661508,0.00027056574,0.0002480498],"domain_scores_gemma":[0.99933094,0.00012011638,0.00008426737,0.0003207846,0.000052788717,0.00009110516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012562599,0.00014682405,0.00016725772,0.00007190084,0.00013338013,0.00025125153,0.00020370142,0.000059718473,0.0000062394593],"category_scores_gemma":[0.000016483868,0.00013461482,0.000025370586,0.00024110866,0.000039246122,0.00027209052,0.000056537527,0.00012587025,0.000028546452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034315901,0.000040577746,0.0007675203,0.000027648717,0.00003113554,0.00076536846,0.0015680586,0.9587984,0.022240022,0.002120722,0.004961954,0.008675163],"study_design_scores_gemma":[0.00035841018,0.00015544686,0.0070679947,0.0001115281,0.0000140592965,0.00017758993,0.000027030863,0.99087965,0.000837731,0.000076280456,0.000052949705,0.00024134215],"about_ca_topic_score_codex":0.000007498347,"about_ca_topic_score_gemma":1.6930852e-7,"teacher_disagreement_score":0.11495943,"about_ca_system_score_codex":0.000022264065,"about_ca_system_score_gemma":0.000055801913,"threshold_uncertainty_score":0.5489433},"labels":[],"label_agreement":null},{"id":"W3138392552","doi":"10.1109/iros47612.2022.9981779","title":"Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Workspace; Computer science; Object (grammar); Mobile manipulator; Planner; Artificial intelligence; Computer vision; Task (project management); Mobile robot; Pipeline (software); Human–computer interaction; Plan (archaeology); Motion planning; Robot; Real-time computing; Engineering","score_opus":0.0530814294407172,"score_gpt":0.297446450377679,"score_spread":0.24436502093696177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138392552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5145913,0.00035449964,0.46619305,0.0012175513,0.01037397,0.0019443393,0.0000564678,0.0003853116,0.004883501],"genre_scores_gemma":[0.99653184,0.00004459076,0.0007373465,0.00010372922,0.00011996496,0.00042006932,0.00009288423,0.000022613998,0.0019269779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688256,0.00030854368,0.0006644244,0.000783073,0.0010043583,0.0003570343],"domain_scores_gemma":[0.9987682,0.000120639226,0.0003564311,0.00042084715,0.00021113054,0.00012272783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006293725,0.00031042262,0.00034569754,0.000554097,0.00027501216,0.0004971916,0.0007544325,0.00007361723,0.00014849681],"category_scores_gemma":[0.000027171543,0.0002787994,0.0000629928,0.00048507442,0.000052638738,0.00049597846,0.00024263441,0.00052293844,0.000058342834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013680063,0.00036168692,0.0047846446,0.000036509664,0.00010710619,0.0002038667,0.0018833012,0.9364121,0.0048241224,0.046026457,0.0003718018,0.0048516267],"study_design_scores_gemma":[0.00049471547,0.0005644895,0.0026961642,0.00025092796,0.0000100956395,0.00023878996,0.001101068,0.99176306,0.0007452848,0.00024441228,0.0015294004,0.00036161309],"about_ca_topic_score_codex":0.0004829906,"about_ca_topic_score_gemma":0.000032907697,"teacher_disagreement_score":0.48194054,"about_ca_system_score_codex":0.0004531242,"about_ca_system_score_gemma":0.00010451872,"threshold_uncertainty_score":0.99996644},"labels":[],"label_agreement":null},{"id":"W3138591890","doi":"10.3389/frobt.2021.624333","title":"Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment","year":2021,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Motion planning; Path (computing); Mobile robot; Graph; Robot; Representation (politics); Task (project management); Any-angle path planning; Real-time computing; Artificial intelligence; Computer network; Theoretical computer science","score_opus":0.019908434107277675,"score_gpt":0.2719147420183601,"score_spread":0.2520063079110824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138591890","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04208561,0.0006431064,0.9562947,0.000066645276,0.0006265248,0.00024247392,0.0000064033197,0.000020380548,0.000014116624],"genre_scores_gemma":[0.29935256,0.000010611831,0.700527,0.000050281622,0.000020333273,0.000011242162,0.00001072619,0.000009623677,0.0000076183514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987422,0.00006940831,0.00031417308,0.00040417272,0.00021043289,0.0002595627],"domain_scores_gemma":[0.9993072,0.00013030993,0.00015829512,0.00030102988,0.00004186662,0.00006130556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002505027,0.00013336907,0.00027678997,0.00014073093,0.00011184866,0.00007069623,0.00015112064,0.00006314402,5.5329485e-7],"category_scores_gemma":[0.00006019932,0.00014332772,0.000067283916,0.00029303468,0.000045638073,0.000077646364,0.00010247134,0.0001101824,1.3637221e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051791817,0.00008560595,0.03675151,0.000043893142,0.000013703299,0.000029944926,0.0003844887,0.9590224,0.00050549174,0.0001550158,0.000057520938,0.0029452555],"study_design_scores_gemma":[0.00063931855,0.00006593413,0.0031651156,0.00015159088,0.000015831843,0.000006597583,0.00013016682,0.9934041,0.0015056824,0.00075120747,0.00001119685,0.00015324449],"about_ca_topic_score_codex":0.000012389836,"about_ca_topic_score_gemma":1.04461506e-7,"teacher_disagreement_score":0.25726694,"about_ca_system_score_codex":0.00006341999,"about_ca_system_score_gemma":0.000063281026,"threshold_uncertainty_score":0.5844735},"labels":[],"label_agreement":null},{"id":"W3138727095","doi":"","title":"On Multi-Robot Area Coverage","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robot; Task (project management); Path (computing); Point (geometry); Mobile robot; Human–computer interaction; Actuator; Artificial intelligence; Real-time computing; Motion planning; Computer vision; Computer network; Engineering; Systems engineering","score_opus":0.04605794693522053,"score_gpt":0.24511521676381792,"score_spread":0.1990572698285974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138727095","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.073394e-7,0.00004088342,0.51483727,0.00013467722,0.00061750395,0.00017055235,0.0000045595216,0.00028741304,0.48390672],"genre_scores_gemma":[0.00008800385,0.000018872757,0.27384335,0.0009946595,0.000080558806,0.000009159179,0.00001321109,0.000036424968,0.72491574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795353,0.000015591691,0.00032641093,0.00081623317,0.00051867514,0.00036958294],"domain_scores_gemma":[0.99794364,0.00021802116,0.00019553931,0.001357638,0.00009465465,0.0001905151],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012689424,0.0004737479,0.00042691335,0.00020063356,0.00008845016,0.00022994484,0.0014010612,0.00037189148,0.0009238019],"category_scores_gemma":[0.000033158187,0.0003986503,0.00015478922,0.000033519722,0.000046865065,0.00020974789,0.00035916315,0.00053266546,0.012375747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020633788,0.0000517821,0.0000026879186,0.000019523617,0.00008179112,0.0003333578,0.000112707945,0.0049797846,0.000011666121,0.8758586,0.07476207,0.043783937],"study_design_scores_gemma":[0.0014781884,0.0005969042,0.0002810585,0.00088593044,0.000042285574,0.00016794637,0.0000020274026,0.7527845,0.00009881976,0.10161033,0.13923217,0.002819812],"about_ca_topic_score_codex":0.000026034222,"about_ca_topic_score_gemma":9.941987e-7,"teacher_disagreement_score":0.7742483,"about_ca_system_score_codex":0.00009909922,"about_ca_system_score_gemma":0.00008446518,"threshold_uncertainty_score":0.9999895},"labels":[],"label_agreement":null},{"id":"W3144568485","doi":"","title":"Effective Virtual Reality Based Building Navigation Using Dynamic Loading and Path Optimization","year":2009,"lang":"en","type":"article","venue":"国际自动化与计算杂志","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Virtual reality; Path (computing); Computer science; Computer graphics (images); Computer vision; Artificial intelligence","score_opus":0.013233211894843562,"score_gpt":0.28356788451800846,"score_spread":0.2703346726231649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144568485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10279065,0.000039327922,0.89629334,0.00017750241,0.00020202048,0.00022085087,0.0000027538852,0.00020648408,0.00006707622],"genre_scores_gemma":[0.5993453,7.2287213e-7,0.40050906,0.00009396529,0.000029010236,0.0000026392713,0.000010361364,0.000005758992,0.0000031814757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867684,0.00015378708,0.00020425432,0.00044720987,0.0002533067,0.00026458973],"domain_scores_gemma":[0.9992575,0.00013539515,0.00014035641,0.00030232727,0.00007006145,0.00009433178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051809533,0.00016117177,0.00017373878,0.00012256157,0.00022722593,0.0001793133,0.00024262015,0.000091749476,0.0000011590379],"category_scores_gemma":[0.00007925169,0.00016843218,0.000033248965,0.00040648354,0.00003247893,0.00058694754,0.000057648624,0.00015896872,0.0000019649867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055524506,0.000024699406,0.00016741364,0.000007042834,0.0000053716194,0.000021481053,0.00028159516,0.9550952,0.0043450105,0.0010707694,0.000008033003,0.038967866],"study_design_scores_gemma":[0.0003400234,0.000136723,0.006080681,0.0001281743,0.000010309008,0.000029161742,0.00001115171,0.9915311,0.00088696275,0.0006544719,0.0000036175484,0.0001876654],"about_ca_topic_score_codex":0.000039421226,"about_ca_topic_score_gemma":1.8373751e-7,"teacher_disagreement_score":0.49655464,"about_ca_system_score_codex":0.00017670308,"about_ca_system_score_gemma":0.000047870122,"threshold_uncertainty_score":0.6868465},"labels":[],"label_agreement":null},{"id":"W3144645476","doi":"10.1109/wsc.2008.4736198","title":"A hybrid approach based on multi-agent geosimulation and reinforcement learning to solve a UAV patrolling problem","year":2008,"lang":"en","type":"article","venue":"2008 Winter Simulation Conference","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Université Laval; The North South Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Patrolling; Reinforcement learning; Computer science; Set (abstract data type); Drone; Artificial intelligence; Distributed computing; Autonomous agent","score_opus":0.05845513095134897,"score_gpt":0.27393946832983823,"score_spread":0.21548433737848927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144645476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014950022,0.000011915648,0.9831565,0.000266787,0.00013552124,0.00063816627,0.0000015508135,0.00025747242,0.0005820511],"genre_scores_gemma":[0.6675294,0.000001620849,0.3317963,0.00039433685,0.00003596332,0.000031664025,0.000017482085,0.000012538296,0.00018073355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978382,0.00008782863,0.00046012775,0.0007054448,0.0005134221,0.00039495766],"domain_scores_gemma":[0.9986516,0.00019680623,0.00018879687,0.00043902008,0.00031816,0.00020559154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033308478,0.00027949878,0.00026196206,0.00027887936,0.00033569473,0.00017337622,0.00036841925,0.000066596,0.000013036936],"category_scores_gemma":[0.00015751079,0.00027458806,0.00006297171,0.0002297069,0.00003913328,0.0003973321,0.00015178446,0.00025996479,0.00006079582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027442175,0.000062747575,0.0013664182,0.000014806977,0.00001019605,0.000015870759,0.002369566,0.99105054,0.00006349257,0.00014679934,0.000042401516,0.004829722],"study_design_scores_gemma":[0.0010616853,0.00028141748,0.0039626234,0.000108904,0.000006452547,0.000012039825,0.000024406878,0.9938011,0.00013530608,0.000038655562,0.00025864475,0.0003087652],"about_ca_topic_score_codex":0.000024780036,"about_ca_topic_score_gemma":4.3473293e-7,"teacher_disagreement_score":0.65257937,"about_ca_system_score_codex":0.000098822355,"about_ca_system_score_gemma":0.000103661034,"threshold_uncertainty_score":0.9999706},"labels":[],"label_agreement":null},{"id":"W3146613331","doi":"10.1109/his.2006.264936","title":"Optimum Path Planning for Mobile Robots Based on a Hybrid Genetic Algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Crossover; Motion planning; Genetic algorithm; Mobile robot; Computer science; Robot; Path (computing); Algorithm; Mutation; Hybrid algorithm (constraint satisfaction); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning","score_opus":0.013012927536119422,"score_gpt":0.24937172324400833,"score_spread":0.2363587957078889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146613331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009404703,0.000101773316,0.9953134,0.00015323408,0.00052779267,0.0005789913,0.00001640251,0.00051572913,0.0018522162],"genre_scores_gemma":[0.025095208,4.897266e-7,0.97311157,0.000535861,0.00024147362,0.00023537163,0.000027908174,0.000028496886,0.00072360993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790245,0.00005021699,0.00033572203,0.0006959705,0.000404931,0.0006106807],"domain_scores_gemma":[0.9986334,0.00031557423,0.00011094612,0.00072754885,0.00008725735,0.00012528925],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002741048,0.00026958683,0.0002591247,0.00018064932,0.00019207122,0.00020266499,0.00081992877,0.000066584726,0.000010900085],"category_scores_gemma":[0.000022697788,0.00024534942,0.00011839969,0.00025283653,0.000031709103,0.00017151212,0.0000927136,0.00013248199,0.00006284757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004583849,0.00012427005,0.00017473631,0.000009953498,0.0000062094045,0.00012685392,0.00003074514,0.9336976,0.000060637798,0.00018694466,0.009069926,0.056507554],"study_design_scores_gemma":[0.00076095085,0.0004616834,0.0017795824,0.000048964775,0.000007113023,0.000033950953,0.0000065183126,0.9931528,0.0014861653,0.0007388791,0.0011887904,0.000334579],"about_ca_topic_score_codex":0.000042691347,"about_ca_topic_score_gemma":9.411473e-8,"teacher_disagreement_score":0.059455235,"about_ca_system_score_codex":0.000070291215,"about_ca_system_score_gemma":0.00010451928,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W3146841301","doi":"10.1109/lra.2021.3068554","title":"A Gravity-Referenced Moving Frame for Vehicle Path Following Applications in 3D","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Reference frame; Frame (networking); Trajectory; Path (computing); Curvature; Moving frame; Gravitational field; Motion planning; Computer vision; Control theory (sociology); Artificial intelligence; Mathematics; Control (management); Physics; Geometry; Classical mechanics; Robot","score_opus":0.016375031968111898,"score_gpt":0.25795880835869783,"score_spread":0.24158377639058592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146841301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105618864,0.000039324856,0.8909405,0.0027893889,0.0002603203,0.00021141335,0.0000031397926,0.00012309475,0.000013970765],"genre_scores_gemma":[0.43625185,0.0000023270823,0.56279486,0.0008171478,0.0000387376,0.000058572725,0.000014098646,0.000008480444,0.000013933536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989482,0.00004607762,0.0002464784,0.00034492614,0.0001713973,0.00024289514],"domain_scores_gemma":[0.99930894,0.00021663481,0.00008527728,0.00028388738,0.00004373039,0.000061507344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020838894,0.00011835278,0.00017159787,0.000098074896,0.0001578293,0.00020901553,0.00021456428,0.000058695234,3.8085477e-7],"category_scores_gemma":[0.000048438014,0.00012608596,0.00005277657,0.00035391073,0.000016317279,0.00028688394,0.00005233211,0.00011418596,0.000003728117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002467939,0.00020084073,0.015802799,0.00016895482,0.00007077902,0.000088814966,0.0033235997,0.82243156,0.09971016,0.02177916,0.000407161,0.036013708],"study_design_scores_gemma":[0.00037507518,0.000012494402,0.012002578,0.00006696761,0.000009717574,0.000005062508,0.00003562978,0.9849838,0.0010285358,0.001227397,0.000076889315,0.00017584105],"about_ca_topic_score_codex":0.00001088269,"about_ca_topic_score_gemma":0.0000018300235,"teacher_disagreement_score":0.33063298,"about_ca_system_score_codex":0.00004493793,"about_ca_system_score_gemma":0.000049793834,"threshold_uncertainty_score":0.51416355},"labels":[],"label_agreement":null},{"id":"W3146880392","doi":"10.1109/robot.1990.126258","title":"Fast collision avoidance for manipulator arms: a sequential search strategy","year":2002,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Link (geometry); Path (computing); Motion (physics); Discretization; Computer science; Base (topology); Space (punctuation); Mathematics; Topology (electrical circuits); Algorithm; Artificial intelligence; Combinatorics; Robot; Mathematical analysis","score_opus":0.09605922199701503,"score_gpt":0.2977210101620368,"score_spread":0.20166178816502175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146880392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007173941,0.00008500144,0.9878137,0.0005789178,0.00040113062,0.0003476698,0.0000066557127,0.00026359066,0.0033294107],"genre_scores_gemma":[0.3813643,0.000008159297,0.6116689,0.00020793945,0.00019015408,0.0000384126,0.0000041538287,0.000016774411,0.0065012146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984306,0.00005003044,0.0002249627,0.00047418004,0.00036311787,0.0004570813],"domain_scores_gemma":[0.99911183,0.0001069464,0.000047352198,0.0004898194,0.00010454057,0.00013951292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838091,0.00014662225,0.00016120599,0.000088361354,0.00016112546,0.000247182,0.0007944619,0.00007622749,0.00005535017],"category_scores_gemma":[0.000026319285,0.00013029874,0.00006833904,0.0003037356,0.000035065787,0.00040648197,0.00013905573,0.00013094785,0.0002722888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054461412,0.0010230426,0.0013657513,0.00027442386,0.00017722599,0.00048054708,0.008293279,0.51185066,0.01394036,0.1500192,0.07399857,0.23852251],"study_design_scores_gemma":[0.0004382373,0.00016933636,0.00032818996,0.000019491548,0.0000029460389,0.000036126607,0.000068860994,0.99481195,0.003080365,0.0003538631,0.0005080521,0.00018258402],"about_ca_topic_score_codex":0.000026845079,"about_ca_topic_score_gemma":0.0000018563331,"teacher_disagreement_score":0.4829613,"about_ca_system_score_codex":0.000060495826,"about_ca_system_score_gemma":0.000039276616,"threshold_uncertainty_score":0.5313428},"labels":[],"label_agreement":null},{"id":"W3147166570","doi":"10.5555/1750496.1750503","title":"Kinematics-based characterization of the collision course","year":2008,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kinematics; Collision; Course (navigation); Object (grammar); Motion (physics); Characterization (materials science); Kinematics equations; Computer science; Mobile robot; Robot kinematics; Artificial intelligence; Computer vision; Physics; Robot; Classical mechanics; Astronomy; Computer security; Optics","score_opus":0.014691747760442532,"score_gpt":0.2525867213101402,"score_spread":0.23789497354969769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147166570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24893883,0.000016929933,0.7475527,0.0024603356,0.0009477702,0.000048214493,0.000002588159,0.000010134768,0.000022538075],"genre_scores_gemma":[0.8701021,0.000022286304,0.12965979,0.00011028251,0.00007537424,4.3353046e-7,0.000003076583,0.0000035923,0.000023063925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988628,0.00004685845,0.00040204133,0.000065521956,0.0005639603,0.000058826074],"domain_scores_gemma":[0.99846876,0.00007309331,0.0007219492,0.000120471304,0.0005843607,0.000031390045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002439338,0.0000632844,0.00011732644,0.00010464668,0.000059253933,0.00004426938,0.0004912771,0.00003448581,0.0000015077982],"category_scores_gemma":[0.00009497236,0.000044527907,0.00005386558,0.00013788107,0.00004629502,0.0003187117,0.000057300287,0.000078018726,9.43891e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027441898,0.00045776286,0.017834453,0.000047551104,0.00015403067,0.000085613625,0.0018562985,0.90396214,0.046241533,0.014342502,0.00037863862,0.014612031],"study_design_scores_gemma":[0.0003771908,0.000050921735,0.1463249,0.00015874748,0.000010053505,0.00022236245,0.0000052711835,0.84721214,0.0052335477,0.0003221613,0.0000343219,0.000048368518],"about_ca_topic_score_codex":0.0000015593204,"about_ca_topic_score_gemma":7.8088554e-8,"teacher_disagreement_score":0.62116325,"about_ca_system_score_codex":0.000029331044,"about_ca_system_score_gemma":0.0001323263,"threshold_uncertainty_score":0.18157953},"labels":[],"label_agreement":null},{"id":"W3153015002","doi":"10.1016/j.ifacol.2020.12.1549","title":"Fast Trajectory Planning in Cartesian rather than Frenet Frame: A Precise Solution for Autonomous Driving in Complex Urban Scenarios","year":2020,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Frenet–Serret formulas; Trajectory; Cartesian coordinate system; Computer science; Control theory (sociology); Frame (networking); Mathematical optimization; Constraint (computer-aided design); Motion planning; Curvature; Simulation; Mathematics; Robot; Control (management); Artificial intelligence; Geometry","score_opus":0.04216438017253147,"score_gpt":0.273681377512884,"score_spread":0.23151699734035255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153015002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17924626,0.000555903,0.81353915,0.004402271,0.00042024534,0.001080803,0.000054744996,0.00046418558,0.00023644487],"genre_scores_gemma":[0.33492154,0.000002435195,0.6640619,0.0005158717,0.0002804077,0.000060818296,0.00006147285,0.000034595876,0.00006094221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974139,0.00014192596,0.0005337547,0.00084500836,0.00031573436,0.0007497059],"domain_scores_gemma":[0.99890566,0.00023083713,0.00017334294,0.00040984925,0.000044740198,0.00023555063],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037832343,0.0003470228,0.000499562,0.00023512857,0.00012843353,0.00013528121,0.0008781031,0.00018834304,0.000011718435],"category_scores_gemma":[0.00018663854,0.00036740612,0.00011333684,0.00061538373,0.00005903511,0.00039246056,0.000161453,0.0004648882,0.00001792638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020320344,0.0009697239,0.23175184,0.0003086622,0.00013346701,0.00079168426,0.1557166,0.45383897,0.06220103,0.00034416813,0.0007137877,0.09302685],"study_design_scores_gemma":[0.0013587581,0.00022974306,0.06645257,0.00017428244,0.000008072917,0.000013365992,0.00031088985,0.9306003,0.00012185047,0.000029912066,0.0003054025,0.00039489145],"about_ca_topic_score_codex":0.00015999949,"about_ca_topic_score_gemma":0.0002040367,"teacher_disagreement_score":0.47676128,"about_ca_system_score_codex":0.00021872774,"about_ca_system_score_gemma":0.00019900834,"threshold_uncertainty_score":0.9998778},"labels":[],"label_agreement":null},{"id":"W3153787113","doi":"10.1017/s0373463321000345","title":"Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field","year":2021,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Underwater; Motion planning; Path (computing); Potential field; Function (biology); Computer science; Field (mathematics); Shortest path problem; Marine engineering; Algorithm; Engineering; Simulation; Artificial intelligence; Geology; Mathematics; Robot; Graph","score_opus":0.023965064581697965,"score_gpt":0.33841057078095943,"score_spread":0.3144455061992615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153787113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16224481,0.00003255824,0.8363721,0.0005263609,0.0007206662,0.00007939081,0.0000022686397,0.0000107895985,0.000011078462],"genre_scores_gemma":[0.6473279,3.5275104e-7,0.35242176,0.00006610807,0.00016466042,0.0000020360628,0.0000074278896,0.000005863975,0.00000387842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985034,0.00029046088,0.00046556932,0.00015784887,0.00042461097,0.00015810187],"domain_scores_gemma":[0.9979837,0.0010003747,0.0005323069,0.00014414907,0.00028719773,0.00005228984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013303879,0.00010434338,0.00024707496,0.00011933903,0.00008642453,0.00007251968,0.00023660569,0.00007298219,0.0000014650133],"category_scores_gemma":[0.0004144539,0.000086794724,0.00014737502,0.00019062754,0.000008536946,0.00023217492,0.000031992142,0.0002067176,8.8513553e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040568426,0.00022233836,0.001792427,0.00035949258,0.00008277459,0.0003152599,0.00095533545,0.5469334,0.10508556,0.00024439595,0.00048019062,0.34312314],"study_design_scores_gemma":[0.0006132169,0.0009439045,0.00066281745,0.00063564535,0.000030388577,0.00004071479,0.000024337356,0.7196674,0.27652597,0.0007797497,0.00000806927,0.00006782528],"about_ca_topic_score_codex":0.0000017685521,"about_ca_topic_score_gemma":2.4246173e-8,"teacher_disagreement_score":0.4850831,"about_ca_system_score_codex":0.00004060562,"about_ca_system_score_gemma":0.00008498834,"threshold_uncertainty_score":0.35393864},"labels":[],"label_agreement":null},{"id":"W3159126237","doi":"10.1016/j.robot.2021.103800","title":"Safe path planning for UAV urban operation under GNSS signal occlusion risk","year":2021,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; St. Francis Xavier University","funders":"Agence de l'innovation de Défense; Office National d'études et de Recherches Aérospatiales; Ministry of Defense; Ministère de la Défense; Agence Nationale de la Recherche","keywords":"Computer science; Partially observable Markov decision process; GNSS applications; Real-time computing; Motion planning; Global Positioning System; Obstacle; Benchmark (surveying); Probabilistic logic; Simulation; Artificial intelligence; Markov chain; Markov model; Robot; Machine learning; Telecommunications","score_opus":0.021019329597167374,"score_gpt":0.25038277343504145,"score_spread":0.22936344383787408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159126237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022341344,0.0018904157,0.99341625,0.00030241392,0.0013064203,0.00034029596,0.00002252245,0.00014355776,0.00034401915],"genre_scores_gemma":[0.72533715,0.000030654657,0.27252838,0.00016008312,0.00045868673,0.000027871782,0.00005991401,0.000032420416,0.0013648482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817246,0.00015109543,0.0004325135,0.0005870459,0.00025688933,0.00039999033],"domain_scores_gemma":[0.99881786,0.0002368955,0.00020946476,0.00042017712,0.00016085365,0.00015476867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005662333,0.0002232743,0.00034095164,0.0000769948,0.0005516744,0.0006243245,0.00030476516,0.00013899087,0.0000017670164],"category_scores_gemma":[0.00004036594,0.0002113274,0.00006841395,0.00016708364,0.000026181919,0.00026497815,0.00021537607,0.00017283608,0.000012776462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027731758,0.000042450294,0.0013464397,0.00006195293,0.000035536003,0.000070358234,0.0009038869,0.9787379,0.0005573497,0.01609083,0.000906512,0.0012440494],"study_design_scores_gemma":[0.0004910914,0.00011938437,0.0010482662,0.0001556559,0.000025307234,0.00016169749,0.00021101125,0.9957265,0.00018407832,0.0003669061,0.0012214668,0.00028859294],"about_ca_topic_score_codex":0.00005412439,"about_ca_topic_score_gemma":9.78e-7,"teacher_disagreement_score":0.723103,"about_ca_system_score_codex":0.000102351405,"about_ca_system_score_gemma":0.00023554346,"threshold_uncertainty_score":0.8617681},"labels":[],"label_agreement":null},{"id":"W3160323480","doi":"10.1017/s0263574722000777","title":"Stability-constrained mobile manipulation planning on rough terrain","year":2022,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Terrain; Zero moment point; Computer science; Kinematics; Mobile robot; Stability (learning theory); Control theory (sociology); Mobile manipulator; Trajectory; Robot; Constraint (computer-aided design); Point (geometry); Robotics; Control engineering; Artificial intelligence; Humanoid robot; Engineering; Control (management); Mathematics","score_opus":0.039744821113402155,"score_gpt":0.27645296009340786,"score_spread":0.2367081389800057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160323480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016759967,0.000058737154,0.97277117,0.0011701024,0.0010741566,0.0005207919,0.000010719959,0.0006360813,0.0069982517],"genre_scores_gemma":[0.8670183,2.46154e-7,0.13215859,0.00042430015,0.00007345376,0.000113994574,0.000021284855,0.000016390519,0.00017348104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978353,0.0002726213,0.0003064208,0.0005801029,0.00058314315,0.00042241544],"domain_scores_gemma":[0.99871194,0.00029123636,0.000110930116,0.00074529106,0.000032235777,0.00010837914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069433445,0.00018219084,0.00020665374,0.000134275,0.00048385517,0.00010632635,0.0008337277,0.00004688676,0.00013340799],"category_scores_gemma":[0.00008274193,0.00019465713,0.000071214345,0.00045397264,0.000044576205,0.0002311293,0.0003936014,0.00041657512,0.00006403896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009899171,0.00012619226,0.0006149667,0.000007992895,0.000013192012,0.00009502775,0.002919885,0.9792634,0.0003117484,0.011455199,0.0003925176,0.004789974],"study_design_scores_gemma":[0.00039133473,0.00050538126,0.0034625547,0.000021529411,0.0000069650696,0.00007887656,0.000343578,0.99197286,0.00019899572,0.0017799025,0.00093548873,0.00030252803],"about_ca_topic_score_codex":0.0000128608335,"about_ca_topic_score_gemma":1.9515761e-7,"teacher_disagreement_score":0.8502583,"about_ca_system_score_codex":0.00020084703,"about_ca_system_score_gemma":0.00009287138,"threshold_uncertainty_score":0.79378873},"labels":[],"label_agreement":null},{"id":"W3163098214","doi":"10.31234/osf.io/x7jbw","title":"The Moon Illusion Explained by Visual Motion Perception","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Illusion; Optical illusion; Rocket (weapon); Geodesy; Space (punctuation); Perception; Motion (physics); Aerospace engineering; Launched; Physics; Computer science; Computer graphics (images); Computer vision; Geology; Engineering; Psychology; Cognitive psychology","score_opus":0.022259295744321225,"score_gpt":0.27375482474666013,"score_spread":0.2514955290023389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163098214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025499994,0.00008315624,0.9849618,0.007671687,0.0019302944,0.00035598603,0.0000046816676,0.0017137765,0.00072861515],"genre_scores_gemma":[0.48221704,0.00028466998,0.5126893,0.0014493299,0.0009710785,0.00014987547,0.00021656373,0.0001252397,0.0018968907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997675,0.0002132044,0.00035226796,0.00082300464,0.00061535015,0.0003211663],"domain_scores_gemma":[0.99867016,0.00012898873,0.00020130508,0.00076579116,0.00009675517,0.00013700787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048975146,0.0002895303,0.00022911833,0.000056304594,0.00038257815,0.00052074745,0.0014877181,0.0002615944,0.00000867107],"category_scores_gemma":[0.00011056183,0.00020515974,0.0001181035,0.00018588042,0.000047791797,0.00020837963,0.0022344226,0.00062937045,0.00045190722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005152175,0.00033105773,0.00014128862,0.00019196057,0.0001537451,0.000082346676,0.010797741,0.02380558,0.026359135,0.01023654,0.19887133,0.72897774],"study_design_scores_gemma":[0.000141285,0.000074821255,0.0014447594,0.00005213391,0.000008084291,0.000007030332,0.00011474241,0.994242,0.00024493836,0.0025829605,0.00080664235,0.00028058593],"about_ca_topic_score_codex":0.00008493156,"about_ca_topic_score_gemma":0.0000012411856,"teacher_disagreement_score":0.97043645,"about_ca_system_score_codex":0.00015857538,"about_ca_system_score_gemma":0.000077139084,"threshold_uncertainty_score":0.8366172},"labels":[],"label_agreement":null},{"id":"W3165166948","doi":"10.48550/arxiv.2006.15664","title":"Minimizing The Maximum Distance Traveled To Form Patterns With Systems\\n of Mobile Robots","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Carleton University","funders":"","keywords":"Computer science; Mobile robot; Robot; Artificial intelligence","score_opus":0.07007722403764295,"score_gpt":0.19123088068268765,"score_spread":0.1211536566450447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165166948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11847268,0.00010817999,0.8773179,0.00020433374,0.0011105741,0.0019603118,0.00012155681,0.0001509019,0.0005535842],"genre_scores_gemma":[0.98690283,0.000088545174,0.011919367,0.00011766099,0.00014755329,0.000016506538,0.000015737482,0.000063849184,0.00072793744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946195,0.00042231462,0.00085407996,0.0025626794,0.00049081753,0.0010506363],"domain_scores_gemma":[0.9942968,0.00048004606,0.0012186273,0.0028684027,0.0004965519,0.0006395633],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0006319285,0.0009272241,0.0013397741,0.00030235262,0.00044271277,0.00033193524,0.0054028546,0.00035172226,0.000016754551],"category_scores_gemma":[0.000056552894,0.00080401846,0.0003652836,0.002011443,0.00030632885,0.0004933769,0.0025021522,0.001111018,0.00008855027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019882443,0.00014073304,0.0054256227,0.0006297303,0.00032800357,0.001029546,0.005480594,0.9782724,0.000053480468,0.007409607,0.00005979526,0.00097171176],"study_design_scores_gemma":[0.0010985755,0.00080788526,0.00564686,0.0020209027,0.00030946225,0.000053607822,0.0029317753,0.98492295,0.00019598837,0.00071022194,0.00019408003,0.0011076818],"about_ca_topic_score_codex":0.00072703155,"about_ca_topic_score_gemma":0.000050852344,"teacher_disagreement_score":0.86843014,"about_ca_system_score_codex":0.00044438994,"about_ca_system_score_gemma":0.000568296,"threshold_uncertainty_score":0.99997836},"labels":[],"label_agreement":null},{"id":"W3171961868","doi":"10.3390/en14123517","title":"An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Energy consumption; Motion planning; Mobile robot; Energy (signal processing); Battery (electricity); Motion (physics); Efficient energy use; Robot; Computer science; Work (physics); Automotive engineering; Key (lock); Process (computing); Motion control; Simulation; Engineering; Artificial intelligence; Electrical engineering; Mechanical engineering; Power (physics)","score_opus":0.010989025784217647,"score_gpt":0.23635535440475855,"score_spread":0.2253663286205409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171961868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5644524,0.0004913497,0.43390957,0.00032090957,0.00048245408,0.000045439658,6.961494e-7,0.00015928976,0.0001379133],"genre_scores_gemma":[0.8711951,0.000011670399,0.12833035,0.00011934859,0.00006526517,0.000031578988,0.000014035969,0.000007658211,0.0002250047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987972,0.00020373244,0.00023317932,0.0003142327,0.0002828662,0.00016878339],"domain_scores_gemma":[0.9988636,0.000094140945,0.00012303513,0.0007083373,0.00015112445,0.00005976586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027751128,0.00011153697,0.00013901836,0.000068748675,0.00014150642,0.000103445695,0.0005618167,0.000053849446,0.0000047443777],"category_scores_gemma":[0.00004183896,0.00008734207,0.000045331282,0.000449371,0.00008746238,0.00023249058,0.00015193562,0.00007251601,0.00000500412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.0139644e-7,0.000040055405,0.00012865048,0.0000046362493,0.000007728786,0.000008059244,0.0053735073,0.93904835,0.017737528,0.018268157,0.000066416964,0.01931629],"study_design_scores_gemma":[0.00010098796,0.00005703624,0.0026924545,0.000018403898,0.000005704885,0.000010956126,0.00034461592,0.84130156,0.15371814,0.0015255514,0.00011942916,0.00010518253],"about_ca_topic_score_codex":0.00028228504,"about_ca_topic_score_gemma":0.000014030976,"teacher_disagreement_score":0.3067427,"about_ca_system_score_codex":0.000046327397,"about_ca_system_score_gemma":0.0001544495,"threshold_uncertainty_score":0.35617062},"labels":[],"label_agreement":null},{"id":"W3172523115","doi":"10.48550/arxiv.2106.03836","title":"Tunable Trajectory Planner Using G3 Curves","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Acceleration; Discretization; Trajectory; Path (computing); Mathematical optimization; Curvature; Motion planning; Computer science; Trajectory optimization; Gradient descent; Control theory (sociology); Set (abstract data type); Mathematics; Optimal control; Mathematical analysis; Geometry; Robot; Artificial intelligence; Physics; Classical mechanics; Control (management)","score_opus":0.13106806573241878,"score_gpt":0.20195382179690752,"score_spread":0.07088575606448874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172523115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09027824,0.0015030805,0.9039307,0.00006705558,0.0016783169,0.00022126664,0.000014394457,0.00040620056,0.0019007266],"genre_scores_gemma":[0.90714294,0.00064437365,0.08829583,0.00042804927,0.00021221314,0.0000011281169,0.00006248412,0.000049291833,0.003163695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973463,0.00025205963,0.00023718934,0.0014343866,0.0001709557,0.0005591521],"domain_scores_gemma":[0.99755204,0.00012186414,0.00027159537,0.0016346334,0.00018681052,0.00023303773],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034026016,0.0003961379,0.00049078255,0.00026553508,0.00019132752,0.00020242088,0.0021096927,0.00032750345,0.00004091543],"category_scores_gemma":[0.000051697818,0.00048543155,0.00024619038,0.00070676033,0.00009505376,0.0005553592,0.0021157416,0.0008095962,0.000047047844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050416043,0.000091696646,0.0016945628,0.00028782303,0.00012733015,0.0030037363,0.00032036466,0.9909717,0.000090966125,0.0022167496,0.00095046684,0.000239549],"study_design_scores_gemma":[0.00027036396,0.000023799643,0.00094480295,0.0009538013,0.00008878308,0.00004728397,0.000096267366,0.9954083,0.00016029006,0.0011629123,0.00019920667,0.00064418965],"about_ca_topic_score_codex":0.0003603134,"about_ca_topic_score_gemma":0.00000778148,"teacher_disagreement_score":0.81686467,"about_ca_system_score_codex":0.00029281553,"about_ca_system_score_gemma":0.00066250237,"threshold_uncertainty_score":0.99975973},"labels":[],"label_agreement":null},{"id":"W3173044790","doi":"10.3390/designs5020037","title":"Swarm Robotic Interactions in an Open and Cluttered Environment: A Survey","year":2021,"lang":"en","type":"article","venue":"Designs","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Swarm behaviour; Computer science; Artificial intelligence; Robot; Population; Open research; Swarm robotics; Human–computer interaction; Machine learning; World Wide Web; Sociology","score_opus":0.16600255701506592,"score_gpt":0.33904795854921715,"score_spread":0.17304540153415124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173044790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041793685,0.000062020015,0.9569485,0.00044244886,0.00021902924,0.00016533118,0.00000430299,0.000041298063,0.0003233801],"genre_scores_gemma":[0.70267665,0.000012013048,0.29654402,0.0002949241,0.000018781833,0.000020729729,0.00002415528,0.000010430399,0.0003983087],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986416,0.00043187253,0.00017097207,0.00042923202,0.00011420507,0.00021210313],"domain_scores_gemma":[0.99911535,0.00021118995,0.000044752953,0.0005049927,0.000019312101,0.00010438829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004736101,0.0000998845,0.00015911141,0.000071058195,0.00007563332,0.0003645173,0.0006455625,0.000027471482,0.000018944897],"category_scores_gemma":[0.000069880705,0.0001070619,0.0000112242105,0.0002347156,0.000024029845,0.00070666266,0.000491624,0.00013831317,0.000051377898],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078464924,0.0031852229,0.593352,0.00006295118,0.00018474714,0.004865008,0.021618774,0.24599643,0.032271508,0.007082386,0.0038592606,0.08744323],"study_design_scores_gemma":[0.00045522652,0.00008540284,0.63087547,0.00003430845,0.000004501703,0.00010969504,0.00005664502,0.36640513,0.00083400897,0.0007437925,0.00016960503,0.00022624181],"about_ca_topic_score_codex":0.00031216547,"about_ca_topic_score_gemma":0.00020343377,"teacher_disagreement_score":0.66088295,"about_ca_system_score_codex":0.000049494265,"about_ca_system_score_gemma":0.00006485696,"threshold_uncertainty_score":0.43658575},"labels":[],"label_agreement":null},{"id":"W3175465930","doi":"10.1609/icaps.v31i1.15961","title":"Jump Point Search with Temporal Obstacles","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council; Monash University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Jump; Point (geometry); Computer science; Variety (cybernetics); Grid; Path (computing); Motion planning; Space (punctuation); Order (exchange); Artificial intelligence; Robotics; Mathematical optimization; Algorithm; Robot; Mathematics; Geometry","score_opus":0.04692968483102595,"score_gpt":0.2901247606502225,"score_spread":0.24319507581919658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175465930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97814995,0.00009131972,0.008560953,0.004609439,0.0003183683,0.00009582506,0.000006184656,0.00043677096,0.0077312016],"genre_scores_gemma":[0.8767742,0.000005415712,0.122859664,0.00014129432,0.000029292802,0.000004061581,0.0000031351997,0.0000080538,0.00017486323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860674,0.0000132067025,0.00022952167,0.00036444,0.0005774848,0.00020857765],"domain_scores_gemma":[0.998917,0.00007778093,0.00016831052,0.00012830152,0.0006381267,0.00007047168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003425989,0.00014905732,0.00017206211,0.00011117898,0.00014848528,0.00039183008,0.0008255208,0.000059903214,0.0000067256833],"category_scores_gemma":[0.00015978575,0.00010577647,0.000036442496,0.0002704898,0.00007287228,0.00032743503,0.00030832653,0.000285088,0.0000037645316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024465346,0.00040733087,0.37640148,0.00032758224,0.0006785148,0.00025776273,0.013585633,0.04635247,0.1204827,0.4335615,0.0012568258,0.006443529],"study_design_scores_gemma":[0.00033043022,0.000069026995,0.010043264,0.0009745182,0.0000073594097,0.00017801604,0.00092777703,0.95200884,0.03426506,0.0010111477,0.00002379401,0.000160761],"about_ca_topic_score_codex":0.000016893837,"about_ca_topic_score_gemma":2.7643551e-7,"teacher_disagreement_score":0.9056564,"about_ca_system_score_codex":0.000032825028,"about_ca_system_score_gemma":0.00015511895,"threshold_uncertainty_score":0.4313439},"labels":[],"label_agreement":null},{"id":"W3180468800","doi":"10.1109/icps49255.2021.9468264","title":"Scenario-based Model Predictive Control for Path Planning and Obstacle Avoidance","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Obstacle avoidance; Motion planning; Mathematical optimization; Obstacle; Convexity; Path (computing); Computer science; Model predictive control; Convex optimization; Function (biology); Any-angle path planning; Control (management); Regular polygon; Mathematics; Artificial intelligence; Mobile robot; Robot","score_opus":0.023329491715479267,"score_gpt":0.25787885376090924,"score_spread":0.23454936204542998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180468800","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014189356,0.00039197944,0.9961863,0.00079989625,0.00016404351,0.00022148323,0.000027483493,0.00021644164,0.0005734462],"genre_scores_gemma":[0.43243602,7.517956e-7,0.56623316,0.0009375955,0.00002957195,0.000039454415,0.0000049412442,0.000008350069,0.0003101436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987628,0.000044270022,0.00017709815,0.000491209,0.00019920008,0.0003254551],"domain_scores_gemma":[0.99890983,0.0003798378,0.000064131695,0.00036206332,0.00016241534,0.000121714605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002414956,0.00013903003,0.00019996575,0.000041303065,0.00017024021,0.00014244272,0.00028245288,0.00006365935,0.0000013434436],"category_scores_gemma":[0.00016339701,0.00013060524,0.000045027144,0.00014057393,0.0000363232,0.00027373893,0.0000742687,0.00011283373,0.0000026818213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000228234,0.00005422166,0.0021845808,0.000027552951,0.000025499301,0.00006498842,0.0006757057,0.9871468,0.00080753886,0.005793028,0.0012219344,0.001975315],"study_design_scores_gemma":[0.0013586258,0.00008593042,0.00135892,0.00005789564,0.000009550231,0.000016046826,0.000047386886,0.9937638,0.0011309918,0.0019416718,0.00006625252,0.00016292548],"about_ca_topic_score_codex":0.000004128748,"about_ca_topic_score_gemma":5.209359e-7,"teacher_disagreement_score":0.43101707,"about_ca_system_score_codex":0.00003494918,"about_ca_system_score_gemma":0.00024982262,"threshold_uncertainty_score":0.5325927},"labels":[],"label_agreement":null},{"id":"W3180852400","doi":"10.3390/s21144731","title":"GPS-Free, Error Tolerant Path Planning for Swarms of Micro Aerial Vehicles with Quality Amplification ‡","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Global Affairs Canada","keywords":"Terrain; Global Positioning System; Computer vision; Path (computing); Computer science; A priori and a posteriori; Artificial intelligence; Motion planning; Real-time computing; Geography; Cartography; Telecommunications; Robot","score_opus":0.05485880521274164,"score_gpt":0.3117381080264911,"score_spread":0.2568793028137495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180852400","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63607335,0.00007620165,0.3625745,0.00052962755,0.00033196586,0.00017723083,0.00003915703,0.00009298255,0.00010500894],"genre_scores_gemma":[0.43012056,0.0000013434741,0.56943214,0.00009737163,0.0001232247,0.000015557956,0.000028923072,0.000015836144,0.00016506795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984787,0.000121954676,0.00036195212,0.0004482444,0.00030007472,0.0002890817],"domain_scores_gemma":[0.99836534,0.00026809992,0.0002372367,0.00080670795,0.0002456918,0.00007693843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040739728,0.00014869201,0.00028351473,0.000056212382,0.000103283346,0.00006736837,0.000494168,0.00007283673,0.0000017056182],"category_scores_gemma":[0.00021420862,0.00013059803,0.00006841833,0.00027470884,0.000060266637,0.00012360768,0.00010108804,0.00009879339,0.00000445338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000903807,0.0010415295,0.02181164,0.0009143372,0.00048180594,0.0004930388,0.03976683,0.45150706,0.4294369,0.023479221,0.0060306755,0.02413317],"study_design_scores_gemma":[0.0059599793,0.0006444375,0.12230425,0.00065290456,0.000079590114,0.00023465554,0.002470927,0.5426349,0.31706578,0.0037954901,0.0028317098,0.0013253989],"about_ca_topic_score_codex":0.000054113018,"about_ca_topic_score_gemma":0.0000020649916,"teacher_disagreement_score":0.2068576,"about_ca_system_score_codex":0.00003032673,"about_ca_system_score_gemma":0.00014232099,"threshold_uncertainty_score":0.5325633},"labels":[],"label_agreement":null},{"id":"W3180950260","doi":"10.1007/s11590-021-01773-6","title":"An exact penalty function method for optimal control of a dubins airplane in the presence of moving obstacles","year":2021,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Airplane; Computational intelligence; Penalty method; Mathematical optimization; Optimal control; Function (biology); Mathematics; Control (management); Computer science; Variable (mathematics); Control theory (sociology); Engineering; Artificial intelligence; Mathematical analysis; Aerospace engineering","score_opus":0.015437600586475936,"score_gpt":0.27257816478995295,"score_spread":0.25714056420347703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180950260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004529119,0.00003590992,0.9931865,0.0018000888,0.00014532245,0.00024146235,0.000012179415,0.000023968423,0.000025468249],"genre_scores_gemma":[0.22284292,0.0000024149592,0.776417,0.0006574029,0.000024717088,0.000021362577,0.000022958762,0.0000057310067,0.0000054675024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868256,0.000344797,0.00029625048,0.00026526762,0.000246852,0.0001642791],"domain_scores_gemma":[0.9985731,0.0006501491,0.00021061227,0.00040712193,0.0001311181,0.00002786699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007373293,0.000092382696,0.00018336657,0.00009344726,0.000054831566,0.00005494659,0.00043830764,0.00004039606,0.0000058024275],"category_scores_gemma":[0.00022228344,0.00007843649,0.00004850298,0.00036917947,0.000029811665,0.00042822756,0.000028452794,0.00007894334,3.699497e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020342428,0.000055652203,0.0005496741,0.000021313215,0.000012823723,0.00000817614,0.00095108384,0.9865257,0.009931198,0.00048449344,0.00009391294,0.0013456132],"study_design_scores_gemma":[0.00060488033,0.0000842928,0.0029248013,0.000031629,0.0000149176785,0.000016337643,0.00014710757,0.994433,0.0016267792,0.00001866587,0.000019426945,0.00007816064],"about_ca_topic_score_codex":0.000033626202,"about_ca_topic_score_gemma":0.0000013241483,"teacher_disagreement_score":0.2183138,"about_ca_system_score_codex":0.000018704282,"about_ca_system_score_gemma":0.00006080096,"threshold_uncertainty_score":0.31985474},"labels":[],"label_agreement":null},{"id":"W3181097251","doi":"10.2514/1.g005914","title":"Cooperative Transportation of a Flexible Payload Using Two Quadrotors","year":2021,"lang":"en","type":"article","venue":"Journal of Guidance Control and Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"Ministère de la Défense Nationale","keywords":"Payload (computing); Astronautics; Aeronautics; Aerospace; George (robot); Engineering; Library science; Operations research; Computer science; Aerospace engineering; Artificial intelligence","score_opus":0.012919927676692453,"score_gpt":0.27157379644423457,"score_spread":0.2586538687675421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181097251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13336448,0.001090284,0.8648683,0.00023887819,0.00032367822,0.00005075489,0.000011241953,0.0000073965944,0.00004496803],"genre_scores_gemma":[0.8060685,0.000026826354,0.19370472,0.00009471988,0.000054928518,8.967656e-7,0.0000010788726,0.000004794146,0.000043579137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989467,0.00007614524,0.00048587457,0.0001256413,0.00023173269,0.00013385469],"domain_scores_gemma":[0.99865425,0.00010414134,0.00044397046,0.00013619782,0.0005866599,0.000074797885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034387503,0.00009303836,0.00031052652,0.0000715537,0.000054224973,0.00004744021,0.00018620961,0.000036722402,0.0000016666694],"category_scores_gemma":[0.00006773017,0.00007967011,0.000073124495,0.00023527988,0.000042385713,0.00037624265,0.000010216328,0.00012958943,2.5892976e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027906493,0.00041535153,0.08421837,0.00013736029,0.0005525664,0.001523665,0.00605827,0.7552386,0.03486208,0.061597634,0.00009091095,0.055026144],"study_design_scores_gemma":[0.0016832246,0.00015320409,0.019580489,0.00014728196,0.000037640762,0.00020115882,0.00014914329,0.9768837,0.00043594502,0.0006144632,0.000016088221,0.0000976395],"about_ca_topic_score_codex":0.000024223204,"about_ca_topic_score_gemma":0.0000105956915,"teacher_disagreement_score":0.672704,"about_ca_system_score_codex":0.000047573183,"about_ca_system_score_gemma":0.00023473363,"threshold_uncertainty_score":0.32488528},"labels":[],"label_agreement":null},{"id":"W3182357916","doi":"10.1109/iccworkshops50388.2021.9473500","title":"UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Teleoperation; Computer science; Obstacle; Motion planning; Real-time computing; Enhanced Data Rates for GSM Evolution; Path (computing); Operator (biology); Mode (computer interface); State (computer science); Telerobotics; Robot; Simulation; Computer vision; Artificial intelligence; Human–computer interaction; Mobile robot; Algorithm; Computer network","score_opus":0.04091371795132301,"score_gpt":0.2833839678980283,"score_spread":0.24247024994670527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182357916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07795685,0.00010387814,0.9112647,0.0003246,0.0006541878,0.0000793884,0.0000044617564,0.00021371302,0.00939823],"genre_scores_gemma":[0.3508422,0.000009245326,0.6467955,0.0012419001,0.00016576322,0.0000040701634,0.000008666707,0.000022457441,0.00091017276],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983362,0.00007176743,0.00023606859,0.0006068238,0.00033018165,0.00041895942],"domain_scores_gemma":[0.99894196,0.00025803043,0.000053818723,0.00050046685,0.00006509507,0.00018060603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026218334,0.0002015294,0.0002372698,0.00008122602,0.00018044836,0.00026627327,0.00034258625,0.000084795225,0.00005380694],"category_scores_gemma":[0.00006177041,0.00018723171,0.00005688461,0.0003028552,0.000047075864,0.00032301815,0.00007921705,0.00021093007,0.000036262252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010412505,0.0018774706,0.13327657,0.00055313867,0.0009748485,0.023225727,0.08860519,0.21058626,0.28975943,0.071803905,0.048079733,0.13115361],"study_design_scores_gemma":[0.0031596334,0.0006985371,0.04700414,0.0006269233,0.00010546076,0.0045620855,0.0017043023,0.8506706,0.07613701,0.0037025574,0.008895123,0.002733667],"about_ca_topic_score_codex":0.000018220944,"about_ca_topic_score_gemma":4.79329e-7,"teacher_disagreement_score":0.64008427,"about_ca_system_score_codex":0.0000387193,"about_ca_system_score_gemma":0.00017490456,"threshold_uncertainty_score":0.76350874},"labels":[],"label_agreement":null},{"id":"W3183411398","doi":"10.1109/icjece.2021.3088294","title":"Multiunmanned Aerial Vehicle Path Planner on Graphics Processing Unit","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Canadian Defence Academy","keywords":"Computer science; Graphics processing unit; Software; Motion planning; Speedup; Point of interest; Graphics; Path (computing); Real-time computing; Planner; Dijkstra's algorithm; Shortest path problem; Artificial intelligence; Computer graphics (images); Parallel computing; Robot; Operating system; Theoretical computer science","score_opus":0.011533667672711178,"score_gpt":0.19506371852209659,"score_spread":0.1835300508493854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183411398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09537291,0.0010566377,0.9021674,0.00050920655,0.0007860159,0.000037433027,0.0000015193107,0.000036994636,0.000031915402],"genre_scores_gemma":[0.9142079,0.00001226423,0.08471526,0.00047039927,0.0005597942,8.6932397e-7,0.0000013392463,0.000014660526,0.000017543402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899226,0.000032000655,0.00024441333,0.00018353756,0.00017339604,0.00037435855],"domain_scores_gemma":[0.99899757,0.000107944084,0.00007206651,0.00012509563,0.0001508658,0.0005464725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014329496,0.00013652869,0.000208144,0.00027424775,0.00010872528,0.0002356228,0.0003188167,0.0000706112,0.0000013574597],"category_scores_gemma":[0.00007007647,0.00012719209,0.000053487845,0.0005520901,0.000014752116,0.00019589442,0.00002694944,0.00038185503,0.0000015491152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004293482,0.00019599612,0.0075673647,0.00015634335,0.00028601804,0.019770343,0.0027795692,0.49227265,0.0019924936,0.03783301,0.0037165456,0.43338674],"study_design_scores_gemma":[0.00039774342,0.00022458666,0.008165515,0.00012749819,0.0000070385704,0.0007627536,0.0000021881424,0.9878281,0.00032493577,0.00013018303,0.0018532393,0.00017625224],"about_ca_topic_score_codex":0.000049471357,"about_ca_topic_score_gemma":0.000013190517,"teacher_disagreement_score":0.81883496,"about_ca_system_score_codex":0.000045685487,"about_ca_system_score_gemma":0.0004740916,"threshold_uncertainty_score":0.5186743},"labels":[],"label_agreement":null},{"id":"W3187649900","doi":"10.1016/j.artint.2021.103574","title":"Pairwise symmetry reasoning for multi-agent path finding search","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Pairwise comparison; Scalability; Bounded function; Computer science; Variety (cybernetics); Context (archaeology); Homogeneous space; Theoretical computer science; Node (physics); Path (computing); Symmetry (geometry); Combinatorial explosion; Combinatorial search; Algorithm; Mathematics; Search algorithm; Artificial intelligence; Combinatorics; Beam search; Physics","score_opus":0.1819009405321721,"score_gpt":0.3722673903453196,"score_spread":0.1903664498131475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187649900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008395867,0.00027674562,0.98901665,0.0005005398,0.001158528,0.00023485815,0.000008468245,0.00021733527,0.00019100201],"genre_scores_gemma":[0.29008245,0.000016273945,0.7091469,0.00020820745,0.00016021421,0.00003360232,0.0000091539705,0.000018094355,0.00032508196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763054,0.00013890277,0.00044360332,0.00074899395,0.00038739652,0.0006505778],"domain_scores_gemma":[0.99827135,0.0004505417,0.00009013442,0.0006938155,0.0003042376,0.00018990773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009542813,0.00019561798,0.00022968245,0.00012036147,0.00036822332,0.00034019205,0.0008321822,0.00009926865,0.000020237312],"category_scores_gemma":[0.0009166424,0.00020645384,0.00013386118,0.0007902639,0.00006173679,0.0003181955,0.00035122133,0.00024761754,0.00025391515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017549404,0.00043447592,0.0016219288,0.00008214155,0.00006461637,0.00055251457,0.0053657736,0.02025989,0.008251363,0.3008036,0.0005013739,0.66204476],"study_design_scores_gemma":[0.000035977046,0.000064939384,0.0002947971,0.000096799915,0.0000073048645,0.0000339792,0.0005156669,0.8733076,0.12158246,0.0035318,0.00027614215,0.00025252494],"about_ca_topic_score_codex":0.000044652556,"about_ca_topic_score_gemma":0.000007047141,"teacher_disagreement_score":0.8530477,"about_ca_system_score_codex":0.00010026851,"about_ca_system_score_gemma":0.00022908201,"threshold_uncertainty_score":0.8418943},"labels":[],"label_agreement":null},{"id":"W3189840574","doi":"10.1109/cacre52464.2021.9501301","title":"Obstacle Avoidance for Autonomous Mobile Robots in Unstructured Human Environments","year":2021,"lang":"en","type":"article","venue":"2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Obstacle avoidance; Mobile robot; Obstacle; Computer science; Robot; Collision avoidance; Artificial intelligence; Computer vision; Human–computer interaction; Computer security; Geography; Collision","score_opus":0.013004897491621575,"score_gpt":0.24751654524321598,"score_spread":0.23451164775159442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189840574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042464817,0.00012717517,0.99283314,0.0010729387,0.00096667925,0.00032911066,0.000033708708,0.00011581955,0.00027492584],"genre_scores_gemma":[0.8187293,0.000032546544,0.18005928,0.00014620156,0.00012493413,0.00011003043,0.00008017018,0.000022119724,0.0006954417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982929,0.00004739074,0.00045599887,0.00053582166,0.00034055216,0.00032729228],"domain_scores_gemma":[0.9990994,0.00015793486,0.00014717273,0.00034035678,0.00014164476,0.000113488284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022316421,0.00025240929,0.00030559857,0.00018364945,0.00011441359,0.00026823947,0.00048717475,0.00011600801,0.00004285072],"category_scores_gemma":[0.00013568324,0.00027165687,0.000065618195,0.0001571568,0.000030076102,0.00031881308,0.00009565455,0.00021585076,0.000016804688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049619107,0.00006093679,0.00030579415,0.000019109313,0.000053277603,0.000030632964,0.00025292928,0.9280265,0.0068719774,0.060631875,0.00003731142,0.0037046697],"study_design_scores_gemma":[0.00150561,0.00006982619,0.014187496,0.00011571121,0.000009661306,0.000029322842,0.00002472516,0.9815848,0.00052630214,0.0013086876,0.00035869572,0.00027914764],"about_ca_topic_score_codex":0.0000078837875,"about_ca_topic_score_gemma":0.0000049074133,"teacher_disagreement_score":0.8144828,"about_ca_system_score_codex":0.00016362978,"about_ca_system_score_gemma":0.000110030895,"threshold_uncertainty_score":0.99997354},"labels":[],"label_agreement":null},{"id":"W3191656955","doi":"10.1109/icc42927.2021.9500370","title":"Simple and Efficient Algorithm for Drone Path Planning","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Drone; Motion planning; Computer science; Software deployment; Path (computing); Travelling salesman problem; Computation; Metric (unit); Energy consumption; Real-time computing; Task (project management); Simple (philosophy); Algorithm; Artificial intelligence; Engineering; Robot; Computer network; Operations management; Systems engineering","score_opus":0.02152452679349274,"score_gpt":0.27368846964387294,"score_spread":0.2521639428503802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191656955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025838506,0.00035602952,0.9954568,0.0002644566,0.00028380577,0.00011847152,0.0000068433405,0.00018051475,0.000749217],"genre_scores_gemma":[0.015075299,0.0000024571802,0.98383915,0.0003525968,0.00007495979,0.00001780144,0.000011608663,0.000009309456,0.00061680627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887294,0.000028335364,0.00016237846,0.00043346875,0.00018319704,0.00031970686],"domain_scores_gemma":[0.99921125,0.00020935849,0.000041599997,0.00033547007,0.00008710325,0.00011520649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000240574,0.00011561621,0.00015862874,0.00004531715,0.00015058565,0.00015553493,0.00024342707,0.000046835165,0.0000058594865],"category_scores_gemma":[0.000059353475,0.00010683537,0.00003615899,0.00020230039,0.000022138334,0.000097522905,0.000232255,0.000072026276,0.000009355847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004423803,0.0002947082,0.0014678995,0.00007123258,0.000092658396,0.00076025695,0.0031577626,0.041430816,0.0019297779,0.023796154,0.015112287,0.91188204],"study_design_scores_gemma":[0.0003920792,0.00005367489,0.0013692483,0.00001802182,0.000004641238,0.00008388001,0.00009002227,0.993004,0.0016552346,0.00086278835,0.0023089992,0.00015740172],"about_ca_topic_score_codex":0.0000075408047,"about_ca_topic_score_gemma":1.1958346e-7,"teacher_disagreement_score":0.9515732,"about_ca_system_score_codex":0.00002041516,"about_ca_system_score_gemma":0.00007140521,"threshold_uncertainty_score":0.435662},"labels":[],"label_agreement":null},{"id":"W3194593943","doi":"10.1109/ro-man50785.2021.9515460","title":"RRT-SMP: Socially-encoded Motion Primitives for Sampling-based Path Planning","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Motion planning; Motion (physics); Planner; Benchmark (surveying); Computer science; Path (computing); Social force model; Robot; Random tree; Artificial intelligence; Social planner; Social robot; Human–computer interaction; Mobile robot; Engineering; Robot control; Computer network; Pedestrian; Geography","score_opus":0.07116702792391091,"score_gpt":0.3240758882188842,"score_spread":0.2529088602949733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194593943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002267368,0.00008859302,0.99348783,0.001323927,0.00058693264,0.00020906689,0.000009555519,0.00041121198,0.0016154983],"genre_scores_gemma":[0.06557591,0.0000011158803,0.93285894,0.0008170361,0.00016120309,0.000040009178,0.0000469858,0.000016227372,0.00048256438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983584,0.00008985019,0.0002688202,0.00056525087,0.00030346125,0.00041418168],"domain_scores_gemma":[0.9986302,0.00049111055,0.00011520856,0.00040536321,0.0002501198,0.00010795768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041328324,0.000174627,0.00022617973,0.00007897024,0.00027859575,0.0002291416,0.00046164295,0.00010542237,0.000013230682],"category_scores_gemma":[0.00035278406,0.00017406719,0.00011825365,0.00030670362,0.00003245753,0.00029537606,0.000115723575,0.00013615219,0.000018628672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008085002,0.0014513918,0.0107857175,0.0004951191,0.0003486892,0.0006358197,0.01358232,0.39075536,0.023868874,0.30741462,0.013369677,0.23721157],"study_design_scores_gemma":[0.0009357961,0.00010284134,0.0063554817,0.00010986643,0.000014702114,0.00001818069,0.00017287706,0.96772015,0.014373696,0.009017756,0.00079621084,0.00038243548],"about_ca_topic_score_codex":0.000007313343,"about_ca_topic_score_gemma":9.5813e-7,"teacher_disagreement_score":0.5769648,"about_ca_system_score_codex":0.00007263553,"about_ca_system_score_gemma":0.0004908815,"threshold_uncertainty_score":0.7098254},"labels":[],"label_agreement":null},{"id":"W3196037996","doi":"10.3390/s21165547","title":"Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Motion planning; Global Positioning System; Computer science; Nonlinear system; Obstacle avoidance; Planner; Feedback linearization; Control engineering; Drone; Path (computing); Computation; Linearization; Control theory (sociology); Robot; Artificial intelligence; Engineering; Mobile robot; Control (management); Algorithm","score_opus":0.033027708570848247,"score_gpt":0.26957752315714023,"score_spread":0.23654981458629198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196037996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45569444,0.000034347904,0.5437915,0.000039868683,0.00016278274,0.00006260691,0.000004772893,0.000042092208,0.00016762283],"genre_scores_gemma":[0.41804653,0.000004040679,0.5816676,0.00003079776,0.00006934883,0.0000026940609,0.000011942029,0.0000153222,0.00015174494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834585,0.000112031375,0.00025702067,0.00053826795,0.0003697615,0.00037708986],"domain_scores_gemma":[0.999344,0.000051806473,0.000089853645,0.00039766042,0.000024115961,0.00009255795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019876339,0.00016919781,0.00019736047,0.00013021343,0.000089950125,0.000065981636,0.00028520523,0.00010901611,0.0000019738382],"category_scores_gemma":[0.000064722866,0.00019140182,0.000047545735,0.0003119059,0.00003372075,0.0003078769,0.00020748866,0.00025695836,0.00002063148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006079811,0.000053100393,0.000984952,0.0000036397325,0.000010805232,0.0003405987,0.0012733069,0.99259806,0.004000674,0.000032573884,0.0000063120447,0.0006898998],"study_design_scores_gemma":[0.0003752744,0.00004697897,0.0021206154,0.00006307688,0.000007402537,0.00006090459,0.0001718929,0.99103916,0.0058427504,0.00008037233,0.000011124018,0.00018044002],"about_ca_topic_score_codex":0.0000144218,"about_ca_topic_score_gemma":3.3000484e-7,"teacher_disagreement_score":0.03787612,"about_ca_system_score_codex":0.00017278535,"about_ca_system_score_gemma":0.00007782191,"threshold_uncertainty_score":0.78051394},"labels":[],"label_agreement":null},{"id":"W3196677378","doi":"10.3390/electronics10172136","title":"Agent in a Box: A Framework for Autonomous Mobile Robots with Beliefs, Desires, and Intentions","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Variety (cybernetics); Mobile robot; Semantic reasoner; Human–computer interaction; Domain (mathematical analysis); Computer science; Artificial intelligence; Resource (disambiguation); Embedded system","score_opus":0.016980398383254974,"score_gpt":0.2658396605072492,"score_spread":0.24885926212399426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196677378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006026342,0.008929041,0.9838731,0.00059404457,0.00010761026,0.00029069473,0.000002376799,0.00009671736,0.00008005607],"genre_scores_gemma":[0.049772415,0.0002378202,0.94924253,0.00023672152,0.000036611436,0.00021626583,0.000008647749,0.000016916094,0.00023206807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987384,0.000043708747,0.00016930739,0.00041356764,0.00016445063,0.00047053932],"domain_scores_gemma":[0.9992502,0.00016121456,0.000056513614,0.00037895306,0.00007609017,0.00007702873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020224498,0.00012797673,0.00017356254,0.00007092115,0.00010372618,0.00012385398,0.0002737185,0.000077357116,0.0000017783173],"category_scores_gemma":[0.0000906569,0.00012291556,0.000033210745,0.000407503,0.000027312904,0.00013863582,0.00011163444,0.0002660275,0.0000031490126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006518807,0.0012036631,0.0037983118,0.00019152214,0.0002877731,0.0008433438,0.0072671063,0.11317689,0.0022026864,0.5740215,0.0013250767,0.29561695],"study_design_scores_gemma":[0.003264283,0.0032173356,0.010487848,0.00082096906,0.00007852419,0.0011737029,0.00029065876,0.8112876,0.009212381,0.12706694,0.03159744,0.0015023607],"about_ca_topic_score_codex":0.000006149694,"about_ca_topic_score_gemma":0.00006211361,"teacher_disagreement_score":0.69811064,"about_ca_system_score_codex":0.0001592278,"about_ca_system_score_gemma":0.0005379386,"threshold_uncertainty_score":0.50123507},"labels":[],"label_agreement":null},{"id":"W3196853986","doi":"10.1007/s10846-021-01485-0","title":"Cooperative Sensor-based Selective Graph Exploration Strategy for a Team of Quadrotors","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Institute of Food and Agriculture","keywords":"Frontier; Backtracking; Computer science; Workspace; Graph; Tree (set theory); Path (computing); Artificial intelligence; Robot; Theoretical computer science; Algorithm; Mathematics; Geography","score_opus":0.06537510533583675,"score_gpt":0.3073702323196286,"score_spread":0.2419951269837919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196853986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022572912,0.0007681115,0.994112,0.00020751197,0.001932385,0.000553446,0.0000063091447,0.00002762547,0.00013532359],"genre_scores_gemma":[0.8409931,0.000034560395,0.15827024,0.000061297214,0.00032780308,0.00004151521,0.0000065552554,0.000027015882,0.0002379087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701446,0.00044602738,0.0012955713,0.0003057659,0.0006331975,0.00030494836],"domain_scores_gemma":[0.9947456,0.0006891342,0.0011922638,0.00035647085,0.0028571924,0.00015931622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00107168,0.00023529443,0.00070629857,0.00034009502,0.00010029538,0.00017902818,0.00052004,0.00011511927,0.000003028109],"category_scores_gemma":[0.0004878284,0.00019705272,0.0002835302,0.0008099131,0.000051701292,0.0005280338,0.00003498918,0.00024586514,0.0000060869197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038727438,0.0002240361,0.00029699758,0.000115220486,0.00019929669,0.000064193795,0.0013371614,0.9911544,0.0021395052,0.0028680374,0.00088685536,0.0006755948],"study_design_scores_gemma":[0.000851142,0.0019487923,0.00014260094,0.0009322567,0.000084160245,0.00052434305,0.004810224,0.9239975,0.06569162,0.00053111813,0.00017007484,0.00031613253],"about_ca_topic_score_codex":0.000032042393,"about_ca_topic_score_gemma":0.0000025507484,"teacher_disagreement_score":0.8387358,"about_ca_system_score_codex":0.00019360709,"about_ca_system_score_gemma":0.0009808111,"threshold_uncertainty_score":0.8035577},"labels":[],"label_agreement":null},{"id":"W3197573625","doi":"10.1609/socs.v12i1.18594","title":"Speeding Up Heuristic Function Synthesis via Extending the Formula Grammar","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Heuristic; Computer science; Grammar; Consistent heuristic; Function (biology); Incremental heuristic search; Null-move heuristic; Quality (philosophy); Pathfinding; Artificial intelligence; Beam search; Algorithm; Theoretical computer science; Search algorithm","score_opus":0.019314497431079942,"score_gpt":0.2549824422958675,"score_spread":0.23566794486478754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197573625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57009196,0.0003893707,0.10215405,0.0923912,0.15624742,0.0024387497,0.00004398821,0.0009986634,0.07524459],"genre_scores_gemma":[0.9964698,0.000023923223,0.00189201,0.0001500101,0.0008170753,0.000033681627,0.0000016301517,0.000022111079,0.0005897823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969755,0.00004232961,0.00039945275,0.00049778377,0.0017069698,0.0003779836],"domain_scores_gemma":[0.99771446,0.0006756711,0.00024228742,0.0003033494,0.0009767319,0.00008752643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012479096,0.00020343592,0.00021479996,0.00010497333,0.00043003433,0.00048205297,0.002666554,0.00009288414,0.000018682034],"category_scores_gemma":[0.00091576093,0.00014085561,0.00018659655,0.00064202497,0.000091592665,0.00046310097,0.0010609296,0.0004592745,0.000030127749],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001579369,0.00031252066,0.0036114142,0.00007349435,0.0002589922,0.000011361287,0.00077566376,0.0008152434,0.12962839,0.85080445,0.00303944,0.010511111],"study_design_scores_gemma":[0.0017036591,0.00039537845,0.008056848,0.0007479741,0.000114159826,0.0002744428,0.00030371227,0.21764924,0.64555955,0.1171612,0.0073141293,0.00071970734],"about_ca_topic_score_codex":0.000055120887,"about_ca_topic_score_gemma":3.2460912e-7,"teacher_disagreement_score":0.73364323,"about_ca_system_score_codex":0.00028726648,"about_ca_system_score_gemma":0.000091930895,"threshold_uncertainty_score":0.57439256},"labels":[],"label_agreement":null},{"id":"W3199346716","doi":"10.32393/csme.2021.201","title":"Review On Current Path Planning Algorthms For Autonomous Robotic Applications","year":2021,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering. Volume 4","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Current (fluid); Computer science; Motion planning; Path (computing); Robot; Artificial intelligence; Engineering; Electrical engineering; Computer network","score_opus":0.0223821771415807,"score_gpt":0.2844696562043636,"score_spread":0.2620874790627829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199346716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010621202,0.056003474,0.9399239,0.0021114545,0.0007828296,0.0008760194,0.000026545038,0.00022886806,0.00003631397],"genre_scores_gemma":[0.025193779,0.0011361868,0.96596646,0.0025318942,0.00045978572,0.00426129,0.00019866199,0.00012519523,0.00012677032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977123,0.000038395225,0.0004326043,0.00069606793,0.00023591967,0.0008847253],"domain_scores_gemma":[0.9982792,0.000121360485,0.00007745441,0.0007457911,0.00011176738,0.00066440075],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046763592,0.00025876562,0.00039333187,0.00020652702,0.000105268184,0.00011062736,0.00080336654,0.000113713875,0.000008919012],"category_scores_gemma":[0.00023782798,0.00029223796,0.00010847898,0.000717404,0.000016422064,0.000122026686,0.00009959668,0.00041166184,0.000040505165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030076471,0.00026323393,0.0009068213,0.0026250049,0.00005768752,0.0005839683,0.00015073293,0.13950469,0.000010482789,0.08121178,0.007952412,0.7667302],"study_design_scores_gemma":[0.00019054765,0.00005810039,0.00030868253,0.0017626443,0.000013307361,0.00004367529,0.0000015910828,0.8717055,0.000025043691,0.00027024344,0.12528715,0.00033357122],"about_ca_topic_score_codex":0.0003386983,"about_ca_topic_score_gemma":0.00021603289,"teacher_disagreement_score":0.76639664,"about_ca_system_score_codex":0.00046431774,"about_ca_system_score_gemma":0.0007686743,"threshold_uncertainty_score":0.999953},"labels":[],"label_agreement":null},{"id":"W3200142631","doi":"10.1109/lra.2022.3191939","title":"Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristics; Computer science; Path (computing); Set (abstract data type); Mathematical optimization; Regular polygon; Line (geometry); Time complexity; Line segment; Algorithm; Mathematics; Artificial intelligence","score_opus":0.015312047606541755,"score_gpt":0.24194091139813234,"score_spread":0.22662886379159058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200142631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14389953,0.000021626016,0.85441613,0.0009488894,0.00047549178,0.00016932239,0.000012774106,0.000041624957,0.00001458036],"genre_scores_gemma":[0.7037604,0.0000015187766,0.2953753,0.00071547314,0.000045128392,0.00004181381,0.000019572517,0.000010358291,0.00003045862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989574,0.000042928157,0.00026991352,0.00024997705,0.00026797142,0.00021182495],"domain_scores_gemma":[0.999403,0.0001292301,0.00021534156,0.0001923024,0.000010085311,0.000050028182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026373265,0.00011083192,0.00016699218,0.00010111868,0.0002764505,0.000067202,0.00025239872,0.000025115794,0.0000032873156],"category_scores_gemma":[0.0000120231825,0.00012647484,0.00004559482,0.000113378796,0.0000343566,0.00023577479,0.000098082295,0.000101504294,0.0000019221436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002946156,0.000024649948,0.0005170528,0.000018694247,0.000018477154,0.000009111374,0.0009430122,0.98264414,0.014064261,0.00014304083,0.0009074388,0.0007071881],"study_design_scores_gemma":[0.00048692952,0.00009475092,0.0046679215,0.000023487037,0.0000112259695,0.00001682958,0.00003816292,0.9925877,0.0016605553,0.000056737324,0.00021314091,0.00014259265],"about_ca_topic_score_codex":0.000002675046,"about_ca_topic_score_gemma":1.4848002e-8,"teacher_disagreement_score":0.5598609,"about_ca_system_score_codex":0.00004510902,"about_ca_system_score_gemma":0.000019069737,"threshold_uncertainty_score":0.51574945},"labels":[],"label_agreement":null},{"id":"W3200640745","doi":"10.3849/aimt.01237","title":"Real-time Optimal Control of Multi-wheeled Combat Vehicles - using Artificial Neural Network and Potential Fields","year":2018,"lang":"en","type":"article","venue":"Advances in Military Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Heading (navigation); Artificial neural network; Workspace; Computer science; Path (computing); Control (management); Motion planning; Position (finance); Point (geometry); Robot; Engineering; Artificial intelligence; Simulation; Control engineering","score_opus":0.010667969805386705,"score_gpt":0.266985801936273,"score_spread":0.2563178321308863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200640745","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5797262,0.0025871343,0.41673094,0.00025501612,0.00038839827,0.00014564313,0.0000029583498,0.00014927717,0.000014432617],"genre_scores_gemma":[0.63606024,0.00008443737,0.36374214,0.000030710664,0.00006827035,0.000004352139,9.0464516e-7,0.00000592621,0.0000030117658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851096,0.00009191922,0.0003965618,0.000435855,0.00013084793,0.00043383933],"domain_scores_gemma":[0.999222,0.00012304312,0.0001015961,0.00043908056,0.00007383233,0.000040492083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026220657,0.0001637526,0.0003608389,0.00021429615,0.000113460716,0.0000076001793,0.000608482,0.00021457554,0.0000035637715],"category_scores_gemma":[0.00007999599,0.00016519595,0.000034444045,0.00049043377,0.0006794598,0.0003844492,0.0002571765,0.00023530342,0.0000038499097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020230512,0.00024108433,0.015234641,0.00004191491,0.000053987857,0.00029638154,0.00049442676,0.8028993,0.027459161,0.008719829,0.000038116792,0.14431886],"study_design_scores_gemma":[0.0006061129,0.00040597102,0.0015995883,0.000037892733,0.000008801956,0.000073154566,0.00004372988,0.9884656,0.0010385007,0.0075468663,0.000016426286,0.00015735636],"about_ca_topic_score_codex":0.00008173973,"about_ca_topic_score_gemma":0.000020989162,"teacher_disagreement_score":0.18556632,"about_ca_system_score_codex":0.00002264058,"about_ca_system_score_gemma":0.000029075112,"threshold_uncertainty_score":0.6736495},"labels":[],"label_agreement":null},{"id":"W3201494226","doi":"10.1007/s13042-021-01405-6","title":"An improved neural dynamics based approach with territorial mechanism to online path planning of multi-robot systems","year":2021,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Motion planning; Computer science; Workload; Planner; Robustness (evolution); Robot; Artificial neural network; Variety (cybernetics); Distributed computing; Computational intelligence; Adaptation (eye); Path (computing); Mechanism (biology); Artificial intelligence; Computer network","score_opus":0.014014006813398101,"score_gpt":0.2771642218931179,"score_spread":0.2631502150797198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201494226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080782235,0.0002068167,0.9172229,0.00028534175,0.0013766098,0.000058217374,0.000020306841,0.000027732991,0.000019803467],"genre_scores_gemma":[0.6113703,0.0000047645576,0.38819474,0.000045897537,0.00029837503,8.778951e-7,0.000028890925,0.000012670165,0.00004344569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823815,0.00020641265,0.00048336538,0.0002536354,0.0006390269,0.00017942734],"domain_scores_gemma":[0.9983325,0.0001257687,0.0004974869,0.00016474527,0.0007040439,0.00017545171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042441106,0.00017149857,0.000301311,0.00019045785,0.000055056025,0.00021396835,0.00066177815,0.00007182808,0.0000010069202],"category_scores_gemma":[0.00016618891,0.00014295598,0.00005598278,0.00014643991,0.00002695789,0.00017918184,0.00012477841,0.00050233584,2.140305e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010457386,0.00036838712,0.011926388,0.000022979928,0.00012400025,0.00035954211,0.00089532847,0.97498554,0.0032346211,0.000699284,0.000006330782,0.0072730226],"study_design_scores_gemma":[0.0013476699,0.00078620244,0.0024510508,0.00018448006,0.000023315433,0.00074039877,0.00025800098,0.9937419,0.0002193193,0.000023945217,0.00006808263,0.00015562949],"about_ca_topic_score_codex":0.00010906204,"about_ca_topic_score_gemma":0.000004865442,"teacher_disagreement_score":0.5305881,"about_ca_system_score_codex":0.00006661288,"about_ca_system_score_gemma":0.00014376131,"threshold_uncertainty_score":0.58295757},"labels":[],"label_agreement":null},{"id":"W3202043921","doi":"","title":"An Acrophobic Guard Watchtower Problem on Terrains.","year":2021,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Guard (computer science); Computer science; Computer security; Programming language","score_opus":0.029826325630256632,"score_gpt":0.27213839133728307,"score_spread":0.24231206570702643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202043921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090141945,0.00010899077,0.82445115,0.023580944,0.002742748,0.00060925685,0.00025871294,0.00060167524,0.05750457],"genre_scores_gemma":[0.8380014,0.000002341549,0.1552395,0.0056718863,0.0001253323,0.000017041304,0.00017066112,0.000020649606,0.0007512244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974398,0.00014007463,0.00030175078,0.00085716543,0.00062123523,0.0006399612],"domain_scores_gemma":[0.9976932,0.00017683917,0.00009144054,0.0007231523,0.00049158133,0.0008238131],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025907918,0.0002738863,0.00025334404,0.0005601505,0.000296246,0.0005180452,0.0010177317,0.00013949363,0.000257908],"category_scores_gemma":[0.00008655862,0.0002925734,0.00007199338,0.0010547597,0.00007550279,0.0003154528,0.00006671909,0.00038268472,0.0006774427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014278414,0.00038505121,0.0016457291,0.00003781399,0.00011313194,0.0026501252,0.0018882446,0.46396044,0.00034830472,0.38294595,0.016505366,0.12950556],"study_design_scores_gemma":[0.0009484309,0.0007114827,0.041520085,0.00024299014,0.00001103928,0.0002613859,0.00022055004,0.90557116,0.00042040786,0.039416168,0.0095085045,0.0011677869],"about_ca_topic_score_codex":0.0017404644,"about_ca_topic_score_gemma":0.0011428775,"teacher_disagreement_score":0.7478594,"about_ca_system_score_codex":0.00032385025,"about_ca_system_score_gemma":0.0028905089,"threshold_uncertainty_score":0.9999526},"labels":[],"label_agreement":null},{"id":"W3202915137","doi":"10.1109/rtcsa52859.2021.00028","title":"Optimal Recharging of Teams of Mobile Robots","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ford Motor Company (Canada)","funders":"","keywords":"Robot; Computer science; Mobile robot; Energy consumption; A priori and a posteriori; Motion planning; Task (project management); Real-time computing; Set (abstract data type); Path (computing); Distributed computing; Artificial intelligence; Computer network; Engineering","score_opus":0.013688798485950806,"score_gpt":0.25869698418069936,"score_spread":0.24500818569474855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202915137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031537402,0.00017159691,0.9621179,0.00008606636,0.0002259826,0.000051956496,0.0000011760227,0.000065876666,0.0057420344],"genre_scores_gemma":[0.15206704,0.000005529381,0.8469303,0.000028801887,0.000014937729,0.000003620092,0.0000012076862,0.0000039934166,0.0009445694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991342,0.000040875162,0.00024228964,0.00021605834,0.00021311386,0.00015345366],"domain_scores_gemma":[0.99913305,0.00009436024,0.00013237966,0.00046649264,0.0001303319,0.000043380573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022129323,0.00006767671,0.00018834724,0.00005220076,0.00002008258,0.000014955524,0.00041923753,0.00003783011,0.00003737439],"category_scores_gemma":[0.00007203578,0.00006295709,0.000051786774,0.0003602401,0.00002572666,0.00014596929,0.0002246118,0.000067436085,0.000012441367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004707938,0.00038113896,0.0036067327,0.0001214447,0.00008161375,0.00018448228,0.003426075,0.795804,0.09482289,0.021112524,0.0018391713,0.078615166],"study_design_scores_gemma":[0.0002494791,0.00010584935,0.001192016,0.00008177674,0.000005455096,0.000060027753,0.00022131547,0.64801323,0.34958163,0.0002109867,0.00013885876,0.00013934368],"about_ca_topic_score_codex":0.000024324852,"about_ca_topic_score_gemma":2.376271e-7,"teacher_disagreement_score":0.25475875,"about_ca_system_score_codex":0.000013205364,"about_ca_system_score_gemma":0.00011826446,"threshold_uncertainty_score":0.25673157},"labels":[],"label_agreement":null},{"id":"W3203175586","doi":"10.31224/osf.io/7h4ep","title":"A Sufficient Condition for Convex Hull Property in General Convex Spatio-Temporal Corridors","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Convex hull; Smoothness; Regular polygon; Mathematical optimization; Property (philosophy); Parametric statistics; Parametric equation; Computer science; Hull; Motion planning; Mathematics; Geometry; Mathematical analysis; Robot; Engineering; Artificial intelligence","score_opus":0.036299922396561185,"score_gpt":0.28474643437157343,"score_spread":0.24844651197501225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203175586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077258594,0.00011946908,0.91427004,0.0011409002,0.003917197,0.0018396939,0.000060907787,0.0003468851,0.0010463038],"genre_scores_gemma":[0.3851285,0.000011871593,0.6059035,0.000976058,0.00030762728,0.00072404,0.001746291,0.00005125893,0.0051508574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962123,0.00022637409,0.00084829045,0.0014734686,0.00058348814,0.00065607287],"domain_scores_gemma":[0.9976661,0.00015059115,0.00040240271,0.0011575627,0.00042982938,0.00019348091],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083073723,0.00048803468,0.00080794364,0.00031766045,0.00010913746,0.00054605934,0.0013336702,0.00043428014,0.00008002172],"category_scores_gemma":[0.00016808626,0.00038998,0.00022762967,0.00034768958,0.000102793,0.00027308465,0.0012175709,0.00065996853,0.0000343819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013665631,0.0020570173,0.030695943,0.0017374468,0.00033563314,0.0013767215,0.012910383,0.8677027,0.00053941825,0.011214958,0.05906779,0.012225309],"study_design_scores_gemma":[0.00091052643,0.00014430637,0.0030228703,0.00026238864,0.000015280602,0.000035758014,0.00012451092,0.99228334,0.0007306533,0.000397266,0.0014481277,0.0006249673],"about_ca_topic_score_codex":0.0015411208,"about_ca_topic_score_gemma":0.00013484634,"teacher_disagreement_score":0.30836657,"about_ca_system_score_codex":0.00031973206,"about_ca_system_score_gemma":0.0011017987,"threshold_uncertainty_score":0.9998552},"labels":[],"label_agreement":null},{"id":"W3203199868","doi":"10.23977/jeis.2021.61005","title":"Establishment of UAV Path Planning Model Based on Ant Colony and Simulated Annealing Algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Motion planning; Computer science; Ant colony optimization algorithms; Terrain; Mathematical optimization; Path (computing); Algorithm; Real-time computing; Operations research; Engineering; Artificial intelligence; Robot; Mathematics; Geography","score_opus":0.011720225652647501,"score_gpt":0.2620283427334895,"score_spread":0.250308117080842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203199868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0873864,0.00035745755,0.91157573,0.0002891491,0.000112363006,0.000047363603,0.0000027628164,0.000008585186,0.00022020434],"genre_scores_gemma":[0.7532601,0.00013608884,0.24614209,0.0004423688,0.000012021079,3.2801702e-7,0.000001317017,0.0000020740474,0.0000036410665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846417,0.000023485964,0.0004834893,0.00011425455,0.0006669148,0.00024767304],"domain_scores_gemma":[0.9984297,0.00010651389,0.0005089547,0.0001554066,0.00067753973,0.00012188185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015408914,0.000086386935,0.00017500577,0.00031850903,0.00017033996,0.00029587926,0.00031190415,0.00003498802,4.0071922e-7],"category_scores_gemma":[0.00023644217,0.00007234063,0.000025557983,0.00071545306,0.00007643693,0.0035829488,0.00008316388,0.00019810814,2.299689e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008612434,0.000021666263,0.00015376622,0.000011906812,0.000004124409,0.000009034541,0.0010331614,0.9714402,0.0009342146,0.0021472184,0.00003901316,0.024197077],"study_design_scores_gemma":[0.00043703656,0.00036198637,0.0008016282,0.000103386694,0.000004414368,0.00007917316,0.00007292341,0.99414945,0.003395213,0.00031519824,0.0002013869,0.0000782252],"about_ca_topic_score_codex":0.0000017739205,"about_ca_topic_score_gemma":3.214104e-8,"teacher_disagreement_score":0.66587365,"about_ca_system_score_codex":0.000081943115,"about_ca_system_score_gemma":0.0010445442,"threshold_uncertainty_score":0.2949965},"labels":[],"label_agreement":null},{"id":"W3203299716","doi":"10.1155/2021/3606895","title":"An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Benchmark (surveying); Path (computing); Convergence (economics); Robot; Mathematical optimization; Algorithm; Automotive industry; Artificial intelligence; Mathematics; Engineering","score_opus":0.020750938601356832,"score_gpt":0.2827177665499982,"score_spread":0.26196682794864135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203299716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013547848,0.00039530787,0.99716073,0.00014864684,0.0007209766,0.00014782554,0.000013147825,0.000047449015,0.000011155546],"genre_scores_gemma":[0.025457012,0.000039403603,0.9741543,0.0000885973,0.00015034043,0.000010333904,0.00006643728,0.000017534423,0.000016021071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842834,0.00006435712,0.0006051963,0.0002793924,0.00038721418,0.00023548199],"domain_scores_gemma":[0.9981574,0.0001294079,0.000567436,0.00021846114,0.0007842952,0.00014301701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030969942,0.00015885965,0.0002947486,0.00014284799,0.00011469261,0.00007854281,0.00032959128,0.00007582133,0.0000025662043],"category_scores_gemma":[0.00003844678,0.00015884827,0.00012454783,0.00035043852,0.000018683806,0.0016206657,0.0000034597292,0.00018883678,5.7967736e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035026445,0.000094959614,0.00006244034,0.000009462141,0.0000314788,0.00020553492,0.0013633108,0.9348385,0.0005973674,0.0003876356,0.0000134690945,0.06236082],"study_design_scores_gemma":[0.0016220298,0.000799616,0.009791813,0.0001571185,0.00005097768,0.00010419752,0.0005654454,0.9840521,0.000975201,0.0015997873,0.00006959078,0.00021211221],"about_ca_topic_score_codex":0.0000012512064,"about_ca_topic_score_gemma":5.3371673e-7,"teacher_disagreement_score":0.06214871,"about_ca_system_score_codex":0.00005792715,"about_ca_system_score_gemma":0.00020741063,"threshold_uncertainty_score":0.64776444},"labels":[],"label_agreement":null},{"id":"W3204763917","doi":"10.1109/icas49788.2021.9551158","title":"State-Of-The-Art And Directions For The Conceptual Design Of Safety-Critical Unmanned And Autonomous Aerial Vehicles","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sizing; Redundancy (engineering); Conceptual design; Reliability engineering; Reliability (semiconductor); Computer science; Fault tree analysis; Reliability theory; Risk analysis (engineering); Systems engineering; Engineering","score_opus":0.030299100828934524,"score_gpt":0.26689734598536163,"score_spread":0.2365982451564271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204763917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003435595,0.00037018475,0.99310213,0.002279984,0.00045288814,0.00019362108,0.0000131976485,0.000031255528,0.000121169884],"genre_scores_gemma":[0.3444635,0.000052529576,0.6542737,0.00020076986,0.00006187085,0.000026270212,0.0000012683067,0.000009909454,0.00091019773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924374,0.000113631664,0.00019361188,0.0001905374,0.00011357785,0.00014493261],"domain_scores_gemma":[0.99769205,0.001875033,0.000041376028,0.00024002594,0.00010955956,0.000041973173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028380033,0.00007114975,0.00014169564,0.000016911066,0.00016823485,0.00004310033,0.00020617068,0.00002826145,0.0000031416162],"category_scores_gemma":[0.00034781484,0.000045640387,0.000030865845,0.00013419011,0.00036862947,0.00009695723,0.00016579933,0.000057943962,6.1323766e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034162618,0.00061691215,0.0025430094,0.0002132968,0.0005291083,0.000044585922,0.020492166,0.050035972,0.040024694,0.49332255,0.011214581,0.3806215],"study_design_scores_gemma":[0.0011828208,0.00031737203,0.008605664,0.00005820836,0.000056292025,0.00010878469,0.00041271874,0.9310267,0.04730248,0.007638783,0.0030697996,0.00022036991],"about_ca_topic_score_codex":0.000014569189,"about_ca_topic_score_gemma":0.000003328277,"teacher_disagreement_score":0.88099074,"about_ca_system_score_codex":0.0000089661935,"about_ca_system_score_gemma":0.0001305897,"threshold_uncertainty_score":0.1861161},"labels":[],"label_agreement":null},{"id":"W3205663827","doi":"10.1109/icra48506.2021.9560914","title":"LBGP: Learning Based Goal Planning for Autonomous Following in Front","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Trajectory; Planner; Computer science; Robot; Reinforcement learning; Artificial intelligence; Deep learning; Human–computer interaction; Simulation","score_opus":0.022624047965056335,"score_gpt":0.2792098966939343,"score_spread":0.25658584872887796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205663827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008269322,0.00017454904,0.9870904,0.0005264784,0.0006445277,0.00012883231,5.896831e-7,0.00030366261,0.0028616136],"genre_scores_gemma":[0.26644313,2.0960654e-7,0.73131806,0.00037826557,0.00005281449,0.0000314567,0.000010221366,0.000013379543,0.0017524633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984824,0.000087914894,0.00026867742,0.0004986587,0.00020950513,0.00045282562],"domain_scores_gemma":[0.99908453,0.00041244636,0.00005862726,0.0003161273,0.000042285774,0.000085964544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005246697,0.00014506535,0.0002360992,0.00012315092,0.00013605572,0.00017426538,0.00042643977,0.00007880983,0.000012127065],"category_scores_gemma":[0.0003008502,0.00015126295,0.00011722366,0.00019439442,0.000008728104,0.00027947073,0.00013694969,0.00022749438,0.000028189372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007923387,0.00009321115,0.023811104,0.00003047141,0.0000334899,0.0012142926,0.0031098071,0.94939184,0.0013102225,0.001133455,0.0007973374,0.019066824],"study_design_scores_gemma":[0.00081557117,0.0000552028,0.0044270195,0.000082743296,0.0000043837103,0.000012197228,0.00015615101,0.99088925,0.0013965727,0.00020262737,0.001745434,0.00021283272],"about_ca_topic_score_codex":0.00003331923,"about_ca_topic_score_gemma":0.0000052704295,"teacher_disagreement_score":0.2581738,"about_ca_system_score_codex":0.00010230212,"about_ca_system_score_gemma":0.00027759094,"threshold_uncertainty_score":0.61683244},"labels":[],"label_agreement":null},{"id":"W3206082228","doi":"10.1109/icra48506.2021.9560748","title":"Distributed Heuristic Multi-Agent Path Finding with Communication","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Heuristic; Multi-agent system; Path (computing); Distributed computing; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","score_opus":0.038132610383760444,"score_gpt":0.2721603191303243,"score_spread":0.23402770874656384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206082228","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024790997,0.0001285694,0.9956903,0.000709219,0.00009473671,0.00006653835,0.0000061108417,0.00022267197,0.0006027895],"genre_scores_gemma":[0.3007006,0.000008587925,0.6986186,0.00012837675,0.000007866118,0.0000077354425,0.0000555102,0.0000053452272,0.00046738418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999052,0.00010477099,0.00015198454,0.00027553202,0.00020525392,0.00021048261],"domain_scores_gemma":[0.9987152,0.00013291003,0.0000643373,0.0008880383,0.00011763585,0.000081883874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016892175,0.00010086455,0.00011783669,0.000029302777,0.0001606938,0.00015555888,0.00057201425,0.00003326527,0.000012847823],"category_scores_gemma":[0.0000918511,0.000082833474,0.000023520603,0.00039049194,0.000027074931,0.00018269605,0.0002764406,0.0001281059,0.00006370452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060636386,0.005468382,0.2031183,0.0004332606,0.00097161287,0.009699961,0.02113601,0.284758,0.008334333,0.2594936,0.046704065,0.15982185],"study_design_scores_gemma":[0.00056802365,0.00004287439,0.03594026,0.00008796972,0.0000097089805,0.00013789766,0.000116732874,0.9606447,0.0012095879,0.00017029393,0.0008377256,0.00023422077],"about_ca_topic_score_codex":0.000025201294,"about_ca_topic_score_gemma":0.000004890827,"teacher_disagreement_score":0.6758867,"about_ca_system_score_codex":0.000054769447,"about_ca_system_score_gemma":0.00008922265,"threshold_uncertainty_score":0.3377851},"labels":[],"label_agreement":null},{"id":"W3209614597","doi":"","title":"Design and Optimization of Automatic Glass Window Cleaning Robot","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Window (computing); Climb; sort; Computer science; Robot; Window of opportunity; Space (punctuation); Task (project management); Simulation; Artificial intelligence; Engineering; Real-time computing; Operating system; Aerospace engineering","score_opus":0.01861546944148647,"score_gpt":0.2585475255005015,"score_spread":0.239932056059015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209614597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056314743,0.00024424685,0.99212325,0.0013194615,0.00058293383,0.000045309313,6.329128e-7,0.000023922068,0.000028786195],"genre_scores_gemma":[0.22893402,0.00009116218,0.7708546,0.00005153154,0.000050042676,3.9247584e-7,0.0000021685041,0.00000501331,0.000011089735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987691,0.00010015903,0.00047893374,0.0001233387,0.0004396034,0.00008884236],"domain_scores_gemma":[0.9984067,0.00021423811,0.00055477524,0.00009810544,0.00066852913,0.00005762336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004782915,0.00008808773,0.00018261961,0.00018405828,0.000043537213,0.00018264899,0.00026208448,0.00004814664,0.00000373007],"category_scores_gemma":[0.00021072637,0.000083788414,0.00003500427,0.00014590594,0.000030451116,0.00061346136,0.00010221743,0.000096253396,5.9981244e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003182342,0.000034390774,0.00026776295,0.000014294261,0.00006147876,0.000053172065,0.00040192244,0.9672517,0.0014029455,0.002372028,0.000020555653,0.028116537],"study_design_scores_gemma":[0.00047054313,0.00007647779,0.004537234,0.00022769305,0.000019169025,0.0007001549,0.00004445594,0.99095637,0.0017933385,0.0010943101,0.0000046502873,0.00007560333],"about_ca_topic_score_codex":0.0000016476055,"about_ca_topic_score_gemma":9.2789556e-8,"teacher_disagreement_score":0.22330254,"about_ca_system_score_codex":0.000038193,"about_ca_system_score_gemma":0.00011802721,"threshold_uncertainty_score":0.34167922},"labels":[],"label_agreement":null},{"id":"W3210854080","doi":"10.1109/tcyb.2021.3110196","title":"Toward Safer Navigation of Heterogeneous Mobile Robots in Distributed Scheme: A Novel Time-to-Collision-Based Method","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Collision; Robot; Computer science; Inertia; Collision avoidance; Mobile robot; Controller (irrigation); Motion (physics); Work (physics); Scheme (mathematics); SAFER; Simulation; Control engineering; Control theory (sociology); Engineering; Control (management); Artificial intelligence; Mathematics; Computer security","score_opus":0.022629296713322818,"score_gpt":0.28055047099673586,"score_spread":0.25792117428341305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210854080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0123703,0.000031800413,0.9863734,0.00027821056,0.00032469822,0.000328397,0.00014577336,0.00010466669,0.000042717387],"genre_scores_gemma":[0.2955273,0.0000027835945,0.70406127,0.00012519358,0.00001282853,0.000069042966,0.000026296402,0.000020436672,0.00015483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980049,0.00014169338,0.0004958901,0.0005462084,0.00048453454,0.00032675292],"domain_scores_gemma":[0.99840766,0.0003889946,0.00010817645,0.00068028783,0.00025732198,0.00015757243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028302596,0.00022084915,0.00033443066,0.00020344368,0.00006686419,0.00006193146,0.00047210645,0.00015654704,0.00004071396],"category_scores_gemma":[0.000027468946,0.00024390762,0.00012841514,0.0013400592,0.000034620694,0.000101588936,0.000008222307,0.0002819103,0.00009952093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015975369,0.00046678452,0.000007617872,0.000019442703,0.000021577966,0.000052785326,0.00027694087,0.9716263,0.018359588,0.000016822065,0.000031241343,0.009104938],"study_design_scores_gemma":[0.00060141936,0.00015735636,0.00012287092,0.00013452042,0.000014641841,0.000037971033,0.0000099724975,0.7351379,0.26342127,0.000034156357,0.00013440032,0.00019357371],"about_ca_topic_score_codex":0.000045666147,"about_ca_topic_score_gemma":0.00000458159,"teacher_disagreement_score":0.28315702,"about_ca_system_score_codex":0.00016322723,"about_ca_system_score_gemma":0.00024517026,"threshold_uncertainty_score":0.9946264},"labels":[],"label_agreement":null},{"id":"W3211198923","doi":"10.1109/ccece53047.2021.9569172","title":"Risky Zone Avoidance Strategies for Drones","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Drone; Terrain; Traverse; Computer science; Tree traversal; Curvilinear coordinates; Real-time computing; Simulation; Geography; Geodesy; Mathematics; Cartography","score_opus":0.034382689835514006,"score_gpt":0.2887355351264228,"score_spread":0.2543528452909088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211198923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016264217,0.00086125755,0.99013424,0.0008567769,0.0025901445,0.00038889286,0.000011934218,0.0005526458,0.0029776632],"genre_scores_gemma":[0.03245223,0.00004087735,0.96391726,0.00020001487,0.00025695545,0.00017511203,0.00003386129,0.000021313905,0.0029024018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978075,0.00007251296,0.0003544415,0.0010080008,0.00032826755,0.00042929393],"domain_scores_gemma":[0.9978486,0.00023310653,0.00020410572,0.0013828288,0.000231606,0.00009980807],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00037724792,0.00032576555,0.00045298418,0.00009488378,0.00012320359,0.0011757523,0.00180837,0.00026975345,0.000013314997],"category_scores_gemma":[0.000060051923,0.00031270116,0.00018061502,0.00016097593,0.00004881368,0.0004003307,0.001560305,0.00045922768,0.000027592981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018669149,0.000494317,0.0005888255,0.0019564475,0.0005891165,0.0004923696,0.010669538,0.6108578,0.0010256062,0.2523836,0.026749939,0.09417381],"study_design_scores_gemma":[0.00030084085,0.00006116022,0.0011141257,0.0002948631,0.000022636217,0.00003053167,0.0005489802,0.9696104,0.0008406882,0.025416052,0.0010516921,0.0007080085],"about_ca_topic_score_codex":0.0002517967,"about_ca_topic_score_gemma":0.000021694412,"teacher_disagreement_score":0.35875264,"about_ca_system_score_codex":0.000057144855,"about_ca_system_score_gemma":0.00073275104,"threshold_uncertainty_score":0.9999325},"labels":[],"label_agreement":null},{"id":"W3211351269","doi":"10.1109/isrimt53730.2021.9596826","title":"Voronoi-based Multi-robot Collision Avoidance with Localization Uncertainty","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Collision avoidance; Robot; Deadlock; Voronoi diagram; Collision; Computer science; Scheme (mathematics); Real-time computing; Mobile robot; Deadlock prevention algorithms; Artificial intelligence; Distributed computing; Simulation; Computer security; Mathematics","score_opus":0.020343683102325524,"score_gpt":0.24895004484454408,"score_spread":0.22860636174221854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211351269","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071724463,0.00007523075,0.9972043,0.0008027835,0.00024333675,0.00012254051,0.0000016822794,0.00030432572,0.00052854227],"genre_scores_gemma":[0.11130703,0.0000029624216,0.88648504,0.0010342909,0.000028020568,0.000013161579,0.000020408834,0.000011857857,0.0010971932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851084,0.000115856266,0.00019722231,0.0005277816,0.00036616484,0.00028212654],"domain_scores_gemma":[0.998735,0.00011992397,0.000080864156,0.00066057366,0.0002915294,0.00011212769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020524046,0.00015664977,0.00016917064,0.000055327862,0.00016470742,0.00016221195,0.00043478547,0.000065243956,0.00001791211],"category_scores_gemma":[0.00008841752,0.00012550042,0.000033576547,0.0007314251,0.0000452859,0.0002827697,0.00009829345,0.000110479865,0.00006555469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005233838,0.00009281462,0.0015224757,0.000011242492,0.000007046175,0.000105670246,0.00011790643,0.9928272,0.00026145313,0.0013324426,0.0003995396,0.0033169752],"study_design_scores_gemma":[0.0007659078,0.00006734959,0.0023869341,0.00007189479,0.000004561211,0.000020464639,0.000027568265,0.9868145,0.008902809,0.00003783277,0.00069819624,0.00020196463],"about_ca_topic_score_codex":0.0000777378,"about_ca_topic_score_gemma":0.00004409111,"teacher_disagreement_score":0.11071924,"about_ca_system_score_codex":0.00008952023,"about_ca_system_score_gemma":0.0003500354,"threshold_uncertainty_score":0.51177585},"labels":[],"label_agreement":null},{"id":"W3212961470","doi":"10.1109/cyber53097.2021.9588273","title":"Obstacle Avoidance of Multiple Manipulators Based on 3D Artificial Potential Field Method","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Shenzhen Research and Development Program","keywords":"Collision avoidance; Obstacle avoidance; Potential field; Obstacle; Trajectory; Computer science; Field (mathematics); Control theory (sociology); Manipulator (device); Collision; Motion planning; Robot manipulator; Artificial intelligence; Robot; Control engineering; Engineering; Mathematics; Mobile robot; Physics; Control (management)","score_opus":0.02612058505895915,"score_gpt":0.2795752603222532,"score_spread":0.25345467526329407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212961470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047107846,0.000016868016,0.9919862,0.00071687053,0.0007986502,0.000070736416,0.000002627464,0.00011829574,0.0015789283],"genre_scores_gemma":[0.35432664,2.5484908e-7,0.64485765,0.0005240928,0.000048293645,0.0000028669613,0.0000023497616,0.0000058173573,0.00023204596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998509,0.00015208608,0.00028395932,0.00042336638,0.00037911133,0.00025246135],"domain_scores_gemma":[0.9985353,0.0005137852,0.00009009261,0.0006747958,0.00010719667,0.0000788301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030058797,0.00012629938,0.0002027151,0.00007609362,0.00007610152,0.00005878375,0.00045146223,0.000081844584,0.00006952731],"category_scores_gemma":[0.0003257493,0.0001212398,0.00009239793,0.00035942276,0.000015824382,0.00012762572,0.000120631106,0.00015388028,0.000038177164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051072428,0.00061628415,0.0028430258,0.00005963732,0.0000487689,0.0006628742,0.00044707637,0.7834751,0.038799163,0.016508296,0.0018101044,0.15467858],"study_design_scores_gemma":[0.00019029972,0.000073111434,0.00164474,0.000021367203,0.000004364534,0.000010259875,0.000017912433,0.8214367,0.17602879,0.00037590426,0.0000785718,0.00011799495],"about_ca_topic_score_codex":0.00006689072,"about_ca_topic_score_gemma":0.000005559061,"teacher_disagreement_score":0.34961584,"about_ca_system_score_codex":0.000021185313,"about_ca_system_score_gemma":0.00011066952,"threshold_uncertainty_score":0.49440154},"labels":[],"label_agreement":null},{"id":"W3213888950","doi":"10.3390/s21227562","title":"A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Motion planning; Obstacle; Computer science; Metric (unit); Path (computing); Context (archaeology); Probabilistic logic; Field (mathematics); Robot; Trajectory; Artificial intelligence; Data mining; Mathematics; Engineering; Geography","score_opus":0.030593060565217826,"score_gpt":0.27300780558228427,"score_spread":0.24241474501706645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213888950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09690346,0.00013360228,0.90180767,0.00014966952,0.0006518564,0.00018002104,0.000032586948,0.00008519809,0.00005594074],"genre_scores_gemma":[0.19617815,0.0000076358365,0.80340844,0.00016175077,0.000097474884,0.0000150453325,0.0000143071165,0.00001838014,0.00009880088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982048,0.00008005064,0.00030705336,0.00062926096,0.0002789633,0.00049984746],"domain_scores_gemma":[0.99874735,0.000361356,0.00017165933,0.000595894,0.00002259979,0.000101139616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023159386,0.00020524998,0.00026605756,0.000118841155,0.00013905349,0.00009051698,0.00044328082,0.00016674606,0.000003941691],"category_scores_gemma":[0.00039062463,0.00021666424,0.000080631806,0.0004280317,0.000028260316,0.00013294953,0.00017412934,0.00037063577,0.00001578097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055465003,0.00044283905,0.2111058,0.00021305875,0.0002672494,0.0022112522,0.035195734,0.65097755,0.0077718254,0.010527393,0.00069430977,0.08053752],"study_design_scores_gemma":[0.0010719153,0.00004291729,0.14070487,0.00044643914,0.000011573681,0.000114489936,0.0007676806,0.8459145,0.001574256,0.0074784798,0.0013549695,0.00051792845],"about_ca_topic_score_codex":0.000017964212,"about_ca_topic_score_gemma":6.4809075e-7,"teacher_disagreement_score":0.19493693,"about_ca_system_score_codex":0.000086186796,"about_ca_system_score_gemma":0.00006831609,"threshold_uncertainty_score":0.88353115},"labels":[],"label_agreement":null},{"id":"W3214818091","doi":"10.22215/etd/2014-10295","title":"Mobile Assistive Robot with Shared Control","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robot; Controller (irrigation); Human–computer interaction; Mobile robot; Haptic technology; Computer science; Interface (matter); Population; Artificial intelligence; Engineering; Control engineering; Simulation","score_opus":0.008381360235684377,"score_gpt":0.24516618619767855,"score_spread":0.23678482596199418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214818091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000132176,0.00011127744,0.9532616,0.000030512236,0.0008097952,0.0004628952,0.000009831642,0.0004192942,0.044762596],"genre_scores_gemma":[0.112887345,0.00000560414,0.79443735,0.0004951549,0.00030236703,0.0006042397,0.00080287526,0.0000850334,0.09038005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808264,0.000076402255,0.00026725142,0.00071653776,0.000499695,0.00035750412],"domain_scores_gemma":[0.99844176,0.00014741819,0.00027793503,0.00074290123,0.00025399672,0.00013599331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019006312,0.00034765052,0.0004745259,0.0001488626,0.00011151371,0.00026151704,0.001101435,0.00023186051,0.000047982183],"category_scores_gemma":[0.000039413168,0.00026333536,0.00007882462,0.00024188288,0.000019725128,0.00023117187,0.00003061508,0.0003177601,0.0002880379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011728631,0.001687304,0.0071404153,0.0016052915,0.0039091147,0.0019693675,0.020967616,0.23250967,0.0019731715,0.041236743,0.15940402,0.5264244],"study_design_scores_gemma":[0.004016827,0.002434669,0.06300254,0.0009864479,0.00023025219,0.00007492559,0.00038499373,0.91841793,0.0010657584,0.00044635145,0.006400656,0.0025386647],"about_ca_topic_score_codex":0.00006284205,"about_ca_topic_score_gemma":0.000022376575,"teacher_disagreement_score":0.68590826,"about_ca_system_score_codex":0.000056826593,"about_ca_system_score_gemma":0.00021277464,"threshold_uncertainty_score":0.9999819},"labels":[],"label_agreement":null},{"id":"W3215393605","doi":"10.1109/tits.2021.3128411","title":"Path Planning of Arbitrary Shaped Mobile Robots With Safety Consideration","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; National Natural Science Foundation of China","keywords":"Mobile robot; Motion planning; Robot; Computer science; Path (computing); Engineering; Simulation; Control engineering; Artificial intelligence; Computer network","score_opus":0.027609903095940056,"score_gpt":0.26009705817303785,"score_spread":0.2324871550770978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215393605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013476563,0.00028212176,0.9840409,0.000052293002,0.0011061605,0.00050203665,0.00008123581,0.00023412977,0.0002245201],"genre_scores_gemma":[0.9500946,0.0000454951,0.049334984,0.00006378382,0.000030052479,0.00013422692,0.000054054068,0.00002632606,0.00021649736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974865,0.00015600532,0.00088153384,0.0005662673,0.00063086103,0.00027884557],"domain_scores_gemma":[0.9984056,0.0002739433,0.00027286462,0.0005231056,0.00039012032,0.00013438165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025899708,0.00026785806,0.000415462,0.00021634295,0.00019825523,0.00010232248,0.00026287758,0.00013174576,0.000031831616],"category_scores_gemma":[0.0000032313715,0.00025420773,0.0001319898,0.0006662915,0.00005680906,0.00046300588,4.7613347e-7,0.00028303213,0.00002329937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060943425,0.0002262657,0.0002407332,0.0001474857,0.00012814305,0.0001763717,0.0036306386,0.98979753,0.0021755425,0.0013316633,0.000023912535,0.0020607673],"study_design_scores_gemma":[0.0017521575,0.0010604431,0.0029562067,0.0021391825,0.00019246705,0.00032877785,0.0037424928,0.7838762,0.2022198,0.00014868645,0.00051219075,0.0010713977],"about_ca_topic_score_codex":0.00007131218,"about_ca_topic_score_gemma":0.000013549567,"teacher_disagreement_score":0.93661803,"about_ca_system_score_codex":0.00007257911,"about_ca_system_score_gemma":0.00028541585,"threshold_uncertainty_score":0.999991},"labels":[],"label_agreement":null},{"id":"W3215464764","doi":"10.1109/ccdc52312.2021.9602686","title":"Fast Trajectory Planning for AGV in the Presence of Moving Obstacles: A Combination of 3-dim A* Search and QCQP","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Solver; Mathematical optimization; Motion planning; Sequential quadratic programming; Computer science; Local optimum; Discretization; Quadratic programming; Path (computing); Quadratic growth; Control theory (sociology); Mathematics; Algorithm; Control (management); Artificial intelligence; Robot","score_opus":0.04521083658362462,"score_gpt":0.29995563846087847,"score_spread":0.25474480187725385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215464764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35245183,0.00026335963,0.64620185,0.00023075238,0.00007071069,0.00019271173,0.0000027756278,0.000017620983,0.00056837563],"genre_scores_gemma":[0.85764676,0.0000035713845,0.14222257,0.000034298984,0.0000070600167,0.000009685728,0.0000018542602,0.0000030817512,0.00007113683],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905396,0.00012411017,0.00020743343,0.00020189345,0.0002575396,0.0001550912],"domain_scores_gemma":[0.99865055,0.0009070158,0.000053498483,0.00025406835,0.00011304214,0.000021827336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069492275,0.000062347724,0.00013609082,0.00008473899,0.000041595555,0.000038105012,0.00038472138,0.000033159522,0.0000010700358],"category_scores_gemma":[0.00020648024,0.000049251234,0.00002465503,0.00032824613,0.000051585786,0.00019579951,0.00010966319,0.00008832226,2.5154588e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006885472,0.0013304295,0.12853432,0.0018555379,0.00012773409,0.00025963175,0.28203395,0.2648598,0.079426534,0.11906285,0.0008949031,0.12154548],"study_design_scores_gemma":[0.0005208943,0.00011400281,0.08307963,0.0001745192,0.0000032479809,0.000018886356,0.0038608306,0.89963406,0.011831159,0.0006732638,0.0000075408093,0.000081977916],"about_ca_topic_score_codex":0.000066389686,"about_ca_topic_score_gemma":0.0000048811894,"teacher_disagreement_score":0.63477427,"about_ca_system_score_codex":0.00001194783,"about_ca_system_score_gemma":0.00008872885,"threshold_uncertainty_score":0.2008407},"labels":[],"label_agreement":null},{"id":"W3215761265","doi":"10.1109/lra.2023.3242201","title":"Learning to Search in Task and Motion Planning With Streams","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"STREAMS; Task (project management); Motion (physics); Computer science; Artificial intelligence; Economics; Computer network; Management","score_opus":0.018195057553560334,"score_gpt":0.2579148893678889,"score_spread":0.2397198318143286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215761265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49524522,0.0000048029906,0.50119543,0.0032222003,0.00006961998,0.00008076238,3.4739764e-7,0.00017023197,0.000011408003],"genre_scores_gemma":[0.89967215,0.0000036275508,0.09988112,0.000358564,0.000031940173,0.0000070069227,0.0000056594085,0.000010087377,0.000029839095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990402,0.000060815135,0.00014002972,0.00028627162,0.00022206763,0.0002506028],"domain_scores_gemma":[0.999639,0.00009688133,0.00003889401,0.00012326923,0.000019952797,0.000082023784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032864936,0.00010171543,0.00011519706,0.0003186224,0.00011648867,0.00019078543,0.0001216897,0.000033325046,2.1445524e-7],"category_scores_gemma":[0.000019995729,0.00009499249,0.000007914496,0.0006026227,0.000023776005,0.0002672338,0.000060505674,0.00014952348,0.00001496499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013972025,0.000004513573,0.011136292,0.000015295092,0.0000043257187,0.000055413628,0.003144931,0.96664375,0.0019753322,0.00010833333,0.00009916678,0.016811272],"study_design_scores_gemma":[0.00021287015,0.0000636807,0.099394046,0.00009912067,0.000001974451,0.000017211149,0.00013496194,0.8998095,0.00011978191,0.000020519723,0.000011301904,0.000115041454],"about_ca_topic_score_codex":0.00002399985,"about_ca_topic_score_gemma":9.4392954e-7,"teacher_disagreement_score":0.40442693,"about_ca_system_score_codex":0.000027594342,"about_ca_system_score_gemma":0.000012715121,"threshold_uncertainty_score":0.3873681},"labels":[],"label_agreement":null},{"id":"W3216355168","doi":"10.1109/wisee50203.2021.9613837","title":"Dynamic Feasibility of Space Environment Emulation using an Omnidirectional Drone","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Emulation; Drone; Omnidirectional antenna; Computer science; Space (punctuation); Aerospace engineering; Engineering; Telecommunications; Operating system","score_opus":0.05252801618349671,"score_gpt":0.30053281095759865,"score_spread":0.24800479477410195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216355168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24872898,0.000029392035,0.75063634,0.00012413427,0.0001475072,0.00004576936,0.0000012880545,0.00004554898,0.00024107257],"genre_scores_gemma":[0.47098297,0.0000011789245,0.5287993,0.000012148014,0.00000793701,5.4273556e-7,0.0000045793695,0.0000025906083,0.0001887614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990172,0.000093298026,0.00016684328,0.0003265301,0.00027129284,0.00012483362],"domain_scores_gemma":[0.99933356,0.000040784907,0.0000686014,0.00045786102,0.0000422791,0.000056917885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022277771,0.00007283511,0.00010967638,0.00003736668,0.000059578768,0.000026704894,0.00016103755,0.000038142833,0.000039648858],"category_scores_gemma":[0.000024527775,0.00007555346,0.000032747008,0.00015701364,0.000027395381,0.00029834936,0.00010889889,0.000056274435,0.000009429789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047277067,0.00061135576,0.02499458,0.000018580573,0.000025653848,0.000029856794,0.00055867876,0.88594973,0.07657049,0.0042800875,0.000016549342,0.006939703],"study_design_scores_gemma":[0.00011964435,0.00002689979,0.08716512,0.0000070099254,0.000003562817,0.00002912517,0.000021310485,0.9070102,0.0046450687,0.00088708545,0.000008845832,0.000076118486],"about_ca_topic_score_codex":0.000054952085,"about_ca_topic_score_gemma":0.000003618527,"teacher_disagreement_score":0.22225401,"about_ca_system_score_codex":0.00013899039,"about_ca_system_score_gemma":0.00008052407,"threshold_uncertainty_score":0.30809805},"labels":[],"label_agreement":null},{"id":"W3216623747","doi":"10.1007/978-3-030-91608-4_9","title":"Fast and Optimal Planner for the Discrete Grid-Based Coverage Path-Planning Problem","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Grid; Mathematical optimization; Heuristic; Path (computing); Planner; Motion planning; Algorithm; Branch and bound; Mathematics; Artificial intelligence; Robot","score_opus":0.01851515627556675,"score_gpt":0.25225026786352184,"score_spread":0.2337351115879551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216623747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019121177,0.0014477335,0.9939037,0.0014960448,0.0018205087,0.00072183396,0.000043485314,0.00013941046,0.0004081509],"genre_scores_gemma":[0.0063465666,0.000021949201,0.9906985,0.001731241,0.00071902137,0.00004955884,0.000031678337,0.000048646547,0.00035282256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960068,0.000047185804,0.0004960967,0.0017212935,0.00089837564,0.0008302881],"domain_scores_gemma":[0.9960789,0.0019525031,0.00031470926,0.0012476391,0.00022066945,0.00018557577],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011035036,0.00061322574,0.00057366333,0.00033919138,0.0006230273,0.001198662,0.002644328,0.00029108362,0.0000046188698],"category_scores_gemma":[0.000118793665,0.00044859565,0.00014280782,0.00040119025,0.00063478254,0.00042677057,0.0010956014,0.0008552305,0.000004651197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008643316,0.000011180755,0.000069342685,0.00006574974,0.000018707156,0.00019563745,0.0007417103,0.8350994,0.000029261753,0.0023680478,0.00009848109,0.16129382],"study_design_scores_gemma":[0.00043681875,0.00020440578,0.00012996183,0.00079257414,0.000018512968,0.00013332217,6.188586e-7,0.9900419,0.00019894334,0.005625482,0.0018076537,0.00060979486],"about_ca_topic_score_codex":0.000015282534,"about_ca_topic_score_gemma":0.0000035735118,"teacher_disagreement_score":0.16068403,"about_ca_system_score_codex":0.00014660557,"about_ca_system_score_gemma":0.00066289643,"threshold_uncertainty_score":0.9998382},"labels":[],"label_agreement":null},{"id":"W3217571039","doi":"10.1109/icra46639.2022.9811540","title":"Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research; Defense Advanced Research Projects Agency","keywords":"Component (thermodynamics); Computer science; Generalization; Metric (unit); Artificial intelligence; Obstacle; Machine learning; Geometric shape; Mathematics; Geometry; Engineering","score_opus":0.026624607376047812,"score_gpt":0.2623421616210431,"score_spread":0.2357175542449953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217571039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19784178,0.00024305264,0.79194164,0.003642729,0.00081468344,0.0006029741,0.0001825422,0.00013979962,0.0045907735],"genre_scores_gemma":[0.9804206,0.000050781087,0.018832183,0.00022713536,0.000018278502,0.00006272326,0.000084386775,0.000009767299,0.00029414857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834967,0.00011282452,0.00023874335,0.0004448589,0.00066257274,0.00019133404],"domain_scores_gemma":[0.99938995,0.00013843311,0.00019114888,0.00015826951,0.000048828104,0.000073372306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031165915,0.0001610009,0.00016375259,0.000515672,0.00019589107,0.0001644041,0.00038802225,0.000024138155,0.000039160677],"category_scores_gemma":[0.00005450714,0.00016053859,0.000015916457,0.0002834916,0.000059924736,0.0003363723,0.00035618816,0.00029376606,0.000005365302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010414804,0.00036164778,0.018761797,0.000014608356,0.0001561636,0.00032779205,0.0032257212,0.82068175,0.00028656467,0.05417304,0.00045492017,0.10145187],"study_design_scores_gemma":[0.00062854623,0.00034245529,0.11024455,0.00004967253,0.0000052786127,0.000055655782,0.00034345055,0.88728243,0.000040090858,0.0007681207,0.000074664385,0.00016509728],"about_ca_topic_score_codex":0.000015588499,"about_ca_topic_score_gemma":5.8829244e-7,"teacher_disagreement_score":0.7825788,"about_ca_system_score_codex":0.00022782256,"about_ca_system_score_gemma":0.00006631228,"threshold_uncertainty_score":0.65465736},"labels":[],"label_agreement":null},{"id":"W329093344","doi":"10.1007/978-1-4614-5659-9_4","title":"Path Planning for Autonomous Underwater Vehicles","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Motion planning; Underwater; Task (project management); Path (computing); Computer science; Real-time computing; Marine engineering; Engineering; Simulation; Artificial intelligence; Systems engineering; Geography; Robot; Computer network","score_opus":0.05208157505628954,"score_gpt":0.2646501516909588,"score_spread":0.21256857663466924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W329093344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000014946627,0.0007254044,0.7473889,0.00023267523,0.0007652515,0.0002972453,0.000013644826,0.00047387258,0.25010154],"genre_scores_gemma":[0.0003994992,0.0000089657715,0.5083572,0.00045172928,0.00049785204,0.000025299556,0.000037412967,0.00006540208,0.4901566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99811995,0.000011523128,0.0003694614,0.00061576243,0.0003029482,0.0005803788],"domain_scores_gemma":[0.99853975,0.00021727415,0.00020557982,0.00077000726,0.00007973504,0.0001876816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031540924,0.00042121473,0.00042506974,0.00016914289,0.00014955134,0.00018005789,0.0010362075,0.0003853031,0.000051721094],"category_scores_gemma":[0.000009937662,0.0003628505,0.00017949966,0.000021454809,0.000048368867,0.0003005636,0.00032670435,0.00029828792,0.00043034184],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008306555,0.000034623197,0.00005092457,0.00016071177,0.00020290248,0.00011193999,0.0012112359,0.0010301602,0.000049493687,0.8581741,0.036413413,0.10255222],"study_design_scores_gemma":[0.0007299081,0.00028011377,0.00012620914,0.0005381769,0.00009909277,0.00016796924,0.000013904584,0.14143991,0.00027568417,0.10812222,0.74632406,0.0018827422],"about_ca_topic_score_codex":0.0000060696375,"about_ca_topic_score_gemma":1.5733109e-7,"teacher_disagreement_score":0.75005186,"about_ca_system_score_codex":0.000107847976,"about_ca_system_score_gemma":0.0001196063,"threshold_uncertainty_score":0.99988234},"labels":[],"label_agreement":null},{"id":"W343032421","doi":"","title":"A Navigation and Decision Making Architecture for Unmanned Ground Vehicles: Implementation and Results with the Raptor UGV","year":2007,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Motion planning; Obstacle avoidance; Architecture; Unmanned ground vehicle; Traverse; Computer science; Terrain; Software; Obstacle; Path (computing); Software architecture; Mission control center; Real-time computing; Systems engineering; Robot; Human–computer interaction; Artificial intelligence; Engineering; Mobile robot; Geography; Operating system","score_opus":0.016800566517310957,"score_gpt":0.3103429548131665,"score_spread":0.29354238829585555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W343032421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23832014,0.000035825757,0.7602307,0.0009769611,0.000043210086,0.00030824982,0.0000027610843,0.000045572666,0.000036539743],"genre_scores_gemma":[0.48797202,0.0000012355537,0.51180196,0.00016593741,0.000032564225,0.000007822141,0.000004709505,0.0000043056107,0.000009435503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911785,0.000018222445,0.00018657681,0.00029195385,0.0001916254,0.00019378091],"domain_scores_gemma":[0.9988472,0.0007660437,0.000103599035,0.000191014,0.00005136077,0.000040767685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097282825,0.00009468214,0.00007595236,0.0000610698,0.00027248217,0.00018758152,0.0001659101,0.000032245676,2.6895006e-7],"category_scores_gemma":[0.000025895906,0.000056319986,0.000011650353,0.00017433465,0.000040834013,0.00020548387,0.0000675947,0.00006692255,4.550143e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028544312,0.000012537016,0.0009422688,0.0000214708,0.000019972336,0.000010218622,0.006443592,0.0010064486,0.00030526207,0.0035711622,0.00063221337,0.9867494],"study_design_scores_gemma":[0.013108613,0.0029824409,0.3842727,0.0007761134,0.00008182579,0.00070267066,0.007543204,0.54101026,0.003004155,0.040073562,0.005333418,0.0011110138],"about_ca_topic_score_codex":0.000028232627,"about_ca_topic_score_gemma":0.00006906691,"teacher_disagreement_score":0.9856384,"about_ca_system_score_codex":0.000020118408,"about_ca_system_score_gemma":0.000014910791,"threshold_uncertainty_score":0.22966623},"labels":[],"label_agreement":null},{"id":"W3941961","doi":"","title":"A Genetic Algorithm for calculating minimum distance between convex and concave bodies","year":2001,"lang":"en","type":"article","venue":"Surgery annual","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Regular polygon; Focus (optics); Convergence (economics); Genetic algorithm; Mathematical optimization; Simple (philosophy); Path (computing); Algorithm; Mathematics; Robot; Motion planning; Separation (statistics); Computer science; Artificial intelligence; Geometry","score_opus":0.026663109405124744,"score_gpt":0.2647983166763428,"score_spread":0.23813520727121806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3941961","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0414416,0.0006143172,0.9565939,0.0003908079,0.00041577098,0.00019676916,0.00008162478,0.00015883472,0.00010641329],"genre_scores_gemma":[0.36524704,0.000043634333,0.6327489,0.00040859054,0.0006974586,0.00006690693,0.000027211872,0.000033190387,0.0007270482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983263,0.00007794192,0.00036638658,0.00049040763,0.0002298265,0.0005091537],"domain_scores_gemma":[0.9976911,0.001568672,0.00014523187,0.00030280752,0.00013093853,0.00016124189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047887425,0.00019823496,0.00039708975,0.00009727913,0.00022098685,0.00011327102,0.0002616715,0.00008627139,0.0000022449428],"category_scores_gemma":[0.0001698344,0.00019833572,0.000088452725,0.00018077785,0.00012433516,0.00033846713,0.000119799945,0.000104751416,0.000008122293],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008504346,0.000034502194,0.14841047,0.000051374227,0.000088810266,0.00027540606,0.0030980585,0.00019501212,0.000015082333,0.00031097664,0.004959856,0.84255195],"study_design_scores_gemma":[0.00080407195,0.00017460117,0.20823425,0.00020051988,0.000049569026,0.00021239657,0.0005442415,0.757098,0.00016722291,0.0024074034,0.029025145,0.001082574],"about_ca_topic_score_codex":0.000021656035,"about_ca_topic_score_gemma":8.9952044e-7,"teacher_disagreement_score":0.84146935,"about_ca_system_score_codex":0.000026294309,"about_ca_system_score_gemma":0.00007848869,"threshold_uncertainty_score":0.8087896},"labels":[],"label_agreement":null},{"id":"W4200153275","doi":"10.3390/electronics10243093","title":"Occlusion-Aware Path Planning to Promote Infrared Positioning Accuracy for Autonomous Driving in a Warehouse","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Computer science; Frame (networking); Frenet–Serret formulas; Computer vision; Path (computing); Infrared; Simulation; Artificial intelligence; Curvature; Real-time computing; Mathematics; Optics; Telecommunications; Geometry; Physics","score_opus":0.01410180025522239,"score_gpt":0.278257626393551,"score_spread":0.2641558261383286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200153275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069253124,0.001160527,0.9270952,0.001199804,0.00029493164,0.0004813278,0.0000061333863,0.000376985,0.00013201928],"genre_scores_gemma":[0.2851795,0.000027531529,0.7125318,0.001350022,0.00016296381,0.00014807361,0.000063637286,0.000064338856,0.0004721371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976665,0.00008157329,0.00038361043,0.00068063947,0.00028599097,0.00090169837],"domain_scores_gemma":[0.99858904,0.00031784974,0.00012434488,0.0006081041,0.0002021965,0.00015846986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045179095,0.00022697818,0.00028434952,0.00019247297,0.0003006291,0.00029813944,0.0007237049,0.00011033418,0.0000036280292],"category_scores_gemma":[0.00071245636,0.00025962232,0.0000778648,0.00084110326,0.000009436879,0.00048138847,0.0004481422,0.0003704077,0.000016043186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089356974,0.0007113697,0.010643368,0.0002613943,0.00020511018,0.00435178,0.06207091,0.64528406,0.02727358,0.010621271,0.0099630775,0.22852473],"study_design_scores_gemma":[0.0010658082,0.00045131697,0.0035248392,0.000622778,0.000012841875,0.0003293591,0.00016491563,0.96691155,0.009888291,0.0040793447,0.012234883,0.0007140522],"about_ca_topic_score_codex":0.0000049184637,"about_ca_topic_score_gemma":0.0000075092585,"teacher_disagreement_score":0.32162753,"about_ca_system_score_codex":0.00047963057,"about_ca_system_score_gemma":0.0009226193,"threshold_uncertainty_score":0.9999856},"labels":[],"label_agreement":null},{"id":"W4200163943","doi":"10.1109/iros51168.2021.9636609","title":"Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Science and Engineering Research Council","keywords":"Deadlock; Heuristics; Computer science; Deadlock prevention algorithms; Voronoi diagram; Distributed computing; Simple (philosophy); Collision avoidance; Robot; Collision; Artificial intelligence; Mathematics; Computer security","score_opus":0.041217035884472,"score_gpt":0.26518750893579945,"score_spread":0.22397047305132745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200163943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01956403,0.0003463238,0.97433335,0.0006978519,0.0034763834,0.00054621143,0.00039392154,0.000072670155,0.0005692499],"genre_scores_gemma":[0.9671153,0.001119736,0.02540331,0.00016875367,0.0005053784,0.00024591986,0.00036716074,0.000037923182,0.0050365487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974363,0.00014471936,0.0005636577,0.00091426435,0.0005936189,0.00034742514],"domain_scores_gemma":[0.9980158,0.00029813137,0.00029004028,0.00043514435,0.0007558772,0.00020501079],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040891848,0.00032480602,0.00040757455,0.00013604244,0.00023432821,0.0008426362,0.0004612827,0.00015618239,0.000022363203],"category_scores_gemma":[0.00009968532,0.00027843376,0.00007026081,0.0002064953,0.00007898032,0.00040081932,0.00013641223,0.00023123891,0.000019875517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011522927,0.0014140868,0.007769567,0.0009419549,0.0015956311,0.0006744976,0.0034179054,0.55386573,0.0478685,0.2864948,0.01824764,0.076557435],"study_design_scores_gemma":[0.0007933597,0.000544946,0.0013158744,0.0009867157,0.000030067566,0.00016639926,0.00035818442,0.9810141,0.011234749,0.0006181869,0.0025246406,0.0004128158],"about_ca_topic_score_codex":0.0000897754,"about_ca_topic_score_gemma":0.00001577599,"teacher_disagreement_score":0.94893,"about_ca_system_score_codex":0.00017319697,"about_ca_system_score_gemma":0.0001971105,"threshold_uncertainty_score":0.9999668},"labels":[],"label_agreement":null},{"id":"W4200210047","doi":"10.18280/ria.350601","title":"Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Odometry; Mobile robot; Mobile robot navigation; Computer science; Motion planning; Robot; Artificial intelligence; Computation; Computer vision; Robot control; Visual odometry; Position (finance); Controller (irrigation); Real-time computing","score_opus":0.02311004959976963,"score_gpt":0.2669078035619518,"score_spread":0.24379775396218217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200210047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110107996,0.0001802128,0.8885133,0.00005853337,0.0002916738,0.0007936586,0.000010067337,0.000035436908,0.0000090796175],"genre_scores_gemma":[0.92944354,0.0000072855637,0.0701462,0.000014668888,0.00003529802,0.00021599504,0.00005376651,0.000011222264,0.00007204501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868643,0.000061727784,0.00037956494,0.0005167195,0.00012654484,0.00022900615],"domain_scores_gemma":[0.9992905,0.0002569383,0.00012812302,0.00019119646,0.000066736706,0.00006646451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025344614,0.00013354231,0.00020148887,0.0000837172,0.00015559529,0.00012046419,0.00012912376,0.00007646926,8.4831083e-7],"category_scores_gemma":[0.00005886971,0.00014398636,0.000036272304,0.00021584908,0.00004598168,0.000093475624,0.000096283264,0.0000639692,0.0000052009746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012972771,0.00010905207,0.00042652414,0.00023089357,0.000009040096,0.000009146789,0.002440755,0.95940703,0.004906849,0.0020207476,0.000006490744,0.030420478],"study_design_scores_gemma":[0.00019868278,0.000117761345,0.00029195874,0.00021841626,0.000010712244,0.000033151173,0.0007195873,0.970915,0.027020168,0.0002831738,0.000046077825,0.0001452788],"about_ca_topic_score_codex":0.00000844922,"about_ca_topic_score_gemma":0.0000013968242,"teacher_disagreement_score":0.8193355,"about_ca_system_score_codex":0.000086940374,"about_ca_system_score_gemma":0.000026533464,"threshold_uncertainty_score":0.5871593},"labels":[],"label_agreement":null},{"id":"W4205699800","doi":"10.1109/tiv.2022.3141881","title":"Tunable Trajectory Planner Using G<sup>3</sup> Curves","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Trajectory; Motion planning; Path (computing); Jerk; Curvature; Mathematical optimization; Computer science; Mathematics; Control theory (sociology); Set (abstract data type); Path length; Mathematical analysis; Robot; Geometry; Acceleration; Physics; Artificial intelligence; Classical mechanics; Control (management)","score_opus":0.045028324561115995,"score_gpt":0.2671886598073157,"score_spread":0.2221603352461997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205699800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023249125,0.0005253789,0.97375673,0.0003699483,0.001089346,0.0002849143,0.000053225023,0.00043482395,0.00023653291],"genre_scores_gemma":[0.9348466,0.0001729048,0.06103161,0.0015984921,0.00010875893,0.00018763884,0.000007885222,0.00006560933,0.0019805154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974211,0.00028132735,0.0004139704,0.000620649,0.00072456145,0.00053841755],"domain_scores_gemma":[0.9987345,0.00024247044,0.00010083005,0.0006911112,0.000062457715,0.00016868218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005008726,0.00027628415,0.00027034347,0.00036597368,0.000885925,0.00010114319,0.00107692,0.00006382622,0.00030224834],"category_scores_gemma":[0.000006574969,0.0002941028,0.00019181683,0.0007060309,0.00007207501,0.0003655448,0.000015698794,0.00066383544,0.00011953052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020652666,0.0002895626,0.000025278705,0.000032387463,0.000057817266,0.000055443066,0.0011891505,0.9834792,0.00053243566,0.000090008514,0.0011762411,0.013051797],"study_design_scores_gemma":[0.0002143713,0.0002813949,0.000028756176,0.00011387235,0.000036451216,0.00017913758,0.00046479187,0.97440445,0.020657009,0.00019922471,0.0030000769,0.00042047285],"about_ca_topic_score_codex":0.00016209055,"about_ca_topic_score_gemma":0.0000017561835,"teacher_disagreement_score":0.9127251,"about_ca_system_score_codex":0.0002848909,"about_ca_system_score_gemma":0.00016115046,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W4205821028","doi":"10.2514/6.2022-2215","title":"Autonomous Strategic Defense: An Adaptive Clustering Approach to Capture Order Optimization","year":2022,"lang":"en","type":"article","venue":"AIAA SCITECH 2022 Forum","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Mathematical optimization; Computer science; Set (abstract data type); Travelling salesman problem; Computational complexity theory; Path (computing); Set cover problem; Constraint (computer-aided design); Realization (probability); Optimization problem; Artificial intelligence; Algorithm; Mathematics","score_opus":0.028279768842766808,"score_gpt":0.24227210340810687,"score_spread":0.21399233456534006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205821028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007980293,0.000078903475,0.99014,0.00088086136,0.0007021386,0.00050762575,0.000026881233,0.0005343654,0.0063312002],"genre_scores_gemma":[0.17184398,8.9835356e-7,0.8258441,0.001162694,0.000052660416,0.0002344218,0.00006698275,0.00003681108,0.0007574312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972873,0.00019170073,0.0002922296,0.0008909129,0.0006455033,0.00069238455],"domain_scores_gemma":[0.9986155,0.000036427355,0.00012858013,0.0008756943,0.000111118905,0.00023268371],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056379073,0.0002604146,0.0002371068,0.00032295866,0.0007668547,0.00026050195,0.0016099068,0.0000805221,0.000047410613],"category_scores_gemma":[0.00002151059,0.0002831698,0.00005763083,0.0018548927,0.000031221498,0.0005710989,0.0013168417,0.00048456842,0.000021231433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012273527,0.00009701797,0.000026175097,0.000004190119,0.00001386829,0.000032355823,0.0012484014,0.9890778,0.00007841517,0.006378077,0.0007143348,0.0023170607],"study_design_scores_gemma":[0.00025212253,0.000444736,0.000030351084,0.0000054243965,0.000006930259,0.0001679797,0.0022514171,0.9951754,0.000021761016,0.0007208581,0.00055682386,0.0003661625],"about_ca_topic_score_codex":0.0001388949,"about_ca_topic_score_gemma":0.000007811997,"teacher_disagreement_score":0.17104594,"about_ca_system_score_codex":0.00028308842,"about_ca_system_score_gemma":0.00023869977,"threshold_uncertainty_score":0.99996203},"labels":[],"label_agreement":null},{"id":"W4205958651","doi":"10.1109/mis.2001.956078","title":"Experiences with an autonomous robot attending AAAI","year":2001,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Presentation (obstetrics); Human–computer interaction; Robotics; Autonomous robot; Artificial intelligence; Human–robot interaction; Mobile robot; Multimedia","score_opus":0.04482143113377924,"score_gpt":0.28408121137113934,"score_spread":0.2392597802373601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205958651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12018981,0.0002497082,0.8739462,0.00008453576,0.0034939882,0.00030472732,7.544385e-7,0.00052988343,0.0012003811],"genre_scores_gemma":[0.96569043,0.000008851713,0.032378767,0.00007525216,0.00041194606,0.00013605921,0.00000241262,0.000025137935,0.0012711249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747914,0.000151468,0.00046682582,0.0007231061,0.0005736556,0.00060583226],"domain_scores_gemma":[0.998379,0.000098872704,0.00020836333,0.0009298496,0.000106267486,0.00027768206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048583123,0.00028793295,0.0003375367,0.00019627599,0.00022632543,0.0005358165,0.0014487351,0.000092708186,0.000012847724],"category_scores_gemma":[0.000015765072,0.00022650167,0.000054973305,0.0005133093,0.00008240052,0.00080722274,0.00006585509,0.00017738537,0.00027810334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007839692,0.0007355055,0.017141411,0.00014010922,0.00027128126,0.0038274615,0.16681026,0.75224006,0.0018550192,0.007495992,0.0021823798,0.047222137],"study_design_scores_gemma":[0.00018198472,0.00067810994,0.00026078874,0.00025322105,0.00001047079,0.001790538,0.010714217,0.9787425,0.003325675,0.00003099518,0.0033591972,0.00065235025],"about_ca_topic_score_codex":0.0004135837,"about_ca_topic_score_gemma":0.000006989329,"teacher_disagreement_score":0.84550065,"about_ca_system_score_codex":0.00016496787,"about_ca_system_score_gemma":0.00009248673,"threshold_uncertainty_score":0.923647},"labels":[],"label_agreement":null},{"id":"W4205966649","doi":"10.2514/6.2022-0757","title":"Model of UAV and Downwash for Multi-UAV Path Planning","year":2022,"lang":"en","type":"article","venue":"AIAA SCITECH 2022 Forum","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Downwash; Motion planning; Computer science; Path (computing); Aerospace engineering; Artificial intelligence; Computer network; Engineering; Robot","score_opus":0.05960477314089838,"score_gpt":0.2917093248794757,"score_spread":0.23210455173857733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205966649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011879115,0.00042715177,0.98562694,0.0007642212,0.00043088553,0.00040818893,0.0001337685,0.00017565569,0.0001540879],"genre_scores_gemma":[0.28362587,0.0000056976114,0.7147742,0.0005187855,0.00002529492,0.00021617587,0.000022240172,0.00002614532,0.0007856158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811804,0.00005210327,0.00031703908,0.0005417837,0.00045297405,0.0005180473],"domain_scores_gemma":[0.99896604,0.00013387186,0.0001843284,0.0005476707,0.00006240234,0.000105675754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071459793,0.00017791218,0.00026616632,0.0002219126,0.0004984912,0.00006294796,0.0010106808,0.00005353281,0.00000564293],"category_scores_gemma":[0.00006985783,0.0001866187,0.00008391668,0.00045157282,0.000057993057,0.00029564425,0.0012190924,0.00025896056,0.000001952319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081732935,0.0004727196,0.011283798,0.0001766241,0.00013797198,0.00008824805,0.007472047,0.8262903,0.022814779,0.03612998,0.03676458,0.058287196],"study_design_scores_gemma":[0.00071079814,0.0002384898,0.00021906909,0.00002128262,0.0000069548378,0.000030058423,0.000289827,0.99269575,0.0006854049,0.0032992526,0.001580982,0.00022215737],"about_ca_topic_score_codex":0.000023819795,"about_ca_topic_score_gemma":7.592451e-7,"teacher_disagreement_score":0.27174675,"about_ca_system_score_codex":0.00006087014,"about_ca_system_score_gemma":0.000112311776,"threshold_uncertainty_score":0.761009},"labels":[],"label_agreement":null},{"id":"W4206322160","doi":"10.1109/icjece.2021.3120324","title":"3-D Path Planning Using Improved RRT* Algorithm for Robot-Assisted Flexible Needle Insertion in Multilayer Tissues","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Motion planning; Path (computing); Computer science; Robot; Algorithm; Biomedical engineering; Artificial intelligence; Engineering; Computer network","score_opus":0.023007989497957074,"score_gpt":0.24147185678769018,"score_spread":0.2184638672897331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206322160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02409815,0.0018681921,0.9732992,0.000089615765,0.000541545,0.000073870855,0.0000015859886,0.00002438311,0.000003446274],"genre_scores_gemma":[0.19387715,0.000005871603,0.805744,0.00009295303,0.0002476639,0.0000023940245,0.0000018732914,0.000014674256,0.000013429083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987947,0.0000296058,0.0003755671,0.00022480197,0.00011369123,0.000461657],"domain_scores_gemma":[0.9990827,0.00013870334,0.0001000147,0.00012324593,0.00016162722,0.00039373056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021203363,0.0001575752,0.0002980659,0.0004207218,0.000084453415,0.0002079731,0.0002577669,0.0000899441,8.205898e-7],"category_scores_gemma":[0.00006605577,0.00016179665,0.00006683646,0.0005951292,0.000009547651,0.00031532886,0.000032812717,0.0002852362,2.4942676e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066389857,0.000048957158,0.0018169187,0.0000559438,0.000082054925,0.0015029584,0.0008742256,0.43009302,0.0074783815,0.00028369224,0.00014513498,0.55761206],"study_design_scores_gemma":[0.00054630643,0.00014344483,0.010873619,0.00017806768,0.000008811607,0.000777746,0.0000070162896,0.98578286,0.0010897083,0.00005059321,0.00035565125,0.00018618582],"about_ca_topic_score_codex":0.00029505658,"about_ca_topic_score_gemma":0.000016603288,"teacher_disagreement_score":0.55742586,"about_ca_system_score_codex":0.00017658397,"about_ca_system_score_gemma":0.0004732515,"threshold_uncertainty_score":0.6597876},"labels":[],"label_agreement":null},{"id":"W4206404584","doi":"10.1109/smc52423.2021.9659071","title":"RRT-Rope: A deterministic shortening approach for fast near-optimal path planning in large-scale uncluttered 3D environments","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Random tree; Path (computing); Computation; Computer science; Rope; Motion planning; Probabilistic logic; Mathematical optimization; Scale (ratio); Tree (set theory); Distributed computing; Algorithm; Artificial intelligence; Mathematics; Computer network; Robot","score_opus":0.05086619524788937,"score_gpt":0.28787711645813746,"score_spread":0.23701092121024808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206404584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05004331,0.00019850339,0.942321,0.00011646838,0.0012793059,0.0004046601,0.00011639538,0.000043551714,0.0054768166],"genre_scores_gemma":[0.8670881,0.000042067735,0.12928562,0.00016328151,0.00024075281,0.00013932955,0.00015511185,0.00003173282,0.0028540005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970801,0.00016029335,0.00062872644,0.0009386723,0.00067365833,0.00051856466],"domain_scores_gemma":[0.99883926,0.00015597872,0.00024323638,0.00045749018,0.00014224176,0.00016177661],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047832442,0.0003342809,0.00043969267,0.00014430868,0.00016993785,0.00078334805,0.0007573092,0.00016022372,0.00001809323],"category_scores_gemma":[0.00005205636,0.00034815792,0.000074593845,0.0001542102,0.00007563931,0.00022633556,0.000268296,0.00031149562,0.00002541265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005772827,0.0038498587,0.094287746,0.0011834904,0.0013891873,0.005797523,0.040527835,0.6953807,0.012280639,0.077084504,0.004153832,0.063487396],"study_design_scores_gemma":[0.0009851122,0.00013654631,0.0036998321,0.00037952414,0.000018366014,0.00013869014,0.00094487664,0.9911856,0.00012889782,0.00007713807,0.0019254141,0.00037997076],"about_ca_topic_score_codex":0.00003636146,"about_ca_topic_score_gemma":0.000004249237,"teacher_disagreement_score":0.8170448,"about_ca_system_score_codex":0.00012703963,"about_ca_system_score_gemma":0.0001619981,"threshold_uncertainty_score":0.99989706},"labels":[],"label_agreement":null},{"id":"W4207006311","doi":"10.21203/rs.3.rs-1229897/v1","title":"A Hybrid Technique for Active SLAM Based on RPPO Model with Transfer Learning","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Simultaneous localization and mapping; Reinforcement learning; Convolution (computer science); Action (physics); A priori and a posteriori; RGB color model; Robot; Active learning (machine learning); Mobile robot; Computer vision; Artificial neural network","score_opus":0.07939926041669168,"score_gpt":0.37843427486832665,"score_spread":0.29903501445163494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207006311","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007385077,0.000028250195,0.99063075,0.0011279146,0.000098953526,0.0037880812,0.00018805987,0.00041140264,0.0029880698],"genre_scores_gemma":[0.4391058,0.000015242869,0.5463085,0.00011063203,0.00013653334,0.012209417,0.00031660547,0.00014865496,0.0016485831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99388665,0.0009207761,0.0002867914,0.001561173,0.0022724476,0.0010721507],"domain_scores_gemma":[0.9963835,0.0011890839,0.00007342041,0.0015100902,0.00060420134,0.00023973624],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0030582764,0.00041032227,0.00047618526,0.0009340446,0.00072912604,0.00032072316,0.0024232438,0.00020908593,0.000028630513],"category_scores_gemma":[0.00035024446,0.00037378358,0.00021666971,0.00055871776,0.00012226748,0.00016676121,0.0010663999,0.00455669,0.000009938571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022692344,0.00016738121,0.000024467594,0.00043414143,0.000036723213,0.00016295642,0.0007274423,0.99092984,0.00021776491,0.001657932,0.00042783952,0.0049865893],"study_design_scores_gemma":[0.0005782618,0.0016239469,0.000062988845,0.00072193914,0.000008335286,0.000010378066,0.00008504655,0.9880358,0.0045526805,0.0032751078,0.0006079542,0.00043756122],"about_ca_topic_score_codex":0.000064105814,"about_ca_topic_score_gemma":0.0000014399009,"teacher_disagreement_score":0.44432223,"about_ca_system_score_codex":0.0007113723,"about_ca_system_score_gemma":0.0020862948,"threshold_uncertainty_score":0.99987143},"labels":[],"label_agreement":null},{"id":"W4210436559","doi":"10.23919/jsee.2021.000123","title":"Experimental study of path planning problem using EMCOA for a holonomic mobile robot","year":2021,"lang":"en","type":"article","venue":"Journal of Systems Engineering and Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Motion planning; Holonomic; Mobile robot; Computer science; Path (computing); Genetic algorithm; Shortest path problem; Any-angle path planning; Mathematical optimization; Occupancy grid mapping; Grid; Robot; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.019932314184419857,"score_gpt":0.26429902550928847,"score_spread":0.24436671132486862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210436559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5718016,0.009369798,0.4183771,0.000002899245,0.00029092003,0.0001412066,5.941018e-7,0.0000141465225,0.0000017480168],"genre_scores_gemma":[0.9171594,0.000011660932,0.082694665,0.0000017134549,0.000099219185,0.000010789187,3.2359122e-7,0.000013854139,0.000008399091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998895,0.000033342203,0.00048046396,0.00015707094,0.00017692865,0.00025719783],"domain_scores_gemma":[0.9992778,0.000074323776,0.00030013802,0.00015641045,0.00012115377,0.00007020093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045811757,0.00012433661,0.00036228323,0.000113650494,0.000052723306,0.000084387044,0.0002105833,0.00004594585,8.621314e-8],"category_scores_gemma":[0.000018845933,0.00011851866,0.000055412336,0.00014573781,0.000005155808,0.00019312334,0.000053665626,0.00017296252,6.643539e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070573387,0.00014965373,0.00015839763,0.00008662817,0.000089735186,0.000048981245,0.00236163,0.94772935,0.04910206,0.00010957587,0.000014751418,0.00014215912],"study_design_scores_gemma":[0.00097169227,0.0018800306,0.00007793246,0.0003512919,0.000028469369,0.00128675,0.0014731344,0.98657906,0.007076315,0.000005534227,0.00012218213,0.00014761275],"about_ca_topic_score_codex":0.0000061862215,"about_ca_topic_score_gemma":4.4160156e-8,"teacher_disagreement_score":0.34535778,"about_ca_system_score_codex":0.00012433503,"about_ca_system_score_gemma":0.00015482969,"threshold_uncertainty_score":0.4833051},"labels":[],"label_agreement":null},{"id":"W4210687899","doi":"10.1109/cdc45484.2021.9683031","title":"PA-FaSTrack: Planner-Aware Real-Time Guaranteed Safe Planning","year":2021,"lang":"en","type":"article","venue":"2021 60th IEEE Conference on Decision and Control (CDC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Planner; Trajectory; Computer science; Motion planning; Sequence (biology); Tracking (education); Tree (set theory); Path (computing); State space; Mathematical optimization; Robot; Real-time computing; Artificial intelligence; Mathematics","score_opus":0.02704713068040716,"score_gpt":0.27796754231815135,"score_spread":0.2509204116377442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210687899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040287033,0.00048405008,0.939383,0.002459925,0.0018863054,0.00042055987,0.000105577354,0.00032380855,0.0146497525],"genre_scores_gemma":[0.9551929,0.00021799274,0.038967278,0.0019744625,0.00031004698,0.00004005173,0.00003392219,0.000045257355,0.0032180578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960959,0.00029375733,0.0006942318,0.0012747983,0.0008908446,0.0007504502],"domain_scores_gemma":[0.99665487,0.0010859321,0.00025722614,0.0011700833,0.00041466168,0.00041720088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064184616,0.00048625906,0.00077289063,0.000232788,0.0003528155,0.0007696641,0.0010055942,0.00026613567,0.00030324588],"category_scores_gemma":[0.00030621036,0.0004387535,0.00015592374,0.00047616,0.00009777045,0.00038881483,0.00019703714,0.00050436077,0.00070810685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007987786,0.00065411744,0.0026609516,0.0000566817,0.00034714388,0.009149619,0.0021957862,0.012402523,0.03521145,0.017333893,0.022449136,0.8967399],"study_design_scores_gemma":[0.005839679,0.0005597522,0.012636934,0.0010853251,0.000055112687,0.00042404764,0.00035283196,0.9691675,0.0013963695,0.0036992459,0.0037155317,0.0010677188],"about_ca_topic_score_codex":0.000027605229,"about_ca_topic_score_gemma":0.0000040956866,"teacher_disagreement_score":0.95676494,"about_ca_system_score_codex":0.00005691463,"about_ca_system_score_gemma":0.00047290014,"threshold_uncertainty_score":0.9998064},"labels":[],"label_agreement":null},{"id":"W4211129186","doi":"10.3389/fnbot.2021.798428","title":"MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neurorobotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Shenzhen Research and Development Program; National Natural Science Foundation of China","keywords":"Motion planning; Computer science; Path (computing); Planner; Mobile robot; Robot; Real-time computing; Any-angle path planning; Tracking system; Tracking (education); Artificial intelligence; Kalman filter","score_opus":0.033532850464956986,"score_gpt":0.2610838522926637,"score_spread":0.22755100182770668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211129186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01934149,0.00065122946,0.97748494,0.00028242913,0.0015811749,0.0004601634,0.00006141982,0.00011072431,0.000026423215],"genre_scores_gemma":[0.16363604,0.000013901067,0.83595324,0.00019549031,0.00005294324,0.00003794325,0.000046083165,0.000028858558,0.000035483317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787563,0.00016305635,0.0005064917,0.00061407714,0.00038157977,0.00045915073],"domain_scores_gemma":[0.99878895,0.00023848575,0.00030411733,0.00052093813,0.00005249549,0.00009502001],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044777876,0.00022821488,0.00046078785,0.00034419022,0.00021185522,0.00006437498,0.00095901923,0.00006468083,5.676629e-7],"category_scores_gemma":[0.00025990725,0.00025569304,0.00006826462,0.0006435529,0.00008243892,0.00027802007,0.0004901113,0.00038564068,8.87851e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059171933,0.00045472378,0.027892616,0.00016555398,0.000040788535,0.00012693385,0.0023714579,0.9300958,0.00038175052,0.0008294403,0.0037305173,0.033851247],"study_design_scores_gemma":[0.001011432,0.000323048,0.011750395,0.000031500444,0.000018514878,0.00004771145,0.00036903535,0.98496485,0.00010271738,0.0006990173,0.00041633303,0.00026544562],"about_ca_topic_score_codex":0.000014962287,"about_ca_topic_score_gemma":1.2059623e-7,"teacher_disagreement_score":0.14429456,"about_ca_system_score_codex":0.00007683241,"about_ca_system_score_gemma":0.00009078834,"threshold_uncertainty_score":0.9999895},"labels":[],"label_agreement":null},{"id":"W4214603083","doi":"10.1155/2022/9938975","title":"Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Volcano; Algorithm; Acceleration; Computer science; Path (computing); Collision; Civil aviation; Star (game theory); Range (aeronautics); Volcanic ash; A* search algorithm; Aviation; Engineering; Aerospace engineering; Geology; Mathematics; Physics","score_opus":0.013448878803940422,"score_gpt":0.24543843653977643,"score_spread":0.23198955773583602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214603083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027973711,0.00012850807,0.96985936,0.00039375128,0.0013786298,0.00015008828,0.000011664825,0.000068633824,0.00003563688],"genre_scores_gemma":[0.47780356,0.000007077845,0.5215811,0.0004033215,0.00011193062,0.000013028503,0.000024876514,0.000020124879,0.000034981687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804825,0.00010288482,0.00058913085,0.00027206208,0.0007247544,0.0002629257],"domain_scores_gemma":[0.9985338,0.00012619566,0.000768661,0.00028366945,0.00016758732,0.00012008899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046854027,0.00016997388,0.00025942476,0.0002968931,0.00023517612,0.000037601643,0.00058074645,0.00003904518,0.000006993187],"category_scores_gemma":[0.000026437765,0.00016936855,0.0001447739,0.0004911527,0.000016259206,0.00059606385,0.000008756695,0.00052966323,0.0000012904289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009791883,0.000116092626,0.0002936707,0.000010915133,0.000017328142,0.0006987121,0.0014556022,0.9482575,0.0016403133,0.00010752548,0.00016595311,0.047138456],"study_design_scores_gemma":[0.0032485886,0.0027701582,0.055096205,0.00017427366,0.000042771815,0.00015768323,0.0006497513,0.9328219,0.0007456209,0.0011590648,0.0027322301,0.00040174698],"about_ca_topic_score_codex":0.000006024609,"about_ca_topic_score_gemma":6.312825e-7,"teacher_disagreement_score":0.44982985,"about_ca_system_score_codex":0.00018450586,"about_ca_system_score_gemma":0.00020246433,"threshold_uncertainty_score":0.6906649},"labels":[],"label_agreement":null},{"id":"W4220888532","doi":"10.18280/jesa.550106","title":"Online Optimization Application on Path Planning in Unknown Environments","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maxima and minima; Motion planning; Particle swarm optimization; Mathematical optimization; Path (computing); Shortest path problem; Computer science; Smoothing; Mobile robot; Function (biology); Robot; Algorithm; Mathematics; Artificial intelligence; Computer vision; Theoretical computer science","score_opus":0.020612166368954046,"score_gpt":0.2587870972630553,"score_spread":0.23817493089410127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220888532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032920744,0.00026312476,0.96519476,0.0002637328,0.00045781437,0.00023080182,0.000008926853,0.00016905279,0.0004910402],"genre_scores_gemma":[0.51470584,0.000044245062,0.48402748,0.00042833513,0.00017138655,0.000057104655,0.000038490423,0.0000510718,0.00047603704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741066,0.0004999174,0.00057335774,0.0003749692,0.00076965167,0.0003714526],"domain_scores_gemma":[0.99886924,0.000116355775,0.00045587428,0.00040977847,0.000022688075,0.00012605151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079422223,0.00019827684,0.00024048417,0.0003788425,0.0005250353,0.00018184472,0.0009474743,0.00004125251,0.000025023197],"category_scores_gemma":[0.00007631475,0.00019774488,0.00006016139,0.0006152489,0.00003103328,0.00043134007,0.00032183333,0.00057489605,0.000030457955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054865873,0.00022344703,0.0015339599,0.000008363387,0.000011676093,0.00024533487,0.0007616763,0.91321164,0.00012607442,0.000479848,0.0002662619,0.08312622],"study_design_scores_gemma":[0.00044089253,0.00022755732,0.1290417,0.000090203976,0.0000049957725,0.00061360165,0.00006717105,0.8677598,0.000013755894,0.0005701208,0.0009900868,0.0001800813],"about_ca_topic_score_codex":0.000009740277,"about_ca_topic_score_gemma":2.2056601e-7,"teacher_disagreement_score":0.48178512,"about_ca_system_score_codex":0.0006324775,"about_ca_system_score_gemma":0.00007724707,"threshold_uncertainty_score":0.8063803},"labels":[],"label_agreement":null},{"id":"W4221011623","doi":"10.31590/ejosat.1081097","title":"TurtleBot 3 İle Ros Tabanlı Yol Planlama Uygulaması","year":2022,"lang":"tr","type":"article","venue":"European Journal of Science and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Humanities; Dijkstra's algorithm; Physics; Computer science; Combinatorics; Mathematics; Art; Shortest path problem","score_opus":0.014302020836422317,"score_gpt":0.21874899077985718,"score_spread":0.20444696994343486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221011623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84147906,0.007912637,0.07378266,0.05098554,0.009900554,0.00054513355,0.000038526774,0.00044093604,0.0149149485],"genre_scores_gemma":[0.9573868,0.00008569067,0.04145679,0.00036499897,0.00020079417,0.0000018379221,3.5028793e-7,0.000025912343,0.0004768394],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99581903,0.00033075202,0.000836207,0.00069447147,0.001485516,0.0008339978],"domain_scores_gemma":[0.9973961,0.000076137265,0.00095115433,0.00061444833,0.00066313404,0.00029902637],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.007461972,0.00027822625,0.0004463721,0.0019141592,0.0018100268,0.00037700884,0.0049214526,0.000050446728,0.000030630155],"category_scores_gemma":[0.0005661487,0.00026292188,0.00007749844,0.0041998914,0.003094877,0.00084632187,0.003110882,0.0014891798,0.00004946209],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001718839,0.0014671414,0.010686453,0.000099229816,0.00028308178,0.035376433,0.015027056,0.0066212625,0.11902448,0.099546686,0.0320285,0.6796678],"study_design_scores_gemma":[0.013574075,0.026378915,0.064068526,0.0010956425,0.00031512618,0.14910874,0.026423829,0.22969659,0.021574188,0.01365115,0.44962868,0.0044845375],"about_ca_topic_score_codex":0.000005395167,"about_ca_topic_score_gemma":1.7036155e-7,"teacher_disagreement_score":0.67518324,"about_ca_system_score_codex":0.000163431,"about_ca_system_score_gemma":0.0011210836,"threshold_uncertainty_score":0.9999823},"labels":[],"label_agreement":null},{"id":"W4221066356","doi":"10.18280/mmep.090131","title":"Motion Planning Solution with Constraints Based on Minimum Distance Model for Lane Change Problem of Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Jerk; Motion planning; Acceleration; Computer science; Trajectory; Constraint (computer-aided design); Motion (physics); Path (computing); Process (computing); Set (abstract data type); MATLAB; Control theory (sociology); Collision avoidance; Nonlinear system; Simulation; Control engineering; Collision; Control (management); Robot; Engineering; Artificial intelligence","score_opus":0.05133632314811408,"score_gpt":0.2279617048390645,"score_spread":0.17662538169095043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221066356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025938742,0.0000565288,0.9964488,0.00016592721,0.000031004245,0.0004532513,0.000017506825,0.00018135541,0.000051741208],"genre_scores_gemma":[0.5114952,4.7485688e-7,0.48826447,0.000011995265,0.000008264966,0.00018894412,0.000005641431,0.000012682964,0.000012370444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998881,0.00001596027,0.0002583735,0.00030486452,0.00025735176,0.00028249493],"domain_scores_gemma":[0.99943477,0.00016226094,0.0000961852,0.00020455665,0.000033495737,0.00006875218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043163984,0.00016167609,0.0002343994,0.000107344786,0.0001342396,0.000037777794,0.00020337028,0.00004022885,6.929617e-7],"category_scores_gemma":[0.0000093895615,0.00014711558,0.0000332064,0.00013067544,0.000035458215,0.00010657418,0.000050985895,0.00016079587,3.6716148e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010664779,0.0000745984,0.000008228688,0.00047466907,0.000008771946,0.0000022037316,0.0015108275,0.9818188,0.00009365813,0.014761078,0.0000038726957,0.0012326366],"study_design_scores_gemma":[0.00038214974,0.00023111133,0.000003887758,0.00036772722,0.000010777065,0.000013053072,0.000012862308,0.9927457,0.000045963276,0.0060036313,0.000008571258,0.00017455593],"about_ca_topic_score_codex":0.0000023477435,"about_ca_topic_score_gemma":2.4625047e-8,"teacher_disagreement_score":0.5089013,"about_ca_system_score_codex":0.00004714914,"about_ca_system_score_gemma":0.000024454748,"threshold_uncertainty_score":0.5999199},"labels":[],"label_agreement":null},{"id":"W4223987358","doi":"10.1109/accai53970.2022.9752654","title":"Omni-Directional Robot Based on Swerve Drive","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Robot; Degrees of freedom (physics and chemistry); Snake-arm robot; Computer science; Mobile robot; Bang-bang robot; Terrain; Control engineering; Holonomic; Cartesian coordinate robot; Articulated robot; Mechanism (biology); Rotation (mathematics); Robot kinematics; Artificial intelligence; Engineering; Simulation; Physics","score_opus":0.02145888664215345,"score_gpt":0.29382387753913175,"score_spread":0.2723649908969783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223987358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035921861,0.00013890631,0.72570664,0.0024167718,0.0012123515,0.0005422291,0.000038130955,0.00031503555,0.26603776],"genre_scores_gemma":[0.8083935,0.00015531402,0.18891647,0.0020840715,0.000032746175,0.00011065786,0.00016371052,0.000012202239,0.00013134474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979081,0.00015442373,0.0006814997,0.0003083783,0.00068368064,0.0002638742],"domain_scores_gemma":[0.9979423,0.0005186306,0.0005393673,0.00078016846,0.00013666828,0.00008285946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008603137,0.00024131442,0.00024501723,0.00046606973,0.0005406315,0.00025378386,0.0022146064,0.000050591505,0.00020333221],"category_scores_gemma":[0.00007123875,0.00026352442,0.000051186416,0.00045836277,0.000098976554,0.00047289577,0.0011249378,0.0008446132,0.00003432327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000363765,0.0001674407,0.00027919456,0.000010125556,0.0000110470055,0.0000014731019,0.0011315822,0.5439481,0.000018427683,0.41830337,0.00027792162,0.035814945],"study_design_scores_gemma":[0.000843873,0.00013000806,0.00082404475,0.00007134817,0.0000021991018,0.000011696198,0.0007446165,0.98090315,0.000094003444,0.0071774432,0.008919746,0.00027788614],"about_ca_topic_score_codex":0.000011087617,"about_ca_topic_score_gemma":0.0000040975438,"teacher_disagreement_score":0.8048013,"about_ca_system_score_codex":0.0002822261,"about_ca_system_score_gemma":0.00012484734,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W4224057320","doi":"10.3390/robotics11020050","title":"Investigating the Impact of Triangle and Quadrangle Mesh Representations on AGV Path Planning for Various Indoor Environments: With or Without Inflation","year":2022,"lang":"en","type":"article","venue":"Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Motion planning; Representation (politics); Discretization; Robot; Metric (unit); Path (computing); Constant (computer programming); Computer science; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence; Engineering","score_opus":0.0424088244709771,"score_gpt":0.31122187757478925,"score_spread":0.26881305310381215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224057320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09758789,0.00004641302,0.9009554,0.00033998903,0.00011567411,0.00076798885,0.000025639214,0.000055835302,0.00010519791],"genre_scores_gemma":[0.7566323,0.0000013895055,0.24301195,0.00008814152,0.00003420826,0.000080338665,0.000019522557,0.000017256787,0.00011486204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987036,0.0001591192,0.00027978493,0.00028911975,0.00034965685,0.00021871459],"domain_scores_gemma":[0.998424,0.0007352418,0.0003235456,0.00042393807,0.00002468551,0.00006857096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047913523,0.00014342311,0.00022131085,0.00009187159,0.00045717243,0.00007213973,0.00035894735,0.000031159645,0.0000029027021],"category_scores_gemma":[0.00023228858,0.000092950984,0.000047411333,0.0003274537,0.000073599746,0.00018873323,0.00016758722,0.0001920004,7.250432e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005192232,0.00006134286,0.030108506,0.000008542956,0.000056274166,0.0000066787748,0.005878787,0.96158576,0.00017519863,0.0008306515,0.00017349787,0.0010628625],"study_design_scores_gemma":[0.0012949414,0.0016300913,0.029914752,0.000051552903,0.000033035016,0.000048780323,0.0005646943,0.96551,0.000103001,0.00066528475,0.000025298272,0.00015854122],"about_ca_topic_score_codex":0.000083896666,"about_ca_topic_score_gemma":0.000001096576,"teacher_disagreement_score":0.65904444,"about_ca_system_score_codex":0.00010704706,"about_ca_system_score_gemma":0.00013868902,"threshold_uncertainty_score":0.37904313},"labels":[],"label_agreement":null},{"id":"W4224247240","doi":"10.1155/2022/2486561","title":"Transportation of Payload Using Multiple Quadrotors via Rigid Connection","year":2022,"lang":"en","type":"article","venue":"International Journal of Aerospace Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Payload (computing); Controller (irrigation); Position (finance); Robot; Control theory (sociology); Control engineering; Computer science; Control (management); Orientation (vector space); Rigid body; Field (mathematics); Engineering; Simulation; Artificial intelligence; Mathematics","score_opus":0.011044528891786679,"score_gpt":0.2327099835974521,"score_spread":0.22166545470566543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224247240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29155806,0.000050255065,0.70605475,0.000116252246,0.0021503349,0.000039963037,0.000005417214,0.000021257163,0.0000037178788],"genre_scores_gemma":[0.8456078,0.0000029250623,0.15425341,0.000012475351,0.00010379026,0.0000022128697,0.000002414621,0.000008421341,0.000006510192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986905,0.000029015027,0.00039652578,0.000107389256,0.0006621167,0.0001144591],"domain_scores_gemma":[0.99909574,0.00010398418,0.0004119962,0.00010062884,0.00023714201,0.00005050494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037454825,0.00009039096,0.00015265016,0.0002755591,0.000045126686,0.000030097837,0.00059145037,0.000022757868,0.000010586634],"category_scores_gemma":[0.00006941315,0.00009864935,0.000100397105,0.00021635486,0.000009173969,0.00040806434,0.000039586117,0.00022456668,5.8117786e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001477315,0.00003727649,0.004232728,0.000005205752,0.000067985355,0.00006119522,0.0006620685,0.9587675,0.034946665,0.0002712969,0.000020166035,0.0009131694],"study_design_scores_gemma":[0.0005689926,0.0001182666,0.0069219945,0.00004842856,0.000009853551,0.00029859197,0.00008399607,0.9816721,0.00987935,0.00004828426,0.00023916597,0.00011098258],"about_ca_topic_score_codex":0.000060683124,"about_ca_topic_score_gemma":0.0000010340525,"teacher_disagreement_score":0.5540498,"about_ca_system_score_codex":0.00020097232,"about_ca_system_score_gemma":0.00005430281,"threshold_uncertainty_score":0.40228036},"labels":[],"label_agreement":null},{"id":"W4224284963","doi":"10.1109/isas55863.2022.9757293","title":"Maneuvering Planning for UAVs in Forest Surveillance and Fire Detection Missions with Kinematic Uncertainties","year":2022,"lang":"en","type":"article","venue":"2022 5th International Symposium on Autonomous Systems (ISAS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Flexibility (engineering); Computer science; Kinematics; Task (project management); Flight planning; Field (mathematics); Real-time computing; Aeronautics; Simulation; Aerospace engineering; Systems engineering; Engineering","score_opus":0.017286909500627768,"score_gpt":0.2546549098892175,"score_spread":0.23736800038858974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224284963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55502445,0.0007810889,0.4212948,0.0038732938,0.010023703,0.0031291752,0.0001994984,0.0008549039,0.004819097],"genre_scores_gemma":[0.9943676,0.0000047367857,0.0034322175,0.00008836424,0.00012011817,0.0007047199,0.000033203887,0.00002901259,0.0012200142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978806,0.00015083606,0.00046425767,0.0006009318,0.0005591748,0.000344246],"domain_scores_gemma":[0.99885005,0.00035294655,0.00029531302,0.00033880302,0.00007540162,0.0000874896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056685833,0.00023562307,0.00030120395,0.00025209633,0.0004119547,0.00023606942,0.00071852293,0.00005057937,0.000010818314],"category_scores_gemma":[0.000056978337,0.00022624769,0.000050123617,0.00029275083,0.000031320214,0.00024199979,0.00022616086,0.0002760882,0.0000049100345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007252675,0.00007843017,0.010146085,0.00011118822,0.00005860136,0.00009113314,0.0019064292,0.98171,0.00076738826,0.0035544913,0.00021402244,0.0012896954],"study_design_scores_gemma":[0.00075005123,0.0003885949,0.005998194,0.00018390515,0.0000047069484,0.0003077163,0.0004260892,0.98865443,0.00007049345,0.00007612651,0.0028544595,0.00028524807],"about_ca_topic_score_codex":0.0004620526,"about_ca_topic_score_gemma":0.000034022076,"teacher_disagreement_score":0.43934318,"about_ca_system_score_codex":0.0006000238,"about_ca_system_score_gemma":0.00008442288,"threshold_uncertainty_score":0.9226113},"labels":[],"label_agreement":null},{"id":"W4224981430","doi":"10.18280/mmep.090211","title":"Towards Comparison and Real Time Implementation of Path Planning Methods for 2R Planar Manipulator with Obstacles Avoidance","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Path (computing); Bézier curve; Computer science; Fast path; Any-angle path planning; Obstacle avoidance; Point (geometry); Parametric statistics; Control theory (sociology); Set (abstract data type); Actuator; Robot; Mathematical optimization; Simulation; Algorithm; Control (management); Mathematics; Mobile robot; Artificial intelligence","score_opus":0.04643720057293986,"score_gpt":0.31413674166088845,"score_spread":0.2676995410879486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224981430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031174477,0.00024998598,0.9680763,0.000044194167,0.000034144265,0.00026837896,0.00000771964,0.000121368466,0.000023462766],"genre_scores_gemma":[0.15817821,0.0000051143434,0.84168786,0.0000042466186,0.000009848044,0.00008115513,0.0000059930203,0.00001692204,0.000010674284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895126,0.00003481035,0.00031874116,0.00027346268,0.00018245594,0.00023925258],"domain_scores_gemma":[0.999366,0.00026242953,0.00010448145,0.00016772849,0.000025708066,0.0000736466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006863126,0.0001481374,0.00030891513,0.00007558195,0.00013034916,0.00005444894,0.00017977823,0.000029507008,0.000001544822],"category_scores_gemma":[0.000010680753,0.00013306069,0.000023157834,0.000115848656,0.00001842185,0.00010703559,0.0000863079,0.00012276667,2.3416744e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005926339,0.000023481656,0.000083751285,0.0004077319,0.000029214509,0.0000016589115,0.0041688485,0.9771454,0.0009828581,0.013880749,0.000009402037,0.0032609997],"study_design_scores_gemma":[0.00029606282,0.00022793982,0.00007974767,0.00012115033,0.000018713332,0.000028181725,0.00015459486,0.99261135,0.0003534313,0.0059017334,0.000042254258,0.00016481603],"about_ca_topic_score_codex":0.00002297087,"about_ca_topic_score_gemma":3.4696093e-8,"teacher_disagreement_score":0.12700373,"about_ca_system_score_codex":0.000025612,"about_ca_system_score_gemma":0.000019402158,"threshold_uncertainty_score":0.54260576},"labels":[],"label_agreement":null},{"id":"W4226061075","doi":"10.3390/vehicles4020021","title":"Motion Planning for Autonomous Vehicles Based on Sequential Optimization","year":2022,"lang":"en","type":"article","venue":"Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kinematics; Acceleration; Trajectory; Trajectory optimization; Control theory (sociology); Nonlinear system; Computer science; Finite element method; Node (physics); Mathematics; Engineering; Physics; Artificial intelligence; Classical mechanics; Structural engineering","score_opus":0.034962854737969395,"score_gpt":0.2689507922467902,"score_spread":0.2339879375088208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226061075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007970813,0.00003739064,0.98945755,0.00096020766,0.0006249338,0.00028512735,0.000023463357,0.00040620493,0.00023431578],"genre_scores_gemma":[0.5611535,3.0793984e-7,0.43780735,0.0006007293,0.00011250266,0.00014375261,0.000063388565,0.000020490816,0.00009804707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985751,0.00013112958,0.00021045883,0.00042386944,0.0003667612,0.00029271288],"domain_scores_gemma":[0.9992119,0.00019371338,0.00012551191,0.00035762155,0.00004736289,0.00006390278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048058745,0.0001379547,0.00013524684,0.00020231158,0.0005812561,0.00013278656,0.00057538395,0.00004363486,0.000013970697],"category_scores_gemma":[0.00004871617,0.0001521976,0.00007054373,0.00029802154,0.000021806429,0.00021171027,0.00014634922,0.00015904632,0.000007653973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018234194,0.000065115986,0.0002839223,0.000007954329,0.0000063339867,0.000015143314,0.00026078793,0.9848963,0.00040984133,0.0010544811,0.0006191082,0.012362752],"study_design_scores_gemma":[0.00056344364,0.0002677265,0.00071692775,0.0000146640505,0.0000066739503,0.00000882685,0.000034875906,0.99574286,0.0012333281,0.00036923605,0.00086552976,0.00017588114],"about_ca_topic_score_codex":0.000012213517,"about_ca_topic_score_gemma":7.267805e-8,"teacher_disagreement_score":0.5531826,"about_ca_system_score_codex":0.00015567573,"about_ca_system_score_gemma":0.00009857578,"threshold_uncertainty_score":0.62064373},"labels":[],"label_agreement":null},{"id":"W4226322432","doi":"10.1088/1742-6596/2216/1/012035","title":"An Improved Genetic Algorithm for Rapid UAV Path Planning","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Robustness (evolution); Fitness function; Genetic algorithm; Computer science; Obstacle; Swarm behaviour; Path (computing); Mathematical optimization; Algorithm; Real-time computing; Artificial intelligence; Mathematics; Robot; Machine learning; Geography","score_opus":0.029507878577005108,"score_gpt":0.2696342385371157,"score_spread":0.2401263599601106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226322432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030381572,0.0001710148,0.99520624,0.0002189561,0.0010870557,0.00015109821,0.00002809041,0.0000495029,0.000049908747],"genre_scores_gemma":[0.19933034,0.000011100313,0.79993343,0.00014188231,0.0004711291,0.00002585062,0.0000066046514,0.00001895905,0.000060698963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983212,0.00014194113,0.00045711867,0.0002671106,0.0004636537,0.00034899634],"domain_scores_gemma":[0.9983234,0.00009938793,0.0006245683,0.00039966294,0.0004015044,0.00015150607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005000991,0.0001920323,0.000353103,0.00009556002,0.0003898863,0.0002602218,0.0013895838,0.000034172088,0.000010245248],"category_scores_gemma":[0.000027106833,0.00018491616,0.00013210395,0.00025576985,0.000058297497,0.001139194,0.00020549593,0.00037402593,0.0000010934799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034539255,0.00016874657,0.00010873744,0.000017009263,0.00007223438,0.00009211171,0.0045191855,0.016832624,0.00724985,0.0039285515,0.0004350301,0.9665414],"study_design_scores_gemma":[0.0010283471,0.004163085,0.0011675506,0.00005143308,0.00003771222,0.00049563847,0.0015290607,0.95066047,0.013796663,0.024783112,0.0018369318,0.00044997377],"about_ca_topic_score_codex":0.000004656039,"about_ca_topic_score_gemma":5.4438388e-8,"teacher_disagreement_score":0.9660914,"about_ca_system_score_codex":0.00006526434,"about_ca_system_score_gemma":0.00050343137,"threshold_uncertainty_score":0.7540662},"labels":[],"label_agreement":null},{"id":"W4226420906","doi":"10.1139/dsa-2021-0049","title":"Integrating an electronic compass for position tracking on a wheeled tricycle mobile robot","year":2022,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Victoria University; Victoria University of Wellington; University of Waikato","keywords":"Compass; Odometry; Computer science; Sensor fusion; Computer vision; Mobile robot; Heading (navigation); Kalman filter; Gyroscope; Artificial intelligence; Encoder; Visual odometry; Robot; Global Positioning System; Engineering; Geography","score_opus":0.01707337868165313,"score_gpt":0.27844568119189933,"score_spread":0.2613723025102462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226420906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0092178825,0.00026595016,0.98854864,0.00019115274,0.00008665923,0.0013469444,0.000024776888,0.00017017212,0.0001478117],"genre_scores_gemma":[0.9772228,0.0000017059283,0.016391467,0.000054958615,0.00011116038,0.006046047,0.000066619155,0.000012410039,0.00009284657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988964,0.00008677765,0.00022446379,0.00037450853,0.00016632138,0.0002514925],"domain_scores_gemma":[0.9993013,0.00011065223,0.0001332741,0.00034535074,0.00004528332,0.00006411285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039839622,0.000104425424,0.00015743956,0.00009207369,0.0007607,0.00016728262,0.00033651403,0.000025455362,0.0000013332734],"category_scores_gemma":[0.0000044612516,0.00010669492,0.00003238161,0.00029726853,0.0000124508215,0.00016600639,0.000057532212,0.00016191269,0.000003180755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015953638,0.00059721374,0.00011256039,0.000081329585,0.00004115111,0.0000025765678,0.002198174,0.28779736,0.011321974,0.60510147,0.00042237196,0.09230785],"study_design_scores_gemma":[0.00038957223,0.00053485786,0.00025756218,0.000014224605,0.000009991064,0.000057771467,0.00070845947,0.9901009,0.00012451876,0.0011539294,0.0064741145,0.00017408843],"about_ca_topic_score_codex":0.00007892672,"about_ca_topic_score_gemma":0.0000024108256,"teacher_disagreement_score":0.9721572,"about_ca_system_score_codex":0.00013910081,"about_ca_system_score_gemma":0.000050823495,"threshold_uncertainty_score":0.5850766},"labels":[],"label_agreement":null},{"id":"W4229008461","doi":"10.1145/3524134","title":"Coordinated Motion Planning Through Randomized <i>k</i> -Opt","year":2022,"lang":"en","type":"article","venue":"ACM Journal of Experimental Algorithmics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Job shop scheduling; Mathematical optimization; Computer science; Square (algebra); Sequence (biology); Motion (physics); Robot; Phase (matter); Mathematics; Motion planning; Artificial intelligence; Schedule; Geometry; Physics","score_opus":0.02827921308986038,"score_gpt":0.2957232502654412,"score_spread":0.26744403717558085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229008461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02227017,0.005000314,0.9666252,0.0009440733,0.003911454,0.00031684406,0.0000075609755,0.000118763484,0.0008055995],"genre_scores_gemma":[0.39442322,0.000024793897,0.60449827,0.0006367742,0.00026612094,0.000028518982,0.00000841636,0.000028360811,0.00008555212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969241,0.0005381871,0.00090259395,0.00031690608,0.0009188241,0.00039937187],"domain_scores_gemma":[0.9978229,0.00041886786,0.00088916253,0.0005715509,0.00015733611,0.00014020488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015901094,0.00025906874,0.0006962347,0.00021523834,0.00045420486,0.00013938882,0.002305624,0.000057385503,0.000041251493],"category_scores_gemma":[0.00023481878,0.00024040631,0.00031355035,0.0005592547,0.0001066265,0.0009888208,0.001143543,0.0006429178,0.000007917136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.030048165,0.007958715,0.0015013647,0.000072190196,0.0035618525,0.015982589,0.118441336,0.5109979,0.13035062,0.047968846,0.061424542,0.07169187],"study_design_scores_gemma":[0.17458804,0.002494623,0.000120673074,0.00015861868,0.0001419455,0.012776393,0.006715474,0.6940102,0.086855784,0.016163407,0.0044951197,0.0014797398],"about_ca_topic_score_codex":0.000023228384,"about_ca_topic_score_gemma":1.1045281e-8,"teacher_disagreement_score":0.37215304,"about_ca_system_score_codex":0.00033100913,"about_ca_system_score_gemma":0.00012394892,"threshold_uncertainty_score":0.98034847},"labels":[],"label_agreement":null},{"id":"W4230204916","doi":"10.20944/preprints201805.0164.v1","title":"Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Flexibility (engineering); Mathematical optimization; Motion planning; Constraint (computer-aided design); SAFER; Convexity; Range (aeronautics); Planner; Robot; Engineering; Mathematics; Artificial intelligence","score_opus":0.1740030006361202,"score_gpt":0.3555264488754764,"score_spread":0.1815234482393562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230204916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03381791,0.00011695755,0.960142,0.0007190295,0.0016568996,0.0013558149,0.00002715633,0.0008931007,0.0012711085],"genre_scores_gemma":[0.38919064,0.000016575206,0.60916173,0.00031881078,0.00046196702,0.00015708353,0.00012927274,0.00008305274,0.00048083815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582756,0.0001474994,0.000802248,0.0020045342,0.0004809441,0.000737244],"domain_scores_gemma":[0.99658436,0.00031024383,0.0006359453,0.0017906369,0.00038333854,0.00029550915],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011047603,0.0006244626,0.0008307348,0.00035439772,0.00030800526,0.00024429182,0.0019362409,0.00048851466,0.000054756565],"category_scores_gemma":[0.00038304485,0.00069276016,0.00017851076,0.00024240573,0.000221633,0.00037904625,0.0046546645,0.0007192918,0.00012992664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041827534,0.000107025226,0.12291141,0.0005093424,0.00027623915,0.000060265513,0.008064462,0.8649897,0.00084736774,0.00040223787,0.00023268472,0.0015574233],"study_design_scores_gemma":[0.0005854667,0.00004958003,0.07130927,0.0004815331,0.00005187607,0.000072230956,0.000040056686,0.92309797,0.0015114002,0.0016923526,0.00039345102,0.0007148371],"about_ca_topic_score_codex":0.000073295334,"about_ca_topic_score_gemma":3.6514766e-7,"teacher_disagreement_score":0.35537276,"about_ca_system_score_codex":0.00021662944,"about_ca_system_score_gemma":0.00027865253,"threshold_uncertainty_score":0.99955237},"labels":[],"label_agreement":null},{"id":"W4231720604","doi":"10.1002/rob.20344","title":"Vision‐based autonomous convoying with constant time delay","year":2010,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"","keywords":"Heading (navigation); Trajectory; Constant (computer programming); Tracking (education); Track (disk drive); Computer science; Field (mathematics); Control theory (sociology); Real-time computing; Simulation; Engineering; Artificial intelligence; Control (management); Aerospace engineering; Mathematics","score_opus":0.008398226440718843,"score_gpt":0.24454877125363375,"score_spread":0.23615054481291492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231720604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040048175,0.000040831692,0.98823196,0.0053639095,0.00092392,0.000065251654,4.919558e-7,0.00004764408,0.0013211825],"genre_scores_gemma":[0.17469458,0.0000021664525,0.82393956,0.0010772645,0.00013070636,3.8318746e-7,3.0871684e-7,0.000009911649,0.0001451416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869865,0.0000428928,0.0004318928,0.00015768186,0.000410107,0.00025879926],"domain_scores_gemma":[0.9981617,0.0005095915,0.0004271168,0.0003996894,0.00030941784,0.00019249386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005032116,0.0001443199,0.00028151076,0.00016145209,0.00010356276,0.0001637879,0.00079173286,0.00012369126,0.000035475972],"category_scores_gemma":[0.00015275097,0.00010465673,0.00008358759,0.00021179364,0.000059440903,0.00033077915,0.00006265971,0.00075157406,0.000028128256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015457455,0.0006176817,0.006491856,0.00006478211,0.00027430902,0.0062168404,0.0011809405,0.8979953,0.017237945,0.01581886,0.0137178,0.040229134],"study_design_scores_gemma":[0.0010860604,0.0012634977,0.00057302945,0.00016402527,0.000040450293,0.0020960046,0.000013241592,0.99018383,0.0026755931,0.00044706152,0.0011763319,0.00028090226],"about_ca_topic_score_codex":0.0000035196367,"about_ca_topic_score_gemma":0.0000011475726,"teacher_disagreement_score":0.17068976,"about_ca_system_score_codex":0.000024083489,"about_ca_system_score_gemma":0.00041827728,"threshold_uncertainty_score":0.42677775},"labels":[],"label_agreement":null},{"id":"W4232524904","doi":"10.1504/ijhvs.2019.101498","title":"Comparative study of dynamic programming and Pontryagin's minimum principle for autonomous multi-wheeled combat vehicle path planning","year":2019,"lang":"en","type":"article","venue":"International Journal of Heavy Vehicle Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Optimal control; Dynamic programming; Pontryagin's minimum principle; Motion planning; Path (computing); Mathematical optimization; Control theory (sociology); Vehicle dynamics; Computation; Maximum principle; Engineering; Mathematics; Control (management); Computer science; Algorithm; Robot; Automotive engineering; Artificial intelligence","score_opus":0.03184075096607668,"score_gpt":0.33765560496367636,"score_spread":0.30581485399759967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232524904","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89156437,0.00054625067,0.10507989,0.00010381692,0.0017979942,0.0008470609,0.000009834097,0.000033815388,0.000016994263],"genre_scores_gemma":[0.96963453,0.0000019671581,0.030135958,0.000016072832,0.000091602444,0.00002080377,0.000002606933,0.000014439512,0.0000819955],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975759,0.00017159802,0.00095834985,0.00030812365,0.00071478664,0.000271235],"domain_scores_gemma":[0.9973621,0.0003893055,0.0010475374,0.00025726215,0.0008208192,0.0001230259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093844626,0.00019436807,0.0005962511,0.0002654597,0.00007425469,0.00022394811,0.0009941707,0.00006580881,7.1339684e-7],"category_scores_gemma":[0.00005412731,0.00017100366,0.00009536716,0.0001374562,0.000037935602,0.000522069,0.0001844814,0.00023255534,0.0000036646475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018557383,0.008064896,0.5670889,0.0006941339,0.004260165,0.0011610764,0.0875433,0.25548372,0.043048047,0.0026029446,0.00047243116,0.02772464],"study_design_scores_gemma":[0.004768481,0.0015471334,0.048381798,0.00041072327,0.00002190896,0.00027377973,0.003029053,0.94085413,0.00025329122,0.000017699198,0.00025396104,0.00018803078],"about_ca_topic_score_codex":0.00007038301,"about_ca_topic_score_gemma":0.0000021336255,"teacher_disagreement_score":0.68537045,"about_ca_system_score_codex":0.00017346925,"about_ca_system_score_gemma":0.000143734,"threshold_uncertainty_score":0.6973327},"labels":[],"label_agreement":null},{"id":"W4232695030","doi":"10.4018/978-1-4666-3634-7.ch002","title":"Optimal Robot Path Planning with Cellular Neural Network","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Artificial neural network; Computer science; Path (computing); Robot; Analogy; Mobile robot; Cellular neural network; State space; Artificial intelligence; Topology (electrical circuits); Engineering; Mathematics","score_opus":0.02045954712931707,"score_gpt":0.2256662982027004,"score_spread":0.20520675107338332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232695030","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007473457,0.0006722013,0.35803276,0.00005296592,0.0010561724,0.00043078855,0.000016600354,0.00057205954,0.63909173],"genre_scores_gemma":[0.022923319,0.000001912895,0.85939646,0.001082768,0.0021423344,0.000060497492,0.000023558572,0.0001859275,0.1141832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961814,0.000048336784,0.000549917,0.0012286358,0.00090304954,0.0010886177],"domain_scores_gemma":[0.99743843,0.000075232325,0.00048456498,0.0014140479,0.00016145974,0.00042625458],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019698447,0.00089968735,0.00081487565,0.00007655195,0.00026467515,0.00051119743,0.0019334707,0.00052629126,0.000019877965],"category_scores_gemma":[0.000007864885,0.0007866437,0.00020931278,0.00005907924,0.00015402192,0.00022036393,0.0006773543,0.0007996215,0.00046872767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022308104,0.000010512901,0.000066130364,0.000031652387,0.00017536685,0.00265166,0.00017244501,0.27926868,0.000006948583,0.70088464,0.011347163,0.0053624944],"study_design_scores_gemma":[0.002397113,0.0021596448,0.00046780056,0.003552717,0.00038362227,0.0024532084,0.000029409463,0.83664864,0.00005374671,0.11446783,0.031330004,0.0060562403],"about_ca_topic_score_codex":0.000052561623,"about_ca_topic_score_gemma":7.868874e-7,"teacher_disagreement_score":0.5864168,"about_ca_system_score_codex":0.00020774838,"about_ca_system_score_gemma":0.00027017135,"threshold_uncertainty_score":0.99945843},"labels":[],"label_agreement":null},{"id":"W4232724057","doi":"10.1007/978-3-642-41610-1_35-1","title":"Mining Robotics","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Robotics; Computer science; Robot","score_opus":0.04455119785760769,"score_gpt":0.2406473585088388,"score_spread":0.19609616065123112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232724057","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.1394642e-8,0.000082827064,0.48776522,0.0011540697,0.00045875236,0.000053491494,0.0000010732948,0.00033943992,0.5101451],"genre_scores_gemma":[0.000008998896,0.0000073947012,0.56482947,0.00074923056,0.00018644995,8.8001997e-7,0.0000059317267,0.000025137831,0.4341865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844563,0.000008480877,0.00029085035,0.0006093113,0.00041066023,0.00023508667],"domain_scores_gemma":[0.99878067,0.00010654926,0.00015248991,0.00071535807,0.000057916863,0.00018701448],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009068492,0.00031130586,0.00036106884,0.00009765272,0.000061017483,0.00014662162,0.0013122457,0.00024290514,0.00006222093],"category_scores_gemma":[0.000027151978,0.00030018078,0.00011610384,0.00004342483,0.000035500063,0.00012943038,0.00054178294,0.00034962627,0.0014894124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.998951e-7,0.0000028320421,0.0000021294866,0.00001500589,0.000040209834,0.00035610568,0.00012816669,0.002280595,0.0000019849397,0.9596396,0.024495877,0.013037003],"study_design_scores_gemma":[0.00030186027,0.00022009367,0.000014726124,0.00033552837,0.0000576544,0.00017681027,0.000007640844,0.67090297,0.000030495197,0.055147216,0.2712967,0.0015083181],"about_ca_topic_score_codex":0.0000016066891,"about_ca_topic_score_gemma":2.2355665e-7,"teacher_disagreement_score":0.9044924,"about_ca_system_score_codex":0.000042715656,"about_ca_system_score_gemma":0.000120854704,"threshold_uncertainty_score":0.99994504},"labels":[],"label_agreement":null},{"id":"W4234407439","doi":"10.24124/2009/bpgub634","title":"Evolving artificial neural network controllers for autonomous agents navigating dynamic environments.","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.016327593470133493,"score_gpt":0.28383369781064866,"score_spread":0.26750610434051514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234407439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011206761,0.00064153556,0.97444975,0.00042628867,0.007063817,0.001982569,0.000016591159,0.00062050374,0.00359218],"genre_scores_gemma":[0.22470933,0.000016728825,0.75390667,0.0007608773,0.00095917215,0.00028645652,0.0021194085,0.00014009765,0.017101279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706745,0.00007775041,0.0006958657,0.0008892001,0.0004511583,0.0008185934],"domain_scores_gemma":[0.99848074,0.00022149444,0.0005348131,0.000572063,0.000042094867,0.00014882545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039800722,0.0004675911,0.0005587498,0.00007566596,0.00043196545,0.00035796006,0.0010810546,0.00032264952,0.000011766737],"category_scores_gemma":[0.0000833857,0.00048564127,0.0002506395,0.00019576213,0.000019944857,0.00033745362,0.000058494825,0.0004942541,0.000045539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044667213,0.000094224495,0.000064731685,0.000048552898,0.00013811784,0.00006207542,0.0010799351,0.43767253,0.00032097663,0.0006023235,0.0028716966,0.55700016],"study_design_scores_gemma":[0.0004085899,0.00012847103,0.00516223,0.00015217242,0.000044479515,0.000006112895,0.000084616666,0.9920684,0.000052741227,0.0011967228,0.00020918698,0.00048624648],"about_ca_topic_score_codex":0.0000295003,"about_ca_topic_score_gemma":0.00002307918,"teacher_disagreement_score":0.5565139,"about_ca_system_score_codex":0.0002470578,"about_ca_system_score_gemma":0.000115023,"threshold_uncertainty_score":0.99975955},"labels":[],"label_agreement":null},{"id":"W4235170104","doi":"10.22215/etd/2018-13444","title":"Autonomous Navigation of Unmanned Aerial Vehicles Subjected to Time Delays","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Waypoint; Heading (navigation); Filter (signal processing); Real-time computing; Computer science; Control theory (sociology); Simulation; Control (management); Engineering; Artificial intelligence; Computer vision; Aerospace engineering","score_opus":0.00968867646280685,"score_gpt":0.257163427996133,"score_spread":0.24747475153332615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235170104","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8037518,0.000041394935,0.17664102,0.00012640544,0.0039715525,0.00081745494,0.000031098403,0.0008134207,0.013805805],"genre_scores_gemma":[0.084757455,0.0000019099557,0.88900226,0.00012896418,0.00067813264,0.00006259837,0.0019786009,0.00007611474,0.023313986],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981277,0.00006884386,0.0005000297,0.00056229025,0.0004353238,0.00030581266],"domain_scores_gemma":[0.9984272,0.00007438267,0.00032042534,0.00063752243,0.0004147062,0.00012574306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025879758,0.00026996835,0.0003925563,0.0002577868,0.00008594397,0.00010044105,0.0010606425,0.00030511824,0.000048317437],"category_scores_gemma":[0.00008508959,0.0002683745,0.000086062464,0.00055279164,0.000022469087,0.00021334675,0.00008638037,0.00014772666,0.0007746326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010214591,0.0010640142,0.00030136987,0.0010633781,0.0009451198,0.00027333907,0.05939198,0.0066797463,0.52579945,0.013021888,0.087541424,0.30289683],"study_design_scores_gemma":[0.0016951398,0.0021106855,0.018852176,0.0014563012,0.00015032018,0.00005494053,0.0003158786,0.4642798,0.5040591,0.0029085337,0.0017353131,0.002381843],"about_ca_topic_score_codex":0.00018266418,"about_ca_topic_score_gemma":0.000012637179,"teacher_disagreement_score":0.7189944,"about_ca_system_score_codex":0.00007765689,"about_ca_system_score_gemma":0.00030201432,"threshold_uncertainty_score":0.9999769},"labels":[],"label_agreement":null},{"id":"W4236553248","doi":"10.22215/etd/2014-10131","title":"Development and Testing of a Simulation Environment for an Unmanned Aerial Vehicle","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Stereoscopy; Systems engineering; Computer science; Development environment; Real-time computing; Engineering; Protocol (science); Simulation; Systems simulation; Embedded system; Control engineering; Artificial intelligence; Software engineering","score_opus":0.047942799639089054,"score_gpt":0.28603643541104323,"score_spread":0.2380936357719542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236553248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18433869,0.000012782378,0.8147064,0.000005673569,0.00024875882,0.0003103685,0.0000012458114,0.000057108304,0.00031901692],"genre_scores_gemma":[0.17438506,2.7503876e-7,0.82507944,0.000011270483,0.000052417246,0.000026228217,0.00010940116,0.000010816504,0.00032509377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990364,0.000022781449,0.0002870734,0.0003244284,0.0001882041,0.00014111881],"domain_scores_gemma":[0.9992816,0.00019417972,0.00021734847,0.00020554426,0.000048037524,0.000053327196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026229033,0.00013402586,0.00018655058,0.00006963094,0.00008033053,0.000043553602,0.0002208147,0.000104262,0.0000011574575],"category_scores_gemma":[0.00007751043,0.00013044484,0.000016844442,0.000050272054,0.000008040609,0.00012454829,0.000031370946,0.000046156045,0.000002680219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063688036,0.00013319697,0.0002790655,0.00036094032,0.000049620576,0.0000015342331,0.0070656682,0.2930296,0.0064522494,0.0013548678,0.000032936005,0.69117665],"study_design_scores_gemma":[0.00033886716,0.00016743434,0.0056353253,0.00006420416,0.0000094356765,4.583051e-7,0.000055377175,0.9868908,0.0058631054,0.00016840512,0.00062546827,0.00018114464],"about_ca_topic_score_codex":0.000013903906,"about_ca_topic_score_gemma":0.0000048104785,"teacher_disagreement_score":0.6938612,"about_ca_system_score_codex":0.000026321059,"about_ca_system_score_gemma":0.00006516417,"threshold_uncertainty_score":0.5319386},"labels":[],"label_agreement":null},{"id":"W4237375428","doi":"10.1504/ijaac.2018.092850","title":"Optimal path planning for an autonomous articulated vehicle with two trailers","year":2018,"lang":"en","type":"article","venue":"International Journal of Automation and Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Obstacle avoidance; Motion planning; Obstacle; Collision avoidance; Control theory (sociology); Sigmoid function; Path (computing); Optimal control; Computer science; Point (geometry); MATLAB; Vehicle dynamics; Field (mathematics); Control engineering; Mathematical optimization; Mobile robot; Engineering; Control (management); Collision; Artificial intelligence; Robot; Mathematics; Artificial neural network; Aerospace engineering","score_opus":0.015655825956710907,"score_gpt":0.29216470364658237,"score_spread":0.2765088776898715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237375428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28607726,0.000018991406,0.71259093,0.0008049937,0.00032807357,0.00007401731,0.0000030010553,0.00004233077,0.00006041934],"genre_scores_gemma":[0.8531214,4.1312964e-7,0.14627759,0.00032771437,0.00024931662,0.0000036323563,0.000001785246,0.000005090996,0.000013061296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911624,0.000040710103,0.00028918363,0.00012601848,0.0003029636,0.00012486357],"domain_scores_gemma":[0.99883235,0.00008769324,0.00030545765,0.000082812716,0.0005970298,0.000094645016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034811525,0.00008398379,0.00013254528,0.00013457975,0.000072162344,0.00022189335,0.00034887553,0.000027703632,0.0000045458974],"category_scores_gemma":[0.00004607239,0.00006578171,0.000035299607,0.000060849605,0.000044988188,0.0007532819,0.000014699912,0.000072308074,0.0000023969187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019577425,0.0006637406,0.009950972,0.000021119618,0.0016449856,0.00051190075,0.02122111,0.5545167,0.041192777,0.016296657,0.00204951,0.3499728],"study_design_scores_gemma":[0.0033561664,0.0008549603,0.016641326,0.000055717832,0.000016388269,0.00034839907,0.00006774706,0.97749823,0.0005453694,0.00023455425,0.00029392855,0.00008721142],"about_ca_topic_score_codex":0.000004475717,"about_ca_topic_score_gemma":3.3573028e-7,"teacher_disagreement_score":0.56704414,"about_ca_system_score_codex":0.00003917323,"about_ca_system_score_gemma":0.00009321043,"threshold_uncertainty_score":0.26825002},"labels":[],"label_agreement":null},{"id":"W4238045770","doi":"10.1017/s0263574707003499","title":"Editorial","year":2007,"lang":"en","type":"editorial","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Robotics; Artificial intelligence; Context (archaeology); Computer science; Robot; Key (lock); Service (business); Human–computer interaction; Computer security","score_opus":0.009650446808644436,"score_gpt":0.2727984155864476,"score_spread":0.2631479687778031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238045770","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3327387e-8,0.000065209315,0.41703728,0.00007907625,0.5792054,0.00010258057,0.000010704687,0.00041409003,0.0030856687],"genre_scores_gemma":[5.7249457e-7,0.000017354134,0.2334449,0.00003442311,0.76500875,0.0000091032825,0.00007550715,0.000046742865,0.0013626787],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940531,0.00011787994,0.0005671364,0.0010846936,0.0032389846,0.000938171],"domain_scores_gemma":[0.9947742,0.002268518,0.0002905131,0.0016818562,0.00065560406,0.00032929468],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0015584609,0.00052092725,0.00064284523,0.00030704713,0.0001768435,0.0004284488,0.00326443,0.0020614562,0.000013603842],"category_scores_gemma":[0.0026056387,0.0005164819,0.00019829525,0.0005377177,0.00008816411,0.00031538392,0.00070418225,0.0025564427,0.0012397371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049119867,0.000039705534,4.4167228e-7,0.000028739842,0.000035468547,0.00008401557,0.00008161323,0.0009578563,0.0000024511223,0.00052994647,0.9966061,0.0016287303],"study_design_scores_gemma":[0.00031997092,0.00010796799,0.0000035987994,0.000110207344,0.000028383625,0.0000043652153,0.000002115882,0.0071977144,0.000009327948,0.00047337133,0.9911717,0.0005712455],"about_ca_topic_score_codex":0.000048617214,"about_ca_topic_score_gemma":0.0000011756838,"teacher_disagreement_score":0.18580334,"about_ca_system_score_codex":0.0002333053,"about_ca_system_score_gemma":0.0010150259,"threshold_uncertainty_score":0.9997447},"labels":[],"label_agreement":null},{"id":"W4239201751","doi":"10.22215/etd/2012-07106","title":"Rigorous movement of convex polygons on a path using multiple robots","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Regular polygon; Path (computing); Combinatorics; Computer science; Movement (music); Mathematics; Art; Geometry; Programming language","score_opus":0.033137200568155806,"score_gpt":0.28328517466166553,"score_spread":0.25014797409350975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239201751","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06421295,0.000511352,0.9155485,0.00004716353,0.005403926,0.00082138897,0.000038853763,0.0003485205,0.013067351],"genre_scores_gemma":[0.43630424,0.000009355418,0.5570871,0.00029559724,0.00020486933,0.000031299922,0.00027112398,0.000062503525,0.0057338974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788994,0.00006907183,0.0004767796,0.00049148116,0.0006186919,0.00045403015],"domain_scores_gemma":[0.99831563,0.0001513047,0.00044950293,0.0008005335,0.00013584914,0.00014716247],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024601026,0.00034087003,0.00047092375,0.00024647306,0.00009531691,0.00005354831,0.00081658893,0.00024722415,0.000030520958],"category_scores_gemma":[0.00005925443,0.00030829123,0.00013132014,0.0002572473,0.000019886122,0.00019730392,0.00008474589,0.0002660744,0.000052007177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005026508,0.008644105,0.028310303,0.0031737948,0.0025099134,0.00067724515,0.06336108,0.29025963,0.11293287,0.10531119,0.009710718,0.37460652],"study_design_scores_gemma":[0.00091373123,0.0003114063,0.030068027,0.00076234614,0.00008314401,0.000012431993,0.0004388946,0.9472515,0.01860159,0.0004985217,0.00008258926,0.00097582745],"about_ca_topic_score_codex":0.0010024011,"about_ca_topic_score_gemma":0.00002793649,"teacher_disagreement_score":0.6569919,"about_ca_system_score_codex":0.00010645917,"about_ca_system_score_gemma":0.00018211507,"threshold_uncertainty_score":0.99993694},"labels":[],"label_agreement":null},{"id":"W4239773906","doi":"10.1109/cefc.1992.720686","title":"A Density driven Mesh Generator guided by a Neural Network","year":2005,"lang":"en","type":"article","venue":"Digest of the Fifth Biennial IEEE Conference on Electromagnetic Field Computation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Generator (circuit theory); Computer science; Artificial neural network; Artificial intelligence; Physics; Power (physics)","score_opus":0.01857453145208237,"score_gpt":0.24935864412600026,"score_spread":0.23078411267391788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239773906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80741316,0.00007362906,0.18257615,0.00736381,0.0011412695,0.00043090954,0.0000039210963,0.00017049446,0.00082667696],"genre_scores_gemma":[0.96655804,0.000010039339,0.031642154,0.0013157421,0.00026156844,0.000016444861,0.0000064145984,0.000012091936,0.00017750048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978808,0.00021715104,0.00043227052,0.00048749775,0.00047305433,0.0005092223],"domain_scores_gemma":[0.99859864,0.0002228998,0.00030115634,0.00056272605,0.00020854035,0.00010603446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020297326,0.00025558687,0.00030318878,0.00006777558,0.00020885134,0.00013688012,0.001183072,0.00013746941,0.000011682372],"category_scores_gemma":[0.00007900423,0.00021285511,0.00011148939,0.0004622433,0.00006617007,0.00020023422,0.00012887601,0.00032819033,0.00002125909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017710004,0.00079516566,0.002301835,0.00006647797,0.00014857657,0.000032967797,0.004033668,0.49211922,0.2534559,0.032407813,0.09071211,0.12374914],"study_design_scores_gemma":[0.000515176,0.0010972618,0.003484259,0.000056456956,0.000017618004,0.000027577247,0.0000031358243,0.97113204,0.021951346,0.0012964839,0.0001413158,0.00027733037],"about_ca_topic_score_codex":0.00006332125,"about_ca_topic_score_gemma":0.000012281543,"teacher_disagreement_score":0.47901282,"about_ca_system_score_codex":0.00005612495,"about_ca_system_score_gemma":0.00015496179,"threshold_uncertainty_score":0.86799794},"labels":[],"label_agreement":null},{"id":"W4240220159","doi":"10.1109/isic.2004.1387675","title":"A framework for coordinated control of multi-agent systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Layer (electronics); Multi-agent system; Computer science; Control (management); Distributed computing; Process (computing); Control system; Process control; Control engineering; Intelligent control; Hybrid system; Decentralised system; Computation; Engineering; Artificial intelligence","score_opus":0.03801734021016922,"score_gpt":0.2935482831391991,"score_spread":0.2555309429290299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240220159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003911606,0.00017556638,0.99751174,0.00041712535,0.0007442028,0.0004891206,0.00000738812,0.0001819806,0.000081727434],"genre_scores_gemma":[0.4661753,4.996787e-7,0.53359604,0.00007954484,0.000020337675,0.0000313867,6.681672e-7,0.000004516229,0.000091751135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990835,0.000027418464,0.00025668787,0.0002405803,0.00015299051,0.00023881075],"domain_scores_gemma":[0.99905866,0.0002122948,0.00011570411,0.00039560153,0.00014086136,0.00007687223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026597237,0.0001017468,0.00022714966,0.000063299405,0.000048101065,0.000048787297,0.0005354912,0.00008323181,0.0000011984926],"category_scores_gemma":[0.0001683222,0.00008423758,0.00006229184,0.00020921747,0.000025556605,0.00010451058,0.000043964286,0.00007311481,0.000019768462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012612303,0.00025477287,0.00041351616,0.00008747595,0.00009413012,0.000022028338,0.0007140486,0.35707837,0.00089184777,0.6379059,0.00023548622,0.002289813],"study_design_scores_gemma":[0.0019918666,0.00016288452,0.00042747392,0.00011823056,0.0000091744305,0.000013596329,0.000040496554,0.99277306,0.0011160215,0.0030728965,0.00013569415,0.00013862827],"about_ca_topic_score_codex":0.00010114236,"about_ca_topic_score_gemma":3.4402225e-7,"teacher_disagreement_score":0.6356947,"about_ca_system_score_codex":0.000045656696,"about_ca_system_score_gemma":0.00006674003,"threshold_uncertainty_score":0.3435109},"labels":[],"label_agreement":null},{"id":"W4242861235","doi":"10.32920/16636006","title":"Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Obstacle avoidance; Trajectory; Solver; Computer science; Obstacle; Trajectory optimization; Computation; Control theory (sociology); Fixed wing; Nonlinear programming; Mathematical optimization; Control engineering; Optimal control; Nonlinear system; Mobile robot; Robot; Mathematics; Engineering; Wing; Artificial intelligence; Algorithm; Control (management); Aerospace engineering","score_opus":0.0240429945197342,"score_gpt":0.26756546175473994,"score_spread":0.24352246723500573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242861235","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02582296,0.0001620852,0.96264744,0.0012496097,0.004377441,0.0008353977,0.000045751836,0.0013377505,0.003521542],"genre_scores_gemma":[0.010135753,0.000024752566,0.98253626,0.00017886794,0.0006215212,0.00019586968,0.0001219912,0.00004588516,0.0061391275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694496,0.00013467901,0.00053962274,0.0013623553,0.00037705584,0.0006413342],"domain_scores_gemma":[0.99717003,0.00038246595,0.000296684,0.0017130108,0.0002527454,0.00018508467],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052834715,0.00042319144,0.00064896664,0.000113964714,0.0001799239,0.00077612,0.0021839729,0.00040439155,0.000030287898],"category_scores_gemma":[0.00018300935,0.0004373669,0.00026817297,0.00019084915,0.000057367364,0.0002661655,0.0020713613,0.00040511938,0.00011088313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022550525,0.0016789484,0.0005022908,0.0022608375,0.0015255248,0.0016745966,0.01696957,0.56421494,0.0715087,0.046105627,0.09953979,0.19379365],"study_design_scores_gemma":[0.0005990882,0.000092526905,0.000906016,0.00020440399,0.00002623507,0.000031325155,0.000032120097,0.9862368,0.00687705,0.0034514517,0.00077974,0.00076325977],"about_ca_topic_score_codex":0.00026159425,"about_ca_topic_score_gemma":0.0000037458483,"teacher_disagreement_score":0.42202184,"about_ca_system_score_codex":0.00019111055,"about_ca_system_score_gemma":0.00082149863,"threshold_uncertainty_score":0.99980783},"labels":[],"label_agreement":null},{"id":"W4243347437","doi":"10.1007/978-3-642-41610-1_167-1","title":"Potential Fields Approach","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.025266796063856505,"score_gpt":0.21558675947261643,"score_spread":0.19031996340875992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243347437","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5462259e-8,0.000053719355,0.50694096,0.00051696075,0.0003588926,0.00006763272,0.0000016792543,0.0002427546,0.49181738],"genre_scores_gemma":[0.00014093608,0.000007510795,0.47790337,0.00085273077,0.00033826035,0.0000026291514,0.000016581162,0.000020157053,0.52071786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849653,0.000010056246,0.0002330401,0.0006453934,0.00040878335,0.0002062156],"domain_scores_gemma":[0.9989963,0.000024666033,0.000102529215,0.0006823723,0.000042127256,0.00015196226],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007834056,0.00027426943,0.00030639773,0.00007908327,0.0000581197,0.00013855025,0.0013301228,0.00035003756,0.00009442525],"category_scores_gemma":[0.0000090623,0.00024965373,0.00014926285,0.00003290437,0.000032064992,0.00011757807,0.0005071797,0.0005048637,0.00083532406],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014614989,0.000009896762,5.367533e-7,0.000028888715,0.000061939616,0.00030382833,0.00011175299,0.001247532,0.000002358532,0.9364334,0.046084788,0.015713653],"study_design_scores_gemma":[0.00029782238,0.00015749919,0.000011232033,0.00005701944,0.000044097615,0.0002359948,0.0000039663864,0.82908344,0.00001814011,0.06345505,0.10561466,0.0010210949],"about_ca_topic_score_codex":0.0000051475336,"about_ca_topic_score_gemma":7.212655e-8,"teacher_disagreement_score":0.8729783,"about_ca_system_score_codex":0.000022533615,"about_ca_system_score_gemma":0.000086211854,"threshold_uncertainty_score":0.9999956},"labels":[],"label_agreement":null},{"id":"W4243557204","doi":"10.22215/etd/2014-10209","title":"Motion Planning with Directional Constraints","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Piecewise linear function; Path (computing); Polygon (computer graphics); Motion planning; Constraint (computer-aided design); Piecewise; Mathematical optimization; Motion (physics); Computer science; Algorithm; Mathematics; Robot; Geometry; Artificial intelligence; Mathematical analysis; Telecommunications","score_opus":0.014294627879228023,"score_gpt":0.2589275249449518,"score_spread":0.24463289706572378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243557204","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093681295,0.00003123865,0.87571484,0.0000427514,0.0012742622,0.00011626395,0.000001623733,0.00040672522,0.12147548],"genre_scores_gemma":[0.11419365,0.0000021818566,0.85551536,0.00020019985,0.00040158964,0.000053802974,0.0007381216,0.000050239916,0.02884485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985171,0.000047128025,0.00020822683,0.00051521295,0.00046383805,0.00024847675],"domain_scores_gemma":[0.999147,0.00009608499,0.00019026748,0.00034195173,0.00013605713,0.00008863396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002004991,0.00023902902,0.00023696366,0.00018710698,0.00013277914,0.00015773322,0.00047900836,0.00018593599,0.00004407779],"category_scores_gemma":[0.000035185218,0.00019739434,0.000045187007,0.00020796528,0.00003402426,0.00020644962,0.000020525096,0.00028776313,0.00011154614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015167384,0.00039980147,0.009145039,0.00062607793,0.00077879574,0.0006758209,0.008439275,0.06567198,0.0008072177,0.109561555,0.024502857,0.7792399],"study_design_scores_gemma":[0.003120251,0.0010432216,0.22139293,0.0034739748,0.00019146895,0.0010989141,0.0011128528,0.7451923,0.0072927023,0.005295199,0.0062130927,0.004573111],"about_ca_topic_score_codex":0.000019621148,"about_ca_topic_score_gemma":0.000004204565,"teacher_disagreement_score":0.7746668,"about_ca_system_score_codex":0.00004432732,"about_ca_system_score_gemma":0.00013686567,"threshold_uncertainty_score":0.8049508},"labels":[],"label_agreement":null},{"id":"W4243980344","doi":"10.1007/978-90-481-9707-1_108","title":"Modeling of a Micro UAV with Slung Payload","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Payload (computing); Control theory (sociology); Computer science; Trajectory; Swing; Aerospace engineering; Stability (learning theory); Simulation; Engineering; Control (management); Physics; Mechanical engineering","score_opus":0.019958296576513233,"score_gpt":0.21468665789844482,"score_spread":0.19472836132193158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243980344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000057133516,0.00013068701,0.7079881,0.000081459235,0.00013669423,0.00010669547,0.0000024126557,0.00012905676,0.2914192],"genre_scores_gemma":[0.0016077122,0.000012908142,0.7368511,0.00017119343,0.000098304605,0.0000033154643,0.0000059977415,0.000040170155,0.2612093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983425,0.000013276999,0.00037171753,0.0005684752,0.00043821402,0.00026578258],"domain_scores_gemma":[0.99846613,0.00006616725,0.00019794273,0.000999941,0.00017158895,0.000098210076],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002250983,0.00032649524,0.000495177,0.00017827521,0.000045481553,0.000057160993,0.0010651757,0.00021439833,0.000021772148],"category_scores_gemma":[0.000008513974,0.00024910472,0.00009801656,0.000040863662,0.000052365427,0.00009231547,0.00025909327,0.0002927479,0.000095724485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002305149,0.000033730394,0.000033002245,0.00029157288,0.00037244166,0.00021193291,0.000861252,0.28204656,0.0002387803,0.67785555,0.0024143737,0.03561776],"study_design_scores_gemma":[0.00021320273,0.00011872926,0.0000017186865,0.00044747247,0.000027519802,0.000065915825,0.0000021219532,0.99338627,0.00008328285,0.0034382094,0.0018242621,0.0003912886],"about_ca_topic_score_codex":0.000057669295,"about_ca_topic_score_gemma":0.0000031511877,"teacher_disagreement_score":0.7113397,"about_ca_system_score_codex":0.0000342366,"about_ca_system_score_gemma":0.0001202759,"threshold_uncertainty_score":0.9999961},"labels":[],"label_agreement":null},{"id":"W4244080821","doi":"10.1145/780611.780612","title":"Touring a sequence of polygons","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Combinatorics; Simple polygon; Regular polygon; Polygon (computer graphics); Disjoint sets; Path (computing); Sequence (biology); Mathematics; Shortest path problem; Generalization; Time complexity; Point (geometry); Simple (philosophy); Binary logarithm; Point in polygon; Convex polygon; Computer science; Geometry","score_opus":0.04010925928203262,"score_gpt":0.2693167653220623,"score_spread":0.22920750604002965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244080821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036075371,0.000033565397,0.95792395,0.00011860757,0.0001566826,0.000032908974,3.9547444e-7,0.00009321586,0.038033154],"genre_scores_gemma":[0.3804767,8.409717e-7,0.61879915,0.000063140695,0.0000052365363,0.0000012200106,8.232394e-8,0.0000019116305,0.00065169565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942946,0.000030030367,0.00011217211,0.00014623682,0.00013128562,0.00015078745],"domain_scores_gemma":[0.9995052,0.000050819068,0.000036325233,0.00033357754,0.00002718244,0.000046857698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016821896,0.000051000698,0.00007714167,0.000047819976,0.000027592156,0.000018624567,0.00036595803,0.000019926856,0.00001370465],"category_scores_gemma":[0.00007445458,0.00004482062,0.000023164526,0.0002239584,0.000020227268,0.00015199448,0.000046295718,0.000045496854,0.000029100529],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.597417e-7,0.00009962545,0.0070181102,0.00002435,0.000026559697,0.00011183613,0.0014933443,0.005166302,0.04034956,0.9277089,0.0012365058,0.016763907],"study_design_scores_gemma":[0.00096948637,0.00029116764,0.008590352,0.00015444106,0.000012926582,0.00060453673,0.00019555267,0.6755144,0.28224573,0.025793483,0.0047205444,0.0009073461],"about_ca_topic_score_codex":0.000027384922,"about_ca_topic_score_gemma":3.1844866e-7,"teacher_disagreement_score":0.90191543,"about_ca_system_score_codex":0.000011454934,"about_ca_system_score_gemma":0.00005483368,"threshold_uncertainty_score":0.18277319},"labels":[],"label_agreement":null},{"id":"W4244231788","doi":"10.23952/jano.1.2019.1.08","title":"A descent-like method for fixed points and split conclusion problems","year":2019,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Descent (aeronautics); Fixed point; Computer science; Mathematics; Mathematical analysis; Engineering; Aerospace engineering","score_opus":0.00983490032252842,"score_gpt":0.2469238633489255,"score_spread":0.23708896302639706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244231788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010998247,0.00007765078,0.9976733,0.00052870635,0.00018510716,0.00025893032,7.6196756e-7,0.000016367807,0.00015936994],"genre_scores_gemma":[0.017252477,0.000034844812,0.9823445,0.0002885077,0.000043419903,0.0000034570983,0.0000018209902,0.0000078869725,0.000023100836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991647,0.00002933666,0.00029672624,0.00018288265,0.0001848887,0.00014147708],"domain_scores_gemma":[0.999232,0.0001630886,0.00029524902,0.00009892287,0.00010104621,0.000109684996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048159508,0.00009728211,0.00024272714,0.000077605895,0.00006886682,0.000079858946,0.00015402933,0.000060337083,0.0000037521932],"category_scores_gemma":[0.000025993679,0.00007662693,0.00003353273,0.00013365295,0.000014482912,0.00021454332,0.00008803033,0.000104512044,0.0000013799851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022741985,0.0001291383,0.0003623288,0.00011430578,0.0000613059,0.0000057127204,0.0014998479,0.89187104,0.0035466761,0.009140357,0.00033978454,0.0927021],"study_design_scores_gemma":[0.0011173874,0.0002575169,0.00026476764,0.00004037949,0.00001505825,0.00006277067,0.000020102292,0.9958209,0.00019904505,0.0016059628,0.00049691997,0.00009918067],"about_ca_topic_score_codex":0.0000011871214,"about_ca_topic_score_gemma":6.7873374e-9,"teacher_disagreement_score":0.10394989,"about_ca_system_score_codex":0.000022072536,"about_ca_system_score_gemma":0.00003358995,"threshold_uncertainty_score":0.31247553},"labels":[],"label_agreement":null},{"id":"W4246022494","doi":"10.4018/978-1-5225-8060-7.ch023","title":"Optimal Robot Path Planning With Cellular Neural Network","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Artificial neural network; Robot; Computer science; Path (computing); Analogy; Mobile robot; Cellular neural network; State space; Artificial intelligence; Topology (electrical circuits); Engineering; Mathematics","score_opus":0.01991386139712844,"score_gpt":0.22903201749818528,"score_spread":0.20911815610105683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246022494","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000068851376,0.00067401375,0.3538508,0.00004293933,0.0014810943,0.00042288844,0.00002473167,0.0004949547,0.6429397],"genre_scores_gemma":[0.0383567,0.0000023998703,0.77529687,0.0015188055,0.0025677036,0.000030087598,0.000039227092,0.00026442355,0.18192378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961662,0.000047180492,0.0005197197,0.0012879528,0.0009398707,0.0010390389],"domain_scores_gemma":[0.99740475,0.00008605404,0.00048661203,0.0015648151,0.00013135084,0.00032639946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023502439,0.0008735704,0.00086407166,0.00007890699,0.00020587664,0.00038334422,0.0019522082,0.0005287173,0.0000080766295],"category_scores_gemma":[0.000007142279,0.00078051625,0.00022679706,0.000059771457,0.00012044085,0.00017556331,0.00069923874,0.0008165087,0.00034870903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003425416,0.000007844765,0.00013163085,0.000034935714,0.00014533378,0.0022927786,0.00013053275,0.39840806,0.000005477615,0.5926849,0.0038236312,0.0023005921],"study_design_scores_gemma":[0.0033235033,0.0029180644,0.0005965779,0.00477604,0.00047246853,0.0026824593,0.00003165511,0.8758201,0.000058166082,0.06515915,0.037366707,0.0067950906],"about_ca_topic_score_codex":0.000024998606,"about_ca_topic_score_gemma":8.429863e-7,"teacher_disagreement_score":0.5275258,"about_ca_system_score_codex":0.00022294726,"about_ca_system_score_gemma":0.00045543927,"threshold_uncertainty_score":0.9994646},"labels":[],"label_agreement":null},{"id":"W4247354528","doi":"10.22215/etd/2007-07581","title":"Navigation of mobile robots using occupancy grids","year":2007,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage","funders":"","keywords":"Occupancy; Computer science; Geography; Engineering; Architectural engineering","score_opus":0.026344318290214164,"score_gpt":0.35392747080385034,"score_spread":0.3275831525136362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247354528","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073592514,0.0003885686,0.9143715,0.0000025764555,0.0030580135,0.00032583627,0.0000042747997,0.00019831499,0.008058423],"genre_scores_gemma":[0.02965266,0.000009008456,0.96571964,0.00002419906,0.00018839173,0.000014974603,0.0004248754,0.000032498043,0.0039337766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981618,0.000036495006,0.00053827744,0.00044431855,0.0005344615,0.0002846361],"domain_scores_gemma":[0.9985619,0.000077366625,0.00044493657,0.00057510566,0.00026605252,0.00007463002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035408884,0.00023865238,0.0003595438,0.0002804676,0.000070963295,0.000055940574,0.00074555166,0.00030346683,0.000013991384],"category_scores_gemma":[0.000026010448,0.00023559862,0.000105046456,0.00057392026,0.000017892035,0.00030720836,0.00005022947,0.0002462334,0.00002675984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010462908,0.00091082643,0.004911163,0.0028608115,0.00047252188,0.00049464,0.019936968,0.4151328,0.05076321,0.019671679,0.0029920982,0.48174864],"study_design_scores_gemma":[0.00055339065,0.0002867426,0.011801795,0.001750853,0.00008900403,0.000076908334,0.0006443897,0.92083514,0.061360214,0.0012807578,0.00014296264,0.0011778508],"about_ca_topic_score_codex":0.00019806964,"about_ca_topic_score_gemma":0.0000038138553,"teacher_disagreement_score":0.5057023,"about_ca_system_score_codex":0.00007456303,"about_ca_system_score_gemma":0.00022279458,"threshold_uncertainty_score":0.9607433},"labels":[],"label_agreement":null},{"id":"W4248150365","doi":"10.1109/iros.2016.7759623","title":"An anticipative kinematic limitation avoidance algorithm for collaborative robots: Two-dimensional case","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Kinematics; Robot; Computer science; Task (project management); Obstacle avoidance; Obstacle; Robot kinematics; Position (finance); Collision avoidance; Principal (computer security); Artificial intelligence; Simulation; Control theory (sociology); Control engineering; Algorithm; Engineering; Mobile robot; Control (management); Collision","score_opus":0.035923149138637214,"score_gpt":0.31716236608721,"score_spread":0.2812392169485728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248150365","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075512715,0.000028769424,0.9900084,0.0009500232,0.00035614808,0.0006272093,0.000049323484,0.0002690181,0.00015980782],"genre_scores_gemma":[0.075204544,9.4146725e-7,0.9237819,0.00028900476,0.00010634413,0.00015102723,0.0000109631155,0.000017038119,0.00043829062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980442,0.00020750813,0.0003739423,0.000626273,0.0003315909,0.0004164846],"domain_scores_gemma":[0.9971621,0.0012004295,0.00021099938,0.00055275584,0.0006825873,0.00019111203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005526898,0.0002354043,0.0002808269,0.00014747087,0.00025526457,0.00011571622,0.0004479341,0.00006567864,0.0000114498125],"category_scores_gemma":[0.000244401,0.00015787149,0.00005276515,0.0005160835,0.00008874689,0.0012997518,0.000078549485,0.00006795638,0.0000911293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051432424,0.0011120782,0.00038314302,0.00006566423,0.0002939809,0.0027593868,0.011451497,0.15500244,0.014102278,0.058745913,0.0077917073,0.7482405],"study_design_scores_gemma":[0.0014416261,0.0004498473,0.00036819006,0.000093771785,0.000014970752,0.0003737779,0.0001763844,0.99126416,0.0027811427,0.002696055,0.000021914395,0.00031813546],"about_ca_topic_score_codex":0.000025861567,"about_ca_topic_score_gemma":0.000009347483,"teacher_disagreement_score":0.83626175,"about_ca_system_score_codex":0.0000892291,"about_ca_system_score_gemma":0.00017276697,"threshold_uncertainty_score":0.64378124},"labels":[],"label_agreement":null},{"id":"W4248732262","doi":"10.1109/robot.1992.220115","title":"Motion planning for many degrees of freedom: sequential search with backtracking","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Backtracking; Planner; Motion planning; Computer science; Path (computing); Motion (physics); Completeness (order theory); Polynomial; Trajectory; Mathematical optimization; Artificial intelligence; Algorithm; Theoretical computer science; Mathematics; Robot; Programming language","score_opus":0.0694323400826482,"score_gpt":0.30209430159736406,"score_spread":0.23266196151471585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248732262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014141386,0.000039278926,0.9823152,0.000049562626,0.00020439533,0.00021217516,0.0000017635198,0.000101394406,0.0029348645],"genre_scores_gemma":[0.34442374,3.7105582e-7,0.65532357,0.000010534333,0.000027911148,0.000007268339,0.0000019811753,0.000007879024,0.00019672634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987875,0.000062829335,0.0002014492,0.00031244947,0.00031666597,0.00031908965],"domain_scores_gemma":[0.99923855,0.00014174983,0.00007698084,0.0003307187,0.00014507012,0.00006690992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005493419,0.00011772895,0.00016300083,0.000118423144,0.00009967761,0.000075476775,0.00040500052,0.000053457163,0.000010491442],"category_scores_gemma":[0.000047146284,0.00009562358,0.00004246712,0.0002530226,0.000049825307,0.000341421,0.00004743111,0.00009710983,0.000006864258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021069236,0.000089626235,0.012491438,0.00008406592,0.00006739885,0.00003882183,0.0015332616,0.857991,0.0024629855,0.119311616,0.00034999652,0.0055587185],"study_design_scores_gemma":[0.0007115392,0.00024874898,0.0049791955,0.000086562264,0.000010011898,0.000072119205,0.0001370677,0.98389125,0.009360588,0.00023541451,0.000078736026,0.00018879329],"about_ca_topic_score_codex":0.000040039195,"about_ca_topic_score_gemma":0.0000014720069,"teacher_disagreement_score":0.33028236,"about_ca_system_score_codex":0.000028025155,"about_ca_system_score_gemma":0.00007828089,"threshold_uncertainty_score":0.38994163},"labels":[],"label_agreement":null},{"id":"W4249127954","doi":"10.1504/ijdsss.2018.090875","title":"Massively parallel hybrid algorithm on embedded graphics processing unit for unmanned aerial vehicle path planning","year":2018,"lang":"en","type":"article","venue":"International Journal of Digital Signals and Smart Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Graphics processing unit; Computer science; Massively parallel; Motion planning; Path (computing); Particle swarm optimization; Genetic algorithm; Terrain; Parallel computing; Real-time computing; Algorithm; Artificial intelligence; Robot; Operating system","score_opus":0.03658986226919345,"score_gpt":0.29469456534494914,"score_spread":0.25810470307575567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249127954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064903535,0.0002974643,0.9311025,0.00033706558,0.0026926894,0.00019570862,0.00006712938,0.0000427501,0.00036116558],"genre_scores_gemma":[0.97847253,0.0000067714636,0.01964226,0.00014031859,0.0016080164,0.000008769398,0.0000111050995,0.000017937491,0.00009228606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795693,0.000051177907,0.00066975644,0.00027453378,0.00076949503,0.00027812886],"domain_scores_gemma":[0.9977173,0.0002382674,0.00069065613,0.0001256976,0.0010520562,0.0001759801],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005423139,0.00020020813,0.00032108836,0.00026764296,0.00016885423,0.0013061602,0.00079102133,0.0000637546,0.0000011918219],"category_scores_gemma":[0.00013680114,0.00016498781,0.000115086244,0.0001318875,0.00008584629,0.0011358178,0.00010240208,0.00016069681,0.0000057154257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048257476,0.0033064557,0.038419403,0.0006306202,0.005412829,0.0073707965,0.020827731,0.08089934,0.00835088,0.041650936,0.038990874,0.74931437],"study_design_scores_gemma":[0.0029395479,0.0020910006,0.0013638628,0.0013074758,0.000024827541,0.0012673268,0.00039021735,0.98113805,0.0006284205,0.0037619115,0.004632108,0.0004552552],"about_ca_topic_score_codex":0.000005494027,"about_ca_topic_score_gemma":6.274646e-8,"teacher_disagreement_score":0.91356903,"about_ca_system_score_codex":0.000043909014,"about_ca_system_score_gemma":0.00013493902,"threshold_uncertainty_score":0.9997306},"labels":[],"label_agreement":null},{"id":"W4249282045","doi":"10.24124/2006/bpgub432","title":"The generalized tutor-student learning algorithm for autonomous mobile robots.","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"TUTOR; Computer science; Artificial intelligence; Humanities; Mathematics education; Mathematics; Art","score_opus":0.010490428915706722,"score_gpt":0.2866202118377292,"score_spread":0.27612978292202245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249282045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033414073,0.0015745353,0.98401594,0.00007074045,0.0044713016,0.0013059084,0.000009014764,0.000761912,0.0074564843],"genre_scores_gemma":[0.00013022636,0.000059121212,0.7500405,0.000074992415,0.00055327825,0.0012786455,0.0009379792,0.0000782476,0.24684705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969355,0.00013260242,0.000663508,0.00088646554,0.00063964434,0.00074229896],"domain_scores_gemma":[0.99789745,0.0004348245,0.00046622532,0.0008005262,0.00028707786,0.00011389365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006766619,0.0004937586,0.0004985231,0.00014146086,0.0009659613,0.0008966728,0.0020935119,0.00033473983,0.0000076135607],"category_scores_gemma":[0.00004428214,0.0003672577,0.00030665402,0.0002800987,0.000033192082,0.00026058362,0.0001365154,0.00050490507,0.00006728173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008920592,0.00012745496,0.000035837395,0.000045358986,0.00018385457,0.000035876452,0.0013642184,0.10310663,0.000103820705,0.0061664893,0.01911994,0.8697016],"study_design_scores_gemma":[0.0006400522,0.0002112763,0.0006266158,0.00004864885,0.000048758477,0.000013795996,0.00026077678,0.9281893,0.0004393304,0.00051152875,0.06834922,0.0006607446],"about_ca_topic_score_codex":0.00025002554,"about_ca_topic_score_gemma":0.000033127097,"teacher_disagreement_score":0.86904085,"about_ca_system_score_codex":0.00019389429,"about_ca_system_score_gemma":0.00032508557,"threshold_uncertainty_score":0.9998779},"labels":[],"label_agreement":null},{"id":"W4249700811","doi":"10.1504/ijvp.2018.088783","title":"Advanced control techniques for unmanned ground vehicle: literature survey","year":2017,"lang":"en","type":"article","venue":"International Journal of Vehicle Performance","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Motion planning; Unmanned ground vehicle; Robustness (evolution); Computer science; Reliability (semiconductor); Engineering; Control engineering; Simulation; Artificial intelligence; Robot","score_opus":0.020332818347738906,"score_gpt":0.3092517567214546,"score_spread":0.2889189383737157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249700811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57403666,0.00038458116,0.4177559,0.0035616283,0.0035960036,0.00026526174,0.00006035329,0.00007708804,0.0002625135],"genre_scores_gemma":[0.9236721,0.00010568001,0.07519013,0.00031352314,0.0005278547,0.000010552759,0.0000055725695,0.000012891897,0.00016173282],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983582,0.000058659796,0.00049769593,0.00022079975,0.00060594414,0.00025870863],"domain_scores_gemma":[0.99658,0.00023899974,0.00094717095,0.00046294078,0.001667238,0.00010365836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012972116,0.00016441786,0.00026269717,0.00018231003,0.00028781875,0.00080166024,0.0032168266,0.00008933811,0.0000022981556],"category_scores_gemma":[0.0004943015,0.00014150138,0.00012269095,0.000079505604,0.00007197793,0.0026771727,0.00016005614,0.0003065549,0.000006925517],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014923197,0.00039263425,0.15061237,0.00007270386,0.000533084,0.00033069862,0.001058411,0.0043701823,0.011887541,0.0018527242,0.0026276547,0.8247697],"study_design_scores_gemma":[0.0034230642,0.0007629835,0.6246933,0.00062935456,0.000013432136,0.00033872234,0.000007398657,0.35518843,0.010432668,0.00079383363,0.0033859846,0.0003308757],"about_ca_topic_score_codex":0.000011632908,"about_ca_topic_score_gemma":0.0000019950207,"teacher_disagreement_score":0.8244388,"about_ca_system_score_codex":0.000103940474,"about_ca_system_score_gemma":0.0001151563,"threshold_uncertainty_score":0.77304286},"labels":[],"label_agreement":null},{"id":"W4249866016","doi":"10.36288/roscon2017-900799","title":"From simulation to the field: Learning to swim with the AQUA robot","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robot; Computer science; Field (mathematics); Artificial intelligence; Simulation; Human–computer interaction; Mathematics","score_opus":0.023472781327984908,"score_gpt":0.30580494586079976,"score_spread":0.28233216453281484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249866016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009228878,0.0000047443295,0.92738473,0.060196117,0.00023952105,0.00017160983,2.5797866e-7,0.000091098416,0.0026830602],"genre_scores_gemma":[0.81324536,1.4991663e-7,0.18072739,0.003713666,0.00021687661,0.000010153534,4.6171627e-7,0.0000057041607,0.0020802643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991404,0.00005763774,0.00008450069,0.00026708507,0.00025835697,0.00019202003],"domain_scores_gemma":[0.9980546,0.00064615556,0.000066827364,0.0011141978,0.000047190966,0.00007098154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000293562,0.00009097351,0.000082458006,0.000023471399,0.0007546354,0.0006467781,0.0016968132,0.000028910845,0.000014702076],"category_scores_gemma":[0.0003601961,0.000043105305,0.00002038854,0.000096704905,0.000014403106,0.0002219018,0.00038987957,0.00016029112,0.00032557014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004469931,0.0000039631122,0.00090529723,3.1690726e-7,0.000007728344,0.000004846354,0.0029871361,0.95465994,0.00003488377,0.00023507298,0.0031645051,0.037991848],"study_design_scores_gemma":[0.000097912955,0.00013080031,0.031829868,0.000018317021,0.0000043238388,0.0000012246156,0.00015008684,0.9553271,0.00024734435,0.00012083706,0.011958726,0.000113441376],"about_ca_topic_score_codex":0.0008748707,"about_ca_topic_score_gemma":0.0000702141,"teacher_disagreement_score":0.8040165,"about_ca_system_score_codex":0.0000127143185,"about_ca_system_score_gemma":0.000022540682,"threshold_uncertainty_score":0.6236896},"labels":[],"label_agreement":null},{"id":"W4250870185","doi":"10.1109/ijcnn.2006.1716487","title":"A Neuro—Fuzzy Approach for the Motion Planning of Redundant Manipulators","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Obstacle avoidance; Computer science; Motion planning; Control theory (sociology); Kinematics; Robot; Inverse kinematics; Adaptive neuro fuzzy inference system; Gravitational singularity; Artificial intelligence; Control engineering; Fuzzy logic; Fuzzy control system; Mathematics; Mobile robot; Engineering","score_opus":0.07641989096458972,"score_gpt":0.2787647750373605,"score_spread":0.2023448840727708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250870185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04254331,0.000096259806,0.9326367,0.008077009,0.0029733905,0.0011684236,0.000015886073,0.00028664866,0.01220239],"genre_scores_gemma":[0.9738962,0.000006100317,0.024074716,0.00047162198,0.0010530413,0.000094400195,0.000011130465,0.000021209456,0.00037158543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997909,0.000029643003,0.0005231024,0.00047098714,0.00064629177,0.0004209935],"domain_scores_gemma":[0.99851054,0.00027667804,0.0004918583,0.00027169278,0.00040217114,0.0000470884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006552577,0.00025849827,0.00024150671,0.00010261725,0.0002962232,0.00031056337,0.0016653488,0.00007215049,0.0000033541744],"category_scores_gemma":[0.00007422498,0.0001614273,0.00014398256,0.00031968026,0.00013935956,0.00032902154,0.0001505429,0.0003373249,0.0000049588566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082156956,0.00010471582,0.0011905025,0.000025443727,0.000060070844,0.0000046471396,0.00035641287,0.72609687,0.0017299413,0.24127378,0.024769133,0.004306325],"study_design_scores_gemma":[0.00029952987,0.00012103561,0.00669877,0.000084937354,0.000018094044,0.00006924306,0.000045917805,0.98027474,0.00070639065,0.011284792,0.00021862336,0.00017794104],"about_ca_topic_score_codex":0.00007132459,"about_ca_topic_score_gemma":4.8665385e-7,"teacher_disagreement_score":0.9313529,"about_ca_system_score_codex":0.000053572898,"about_ca_system_score_gemma":0.000035901816,"threshold_uncertainty_score":0.65828145},"labels":[],"label_agreement":null},{"id":"W4252671680","doi":"10.22215/etd/2015-11093","title":"Evolutionary Neural Network-Based Obstacle Avoidance for a Planetary Exploration Rover","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Traverse; Obstacle avoidance; Computer science; Motion planning; Artificial neural network; Path (computing); Mobile robot; Artificial intelligence; Set (abstract data type); Obstacle; Robot; Real-time computing; Geography; Computer network","score_opus":0.041493371747916535,"score_gpt":0.28383120568574377,"score_spread":0.24233783393782724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252671680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011041814,0.0015102897,0.9865395,0.00046453776,0.0062288265,0.0010428174,0.000054875014,0.0006672774,0.0023877334],"genre_scores_gemma":[0.015404646,0.000007804183,0.9554204,0.0008758638,0.0013680264,0.00045727636,0.013067231,0.00007894082,0.013319822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977617,0.000087234905,0.00040783736,0.00069270807,0.0005626877,0.00048779123],"domain_scores_gemma":[0.9983335,0.0002612479,0.00029574736,0.00062414445,0.0003337195,0.00015165057],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032272344,0.000347506,0.00034235528,0.00012505981,0.00018988248,0.000144819,0.00084010046,0.00028592578,0.000010271576],"category_scores_gemma":[0.00009555301,0.00034562327,0.000116751595,0.00030078978,0.000016763828,0.0008372505,0.00003519453,0.00026987083,0.00007251431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111148336,0.000060467446,0.0001263762,0.00010590031,0.000033462413,0.000025249366,0.00041212986,0.7756627,0.000019025294,0.0011308213,0.20715062,0.015162051],"study_design_scores_gemma":[0.0005241958,0.00017538028,0.001885055,0.000094094,0.000022003094,0.0000063579564,0.000046987305,0.98898137,0.000030878902,0.005074445,0.0027291167,0.00043014443],"about_ca_topic_score_codex":0.000059415124,"about_ca_topic_score_gemma":0.000045912842,"teacher_disagreement_score":0.2133186,"about_ca_system_score_codex":0.00013051815,"about_ca_system_score_gemma":0.0005219379,"threshold_uncertainty_score":0.99989957},"labels":[],"label_agreement":null},{"id":"W4253791292","doi":"10.32920/16636006.v1","title":"Real-Time Autonomous Obstacle Avoidance For Unmanned Aerial Vehicles","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Obstacle avoidance; Trajectory; Solver; Computer science; Trajectory optimization; Obstacle; Computation; Control theory (sociology); Nonlinear programming; Fixed wing; Mathematical optimization; Control engineering; Optimal control; Nonlinear system; Mobile robot; Mathematics; Robot; Engineering; Wing; Artificial intelligence; Algorithm; Control (management); Aerospace engineering","score_opus":0.0240429945197342,"score_gpt":0.26756546175473994,"score_spread":0.24352246723500573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253791292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02582296,0.0001620852,0.96264744,0.0012496097,0.004377441,0.0008353977,0.000045751836,0.0013377505,0.003521542],"genre_scores_gemma":[0.010135753,0.000024752566,0.98253626,0.00017886794,0.0006215212,0.00019586968,0.0001219912,0.00004588516,0.0061391275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694496,0.00013467901,0.00053962274,0.0013623553,0.00037705584,0.0006413342],"domain_scores_gemma":[0.99717003,0.00038246595,0.000296684,0.0017130108,0.0002527454,0.00018508467],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052834715,0.00042319144,0.00064896664,0.000113964714,0.0001799239,0.00077612,0.0021839729,0.00040439155,0.000030287898],"category_scores_gemma":[0.00018300935,0.0004373669,0.00026817297,0.00019084915,0.000057367364,0.0002661655,0.0020713613,0.00040511938,0.00011088313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022550525,0.0016789484,0.0005022908,0.0022608375,0.0015255248,0.0016745966,0.01696957,0.56421494,0.0715087,0.046105627,0.09953979,0.19379365],"study_design_scores_gemma":[0.0005990882,0.000092526905,0.000906016,0.00020440399,0.00002623507,0.000031325155,0.000032120097,0.9862368,0.00687705,0.0034514517,0.00077974,0.00076325977],"about_ca_topic_score_codex":0.00026159425,"about_ca_topic_score_gemma":0.0000037458483,"teacher_disagreement_score":0.42202184,"about_ca_system_score_codex":0.00019111055,"about_ca_system_score_gemma":0.00082149863,"threshold_uncertainty_score":0.99980783},"labels":[],"label_agreement":null},{"id":"W4254233034","doi":"10.22215/etd/2009-09260","title":"A component-based three-layer autonomy architecture for unmanned aerial vehicles","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Architecture; Layer (electronics); Component (thermodynamics); Computer science; Art; Humanities; Visual arts; Physics","score_opus":0.022113808678541687,"score_gpt":0.2730183210544937,"score_spread":0.250904512375952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254233034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004779417,0.00010738243,0.9877719,0.00089962885,0.0021622851,0.0010661691,0.00002322859,0.0006899164,0.002500039],"genre_scores_gemma":[0.013706818,6.7716354e-7,0.980089,0.00074562157,0.0005950091,0.00020187438,0.0014630045,0.00005761052,0.0031403797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976194,0.00005774676,0.00046093212,0.00086022215,0.00043403587,0.00056765985],"domain_scores_gemma":[0.9983653,0.0002488696,0.00029844668,0.0007711674,0.00015944219,0.0001567778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002460517,0.00049034215,0.00054657337,0.0003055962,0.0002150282,0.00029797707,0.0014859849,0.0004092876,0.000010950945],"category_scores_gemma":[0.000046707064,0.0004427649,0.00027234884,0.00025040787,0.000023178796,0.00013279327,0.000038382903,0.00036306365,0.000044626857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010258851,0.0009610194,0.00014317183,0.00081106287,0.00046629054,0.0001714378,0.0032411786,0.11177513,0.009649019,0.022053674,0.023165746,0.82653636],"study_design_scores_gemma":[0.0027923195,0.0006189581,0.008303025,0.0003928702,0.00009158204,0.000012681108,0.000035575293,0.9567468,0.009678843,0.0103516495,0.009548129,0.0014275556],"about_ca_topic_score_codex":0.00009088053,"about_ca_topic_score_gemma":0.00012140601,"teacher_disagreement_score":0.84497166,"about_ca_system_score_codex":0.00010864149,"about_ca_system_score_gemma":0.00071935897,"threshold_uncertainty_score":0.9998024},"labels":[],"label_agreement":null},{"id":"W4256103385","doi":"10.1007/978-3-662-53120-4_300713","title":"Trajectory Generation","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Trajectory; Computer science; Physics; Astronomy","score_opus":0.04425891408943667,"score_gpt":0.23398512075379094,"score_spread":0.18972620666435427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256103385","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.7596419e-7,0.00011182448,0.45178288,0.00007638906,0.0010325469,0.00008530536,0.0000013314888,0.00015778176,0.54675144],"genre_scores_gemma":[0.000030623083,0.000012316435,0.17803842,0.0003225124,0.00034360646,0.0000013936211,0.000017694776,0.000018908439,0.82121456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889183,0.000009148249,0.00018794548,0.00045382197,0.00030246424,0.0001547867],"domain_scores_gemma":[0.99900776,0.00003480429,0.00009980853,0.0007494872,0.000052256353,0.000055903693],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011969107,0.00020489925,0.00020758934,0.00011789533,0.000037249825,0.000092725204,0.0006364923,0.0002278521,0.00016700767],"category_scores_gemma":[0.000004958834,0.00018829248,0.00008254843,0.000016692371,0.000015918169,0.00015752517,0.00011649315,0.00021587935,0.0029096554],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.240528e-7,0.0000051033394,0.0000019345218,0.00001187793,0.000032464348,0.0000394627,0.00008345061,0.001228611,0.00015738023,0.9219895,0.05112082,0.025329074],"study_design_scores_gemma":[0.00027460526,0.00014763606,0.00004265342,0.00009560604,0.000023256756,0.0000757128,9.701268e-7,0.7012808,0.00022385798,0.0069318344,0.28988853,0.0010145615],"about_ca_topic_score_codex":0.000003018918,"about_ca_topic_score_gemma":9.539164e-7,"teacher_disagreement_score":0.91505766,"about_ca_system_score_codex":0.000056866756,"about_ca_system_score_gemma":0.0001708196,"threshold_uncertainty_score":0.9978667},"labels":[],"label_agreement":null},{"id":"W4256212102","doi":"10.1017/s026357470700389x","title":"Modified Newton's method applied to potential field based navigation for nonholonomic robots in dynamic environments","year":2007,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Ontario Tech University","funders":"","keywords":"Nonholonomic system; Robot; Computer science; Control theory (sociology); Field (mathematics); Mobile robot; Control engineering; Artificial intelligence; Engineering; Mathematics; Control (management)","score_opus":0.012028382290166442,"score_gpt":0.2924205957372746,"score_spread":0.28039221344710813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256212102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002837288,0.0000073806273,0.99349856,0.0021466739,0.00045441563,0.00074006524,0.0000027958624,0.00009203326,0.00022080923],"genre_scores_gemma":[0.35968226,2.4064508e-7,0.6395368,0.00056118524,0.000037738606,0.000042457003,0.000017426557,0.000015742007,0.000106151885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979943,0.000056928784,0.00041405665,0.000663271,0.00027107526,0.0006003386],"domain_scores_gemma":[0.99865055,0.00044544792,0.000107969594,0.0005903039,0.000016486674,0.00018924517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010527711,0.00021600867,0.00027847732,0.0002500385,0.00009998618,0.00007498279,0.0007292179,0.0001721533,0.0000035516277],"category_scores_gemma":[0.0000680544,0.00023981361,0.00007572072,0.00031355387,0.000017291084,0.00014833866,0.00014231881,0.00020821985,0.00006156239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056333873,0.00007605916,0.00004087874,0.00001116226,0.000008600796,0.000025572177,0.0001313874,0.96702343,0.0094602825,0.0014713212,0.00007485654,0.021620134],"study_design_scores_gemma":[0.0009577391,0.00013497159,0.006525359,0.000032562963,0.000011409357,0.0000073869955,0.000010077987,0.986968,0.0040808734,0.0009249738,0.00007132049,0.0002753261],"about_ca_topic_score_codex":0.00002768658,"about_ca_topic_score_gemma":0.000004569026,"teacher_disagreement_score":0.35684496,"about_ca_system_score_codex":0.00021174885,"about_ca_system_score_gemma":0.000057828533,"threshold_uncertainty_score":0.9779315},"labels":[],"label_agreement":null},{"id":"W4281262165","doi":"10.1155/2022/6184790","title":"Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Curvilinear coordinates; Trajectory; Controllability; Computer science; Kinematics; Flexibility (engineering); Coordinate system; Cartesian coordinate system; Domain (mathematical analysis); Trajectory optimization; Curvature; Mathematical optimization; Control theory (sociology); Optimal control; Control (management); Mathematics; Artificial intelligence","score_opus":0.005210448015248986,"score_gpt":0.21875776442397651,"score_spread":0.21354731640872754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281262165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2891917,0.000056268065,0.70991,0.00013099414,0.00036991626,0.00012482259,0.000010454781,0.00018920569,0.000016689693],"genre_scores_gemma":[0.58644825,0.0000044481717,0.41341555,0.000058366048,0.000019043486,0.0000047671388,0.00003172785,0.000011311166,0.0000065028203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840397,0.000080533464,0.00051597686,0.00019413694,0.0006171371,0.00018823128],"domain_scores_gemma":[0.99854577,0.00006218915,0.0007792847,0.00017667092,0.00035421632,0.000081902595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020188362,0.00014273482,0.000232399,0.00019618869,0.00019874287,0.00003798908,0.00039419235,0.000038979775,0.00002348759],"category_scores_gemma":[0.000019974654,0.00012897313,0.00005659182,0.00076254585,0.000020754545,0.00097423076,0.000005810203,0.000324786,3.1924168e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017924022,0.000045837536,0.00092005933,0.000010541698,0.000036875932,0.00015298712,0.0015464041,0.99128526,0.0035891992,0.00011638325,0.00004129374,0.0020758999],"study_design_scores_gemma":[0.0021991679,0.00075092405,0.20312794,0.000047007517,0.000032700165,0.000103029175,0.00017651825,0.79245543,0.00067330396,0.00011737186,0.00012568712,0.00019092597],"about_ca_topic_score_codex":0.0000037459126,"about_ca_topic_score_gemma":0.000004266838,"teacher_disagreement_score":0.29725656,"about_ca_system_score_codex":0.00015165079,"about_ca_system_score_gemma":0.0002561192,"threshold_uncertainty_score":0.52593714},"labels":[],"label_agreement":null},{"id":"W4281989457","doi":"10.3390/robotics11030064","title":"Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team","year":2022,"lang":"en","type":"article","venue":"Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"A priori and a posteriori; Robot; Novelty; Search and rescue; Wilderness; Artificial intelligence; Computer science; Robotics; Mobile robot; Trajectory; Motion planning; Human–computer interaction; Machine learning","score_opus":0.06279666526434236,"score_gpt":0.3086709622278999,"score_spread":0.24587429696355756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281989457","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007730929,0.000044399432,0.9868976,0.0007044694,0.0035655042,0.00068661,0.000049543403,0.00019083325,0.00013009651],"genre_scores_gemma":[0.17735188,0.0000010748548,0.82121193,0.00014604251,0.00073457556,0.000044528246,0.000028080696,0.000047869817,0.00043398925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972753,0.00023769002,0.00035721442,0.0006564742,0.0006999126,0.0007733989],"domain_scores_gemma":[0.9985614,0.00032751387,0.00013807118,0.00071209035,0.000066820816,0.00019411571],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069054845,0.0002577795,0.00034770006,0.0001962331,0.0008294509,0.00024458673,0.0014494152,0.000086582426,0.00001669314],"category_scores_gemma":[0.00009131048,0.00028854536,0.00016458874,0.00053439476,0.000073619514,0.0002759194,0.00089939084,0.0004083372,0.000020611633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036804147,0.00015286026,0.00026958055,0.000013416921,0.000039149196,0.000042979613,0.001145137,0.99318707,0.0009837701,0.001119546,0.0009529235,0.002056746],"study_design_scores_gemma":[0.0014477347,0.00024191765,0.00019101321,0.000015845822,0.00003092656,0.0001432037,0.00023767079,0.9964311,0.00028594493,0.00013065987,0.0004859657,0.00035802112],"about_ca_topic_score_codex":0.00015419968,"about_ca_topic_score_gemma":0.0000015584924,"teacher_disagreement_score":0.16962095,"about_ca_system_score_codex":0.00032157236,"about_ca_system_score_gemma":0.00048904424,"threshold_uncertainty_score":0.99995667},"labels":[],"label_agreement":null},{"id":"W4282978153","doi":"10.1007/s43154-022-00083-8","title":"Graph-Based Multi-Robot Path Finding and Planning","year":2022,"lang":"en","type":"article","venue":"Current Robotics Reports","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Motion planning; Scheduling (production processes); Distributed computing; Holonomic; Kinematics; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.06814399243194001,"score_gpt":0.3155495869602875,"score_spread":0.24740559452834748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282978153","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004578519,0.0018703553,0.9876872,0.00018114371,0.0049871947,0.0002693779,0.0000040240157,0.00035635615,0.000065868335],"genre_scores_gemma":[0.4692773,0.000009326948,0.5302695,0.000117982374,0.00010858251,0.00006835689,0.00004402177,0.000034173365,0.00007078559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971781,0.00014021022,0.0005686161,0.00084626174,0.0007124327,0.0005544005],"domain_scores_gemma":[0.9982628,0.00013656922,0.00045536272,0.00083582645,0.00007877721,0.00023064908],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082938955,0.00028058852,0.0003135448,0.0003333828,0.00071334036,0.00019626896,0.00051720353,0.000047065645,0.0000072776884],"category_scores_gemma":[0.00016642085,0.00030191024,0.00009832389,0.00064251124,0.00006283093,0.00022822028,0.0007581118,0.0005722114,0.0000032875255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000195567,0.00016976809,0.042664114,0.000038507726,0.000015088241,0.0019118723,0.00072004844,0.94533324,0.00007742793,0.00038078174,0.00081494666,0.007872237],"study_design_scores_gemma":[0.00042229978,0.00009600827,0.014634288,0.00011942089,0.000023841383,0.0008844655,0.00007287068,0.98147315,0.00007131729,0.0006032637,0.0010861036,0.00051294605],"about_ca_topic_score_codex":0.000008328107,"about_ca_topic_score_gemma":1.6600583e-7,"teacher_disagreement_score":0.46469876,"about_ca_system_score_codex":0.00011786659,"about_ca_system_score_gemma":0.00019067418,"threshold_uncertainty_score":0.9999433},"labels":[],"label_agreement":null},{"id":"W4285099387","doi":"10.18280/jesa.550313","title":"Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Maxima and minima; Mobile robot; Computer science; Robot; Path (computing); Motion planning; Mathematical optimization; Optimization problem; Particle swarm optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.017291516112018353,"score_gpt":0.25362448147460603,"score_spread":0.2363329653625877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285099387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011635837,0.0004760072,0.9864138,0.00009669698,0.0005540303,0.00030597317,0.00004182624,0.0001285346,0.00034733815],"genre_scores_gemma":[0.25565463,0.00002158675,0.74379176,0.00014827262,0.000082117534,0.00003928758,0.000028036213,0.00004068738,0.00019360584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643,0.0007903876,0.0008313835,0.00040727816,0.0010906707,0.00045028943],"domain_scores_gemma":[0.9983733,0.00021638194,0.00074653176,0.00045750153,0.000046314093,0.00015993572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00092442613,0.00027253773,0.0004419911,0.00055077975,0.00039439308,0.00013474812,0.0010984326,0.000056217697,0.00005363982],"category_scores_gemma":[0.00010238885,0.00025459583,0.00010947046,0.000838543,0.00006619818,0.00035836038,0.00030989564,0.00061349303,0.0000050375506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008530081,0.00042786595,0.0006324939,0.00001737762,0.000018843106,0.00043190297,0.0011424634,0.9079782,0.00010242837,0.000033567132,0.00019144369,0.08901493],"study_design_scores_gemma":[0.00075640157,0.000746058,0.0532292,0.00023425551,0.000011053031,0.00031540942,0.00020716655,0.9438134,0.000051914874,0.00012962977,0.0002693226,0.00023619687],"about_ca_topic_score_codex":0.000020305746,"about_ca_topic_score_gemma":2.6173925e-7,"teacher_disagreement_score":0.24401881,"about_ca_system_score_codex":0.0004879951,"about_ca_system_score_gemma":0.00015419096,"threshold_uncertainty_score":0.99999064},"labels":[],"label_agreement":null},{"id":"W4285099531","doi":"10.18280/jesa.550306","title":"Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Mosul","keywords":"Obstacle; Mobile robot; Obstacle avoidance; Robot; Fuzzy logic; Artificial neural network; Computer science; Artificial intelligence; Control theory (sociology); Simulation; Control engineering; Engineering; Control (management); Geography","score_opus":0.03218229702032457,"score_gpt":0.2686398953011813,"score_spread":0.23645759828085675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285099531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0142179495,0.00034025704,0.9827146,0.0004926621,0.00066947594,0.0005222409,0.000011012861,0.00055899174,0.00047283716],"genre_scores_gemma":[0.5676807,0.000005477442,0.43177798,0.00026038653,0.0001163541,0.00006836317,0.000011167808,0.000027799579,0.000051749237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971088,0.00082009035,0.00054852804,0.00042356705,0.0005953644,0.00050362415],"domain_scores_gemma":[0.99848944,0.0004795845,0.00047209044,0.00031554583,0.00010964624,0.00013368996],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0020895358,0.00024955478,0.00032952373,0.0002986747,0.0019314504,0.0005885675,0.0007404915,0.00005707107,0.00000391451],"category_scores_gemma":[0.0002112569,0.00023303683,0.000116025134,0.00051713537,0.000072520714,0.00038294715,0.00024190682,0.0005944187,0.0000017682462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003965783,0.00025216886,0.00050356216,0.00010780026,0.000035831887,0.00012688713,0.0005689941,0.51093686,0.000524724,0.0140557345,0.0011311639,0.47171664],"study_design_scores_gemma":[0.00024970548,0.00081695255,0.0140573615,0.0000626315,0.000017365528,0.0006378017,0.000073111514,0.9572505,0.00004691445,0.026475914,0.00006315197,0.00024860862],"about_ca_topic_score_codex":0.0000037682848,"about_ca_topic_score_gemma":2.575131e-7,"teacher_disagreement_score":0.55346274,"about_ca_system_score_codex":0.00032596666,"about_ca_system_score_gemma":0.000088048444,"threshold_uncertainty_score":0.9993679},"labels":[],"label_agreement":null},{"id":"W4285102651","doi":"10.1109/icra46639.2022.9811994","title":"Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Huawei Technologies","keywords":"Trajectory; Computer science; Motion planning; Task (project management); Probabilistic logic; Artificial intelligence; Information gain; Path (computing); Collision avoidance; Motion (physics); Machine learning; Computer vision; Collision; Robot; Computer security; Engineering","score_opus":0.03984109665198412,"score_gpt":0.29684880234539945,"score_spread":0.25700770569341536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285102651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018510015,0.00001742623,0.990845,0.00371152,0.00088188885,0.00051436154,0.00014163357,0.00014594122,0.0018912312],"genre_scores_gemma":[0.5817318,0.000006910487,0.4154889,0.00074575027,0.00011896118,0.0005017389,0.0003433049,0.00002159926,0.0010410615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861133,0.00003840411,0.000303485,0.0003175014,0.00049745064,0.0002318084],"domain_scores_gemma":[0.99889475,0.00030722792,0.0002678727,0.00018088095,0.000284086,0.00006515821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604431,0.00017068678,0.00018205792,0.00023229259,0.0007488764,0.0003008293,0.00055938796,0.000035467918,0.00003617001],"category_scores_gemma":[0.000077928635,0.00016100782,0.000048031892,0.00019036565,0.000037817066,0.00046198937,0.00022685016,0.00018181991,0.0000018906534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070091366,0.00006276588,0.0002995527,0.00002437982,0.000076259596,0.0000051512948,0.0032693085,0.43030873,0.00013553935,0.55854946,0.0011561114,0.0060426295],"study_design_scores_gemma":[0.00094961666,0.00048450596,0.0012387845,0.000058220732,0.0000119572405,0.000028061864,0.00091228593,0.98886293,0.00009811774,0.0057757497,0.0013602437,0.00021952699],"about_ca_topic_score_codex":0.0000056643676,"about_ca_topic_score_gemma":0.0000011427317,"teacher_disagreement_score":0.5798808,"about_ca_system_score_codex":0.00012449526,"about_ca_system_score_gemma":0.00019316578,"threshold_uncertainty_score":0.65657085},"labels":[],"label_agreement":null},{"id":"W4285168987","doi":"10.1109/tro.2022.3181055","title":"Closed-Loop Motion Control of Robotic Swarms – A Tether-Based Strategy","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Swarm behaviour; Motion control; Swarm robotics; Computer science; Motion planning; Novelty; Motion (physics); Mobile robot; Robot; Control engineering; Task (project management); Closed loop; Control theory (sociology); Artificial intelligence; Engineering; Control (management)","score_opus":0.025048900071996723,"score_gpt":0.24915903453948343,"score_spread":0.2241101344674867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285168987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009217186,0.00003939365,0.995595,0.0010023422,0.0015692017,0.00041075208,0.000041261792,0.00030397144,0.000116404284],"genre_scores_gemma":[0.89413553,0.0000027739509,0.10510667,0.0003077637,0.000026472007,0.00006717625,0.000004484746,0.00003248929,0.00031665276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733883,0.00034230866,0.0005336568,0.00052954315,0.0007973819,0.00045827398],"domain_scores_gemma":[0.9982217,0.00036325498,0.00024666742,0.0008913001,0.00012834236,0.00014875832],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044363205,0.00028226976,0.00040313526,0.00038611682,0.00043262102,0.000063088104,0.00096817245,0.00009521358,0.00006975364],"category_scores_gemma":[0.000009700234,0.00030349364,0.00022220527,0.0009335136,0.00008999014,0.000202739,0.000005943735,0.0006351843,0.000034494158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030029163,0.00065549224,0.000029022933,0.000022116543,0.000057261637,0.000033625467,0.00014525768,0.98434806,0.000982335,0.0006067337,0.000045475972,0.0130445855],"study_design_scores_gemma":[0.0014745241,0.00080035534,0.00022810254,0.000022855287,0.00007126852,0.000027761262,0.000072016155,0.99257094,0.004028021,0.0004008342,0.000009590334,0.0002937423],"about_ca_topic_score_codex":0.000053461612,"about_ca_topic_score_gemma":0.0000027974409,"teacher_disagreement_score":0.8932138,"about_ca_system_score_codex":0.00019606944,"about_ca_system_score_gemma":0.00026114337,"threshold_uncertainty_score":0.9999417},"labels":[],"label_agreement":null},{"id":"W4285184932","doi":"10.1109/tiv.2022.3178061","title":"Adaptive Lane Change Trajectory Planning Scheme for Autonomous Vehicles Under Various Road Frictions and Vehicle Speeds","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheme (mathematics); Trajectory; Computer science; Control theory (sociology); Automotive engineering; Engineering; Artificial intelligence; Mathematics; Control (management); Physics","score_opus":0.07861061887554463,"score_gpt":0.28489119627613196,"score_spread":0.20628057740058733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285184932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04907569,0.00052796025,0.94694245,0.00075298676,0.0012515811,0.0006972442,0.00012678739,0.00052781217,0.000097504846],"genre_scores_gemma":[0.9357929,0.000040491264,0.062118385,0.00059319386,0.00013257719,0.0007092383,0.000008501798,0.00005195009,0.00055274455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976216,0.00016881336,0.00041849827,0.00075558084,0.00045875696,0.0005767443],"domain_scores_gemma":[0.99865663,0.00040899703,0.00014524192,0.0004991183,0.000084705876,0.00020528701],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004167325,0.00034165228,0.00032788058,0.0004609014,0.0013682812,0.00014787812,0.0006428339,0.000114274524,0.000035306493],"category_scores_gemma":[0.0000057795673,0.00037785296,0.00016927907,0.0005365739,0.00010211961,0.0003973789,0.000024325816,0.0006431499,0.000029402905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036019436,0.0015639303,0.00039315975,0.0000734497,0.00058771984,0.00012006933,0.01808129,0.67141986,0.005284586,0.0055559766,0.00070621766,0.29585356],"study_design_scores_gemma":[0.0008158092,0.0014095777,0.0032200655,0.00006179795,0.000075237054,0.000150517,0.0018710245,0.9804354,0.008789821,0.0011337803,0.0013540889,0.00068291434],"about_ca_topic_score_codex":0.00029894063,"about_ca_topic_score_gemma":0.000012180006,"teacher_disagreement_score":0.8867172,"about_ca_system_score_codex":0.00031567403,"about_ca_system_score_gemma":0.000110662324,"threshold_uncertainty_score":0.9999318},"labels":[],"label_agreement":null},{"id":"W4285267709","doi":"10.2316/j.2022.206-0510","title":"MLC-SLAM: MASK LOOP CLOSING FOR MONOCULAR SLAM, 107-114.","year":2022,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Closing (real estate); Monocular; Loop (graph theory); Simultaneous localization and mapping; Artificial intelligence; Computer vision; Computer science; Political science; Mathematics; Robot; Law; Mobile robot","score_opus":0.017480433809966174,"score_gpt":0.27211997951356576,"score_spread":0.2546395457035996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285267709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013542428,0.00023714724,0.9788613,0.004807269,0.0023248582,0.000101245525,0.000007840511,0.000036357924,0.00008160164],"genre_scores_gemma":[0.35779497,0.000029560533,0.64133227,0.0003180998,0.00032611057,0.0000072312364,0.0000126991345,0.000013044767,0.00016600263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985277,0.00006235336,0.0004507058,0.00015690153,0.00065781473,0.00014450109],"domain_scores_gemma":[0.99878395,0.00013716564,0.0005138504,0.0001217424,0.00038279445,0.000060475628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066917884,0.00010254288,0.0001580171,0.0002740928,0.00017951644,0.0002492306,0.0006919971,0.000031325966,0.0000070821143],"category_scores_gemma":[0.00010636165,0.00010244473,0.00009422318,0.0001296824,0.000019200785,0.0005199341,0.00020827669,0.00017323765,0.000001857776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027372334,0.00012293791,0.0007762633,0.000015991767,0.00018226368,0.00012438915,0.0010775917,0.8739207,0.0015800865,0.030491188,0.0013018602,0.09037938],"study_design_scores_gemma":[0.0006798069,0.00017696411,0.0015604225,0.000043387157,0.000018715838,0.00055151683,0.0000700627,0.98684084,0.00029177548,0.008266495,0.0013815402,0.00011846584],"about_ca_topic_score_codex":0.0000044258627,"about_ca_topic_score_gemma":1.7683291e-7,"teacher_disagreement_score":0.34425253,"about_ca_system_score_codex":0.00013938468,"about_ca_system_score_gemma":0.00009854614,"threshold_uncertainty_score":0.41775748},"labels":[],"label_agreement":null},{"id":"W4285685133","doi":"10.5220/0011123600003271","title":"Nonholonomic Robot Navigation of Mazes using Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Reinforcement learning; Computer science; Nonholonomic system; Robot; Artificial intelligence; Mobile robot; Computer vision; Human–computer interaction","score_opus":0.02904924891392879,"score_gpt":0.26390483951701244,"score_spread":0.23485559060308364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285685133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08512558,0.00001982458,0.9132382,0.000060610688,0.00026437172,0.000073744544,2.2010208e-7,0.00008729694,0.001130149],"genre_scores_gemma":[0.7265985,2.9223006e-7,0.27300343,0.00004672109,0.000013152658,0.0000051050665,0.00000384256,0.000004129981,0.00032479007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914855,0.000069403286,0.00021047557,0.00018212557,0.00023927039,0.0001502065],"domain_scores_gemma":[0.99953717,0.00003861427,0.00014512778,0.00022823153,0.000024999086,0.00002588446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003607909,0.00006512234,0.00010112359,0.00008326819,0.00018370945,0.000026093467,0.00041630582,0.000013414191,0.00003606127],"category_scores_gemma":[0.000012096648,0.000069790614,0.000032788375,0.00023007092,0.000014812441,0.00018409445,0.00042371836,0.00014021147,0.000008617418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015057444,0.00000867701,0.00083653425,0.000004092026,0.00000595579,0.0000053413473,0.00052256987,0.9908112,0.0041839588,0.0016525433,0.000021011898,0.0019466041],"study_design_scores_gemma":[0.0001353999,0.00010012082,0.0005748779,0.000008419043,0.000002905002,0.000031177442,0.000100419486,0.99616784,0.00253113,0.00009231199,0.00017322302,0.00008219763],"about_ca_topic_score_codex":0.00011393606,"about_ca_topic_score_gemma":6.776142e-8,"teacher_disagreement_score":0.64147294,"about_ca_system_score_codex":0.000103846454,"about_ca_system_score_gemma":0.00005549481,"threshold_uncertainty_score":0.28459787},"labels":[],"label_agreement":null},{"id":"W4285722121","doi":"10.1007/978-3-031-01506-9_3","title":"Path and Speed Planning for Intelligent Vehicles","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on advances in automotive technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motion planning; Obstacle; Path (computing); Obstacle avoidance; Mathematical optimization; Computer science; Regular polygon; Convex optimization; Artificial intelligence; Mathematics; Geography; Robot","score_opus":0.021245279862175732,"score_gpt":0.2783516758311411,"score_spread":0.2571063959689654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285722121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000981382,0.027313393,0.9046512,0.0060493127,0.0011292177,0.0019233194,0.000109178094,0.0024293824,0.05629686],"genre_scores_gemma":[0.08006933,0.0042060367,0.9042341,0.0025540122,0.00051376986,0.00043099016,0.000031383744,0.0004263245,0.007534044],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973322,0.00004418765,0.00049590674,0.0013306362,0.00029135658,0.0005056972],"domain_scores_gemma":[0.99732935,0.0014429801,0.00039085365,0.00065407046,0.00009059604,0.000092133894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024213134,0.0006206063,0.00091228803,0.0010033774,0.00012296204,0.000062887084,0.0013065678,0.0007137525,0.000006950455],"category_scores_gemma":[0.0012049255,0.0005789083,0.00011780808,0.00019485944,0.00037060975,0.00015869964,0.00039772314,0.0009431331,0.000028448954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000945791,0.00004876529,0.00013464449,0.00023687455,0.00012684334,0.0004191896,0.0005191351,0.0044972613,0.000110403176,0.427803,0.0002969387,0.56571233],"study_design_scores_gemma":[0.0005854444,0.0015087872,0.00014765801,0.0032075692,0.000070849186,0.000121116514,0.00009606503,0.04098151,0.01328972,0.86877096,0.06952176,0.0016985411],"about_ca_topic_score_codex":9.641618e-7,"about_ca_topic_score_gemma":0.000001841277,"teacher_disagreement_score":0.56401384,"about_ca_system_score_codex":0.00018730074,"about_ca_system_score_gemma":0.00007966161,"threshold_uncertainty_score":0.9996662},"labels":[],"label_agreement":null},{"id":"W4285813121","doi":"10.1109/iv51971.2022.9827156","title":"A Sufficient Condition for Convex Hull Property in General Convex Spatio-Temporal Corridors","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Convex hull; Smoothness; Regular polygon; Mathematical optimization; Computer science; Motion planning; Parametric equation; Parametric statistics; Convex combination; Property (philosophy); Hull; Mathematics; Convex optimization; Geometry; Robot; Mathematical analysis; Engineering; Artificial intelligence","score_opus":0.02546569992971375,"score_gpt":0.2626503696655514,"score_spread":0.23718466973583763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285813121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5815546,0.00023124841,0.40355486,0.0030902892,0.0070847454,0.0030680958,0.00027507413,0.00053334975,0.0006077716],"genre_scores_gemma":[0.97417426,0.000032801487,0.017037105,0.0014821548,0.00032748736,0.0014810346,0.00043332687,0.00008124919,0.0049505616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954676,0.0004563596,0.0009903808,0.0011658819,0.0010101398,0.0009096078],"domain_scores_gemma":[0.998075,0.0002631605,0.0003986999,0.0008387148,0.00018752347,0.00023690384],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014022182,0.0004398936,0.0005721088,0.0004885776,0.0005377959,0.00022255084,0.0016475852,0.00012447739,0.0001743883],"category_scores_gemma":[0.000051094048,0.0004051244,0.00023729936,0.0008697855,0.00014008243,0.00036560293,0.00052616105,0.000609628,0.00010184208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005641091,0.0020344628,0.025082955,0.00022362384,0.00016300603,0.0005172042,0.013280354,0.86664844,0.029607413,0.005639134,0.049705576,0.006533742],"study_design_scores_gemma":[0.0009844558,0.0010224867,0.0005877737,0.00004134252,0.000021726435,0.00008876412,0.00045752517,0.96121365,0.012251762,0.00038670516,0.022308115,0.0006357188],"about_ca_topic_score_codex":0.00063718634,"about_ca_topic_score_gemma":0.00003620963,"teacher_disagreement_score":0.39261973,"about_ca_system_score_codex":0.00077025505,"about_ca_system_score_gemma":0.00036999452,"threshold_uncertainty_score":0.9998401},"labels":[],"label_agreement":null},{"id":"W4286973027","doi":"10.48550/arxiv.2109.08185","title":"Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Heuristics; Computer science; Path (computing); Motion planning; Mathematical optimization; Regular polygon; Line (geometry); Robot; Set (abstract data type); Line segment; Time complexity; Quality (philosophy); Algorithm; Mathematics; Artificial intelligence","score_opus":0.06343477976045878,"score_gpt":0.20635504698566923,"score_spread":0.14292026722521045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286973027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2947885,0.0000537753,0.7040211,0.000015148981,0.0005278566,0.00023108892,0.000025925436,0.000048905273,0.0002877043],"genre_scores_gemma":[0.9118577,0.000026788388,0.08734487,0.00004378386,0.00006628582,0.0000032906148,0.00007418869,0.00002023262,0.00056285615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979843,0.000085211344,0.00030596653,0.0010628515,0.00014001809,0.00042165435],"domain_scores_gemma":[0.998142,0.00021996327,0.00045316416,0.00096587336,0.000069793045,0.00014920846],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002695192,0.00031219108,0.00049496884,0.00018334962,0.0001465372,0.00011245883,0.001279217,0.00028289435,0.000012292301],"category_scores_gemma":[0.000050097366,0.00040450974,0.000255364,0.00024941502,0.000093656105,0.00037571436,0.0014023058,0.00040954468,0.000011833512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017077942,0.00008149853,0.0034227907,0.00010058628,0.00012730366,0.00040047683,0.00081392174,0.9937833,0.00037278645,0.0006109677,0.000114323324,0.00015498267],"study_design_scores_gemma":[0.0007239092,0.0000880015,0.004155818,0.0003600325,0.00007487875,0.000008011412,0.00015426574,0.9915182,0.0017157467,0.00069378084,0.000094352006,0.000412975],"about_ca_topic_score_codex":0.000026924343,"about_ca_topic_score_gemma":4.1917562e-7,"teacher_disagreement_score":0.6170692,"about_ca_system_score_codex":0.00014930185,"about_ca_system_score_gemma":0.00019445251,"threshold_uncertainty_score":0.9998407},"labels":[],"label_agreement":null},{"id":"W4287265047","doi":"10.48550/arxiv.2103.10534","title":"Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Workspace; Computer science; Object (grammar); Planner; Mobile manipulator; Artificial intelligence; Computer vision; Task (project management); Pipeline (software); Human–computer interaction; Control (management); Plan (archaeology); Mobile robot; Robot; Real-time computing; Engineering","score_opus":0.05765218474886178,"score_gpt":0.2060978476200804,"score_spread":0.14844566287121863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287265047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5501364,0.000035544235,0.44870287,0.000027157439,0.00043886137,0.000249823,0.0000013349463,0.00017173716,0.00023624966],"genre_scores_gemma":[0.990743,0.000019409475,0.008663987,0.000024281608,0.00004072641,0.0000035195148,0.00006820697,0.000018046889,0.00041882313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979633,0.00021858289,0.00023937538,0.0011148491,0.00013218404,0.0003316894],"domain_scores_gemma":[0.9984293,0.00008454547,0.00025575113,0.0009387652,0.00018782707,0.00010380024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001990083,0.00030308188,0.00033950456,0.00040207396,0.00008782946,0.00022953084,0.0007286599,0.00024340993,0.000011243036],"category_scores_gemma":[0.000022915652,0.00033088357,0.00009400299,0.0010457042,0.00004855743,0.0007126724,0.00074144325,0.00062789785,0.000041714517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022655047,0.00011222441,0.010389085,0.000040865136,0.0000486511,0.0012976299,0.0004397604,0.9858509,0.00015774442,0.0011935229,0.000010652881,0.00043634747],"study_design_scores_gemma":[0.0004937165,0.000078434336,0.025073923,0.00046420487,0.000038052163,0.000031828265,0.00018707284,0.9723286,0.00040959247,0.00046980675,0.000035988418,0.00038877456],"about_ca_topic_score_codex":0.00035861265,"about_ca_topic_score_gemma":0.00009532759,"teacher_disagreement_score":0.4406066,"about_ca_system_score_codex":0.00039106383,"about_ca_system_score_gemma":0.00021124941,"threshold_uncertainty_score":0.99991435},"labels":[],"label_agreement":null},{"id":"W4287864852","doi":"10.48550/arxiv.2002.10563","title":"A Double Q-Learning Approach for Navigation of Aerial Vehicles with\\n Connectivity Constraint","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Computer science; Trajectory; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.10414019235802163,"score_gpt":0.2087922182107358,"score_spread":0.10465202585271416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287864852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2991086,0.000015955482,0.6983155,0.0000578917,0.00037676655,0.0011778807,0.00004134603,0.00016318716,0.00074286485],"genre_scores_gemma":[0.8821753,0.000013186327,0.11733658,0.000016234195,0.00017631838,0.0000071026416,0.00010073017,0.000037958456,0.00013658825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99580956,0.00040923597,0.0005470263,0.0022955416,0.0002623286,0.00067633484],"domain_scores_gemma":[0.99621546,0.0005124203,0.0012900494,0.00094871223,0.00066183874,0.00037154197],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00087186875,0.00065291504,0.0010742953,0.0002326395,0.00043843803,0.00018922475,0.0017104511,0.000518424,0.000007988491],"category_scores_gemma":[0.00012150137,0.00076297106,0.00037843338,0.0010695831,0.0007426915,0.00055553054,0.0011650552,0.0011781435,0.000011083515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011828246,0.00025361046,0.0022850288,0.0006533725,0.0003559913,0.0001458837,0.002043038,0.9373144,0.0007635153,0.05403416,0.000013355669,0.0009548397],"study_design_scores_gemma":[0.0047173477,0.00084042916,0.00056198525,0.00042513592,0.0002996607,0.00003710329,0.0006993918,0.98635817,0.0020363166,0.0031873386,0.000043527856,0.0007936219],"about_ca_topic_score_codex":0.0002934686,"about_ca_topic_score_gemma":0.000002077174,"teacher_disagreement_score":0.5830667,"about_ca_system_score_codex":0.0002817275,"about_ca_system_score_gemma":0.0009329736,"threshold_uncertainty_score":0.99948215},"labels":[],"label_agreement":null},{"id":"W4288047686","doi":"10.1109/icuas54217.2022.9836163","title":"Experiments on Collaborative Transport of Cable-suspended Payload with Quadrotor UAVs","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Unmanned Aircraft Systems (ICUAS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"NCR","keywords":"Payload (computing); Drone; Ethernet; Computer science; Control (management); Aerospace engineering; Engineering; Aeronautics; Computer network","score_opus":0.03259018551752736,"score_gpt":0.27559283451447597,"score_spread":0.24300264899694862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288047686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28390244,0.0009033159,0.3548207,0.013373205,0.032641273,0.013430343,0.004842928,0.0024234138,0.2936624],"genre_scores_gemma":[0.99204415,0.00000953305,0.002909328,0.00021625264,0.000097816555,0.0008256807,0.00013714048,0.000032510157,0.0037276135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952985,0.00039334464,0.00071784575,0.0009330247,0.0022239871,0.00043330493],"domain_scores_gemma":[0.99774367,0.00015528529,0.00058193365,0.0007967177,0.00055417017,0.00016823482],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060646195,0.0004051616,0.00054373645,0.00046206597,0.0003082014,0.00012368751,0.0021276022,0.000079832105,0.00050000445],"category_scores_gemma":[0.00003524834,0.00036894096,0.000102619226,0.0007107338,0.00008766706,0.0003596251,0.000217685,0.0004798804,0.00007414642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021326079,0.0027892517,0.0074817976,0.00015181354,0.0014312769,0.001824874,0.01337005,0.28294167,0.009078076,0.6682476,0.008946827,0.0016041824],"study_design_scores_gemma":[0.008907683,0.009855825,0.0045188973,0.0012237864,0.00006923447,0.00028043144,0.013350898,0.92872983,0.013646157,0.0012342186,0.015741816,0.0024412337],"about_ca_topic_score_codex":0.00030924004,"about_ca_topic_score_gemma":0.000009804736,"teacher_disagreement_score":0.7081417,"about_ca_system_score_codex":0.0004650069,"about_ca_system_score_gemma":0.000566818,"threshold_uncertainty_score":0.99987626},"labels":[],"label_agreement":null},{"id":"W4288391271","doi":"10.1109/lra.2022.3194691","title":"TAPE: Tether-Aware Path Planning for Autonomous Exploration of Unknown 3D Cavities Using a Tangle-Compatible Tethered Aerial Robot","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Université de Sherbrooke","keywords":"Travelling salesman problem; Motion planning; Path (computing); Computer science; Path length; Robot; Robotics; Function (biology); Mathematical optimization; Simulation; Algorithm; Artificial intelligence; Mathematics","score_opus":0.054080465320992206,"score_gpt":0.27731925043374267,"score_spread":0.22323878511275047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288391271","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059913035,0.000060854883,0.9370274,0.0010491649,0.0012733188,0.00043102165,0.000030673025,0.00020398642,0.000010575402],"genre_scores_gemma":[0.4625124,0.000001525854,0.5367474,0.0003991591,0.00015965506,0.00007846467,0.000042045853,0.000030302657,0.0000290689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828684,0.00015416445,0.00048103466,0.00037784255,0.00038005738,0.00032005247],"domain_scores_gemma":[0.9989115,0.00019525882,0.0004362442,0.00031202086,0.000080480124,0.000064522756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042073175,0.00021280235,0.00032118426,0.00022551235,0.0005046569,0.00016139509,0.00037293878,0.000054635282,0.0000040198174],"category_scores_gemma":[0.000026330139,0.00023206325,0.000069854716,0.00031767687,0.00005929298,0.00068172335,0.0001157437,0.00015207987,8.400979e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009531953,0.00004178885,0.00010498607,0.00005357814,0.000027111582,0.000009216068,0.0040779067,0.97573787,0.017046444,0.0005726034,0.00032789586,0.001991092],"study_design_scores_gemma":[0.0006834354,0.0001548558,0.00015911677,0.00006746357,0.00002560143,0.000025848907,0.00031970095,0.99662685,0.0012321599,0.0002749278,0.00016333115,0.0002666879],"about_ca_topic_score_codex":0.000052697727,"about_ca_topic_score_gemma":6.1598433e-7,"teacher_disagreement_score":0.40259936,"about_ca_system_score_codex":0.00014546244,"about_ca_system_score_gemma":0.00011577404,"threshold_uncertainty_score":0.94632643},"labels":[],"label_agreement":null},{"id":"W4288702346","doi":"10.36227/techrxiv.20379546.v1","title":"Hardware Agnostic Robotic Hand Algorithms with Recreational, Educational and Healthcare Applications","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Robot; Controller (irrigation); Human–computer interaction; Simulation","score_opus":0.03632931957684964,"score_gpt":0.300181135166595,"score_spread":0.26385181558974535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288702346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010908002,0.0021993213,0.9750213,0.019168328,0.0005386579,0.0012389447,0.00008441384,0.00024590723,0.0013940204],"genre_scores_gemma":[0.008704057,0.00018615069,0.9816246,0.0007871939,0.00034866884,0.002799686,0.0010076517,0.000045785546,0.004496227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970133,0.00015125082,0.00040373387,0.0013020746,0.00074667105,0.0003830202],"domain_scores_gemma":[0.9972926,0.00059821055,0.0002872683,0.0012083448,0.00031922176,0.00029435696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028965913,0.00038617765,0.0004042765,0.00028278615,0.00072922726,0.0005361753,0.0012281172,0.00015958372,0.00014425318],"category_scores_gemma":[0.000069373134,0.00036169082,0.000060328854,0.00044887932,0.00015436295,0.00020827146,0.0015191076,0.00080247433,0.000033232747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002475771,0.0009063129,0.024401838,0.0014685033,0.0005725976,0.00011395709,0.0043894476,0.66226816,0.000012539078,0.22747412,0.015720185,0.0626476],"study_design_scores_gemma":[0.0012844562,0.0005647082,0.11076002,0.0010311281,0.00021175927,0.0009291258,0.00039944216,0.79619753,0.00003605638,0.07256989,0.013240556,0.0027753531],"about_ca_topic_score_codex":0.0003151024,"about_ca_topic_score_gemma":0.000020194202,"teacher_disagreement_score":0.15490423,"about_ca_system_score_codex":0.00026116526,"about_ca_system_score_gemma":0.0016532382,"threshold_uncertainty_score":0.99988353},"labels":[],"label_agreement":null},{"id":"W4289785486","doi":"10.48550/arxiv.2208.01222","title":"Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Southern California; Simon Fraser University; National Science Foundation","keywords":"Task (project management); Benchmark (surveying); Path (computing); Bounded function; Sequence (biology); Computer science; Assignment problem; Weapon target assignment problem; Mathematical optimization; Generalized assignment problem; Variety (cybernetics); Algorithm; Mathematics; Artificial intelligence","score_opus":0.07614609090562321,"score_gpt":0.2082556388280741,"score_spread":0.1321095479224509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289785486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26789454,0.00016848868,0.73054963,0.000065130494,0.0005277833,0.00026421572,0.000031980766,0.00019494041,0.00030327047],"genre_scores_gemma":[0.81810296,0.0001384003,0.18055734,0.00006154173,0.00004970802,0.0000031014679,0.000020985635,0.000023672994,0.001042304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726987,0.00020375852,0.0002246163,0.0015909693,0.00018905057,0.0005217344],"domain_scores_gemma":[0.99840456,0.00015052519,0.00026463773,0.000845365,0.00004711958,0.00028781279],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046793086,0.00041465412,0.00041012553,0.00027892808,0.00048826582,0.00032362362,0.0012106986,0.00022794082,0.000023101074],"category_scores_gemma":[0.000034001074,0.0005047965,0.00010244311,0.00031056537,0.0001822687,0.00037544896,0.0055865897,0.0009173858,0.0000112635025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003470642,0.00018152191,0.015406937,0.00010908632,0.00018802124,0.002893635,0.0017605199,0.95830214,0.00007035127,0.019533457,0.00023295605,0.0012866805],"study_design_scores_gemma":[0.0008132371,0.0001343383,0.007051908,0.000064237596,0.00006259012,0.000048343605,0.00020641074,0.9895631,0.0000125652405,0.001141892,0.00032084962,0.0005805428],"about_ca_topic_score_codex":0.00010465612,"about_ca_topic_score_gemma":0.00000162418,"teacher_disagreement_score":0.5502084,"about_ca_system_score_codex":0.00033079323,"about_ca_system_score_gemma":0.000203058,"threshold_uncertainty_score":0.99974036},"labels":[],"label_agreement":null},{"id":"W4291744173","doi":"10.3390/e24081128","title":"A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; Youth Innovation Promotion Association; Chinese Academy of Sciences","keywords":"Differential evolution; Benchmark (surveying); Computer science; Dead reckoning; Evolutionary algorithm; Evolutionary computation; Calibration; Genetic algorithm; Algorithm; Scheme (mathematics); Artificial intelligence; Control theory (sociology); Mathematics; Global Positioning System; Control (management); Machine learning","score_opus":0.017131655966007055,"score_gpt":0.23555793114365364,"score_spread":0.2184262751776466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291744173","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006387218,0.000024845822,0.99753755,0.0003268279,0.00027577576,0.00058937754,0.00002917414,0.00054620963,0.000031524563],"genre_scores_gemma":[0.055191524,7.773737e-7,0.9436594,0.00042292257,0.0001012961,0.0002915261,0.00028058942,0.000030353493,0.00002155704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802417,0.00027523361,0.00024075268,0.00056428165,0.0005796637,0.00031588957],"domain_scores_gemma":[0.99902105,0.00012784374,0.00019616449,0.0004060832,0.0001438102,0.000105028535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031478194,0.00018347027,0.0001599913,0.0001845572,0.0007607787,0.000104089675,0.00040259908,0.000051939962,0.000014797846],"category_scores_gemma":[0.000030040921,0.00018749203,0.000043186235,0.00064004585,0.000023366936,0.0006777483,0.000073997035,0.00015731627,0.0000029006358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100760735,0.00022194312,0.0002806997,0.0000042298357,0.000015464451,0.000012482193,0.0003379699,0.9893568,0.000053167947,0.007307423,0.00020958163,0.0020994334],"study_design_scores_gemma":[0.0013056567,0.0023456118,0.00018828051,0.00002287961,0.000013213766,0.000009990265,0.000085154184,0.9951727,0.00018826756,0.00026822544,0.00015132417,0.00024874337],"about_ca_topic_score_codex":0.000025992587,"about_ca_topic_score_gemma":3.572864e-7,"teacher_disagreement_score":0.054552805,"about_ca_system_score_codex":0.0003640574,"about_ca_system_score_gemma":0.00026314688,"threshold_uncertainty_score":0.7645703},"labels":[],"label_agreement":null},{"id":"W4293115961","doi":"10.11159/icmie22.124","title":"Path Planning with Modified RRT* Algorithm for Lung Biopsy","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Sichuan Province","keywords":"Computer science; Path (computing); Motion planning; Lung; Algorithm; Artificial intelligence; Medicine; Robot; Internal medicine; Operating system","score_opus":0.009162754981923177,"score_gpt":0.21402656007149168,"score_spread":0.2048638050895685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293115961","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.917982,0.0000681418,0.06952858,0.0012746812,0.007026999,0.0023065223,0.00022209302,0.0012066144,0.00038435688],"genre_scores_gemma":[0.6370502,0.0000030211152,0.36194575,0.000111671914,0.00022801489,0.00038062045,0.000006458346,0.00006030456,0.00021391173],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998427,0.0000062048225,0.00030529356,0.0004850998,0.00037990598,0.00039652103],"domain_scores_gemma":[0.999289,0.0001009472,0.00020609311,0.0002104489,0.00008424886,0.00010928691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003171135,0.0002583291,0.00034690113,0.00012688772,0.00022082045,0.00016984982,0.0010162327,0.00005011452,0.0000039252873],"category_scores_gemma":[0.00004928794,0.0002016782,0.0000686531,0.00035779303,0.00003905755,0.000173969,0.0006682767,0.00025331983,1.3320329e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001451681,0.0001009798,0.000010340436,0.00035692373,0.00010005964,0.000008562616,0.00006677248,0.007957686,0.9614105,0.026828239,0.001279643,0.0017351245],"study_design_scores_gemma":[0.0005454917,0.00009331216,0.0000032979938,0.0001903858,0.000022457278,0.000044983095,0.000025070207,0.53984594,0.4584765,0.00022670973,0.00029780887,0.00022802579],"about_ca_topic_score_codex":0.0000033763058,"about_ca_topic_score_gemma":1.0713269e-8,"teacher_disagreement_score":0.53188825,"about_ca_system_score_codex":0.000062965846,"about_ca_system_score_gemma":0.000023374088,"threshold_uncertainty_score":0.8224198},"labels":[],"label_agreement":null},{"id":"W4293551731","doi":"","title":"Simulations for Vision-based Navigation and Collision Avoidance in Unmanned Aerial Systems","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Collision avoidance; Computer science; Aeronautics; Collision; Computer vision; Artificial intelligence; Human–computer interaction; Real-time computing; Simulation; Computer security; Engineering","score_opus":0.016129169579655246,"score_gpt":0.26262484836762723,"score_spread":0.246495678787972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293551731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056983054,0.0005680916,0.93554443,0.0037191526,0.0008905132,0.0013032001,0.0001766735,0.00023894905,0.0005759368],"genre_scores_gemma":[0.6834123,0.00002871259,0.3144565,0.00004690715,0.000029677223,0.0003150242,0.00095385965,0.00003344276,0.00072358607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99365616,0.0038415785,0.000646949,0.0010084359,0.0005074238,0.0003394726],"domain_scores_gemma":[0.99278253,0.003709689,0.00057714526,0.0017897284,0.0010085452,0.00013235632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005275738,0.0002929063,0.00039543962,0.00032560277,0.0005413677,0.0007360741,0.0014846092,0.0002314508,0.00000642672],"category_scores_gemma":[0.001294155,0.00034153706,0.000100914636,0.00059845677,0.00010303408,0.00025204255,0.0013024324,0.0005313533,0.0000031701256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037395523,0.0006330039,0.002021747,0.00053720496,0.00003726462,0.000014076563,0.007834274,0.9208546,0.0011118993,0.055844054,0.0007428635,0.010331612],"study_design_scores_gemma":[0.0008497705,0.0000014416274,0.001844075,0.0015326062,0.000011258924,0.0000033323952,0.000038992268,0.99015355,0.00106431,0.0021570406,0.002022227,0.00032139683],"about_ca_topic_score_codex":0.0007519869,"about_ca_topic_score_gemma":0.00007625654,"teacher_disagreement_score":0.62642926,"about_ca_system_score_codex":0.00026436348,"about_ca_system_score_gemma":0.0004976413,"threshold_uncertainty_score":0.9999037},"labels":[],"label_agreement":null},{"id":"W4293793189","doi":"10.1155/2022/1675736","title":"Motion Planning and Tracking Control of Autonomous Vehicle Based on Improved <a:math xmlns:a=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"> <a:msup> <a:mrow> <a:mi>A</a:mi> </a:mrow> <a:mi>∗</a:mi> </a:msup> </a:math> Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Tracking (education); Motion (physics); Control (management); Computer science; Motion control; Artificial intelligence; Robot; Psychology","score_opus":0.012677798595867169,"score_gpt":0.2412352554798217,"score_spread":0.2285574568839545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293793189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4433967,0.0005033328,0.55385184,0.00046464798,0.0010934365,0.00033620192,0.00013912376,0.00017164185,0.000043111268],"genre_scores_gemma":[0.86036915,0.00003263672,0.13858186,0.00046663755,0.00024662082,0.00007344288,0.00010236694,0.00009563022,0.000031675503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994203,0.00029407002,0.0019781846,0.00096441316,0.0016548634,0.0009054983],"domain_scores_gemma":[0.99526465,0.00062251784,0.00250526,0.00080711686,0.0004138302,0.00038661645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020266478,0.00066403527,0.0009966883,0.000718528,0.00070385356,0.00019531556,0.0010704705,0.00026178124,0.000011212531],"category_scores_gemma":[0.00015770819,0.00069559045,0.00045538732,0.00082173443,0.00016073327,0.0019478564,0.000059432077,0.0013218492,0.000009852571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007093234,0.000778739,0.001249826,0.00028227983,0.0002536423,0.0007758125,0.0062922924,0.87719274,0.019902091,0.027468313,0.00003227287,0.06506267],"study_design_scores_gemma":[0.0058987634,0.0020400328,0.02388751,0.00040851152,0.00020723238,0.000251195,0.0009134086,0.9629258,0.0019178415,0.000307577,0.0006497075,0.0005924075],"about_ca_topic_score_codex":0.000039729133,"about_ca_topic_score_gemma":0.0000044032536,"teacher_disagreement_score":0.41697246,"about_ca_system_score_codex":0.00026239583,"about_ca_system_score_gemma":0.00048277248,"threshold_uncertainty_score":0.9995495},"labels":[],"label_agreement":null},{"id":"W4294142959","doi":"10.3390/robotics11050087","title":"A Backstepping Approach to Nonlinear Model Predictive Horizon for Optimal Trajectory Planning","year":2022,"lang":"en","type":"article","venue":"Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Backstepping; Control theory (sociology); Nonlinear system; Computer science; Trajectory; Model predictive control; Computation; Trajectory optimization; Control engineering; Horizon; Rotor (electric); Motion planning; Flexibility (engineering); Time horizon; Mathematical optimization; Engineering; Optimal control; Control (management); Robot; Artificial intelligence; Mathematics; Adaptive control; Algorithm","score_opus":0.04494290939216148,"score_gpt":0.2756277460629841,"score_spread":0.23068483667082265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294142959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009191589,0.00007324365,0.99589664,0.00021030354,0.00061349716,0.00069325726,0.00005540422,0.00033579007,0.0012026898],"genre_scores_gemma":[0.021291096,5.0105245e-7,0.9776336,0.00026030946,0.00019461584,0.0002288666,0.000036789195,0.000038907452,0.00031529294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977674,0.000078954065,0.0003184744,0.00069363957,0.0005364485,0.0006050858],"domain_scores_gemma":[0.99885654,0.00016294372,0.00011458445,0.000570061,0.00009017815,0.00020568275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005489898,0.00023600252,0.00029746816,0.00021626147,0.0005230679,0.000109413566,0.0012372015,0.000065126944,8.937618e-7],"category_scores_gemma":[0.00007578266,0.00026835792,0.00010808417,0.00051302975,0.000023897906,0.00023709954,0.0006695918,0.000376044,0.000005996012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026531005,0.00012874414,0.000027524962,0.000020694022,0.00003063967,0.000009789175,0.0028460452,0.9924493,0.000107170876,0.0019405971,0.001352365,0.0010606188],"study_design_scores_gemma":[0.00043472182,0.0005545231,0.000037361053,0.000018789344,0.000019862124,0.000039347236,0.00039021272,0.9977575,0.000053643947,0.00012673473,0.00025260227,0.0003147088],"about_ca_topic_score_codex":0.000002729596,"about_ca_topic_score_gemma":3.8144453e-8,"teacher_disagreement_score":0.020371936,"about_ca_system_score_codex":0.00024136732,"about_ca_system_score_gemma":0.00023636459,"threshold_uncertainty_score":0.9999769},"labels":[],"label_agreement":null},{"id":"W4294224162","doi":"10.1051/itmconf/20224803007","title":"Parallel Hybrid 2-Opt Flower Pollination Algorithm for Real-Time UAV Trajectory Planning on GPU","year":2022,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Nvidia","keywords":"CUDA; Graphics processing unit; Computer science; Trajectory; Speedup; Pollination; Multi-core processor; Trajectory optimization; Parallel computing; Central processing unit; Graphics; Computer graphics (images); Computer hardware","score_opus":0.03137554628453916,"score_gpt":0.276185974121945,"score_spread":0.24481042783740586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294224162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022602217,0.00017750401,0.95764756,0.00074155134,0.0017013283,0.00067840656,0.00015878605,0.00038251962,0.015910113],"genre_scores_gemma":[0.6757531,0.000013683577,0.322535,0.00015343394,0.00016041489,0.00017887565,0.00007115162,0.000020677642,0.0011136229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978366,0.00016272912,0.00041348595,0.000522939,0.000680715,0.0003835386],"domain_scores_gemma":[0.9985187,0.00052366324,0.00029335552,0.00042709493,0.000144806,0.00009235869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070517213,0.0002201472,0.00036035452,0.00028792952,0.00030070858,0.000087998356,0.0011185571,0.00004821017,0.00009594012],"category_scores_gemma":[0.000059419748,0.00021903637,0.00011947355,0.00025704232,0.000069879105,0.00023862095,0.00016435352,0.00020701617,0.00001782872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027926042,0.0013847704,0.0017985352,0.00012970506,0.00047664501,0.00032645251,0.007099809,0.29762596,0.005784781,0.0633931,0.038734842,0.58296615],"study_design_scores_gemma":[0.0008153357,0.0007780192,0.0013687854,0.000052648713,0.000014917147,0.00002100823,0.00017359295,0.989012,0.0013387192,0.0018268456,0.0042773373,0.00032077546],"about_ca_topic_score_codex":0.000048596627,"about_ca_topic_score_gemma":5.774485e-7,"teacher_disagreement_score":0.69138604,"about_ca_system_score_codex":0.00006483865,"about_ca_system_score_gemma":0.0008118768,"threshold_uncertainty_score":0.8932044},"labels":[],"label_agreement":null},{"id":"W4294982655","doi":"10.1109/lra.2022.3205122","title":"Experimental Performance Comparison of Bidirectional Actuator Configurations for Suspended Aerial Platforms","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Thrust; Payload (computing); Actuator; Bandwidth (computing); Marine engineering; Computer science; Propeller; Sampling (signal processing); Simulation; Aerospace engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.031582876850239805,"score_gpt":0.283864397842166,"score_spread":0.2522815209919262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294982655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42972258,0.000017558226,0.5679451,0.0007310721,0.0012262014,0.00022641521,0.000014855262,0.00008809636,0.000028097345],"genre_scores_gemma":[0.87014,5.3189615e-7,0.12950183,0.00017893325,0.00006876852,0.000058611193,0.00002800676,0.000006628847,0.000016665479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990531,0.00001995208,0.00029102826,0.00019646257,0.00028401802,0.00015541038],"domain_scores_gemma":[0.9994817,0.00009783115,0.00019327007,0.00014789519,0.000035732934,0.00004359745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016690066,0.00010019807,0.00016382361,0.00011318822,0.00039969798,0.000059625854,0.00022156621,0.000023628483,0.000013723185],"category_scores_gemma":[0.000010042195,0.000104231796,0.000041205276,0.00016278728,0.00004077589,0.00029174122,0.000053487,0.00008466225,0.0000017112379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002512573,0.00016521591,0.00040268924,0.000027918773,0.00004105238,0.0000012623846,0.0024498708,0.83361757,0.14751582,0.0093761915,0.0033779808,0.0029992736],"study_design_scores_gemma":[0.00059278763,0.00016659741,0.0010283286,0.000006815851,0.0000063675875,0.000015354677,0.000109551926,0.9552779,0.042437725,0.000057660993,0.00017359683,0.00012728378],"about_ca_topic_score_codex":0.0000040493637,"about_ca_topic_score_gemma":1.2279376e-7,"teacher_disagreement_score":0.44041744,"about_ca_system_score_codex":0.000066092856,"about_ca_system_score_gemma":0.000048285638,"threshold_uncertainty_score":0.42504492},"labels":[],"label_agreement":null},{"id":"W4296825636","doi":"10.3390/su141912021","title":"Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Terrain; Curvature; Trajectory; Computer science; Path (computing); Model predictive control; Tracking (education); Noise (video); Computer vision; Simulation; Artificial intelligence; Algorithm; Mathematics; Control (management); Geography","score_opus":0.01684155646848615,"score_gpt":0.2793122734048111,"score_spread":0.2624707169363249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296825636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8370425,0.00008554837,0.15829332,0.0013200623,0.00025580198,0.0020741974,0.00014998714,0.0007090468,0.000069495334],"genre_scores_gemma":[0.99596435,8.8603906e-7,0.0032276867,0.00019796439,0.000034814468,0.0004557391,0.000017515902,0.000015901014,0.00008514835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972698,0.00038073017,0.00043180198,0.0008548121,0.00043499295,0.0006278761],"domain_scores_gemma":[0.998299,0.00045063265,0.00015529494,0.0005264757,0.0004289575,0.00013962292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012198763,0.0002667812,0.0003847331,0.0001432225,0.0004327572,0.000111921356,0.0005839437,0.00008957359,0.0000039683746],"category_scores_gemma":[0.00062786276,0.00023873143,0.00010194583,0.0004570176,0.00009406841,0.00046799952,0.00025325222,0.0003759202,5.2472006e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018368429,0.0003171899,0.045918707,0.00012867771,0.000035435907,0.000045311794,0.0036153754,0.9455314,0.00007282267,0.0005370889,0.0010526613,0.002561591],"study_design_scores_gemma":[0.0016844859,0.00030711223,0.33119938,0.000009382463,0.00000906228,0.000008640283,0.0003797096,0.66396457,0.0000151895265,0.0022106788,0.000018097526,0.0001937303],"about_ca_topic_score_codex":0.00020715021,"about_ca_topic_score_gemma":0.000009206532,"teacher_disagreement_score":0.28528064,"about_ca_system_score_codex":0.0013740227,"about_ca_system_score_gemma":0.0004710343,"threshold_uncertainty_score":0.97351855},"labels":[],"label_agreement":null},{"id":"W4297193076","doi":"10.3390/drones6100272","title":"Expandable Fully Actuated Aerial Vehicle Assembly: Geometric Control Adapted from an Existing Flight Controller and Real-World Prototype Implementation","year":2022,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; State Key Laboratory of Robotics","keywords":"Controller (irrigation); Payload (computing); Software; Control theory (sociology); Engineering; Trajectory; Control engineering; Computer science; Simulation; Control (management)","score_opus":0.030477861747344417,"score_gpt":0.3081970858070697,"score_spread":0.2777192240597253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297193076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79242593,0.00018215859,0.20229563,0.0008045382,0.0010035562,0.0020132272,0.00013797214,0.0006041705,0.00053280365],"genre_scores_gemma":[0.98146755,0.0000031550276,0.017505018,0.00018735474,0.00018790417,0.00037134285,0.000094656854,0.000020660662,0.00016239156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787956,0.00031218998,0.00036937412,0.00055082905,0.00047824462,0.00040980332],"domain_scores_gemma":[0.9989212,0.0002777246,0.00023945255,0.000335754,0.0000913659,0.00013449912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061111397,0.000192197,0.00032899744,0.00033407818,0.0005529827,0.00024297893,0.00046810677,0.00003746993,0.000095145646],"category_scores_gemma":[0.000056559235,0.00019280196,0.00003557739,0.00091432093,0.000023834602,0.00052562513,0.00020508432,0.0001713333,0.000010684679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003988264,0.001685824,0.17439379,0.000099225275,0.0012561759,0.0007971334,0.017415034,0.034783695,0.23215619,0.0139087215,0.005496399,0.51401955],"study_design_scores_gemma":[0.010882091,0.0013429468,0.15238544,0.000018454537,0.00007244238,0.000018710714,0.00047500362,0.82630587,0.0033084715,0.0010491201,0.0034638243,0.0006776346],"about_ca_topic_score_codex":0.0034248536,"about_ca_topic_score_gemma":0.00008900454,"teacher_disagreement_score":0.79152215,"about_ca_system_score_codex":0.0001151148,"about_ca_system_score_gemma":0.00010564386,"threshold_uncertainty_score":0.7862236},"labels":[],"label_agreement":null},{"id":"W4297493126","doi":"10.1145/3564696","title":"Multiple Mobile Robot Task and Motion Planning: A Survey","year":2022,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Mobile robot; Robot; Motion planning; Task (project management); Robotics; Artificial intelligence; Human–computer interaction; Field (mathematics); Motion (physics); Systems engineering; Engineering","score_opus":0.1334261220052098,"score_gpt":0.3533795191759313,"score_spread":0.2199533971707215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297493126","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020320815,0.59625244,0.40123606,0.0000043201258,0.0013609395,0.000594409,0.000057038087,0.00043390575,0.000040575087],"genre_scores_gemma":[0.00046385857,0.85541475,0.14188015,0.000052312967,0.00033951565,0.00013860276,0.001340838,0.00017605747,0.00019389341],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98753667,0.008086229,0.0010775479,0.0017143459,0.00071626395,0.00086895167],"domain_scores_gemma":[0.9856286,0.010779661,0.0009011525,0.0023454023,0.00009602501,0.00024915204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.012118469,0.0008016422,0.002031436,0.00047680584,0.00064363034,0.00046524254,0.0037601986,0.0003270117,0.000010940962],"category_scores_gemma":[0.0031324099,0.00079237885,0.00028838802,0.0014794879,0.00008614607,0.00024783646,0.004526347,0.0012121118,0.0000573568],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.5594484e-7,0.000059444348,0.0019802058,0.0010507772,0.000094040624,0.00010170827,0.00030634846,0.0051038666,7.654089e-8,0.000007862874,0.0004242347,0.9908709],"study_design_scores_gemma":[0.0007995141,0.00040886106,0.026841868,0.0053351736,0.00020032964,0.00082465494,0.00003080966,0.2771253,3.851204e-7,0.00009004203,0.6855189,0.0028241235],"about_ca_topic_score_codex":0.00055788603,"about_ca_topic_score_gemma":0.0000066110842,"teacher_disagreement_score":0.98804677,"about_ca_system_score_codex":0.0002207481,"about_ca_system_score_gemma":0.0003050866,"threshold_uncertainty_score":0.9994527},"labels":[],"label_agreement":null},{"id":"W4298214367","doi":"","title":"Dynamics and Control for Surface Exploration of Small Bodies","year":2008,"lang":"en","type":"article","venue":"Deep Blue (University of Michigan)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Jet Propulsion Laboratory; Natural Sciences and Engineering Research Council of Canada; California Institute of Technology","keywords":"Dynamics (music); Surface (topology); Geology; Physics; Mathematics; Geometry","score_opus":0.023708376065363947,"score_gpt":0.1946056943995244,"score_spread":0.17089731833416047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298214367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44359228,0.00005229047,0.5559673,0.00020793048,0.000038464615,0.000081557126,0.000011407614,0.000020464326,0.000028356026],"genre_scores_gemma":[0.6164947,0.000031756717,0.38327968,0.000015839401,0.0000057806187,1.0031879e-7,0.0000064838946,0.0000032634318,0.00016238827],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950296,0.000029165221,0.00008461159,0.00017371713,0.00009322842,0.00011632107],"domain_scores_gemma":[0.9994014,0.00012225435,0.00012930357,0.00018828771,0.000116427495,0.000042330154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011847353,0.00007153263,0.00018472034,0.00006202029,0.00012978817,0.0000051634142,0.00030566438,0.000049330283,6.3422755e-7],"category_scores_gemma":[0.000017935823,0.00009054497,0.000043377797,0.00008190926,0.000119440316,0.00035763666,0.0000718554,0.000045064546,0.0000016563893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034932082,0.00044613378,0.0058706882,0.0004448427,0.00046654855,0.00016038411,0.7929217,0.12972513,0.009627803,0.044650946,0.000078897865,0.015257573],"study_design_scores_gemma":[0.0010138003,0.0001290608,0.0026566728,0.000018034205,0.000020557505,0.000012407837,0.0066198083,0.98845935,0.0003786826,0.00056134566,0.000024008845,0.00010629699],"about_ca_topic_score_codex":0.000054080694,"about_ca_topic_score_gemma":0.00092191424,"teacher_disagreement_score":0.8587342,"about_ca_system_score_codex":0.000012134344,"about_ca_system_score_gemma":0.000031934564,"threshold_uncertainty_score":0.36923167},"labels":[],"label_agreement":null},{"id":"W4298223224","doi":"","title":"Path following control of unmanned quadrotor helicopter with obstacle avoidance capability","year":2017,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Obstacle avoidance; Collision avoidance; Path (computing); Computer science; Obstacle; Remotely operated underwater vehicle; Control (management); Aeronautics; Aerospace engineering; Mobile robot; Engineering; Computer network; Artificial intelligence; Robot; Computer security; Geography","score_opus":0.010937869175814772,"score_gpt":0.2251751425091364,"score_spread":0.21423727333332163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298223224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21216808,0.00014389583,0.7787114,0.0036012877,0.00015438403,0.00032324114,0.000011672076,0.00014290164,0.004743167],"genre_scores_gemma":[0.78611183,0.000005653012,0.21309519,0.000047526548,0.000008057185,0.000031366824,0.0000059344043,0.000013819373,0.0006806114],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964546,0.0016977371,0.000386346,0.00060967123,0.00048234095,0.00036928194],"domain_scores_gemma":[0.9936605,0.0009849964,0.00057594787,0.0036773055,0.0009382809,0.00016295139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041011195,0.00021113681,0.00034783478,0.000070677444,0.00066551485,0.00042056976,0.0025410485,0.00008680591,0.0000089723135],"category_scores_gemma":[0.0016050125,0.00019197032,0.00013755291,0.00018206247,0.0002692758,0.0006371283,0.00040071137,0.00021754204,0.000021062955],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001216973,0.002610107,0.5848425,0.00045218188,0.0005129094,0.00016264927,0.04340706,0.0010722796,0.0362609,0.18191722,0.00038666456,0.14825383],"study_design_scores_gemma":[0.0050277417,0.000008217322,0.35357323,0.0027854878,0.00006951028,0.000040640174,0.00016194307,0.50150716,0.13172738,0.002849167,0.0011911474,0.0010583906],"about_ca_topic_score_codex":0.00085231184,"about_ca_topic_score_gemma":0.00014617245,"teacher_disagreement_score":0.5739438,"about_ca_system_score_codex":0.00006058758,"about_ca_system_score_gemma":0.00016547536,"threshold_uncertainty_score":0.78283226},"labels":[],"label_agreement":null},{"id":"W4299543848","doi":"10.1007/978-3-031-02120-6_2","title":"Contrasting Representations for Maze Generation","year":2018,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on games and computational intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Representation (politics); Computer science","score_opus":0.05709946370850733,"score_gpt":0.29738274379957663,"score_spread":0.2402832800910693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299543848","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016836362,0.00047969658,0.98500484,0.00067163835,0.00043431093,0.00040257504,0.000055906996,0.00012193973,0.012812258],"genre_scores_gemma":[0.045993596,0.00015659262,0.91067505,0.0017165276,0.0017476489,0.00023308785,0.00015854636,0.00010449596,0.039214466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980428,0.00004036156,0.00044767346,0.00083020667,0.00038664136,0.0002522993],"domain_scores_gemma":[0.99647516,0.0024125671,0.00030571438,0.0003508592,0.00035409606,0.00010162173],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026679327,0.00035126883,0.00033892476,0.00022513625,0.00025144935,0.00031705663,0.0004907776,0.0002010439,0.00007863554],"category_scores_gemma":[0.0004044816,0.00033263283,0.00013431709,0.000055262084,0.0001520099,0.00012787286,0.00009185151,0.00019795712,0.000058943457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000214314,0.000028981389,0.0000031472334,0.00005087167,0.00018403813,0.000012695745,0.00076800433,0.17392324,0.000032312426,0.53270775,0.005969887,0.28629765],"study_design_scores_gemma":[0.000047771282,0.00016311464,0.000040975512,0.00022588753,0.00003963708,0.00003916576,0.000009851111,0.7544545,0.00085053185,0.23853028,0.00520521,0.00039307616],"about_ca_topic_score_codex":0.0000046524033,"about_ca_topic_score_gemma":0.0000021026185,"teacher_disagreement_score":0.5805313,"about_ca_system_score_codex":0.000056340305,"about_ca_system_score_gemma":0.000119162876,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W4299651327","doi":"","title":"A Two-Step Hybrid Metaheuristic for Flight Level Allocation in 4D Trajectory Generation","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Trajectory; Metaheuristic; Computer science; Mathematical optimization; Trajectory optimization; Control theory (sociology); Algorithm; Mathematics; Artificial intelligence; Optimal control; Physics; Control (management)","score_opus":0.0396993748711207,"score_gpt":0.25860650639271876,"score_spread":0.21890713152159808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299651327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010296823,0.0005646891,0.98044086,0.004339006,0.0008539011,0.0009458633,0.000055996952,0.00030078363,0.0022020948],"genre_scores_gemma":[0.33055454,0.000046341454,0.6667655,0.00012940718,0.00008486264,0.00031880665,0.00066041836,0.00004168335,0.0013984303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99207336,0.0046269153,0.0008671921,0.0013540117,0.00056622823,0.0005122666],"domain_scores_gemma":[0.99280775,0.0017466082,0.000705476,0.002861527,0.0016944669,0.00018417102],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008626145,0.00044916166,0.0005620205,0.00045163214,0.00034776493,0.00082241546,0.0025044933,0.00023562094,0.000009018057],"category_scores_gemma":[0.002162154,0.00050518534,0.0002270806,0.000396,0.00013399255,0.0002857211,0.0009915894,0.0006145474,0.000027332982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036098514,0.0022125377,0.0010913033,0.0010871215,0.0003834402,0.00004711596,0.020260977,0.10659061,0.018888105,0.53759485,0.009389843,0.302418],"study_design_scores_gemma":[0.00076463446,8.7541497e-7,0.0009611564,0.0006593477,0.000035460656,0.000015025693,0.000010659118,0.9738352,0.018732276,0.0029973087,0.0014897019,0.0004983662],"about_ca_topic_score_codex":0.0007718349,"about_ca_topic_score_gemma":0.0007643115,"teacher_disagreement_score":0.8672446,"about_ca_system_score_codex":0.00028333865,"about_ca_system_score_gemma":0.000615482,"threshold_uncertainty_score":0.99974},"labels":[],"label_agreement":null},{"id":"W4301905934","doi":"10.48550/arxiv.1609.06662","title":"On Efficient Computation of Shortest Dubins Paths Through Three\\n Consecutive Points","year":2016,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heading (navigation); Midpoint; Shortest path problem; Discretization; Path (computing); Computation; Mathematical optimization; Mathematics; Point (geometry); Motion planning; Computer science; Control theory (sociology); Algorithm; Geometry; Robot; Artificial intelligence; Combinatorics; Engineering; Mathematical analysis","score_opus":0.08489988401970419,"score_gpt":0.21467593176239969,"score_spread":0.1297760477426955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301905934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23033795,0.000051215673,0.76146966,0.000172527,0.002150512,0.0008874669,0.00015955142,0.00018326387,0.0045878273],"genre_scores_gemma":[0.98950523,0.00006524962,0.0098598,0.00012972942,0.00009981328,0.0000018809495,0.00002651516,0.000053296506,0.0002584727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936936,0.000661266,0.00096463057,0.0030589749,0.0005546878,0.0010668266],"domain_scores_gemma":[0.99334264,0.0014763933,0.0016519824,0.002169774,0.00096164807,0.0003975826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081290666,0.001044524,0.0012713264,0.0005671554,0.00044772556,0.00013059816,0.002834656,0.00070163544,0.000052304877],"category_scores_gemma":[0.00027439193,0.0010783899,0.000594232,0.0013747854,0.0010967748,0.00042956,0.0024005494,0.0010531398,0.00056885777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017297275,0.00070578465,0.0022863012,0.0000972455,0.00029098184,0.00095775345,0.0018059456,0.80092144,0.000060698127,0.19153863,0.00013616747,0.0010260806],"study_design_scores_gemma":[0.0018369642,0.00068750454,0.0077885035,0.0020532366,0.00019593521,0.000028610542,0.0001753367,0.8971479,0.00046158122,0.08856341,0.000015002698,0.001045994],"about_ca_topic_score_codex":0.00021164013,"about_ca_topic_score_gemma":0.000009384972,"teacher_disagreement_score":0.7591673,"about_ca_system_score_codex":0.0007827271,"about_ca_system_score_gemma":0.0009004099,"threshold_uncertainty_score":0.9991666},"labels":[],"label_agreement":null},{"id":"W4307096839","doi":"10.1093/jcde/qwac109","title":"A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Shenzhen Graduate School, Peking University","keywords":"Motion planning; Computer science; Path (computing); Robot; Stability (learning theory); Computation; Interference (communication); Artificial intelligence; Algorithm; Machine learning","score_opus":0.06823519760274802,"score_gpt":0.23910233431687178,"score_spread":0.17086713671412376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307096839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021492364,0.00031270916,0.99715644,0.00003227655,0.00009617816,0.00023433089,0.0000038739195,0.000014121079,8.554636e-7],"genre_scores_gemma":[0.414433,7.431597e-7,0.585524,0.0000077424775,0.000012967265,0.000012353483,0.000002064434,0.000005785061,0.0000013009919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897456,0.000047796377,0.00036979557,0.00012836794,0.00032800977,0.00015148059],"domain_scores_gemma":[0.99922514,0.00034980843,0.00027190382,0.00007461051,0.000035789173,0.00004274492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080747553,0.00010681139,0.00020172047,0.00021592814,0.00012900365,0.00003240023,0.00028517467,0.00001944784,1.3518327e-7],"category_scores_gemma":[0.000019351453,0.000084247746,0.000038193182,0.00021627644,0.0000179207,0.00014348677,0.00006858224,0.0001627237,5.300295e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021559652,0.00007172609,0.0001768276,0.00004958272,0.000045632765,0.0000107171445,0.0010427345,0.9955264,0.0023228924,0.00022712752,0.0000070542605,0.00049779826],"study_design_scores_gemma":[0.0015333127,0.00012935759,0.03710134,0.00010217408,0.000009154661,0.00036633183,0.0002663539,0.9603581,0.000012852695,0.0000059004383,0.000025314948,0.000089835485],"about_ca_topic_score_codex":0.0000039277384,"about_ca_topic_score_gemma":1.2111428e-8,"teacher_disagreement_score":0.41228378,"about_ca_system_score_codex":0.00011185322,"about_ca_system_score_gemma":0.000046614572,"threshold_uncertainty_score":0.34355235},"labels":[],"label_agreement":null},{"id":"W4307349940","doi":"10.3390/mi13111822","title":"Design and Implementation of a Fully-Actuated Integrated Aerial Platform Based on Geometric Model Predictive Control","year":2022,"lang":"en","type":"article","venue":"Micromachines","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; State Key Laboratory of Robotics; National Natural Science Foundation of China","keywords":"Controller (irrigation); Engineering; Underactuation; Payload (computing); Control engineering; Scalability; Hardware-in-the-loop simulation; Control system; Control theory (sociology); Computer science; Robot; Control (management); Artificial intelligence","score_opus":0.01744359462396995,"score_gpt":0.2588563447660948,"score_spread":0.24141275014212488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307349940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15412381,0.000031479176,0.8449721,0.00009425349,0.00015452878,0.0004062953,0.00011586051,0.00008801453,0.0000136517765],"genre_scores_gemma":[0.85844874,8.053392e-7,0.14126138,0.00014779787,0.000012278097,0.00006649393,0.000043844626,0.000009937527,0.000008723398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987841,0.00013857493,0.00026821022,0.00031289537,0.00028939184,0.00020680428],"domain_scores_gemma":[0.99923664,0.00023718076,0.00017700954,0.00022700525,0.00006868486,0.00005348513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049753505,0.00015746095,0.00022240433,0.0004487617,0.00017975402,0.000047037607,0.00038694957,0.000030179901,0.000017548844],"category_scores_gemma":[0.000039291026,0.00014097334,0.00003478686,0.000753044,0.000029648418,0.00016284801,0.00011131543,0.00015302676,0.0000010630099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024886284,0.00010134177,0.00069327914,0.000008569571,0.00004112865,0.000011472481,0.0008593249,0.96091044,0.026755488,0.00007241305,0.00034896968,0.009948731],"study_design_scores_gemma":[0.002333937,0.0008434536,0.0021526644,0.0000068642794,0.000016348635,0.00001379417,0.000055576318,0.98911834,0.0051342766,0.00018780147,0.000007787601,0.00012918346],"about_ca_topic_score_codex":0.00015230334,"about_ca_topic_score_gemma":8.5208603e-7,"teacher_disagreement_score":0.7043249,"about_ca_system_score_codex":0.00009018009,"about_ca_system_score_gemma":0.00016535106,"threshold_uncertainty_score":0.5748726},"labels":[],"label_agreement":null},{"id":"W4307436827","doi":"10.3390/drones6110323","title":"Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning","year":2022,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Trajectory; Fidelity; Computer science; Nonlinear system; Control theory (sociology); Adaptation (eye); Trajectory optimization; Horizon; Time horizon; Artificial intelligence; Stability (learning theory); Scheme (mathematics); High fidelity; Mathematical optimization; Optimal control; Machine learning; Engineering; Mathematics","score_opus":0.04460087280813192,"score_gpt":0.2779166340531157,"score_spread":0.23331576124498377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307436827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012607211,0.00016551028,0.985836,0.000043002707,0.00044432833,0.00038447446,0.0000068498134,0.00024827357,0.0002643428],"genre_scores_gemma":[0.3850853,0.0000010957274,0.6143489,0.000054974895,0.00012881475,0.00012779619,0.000025312414,0.000024900335,0.00020289328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819636,0.00009124837,0.00027566004,0.0004994434,0.00046555523,0.00047175484],"domain_scores_gemma":[0.99920535,0.00014704105,0.00018784453,0.00027091743,0.00009671278,0.00009214119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043466166,0.00020303273,0.00023257898,0.00017202308,0.0008161679,0.00006538543,0.00061030866,0.000048476413,0.0000050323365],"category_scores_gemma":[0.0000541732,0.000228739,0.00009261678,0.00024462488,0.000036634574,0.00038208283,0.00043668158,0.00036658387,0.0000024719998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059598377,0.000029057528,0.000059946047,0.000007988467,0.00003892313,0.000019936466,0.004437379,0.9928749,0.00029282746,0.00039687357,0.000055728706,0.0017268468],"study_design_scores_gemma":[0.0004910835,0.0010592863,0.000035449128,0.000022615803,0.000021930937,0.000029697154,0.0013429947,0.99628097,0.0002170263,0.0001221911,0.000114688424,0.00026207662],"about_ca_topic_score_codex":0.000016394122,"about_ca_topic_score_gemma":1.3135883e-7,"teacher_disagreement_score":0.3724781,"about_ca_system_score_codex":0.00027151857,"about_ca_system_score_gemma":0.00017816304,"threshold_uncertainty_score":0.93277055},"labels":[],"label_agreement":null},{"id":"W4307813561","doi":"10.1016/j.robot.2022.104306","title":"Systematic solution for optimally energy-efficient turning radius for wheeled skid-steer rovers","year":2022,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Turning radius; Karush–Kuhn–Tucker conditions; Traverse; Computer science; Control theory (sociology); Energy consumption; Path (computing); Mathematical optimization; Simulation; Mathematics; Aerospace engineering; Engineering; Artificial intelligence; Electrical engineering; Geodesy","score_opus":0.013928946146687556,"score_gpt":0.22305879433052248,"score_spread":0.20912984818383493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307813561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003446785,0.0018046013,0.99280876,0.0002863434,0.002764815,0.001617691,0.000041065192,0.00022430254,0.00010774207],"genre_scores_gemma":[0.655649,0.0000062399413,0.33964834,0.00015445892,0.0002702265,0.0017976924,0.000059043177,0.00006969711,0.0023453163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974257,0.00018788566,0.00072374905,0.0006453469,0.0003969083,0.0006204584],"domain_scores_gemma":[0.9981249,0.0005567635,0.0004947381,0.0005220477,0.00014347534,0.0001580825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013929745,0.000285247,0.0006312974,0.00019346764,0.0009429521,0.00041015004,0.0006740916,0.0000809538,7.053968e-7],"category_scores_gemma":[0.00010529797,0.00027827837,0.00016557313,0.00023032895,0.000029498908,0.0001313009,0.00029084334,0.00013314094,0.0000019338052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000721925,0.0000562394,0.0000059355057,0.0017530815,0.00007788447,0.000007956713,0.00061586883,0.94814605,0.00024377812,0.048435107,0.00041069803,0.00024019857],"study_design_scores_gemma":[0.00082760776,0.0003436927,0.000008085281,0.00041582165,0.00006790045,0.00016002152,0.00023758547,0.99665457,0.000022104367,0.000106120286,0.0008064066,0.0003500544],"about_ca_topic_score_codex":0.00005832436,"about_ca_topic_score_gemma":7.1769773e-7,"teacher_disagreement_score":0.6553043,"about_ca_system_score_codex":0.0003985738,"about_ca_system_score_gemma":0.00020453594,"threshold_uncertainty_score":0.9999669},"labels":[],"label_agreement":null},{"id":"W4308213848","doi":"10.1109/wisee49342.2022.9926816","title":"Hierarchical Model Predictive Control for Obstacle Avoidance in Airborne Radar Based Detect-and-Avoid Systems Operating in a Varying Environment","year":2022,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Collision avoidance; Obstacle avoidance; Model predictive control; Trajectory; Computer science; Control theory (sociology); Radar; Motion planning; Obstacle; Line-of-sight; Path (computing); Tracking (education); Collision; Mobile robot; Real-time computing; Simulation; Control (management); Engineering; Artificial intelligence; Aerospace engineering; Robot","score_opus":0.016268678410202325,"score_gpt":0.223806233551721,"score_spread":0.20753755514151867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308213848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012180409,0.00026664312,0.9857008,0.00047332546,0.00012260441,0.0010382943,0.000033747507,0.00009044061,0.00009377182],"genre_scores_gemma":[0.6942532,0.0000013085627,0.3047591,0.00025289756,0.000014884379,0.0006664085,0.0000028675076,0.000011722638,0.000037630125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977551,0.00033402035,0.00043196543,0.00063898176,0.000385362,0.0004545832],"domain_scores_gemma":[0.99881804,0.00064789905,0.00008500912,0.00033819384,0.000012881501,0.00009797568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012172562,0.00017829382,0.0003142831,0.00018174513,0.00024257107,0.00008091012,0.00050799974,0.000047211546,0.0000025513896],"category_scores_gemma":[0.00009504042,0.00018184121,0.000038515518,0.00022303236,0.000036530535,0.0002330996,0.0002562983,0.00040747997,0.0000011718844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002667702,0.000055274093,0.00049140427,0.000021350847,0.000006407102,0.000030691615,0.0007992944,0.99506044,0.001031087,0.00088517845,0.000010792707,0.0015814104],"study_design_scores_gemma":[0.002759375,0.00023032872,0.0010262806,0.00004203427,0.0000033668982,0.000014169003,0.000103386286,0.9951036,0.00014561962,0.0003555688,0.000016111275,0.00020015908],"about_ca_topic_score_codex":0.00008864751,"about_ca_topic_score_gemma":0.000001735188,"teacher_disagreement_score":0.6820728,"about_ca_system_score_codex":0.00036216198,"about_ca_system_score_gemma":0.00014138651,"threshold_uncertainty_score":0.74152696},"labels":[],"label_agreement":null},{"id":"W4309066605","doi":"10.3233/ica-220695","title":"An efficient multi-robot path planning solution using A* and coevolutionary algorithms","year":2022,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"H2020 European Research Council; Agencia Estatal de Investigación; Junta de Castilla y León; Centro para el Desarrollo Tecnológico Industrial; European Commission","keywords":"Robot; Motion planning; Computer science; Path (computing); Set (abstract data type); Process (computing); Evolutionary algorithm; Enhanced Data Rates for GSM Evolution; Collision avoidance; Collision; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.027331644148264778,"score_gpt":0.2561648191915628,"score_spread":0.22883317504329803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309066605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06727766,0.00047958316,0.92914927,0.000056679073,0.0017783967,0.00022679634,0.0000175912,0.0010089631,0.000005074596],"genre_scores_gemma":[0.3514061,0.0000012307092,0.648348,0.000050214006,0.00009983733,0.000025948444,0.00003075609,0.000031315758,0.0000066205284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738574,0.00016828428,0.00045981308,0.0008184812,0.0004970647,0.0006705941],"domain_scores_gemma":[0.998805,0.00011998273,0.00014153431,0.0005730545,0.00010776917,0.00025266127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057381863,0.00039252467,0.00034903598,0.00047728658,0.0007039976,0.00022743717,0.0009115388,0.00009246934,0.0000050586214],"category_scores_gemma":[0.000024091481,0.00043653036,0.00007256874,0.0010635825,0.00003987409,0.000415909,0.0006312064,0.00070261786,0.000004487664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044022795,0.00011792779,0.00016848845,0.000013696242,0.00003282438,0.00021031665,0.000824304,0.9855623,0.006174084,0.00038676464,0.00005195786,0.006452881],"study_design_scores_gemma":[0.0005584666,0.00024497163,0.0028114172,0.00010042286,0.000012213076,0.00074652117,0.00008292628,0.9945513,0.0001865045,0.000017835491,0.00019100282,0.0004963892],"about_ca_topic_score_codex":0.00014565831,"about_ca_topic_score_gemma":1.4522661e-7,"teacher_disagreement_score":0.28412846,"about_ca_system_score_codex":0.0004791872,"about_ca_system_score_gemma":0.00012336066,"threshold_uncertainty_score":0.99980867},"labels":[],"label_agreement":null},{"id":"W4309574091","doi":"10.5802/roia.45","title":"Approche locale pour l’exploration autonome d’environnements inconnus par une flottille de robots","year":2022,"lang":"fr","type":"article","venue":"Revue Ouverte d Intelligence Artificielle","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Humanities; Locale (computer software); Computer science; Philosophy; Programming language","score_opus":0.06170611589174485,"score_gpt":0.266231998798233,"score_spread":0.20452588290648815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309574091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022078673,0.0024488592,0.9645325,0.018160172,0.0048273834,0.0007399681,0.000043022195,0.0002755794,0.00676464],"genre_scores_gemma":[0.5297572,0.0006091677,0.2629779,0.0027237302,0.0011764147,0.0007936833,0.00015431929,0.00023325774,0.20157431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945179,0.00070572994,0.0011534799,0.0014427707,0.00074294687,0.0014371483],"domain_scores_gemma":[0.9970221,0.00033219962,0.0005136497,0.0015315254,0.00016406215,0.00043648074],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017784111,0.0005748528,0.0005934436,0.00029509503,0.0015526115,0.0003322337,0.002234369,0.0002194819,0.0010538273],"category_scores_gemma":[0.00020016593,0.0007352022,0.00026590173,0.0019148081,0.00034129276,0.0010947885,0.0014838206,0.0010169451,0.0025684326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019323956,0.0007586925,0.00017320827,0.000099429024,0.00006290513,0.00029923953,0.015163307,0.84139013,0.0010138426,0.008316621,0.011939257,0.12076404],"study_design_scores_gemma":[0.00016624713,0.00041272814,0.00009008761,0.00014948922,0.00005495414,0.00028727643,0.004809607,0.8975523,0.0056399703,0.007144625,0.082983926,0.0007087546],"about_ca_topic_score_codex":0.00071152864,"about_ca_topic_score_gemma":0.000023418983,"teacher_disagreement_score":0.7015546,"about_ca_system_score_codex":0.0011781331,"about_ca_system_score_gemma":0.00066998764,"threshold_uncertainty_score":0.99985933},"labels":[],"label_agreement":null},{"id":"W4309679509","doi":"10.1109/smc53654.2022.9945496","title":"Evolutionary Mapping with Multiple Unmanned Aerial Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Drone; Computer science; Context (archaeology); Motion planning; Obstacle; Plan (archaeology); Real-time computing; Search and rescue; Evolutionary algorithm; Operations research; Artificial intelligence; Robot; Engineering","score_opus":0.04777489517899604,"score_gpt":0.2558853031340413,"score_spread":0.20811040795504526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309679509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5763419,0.0013181466,0.33376163,0.006974045,0.02417918,0.0024049922,0.0007363372,0.0011886257,0.053095113],"genre_scores_gemma":[0.9893414,0.000037080503,0.0057402216,0.00024244918,0.0003304187,0.00016047356,0.00005838627,0.000021664555,0.004067937],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723816,0.00022358917,0.00040919785,0.0006597203,0.0011310292,0.00033828444],"domain_scores_gemma":[0.9988111,0.00016650408,0.0002655067,0.00041675335,0.00021444484,0.00012568249],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040326064,0.00025566234,0.00027026574,0.0002639215,0.00039607348,0.00033854775,0.0011452036,0.00006131774,0.000069636684],"category_scores_gemma":[0.00003141473,0.0002504929,0.000048062593,0.00024210702,0.0000898403,0.00022950523,0.00038342888,0.00036330367,0.00004600802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006882317,0.0010328395,0.031548265,0.00020537889,0.00090247765,0.0012514845,0.008162128,0.15149808,0.008530772,0.7515868,0.034493726,0.010099798],"study_design_scores_gemma":[0.0014021675,0.00047547344,0.008057256,0.00014675014,0.0000129771215,0.00036392832,0.0013680669,0.9658265,0.000103890874,0.0010347193,0.02068503,0.0005232339],"about_ca_topic_score_codex":0.00029251885,"about_ca_topic_score_gemma":0.000009891851,"teacher_disagreement_score":0.81432843,"about_ca_system_score_codex":0.00020135552,"about_ca_system_score_gemma":0.0001615941,"threshold_uncertainty_score":0.99999475},"labels":[],"label_agreement":null},{"id":"W4309690654","doi":"10.1139/dsa-2021-0051","title":"SOFAR and Auto-link: software tools that bridge the gap between multirotor drone design optimization and CAD","year":2022,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba; Magellan Aerospace (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Drone; Multirotor; CAD; Software; Computer science; Computer Aided Design; Process (computing); Bridge (graph theory); Key (lock); Component (thermodynamics); Systems engineering; Software engineering; Engineering drawing; Engineering; Operating system","score_opus":0.07797038407992034,"score_gpt":0.26722249819775307,"score_spread":0.18925211411783271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309690654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017288239,0.0012880807,0.99435204,0.000755945,0.00007256264,0.0014971038,0.0001230479,0.00016112713,0.000021249698],"genre_scores_gemma":[0.7772152,0.00007717307,0.21817029,0.00013687009,0.0003093795,0.003435664,0.00010716195,0.00003589728,0.00051236514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987555,0.00016344522,0.0002254389,0.000434365,0.00021229361,0.00020896652],"domain_scores_gemma":[0.9988879,0.00036239537,0.00015781623,0.00045392016,0.00003537829,0.00010258726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005601855,0.00014813672,0.00020096179,0.000059271493,0.0010260157,0.00032930655,0.0003449523,0.000047983096,0.0000015229409],"category_scores_gemma":[0.000017505437,0.00012226134,0.000017840299,0.00022900803,0.0000813565,0.0002196135,0.00032761943,0.00016563719,0.0000024750275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011594362,0.00020708331,0.029138263,0.00040397342,0.00023762837,0.000017257265,0.010636893,0.5717468,0.00077279104,0.027883535,0.003479693,0.35546446],"study_design_scores_gemma":[0.0004508602,0.00008241567,0.028909301,0.000023867951,0.000032826832,0.00011466209,0.00029330843,0.9622235,0.000027746866,0.0003330883,0.0072069643,0.00030150282],"about_ca_topic_score_codex":0.0001540372,"about_ca_topic_score_gemma":4.1402092e-7,"teacher_disagreement_score":0.77618176,"about_ca_system_score_codex":0.00003839914,"about_ca_system_score_gemma":0.000043238142,"threshold_uncertainty_score":0.78913873},"labels":[],"label_agreement":null},{"id":"W4311127142","doi":"10.1002/aaai.12069","title":"Intelligent planning for large‐scale multi‐agent systems","year":2022,"lang":"en","type":"article","venue":"AI Magazine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Task (project management); Computer science; Scale (ratio); Intelligent agent; Robot; Multi-agent system; Motion planning; Intelligent decision support system; Motion (physics); Artificial intelligence; Human–computer interaction; Operations research; Software engineering; Systems engineering; Engineering","score_opus":0.04019234045748928,"score_gpt":0.2999924591246558,"score_spread":0.2598001186671665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311127142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090000563,0.0009125466,0.99363023,0.00077788904,0.0026225639,0.0005064848,0.000049161372,0.00030064982,0.0003004779],"genre_scores_gemma":[0.31830668,0.000009373648,0.6614214,0.0032196217,0.0005283123,0.0012554758,0.00014750569,0.00008716722,0.015024449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981957,0.00010050749,0.00032183563,0.00048843486,0.00038406337,0.0005094429],"domain_scores_gemma":[0.9989874,0.00012694334,0.00011422912,0.0005746963,0.00007452483,0.00012225205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462144,0.00016809472,0.00022929645,0.00013597714,0.00039814186,0.00012769536,0.00096419593,0.00003465145,0.000019306215],"category_scores_gemma":[0.00004409579,0.00017265846,0.0000851725,0.00032937946,0.000012375493,0.00017138908,0.00065114663,0.00023634158,0.00013377974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004435831,0.00084235985,0.005195931,0.00018595347,0.00013121472,0.00040930242,0.011803831,0.7590919,0.0013332673,0.010142245,0.2021695,0.008650145],"study_design_scores_gemma":[0.0004917171,0.00016931846,0.0006865193,0.000021008093,0.0000074425097,0.00007349473,0.0002321824,0.8290069,0.00007812869,0.000073917,0.1689623,0.00019704495],"about_ca_topic_score_codex":0.000009225691,"about_ca_topic_score_gemma":5.442433e-7,"teacher_disagreement_score":0.3322088,"about_ca_system_score_codex":0.00013444535,"about_ca_system_score_gemma":0.00006008028,"threshold_uncertainty_score":0.7040808},"labels":[],"label_agreement":null},{"id":"W4312205635","doi":"10.3390/agriculture13010056","title":"Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor","year":2022,"lang":"en","type":"article","venue":"Agriculture","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Korea Institute of Industrial Technology","keywords":"Tractor; Tree traversal; Path (computing); Computer science; Field (mathematics); Range (aeronautics); Obstacle; Process (computing); Mathematical optimization; Mathematics; Algorithm; Engineering; Automotive engineering; Aerospace engineering","score_opus":0.019352627415342466,"score_gpt":0.22222784105164958,"score_spread":0.20287521363630712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312205635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43682536,0.00016970432,0.5612576,0.0007071016,0.00038831183,0.00036936215,0.000021455307,0.00017971067,0.00008137924],"genre_scores_gemma":[0.81419516,0.0000019423153,0.18486656,0.00020422571,0.00019529958,0.00014160792,0.00006741106,0.0000060023854,0.00032178135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921346,0.00004864034,0.00009378728,0.0003322472,0.0001518225,0.00016002262],"domain_scores_gemma":[0.99960476,0.000040491133,0.00007240149,0.00016236132,0.000059442213,0.00006057187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012898826,0.00010048217,0.00009386162,0.000020786068,0.00039761208,0.00011724662,0.00020142461,0.000040571456,0.0000030660476],"category_scores_gemma":[0.00001235963,0.00007147652,0.000018390007,0.00015063773,0.000010085798,0.0003910813,0.00004688075,0.00014339591,7.245249e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001080235,0.00082602137,0.0015493091,0.000065543,0.000110338995,0.00013564211,0.021661855,0.7242849,0.14115891,0.018425608,0.04659412,0.045079757],"study_design_scores_gemma":[0.0010301035,0.0028503893,0.018540422,0.000011197365,0.000024435061,0.0008407399,0.0009923574,0.95996684,0.0061463136,0.00011099608,0.008950066,0.00053611945],"about_ca_topic_score_codex":0.000028575623,"about_ca_topic_score_gemma":0.000006441568,"teacher_disagreement_score":0.3773698,"about_ca_system_score_codex":0.00004280874,"about_ca_system_score_gemma":0.000033424916,"threshold_uncertainty_score":0.3058151},"labels":[],"label_agreement":null},{"id":"W4312250476","doi":"10.1609/icaps.v32i1.19798","title":"Multi-Agent Path Finding with Temporal Jump Point Search","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Monash University; National Science Foundation; Northeast Normal University; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Pathfinding; Jump; Path (computing); Computer science; Point (geometry); Grid; Planner; Mathematical optimization; Work (physics); Algorithm; Artificial intelligence; Mathematics; Theoretical computer science; Shortest path problem; Physics","score_opus":0.06576627797060018,"score_gpt":0.3031903675507545,"score_spread":0.23742408958015432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312250476","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98171604,0.000042589312,0.013610191,0.0021459686,0.0004589681,0.00020487096,0.000015344198,0.00046070307,0.0013453361],"genre_scores_gemma":[0.8702564,0.0000018850377,0.1294037,0.00013343389,0.000021698042,0.00002053715,0.0000043286764,0.000010985678,0.00014700307],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819124,0.00002395653,0.00027330918,0.00041303548,0.00083732634,0.00026115662],"domain_scores_gemma":[0.99921155,0.00006612952,0.0002684668,0.00012798439,0.00025100526,0.000074875694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076098565,0.00017430562,0.00018165907,0.0002079592,0.00044278035,0.00027821198,0.0013457369,0.000037965343,0.000012727613],"category_scores_gemma":[0.0000863782,0.00013105519,0.000043037013,0.0003095941,0.000060173366,0.00027158554,0.0007187189,0.00048555844,0.0000025717707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043052292,0.00071619026,0.42509848,0.00024033873,0.0006101788,0.00016945243,0.030815806,0.38403136,0.07320419,0.079526186,0.0012859288,0.003871351],"study_design_scores_gemma":[0.00046483308,0.00016445224,0.008121721,0.0003510525,0.0000065852523,0.00012515836,0.0015701995,0.98644525,0.002397815,0.00015663973,0.000026043204,0.00017026052],"about_ca_topic_score_codex":0.00003504631,"about_ca_topic_score_gemma":1.2191191e-7,"teacher_disagreement_score":0.6024139,"about_ca_system_score_codex":0.00009652558,"about_ca_system_score_gemma":0.000115682655,"threshold_uncertainty_score":0.5344275},"labels":[],"label_agreement":null},{"id":"W4312361846","doi":"10.1016/j.ifacol.2022.10.419","title":"Long-Horizon Informative Path Planning with Obstacles and Time Constraints","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Senter for Autonome Marine Operasjoner og Systemer; Norges Forskningsråd; Réseau de cancérologie Rossy","keywords":"Planner; Path (computing); Motion planning; Time horizon; Obstacle; Constraint (computer-aided design); Mathematical optimization; Computer science; Horizon; Operations research; Set (abstract data type); Variance (accounting); Excursion; Operations management; Mathematics; Engineering; Artificial intelligence; Economics; Robot; Geography","score_opus":0.011435872027306032,"score_gpt":0.2288166528117103,"score_spread":0.21738078078440426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312361846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4850328,0.0009790802,0.49916926,0.0021760531,0.0007341035,0.0009039781,0.0002836947,0.0011372083,0.009583823],"genre_scores_gemma":[0.1004112,0.0000051969846,0.89830536,0.0004953406,0.00007823773,0.000033070923,0.00007328963,0.000021732709,0.000576582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983413,0.00009241951,0.0002468492,0.00039783018,0.00050995644,0.00041160863],"domain_scores_gemma":[0.9990957,0.00018942614,0.00016794934,0.00033842376,0.00005223466,0.00015629822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003325321,0.00023337177,0.00026528293,0.00012876574,0.0004303385,0.00012828253,0.0005564383,0.000043043645,0.00006146226],"category_scores_gemma":[0.000038069822,0.0002024932,0.00003336087,0.00036941303,0.00016972625,0.00050492195,0.00041513023,0.00038131152,0.00003478932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004823474,0.0010063236,0.057747807,0.00029428798,0.0009486874,0.00842992,0.12175057,0.13766468,0.004221421,0.008680808,0.0010327391,0.6577404],"study_design_scores_gemma":[0.0030372837,0.0029473796,0.027555354,0.00023715953,0.000047065896,0.0021709127,0.004133903,0.9571078,0.00026587056,0.00029561843,0.0009328066,0.0012688851],"about_ca_topic_score_codex":0.000011078293,"about_ca_topic_score_gemma":3.0331822e-7,"teacher_disagreement_score":0.8194431,"about_ca_system_score_codex":0.00007678929,"about_ca_system_score_gemma":0.00012993782,"threshold_uncertainty_score":0.8257433},"labels":[],"label_agreement":null},{"id":"W4312499790","doi":"10.1109/access.2022.3216601","title":"Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Position (finance); Computer science; Energy (signal processing); Position paper; Path (computing); Automotive engineering; Artificial intelligence; Engineering; Computer network; Robot; Business; Physics","score_opus":0.027120294633233885,"score_gpt":0.27822452851464685,"score_spread":0.25110423388141295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312499790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1561484,0.00025183772,0.8418849,0.00039809308,0.00065404346,0.00010398824,0.000010038723,0.00017518336,0.000373497],"genre_scores_gemma":[0.99371845,0.0000014769415,0.005558474,0.00059887173,0.000037097732,0.00004441522,0.0000034608952,0.0000110358715,0.000026736023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982036,0.0001882399,0.0002932204,0.00034030218,0.00067756954,0.00029710052],"domain_scores_gemma":[0.99897194,0.00020322698,0.00019617213,0.00048624887,0.000080923535,0.00006151776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051901845,0.00013073708,0.00016473215,0.00006514151,0.00040218813,0.00014284604,0.0014480539,0.000031047617,0.000007339022],"category_scores_gemma":[0.000013985418,0.00011187329,0.000052127507,0.00049121445,0.000054695487,0.00021985853,0.00058656954,0.0001982466,0.0000027545914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007558824,0.0001376453,0.0005955772,0.000012646901,0.000033175314,0.00011206702,0.002306987,0.9761345,0.004685293,0.0007280086,0.012014173,0.0032323734],"study_design_scores_gemma":[0.0003121153,0.000070089045,0.00041806742,0.00002525904,0.000009946661,0.00010501097,0.00011287927,0.9829835,0.014923012,0.00019904113,0.00068780116,0.00015326701],"about_ca_topic_score_codex":0.000289279,"about_ca_topic_score_gemma":3.7200854e-7,"teacher_disagreement_score":0.83757,"about_ca_system_score_codex":0.00018338348,"about_ca_system_score_gemma":0.00016266466,"threshold_uncertainty_score":0.45620602},"labels":[],"label_agreement":null},{"id":"W4312571183","doi":"10.1109/case49997.2022.9926718","title":"Kinematically-Constrained Continuous-Path Polynomial Trajectories for Quadrotors","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Path (computing); Polynomial; Trajectory; Computer science; Control theory (sociology); Mathematical optimization; Mathematics; Control (management); Artificial intelligence; Mathematical analysis; Physics; Computer network","score_opus":0.02838275858512962,"score_gpt":0.27941969564278296,"score_spread":0.25103693705765334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312571183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18553269,0.000009571501,0.806341,0.0015913862,0.004205136,0.0006077341,0.00010353915,0.0005199456,0.0010890206],"genre_scores_gemma":[0.93003654,0.0000016383705,0.0692721,0.00017414651,0.00011110367,0.00024566898,0.000008999795,0.000011252369,0.00013856326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977491,0.000036775804,0.00038940864,0.0005431973,0.0009231498,0.00035836524],"domain_scores_gemma":[0.9988664,0.00023095879,0.00015548161,0.0002849645,0.00030765202,0.00015457266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013878554,0.00019729917,0.00020944374,0.00048958964,0.00060279487,0.0005091277,0.00085040997,0.00003745813,0.00007694275],"category_scores_gemma":[0.00043542893,0.0002036258,0.00005381819,0.00060692005,0.00013917213,0.00069689815,0.00018173768,0.00022657284,0.000006318072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008109593,0.00040270004,0.00030894572,0.00015666874,0.0001724176,0.0038363663,0.009350008,0.14666681,0.055454336,0.7234287,0.004783641,0.055358294],"study_design_scores_gemma":[0.00041561242,0.00020737258,0.0002183097,0.000023984046,0.0000054427464,0.0037659905,0.00044219306,0.9933466,0.0005131463,0.00027260152,0.00054630917,0.00024248005],"about_ca_topic_score_codex":0.000026052327,"about_ca_topic_score_gemma":9.798981e-7,"teacher_disagreement_score":0.84667975,"about_ca_system_score_codex":0.00022624314,"about_ca_system_score_gemma":0.0002932902,"threshold_uncertainty_score":0.8303619},"labels":[],"label_agreement":null},{"id":"W4312858026","doi":"10.1109/iros47612.2022.9981785","title":"Multi-Goal Multi-Agent Pickup and Delivery","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Scalability; Computer science; Task (project management); Pickup; Path (computing); Distributed computing; Artificial intelligence; Computer network; Database","score_opus":0.09668730626368796,"score_gpt":0.3037960912172963,"score_spread":0.20710878495360832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312858026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06338753,0.0012404473,0.9199068,0.00106933,0.0118594505,0.0009109644,0.0001631361,0.00026809794,0.0011942708],"genre_scores_gemma":[0.9775222,0.000426327,0.013467625,0.00047934844,0.00021567261,0.00026553907,0.000051438416,0.000032298343,0.007539575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656415,0.00029433737,0.0006648026,0.0009830504,0.0010535253,0.00044016205],"domain_scores_gemma":[0.998555,0.00014663002,0.00031716822,0.0004985959,0.00021770166,0.0002649002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076415844,0.00037404927,0.00039884396,0.00036604688,0.0005279674,0.0005890494,0.0012508315,0.0000895929,0.00017111168],"category_scores_gemma":[0.000053302254,0.00036003927,0.00009764463,0.00021181561,0.00010777608,0.00031009768,0.0007927605,0.000557905,0.0000798493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040284134,0.003980362,0.038031902,0.00044671103,0.001746244,0.002367264,0.025366245,0.51635104,0.03910757,0.27732298,0.018108526,0.076768294],"study_design_scores_gemma":[0.0006922905,0.0002845299,0.0026462264,0.000107336724,0.000013361819,0.0002506955,0.0010696342,0.99111736,0.00024464447,0.00009273855,0.0030385035,0.00044269869],"about_ca_topic_score_codex":0.00052575144,"about_ca_topic_score_gemma":0.000014108995,"teacher_disagreement_score":0.9141346,"about_ca_system_score_codex":0.00027592955,"about_ca_system_score_gemma":0.00012430485,"threshold_uncertainty_score":0.99988514},"labels":[],"label_agreement":null},{"id":"W4312861910","doi":"10.2139/ssrn.4242223","title":"Optimal Stratified Placement of Balloons and UAVs to Support Users’ Coverage","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science","score_opus":0.012574208130741204,"score_gpt":0.24721670245069918,"score_spread":0.23464249431995798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312861910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26028734,0.00020518548,0.7377397,0.00087909814,0.00029156255,0.00016161712,0.000007784714,0.000033262466,0.0003944815],"genre_scores_gemma":[0.97063965,0.000081239414,0.028064199,0.00019241152,0.000047503607,0.000011484854,0.0000029168878,0.00001144751,0.00094912737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974695,0.000120179146,0.00031320375,0.0002663386,0.00053298206,0.0012978319],"domain_scores_gemma":[0.9993464,0.000053733835,0.00016136885,0.000254661,0.000043360287,0.00014050478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018251892,0.00012967609,0.00019876297,0.0001553162,0.00029582024,0.00007876984,0.0007432446,0.000023781266,0.000038252805],"category_scores_gemma":[0.000026264312,0.00013212705,0.00005503735,0.00027341276,0.000020466146,0.00020204669,0.00035853853,0.0010184832,0.000005763049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025333857,0.0006106302,0.0042557307,0.000027819664,0.000659389,0.0002971629,0.00877322,0.5472722,0.0068678255,0.3792389,0.0032067262,0.048537064],"study_design_scores_gemma":[0.023908064,0.082594655,0.024702495,0.00020581672,0.00045697548,0.042280268,0.032050442,0.5864364,0.0065638158,0.17327154,0.021695744,0.005833783],"about_ca_topic_score_codex":0.00002077942,"about_ca_topic_score_gemma":0.000011218667,"teacher_disagreement_score":0.7103523,"about_ca_system_score_codex":0.0004900714,"about_ca_system_score_gemma":0.0019022417,"threshold_uncertainty_score":0.5387985},"labels":[],"label_agreement":null},{"id":"W4312930837","doi":"10.1109/lra.2022.3230593","title":"A Safety Planning and Control Architecture Applied to a Quadrotor Autopilot","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autopilot; Control theory (sociology); Quadratic programming; Control engineering; Actuator; Computer science; Controller (irrigation); Engineering; Mathematical optimization; Control (management); Mathematics","score_opus":0.010289775218993292,"score_gpt":0.22510635203074517,"score_spread":0.21481657681175187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312930837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015371759,0.000030566745,0.96757936,0.015916862,0.00036958,0.000411263,0.000009721762,0.00025753796,0.00005331914],"genre_scores_gemma":[0.6849755,5.6287155e-7,0.30499697,0.009801267,0.00008802564,0.00008998744,0.0000051956445,0.00001544811,0.000027060516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987297,0.00008354408,0.00023249415,0.0003783908,0.00030657792,0.00026928648],"domain_scores_gemma":[0.99936736,0.0001435001,0.00009899781,0.00024628235,0.000013130944,0.00013070513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032155795,0.00015541624,0.00020219889,0.00017850149,0.0004234798,0.00016386667,0.00028998297,0.000026464755,0.0000025932914],"category_scores_gemma":[0.000015163382,0.00015907286,0.000023493772,0.00024309033,0.000028862158,0.00009230943,0.0001448061,0.00021092517,0.000004407384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011697526,0.000014494528,0.00020875975,0.000014312877,0.000019258841,0.000025691339,0.0019415433,0.98152363,0.007212023,0.0023625018,0.0005501856,0.006115906],"study_design_scores_gemma":[0.00096880697,0.00011830093,0.011749479,0.000023935341,0.000013300234,0.0001027408,0.000042786116,0.98503196,0.00009516923,0.00029446426,0.0012521232,0.00030691267],"about_ca_topic_score_codex":0.000007873277,"about_ca_topic_score_gemma":1.684684e-7,"teacher_disagreement_score":0.6696037,"about_ca_system_score_codex":0.000054803888,"about_ca_system_score_gemma":0.000025944279,"threshold_uncertainty_score":0.64868027},"labels":[],"label_agreement":null},{"id":"W4313305780","doi":"10.36227/techrxiv.21777359.v1","title":"Chance-Constrained Planning for Dynamically Stable Motion of Reconfigurable Vehicles","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Traverse; Motion planning; Rollover (web design); Terrain; Computer science; Constraint (computer-aided design); Trajectory; Backtracking; Path (computing); Monte Carlo method; Motion (physics); Plan (archaeology); Mathematical optimization; Robot; Simulation; Artificial intelligence; Algorithm; Mathematics","score_opus":0.04120770600701337,"score_gpt":0.28781973175708503,"score_spread":0.24661202575007166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313305780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038279411,0.0001581537,0.9842493,0.00045657935,0.0014040759,0.000726189,0.00010885105,0.00033441276,0.008734513],"genre_scores_gemma":[0.10402945,0.00001020049,0.89282763,0.00010664125,0.000089770256,0.000308696,0.000190381,0.000032114734,0.0024051303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976657,0.000096834476,0.0005843374,0.0008119443,0.00039495528,0.0004462385],"domain_scores_gemma":[0.99802613,0.0003344822,0.00048738124,0.00089018786,0.00017307827,0.00008872837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093472865,0.00027653138,0.0005299266,0.00023380318,0.00015587333,0.00012327828,0.0015512813,0.00020125897,0.000080314625],"category_scores_gemma":[0.00013668947,0.00029063242,0.00017020533,0.00021561196,0.000051498617,0.00019423943,0.0006730209,0.000491636,0.0000036327754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000428647,0.00022535778,0.0007212149,0.00088973314,0.00022380259,0.000034088884,0.002069818,0.90882486,0.003852197,0.027803233,0.0021050898,0.053207736],"study_design_scores_gemma":[0.00036590407,0.00012771299,0.00062970293,0.00019232555,0.00001652322,0.00000991978,0.00011570077,0.9839848,0.002073339,0.011841363,0.00029760573,0.00034506072],"about_ca_topic_score_codex":0.00010912329,"about_ca_topic_score_gemma":8.9432166e-7,"teacher_disagreement_score":0.1002015,"about_ca_system_score_codex":0.0001437322,"about_ca_system_score_gemma":0.00033028377,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W4313305785","doi":"10.36227/techrxiv.21777359","title":"Chance-Constrained Planning for Dynamically Stable Motion of Reconfigurable Vehicles","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Traverse; Motion planning; Rollover (web design); Terrain; Constraint (computer-aided design); Computer science; Trajectory; Backtracking; Path (computing); Motion (physics); Plan (archaeology); Monte Carlo method; Mathematical optimization; Robot; Artificial intelligence; Algorithm; Mathematics","score_opus":0.04120770600701337,"score_gpt":0.28781973175708503,"score_spread":0.24661202575007166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313305785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038279411,0.0001581537,0.9842493,0.00045657935,0.0014040759,0.000726189,0.00010885105,0.00033441276,0.008734513],"genre_scores_gemma":[0.10402945,0.00001020049,0.89282763,0.00010664125,0.000089770256,0.000308696,0.000190381,0.000032114734,0.0024051303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976657,0.000096834476,0.0005843374,0.0008119443,0.00039495528,0.0004462385],"domain_scores_gemma":[0.99802613,0.0003344822,0.00048738124,0.00089018786,0.00017307827,0.00008872837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093472865,0.00027653138,0.0005299266,0.00023380318,0.00015587333,0.00012327828,0.0015512813,0.00020125897,0.000080314625],"category_scores_gemma":[0.00013668947,0.00029063242,0.00017020533,0.00021561196,0.000051498617,0.00019423943,0.0006730209,0.000491636,0.0000036327754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000428647,0.00022535778,0.0007212149,0.00088973314,0.00022380259,0.000034088884,0.002069818,0.90882486,0.003852197,0.027803233,0.0021050898,0.053207736],"study_design_scores_gemma":[0.00036590407,0.00012771299,0.00062970293,0.00019232555,0.00001652322,0.00000991978,0.00011570077,0.9839848,0.002073339,0.011841363,0.00029760573,0.00034506072],"about_ca_topic_score_codex":0.00010912329,"about_ca_topic_score_gemma":8.9432166e-7,"teacher_disagreement_score":0.1002015,"about_ca_system_score_codex":0.0001437322,"about_ca_system_score_gemma":0.00033028377,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W4313644344","doi":"10.1109/tmech.2022.3231467","title":"Adaptive Manipulability-Based Path Planning Strategy for Industrial Robot Manipulators","year":2023,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Workspace; Path (computing); Control theory (sociology); Kinematics; Random tree; Robot; Measure (data warehouse); Traverse; Robot end effector; Industrial robot; Inverse kinematics; Computer science; Configuration space; Mathematical optimization; Path length; Mathematics; Artificial intelligence; Control (management)","score_opus":0.153965650953161,"score_gpt":0.31568571257624284,"score_spread":0.16172006162308183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313644344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003994513,0.000027026967,0.9908479,0.00043080904,0.002410144,0.00089511205,0.00010636385,0.0012266416,0.00006150198],"genre_scores_gemma":[0.82618034,0.0000052747487,0.17268799,0.00015382438,0.00021172427,0.0003387672,0.00004718571,0.00008123481,0.0002936376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671507,0.00017279714,0.0005551704,0.00096899923,0.00063246314,0.0009554754],"domain_scores_gemma":[0.99791914,0.0005635225,0.0001832945,0.0009550023,0.00012896201,0.00025009716],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008861725,0.00043605448,0.00042870475,0.00050041423,0.00055985706,0.00019459476,0.000991266,0.00037165894,0.000014576981],"category_scores_gemma":[0.000027713024,0.00045928848,0.0003026864,0.0012393078,0.00005754142,0.00044074986,0.000011468026,0.00075482996,0.00015116789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006985222,0.00012708505,0.000017282706,0.000021215294,0.000063201944,0.000028516028,0.00019965212,0.9813069,0.00015625649,0.0032216457,0.0003901584,0.014398221],"study_design_scores_gemma":[0.0016187491,0.0009478601,0.00007793213,0.0000985554,0.00004963604,0.000008808171,0.00022098828,0.99055296,0.0028840515,0.0025844895,0.00045345695,0.00050248695],"about_ca_topic_score_codex":0.000035818466,"about_ca_topic_score_gemma":0.000004046418,"teacher_disagreement_score":0.8221859,"about_ca_system_score_codex":0.0003605773,"about_ca_system_score_gemma":0.0005323032,"threshold_uncertainty_score":0.9997859},"labels":[],"label_agreement":null},{"id":"W4313887139","doi":"10.1109/tits.2022.3231259","title":"TriPField: A 3D Potential Field Model and Its Applications to Local Path Planning of Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Shanghai Municipal Education Commission","keywords":"Motion planning; Potential field; Field (mathematics); Path (computing); Computer science; Engineering; Artificial intelligence; Physics; Robot; Mathematics; Computer network","score_opus":0.037562140425338786,"score_gpt":0.28562618983728183,"score_spread":0.24806404941194304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313887139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014059223,0.00008321283,0.98400974,0.00018347331,0.00053065625,0.0006627247,0.00007910517,0.00034593054,0.00004595339],"genre_scores_gemma":[0.98828876,0.000034986144,0.010886861,0.00007727838,0.000026693653,0.00033035476,0.000011157187,0.000017383758,0.00032654047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827427,0.00004288337,0.0006012096,0.00044228852,0.0003768204,0.00026253486],"domain_scores_gemma":[0.9990843,0.00019452357,0.000117542295,0.00031178418,0.0001293783,0.00016247091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002446151,0.00018585839,0.00026432946,0.00042007735,0.00016557607,0.000057310375,0.00033905712,0.00013215681,0.0000034105778],"category_scores_gemma":[0.0000031798063,0.00019454407,0.00008663833,0.0007159416,0.00002155755,0.0002013089,0.0000012183698,0.0001901964,0.000051825995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020168864,0.000055493103,0.000009767587,0.0000832902,0.000033949444,0.000013343143,0.002557209,0.9770714,0.0011286171,0.0007785932,0.00008858882,0.018159546],"study_design_scores_gemma":[0.00018219963,0.0001647602,0.00011445384,0.00014071,0.000028140012,0.000008835144,0.00042863184,0.9872884,0.011249097,0.0000747005,0.00012813017,0.00019193559],"about_ca_topic_score_codex":0.00006359132,"about_ca_topic_score_gemma":0.0000031911916,"teacher_disagreement_score":0.9742295,"about_ca_system_score_codex":0.000034972316,"about_ca_system_score_gemma":0.00007510669,"threshold_uncertainty_score":0.79332775},"labels":[],"label_agreement":null},{"id":"W4314946923","doi":"10.1109/cdc51059.2022.9992384","title":"A Pursuit Evasion Approach for Avoiding an Inattentive Human in the Presence of a Static Obstacle","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pursuit-evasion; Obstacle; Evasion (ethics); Computer science; Artificial intelligence; Computer vision; Computer security; Law; Medicine; Political science","score_opus":0.0685445485839018,"score_gpt":0.3137289581258429,"score_spread":0.2451844095419411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4314946923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16053236,0.000088750654,0.8371545,0.00046530997,0.0002570058,0.0011030585,0.00005107598,0.000037947935,0.00031000806],"genre_scores_gemma":[0.9841907,0.000007145142,0.01480631,0.00041121777,0.000021078526,0.00044451182,0.00001065395,0.0000109737075,0.00009739904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675846,0.0007823794,0.00045341856,0.0006695169,0.00097505347,0.00036118607],"domain_scores_gemma":[0.99739754,0.0014234023,0.0002468417,0.00070886646,0.00013172433,0.00009162056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027486086,0.00020245962,0.0003750144,0.000268263,0.0005851761,0.00023925226,0.0017380358,0.0000491817,0.00002269261],"category_scores_gemma":[0.00030881094,0.00015825826,0.00007577672,0.0004975599,0.000082004466,0.00032539194,0.00022023883,0.0004587463,0.0000018990718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018831895,0.004232012,0.0031663054,0.0001849125,0.00016114977,0.0002401967,0.051380433,0.0730025,0.058829572,0.20526218,0.0039124237,0.5977451],"study_design_scores_gemma":[0.003036176,0.0013367906,0.0038587395,0.00005281889,0.00001593589,0.000023637946,0.0043263817,0.9744313,0.0001179799,0.012476263,0.00010085685,0.00022317292],"about_ca_topic_score_codex":0.000058134247,"about_ca_topic_score_gemma":0.000008114474,"teacher_disagreement_score":0.90142876,"about_ca_system_score_codex":0.00005788538,"about_ca_system_score_gemma":0.00010368801,"threshold_uncertainty_score":0.64535844},"labels":[],"label_agreement":null},{"id":"W4315472103","doi":"10.1109/cdc51059.2022.9993279","title":"Autonomous Navigation in Environments with Arbitrary Non-convex Obstacles","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Western University","funders":"","keywords":"Computer science; Regular polygon; Mathematics; Geometry","score_opus":0.01409749991672334,"score_gpt":0.22814744096769907,"score_spread":0.21404994105097572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315472103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33100286,0.00018901071,0.66460806,0.0010678408,0.0008020546,0.0007194548,0.000042494008,0.00011287401,0.0014553234],"genre_scores_gemma":[0.9882686,0.00002659759,0.010178611,0.0009564589,0.000028673989,0.0001633208,0.000012442689,0.000017064232,0.0003481889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974322,0.00018962627,0.00039667965,0.00078060117,0.0008068011,0.000394115],"domain_scores_gemma":[0.9986997,0.00031075452,0.00018292251,0.0006160777,0.000026003878,0.00016452085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053124875,0.00026694807,0.00038374832,0.00024639355,0.00030762109,0.0001955251,0.00081442425,0.00006820793,0.00010138022],"category_scores_gemma":[0.000025498863,0.00024137282,0.00004850357,0.00035648685,0.000076129356,0.0003371392,0.00018463607,0.0005439348,0.000057760233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013027089,0.0013203357,0.015611056,0.000029333429,0.00014505158,0.0025820795,0.004871676,0.05202462,0.013554155,0.015845042,0.0012411977,0.89147276],"study_design_scores_gemma":[0.005349559,0.0010353968,0.043368287,0.00010881827,0.000015009256,0.00010984636,0.00034993116,0.94244426,0.0003247086,0.0047323294,0.0016183698,0.0005435015],"about_ca_topic_score_codex":0.00005128034,"about_ca_topic_score_gemma":0.0000053940716,"teacher_disagreement_score":0.8909292,"about_ca_system_score_codex":0.00014964775,"about_ca_system_score_gemma":0.00015771779,"threshold_uncertainty_score":0.98428977},"labels":[],"label_agreement":null},{"id":"W4315472394","doi":"10.1109/cdc51059.2022.9992658","title":"A Robust Receding-Horizon Collision Avoidance Strategy for Constrained Unmanned Ground Vehicles Moving in Shared Planar Environments","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Collision avoidance; Horizon; Planar; Computer science; Robustness (evolution); Collision; Control theory (sociology); Aerospace engineering; Engineering; Artificial intelligence; Physics; Control (management); Computer security; Computer graphics (images)","score_opus":0.05652072600143875,"score_gpt":0.26484979114322815,"score_spread":0.2083290651417894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315472394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17921942,0.00027527698,0.81621724,0.0010232631,0.0011081129,0.0013464332,0.00028909202,0.00012898164,0.00039215642],"genre_scores_gemma":[0.9816454,0.00008244625,0.01693974,0.00044567534,0.00006618475,0.00037763352,0.000034442066,0.00002857813,0.0003799122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963273,0.00030590125,0.00068472343,0.0011557821,0.00087982323,0.0006465104],"domain_scores_gemma":[0.99765164,0.001137581,0.00031625174,0.0006085633,0.000058710313,0.00022725893],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012829022,0.000384191,0.0005869194,0.0003711904,0.00065649813,0.00042983974,0.0011298887,0.00013369345,0.00006961643],"category_scores_gemma":[0.00021872371,0.00039049672,0.00010063329,0.0004578965,0.000096441276,0.0004508418,0.00019724295,0.00054891437,0.00001706775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038112407,0.0011900713,0.0018973927,0.00006969659,0.00017516284,0.00079837313,0.0023110514,0.30329847,0.056373578,0.0367977,0.004549586,0.58872765],"study_design_scores_gemma":[0.007367083,0.0019419306,0.0050331256,0.0001646523,0.00001774615,0.000044233115,0.0007938736,0.97705114,0.0002101102,0.0058397083,0.0009697397,0.0005666419],"about_ca_topic_score_codex":0.000055228746,"about_ca_topic_score_gemma":0.000024511248,"teacher_disagreement_score":0.802426,"about_ca_system_score_codex":0.00023621033,"about_ca_system_score_gemma":0.00018145108,"threshold_uncertainty_score":0.9998547},"labels":[],"label_agreement":null},{"id":"W4316036779","doi":"10.1504/ijmlo.2023.10053364","title":"Enabling in-car location-based experiential learning with Presentria GO","year":2023,"lang":"en","type":"article","venue":"International Journal of Mobile Learning and Organisation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic","funders":"","keywords":"Experiential learning; M-learning; Computer science; Psychology; Multimedia; Human–computer interaction; Artificial intelligence; Mathematics education; World Wide Web; Mobile device","score_opus":0.010054944115152788,"score_gpt":0.26901177191854675,"score_spread":0.25895682780339396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316036779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58817935,0.00010651836,0.41032034,0.0005607099,0.00059251493,0.00008299635,2.2876493e-7,0.00009196271,0.00006540746],"genre_scores_gemma":[0.98874307,0.00003406107,0.010807666,0.000028503411,0.00020620589,0.000006436646,0.000007743094,0.000011844012,0.00015449007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986143,0.00014406585,0.00033396512,0.00017941483,0.0005774257,0.00015083837],"domain_scores_gemma":[0.99888664,0.00028084515,0.00034563872,0.00006795596,0.00036093564,0.00005799825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006929246,0.00009508779,0.00013375585,0.00047632717,0.00009501829,0.00021574525,0.0003227203,0.00004860161,0.00001661333],"category_scores_gemma":[0.00036030286,0.00008506116,0.000026230375,0.00042140184,0.00002988094,0.00043057982,0.000062239094,0.00034943694,0.000022525646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051036193,0.000042050826,0.08973812,0.000010506488,0.00004162112,0.00017345375,0.008000271,0.8783732,0.0026150215,0.00015297065,0.00004739769,0.02075434],"study_design_scores_gemma":[0.0025809316,0.0006572969,0.032685254,0.0003603845,0.000013004978,0.00023250339,0.0030539043,0.9499724,0.0042465264,0.00016123088,0.0057667797,0.00026980284],"about_ca_topic_score_codex":0.000016669164,"about_ca_topic_score_gemma":8.0045646e-7,"teacher_disagreement_score":0.40056372,"about_ca_system_score_codex":0.0000909317,"about_ca_system_score_gemma":0.0001645568,"threshold_uncertainty_score":0.34686935},"labels":[],"label_agreement":null},{"id":"W4317383755","doi":"10.1109/robio55434.2022.10011753","title":"Dynamic Self-Maintenance Obstacle Costmap Based on Instance Segmentation and Memory Storage Mechanism","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Obstacle; Computer science; Forgetting; Motion planning; Computer vision; Artificial intelligence; Obstacle avoidance; Segmentation; Robot; Mechanism (biology); Cluster analysis; Mobile robot; Path (computing); Fusion mechanism; Image segmentation; Real-time computing; Fusion","score_opus":0.02259636629730195,"score_gpt":0.2628650112192782,"score_spread":0.24026864492197625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317383755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02467735,0.00009983131,0.9594635,0.006955848,0.0048532835,0.0007018184,0.0002963834,0.00025568632,0.002696328],"genre_scores_gemma":[0.8331273,0.00008800119,0.16451567,0.001437458,0.0000316105,0.000054123833,0.000096148244,0.0000232459,0.0006263937],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769044,0.00015409541,0.0003516842,0.0007300463,0.0007496884,0.00032403757],"domain_scores_gemma":[0.9988452,0.00017624394,0.00026131197,0.0004159663,0.00016534253,0.00013591154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049865164,0.00028763682,0.00024268623,0.0003347308,0.00038323307,0.0002873327,0.00080540014,0.00006939801,0.00006964075],"category_scores_gemma":[0.000037235528,0.0002958132,0.000054078857,0.00029715884,0.000084748215,0.0001636878,0.00030764373,0.00040015386,0.000012117402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021071047,0.0012788188,0.00023527317,0.00008152267,0.0002481613,0.00039244167,0.0016554698,0.45020935,0.038950585,0.4864762,0.0016772822,0.018584162],"study_design_scores_gemma":[0.0010114242,0.00054572464,0.00024572283,0.000053385364,0.000015299318,0.000037866383,0.0001765961,0.9914843,0.0011815182,0.004783596,0.00011886455,0.00034570886],"about_ca_topic_score_codex":0.000011566108,"about_ca_topic_score_gemma":0.0000034194047,"teacher_disagreement_score":0.80845,"about_ca_system_score_codex":0.0003429434,"about_ca_system_score_gemma":0.00013723764,"threshold_uncertainty_score":0.9999494},"labels":[],"label_agreement":null},{"id":"W4317553701","doi":"10.1109/tsmc.2023.3233965","title":"A Bionic Dynamic Path Planning Algorithm of the Micro UAV Based on the Fusion of Deep Neural Network Optimization/Filtering and Hawk-Eye Vision","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Chinese Universities Scientific Fund; Ministry of Agriculture, Food and Rural Affairs; National Key Research and Development Program of China; China Agricultural University; State Key Laboratory of Virtual Reality Technology and Systems; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Artificial intelligence; Computer vision; Computer science; Artificial neural network; Motion planning; Path (computing); Fusion; Robot","score_opus":0.01243962138306274,"score_gpt":0.23121681762488905,"score_spread":0.21877719624182632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317553701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025819998,0.00045248112,0.97058636,0.00016859146,0.002090224,0.0006808358,0.000027690901,0.0001136597,0.000060174156],"genre_scores_gemma":[0.9938328,0.000039586725,0.005787753,0.000028099681,0.00004570503,0.00004605437,0.0000030299057,0.000027586175,0.00018933586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977684,0.00041271723,0.0005839749,0.0004181221,0.0004967386,0.00032007383],"domain_scores_gemma":[0.9983892,0.00040088946,0.00035575224,0.00068156695,0.00009141545,0.000081220285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072498573,0.00025084752,0.00035997914,0.0001953505,0.00039684863,0.00018553974,0.00048674562,0.00012664407,9.1265036e-7],"category_scores_gemma":[0.0000067430815,0.00016756188,0.00009087838,0.00076750334,0.00011711432,0.000094753545,0.000020817377,0.00023761703,0.0000029294697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008121272,0.000030925657,0.00008400976,0.00013733166,0.000025800236,0.000006173199,0.00060989696,0.99603826,0.00071012834,0.0000569308,0.00003621765,0.002256181],"study_design_scores_gemma":[0.00035195105,0.00021410774,0.00091722864,0.0011735853,0.000026540443,0.000038253,0.0003471694,0.9965424,0.00019400638,0.0000062931713,0.00002318845,0.00016528236],"about_ca_topic_score_codex":0.00012518244,"about_ca_topic_score_gemma":0.000002131523,"teacher_disagreement_score":0.96801287,"about_ca_system_score_codex":0.00004587675,"about_ca_system_score_gemma":0.0000302046,"threshold_uncertainty_score":0.6832975},"labels":[],"label_agreement":null},{"id":"W4317791222","doi":"10.2316/j.2023.206-0754","title":"TIME-OPTIMAL TRAJECTORY GENERATION FOR INDUSTRIAL ROBOTS BASED ON ELITE MUTATION SPARROW SEARCH ALGORITHM, 126-135.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trajectory; Sparrow; Elite; Computer science; Robot; Mutation; Algorithm; Trajectory optimization; Mathematical optimization; Mathematics; Artificial intelligence; Biology; Physics; Genetics; Political science","score_opus":0.04996422944326542,"score_gpt":0.3004088546636162,"score_spread":0.2504446252203508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317791222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021718303,0.000021222962,0.9728611,0.0029804253,0.0020825258,0.00019783953,0.000017970655,0.00009109426,0.000029500243],"genre_scores_gemma":[0.16509287,0.0000305669,0.83190477,0.00032745468,0.0021622127,0.00001458681,0.00022970534,0.000031309522,0.00020650293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980477,0.00010884273,0.0005240425,0.00023323033,0.0008843231,0.00020188131],"domain_scores_gemma":[0.998372,0.0003378529,0.00038177878,0.00013398891,0.00067503116,0.0000993101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011132809,0.00014758513,0.0001912275,0.00065833476,0.000118953336,0.00032197323,0.00043304535,0.00011476103,0.0000052228083],"category_scores_gemma":[0.00023474815,0.0001411888,0.000098562145,0.0002856833,0.0000334488,0.000583139,0.000044905304,0.00019786312,0.00002236628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020039903,0.000046995854,0.000032853706,0.0000044089247,0.000042999873,0.000040971758,0.00022592767,0.896845,0.001574941,0.00035556595,0.0012008835,0.09960945],"study_design_scores_gemma":[0.0013633147,0.00032955073,0.0013816187,0.00010438216,0.000015797155,0.00006496212,0.000019851166,0.99524444,0.0010727204,0.00018148065,0.00008579502,0.00013606598],"about_ca_topic_score_codex":0.000002973519,"about_ca_topic_score_gemma":2.1791612e-7,"teacher_disagreement_score":0.14337458,"about_ca_system_score_codex":0.00013854407,"about_ca_system_score_gemma":0.00021317137,"threshold_uncertainty_score":0.5757512},"labels":[],"label_agreement":null},{"id":"W4317792302","doi":"10.1109/wsc57314.2022.10015470","title":"A Meta-Heuristic Solution Approach to Isolated Evacuation Problems","year":2022,"lang":"en","type":"article","venue":"2022 Winter Simulation Conference (WSC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Marine Environmental Observation Prediction and Response Network","keywords":"Routing (electronic design automation); Computer science; Heuristic; Key (lock); Set (abstract data type); Meta heuristic; Scale (ratio); Mathematical optimization; Genetic algorithm; Distributed computing; Algorithm; Artificial intelligence; Computer security; Computer network; Mathematics; Machine learning; Geography","score_opus":0.12851225771924438,"score_gpt":0.29401340709840074,"score_spread":0.16550114937915636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317792302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011005948,0.00006770403,0.9937504,0.00089870323,0.0007263752,0.0008163021,0.000022519938,0.00041180494,0.0022055872],"genre_scores_gemma":[0.9383646,8.083747e-7,0.0591313,0.00048381818,0.00006155679,0.0004117395,0.00010833401,0.000021878159,0.0014159154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674845,0.000480494,0.000558013,0.00088092603,0.0008858957,0.00044621775],"domain_scores_gemma":[0.99828845,0.00018736409,0.00025993315,0.00078642904,0.0003134654,0.00016438101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009485503,0.00028636144,0.00038462412,0.000387137,0.00048644596,0.00028875814,0.001144454,0.00006519521,0.00033548303],"category_scores_gemma":[0.0001309327,0.00029115964,0.00015406382,0.001095891,0.000026948228,0.00062270026,0.0007435942,0.0003978299,0.00014353896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017634919,0.00013847015,0.000077056706,0.000013483685,0.00017787854,0.000004491656,0.0033562689,0.9880581,0.00048341992,0.0028113953,0.00078173826,0.0040800897],"study_design_scores_gemma":[0.00035389749,0.00025131082,0.00067285076,0.000009483822,0.0001300192,0.000017848872,0.00007225652,0.9943456,0.000026820633,0.0016732313,0.00211485,0.00033180742],"about_ca_topic_score_codex":0.00005418536,"about_ca_topic_score_gemma":0.0000018255657,"teacher_disagreement_score":0.9372641,"about_ca_system_score_codex":0.0002514961,"about_ca_system_score_gemma":0.00016711156,"threshold_uncertainty_score":0.99995404},"labels":[],"label_agreement":null},{"id":"W4317815191","doi":"10.2316/j.2023.206-0701","title":"TRAJECTORY OPTIMIZATION OF A SPOT-WELDING ROBOT IN THE JOINT AND CARTESIAN SPACES, 109-125.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spot welding; Cartesian coordinate system; Joint (building); Computer science; Welding; Trajectory; Mathematics; Engineering; Mechanical engineering; Structural engineering; Geometry; Physics","score_opus":0.023415373023894837,"score_gpt":0.26397854018844263,"score_spread":0.2405631671645478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317815191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06175294,0.00013620513,0.9332665,0.00415385,0.0005310581,0.00007213179,0.0000013052414,0.000022462442,0.000063499465],"genre_scores_gemma":[0.8163646,0.00012965877,0.18335548,0.000054344135,0.000076751945,9.822869e-7,0.000003330609,0.0000045030465,0.000010373582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988972,0.00008996355,0.00038789332,0.000098616685,0.0004317844,0.00009452039],"domain_scores_gemma":[0.9992269,0.00014834029,0.0003510942,0.00008272783,0.00015928724,0.000031606294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009782754,0.000074053496,0.00013740789,0.00038501646,0.000037401693,0.00014878465,0.00030878262,0.000038045066,0.0000010206448],"category_scores_gemma":[0.000114436814,0.00005697389,0.00003365366,0.00025427184,0.00003187165,0.0003942457,0.000058632657,0.000116001844,5.7487904e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023887287,0.000021967982,0.0011875533,0.000010143543,0.00001982211,0.00004657209,0.0019298511,0.98639596,0.00035528955,0.0025511174,0.000051160194,0.0074281925],"study_design_scores_gemma":[0.00034030768,0.00006220116,0.054594707,0.00014555309,0.0000073261685,0.00020689891,0.00021404168,0.9434964,0.00013381329,0.00073081994,0.000012212691,0.000055705485],"about_ca_topic_score_codex":0.000011857595,"about_ca_topic_score_gemma":0.000001469953,"teacher_disagreement_score":0.7546116,"about_ca_system_score_codex":0.00003317913,"about_ca_system_score_gemma":0.000047593032,"threshold_uncertainty_score":0.2323328},"labels":[],"label_agreement":null},{"id":"W4318192229","doi":"10.3389/frobt.2022.1076897","title":"Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function","year":2023,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Grid; Mathematical optimization; Path (computing); Heuristic; Planner; Context (archaeology); Occupancy grid mapping; Branch and bound; Best-first search; Upper and lower bounds; Motion planning; Search algorithm; Algorithm; Beam search; Mathematics; Artificial intelligence; Mobile robot","score_opus":0.017504864425632753,"score_gpt":0.2559005558220749,"score_spread":0.23839569139644212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318192229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035009568,0.00030233955,0.99335486,0.0013250358,0.0009792951,0.00036834352,0.000027579337,0.000106547406,0.000035054414],"genre_scores_gemma":[0.40421358,0.000049902363,0.59284186,0.0020537241,0.00028996612,0.0001038736,0.00012480935,0.0000559588,0.00026630383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868786,0.000062339415,0.00020542159,0.00047198942,0.00020912594,0.0003632885],"domain_scores_gemma":[0.9991907,0.00029013824,0.0000766315,0.00027260702,0.000032316344,0.00013760911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051080505,0.00019731016,0.00022405155,0.00019377864,0.00034753836,0.00034761318,0.00022339575,0.000089098954,4.853044e-7],"category_scores_gemma":[0.000054544053,0.00015530195,0.000027284053,0.00028297454,0.00007736923,0.00020045514,0.000063434665,0.00021486536,8.0147544e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069894835,0.000033355398,0.011656748,0.000067783105,0.000012677167,0.000023836248,0.00026978282,0.9745414,0.000009590266,0.0002642656,0.0052475594,0.007803109],"study_design_scores_gemma":[0.0010833819,0.00047666166,0.015785834,0.00014439,0.00002076682,0.0000044966073,0.00006161148,0.9805179,0.000010000746,0.0012529679,0.00043763354,0.00020433242],"about_ca_topic_score_codex":0.000013399872,"about_ca_topic_score_gemma":7.6181385e-7,"teacher_disagreement_score":0.4007126,"about_ca_system_score_codex":0.000026048188,"about_ca_system_score_gemma":0.00008086011,"threshold_uncertainty_score":0.633303},"labels":[],"label_agreement":null},{"id":"W4319440207","doi":"10.3390/drones7020110","title":"Decoupled Control Design of Aerial Manipulation Systems for Vegetation Sampling Application","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Control theory (sociology); Underactuation; Control engineering; Nonlinear system; Computer science; Payload (computing); Controller (irrigation); Quadcopter; Controllability; Nonlinear control; Control system; Engineering; Control (management); Artificial intelligence; Mathematics","score_opus":0.06837250196738616,"score_gpt":0.30146188617033487,"score_spread":0.23308938420294872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319440207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004351233,0.000073738476,0.99394906,0.00006305474,0.00062735943,0.0007206651,0.0000025546549,0.00020541671,0.0000069054804],"genre_scores_gemma":[0.8513989,0.0000037015388,0.14817904,0.0000072025564,0.0001352959,0.00022569734,0.000027923978,0.000008194036,0.000014062278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992362,0.000044369128,0.00023525447,0.00019166063,0.0001526997,0.000139793],"domain_scores_gemma":[0.9991156,0.0003918309,0.00016344714,0.00020520567,0.000100306446,0.000023615601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055036304,0.000067751025,0.00013638647,0.00010463427,0.00006808776,0.000044444998,0.00020844773,0.00004734876,1.255506e-7],"category_scores_gemma":[0.000082497936,0.00006953042,0.000026052621,0.00024715887,0.000009606979,0.00016964447,0.000016158294,0.000021823647,0.000021036722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011963267,0.000008831598,0.00024697246,0.000060792558,0.0000111737445,3.099857e-7,0.00021031027,0.9777603,0.009678625,0.008191716,0.000043598888,0.0037753957],"study_design_scores_gemma":[0.0004936535,0.000042556465,0.0053726113,0.000027606799,0.000008907489,0.0000017520013,0.000013234592,0.9918303,0.00036442766,0.0017386666,0.000035942725,0.000070340444],"about_ca_topic_score_codex":0.000023886352,"about_ca_topic_score_gemma":3.7464233e-7,"teacher_disagreement_score":0.8470476,"about_ca_system_score_codex":0.00002390461,"about_ca_system_score_gemma":0.000026594329,"threshold_uncertainty_score":0.28353682},"labels":[],"label_agreement":null},{"id":"W4319835989","doi":"10.1177/09544070221145742","title":"Robust AISMC-neural network observer-based control of high-speed autonomous vehicles with unknown dynamics","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Control theory (sociology); Computer science; Lyapunov function; Robustness (evolution); Vehicle dynamics; Artificial neural network; Parametric statistics; Control engineering; Trajectory; Nonlinear system; System dynamics; Robust control; Adaptive control; Observer (physics); Controller (irrigation); Lyapunov stability; Control system; Control (management); Engineering; Artificial intelligence; Mathematics","score_opus":0.01395742655985725,"score_gpt":0.19582533229942933,"score_spread":0.1818679057395721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319835989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30498016,0.000102644684,0.6925417,0.0004059755,0.0014711618,0.00027601075,0.000012134304,0.00019229465,0.000017908662],"genre_scores_gemma":[0.9337959,0.000008568608,0.06601828,0.000013188535,0.00012558923,0.0000054514544,0.000001380685,0.000022975433,0.00000860911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975792,0.000015564887,0.0010114172,0.00022558233,0.00076616276,0.00040206662],"domain_scores_gemma":[0.99786645,0.00023256283,0.000921262,0.00022724774,0.00060561724,0.00014683956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011786289,0.00026819535,0.0006993555,0.0002895729,0.000066294044,0.000033737713,0.0012460089,0.00014445039,0.0000015493823],"category_scores_gemma":[0.0004067515,0.00020204682,0.00024585606,0.0013422633,0.00009767659,0.00047762587,0.000113749426,0.00047927132,6.9599906e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041218584,0.00005553651,0.00010446863,0.00017664104,0.00011464821,0.0000095496125,0.00005430182,0.9665023,0.0053343433,0.027123492,0.000074263444,0.00040923833],"study_design_scores_gemma":[0.0013034751,0.00046450127,0.0006559913,0.0008697647,0.00007839404,0.00006986408,0.000024528812,0.9734951,0.022674793,0.00012689276,0.00004778604,0.0001888945],"about_ca_topic_score_codex":0.000007339726,"about_ca_topic_score_gemma":2.6639145e-7,"teacher_disagreement_score":0.62881577,"about_ca_system_score_codex":0.00013753682,"about_ca_system_score_gemma":0.00020297851,"threshold_uncertainty_score":0.82392305},"labels":[],"label_agreement":null},{"id":"W4320014657","doi":"10.2139/ssrn.4332012","title":"Design of Anisotropic Patterns in a Continuum Tubular Robot Using Metaheuristics Optimization with a Deep Neural Network","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Metaheuristic; Artificial neural network; Anisotropy; Robot; Computer science; Artificial intelligence; Materials science; Physics","score_opus":0.02058070306519973,"score_gpt":0.24229761265752994,"score_spread":0.2217169095923302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320014657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011089191,0.00029957996,0.98809654,0.00009376503,0.00018637175,0.00016061288,0.0000012520039,0.000068815054,0.000003874427],"genre_scores_gemma":[0.42368817,0.0002458916,0.57576776,0.00003677278,0.0001712826,0.000006879894,0.000008789564,0.000033710992,0.000040706145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968862,0.00030747885,0.0004008906,0.00026752288,0.00036211938,0.0017758074],"domain_scores_gemma":[0.99909824,0.00015253584,0.00030984107,0.00025829283,0.00011301208,0.000068063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013509464,0.00017870353,0.00030483172,0.00024282803,0.00012228897,0.00009191538,0.0005985215,0.00006389558,0.00000470409],"category_scores_gemma":[0.00006328305,0.00015647044,0.000047357218,0.001111346,0.000029021323,0.00029414752,0.00008836043,0.00083909347,0.000002632696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018684234,0.00002075807,0.0034586166,0.0000047941417,0.00005655934,0.00009501699,0.00016277119,0.9937704,0.000058914466,0.00070454815,0.000009922425,0.0016390102],"study_design_scores_gemma":[0.00061975955,0.00039092088,0.0015006064,0.00006899508,0.000028546867,0.0006106032,0.00008853068,0.99227834,0.000019514735,0.004229446,0.0000015307978,0.00016321294],"about_ca_topic_score_codex":0.000036457153,"about_ca_topic_score_gemma":0.000058325702,"teacher_disagreement_score":0.412599,"about_ca_system_score_codex":0.00031382107,"about_ca_system_score_gemma":0.00074807834,"threshold_uncertainty_score":0.63806796},"labels":[],"label_agreement":null},{"id":"W4321017759","doi":"10.1007/s11370-023-00458-6","title":"Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots","year":2023,"lang":"en","type":"article","venue":"Intelligent Service Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Any-angle path planning; Computer science; Topology (electrical circuits); Path (computing); Mobile robot; Computation; Shortest path problem; Graph; Mathematical optimization; Topological map; Robot; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.041080084833961514,"score_gpt":0.31121867850170604,"score_spread":0.2701385936677445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321017759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065530976,0.00013567565,0.9893498,0.0015333117,0.0011534751,0.0006520663,0.000030404934,0.00045560513,0.00013660388],"genre_scores_gemma":[0.11856968,0.000023197937,0.8792274,0.0011803102,0.00026999574,0.00021555342,0.00011652346,0.00006152545,0.00033579423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973787,0.00008096748,0.0006026163,0.00066286506,0.00048297129,0.0007918952],"domain_scores_gemma":[0.99779063,0.00054741674,0.0002585363,0.0008399731,0.00033577028,0.00022764578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003876848,0.0003261086,0.0005229476,0.00016250103,0.00014472632,0.00007257666,0.0014695772,0.00018514693,0.000005862274],"category_scores_gemma":[0.000056078767,0.00029035436,0.00010853725,0.0012190146,0.00006915772,0.00018632498,0.00045924025,0.00022961987,0.00012507019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048547587,0.00010945794,0.0027799027,0.00018713194,0.00007051893,0.00009673987,0.0013977309,0.9716603,0.00007427809,0.019629665,0.0008608569,0.0030848274],"study_design_scores_gemma":[0.00046223684,0.0008365088,0.0014377137,0.00025456867,0.000039060313,0.00008663842,0.00056113716,0.9887015,0.0008955274,0.0055501545,0.0007642828,0.00041064358],"about_ca_topic_score_codex":0.0000614318,"about_ca_topic_score_gemma":0.000008420942,"teacher_disagreement_score":0.11201658,"about_ca_system_score_codex":0.00010381807,"about_ca_system_score_gemma":0.00018852367,"threshold_uncertainty_score":0.9999549},"labels":[],"label_agreement":null},{"id":"W4321325504","doi":"10.2316/j.2023.201-0344","title":"TRAJECTORY TRACKING OF NONHOLONOMIC CONSTRAINT MOBILE ROBOT BASED ON ADRC, 1-9. SI","year":2023,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nonholonomic system; Constraint (computer-aided design); Tracking (education); Mobile robot; Trajectory; Control theory (sociology); Computer science; Robot; Artificial intelligence; Computer vision; Control engineering; Mathematics; Engineering; Control (management); Physics; Psychology","score_opus":0.015118127601490637,"score_gpt":0.23288593288697082,"score_spread":0.21776780528548018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321325504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03867019,0.0011129125,0.95723027,0.00016387037,0.0012731635,0.0008409921,0.000029541872,0.00038204953,0.00029700805],"genre_scores_gemma":[0.99792016,0.000006644351,0.00169331,0.000068157475,0.00009254452,0.00013877185,0.0000031431373,0.000016190232,0.00006108481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827254,0.00016777994,0.0004086474,0.00044875726,0.00027758486,0.00042469372],"domain_scores_gemma":[0.9987436,0.0003836326,0.00020500543,0.0005123492,0.000049931434,0.00010546433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095433806,0.00019650623,0.000456769,0.00021737957,0.00009348393,0.00009199639,0.00041561338,0.000100990495,0.000003791115],"category_scores_gemma":[0.000032919124,0.00017715832,0.00010312545,0.00023095043,0.00005378742,0.0001207693,0.00003682386,0.00015702636,0.000034448836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045990302,0.00011451631,0.001185562,0.00021873403,0.00013314617,0.00012339973,0.0007341037,0.9218657,0.010275803,0.011088953,0.00017607331,0.054038044],"study_design_scores_gemma":[0.0017395888,0.00038508812,0.0017057399,0.00016172956,0.000017627575,0.000022308752,0.00018650397,0.99507695,0.00029054252,0.00004829421,0.00018098524,0.00018466581],"about_ca_topic_score_codex":0.000075762735,"about_ca_topic_score_gemma":0.0000013914344,"teacher_disagreement_score":0.95925,"about_ca_system_score_codex":0.000072120914,"about_ca_system_score_gemma":0.00016106773,"threshold_uncertainty_score":0.72243065},"labels":[],"label_agreement":null},{"id":"W4321325551","doi":"10.2316/j.2023.206-0846","title":"STANDARDS OF MEASUREMENT AND DEVELOPMENTAL CHALLENGES IN PATH PLANNING FOR MANIPULATOR, 208-217.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Path (computing); Computer science; Motion planning; Manipulator (device); Artificial intelligence; Computer network; Robot","score_opus":0.09859575490531718,"score_gpt":0.3203520093752216,"score_spread":0.22175625446990443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321325551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084946886,0.0011356982,0.9108507,0.0020303011,0.0008114275,0.000116257244,0.000008826703,0.00002804202,0.00007183917],"genre_scores_gemma":[0.80399466,0.00027612736,0.195657,0.000013994115,0.00004639445,0.0000017501723,0.0000019161932,0.000004493593,0.0000036411125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987967,0.000021372087,0.00035159284,0.00009530581,0.0006483734,0.00008667954],"domain_scores_gemma":[0.9991535,0.000085650514,0.00024271985,0.000037849946,0.0004464807,0.00003381893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011848969,0.00006444991,0.00013073612,0.00028025528,0.000024688601,0.00005216187,0.000188932,0.000030312098,3.512464e-7],"category_scores_gemma":[0.00017660629,0.00005919388,0.000021943195,0.00007752683,0.000013729692,0.00025299165,0.00006369124,0.000055476037,2.4589804e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110433975,0.0002133983,0.026439449,0.00031647683,0.00043689212,0.00027252873,0.017255241,0.49526933,0.0040815044,0.038656354,0.0012170455,0.41573134],"study_design_scores_gemma":[0.0010853689,0.00015299488,0.14179234,0.00067897787,0.0000074696704,0.00015059886,0.00045319286,0.8504996,0.00041509606,0.0044331546,0.00022340252,0.00010783201],"about_ca_topic_score_codex":0.0000018339554,"about_ca_topic_score_gemma":6.9909663e-7,"teacher_disagreement_score":0.7190478,"about_ca_system_score_codex":0.000106035804,"about_ca_system_score_gemma":0.000103048915,"threshold_uncertainty_score":0.24138564},"labels":[],"label_agreement":null},{"id":"W4321496558","doi":"10.1007/s41315-023-00274-2","title":"A review of recent trend in motion planning of industrial robots","year":2023,"lang":"en","type":"review","venue":"International Journal of Intelligent Robotics and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Mitacs","keywords":"Motion planning; Computer science; Artificial intelligence; Implementation; Reinforcement learning; Heuristic; Motion (physics); Robotics; Generalization; Robot; Trajectory; Task (project management); Engineering; Systems engineering; Mathematics","score_opus":0.20585205724275224,"score_gpt":0.4140833666309902,"score_spread":0.20823130938823797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321496558","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0968761e-7,0.65766233,0.34135142,0.000215949,0.00038910235,0.000299682,0.00002218793,0.0000074563154,0.000051643616],"genre_scores_gemma":[0.0000037084826,0.9724226,0.02721149,0.000028456332,0.0002373827,0.000026203097,0.00004062561,0.000016637843,0.000012912907],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967228,0.00011842914,0.0021952412,0.0002448625,0.00056962617,0.00014899885],"domain_scores_gemma":[0.99612516,0.0004503676,0.0025134704,0.0002917888,0.00053062587,0.00008857221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009636772,0.0002245106,0.0011289789,0.0008467065,0.000020112755,0.000039671853,0.0013645341,0.00016169278,0.000006244985],"category_scores_gemma":[0.00027096202,0.00019078793,0.0002784732,0.0008525746,0.00005581153,0.00012883954,0.00024593444,0.0004707171,0.0000041333938],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021310366,0.00013857073,0.000018672643,0.0039057024,0.00022006994,0.000017837756,0.000064645516,0.0102557335,5.5170887e-7,0.00425542,0.00037836735,0.9807423],"study_design_scores_gemma":[0.0005267384,0.00021929678,0.000018177858,0.37590265,0.0005626765,0.00065249484,0.000055163015,0.0062881527,0.000027789878,0.0027352255,0.61246073,0.0005508962],"about_ca_topic_score_codex":0.0000070933042,"about_ca_topic_score_gemma":0.0000010786188,"teacher_disagreement_score":0.9801914,"about_ca_system_score_codex":0.00014152675,"about_ca_system_score_gemma":0.00030386576,"threshold_uncertainty_score":0.77801067},"labels":[],"label_agreement":null},{"id":"W4322588019","doi":"10.1111/cgf.14737","title":"Multi‐agent Path Planning with Heterogenous Interactions in Tight Spaces","year":2023,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund; National Science Foundation","keywords":"Initialization; Solver; Computer science; Motion planning; Path (computing); Set (abstract data type); Mathematical optimization; Collision detection; Shortest path problem; Space (punctuation); Dijkstra's algorithm; Crowd simulation; Algorithm; Collision; Theoretical computer science; Artificial intelligence; Mathematics; Crowds; Robot","score_opus":0.043122623680349165,"score_gpt":0.2876624075442743,"score_spread":0.24453978386392514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322588019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062336467,0.00010580646,0.9341562,0.0010144358,0.0013372564,0.00024215106,0.000005135482,0.0007458507,0.000056731336],"genre_scores_gemma":[0.57343024,0.000021434937,0.42543414,0.0007318059,0.00012661613,0.000062182655,0.00003087487,0.000043191387,0.00011951567],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978838,0.00008967236,0.0003111566,0.000632154,0.00035397738,0.00072927173],"domain_scores_gemma":[0.99881554,0.00017035838,0.00012095251,0.00067015603,0.000067410816,0.00015556725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029356676,0.00026706042,0.00027123888,0.0007885752,0.000243198,0.00029268605,0.0009501909,0.00006206283,0.000001145581],"category_scores_gemma":[0.000009356835,0.00023511495,0.00008583157,0.0017998529,0.00006704793,0.0004872007,0.00050086365,0.00040306474,0.000089569105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005583837,0.0010319836,0.56069,0.00017580329,0.0004298953,0.008824484,0.025050456,0.31892025,0.00041364587,0.027990455,0.02643527,0.0299819],"study_design_scores_gemma":[0.00054770894,0.00016044485,0.044066317,0.00022441827,0.000004725973,0.00014260356,0.000086222775,0.9511596,0.000056410925,0.00039526506,0.0028324572,0.00032381632],"about_ca_topic_score_codex":0.00005571985,"about_ca_topic_score_gemma":0.000030488838,"teacher_disagreement_score":0.63223934,"about_ca_system_score_codex":0.000050788563,"about_ca_system_score_gemma":0.000057117162,"threshold_uncertainty_score":0.95877093},"labels":[],"label_agreement":null},{"id":"W4322616123","doi":"10.48550/arxiv.2302.12309","title":"Safe and Quasi-Optimal Autonomous Navigation in Sphere Worlds","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; Point (geometry); Computer science; Control theory (sociology); Path (computing); Equilibrium point; Euclidean space; Obstacle avoidance; Space (punctuation); Line segment; Control (management); Exponential stability; Line (geometry); Sight; Line-of-sight; Mathematics; Artificial intelligence; Aerospace engineering; Mobile robot; Engineering; Mathematical analysis; Geography; Geometry; Physics","score_opus":0.07328689393446554,"score_gpt":0.20622814356067576,"score_spread":0.13294124962621023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322616123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2988546,0.00005446987,0.6988443,0.000181684,0.0007118694,0.00024174509,0.000009524177,0.00041029422,0.0006914838],"genre_scores_gemma":[0.94339406,0.00004777008,0.05363217,0.00003078195,0.000059031558,9.939355e-7,0.000027261418,0.000022839995,0.002785065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798787,0.00013855158,0.00023164572,0.0011578756,0.000101164245,0.0003829058],"domain_scores_gemma":[0.99860877,0.00014838579,0.00019557115,0.00083331036,0.00006785636,0.0001461205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003628788,0.00028612715,0.00033528614,0.00029858228,0.00009540674,0.00014204298,0.0011239244,0.00029318765,0.000006493985],"category_scores_gemma":[0.000036131605,0.00036249874,0.00008050011,0.0007696686,0.00009041787,0.00033853133,0.001721589,0.0006955833,0.00014565595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011536441,0.00006659119,0.01239999,0.00008244956,0.000036545032,0.0019507706,0.0008701289,0.963503,0.000006170236,0.017500876,0.00015907008,0.0034128581],"study_design_scores_gemma":[0.00036480773,0.000046680907,0.018426163,0.0002487353,0.000017641069,0.000011902529,0.00008812974,0.9667739,0.000011058851,0.013539897,0.00008638573,0.00038465904],"about_ca_topic_score_codex":0.00048556333,"about_ca_topic_score_gemma":0.000047088586,"teacher_disagreement_score":0.6452122,"about_ca_system_score_codex":0.00027396833,"about_ca_system_score_gemma":0.000222537,"threshold_uncertainty_score":0.9998827},"labels":[],"label_agreement":null},{"id":"W4322742245","doi":"10.3390/drones7030169","title":"Path Planning for Autonomous Drones: Challenges and Future Directions","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Drone; Motion planning; Planner; Path (computing); Computer science; Key (lock); Popularity; Representation (politics); Control (management); Operations research; Robot; Artificial intelligence; Engineering; Computer security; Computer network","score_opus":0.04221454577768203,"score_gpt":0.28049992060411344,"score_spread":0.23828537482643142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322742245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05661461,0.074592136,0.78112364,0.05302888,0.015191801,0.0019060585,0.000086065294,0.0102436105,0.0072132046],"genre_scores_gemma":[0.20429747,0.013275232,0.7672962,0.0006890705,0.0069069793,0.0011710763,0.00012401454,0.00019856059,0.006041395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990183,0.000027803131,0.00013428293,0.00038313956,0.00012466208,0.00031182106],"domain_scores_gemma":[0.99935,0.00019322454,0.000052261024,0.00029218776,0.000032473363,0.00007985219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023917365,0.00013210828,0.00016333004,0.00014212851,0.00024231094,0.00007656599,0.00028604263,0.00007573,8.690976e-7],"category_scores_gemma":[0.000029265251,0.0001247211,0.000037631304,0.00021469411,0.000028568851,0.00022951936,0.000119730656,0.00008387155,0.0000310402],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007969339,0.00006821572,0.00057360064,0.00021078228,0.00008182169,0.00012067025,0.03481369,0.001236076,0.00015090984,0.045481518,0.006276933,0.91097784],"study_design_scores_gemma":[0.0008174577,0.00030798404,0.08007865,0.00015020955,0.000026745556,0.0001514111,0.0031278261,0.5812427,0.000116850424,0.012904473,0.32036078,0.0007149373],"about_ca_topic_score_codex":0.0000059110066,"about_ca_topic_score_gemma":8.8288624e-7,"teacher_disagreement_score":0.9102629,"about_ca_system_score_codex":0.000018855633,"about_ca_system_score_gemma":0.000031186595,"threshold_uncertainty_score":0.5085979},"labels":[],"label_agreement":null},{"id":"W4322770797","doi":"10.3390/drones7030171","title":"Dual-UAV Payload Transportation Using Optimized Velocity Profiles via Real-Time Dynamic Programming","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Khalifa University of Science, Technology and Research","keywords":"Payload (computing); Dynamic programming; Time horizon; Interval (graph theory); Computer science; Trajectory; Function (biology); Energy consumption; Control theory (sociology); Trajectory optimization; Mathematical optimization; Simulation; Real-time computing; Optimal control; Engineering; Mathematics; Algorithm; Control (management); Electrical engineering; Physics","score_opus":0.01941394589490682,"score_gpt":0.27356242432557054,"score_spread":0.25414847843066374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322770797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29673904,0.000025237914,0.70066386,0.00029112495,0.00040770334,0.00040169564,0.000011779198,0.0014048071,0.000054751446],"genre_scores_gemma":[0.084120326,0.000015566013,0.9152128,0.000017501194,0.00006289603,0.000057594258,0.0001494945,0.000031357253,0.00033245012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826604,0.00008235547,0.00032594017,0.00048608027,0.0003681528,0.0004714224],"domain_scores_gemma":[0.9991911,0.00010108412,0.0001505917,0.00038766066,0.00007479981,0.00009474165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043193094,0.0001987098,0.00026259292,0.00018239956,0.00022212972,0.00012550032,0.0003772475,0.00009569807,0.000006855908],"category_scores_gemma":[0.000028452925,0.00019619943,0.000076973505,0.0008890996,0.000055336015,0.00043386815,0.00004851589,0.00011815191,0.00017206077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000879075,0.00041399774,0.0048469594,0.00045070297,0.00029169617,0.0016981034,0.02004429,0.439448,0.13079779,0.0012216847,0.00070948154,0.39998937],"study_design_scores_gemma":[0.00036097248,0.000038251485,0.014163198,0.000060096012,0.000017395516,0.000017810306,0.0000513209,0.9837965,0.0008381287,0.0003611958,0.000038791783,0.00025635876],"about_ca_topic_score_codex":0.00019539666,"about_ca_topic_score_gemma":0.0000032916403,"teacher_disagreement_score":0.5443485,"about_ca_system_score_codex":0.0000710153,"about_ca_system_score_gemma":0.00007743365,"threshold_uncertainty_score":0.80007803},"labels":[],"label_agreement":null},{"id":"W4323361905","doi":"10.3390/math11051273","title":"Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Metaheuristic; Computer science; Mathematical optimization; Algorithm; Convergence (economics); Optimization algorithm; Optimization problem; Engineering optimization; Wilcoxon signed-rank test; Range (aeronautics); Imperialist competitive algorithm; Rank (graph theory); Meta-optimization; Mathematics; Engineering","score_opus":0.03782347851677414,"score_gpt":0.2782499568333361,"score_spread":0.24042647831656194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323361905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012414272,0.000012912099,0.99702907,0.000324511,0.0002808075,0.00024188121,0.000012320834,0.0013870341,0.0005873208],"genre_scores_gemma":[0.00431996,4.4452858e-7,0.9949753,0.00015467311,0.00007193965,0.000059265563,0.000035249326,0.000042040116,0.00034109244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812937,0.00002885551,0.00043682923,0.00039082364,0.0005674162,0.00044668964],"domain_scores_gemma":[0.99832946,0.0003820273,0.00016958031,0.0008720428,0.000119218916,0.00012769904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005975454,0.0002528359,0.00031994318,0.00032923257,0.00021800291,0.00022709627,0.0009338938,0.00008668718,0.000016086551],"category_scores_gemma":[0.00022486178,0.0002475109,0.00009584664,0.0012339936,0.000044455242,0.0001564493,0.00013998561,0.00016312904,0.00018668314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.8246586e-7,0.00014177636,0.000008418274,0.00010554596,0.000018379109,0.00003924178,0.00027090713,0.9954154,0.00019139567,0.0025929254,0.0007046957,0.00051105994],"study_design_scores_gemma":[0.0004982657,0.000053879907,0.000032365828,0.00010294168,0.000021619106,0.000012393151,0.000027450236,0.9975604,0.0004892829,0.00077346555,0.0001450496,0.00028289028],"about_ca_topic_score_codex":0.000005952881,"about_ca_topic_score_gemma":1.4121959e-7,"teacher_disagreement_score":0.0041958173,"about_ca_system_score_codex":0.00008153446,"about_ca_system_score_gemma":0.00011136595,"threshold_uncertainty_score":0.99999774},"labels":[],"label_agreement":null},{"id":"W4360584313","doi":"10.1109/access.2023.3260646","title":"Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Trajectory; Collision avoidance; Energy consumption; Motion planning; Real-time computing; Collision; Obstacle avoidance; Mobile robot; Obstacle; Kinematics; Simulation; Artificial intelligence; Robot; Engineering; Computer security","score_opus":0.03684971833524189,"score_gpt":0.28944565946879436,"score_spread":0.25259594113355244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360584313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66237444,0.00007613159,0.33639196,0.000025945239,0.0008061076,0.00011667148,0.00002291956,0.00015777857,0.00002802609],"genre_scores_gemma":[0.98352355,0.0000065278577,0.01633236,0.0000151086,0.000050251052,0.000046699304,0.000006867049,0.0000104579,0.0000081666485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986209,0.000060393468,0.00035037278,0.00039419913,0.00024743043,0.00032670185],"domain_scores_gemma":[0.9994461,0.00011072331,0.00012387132,0.00024033852,0.00001763853,0.000061307874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037865076,0.00014731666,0.00026071127,0.00013664989,0.000065462424,0.00011915352,0.00060698367,0.000075646436,3.8747427e-7],"category_scores_gemma":[0.0000096753965,0.0001436081,0.000036800757,0.00025804894,0.000028158265,0.00015425135,0.00020038501,0.00004212683,0.0000020608252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037801405,0.00006236736,0.0042545004,0.0003918282,0.00004458867,0.00012639187,0.001456239,0.983473,0.0046813325,0.0005996667,0.0001066499,0.0047656307],"study_design_scores_gemma":[0.00060206425,0.00004534599,0.022049697,0.00018794632,0.000010019117,0.000021952523,0.000069804286,0.97231436,0.0044441344,0.00006499679,0.000026668986,0.0001630372],"about_ca_topic_score_codex":0.00010985659,"about_ca_topic_score_gemma":0.0000013524786,"teacher_disagreement_score":0.3211491,"about_ca_system_score_codex":0.00018364361,"about_ca_system_score_gemma":0.000044765722,"threshold_uncertainty_score":0.5856168},"labels":[],"label_agreement":null},{"id":"W4362474907","doi":"10.1017/s0263574723000322","title":"A comprehensive review of the latest path planning developments for multi-robot formation systems","year":2023,"lang":"en","type":"review","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Motion planning; Robot; Computer science; Robustness (evolution); Scalability; Path (computing); Field (mathematics); Process (computing); Energy consumption; Control engineering; Systems engineering; Distributed computing; Artificial intelligence; Engineering","score_opus":0.2919264844014907,"score_gpt":0.4051998299497815,"score_spread":0.11327334554829077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362474907","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.484308e-9,0.6165509,0.3801002,0.000043665314,0.0010794097,0.0020370865,0.000031108844,0.00012971363,0.000027871585],"genre_scores_gemma":[1.8996575e-7,0.7976282,0.20153306,0.00008244131,0.00005941297,0.0003797576,0.000090967515,0.000049859147,0.00017609377],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99643445,0.00039502818,0.001500637,0.0005779295,0.0005712009,0.0005207341],"domain_scores_gemma":[0.99612945,0.0009162118,0.0014120372,0.0010722346,0.0003658902,0.00010416339],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007885283,0.0005081618,0.0020066253,0.00018321493,0.0002270357,0.00011062306,0.0021334034,0.00025861847,5.068407e-7],"category_scores_gemma":[0.00048145052,0.00034148103,0.0004866233,0.0010971731,0.000050390743,0.00022810363,0.00064165844,0.00039444547,0.000095217],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.895664e-7,0.000068715555,0.000003095988,0.84221053,0.00037134113,0.000030271312,0.000379014,0.015790293,3.9683883e-7,0.0009646304,0.006873681,0.13330704],"study_design_scores_gemma":[0.0002039398,0.00003413614,0.000021588019,0.5129982,0.00032879782,0.00012319478,0.000010073309,0.11653633,3.6065134e-7,0.000012400498,0.3692821,0.00044884646],"about_ca_topic_score_codex":0.0000068794225,"about_ca_topic_score_gemma":8.583071e-8,"teacher_disagreement_score":0.3624084,"about_ca_system_score_codex":0.00020573329,"about_ca_system_score_gemma":0.0005256155,"threshold_uncertainty_score":0.99990374},"labels":[],"label_agreement":null},{"id":"W4362659379","doi":"10.1109/icrai57502.2023.10089571","title":"Comparison Between ANSYS Fluent and Solidworks Internal Flow Simulation for Analysis of A Fuzzy Logic Controller-Based Heating/Cooling System in A Mobile Robot Design","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fluent; Computational fluid dynamics; Software; Mechanical engineering; Robot; Internal flow; Mobile robot; Simulation; Water cooling; Engineering; Flow (mathematics); Computer science; Aerospace engineering; Mechanics; Physics","score_opus":0.08755428215526657,"score_gpt":0.35530136329640616,"score_spread":0.2677470811411396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362659379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038998526,0.00007377103,0.9597848,0.000023838344,0.000083573694,0.0007984767,0.000008971827,0.00021351982,0.000014539438],"genre_scores_gemma":[0.68186367,4.3553072e-7,0.31798103,0.000016021626,0.000024720868,0.000086032705,0.000014780501,0.0000068093364,0.000006494264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979506,0.00021366157,0.000759082,0.00045070722,0.00028444387,0.00034151552],"domain_scores_gemma":[0.9969904,0.002263932,0.000268585,0.00027091478,0.00012123537,0.00008493362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015804742,0.00017820482,0.0007962576,0.00081571745,0.00008357026,0.000104282684,0.00036101384,0.00010186737,9.4304795e-7],"category_scores_gemma":[0.00012283905,0.00015864076,0.00013890548,0.001622534,0.000030207566,0.0001297758,0.00008607741,0.00011537509,0.0000021300546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046240635,0.000029521594,0.02280638,0.000054254717,0.00017113541,0.0000040443833,0.0005608191,0.9684713,0.00008146509,0.00006909646,0.0000064405262,0.007699337],"study_design_scores_gemma":[0.0016156753,0.00023701593,0.0148667125,0.00015582952,0.00015837965,4.3389034e-7,0.0002044223,0.98235315,0.00018933552,0.000066974615,8.4207034e-7,0.00015121933],"about_ca_topic_score_codex":0.0001023741,"about_ca_topic_score_gemma":0.0000063661614,"teacher_disagreement_score":0.6428652,"about_ca_system_score_codex":0.000100151985,"about_ca_system_score_gemma":0.000043193206,"threshold_uncertainty_score":0.64691824},"labels":[],"label_agreement":null},{"id":"W4362670016","doi":"10.54097/hset.v39i.6643","title":"The Research of Different Algorithms on Path Planning","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Computer science; Algorithm; Ideal (ethics); Path (computing); Bionics; Basis (linear algebra); Any-angle path planning; Transportation planning; Operations research; Management science; Artificial intelligence; Transport engineering; Engineering; Mathematics; Robot","score_opus":0.040169267113446754,"score_gpt":0.3135713873961638,"score_spread":0.27340212028271704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362670016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9412149,0.00048564185,0.04442706,0.009769483,0.0018707428,0.0003524967,0.0000022331196,0.0013498731,0.0005275565],"genre_scores_gemma":[0.98761135,0.00010163562,0.012169773,0.000003370999,0.000023311133,0.000029052399,1.9992363e-7,0.0000064136534,0.000054901884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981182,0.000025004061,0.00019961085,0.00041953407,0.000549101,0.0006885351],"domain_scores_gemma":[0.9986872,0.0005684756,0.000036255096,0.00057030766,0.00008023838,0.00005751069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019010059,0.00010665554,0.00014823883,0.0016255775,0.00035883227,0.000081010156,0.0014474954,0.000083169274,7.9891635e-8],"category_scores_gemma":[0.0004516068,0.000068444075,0.000011712451,0.005133888,0.00057131087,0.000116435884,0.0005894336,0.00035890876,0.000023966768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001939124,0.000029521276,0.0009374336,0.0000127641315,0.0000048296174,0.00012488068,0.0007959313,0.01432751,0.011144788,0.9560319,0.00022999341,0.016358495],"study_design_scores_gemma":[0.00019539233,0.0002445682,0.013869747,0.0002005982,7.236528e-7,0.000022735565,0.0001627857,0.95334166,0.022522205,0.007454112,0.0018208639,0.00016462264],"about_ca_topic_score_codex":0.0000047687436,"about_ca_topic_score_gemma":2.111213e-7,"teacher_disagreement_score":0.9485778,"about_ca_system_score_codex":0.00005159831,"about_ca_system_score_gemma":0.000050700248,"threshold_uncertainty_score":0.27910683},"labels":[],"label_agreement":null},{"id":"W4362714526","doi":"10.2316/j.2023.206-0899","title":"ON SOLVING THE KINEMATICS AND CONTROLLING OF ORIGAMI BOX-SHAPED ROBOT, 405-415. SI","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kinematics; Robot; Computer science; Artificial intelligence; Physics; Classical mechanics","score_opus":0.01792043234815103,"score_gpt":0.27459861700553434,"score_spread":0.2566781846573833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362714526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053659078,0.00011130756,0.94029325,0.0047400105,0.0010078218,0.0000777435,0.0000023176478,0.00003792402,0.00007057341],"genre_scores_gemma":[0.87544197,0.000107150576,0.12410302,0.00015704792,0.00014580613,9.771484e-7,0.000002232628,0.0000073066467,0.000034466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986499,0.00005545228,0.00049951696,0.000106851425,0.00057294476,0.00011531633],"domain_scores_gemma":[0.9981995,0.00068556424,0.0005917002,0.00011523522,0.00036087335,0.00004708126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007836467,0.000096297605,0.00019156294,0.00024245743,0.00007900337,0.00019489747,0.00044376534,0.00004147725,0.000001441794],"category_scores_gemma":[0.00039400056,0.000067835994,0.00005169482,0.00017423494,0.000044085387,0.0002985239,0.00010744119,0.00014769474,0.0000030963806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016940969,0.000049730654,0.00056638004,0.000030412853,0.00016569113,0.00006282917,0.0014910983,0.9251237,0.0036569657,0.049853567,0.00025114906,0.018731525],"study_design_scores_gemma":[0.00051864685,0.0001001169,0.007892303,0.00024183064,0.000016720523,0.00011125922,0.00006229347,0.982414,0.00019695274,0.008362306,0.00001704018,0.000066511624],"about_ca_topic_score_codex":0.000004113582,"about_ca_topic_score_gemma":3.552968e-7,"teacher_disagreement_score":0.8217829,"about_ca_system_score_codex":0.000027212312,"about_ca_system_score_gemma":0.000046175057,"threshold_uncertainty_score":0.27662715},"labels":[],"label_agreement":null},{"id":"W4362715094","doi":"10.2316/j.2023.206-0747","title":"TOWARDS AUTOMATED ROBOT MANIPULATION: A UNIFIED ACTIVE VISION FRAMEWORK, 284-295.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Robot; Artificial intelligence; Robot vision; Computer vision; Human–computer interaction; Mobile robot","score_opus":0.027324967717165347,"score_gpt":0.32259689123839064,"score_spread":0.2952719235212253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362715094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013805636,0.000042363372,0.9755083,0.007343287,0.0025251436,0.0000891792,0.000004006139,0.000448895,0.00023322209],"genre_scores_gemma":[0.6410391,0.0000673722,0.3584012,0.00013698608,0.00026269886,0.0000016832395,0.00001736194,0.000012089638,0.00006154984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981104,0.00008535289,0.0005469043,0.00020405535,0.00086888805,0.00018440471],"domain_scores_gemma":[0.99829024,0.00020770844,0.00055883056,0.00017628934,0.0006610244,0.00010593595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055094855,0.00014875024,0.00021067288,0.00055604574,0.00009401473,0.00037149878,0.0006894217,0.00011885702,0.000006830059],"category_scores_gemma":[0.00027761233,0.00013454905,0.000080228834,0.0004973212,0.000033844557,0.0010130747,0.00019884246,0.00024154308,0.000035755016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030506168,0.0000922008,0.00043866353,0.000016445183,0.00018996805,0.00030628554,0.0015879186,0.83527714,0.00081434194,0.0428516,0.0013609522,0.117034],"study_design_scores_gemma":[0.00040802042,0.00011271762,0.0913011,0.00020757059,0.000012885692,0.00023885153,0.000050857827,0.8952532,0.0002136923,0.011957438,0.00011654992,0.00012711446],"about_ca_topic_score_codex":0.000007854481,"about_ca_topic_score_gemma":3.8069027e-7,"teacher_disagreement_score":0.62723345,"about_ca_system_score_codex":0.00011794727,"about_ca_system_score_gemma":0.00011651112,"threshold_uncertainty_score":0.5486751},"labels":[],"label_agreement":null},{"id":"W4366152443","doi":"10.1109/icateee57445.2022.10093717","title":"Route Planning for a Tractor in an Agriculture Field with Obstacles","year":2022,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Tractor; Computer science; Trajectory; Field (mathematics); Real-time computing; Motion planning; Computer vision; Artificial intelligence; Engineering; Mathematics; Automotive engineering","score_opus":0.02671872326012366,"score_gpt":0.2709039870105284,"score_spread":0.24418526375040478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366152443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12389729,0.00007250419,0.87259734,0.0013116358,0.00028119914,0.00038573018,0.000010523349,0.00026952507,0.0011742366],"genre_scores_gemma":[0.5912824,1.9409217e-7,0.40689185,0.0008276979,0.000052223833,0.00015239415,0.00001564116,0.0000080421005,0.0007695298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907726,0.000041563977,0.00012362801,0.00030898323,0.00020493688,0.00024364474],"domain_scores_gemma":[0.99945855,0.00016928643,0.000048093538,0.00024211522,0.000022882845,0.000059072074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016557124,0.000098082965,0.00011882141,0.00007000359,0.00014925223,0.00009121301,0.0006021267,0.000028783184,0.000010027169],"category_scores_gemma":[0.000017645725,0.00007138926,0.00002326145,0.0002893749,0.000005540705,0.00035657675,0.000103033104,0.00018241223,0.0000010191524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027274605,0.0013274765,0.11284946,0.00007344742,0.00008801135,0.0013056712,0.04836089,0.7170012,0.0067711202,0.029575348,0.030495206,0.051879432],"study_design_scores_gemma":[0.0028247875,0.004649414,0.122237004,0.000074703894,0.0000148702175,0.00028776738,0.004855156,0.8466511,0.0022273692,0.0013291138,0.013739565,0.0011091664],"about_ca_topic_score_codex":0.00006403917,"about_ca_topic_score_gemma":0.00002617039,"teacher_disagreement_score":0.46738514,"about_ca_system_score_codex":0.000045930465,"about_ca_system_score_gemma":0.000047217636,"threshold_uncertainty_score":0.29111692},"labels":[],"label_agreement":null},{"id":"W4366782998","doi":"10.54097/hset.v35i.7018","title":"Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Acceleration; Obstacle avoidance; Obstacle; Collision avoidance; Genetic algorithm; Computer science; Survivability; Control theory (sociology); Collision; Simulation; Control (management); Mobile robot; Artificial intelligence; Computer security; Robot","score_opus":0.021551293389341468,"score_gpt":0.2750482761225838,"score_spread":0.25349698273324234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366782998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11415831,0.000058746744,0.8825424,0.0019367082,0.00041716138,0.00024218133,0.0000052104524,0.00056595734,0.00007334894],"genre_scores_gemma":[0.8976281,0.000013565846,0.102276415,0.0000130028175,0.0000141896135,0.000036800535,4.8360664e-7,0.000006143281,0.000011240214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822277,0.000032521144,0.00022615276,0.00043856254,0.0005540099,0.0005259833],"domain_scores_gemma":[0.9987393,0.0004902682,0.00004810941,0.0005425554,0.00011772541,0.00006204381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011825733,0.00011397951,0.00017030771,0.0026567702,0.00015367944,0.00004494411,0.00088738714,0.00010547596,2.3475589e-7],"category_scores_gemma":[0.000397873,0.00010093076,0.0000135575365,0.005161017,0.00032684172,0.0001993845,0.00012444008,0.00028870924,0.0000579016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005305316,0.00007200855,0.00032415037,0.000032110813,0.0000027381616,0.00010350719,0.0002822717,0.88841105,0.015435286,0.052012593,0.000063159525,0.04325585],"study_design_scores_gemma":[0.00042997053,0.00022091332,0.00557367,0.00010490449,5.0787185e-7,0.0000074500435,0.000023771794,0.96205056,0.030902565,0.00037338043,0.00021113703,0.00010118346],"about_ca_topic_score_codex":0.000008399412,"about_ca_topic_score_gemma":5.060197e-7,"teacher_disagreement_score":0.78346986,"about_ca_system_score_codex":0.000084291256,"about_ca_system_score_gemma":0.0000676421,"threshold_uncertainty_score":0.4115837},"labels":[],"label_agreement":null},{"id":"W4367314350","doi":"10.3390/math11092092","title":"Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; King Saud University","keywords":"Heuristic; Computer science; Ant colony optimization algorithms; Path (computing); Motion planning; Particle swarm optimization; Foraging; Swarm behaviour; Real-time computing; Path length; Mathematical optimization; Simulation; Artificial intelligence; Algorithm; Mathematics; Computer network","score_opus":0.06604630547948197,"score_gpt":0.31847088491466474,"score_spread":0.25242457943518276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367314350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041121133,0.000009025527,0.95513064,0.0003028116,0.0005454589,0.00036711755,0.0000066867374,0.0014687033,0.0010484096],"genre_scores_gemma":[0.25510165,0.0000012574623,0.7440235,0.00014523309,0.000047733265,0.00011040126,0.000014186124,0.000034777688,0.0005212316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985367,0.00005083962,0.00035061178,0.00034833848,0.000343701,0.00036983227],"domain_scores_gemma":[0.99886644,0.00026583354,0.00012295351,0.0006069305,0.000029490753,0.00010833996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046954167,0.00018209679,0.00022996216,0.00039323315,0.00015594486,0.00010181643,0.000517044,0.0000991907,0.0000061088085],"category_scores_gemma":[0.00020929534,0.00017038776,0.000054463595,0.0008097193,0.000024029716,0.00013435066,0.00011674664,0.0002622751,0.0002943004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019024932,0.006301182,0.03809999,0.0005872802,0.000046630612,0.0056696823,0.05714684,0.83315414,0.006073027,0.0090572545,0.0130574545,0.030787483],"study_design_scores_gemma":[0.00040100465,0.00006981254,0.017168198,0.00022362129,0.0000058352452,0.000014255506,0.00015228186,0.9810248,0.00033584182,0.0003124571,0.00010059866,0.00019129159],"about_ca_topic_score_codex":0.000013239121,"about_ca_topic_score_gemma":6.758188e-7,"teacher_disagreement_score":0.21398053,"about_ca_system_score_codex":0.00012149575,"about_ca_system_score_gemma":0.00008261322,"threshold_uncertainty_score":0.6948211},"labels":[],"label_agreement":null},{"id":"W4376254807","doi":"10.1139/tcsme-2022-0134","title":"Simulation and experiment on obstacle avoidance control of concrete pump truck boom based on improved danger field and gradient projection method","year":2023,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Boom; Obstacle avoidance; Smoothness; Smoothing; Obstacle; Control theory (sociology); Transformation (genetics); Computer science; Task (project management); Projection (relational algebra); Engineering; Control (management); Mobile robot; Robot; Artificial intelligence; Mathematics; Algorithm; Computer vision","score_opus":0.01724368056647497,"score_gpt":0.2562533278509839,"score_spread":0.23900964728450896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376254807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061650756,0.000014956647,0.99241835,0.0006709311,0.00024347355,0.0004057691,0.000030629624,0.00004805046,0.000002777171],"genre_scores_gemma":[0.89375746,0.0000012768501,0.10601387,0.00013953239,0.000011463697,0.00005134855,0.0000010109287,0.000009294328,0.000014757944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993471,0.000020149002,0.0001540152,0.00018871237,0.000115321316,0.0001747263],"domain_scores_gemma":[0.9990106,0.0006053358,0.000048702233,0.0002053235,0.000034077435,0.00009596157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002887473,0.00009320185,0.0001425418,0.0000590445,0.00012636374,0.000016986925,0.00013090823,0.00008012527,7.5535974e-7],"category_scores_gemma":[0.00007237091,0.000083372724,0.00012749687,0.0002054318,0.000013198739,0.00005924768,0.0000044953154,0.00012401056,9.5625715e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012210677,0.0000045744678,0.00000152925,0.00005433523,0.000042388583,2.0588551e-7,0.00051555526,0.97956014,0.014391062,0.0013291624,0.000007558209,0.0040812856],"study_design_scores_gemma":[0.0005024646,0.00019960335,0.000056040917,0.000048969614,0.000018888793,6.84056e-7,0.000031622523,0.9660987,0.032861445,0.00005305198,0.000052303178,0.00007618252],"about_ca_topic_score_codex":0.0010190074,"about_ca_topic_score_gemma":0.000077990444,"teacher_disagreement_score":0.8875924,"about_ca_system_score_codex":0.000102045145,"about_ca_system_score_gemma":0.000058452322,"threshold_uncertainty_score":0.3399841},"labels":[],"label_agreement":null},{"id":"W4377107306","doi":"10.3390/math11102356","title":"Bio-Inspired Multi-UAV Path Planning Heuristics: A Review","year":2023,"lang":"en","type":"review","venue":"Mathematics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Population and Public Health; King Saud University","keywords":"Motion planning; Computer science; Heuristics; Scope (computer science); Path (computing); Artificial intelligence; Data science; Operations research; Machine learning; Management science; Systems engineering; Robot; Engineering","score_opus":0.22370646151257348,"score_gpt":0.40318068531188417,"score_spread":0.1794742237993107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377107306","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.264336e-9,0.62929237,0.3681631,0.000027110991,0.0005635363,0.00087455515,0.00003499871,0.00082459283,0.00021974825],"genre_scores_gemma":[2.3774986e-9,0.59731287,0.40138632,0.00008604904,0.00010418518,0.00014934462,0.000045221288,0.0000942247,0.00082177133],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.995532,0.0002360739,0.0017817275,0.00092475326,0.00076785986,0.00075763394],"domain_scores_gemma":[0.9950348,0.0010363528,0.0012913892,0.002268401,0.00011858952,0.0002504406],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014487915,0.0008626323,0.0033024575,0.0003477897,0.00017501439,0.00026036164,0.0030440234,0.0003701366,0.0000067340825],"category_scores_gemma":[0.0013850455,0.0006895361,0.0006392846,0.0014475068,0.000060712948,0.00014867088,0.0009477896,0.00071509805,0.0030164358],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.0944609e-7,0.00018371473,5.9051007e-7,0.39599684,0.00027167672,0.0010601137,0.0006567104,0.000025004723,3.1941635e-8,0.0017274389,0.024527205,0.57555056],"study_design_scores_gemma":[0.00010736018,0.00003389087,3.304251e-7,0.35350755,0.0005550016,0.00027662847,0.0000088033485,0.045540277,7.209158e-8,0.00041800455,0.59876657,0.0007855325],"about_ca_topic_score_codex":0.000003194337,"about_ca_topic_score_gemma":8.576223e-8,"teacher_disagreement_score":0.574765,"about_ca_system_score_codex":0.00015433802,"about_ca_system_score_gemma":0.00037581663,"threshold_uncertainty_score":0.9995556},"labels":[],"label_agreement":null},{"id":"W4377200034","doi":"10.1007/978-3-031-32726-1_31","title":"A Fast Combinatorial Algorithm for the Bilevel Knapsack Problem with Interdiction Constraints","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interdiction; Knapsack problem; Bilevel optimization; Computer science; Algorithm; Mathematical optimization; Mathematics; Optimization problem; Engineering","score_opus":0.021792262505053398,"score_gpt":0.2494158704108485,"score_spread":0.2276236079057951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377200034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010658894,0.00006437819,0.9898393,0.00089117547,0.006656975,0.0013950866,0.000038359656,0.00041037187,0.000703294],"genre_scores_gemma":[0.0017074547,0.000013318394,0.99610937,0.000441659,0.0009935583,0.00010918591,0.00001462216,0.000071882765,0.0005389319],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955551,0.00004174532,0.00057701836,0.0017010496,0.0012384065,0.00088669895],"domain_scores_gemma":[0.995939,0.0016727906,0.00041215663,0.0013396256,0.00046384393,0.0001725724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014822953,0.0006433497,0.0005898295,0.00063101755,0.0005661254,0.0008157094,0.0037778148,0.00034202568,0.000004973636],"category_scores_gemma":[0.00008579572,0.0004401172,0.0001513825,0.0008804797,0.0016019375,0.0004723757,0.0010734167,0.0009841584,0.000048034122],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068954528,0.00002219763,0.0000065023382,0.000026010346,0.000043496362,0.000067325964,0.00077148835,0.036721945,0.0000070426927,0.011035769,0.0000824074,0.9512089],"study_design_scores_gemma":[0.0007541684,0.0005011818,0.000044365675,0.0006138171,0.000022848784,0.00019620391,8.678962e-7,0.90573186,0.00010974319,0.09101421,0.00044052888,0.0005701821],"about_ca_topic_score_codex":0.000022329908,"about_ca_topic_score_gemma":0.0000132526275,"teacher_disagreement_score":0.9506387,"about_ca_system_score_codex":0.0003173513,"about_ca_system_score_gemma":0.00090177346,"threshold_uncertainty_score":0.99980503},"labels":[],"label_agreement":null},{"id":"W4378551539","doi":"10.1007/978-981-19-9483-8_15","title":"Neural Network-Based Obstacle and Pothole Avoiding Robot","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mobile robot; Backpropagation; Computer science; Obstacle; Artificial intelligence; Pothole (geology); Robot; Artificial neural network; Robot control; Computer vision; Real-time computing; Geography","score_opus":0.029303783969854575,"score_gpt":0.23326161116901967,"score_spread":0.20395782719916508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378551539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004642741,0.015614387,0.9779984,0.00025480508,0.0034594857,0.0004802927,0.0000068455174,0.00032233432,0.0018170716],"genre_scores_gemma":[0.9282587,0.0008830209,0.041501295,0.0015888653,0.0073459316,0.00011832676,0.00016249837,0.00047761612,0.019663746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997397,0.00015446219,0.0005437596,0.00089860224,0.00033976807,0.0006664264],"domain_scores_gemma":[0.9973871,0.0015055153,0.00028463127,0.0006066458,0.000050688337,0.0001654205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008652792,0.0005267198,0.000782854,0.00019283075,0.0002671802,0.00051930855,0.00047873709,0.0006699538,0.0000014567908],"category_scores_gemma":[0.00007142469,0.00047822887,0.00008425742,0.00019722531,0.00009197219,0.00011789169,0.00027741268,0.0011730595,0.000006075629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046591535,0.0000027068302,0.00049119274,0.00008472342,0.000024754241,0.00022630456,0.000107103464,0.9792493,0.000001400558,0.0042425077,0.0002571215,0.015308202],"study_design_scores_gemma":[0.00028165802,0.00007239268,0.00027269288,0.0011861089,0.000018857798,0.000098026874,0.0000016328935,0.99379176,5.032141e-7,0.0029008032,0.000877685,0.000497882],"about_ca_topic_score_codex":0.00008008029,"about_ca_topic_score_gemma":0.000034968056,"teacher_disagreement_score":0.93649703,"about_ca_system_score_codex":0.000060546157,"about_ca_system_score_gemma":0.00004448402,"threshold_uncertainty_score":0.99976695},"labels":[],"label_agreement":null},{"id":"W4378676263","doi":"10.18280/jesa.560215","title":"Enhancing Path Planning of Assistive Robots in Complex Environments Using Geno-Fuzzy Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Path (computing); Motion planning; Fuzzy logic; Robot; Artificial intelligence; Computer network","score_opus":0.05067448618641463,"score_gpt":0.2942838412847476,"score_spread":0.24360935509833298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378676263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11045178,0.00034814802,0.8879167,0.000036542773,0.000552326,0.00019145019,0.000011813769,0.00018916179,0.0003021122],"genre_scores_gemma":[0.294676,0.00004228367,0.70488375,0.00004289077,0.0001468522,0.0000055060023,0.000007701029,0.00005172774,0.00014327565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960258,0.0005569109,0.001168651,0.00048810997,0.00094901415,0.0008115351],"domain_scores_gemma":[0.99805635,0.00030232835,0.0008596098,0.00047018085,0.00007636963,0.00023514197],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015547998,0.00034491147,0.000657559,0.0007680271,0.0003611147,0.0002428502,0.001064684,0.00011243183,0.000011427679],"category_scores_gemma":[0.00020274868,0.00033412786,0.00014518104,0.0013165622,0.00011905186,0.0007708531,0.00048882404,0.0004897242,0.00008411044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011102322,0.00020263904,0.023166342,0.00020899017,0.00023483745,0.0056893434,0.0078660315,0.6412166,0.05525222,0.00018903203,0.00060691894,0.26535597],"study_design_scores_gemma":[0.00034599763,0.00007735642,0.45579514,0.0005798244,0.000012180921,0.00066926866,0.00013531813,0.5411286,0.0004924889,0.0005339415,0.000024786397,0.00020510072],"about_ca_topic_score_codex":0.000057185203,"about_ca_topic_score_gemma":8.766045e-7,"teacher_disagreement_score":0.4326288,"about_ca_system_score_codex":0.00046780022,"about_ca_system_score_gemma":0.00017448227,"threshold_uncertainty_score":0.99991107},"labels":[],"label_agreement":null},{"id":"W4380075577","doi":"10.1109/lra.2023.3281261","title":"Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Obstacle avoidance; Software deployment; Terrain; Computer science; Artificial intelligence; End-to-end principle; Emulation; Simulation; Human–computer interaction; Real-time computing; Mobile robot; Robot; Computer vision","score_opus":0.017619467290041567,"score_gpt":0.2505866685451731,"score_spread":0.2329672012551315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380075577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078202724,0.00000757256,0.91357106,0.0064671705,0.0008936602,0.000308945,0.000010421753,0.00052533986,0.000013081772],"genre_scores_gemma":[0.68686444,0.000002548182,0.3110643,0.0017117276,0.000120099365,0.000034059667,0.00013553585,0.000023182296,0.00004409806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853283,0.00004732197,0.00028618792,0.000403082,0.0003589065,0.00037164806],"domain_scores_gemma":[0.99890584,0.00052445754,0.00014293427,0.00023587573,0.000057773097,0.00013309809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048421143,0.00017907233,0.00020418175,0.0006210422,0.0002615403,0.00025118946,0.00024731123,0.00006941075,0.0000017006344],"category_scores_gemma":[0.00011735739,0.00019463118,0.00005407535,0.0010678462,0.00003536794,0.000287511,0.000045960147,0.0001477415,0.000060411294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002229999,0.000006879582,0.0005327302,0.00005241392,0.000016693724,0.000023591738,0.00026862722,0.9838391,0.002746835,0.000044790402,0.0029082461,0.00955789],"study_design_scores_gemma":[0.00045905155,0.00010331014,0.008965917,0.000076820215,0.000013417088,0.00001124132,0.000019170668,0.9883471,0.0014002596,0.000042125543,0.00032711102,0.00023443607],"about_ca_topic_score_codex":0.000019656725,"about_ca_topic_score_gemma":5.844072e-7,"teacher_disagreement_score":0.6086617,"about_ca_system_score_codex":0.000060323353,"about_ca_system_score_gemma":0.00006246641,"threshold_uncertainty_score":0.7936829},"labels":[],"label_agreement":null},{"id":"W4380881051","doi":"10.1145/3579371.3589092","title":"Energy-Efficient Realtime Motion Planning","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Speedup; Computer science; Computation; Parallel computing; Collision detection; Motion planning; Data parallelism; Collision; Artificial intelligence; Parallelism (grammar); Robot; Algorithm","score_opus":0.032552674585929614,"score_gpt":0.27221667869840205,"score_spread":0.23966400411247243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380881051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025931373,0.000019753437,0.98076224,0.0006358405,0.0005775975,0.000036591675,5.909869e-7,0.001547126,0.013827108],"genre_scores_gemma":[0.6440479,0.0000046767263,0.34526402,0.0006274863,0.00021569933,0.00002599241,0.000024531106,0.00002371561,0.009765985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988564,0.00004140429,0.00015303573,0.00032718168,0.00029353553,0.00032842488],"domain_scores_gemma":[0.99933577,0.00008977032,0.000043138425,0.0004084738,0.000032599542,0.00009024708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033797414,0.00009433405,0.00009572753,0.00019813457,0.00011910558,0.00008483826,0.0004983836,0.000045294848,0.000008477446],"category_scores_gemma":[0.000027326547,0.000084404426,0.000033314005,0.0008536957,0.000014243344,0.000118190335,0.00020783169,0.000067475616,0.0005889608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015992674,0.000044520653,0.00060369004,0.0000072478,0.00001365143,0.0002464434,0.0011661575,0.8231026,0.0005517558,0.05782028,0.024592612,0.091849424],"study_design_scores_gemma":[0.00009315051,0.000020403428,0.0046032756,0.000014286375,0.0000010914721,0.000015458068,0.000021355643,0.99301493,0.000491178,0.0007515653,0.00086517667,0.00010812988],"about_ca_topic_score_codex":0.000045808378,"about_ca_topic_score_gemma":1.3459484e-7,"teacher_disagreement_score":0.64145476,"about_ca_system_score_codex":0.000031358755,"about_ca_system_score_gemma":0.000026598133,"threshold_uncertainty_score":0.75700957},"labels":[],"label_agreement":null},{"id":"W4381125018","doi":"10.20944/preprints202306.1241.v1","title":"Decomposition and Modeling of the Situational Awareness of Unmanned Aerial Vehicle for Advanced Air Mobility","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Situation awareness; Computer science; Realization (probability); Drone; Human–computer interaction; Risk analysis (engineering); Systems engineering; Simulation; Engineering; Business","score_opus":0.1622141814758957,"score_gpt":0.3833324114159012,"score_spread":0.2211182299400055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381125018","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6181244,0.000015195635,0.380374,0.00022620185,0.0005926516,0.00054483296,0.000038913102,0.00006962811,0.000014165056],"genre_scores_gemma":[0.9569841,0.0000071008226,0.042720687,0.000014537492,0.000058562437,0.00015444134,0.000025737441,0.000014804898,0.00002005302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979947,0.00013314122,0.00056253315,0.0007478416,0.00036326592,0.0001984856],"domain_scores_gemma":[0.9977444,0.00027314032,0.0004264025,0.001156061,0.00034941037,0.00005057553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094321225,0.00018833266,0.0003698971,0.0000811265,0.000115031,0.0000116702095,0.0010771761,0.00016883474,0.0000019359936],"category_scores_gemma":[0.00034652842,0.00017315689,0.00015092055,0.00015887384,0.00008548446,0.00013886453,0.002423554,0.00023578404,0.0000046361674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004560335,0.000077950506,0.03760681,0.00027503973,0.00004118567,3.8661355e-7,0.0007504121,0.9495523,0.010766615,0.00053671544,0.0000013669577,0.00034561523],"study_design_scores_gemma":[0.00038203588,0.000015124112,0.15454112,0.00028692262,0.000017246344,0.0000012183635,0.000018900093,0.79867476,0.029265184,0.016664224,0.0000010590836,0.00013218613],"about_ca_topic_score_codex":0.00013941618,"about_ca_topic_score_gemma":0.0000051579304,"teacher_disagreement_score":0.33885968,"about_ca_system_score_codex":0.000067919646,"about_ca_system_score_gemma":0.0002734888,"threshold_uncertainty_score":0.70611334},"labels":[],"label_agreement":null},{"id":"W4381733106","doi":"10.1109/tits.2023.3285568","title":"Tangent-Based Path Planning for UAV in a 3-D Low Altitude Urban Environment","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Motion planning; Obstacle; Drone; Tangent; Path (computing); Heuristic; Obstacle avoidance; Computer science; Graph; Mathematical optimization; Simulation; Real-time computing; Artificial intelligence; Mobile robot; Robot; Mathematics; Geography; Geometry; Theoretical computer science","score_opus":0.039971044676193626,"score_gpt":0.27313769363459905,"score_spread":0.23316664895840544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381733106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008968335,0.00008789625,0.9871886,0.0001327698,0.0018491131,0.0011413433,0.00014456635,0.0004679419,0.000019467698],"genre_scores_gemma":[0.9844193,0.0000251545,0.013915611,0.00008084774,0.00005378937,0.0010687355,0.00008630354,0.00004107299,0.00030917194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741966,0.00008999141,0.00077049143,0.0006575729,0.0005605408,0.0005017597],"domain_scores_gemma":[0.9988474,0.0003059302,0.00015918822,0.00048235845,0.000051369214,0.00015370012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053869945,0.00028873183,0.00032686582,0.0006279737,0.00017973283,0.00010043178,0.00047600665,0.00014274749,0.000010181281],"category_scores_gemma":[0.0000035370354,0.00030514484,0.0001809867,0.00072505465,0.00003356483,0.0002396414,4.6192818e-7,0.0002241875,0.0001815361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033154665,0.00017414545,0.00043615303,0.00013960568,0.000035819776,0.00006063364,0.0027615628,0.9937535,0.0003064195,0.00025158946,0.00020079172,0.0018466298],"study_design_scores_gemma":[0.0008144542,0.00023349473,0.0018208101,0.0005279212,0.000024510466,0.000002994218,0.00046441273,0.9874068,0.0068861474,0.000026950445,0.0014016578,0.00038984133],"about_ca_topic_score_codex":0.00009427082,"about_ca_topic_score_gemma":0.000009820274,"teacher_disagreement_score":0.975451,"about_ca_system_score_codex":0.0002044224,"about_ca_system_score_gemma":0.000087800385,"threshold_uncertainty_score":0.99994004},"labels":[],"label_agreement":null},{"id":"W4381895033","doi":"10.48550/arxiv.2012.05946","title":"Autonomous Cooperative Wall Building by a Team of Unmanned Aerial Vehicles in the MBZIRC 2020 Competition","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Khalifa University of Science, Technology and Research; University of Pennsylvania","keywords":"Task (project management); Competition (biology); Engineering; Artificial intelligence; Robotics; Stack (abstract data type); Robot; Simulation; Computer science; Systems engineering","score_opus":0.0481925270456768,"score_gpt":0.19936588284834686,"score_spread":0.15117335580267005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381895033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28951764,0.00006947534,0.70691,0.0011041212,0.0005336934,0.0005966735,0.00007510078,0.00018134378,0.0010119624],"genre_scores_gemma":[0.9901886,0.000038797232,0.009355746,0.00021927059,0.000078596844,0.0000021802316,0.00003984843,0.000013336015,0.000063634085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978169,0.00046882097,0.00031091718,0.00093284744,0.00015818789,0.00031230482],"domain_scores_gemma":[0.9984903,0.00025356043,0.00033064658,0.00072510436,0.00010286291,0.00009752487],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003950614,0.00030239776,0.0004505057,0.00013852744,0.00011949315,0.00011679221,0.0023697133,0.00022360269,0.0000058843684],"category_scores_gemma":[0.00007203861,0.00029765384,0.00013010508,0.00075626577,0.00014865288,0.00025827068,0.0010964174,0.000726677,0.000020943531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008942742,0.00029833915,0.0011482523,0.000120706354,0.00013113087,0.00067954825,0.005807142,0.86614543,0.0025057707,0.119963594,0.0027971813,0.00031350128],"study_design_scores_gemma":[0.0007843842,0.00016494538,0.000974921,0.00018189763,0.00003891441,0.00000872394,0.00026384162,0.98889345,0.0007620234,0.0072141797,0.00032903877,0.00038367696],"about_ca_topic_score_codex":0.0003289105,"about_ca_topic_score_gemma":0.00001566194,"teacher_disagreement_score":0.70067096,"about_ca_system_score_codex":0.00020201826,"about_ca_system_score_gemma":0.0002693045,"threshold_uncertainty_score":0.99994755},"labels":[],"label_agreement":null},{"id":"W4382050648","doi":"10.1109/icuas57906.2023.10155896","title":"A Multi-Objective Approach for Unmanned Aerial Vehicle Mapping","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Set (abstract data type); Pareto principle; Pareto optimal; Multi-objective optimization; Mathematical optimization; Energy consumption; Fuel efficiency; Rank (graph theory); Drone; Operations research; Engineering; Automotive engineering; Mathematics; Machine learning","score_opus":0.06217562986404657,"score_gpt":0.28437294340894675,"score_spread":0.2221973135449002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382050648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027280157,0.0000071644595,0.9936476,0.00026998622,0.00056827586,0.00042361033,0.0000041963403,0.0010253825,0.0013257669],"genre_scores_gemma":[0.037727505,6.908097e-7,0.95990676,0.0001437584,0.00016160001,0.00013445519,0.000013667089,0.000013299706,0.0018982567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872553,0.000041906547,0.00016869459,0.00046263717,0.00017807075,0.00042312837],"domain_scores_gemma":[0.99929076,0.00015333023,0.000049634957,0.0003579984,0.0000676646,0.00008062925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042986873,0.00012313786,0.00016022484,0.0001565351,0.00016931673,0.000115036324,0.0006248885,0.000065167485,0.0000016673591],"category_scores_gemma":[0.00011994571,0.000112264315,0.000070568305,0.0007506863,0.000024547451,0.00024318753,0.00023328183,0.000078983634,0.000117772615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022962602,0.0019206097,0.0067104185,0.000653419,0.00094756903,0.0003385599,0.071890354,0.42767987,0.104201645,0.06761202,0.11246476,0.20535113],"study_design_scores_gemma":[0.00082152325,0.000049535793,0.004103116,0.0000069596913,0.0000022378586,0.0000049429796,0.00022923434,0.9931669,0.0008686102,0.00031985805,0.00026467032,0.00016235975],"about_ca_topic_score_codex":0.000029492378,"about_ca_topic_score_gemma":4.5585708e-7,"teacher_disagreement_score":0.5654871,"about_ca_system_score_codex":0.00003757407,"about_ca_system_score_gemma":0.00005308928,"threshold_uncertainty_score":0.4578006},"labels":[],"label_agreement":null},{"id":"W4382050786","doi":"10.1109/icuas57906.2023.10155790","title":"Hierarchical Cooperative Assignment Algorithm (CAA) for mission and path planning of multiple fixed-wing UAVs based on maximum independent sets","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Motion planning; Computer science; Task (project management); Probabilistic logic; Graph; Assignment problem; Path (computing); Trajectory; Mathematical optimization; Any-angle path planning; Optimization problem; Algorithm; Artificial intelligence; Robot; Theoretical computer science; Mathematics; Engineering","score_opus":0.03998960154851777,"score_gpt":0.2984802032903826,"score_spread":0.25849060174186483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382050786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011656855,0.000020649382,0.98628294,0.000670487,0.0003299671,0.00052464847,0.000043416057,0.00024671896,0.00022429935],"genre_scores_gemma":[0.2898329,0.000002640447,0.7093307,0.00031684956,0.00005146877,0.00005314311,0.00005089279,0.000024930643,0.0003365012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978431,0.00013260447,0.0003525946,0.00062929257,0.0005962071,0.00044622674],"domain_scores_gemma":[0.99807316,0.0011416846,0.00011456177,0.0003795117,0.00008566848,0.00020543745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007457024,0.0002328077,0.00031202688,0.00024561095,0.00023168487,0.00009766405,0.00041590005,0.00012029155,0.000006015086],"category_scores_gemma":[0.00021926512,0.00018993703,0.00006586754,0.00036095307,0.000051196435,0.00017434875,0.00024804717,0.00022335658,0.0000092741175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018644624,0.0006299035,0.00870468,0.00024080789,0.00013440975,0.0005158259,0.006289909,0.6102827,0.01081322,0.0017840854,0.0076113567,0.35280666],"study_design_scores_gemma":[0.0014274343,0.0005656347,0.0060681836,0.00021004994,0.0000055582395,0.00000729698,0.0001524441,0.9855143,0.0052160597,0.0003633148,0.00023799407,0.00023173039],"about_ca_topic_score_codex":0.000016043758,"about_ca_topic_score_gemma":2.4601667e-7,"teacher_disagreement_score":0.3752316,"about_ca_system_score_codex":0.00006249342,"about_ca_system_score_gemma":0.00011615799,"threshold_uncertainty_score":0.7745407},"labels":[],"label_agreement":null},{"id":"W4382059441","doi":"10.4050/f-0079-2023-18134","title":"Analysis of Aerial Firefighting with Rotorcraft Platforms","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Firefighting; Environmental science; Wildlife; Marine engineering; Aeronautics; Engineering; Geography","score_opus":0.020410473157506256,"score_gpt":0.25310641597227435,"score_spread":0.23269594281476808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382059441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29008746,0.000003927379,0.70617294,0.00011625025,0.000185134,0.00009933546,0.0000031727984,0.00054201606,0.0027897651],"genre_scores_gemma":[0.73368984,0.0000016132537,0.26488864,0.00006060624,0.00005820091,0.000015215232,0.000017359882,0.000009192983,0.0012593025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989872,0.000008800249,0.00019385396,0.00024932547,0.00032701573,0.0002338102],"domain_scores_gemma":[0.9992717,0.00010982253,0.00008620797,0.00042478682,0.000050050196,0.00005745711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027025843,0.00008827438,0.00022923487,0.00040227218,0.000056390174,0.000042983796,0.0005134095,0.00003552905,0.000019810992],"category_scores_gemma":[0.000029192279,0.000059011203,0.00006641852,0.00343078,0.000024045035,0.00022946531,0.00014156329,0.00005416906,0.00005045991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047967453,0.00013362293,0.1886815,0.000057183857,0.0033038456,0.00036465545,0.0075510903,0.73166406,0.0021949045,0.024653167,0.0029970305,0.03835096],"study_design_scores_gemma":[0.00017676355,0.00006325265,0.041081205,0.000010400984,0.00006053187,0.0000027586684,0.00003281049,0.95736235,0.0009138413,0.000110523295,0.00007646134,0.00010907055],"about_ca_topic_score_codex":0.0000767488,"about_ca_topic_score_gemma":0.0000066710118,"teacher_disagreement_score":0.4436024,"about_ca_system_score_codex":0.000013533946,"about_ca_system_score_gemma":0.000040032017,"threshold_uncertainty_score":0.2406407},"labels":[],"label_agreement":null},{"id":"W4382242947","doi":"10.1007/978-3-031-29933-9_18","title":"Onboard Trajectory Coordination of Multiple Unmanned Air Vehicles","year":2023,"lang":"en","type":"book-chapter","venue":"Sustainable aviation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Trajectory; Reliability (semiconductor); Aerospace engineering; Rotor (electric); Computer science; Engineering; Control engineering; Control (management); Simulation; Automotive engineering; Control theory (sociology); Artificial intelligence; Mechanical engineering; Physics","score_opus":0.017277616153630958,"score_gpt":0.2294771530890562,"score_spread":0.21219953693542523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382242947","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000718239,0.0005849743,0.8497027,0.0018165946,0.0023585022,0.0032736512,0.00006140184,0.002373143,0.13911079],"genre_scores_gemma":[0.048399426,0.000031918302,0.02230196,0.000088780536,0.00022099406,0.00008206505,0.00021733955,0.0001044896,0.92855304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786353,0.000051606225,0.00050205394,0.00055257574,0.0006233111,0.0004069326],"domain_scores_gemma":[0.9976825,0.00031038318,0.0005728046,0.00066093984,0.00069737417,0.00007599902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005917505,0.0003025019,0.0004027752,0.0006220592,0.00015478148,0.00005771683,0.0006610599,0.00036255276,0.0000066357975],"category_scores_gemma":[0.00027420183,0.00034056912,0.00014368544,0.0002490527,0.00005639736,0.00051133946,0.00023301365,0.00029371656,0.000115673916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002670466,0.00006289854,0.00024503746,0.0008239727,0.00014851216,0.00025356276,0.0015953543,0.019402254,0.00023255142,0.9491616,0.007495319,0.020552272],"study_design_scores_gemma":[0.0032934889,0.0010755609,0.013305523,0.0011798394,0.0002423807,0.000025775385,0.0011397023,0.55126476,0.0039240243,0.3804398,0.04137397,0.0027351612],"about_ca_topic_score_codex":0.00009900633,"about_ca_topic_score_gemma":0.000003258656,"teacher_disagreement_score":0.82740074,"about_ca_system_score_codex":0.00042002235,"about_ca_system_score_gemma":0.000321932,"threshold_uncertainty_score":0.99990463},"labels":[],"label_agreement":null},{"id":"W4382936011","doi":"10.23919/acc55779.2023.10156634","title":"Safe and Quasi-Optimal Autonomous Navigation in Sphere Worlds","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Université du Québec en Outaouais; Lakehead University","funders":"","keywords":"Obstacle; Computer science; Point (geometry); Control theory (sociology); Exponential stability; Path (computing); Euclidean space; Motion planning; Obstacle avoidance; Euclidean shortest path; Equilibrium point; Trajectory; Control (management); Shortest path problem; Mathematics; Mobile robot; Artificial intelligence; Mathematical analysis; Geometry; Physics; Geography; Robot","score_opus":0.016711634361041745,"score_gpt":0.26189190418365393,"score_spread":0.24518026982261218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382936011","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2488525,0.00006310391,0.74065673,0.0021058936,0.00043217896,0.00016853762,0.0000011562547,0.0007878885,0.006931997],"genre_scores_gemma":[0.40352216,0.0000051462584,0.59276235,0.00012894406,0.000042735282,0.000007714352,0.000007271441,0.000008476753,0.0035151804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916893,0.00003412966,0.00015245842,0.00027532363,0.00014093405,0.00022819517],"domain_scores_gemma":[0.9995846,0.000083788866,0.000029596707,0.00022577196,0.000017223165,0.000058977115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002911755,0.00008140858,0.00010185812,0.00009469544,0.00004532847,0.00009050594,0.00025887508,0.000044015287,0.0000107495225],"category_scores_gemma":[0.000022975988,0.00007727676,0.000014013887,0.00066292856,0.00002174238,0.00027511112,0.00015470014,0.0001004715,0.00025105043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009626549,0.00014977212,0.03527033,0.000064001906,0.000023760686,0.0009613998,0.008996009,0.10187659,0.0005700105,0.043202195,0.0070383213,0.801838],"study_design_scores_gemma":[0.0001964532,0.0000437131,0.043869503,0.000027305887,8.1688444e-7,0.000021563581,0.00007032329,0.95409906,0.00009259751,0.0010268952,0.00043701805,0.00011477237],"about_ca_topic_score_codex":0.000095755466,"about_ca_topic_score_gemma":0.00000871854,"teacher_disagreement_score":0.85222244,"about_ca_system_score_codex":0.000032332136,"about_ca_system_score_gemma":0.000042858323,"threshold_uncertainty_score":0.32268292},"labels":[],"label_agreement":null},{"id":"W4383109101","doi":"10.1109/icra48891.2023.10160494","title":"Generating Stable and Collision-Free Policies through Lyapunov Function Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Lyapunov function; Computer science; Collision avoidance; Robot; Obstacle; Stability (learning theory); Collision; Artificial neural network; Obstacle avoidance; Function (biology); Software deployment; Control theory (sociology); Artificial intelligence; Mathematical optimization; Mobile robot; Machine learning; Mathematics; Computer security","score_opus":0.03004624798412377,"score_gpt":0.2638962564805622,"score_spread":0.23385000849643842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383109101","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0529607,0.00009564719,0.93821514,0.0010150231,0.00043778511,0.00008773864,9.809089e-7,0.001054332,0.0061326358],"genre_scores_gemma":[0.07587254,0.00006348435,0.90524143,0.0007617709,0.00029691754,0.000021747923,0.000011587278,0.000025783535,0.017704766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988924,0.00005896991,0.00016143231,0.0003119761,0.00025874295,0.00031645354],"domain_scores_gemma":[0.9992995,0.00019159517,0.000054032178,0.0003499898,0.0000514728,0.000053366002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003809271,0.00010866156,0.00012237934,0.00009134219,0.00041871812,0.0002855397,0.0003392635,0.000049211907,0.0000065562936],"category_scores_gemma":[0.00020160402,0.00009510562,0.000020225882,0.0007454638,0.000027129374,0.000579206,0.00052234094,0.00013115043,0.00008599932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058661726,0.000028954217,0.0087317275,0.000038443497,0.000047667167,0.000052845513,0.007014483,0.8420783,0.007235414,0.050337233,0.05277523,0.031653836],"study_design_scores_gemma":[0.00021452109,0.00008204758,0.0038005123,0.000017706672,0.0000034000893,0.000014707562,0.0003316431,0.98814124,0.0005348959,0.0022057677,0.004506942,0.00014662185],"about_ca_topic_score_codex":0.0002532111,"about_ca_topic_score_gemma":0.0000034927023,"teacher_disagreement_score":0.14606294,"about_ca_system_score_codex":0.00002098074,"about_ca_system_score_gemma":0.00003608961,"threshold_uncertainty_score":0.38782948},"labels":[],"label_agreement":null},{"id":"W4383109281","doi":"10.1109/icra48891.2023.10160894","title":"Stochastic Planning for ASV Navigation Using Satellite Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satellite; Robot; Computer science; Sampling (signal processing); Remote sensing; Plan (archaeology); Motion planning; Vegetation (pathology); Environmental science; Artificial intelligence; Computer vision; Geography; Engineering","score_opus":0.07616208701857478,"score_gpt":0.3436639477725432,"score_spread":0.2675018607539684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383109281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075179623,0.00005924045,0.9905444,0.00019602086,0.0005257739,0.00019139239,0.0000032546814,0.00070692884,0.00025507162],"genre_scores_gemma":[0.115366444,8.8470415e-7,0.8837284,0.000093590745,0.0001364258,0.00002088393,0.000025922192,0.0000168639,0.0006105844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897504,0.000022510465,0.00017234172,0.00030733438,0.00019512513,0.0003276608],"domain_scores_gemma":[0.9992641,0.0002810261,0.000062812185,0.00026512454,0.000063581465,0.000063330044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037496886,0.00010604196,0.00011642427,0.00013726091,0.00016309394,0.00015471844,0.00036692343,0.000043499305,0.0000012007444],"category_scores_gemma":[0.00007705607,0.000100545396,0.000041303214,0.0005126306,0.000019316509,0.00036498578,0.00011083856,0.00006640957,0.00008265485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061294813,0.000018888652,0.0005448953,0.000056662546,0.000029978519,0.00006985541,0.0019226035,0.9238809,0.009357862,0.0044877683,0.001527362,0.058097117],"study_design_scores_gemma":[0.00016146261,0.00003033126,0.001816475,0.00007336116,0.000004777367,0.000021670325,0.000048333324,0.99337757,0.0010277824,0.0032154447,0.00008252943,0.00014028446],"about_ca_topic_score_codex":0.0000130538,"about_ca_topic_score_gemma":5.036184e-8,"teacher_disagreement_score":0.10784848,"about_ca_system_score_codex":0.000032952445,"about_ca_system_score_gemma":0.00004056313,"threshold_uncertainty_score":0.41001225},"labels":[],"label_agreement":null},{"id":"W4383875147","doi":"10.1109/icat57854.2023.10171197","title":"Vision-guided Unified Task and Motion Planning for Multiple Robots in Cluttered Environments","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Workspace; Motion planning; Computer science; Robot; Task (project management); Scheduling (production processes); Automotive industry; Motion (physics); Robot kinematics; Robotic arm; Artificial intelligence; Mobile robot; Distributed computing; Real-time computing; Human–computer interaction; Engineering; Systems engineering","score_opus":0.04820163388764171,"score_gpt":0.3024940930785046,"score_spread":0.2542924591908629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383875147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06883967,0.00001739796,0.92965615,0.0005653484,0.00024630895,0.00033011826,0.0000040152217,0.00020453142,0.00013647237],"genre_scores_gemma":[0.714281,0.000006018887,0.2846565,0.00016178038,0.00003447871,0.000050182432,0.00003982301,0.000015037881,0.0007551884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987575,0.000047509428,0.00024276903,0.0004362554,0.0001857741,0.0003302187],"domain_scores_gemma":[0.9992797,0.00027877055,0.00005882875,0.00029613258,0.000008448658,0.00007814784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043624543,0.00012514039,0.00015173451,0.00021573639,0.00009571582,0.000081667284,0.0003089774,0.00007061679,0.0000015911887],"category_scores_gemma":[0.000112109316,0.00011952188,0.000024781506,0.00031751418,0.000021985923,0.0003224439,0.0002173234,0.00007775349,0.000036864778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037500376,0.00020843124,0.07262158,0.00008038519,0.00004767623,0.00022219379,0.006410183,0.81207466,0.02665131,0.0016072142,0.012295288,0.0677436],"study_design_scores_gemma":[0.00088180095,0.000049456597,0.12841009,0.000033165787,0.0000014462239,0.0000062124427,0.000049108374,0.8690532,0.0005325435,0.0006470808,0.00020528206,0.000130661],"about_ca_topic_score_codex":0.00003318928,"about_ca_topic_score_gemma":0.0000015789329,"teacher_disagreement_score":0.64544135,"about_ca_system_score_codex":0.000039325296,"about_ca_system_score_gemma":0.00001121964,"threshold_uncertainty_score":0.4873961},"labels":[],"label_agreement":null},{"id":"W4383889003","doi":"10.15866/ireaco.v16i2.23120","title":"Medium and Low-Level Energy Saving Control Strategies for Electric-Powered UAVs","year":2023,"lang":"en","type":"article","venue":"International Review of Automatic Control (IREACO)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Energy consumption; Energy (signal processing); Controller (irrigation); Minification; Electric energy consumption; MATLAB; Point (geometry); Energy minimization; Electric vehicle; Control (management); Computer science; Engineering; Drone; Automotive engineering; Control engineering; Control theory (sociology); Simulation; Electric energy; Artificial intelligence; Electrical engineering","score_opus":0.018636822596107556,"score_gpt":0.28853197430012517,"score_spread":0.2698951517040176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383889003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011310605,0.011527653,0.97637045,0.0076435893,0.0011775217,0.00081776886,0.00009425274,0.0004197602,0.0008179361],"genre_scores_gemma":[0.9625894,0.00461707,0.02880989,0.0028462412,0.00027708206,0.00048781314,0.000053072356,0.000039206374,0.00028025504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975186,0.00012414252,0.00087013916,0.00041883197,0.00069656374,0.00037170248],"domain_scores_gemma":[0.99680567,0.0017176076,0.0005291617,0.0003986222,0.0004390354,0.000109893095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012333454,0.0002505049,0.0006912835,0.00025860328,0.0000738795,0.00014148261,0.0010481551,0.00006994855,0.000019793657],"category_scores_gemma":[0.0012844973,0.00021830149,0.00018674841,0.00038770802,0.000048468028,0.00044531672,0.000102369646,0.00010023215,0.000019372397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004556608,0.00021836681,0.00029165717,0.0061835223,0.0012537744,0.000110524095,0.0005714136,0.001008266,0.012785264,0.12874265,0.012391611,0.83639735],"study_design_scores_gemma":[0.0019077626,0.00011326172,0.00628494,0.0040103006,0.00007255798,0.000045205776,0.000017876197,0.97804636,0.00007343883,0.0077503105,0.0014427814,0.00023519725],"about_ca_topic_score_codex":0.0000371344,"about_ca_topic_score_gemma":0.0000031715563,"teacher_disagreement_score":0.9770381,"about_ca_system_score_codex":0.0000720944,"about_ca_system_score_gemma":0.00032551744,"threshold_uncertainty_score":0.8902076},"labels":[],"label_agreement":null},{"id":"W4384787430","doi":"10.1109/tiv.2023.3296435","title":"Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Planner; Path (computing); Tracking (education); Plan (archaeology); Computer science; Motion planning; Control theory (sociology); Artificial intelligence; Computer vision; Real-time computing; Mathematical optimization; Mathematics; Robot; Control (management); Geography; Psychology","score_opus":0.05724224967894712,"score_gpt":0.2861207910976258,"score_spread":0.22887854141867867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384787430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061225053,0.00002195805,0.9360395,0.00070552056,0.00051485363,0.00059743464,0.00008430177,0.0005605718,0.00025077126],"genre_scores_gemma":[0.87085503,0.00008120408,0.12787738,0.00033608577,0.00009386463,0.00008995295,0.0000045524444,0.000060837105,0.00060108927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973948,0.00016216596,0.0004954648,0.0007012726,0.0006119522,0.00063436705],"domain_scores_gemma":[0.9983139,0.00060508127,0.00010977764,0.00049804326,0.00011725059,0.0003559393],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057182775,0.00034832783,0.00043860968,0.00053423346,0.00040460934,0.0003064307,0.00061969034,0.00015498316,0.000030316405],"category_scores_gemma":[0.000029052175,0.0003254333,0.00013161563,0.00088534626,0.00007737792,0.00033707195,0.00001461627,0.00033452112,0.00039671655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002942944,0.00028483596,0.000049660935,0.00006224803,0.00024891176,0.00037505868,0.0064552696,0.8361222,0.026627045,0.0007548444,0.0007529395,0.12797269],"study_design_scores_gemma":[0.0005059955,0.00050704856,0.000528064,0.00038414542,0.000052354655,0.00008144943,0.00051908207,0.98463494,0.011623732,0.0006345079,0.00005715725,0.00047153162],"about_ca_topic_score_codex":0.00008335304,"about_ca_topic_score_gemma":0.0000085009015,"teacher_disagreement_score":0.80963,"about_ca_system_score_codex":0.00012586098,"about_ca_system_score_gemma":0.000099516634,"threshold_uncertainty_score":0.9999198},"labels":[],"label_agreement":null},{"id":"W4385246654","doi":"10.1007/978-3-031-38318-2_22","title":"Architecture of a Low-Cost Solution for ROVs to Improve Navigation and Data Collection","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Remotely operated underwater vehicle; Data collection; Architecture; Computer science; Geography; Artificial intelligence; Mathematics; Mobile robot; Statistics","score_opus":0.033495510319212746,"score_gpt":0.2726774933411004,"score_spread":0.23918198302188767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385246654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026521815,0.0013092792,0.994753,0.00019872093,0.0018233279,0.0015623065,0.00008823437,0.000071634146,0.00016695316],"genre_scores_gemma":[0.45051056,0.0015117017,0.49615017,0.0009771012,0.012211432,0.0016118971,0.0058939233,0.0008436219,0.030289605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823326,0.000044623725,0.00044291065,0.00078338006,0.0002195367,0.00027630661],"domain_scores_gemma":[0.99823284,0.00061188143,0.00028610457,0.00069455954,0.00009200524,0.00008258767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006691648,0.0002773097,0.00049407873,0.00022235374,0.00010890235,0.00014352553,0.0004389316,0.0004709621,1.721802e-7],"category_scores_gemma":[0.00012318892,0.0002517604,0.000036985137,0.00018252949,0.00003999167,0.000093562936,0.00032801577,0.00036935048,7.188648e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072354625,0.000012896263,0.000086704,0.0010888313,0.00009772411,0.000019757734,0.0009969362,0.69092363,0.00013917517,0.0032144587,0.0012340565,0.3021135],"study_design_scores_gemma":[0.00028770708,0.0001397351,0.000060392264,0.0016541237,0.000020871783,0.000036099624,0.0000015464459,0.99350864,0.0000067084648,0.0031172186,0.0009174854,0.00024945138],"about_ca_topic_score_codex":0.00014197062,"about_ca_topic_score_gemma":0.00007008457,"teacher_disagreement_score":0.49860287,"about_ca_system_score_codex":0.00006010695,"about_ca_system_score_gemma":0.000055052453,"threshold_uncertainty_score":0.99999344},"labels":[],"label_agreement":null},{"id":"W4385271034","doi":"10.1002/rob.22231","title":"Autonomous aerial robotics for package delivery: A technical review","year":2023,"lang":"en","type":"review","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Engineering and Physical Sciences Research Council; UK Research and Innovation","keywords":"Drone; Truck; Robotics; State (computer science); Computer science; Collision avoidance; Grippers; Control (management); Routing (electronic design automation); Engineering; Robot; Systems engineering; Transport engineering; Artificial intelligence; Collision; Embedded system; Computer security; Automotive engineering","score_opus":0.0918850285324049,"score_gpt":0.364628244486263,"score_spread":0.2727432159538581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385271034","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.4381184e-9,0.54345876,0.45222253,0.0010222838,0.002610818,0.00054571294,0.000009744196,0.00008313803,0.0000469989],"genre_scores_gemma":[3.0768206e-8,0.6118643,0.38658494,0.00033667096,0.0009418949,0.000015496293,0.000008127724,0.000050187642,0.00019830468],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953773,0.00025088407,0.002522598,0.0004880428,0.00071125483,0.00064989005],"domain_scores_gemma":[0.9931833,0.002409211,0.0024773271,0.0011061443,0.0005018749,0.00032214445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017779777,0.000608704,0.0034248878,0.00042370922,0.00013442553,0.00020991119,0.0032017177,0.0007381165,0.0000054437483],"category_scores_gemma":[0.0019209177,0.00047837704,0.0016716308,0.0008418817,0.00004729246,0.0002781737,0.0005320056,0.0014774469,0.00007039338],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071530458,0.00013224564,3.6575992e-7,0.042878557,0.00047115103,0.0009063449,0.00002944505,0.0052489894,4.4388395e-7,0.0034184123,0.11446302,0.8324439],"study_design_scores_gemma":[0.00050650607,0.0011107548,5.912086e-7,0.123083055,0.0025678682,0.0031216112,0.000002992666,0.0051643476,0.0000012249805,0.0008408361,0.862659,0.0009412183],"about_ca_topic_score_codex":0.0000021859344,"about_ca_topic_score_gemma":7.974158e-7,"teacher_disagreement_score":0.8315027,"about_ca_system_score_codex":0.0002517797,"about_ca_system_score_gemma":0.0014971083,"threshold_uncertainty_score":0.99976677},"labels":[],"label_agreement":null},{"id":"W4385333971","doi":"10.1109/isscs58449.2023.10190949","title":"Movement Optimization of Robotic Arms for Energy and Time Reduction using Evolutionary Algorithms","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Particle swarm optimization; Trajectory; Robot; Acceleration; Computer science; Robot end effector; Reduction (mathematics); Movement (music); Control theory (sociology); Multi-swarm optimization; Energy consumption; Energy (signal processing); Optimization problem; Artificial intelligence; Engineering; Algorithm; Mathematics; Physics; Control (management)","score_opus":0.027982916697665016,"score_gpt":0.25819003617328506,"score_spread":0.23020711947562006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385333971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046627474,0.000053730262,0.99839365,0.0003235035,0.00035574957,0.00013497953,0.00000248364,0.00018737465,0.000082225575],"genre_scores_gemma":[0.0037897364,0.000016569045,0.9951992,0.000035592584,0.0000709025,0.00001533513,0.000025478128,0.000009900429,0.00083731057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908847,0.00002857757,0.00021814585,0.00028372143,0.00019054321,0.00019052003],"domain_scores_gemma":[0.9994888,0.000058437847,0.0000946764,0.0002065257,0.00010176862,0.00004981105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021476395,0.00009268023,0.00013550003,0.00019311722,0.0000878572,0.000029176548,0.00016890356,0.000048075402,0.0000045592233],"category_scores_gemma":[0.000024057726,0.00009048966,0.000031941425,0.00048188178,0.00003297067,0.00029575173,0.000111408015,0.000022278437,0.0000026494133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019979423,0.000018942763,0.000014949437,0.000010231852,0.000013934672,0.0000011505379,0.00010889542,0.99037075,0.0021304004,0.002454581,0.0006092575,0.004264919],"study_design_scores_gemma":[0.00020248452,0.000075667645,0.00019413771,0.00002256778,0.000008628912,0.000015523976,0.000031434913,0.9956498,0.0012022799,0.00248269,0.000012663775,0.00010207896],"about_ca_topic_score_codex":0.0000472928,"about_ca_topic_score_gemma":5.8966513e-8,"teacher_disagreement_score":0.0052791024,"about_ca_system_score_codex":0.000050008708,"about_ca_system_score_gemma":0.000047807433,"threshold_uncertainty_score":0.36900613},"labels":[],"label_agreement":null},{"id":"W4385357007","doi":"10.1007/978-3-031-38906-1_28","title":"Shortest Coordinated Motion for Square Robots","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Square (algebra); Obstacle; Computer science; Robot; Motion (physics); Euclidean geometry; Least-squares function approximation; Motion planning; Algorithm; Plane (geometry); Artificial intelligence; Mathematics; Geometry; Statistics; Geography","score_opus":0.03417938870758149,"score_gpt":0.2703383494041565,"score_spread":0.23615896069657502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385357007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006699408,0.00011276942,0.99196297,0.0010181464,0.004518473,0.0008396486,0.00002102605,0.0007491726,0.0007711069],"genre_scores_gemma":[0.014084327,0.000014750595,0.9824779,0.0007201598,0.0007562643,0.000055542532,0.00005551048,0.00010165509,0.001733902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536145,0.00003294456,0.0006170098,0.0020535067,0.0009708015,0.00096427195],"domain_scores_gemma":[0.99646044,0.0009899557,0.0003230292,0.0015400378,0.00046418852,0.00022234474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012439229,0.0006287236,0.00065264886,0.0011234928,0.00036817955,0.0006150445,0.003649654,0.00047128389,0.0000049345745],"category_scores_gemma":[0.00031460173,0.0006139795,0.00018687632,0.0011036721,0.0004155305,0.000577379,0.0010673819,0.0007226722,0.00011477482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037396676,0.000023619054,0.00005949008,0.000053439966,0.000014920957,0.0001337705,0.00035579133,0.47785422,0.00005330057,0.010441862,0.00013388968,0.51087195],"study_design_scores_gemma":[0.00028326816,0.00021215357,0.0003062435,0.00047587688,0.000010454837,0.00005821487,1.2609713e-7,0.91520095,0.00028409789,0.08229715,0.00021332904,0.00065816654],"about_ca_topic_score_codex":0.000019354951,"about_ca_topic_score_gemma":0.00001765415,"teacher_disagreement_score":0.5102138,"about_ca_system_score_codex":0.00036006325,"about_ca_system_score_gemma":0.0004542048,"threshold_uncertainty_score":0.99963117},"labels":[],"label_agreement":null},{"id":"W4385430633","doi":"10.15607/rss.2023.xix.041","title":"FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Benchmark (surveying); Computer science; Horizon; Artificial intelligence; Mathematics; Geology; Geodesy","score_opus":0.10588225931183778,"score_gpt":0.3353532847107676,"score_spread":0.2294710253989298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385430633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037436606,0.00001550259,0.96182054,0.0027200512,0.0014530554,0.00065694546,0.0000059198105,0.0017671171,0.027817177],"genre_scores_gemma":[0.19360198,0.000019534942,0.7697917,0.00031244455,0.0008328219,0.00013996656,0.00039654097,0.000049103077,0.034855854],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977948,0.000054578217,0.00035975245,0.0009044058,0.0003755893,0.000510891],"domain_scores_gemma":[0.998137,0.00025583315,0.0001241774,0.0012317284,0.0001348208,0.00011643832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000964246,0.000188404,0.00022659254,0.00038089394,0.0002469471,0.00018505403,0.0007779968,0.000066373046,0.000046075173],"category_scores_gemma":[0.00012665978,0.00017641582,0.00008896021,0.0015760511,0.0000263068,0.00042874514,0.00023304122,0.000113721995,0.000315185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036096473,0.00019454252,0.032798912,0.00022162985,0.000118279815,0.00016575301,0.0013021302,0.069394514,0.0028081909,0.20607863,0.56285524,0.12402607],"study_design_scores_gemma":[0.0003505522,0.00016621158,0.2196525,0.000027491771,0.000009639445,0.000011419377,0.000018801285,0.76304495,0.0007518645,0.010713878,0.0049281176,0.0003245991],"about_ca_topic_score_codex":0.00012494272,"about_ca_topic_score_gemma":0.000046724694,"teacher_disagreement_score":0.6936504,"about_ca_system_score_codex":0.00007007566,"about_ca_system_score_gemma":0.00005802352,"threshold_uncertainty_score":0.71940285},"labels":[],"label_agreement":null},{"id":"W4385452913","doi":"10.1109/tits.2023.3297068","title":"Spatio-Temporal Lattice Planning Using Optimal Motion Primitives","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Notation; Lattice (music); Integer lattice; Algorithm; Set (abstract data type); Mathematics; Discrete mathematics; Combinatorics; Computer science; Theoretical computer science; Artificial intelligence; Programming language; Arithmetic","score_opus":0.07830150321909707,"score_gpt":0.31075451657102005,"score_spread":0.23245301335192298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385452913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10461394,0.000041581312,0.8907915,0.00006468893,0.0028996046,0.0004819834,0.000057617704,0.000987715,0.00006142216],"genre_scores_gemma":[0.96426064,0.0000148275485,0.03508776,0.000031459556,0.00008667405,0.00008606276,0.000075331125,0.000039131504,0.0003180978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972622,0.00016021782,0.0007648878,0.0006442365,0.0007068932,0.00046156003],"domain_scores_gemma":[0.9987382,0.0002187977,0.00023534178,0.00044881587,0.00018836906,0.00017045894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052386895,0.0003096877,0.00031976917,0.0006641042,0.00038246514,0.00021223196,0.00045059994,0.00016233067,0.000013945599],"category_scores_gemma":[0.0000053893186,0.00033049696,0.00016532191,0.0013083242,0.0000511194,0.00078231696,8.3590174e-7,0.00031140842,0.00024409972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021278625,0.00007575114,0.0004897959,0.00006454779,0.00006423863,0.00007438081,0.0045579127,0.99062306,0.00031096002,0.000497462,0.000030410058,0.003190195],"study_design_scores_gemma":[0.00028378735,0.00011429403,0.0020648523,0.00029035364,0.000044397308,0.00002561977,0.0010844956,0.986673,0.008756401,0.000027778276,0.000264076,0.00037096153],"about_ca_topic_score_codex":0.00021445288,"about_ca_topic_score_gemma":0.0000068897884,"teacher_disagreement_score":0.85964674,"about_ca_system_score_codex":0.00014499984,"about_ca_system_score_gemma":0.00009216663,"threshold_uncertainty_score":0.9999147},"labels":[],"label_agreement":null},{"id":"W4385488464","doi":"10.1109/aim46323.2023.10196284","title":"A Parameterized Cubic Bézier Spline-based Informed RRT* for Non-holonomic Path Planning","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Mitacs","keywords":"Parameterized complexity; Path (computing); Motion planning; Algorithm; Computer science; Robotics; Mathematics; Artificial intelligence; Robot","score_opus":0.05020425258526109,"score_gpt":0.32124756337269283,"score_spread":0.27104331078743177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385488464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09752007,0.000007779619,0.8988506,0.00063389394,0.00080155715,0.0005448822,0.000011704242,0.0010081622,0.0006213569],"genre_scores_gemma":[0.17426354,0.0000015792938,0.8231574,0.0010016648,0.0001214051,0.0002714964,0.0000731595,0.000033120294,0.00107661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980718,0.000029335484,0.00042206244,0.0005262005,0.00024720925,0.0007033854],"domain_scores_gemma":[0.99807256,0.0008303805,0.00014759981,0.0007153834,0.00006482754,0.00016926973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005673519,0.00024676885,0.00033600355,0.00032671719,0.00017526095,0.00022305067,0.0009846366,0.000118283126,0.000007656323],"category_scores_gemma":[0.00023305445,0.00021997257,0.00014520952,0.000683925,0.000042136162,0.00036392157,0.00019974283,0.00014792116,0.0004201535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066627184,0.00048954133,0.01618725,0.00088042003,0.00049315835,0.0010228,0.011073434,0.6228241,0.020305788,0.0065958076,0.20626253,0.11319892],"study_design_scores_gemma":[0.0016304514,0.00019563694,0.0051365104,0.000071213035,0.000008212526,0.000008266566,0.000037831924,0.9886613,0.0014189166,0.00042926616,0.0020831588,0.00031923637],"about_ca_topic_score_codex":0.000016243686,"about_ca_topic_score_gemma":3.8432327e-7,"teacher_disagreement_score":0.36583722,"about_ca_system_score_codex":0.00007239226,"about_ca_system_score_gemma":0.00031406188,"threshold_uncertainty_score":0.8970221},"labels":[],"label_agreement":null},{"id":"W4385521945","doi":"10.1109/tase.2023.3297176","title":"Learning-Based Risk-Bounded Path Planning Under Environmental Uncertainty","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Mathematical optimization; Probabilistic logic; Bounded function; Artificial neural network; Path (computing); Gaussian; Motion planning; Artificial intelligence; Robot; Mathematics","score_opus":0.01405278267388568,"score_gpt":0.23539369070329694,"score_spread":0.22134090802941125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385521945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18642963,0.0000059559957,0.81207365,0.00011089131,0.00039347354,0.0000758502,0.0000031750494,0.00089076476,0.00001659343],"genre_scores_gemma":[0.97894716,0.000008026081,0.020907193,0.000053051455,0.0000129012,0.000020501951,0.0000017610776,0.000009686911,0.000039707007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848163,0.000028522385,0.00017067886,0.0003843859,0.0005905374,0.00034425218],"domain_scores_gemma":[0.9993862,0.00017085773,0.000059629223,0.00023019951,0.000023812352,0.00012931006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079224503,0.00014088098,0.00010662523,0.00052732753,0.00062055513,0.00024439997,0.00032085922,0.000048384958,0.0000041087646],"category_scores_gemma":[0.000029707597,0.00014213774,0.000032177737,0.0012362618,0.00009482378,0.0006299396,0.000004992309,0.00025503337,0.00006922624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.51062e-7,0.000011349343,0.000041361636,0.0000048625266,0.000004016434,0.0000046272903,0.00051676494,0.98752564,0.003112804,0.000031844847,0.000008888717,0.008736866],"study_design_scores_gemma":[0.00020532719,0.00006719024,0.009206063,0.000035453442,0.0000046438586,0.000008191069,0.000115065115,0.98797363,0.0021226057,0.00003115831,0.0000671249,0.00016352272],"about_ca_topic_score_codex":0.000009701477,"about_ca_topic_score_gemma":1.2893864e-7,"teacher_disagreement_score":0.79251754,"about_ca_system_score_codex":0.00015596072,"about_ca_system_score_gemma":0.00008796222,"threshold_uncertainty_score":0.5796209},"labels":[],"label_agreement":null},{"id":"W4385574945","doi":"10.1109/tie.2023.3299048","title":"Vision-Based Reactive Temporal Logic Motion Planning for Quadruped Robots in Unstructured Dynamic Environments","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Workspace; Robot; Computer science; Trajectory; Temporal logic; Motion planning; Linear temporal logic; Artificial intelligence; Computer vision; Motion (physics); Focus (optics); Real-time computing; Control engineering; Simulation; Engineering","score_opus":0.04262890341155186,"score_gpt":0.3026465813194784,"score_spread":0.26001767790792657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385574945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015900964,0.000019174437,0.98084736,0.00072488055,0.0013308724,0.0008114476,0.00003342233,0.00031651373,0.000015380509],"genre_scores_gemma":[0.98019934,0.0000077939585,0.019150138,0.00009465212,0.00006699546,0.00019367997,0.0000682389,0.000040673563,0.00017847007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974304,0.00018583219,0.0004617888,0.00070944754,0.00043847106,0.0007740949],"domain_scores_gemma":[0.99874055,0.0004552909,0.00018573794,0.00047770044,0.0000256403,0.0001150734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057376694,0.0003074721,0.0003319499,0.0006317256,0.00027658188,0.00009141513,0.00055179134,0.00041267215,0.000005064428],"category_scores_gemma":[0.000037255373,0.00032324676,0.00013972998,0.0012402994,0.00004960296,0.00034245907,0.0000039035676,0.00090500247,0.00002922179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014203425,0.00012954827,0.000030720665,0.0000046865703,0.000029387387,0.000019246701,0.0001670953,0.9633132,0.0022149624,0.00007216117,0.00008636402,0.033790596],"study_design_scores_gemma":[0.0030567395,0.00076840364,0.00031809072,0.00007478163,0.000021840111,0.000007246647,0.00004050165,0.9858961,0.0080000395,0.0012645,0.00020538723,0.0003463679],"about_ca_topic_score_codex":0.00003835779,"about_ca_topic_score_gemma":0.000017808263,"teacher_disagreement_score":0.96429837,"about_ca_system_score_codex":0.00082273065,"about_ca_system_score_gemma":0.0003565872,"threshold_uncertainty_score":0.999922},"labels":[],"label_agreement":null},{"id":"W4385583369","doi":"10.1016/j.comnet.2023.109963","title":"A Novel Ant Colony-inspired Coverage Path Planning for Internet of Drones","year":2023,"lang":"en","type":"article","venue":"Computer Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Drone; Computer science; Context (archaeology); Ant colony optimization algorithms; The Internet; Ant colony; Path (computing); Motion planning; Energy consumption; Cover (algebra); Robot; Distributed computing; Artificial intelligence; Computer network; World Wide Web","score_opus":0.0352631949391018,"score_gpt":0.2689008076533971,"score_spread":0.23363761271429528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385583369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069209207,0.00013436898,0.98939604,0.00012558854,0.0024730961,0.00034585185,0.000009050382,0.00051559415,0.00007946997],"genre_scores_gemma":[0.47323096,0.000010702737,0.52527744,0.00039922126,0.00070618856,0.00005839011,0.000064795466,0.0000363135,0.00021597724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982217,0.000044189488,0.00042561904,0.000527743,0.00024093625,0.00053980923],"domain_scores_gemma":[0.9985585,0.0005490488,0.00016573978,0.00051358255,0.0001010721,0.00011203272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004667026,0.00022460936,0.00039187897,0.00016815141,0.00008885418,0.00012571819,0.0011257604,0.00012877086,0.0000011180691],"category_scores_gemma":[0.000025411828,0.00022111928,0.0001253872,0.0006180222,0.000050944815,0.00021310187,0.000622692,0.00017248948,0.000013386146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026604326,0.00009381779,0.0019295686,0.000060991508,0.000086770015,0.0000967489,0.0015552505,0.8990596,0.00014410772,0.004559021,0.048274826,0.044112686],"study_design_scores_gemma":[0.00078058307,0.00028211734,0.006475053,0.00024501153,0.000008570944,0.00002998818,0.0000047042986,0.9898276,0.000065153894,0.00029925027,0.0017428072,0.00023917948],"about_ca_topic_score_codex":0.000017798531,"about_ca_topic_score_gemma":3.283671e-7,"teacher_disagreement_score":0.46631005,"about_ca_system_score_codex":0.000037296744,"about_ca_system_score_gemma":0.00005143216,"threshold_uncertainty_score":0.90169823},"labels":[],"label_agreement":null},{"id":"W4385733470","doi":"10.1504/ijbic.2023.132789","title":"UAV path planning in presence of occlusions as noisy combinatorial multi-objective optimisation","year":2023,"lang":"en","type":"article","venue":"International Journal of Bio-Inspired Computation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Motion planning; Path (computing); Artificial intelligence; Computer vision; Mathematical optimization; Mathematics; Computer network","score_opus":0.030115089960459352,"score_gpt":0.32604127262178595,"score_spread":0.2959261826613266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385733470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38166952,0.000040494146,0.6130514,0.00060849154,0.004254423,0.00015659598,0.0000073783594,0.0000725031,0.00013921114],"genre_scores_gemma":[0.91134244,0.000012962643,0.08836625,0.00004788255,0.00018442456,0.000003904865,0.000018449002,0.00001023025,0.000013455404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997485,0.00017928838,0.0008311362,0.0002504315,0.0010501016,0.00020408859],"domain_scores_gemma":[0.9975452,0.00041760437,0.0008929579,0.00014166097,0.000916861,0.000085715285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008586044,0.00015073073,0.00026455935,0.0009937349,0.00006899996,0.000102981045,0.0009919989,0.000094742056,0.000001927942],"category_scores_gemma":[0.0006289175,0.00015209813,0.00010742371,0.0008304346,0.0000522427,0.0008012859,0.00024625752,0.00026118124,0.000018792929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010011742,0.00030725446,0.008819866,0.000013121594,0.00011664716,0.0006001306,0.008318982,0.95421326,0.0037784518,0.002890446,0.00033461902,0.020507084],"study_design_scores_gemma":[0.0020380358,0.0002783292,0.0908095,0.00037409435,0.000008038882,0.00012492441,0.00028500572,0.8991192,0.0015242721,0.005263964,0.0000238586,0.00015079167],"about_ca_topic_score_codex":0.00010273903,"about_ca_topic_score_gemma":0.0000013032433,"teacher_disagreement_score":0.5296729,"about_ca_system_score_codex":0.00022156081,"about_ca_system_score_gemma":0.00028985224,"threshold_uncertainty_score":0.6202382},"labels":[],"label_agreement":null},{"id":"W4385977709","doi":"10.32920/23989458.v1","title":"Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"","keywords":"Motion planning; Path (computing); Computer science; Mobile robot; Robot; Graph; Representation (politics); Task (project management); Any-angle path planning; Real-time computing; Artificial intelligence; Computer network; Engineering; Theoretical computer science","score_opus":0.07810824251421308,"score_gpt":0.3295470703901782,"score_spread":0.25143882787596517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385977709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07758886,0.00007937518,0.91931474,0.000031363368,0.0012025192,0.0013427065,0.000042052612,0.00034504145,0.000053314117],"genre_scores_gemma":[0.34415212,0.0000036758445,0.6553248,0.000025639112,0.00008365597,0.00022193593,0.00009276536,0.0000477549,0.00004762582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968462,0.00014252609,0.00074627873,0.0011408593,0.0006483292,0.00047582557],"domain_scores_gemma":[0.99702066,0.0008314061,0.0007627787,0.0011977408,0.00008062774,0.00010680192],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081548217,0.00037305424,0.0005751994,0.0003911189,0.00018821254,0.00015747041,0.00079995947,0.00022609867,0.0000037577875],"category_scores_gemma":[0.00014168126,0.00037808582,0.0003194197,0.0003454093,0.000056004083,0.00006901581,0.0010322802,0.00032841566,0.0000066594416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007396071,0.000098791024,0.002588742,0.00020044897,0.000047386562,0.000020992306,0.0006035997,0.9939302,0.0008204036,0.000093746894,0.000046897865,0.0015413456],"study_design_scores_gemma":[0.00045047654,0.000103844395,0.001948299,0.0005254445,0.00004129307,0.000003689617,0.000058729263,0.99255913,0.0029972664,0.00093700475,0.00000438576,0.00037042846],"about_ca_topic_score_codex":0.00025539793,"about_ca_topic_score_gemma":2.4730255e-7,"teacher_disagreement_score":0.26656327,"about_ca_system_score_codex":0.00020037535,"about_ca_system_score_gemma":0.0001645435,"threshold_uncertainty_score":0.9998671},"labels":[],"label_agreement":null},{"id":"W4385987066","doi":"10.32920/23989458","title":"Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"","keywords":"Motion planning; Path (computing); Computer science; Mobile robot; Graph; Robot; Representation (politics); Task (project management); Any-angle path planning; Real-time computing; Artificial intelligence; Computer network; Engineering; Theoretical computer science","score_opus":0.07810824251421308,"score_gpt":0.3295470703901782,"score_spread":0.25143882787596517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385987066","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07758886,0.00007937518,0.91931474,0.000031363368,0.0012025192,0.0013427065,0.000042052612,0.00034504145,0.000053314117],"genre_scores_gemma":[0.34415212,0.0000036758445,0.6553248,0.000025639112,0.00008365597,0.00022193593,0.00009276536,0.0000477549,0.00004762582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968462,0.00014252609,0.00074627873,0.0011408593,0.0006483292,0.00047582557],"domain_scores_gemma":[0.99702066,0.0008314061,0.0007627787,0.0011977408,0.00008062774,0.00010680192],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081548217,0.00037305424,0.0005751994,0.0003911189,0.00018821254,0.00015747041,0.00079995947,0.00022609867,0.0000037577875],"category_scores_gemma":[0.00014168126,0.00037808582,0.0003194197,0.0003454093,0.000056004083,0.00006901581,0.0010322802,0.00032841566,0.0000066594416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007396071,0.000098791024,0.002588742,0.00020044897,0.000047386562,0.000020992306,0.0006035997,0.9939302,0.0008204036,0.000093746894,0.000046897865,0.0015413456],"study_design_scores_gemma":[0.00045047654,0.000103844395,0.001948299,0.0005254445,0.00004129307,0.000003689617,0.000058729263,0.99255913,0.0029972664,0.00093700475,0.00000438576,0.00037042846],"about_ca_topic_score_codex":0.00025539793,"about_ca_topic_score_gemma":2.4730255e-7,"teacher_disagreement_score":0.26656327,"about_ca_system_score_codex":0.00020037535,"about_ca_system_score_gemma":0.0001645435,"threshold_uncertainty_score":0.9998671},"labels":[],"label_agreement":null},{"id":"W4386016673","doi":"10.1016/j.compag.2023.108128","title":"An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs","year":2023,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Jiangxi Provincial Department of Science and Technology; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Quadcopter; Stateflow; Computer science; Motion planning; Task (project management); MATLAB; Real-time computing; Sequential quadratic programming; Simulation; Path (computing); Scheme (mathematics); Engineering; Quadratic programming; Mathematical optimization; Artificial intelligence; Robot; Systems engineering; Mathematics","score_opus":0.01292044868101696,"score_gpt":0.2900044637704432,"score_spread":0.27708401508942626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386016673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06293549,0.00092297175,0.9354329,0.000078509765,0.00021547102,0.0002802656,0.0000068092695,0.0001138462,0.000013704932],"genre_scores_gemma":[0.4517831,0.00005411389,0.5480014,0.000076492324,0.000044861164,0.00001635433,0.000011591206,0.000008223438,0.0000038408803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983864,0.00007045523,0.00030839653,0.0005470946,0.00020463724,0.00048304774],"domain_scores_gemma":[0.99823207,0.0012354875,0.00015312365,0.00023633422,0.000049439663,0.00009354218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006817093,0.000204918,0.0003254142,0.00014556969,0.00017090885,0.00015792952,0.00041343548,0.00011567951,1.4906799e-7],"category_scores_gemma":[0.00008497774,0.00016255652,0.000042278487,0.00043517863,0.000020074354,0.00023700384,0.00020852832,0.00024162898,3.6964283e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025471365,0.00008008307,0.0053362898,0.000077710116,0.00004283754,0.000087311404,0.0035737727,0.656082,0.0044280076,0.0040444657,0.001314719,0.32490736],"study_design_scores_gemma":[0.00053784234,0.000311956,0.013190619,0.00029365715,0.000009912288,0.00005677013,0.00008573318,0.9785438,0.00014800052,0.0061844266,0.00040659896,0.00023067665],"about_ca_topic_score_codex":0.00001175905,"about_ca_topic_score_gemma":0.0000035090268,"teacher_disagreement_score":0.3888476,"about_ca_system_score_codex":0.00008817638,"about_ca_system_score_gemma":0.00005797858,"threshold_uncertainty_score":0.66288626},"labels":[],"label_agreement":null},{"id":"W4386038368","doi":"10.1109/lra.2023.3306996","title":"Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning With Efficient Heuristics","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Mathematical optimization; Integer programming; Computer science; Path (computing); Reduction (mathematics); Planner; Mathematics; Artificial intelligence","score_opus":0.026652384698934235,"score_gpt":0.2562367640614579,"score_spread":0.22958437936252366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386038368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031637624,0.00001541478,0.9651714,0.0014251835,0.00055302813,0.0004573864,0.000009419079,0.0007256528,0.0000048779266],"genre_scores_gemma":[0.1276989,0.0000016390741,0.87159204,0.000398772,0.0000932283,0.000053724314,0.000045208246,0.000033701614,0.00008278875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983314,0.00005182128,0.00031616635,0.00047131762,0.00033463124,0.00049462594],"domain_scores_gemma":[0.9990143,0.0002903411,0.00018941176,0.00029744513,0.00008692332,0.00012155041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041433927,0.00024276148,0.00025349468,0.00021331475,0.00028702398,0.00036701915,0.00031097193,0.000071395436,4.532857e-7],"category_scores_gemma":[0.00006340838,0.0002142681,0.00005276354,0.0004578313,0.00006654707,0.00018188315,0.00007967236,0.00015329996,0.0000337811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005817686,0.000038196955,0.00019776616,0.00005223662,0.00002965664,0.000066294495,0.0008366759,0.99163556,0.0012431811,0.00019815394,0.001368117,0.0043283696],"study_design_scores_gemma":[0.0007663175,0.00011598686,0.0020038332,0.00015614033,0.000021270604,0.000031358162,0.000030887863,0.99619,0.00021517879,0.00000929168,0.00016155596,0.00029815215],"about_ca_topic_score_codex":0.000003737845,"about_ca_topic_score_gemma":1.07078506e-7,"teacher_disagreement_score":0.09606128,"about_ca_system_score_codex":0.000051882304,"about_ca_system_score_gemma":0.000041899915,"threshold_uncertainty_score":0.8737599},"labels":[],"label_agreement":null},{"id":"W4386048956","doi":"10.1007/s41315-023-00286-y","title":"Chasing an intruder with limited information","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Robotics and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Carleton University","keywords":"Snapshot (computer storage); Computer science; Guard (computer science); Real-time computing","score_opus":0.021202779892759687,"score_gpt":0.283594039368258,"score_spread":0.2623912594754983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386048956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023806496,0.000027891618,0.994184,0.0027954446,0.00025644005,0.000097112206,0.0000034487427,0.000059568785,0.00019545096],"genre_scores_gemma":[0.4434477,0.00039279723,0.5547466,0.0006949808,0.00054609135,0.000023373426,0.00005798484,0.00001585809,0.0000745838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895215,0.00001862856,0.00038301686,0.00010385839,0.00041957892,0.0001227538],"domain_scores_gemma":[0.998627,0.000083544925,0.00030738194,0.00017393111,0.00069630606,0.00011185375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027955443,0.00009026238,0.000105789,0.00039789386,0.00007896624,0.00031512883,0.000685613,0.00003580447,0.0000024812803],"category_scores_gemma":[0.00003567836,0.000072507646,0.000030321542,0.00038730237,0.000039359664,0.0009902332,0.000098988734,0.0001425435,0.000033792203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022440674,0.0001386975,0.0014279879,0.00001063834,0.00017464822,0.000039644096,0.0017959715,0.45013684,0.00021195559,0.16905862,0.0005088351,0.37647372],"study_design_scores_gemma":[0.000599057,0.0003735561,0.006910019,0.0001489073,0.000033024397,0.0009861735,0.00075660663,0.95228624,0.0011408866,0.012496271,0.023925958,0.0003433087],"about_ca_topic_score_codex":0.000004935014,"about_ca_topic_score_gemma":6.5447637e-7,"teacher_disagreement_score":0.5021494,"about_ca_system_score_codex":0.000042021104,"about_ca_system_score_gemma":0.000068063586,"threshold_uncertainty_score":0.30387944},"labels":[],"label_agreement":null},{"id":"W4386099053","doi":"10.1007/978-3-031-38241-3_10","title":"Towards a Robotic Intervention for On-Land Archaeological Fieldwork in Prehistoric Sites","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Excavation; Domain (mathematical analysis); Archaeology; Robot; Engineering; Robotics; Prehistory; Process (computing); Computer science; Artificial intelligence; Geography","score_opus":0.03060768483375555,"score_gpt":0.2553473454671103,"score_spread":0.22473966063335474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386099053","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003639352,0.00029429616,0.9967066,0.00042424878,0.0014583474,0.00046328965,0.0000040386703,0.00031996268,0.0002928071],"genre_scores_gemma":[0.156425,0.00010762774,0.83687687,0.0003944994,0.001091312,0.00038713802,0.00010379806,0.00025319055,0.004360574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804187,0.000025641079,0.00050422264,0.0006441211,0.00031022518,0.00047389267],"domain_scores_gemma":[0.9979545,0.0013665578,0.000100517944,0.00045991427,0.00003083506,0.00008767816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047058388,0.00040599922,0.0006057079,0.00048557797,0.000026976113,0.00004087145,0.00077039475,0.00068677403,0.0000101701],"category_scores_gemma":[0.0013062761,0.0003847823,0.00022119377,0.00020548857,0.000015809077,0.000058568265,0.00031352695,0.0011363573,0.000020885373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020992087,0.000032182023,0.0000098870305,0.00020218283,0.000029900564,0.000191663,0.00019461976,0.90287924,0.000047512855,0.06756565,0.00001648036,0.028809682],"study_design_scores_gemma":[0.0004778254,0.00040069126,0.00007264043,0.0012419653,0.000011873933,0.000015501235,3.313424e-7,0.8961396,0.00011201377,0.10089094,0.00019841302,0.00043817103],"about_ca_topic_score_codex":0.000015480664,"about_ca_topic_score_gemma":0.000026845797,"teacher_disagreement_score":0.15982977,"about_ca_system_score_codex":0.00028582267,"about_ca_system_score_gemma":0.000032270556,"threshold_uncertainty_score":0.9998604},"labels":[],"label_agreement":null},{"id":"W4386304134","doi":"10.32920/24058704.v1","title":"Modeling and Control of Aerial Manipulation Systems: From Conventional to Continuum Manipulation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Advanced Education; Ministry of Advanced Education and Skills Development","keywords":"Control theory (sociology); Nonlinear system; Robustness (evolution); Linear-quadratic regulator; Control engineering; Control system; Sliding mode control; System dynamics; Engineering; Computer science; Control (management); Physics; Artificial intelligence","score_opus":0.07008494293014146,"score_gpt":0.28238288804921613,"score_spread":0.21229794511907468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386304134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039458025,0.00009042723,0.95539063,0.00023916576,0.0036970077,0.00070756214,0.00006958428,0.00027563036,0.00007198283],"genre_scores_gemma":[0.870393,0.0000029041828,0.12881249,0.000029900742,0.0003593444,0.000051834533,0.00015508317,0.000018678982,0.00017676073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997796,0.00012720768,0.00070070574,0.00068737654,0.00048196196,0.00020674354],"domain_scores_gemma":[0.9986301,0.00016068232,0.00035091897,0.0005223509,0.00023374305,0.00010218253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005158003,0.00022731454,0.00048402918,0.00022665109,0.0000546002,0.00023423853,0.00048594308,0.00024267331,0.0000049180726],"category_scores_gemma":[0.00010338297,0.00023789036,0.000076850556,0.000113980655,0.000015784733,0.00016708128,0.0006065936,0.00018755267,0.000032589043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016539901,0.000013341364,0.0012818243,0.00007002066,0.00007220157,0.0000064142237,0.00020379743,0.990502,0.00040743497,0.0071115023,0.00011918942,0.00019575903],"study_design_scores_gemma":[0.00058996084,0.00002549364,0.008451125,0.0003353158,0.00003155432,0.0000028708441,0.000025009504,0.98470205,0.000037507245,0.0055730427,0.0000052347723,0.0002208102],"about_ca_topic_score_codex":0.0023404234,"about_ca_topic_score_gemma":0.000023443661,"teacher_disagreement_score":0.830935,"about_ca_system_score_codex":0.00006398738,"about_ca_system_score_gemma":0.000075889555,"threshold_uncertainty_score":0.9700887},"labels":[],"label_agreement":null},{"id":"W4386304169","doi":"10.32920/24058704","title":"Modeling and Control of Aerial Manipulation Systems: From Conventional to Continuum Manipulation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Advanced Education; Ministry of Advanced Education and Skills Development","keywords":"Control theory (sociology); Nonlinear system; Robustness (evolution); Linear-quadratic regulator; Control system; Control engineering; Sliding mode control; System dynamics; Engineering; Computer science; Control (management); Physics; Artificial intelligence","score_opus":0.07008494293014146,"score_gpt":0.28238288804921613,"score_spread":0.21229794511907468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386304169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039458025,0.00009042723,0.95539063,0.00023916576,0.0036970077,0.00070756214,0.00006958428,0.00027563036,0.00007198283],"genre_scores_gemma":[0.870393,0.0000029041828,0.12881249,0.000029900742,0.0003593444,0.000051834533,0.00015508317,0.000018678982,0.00017676073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997796,0.00012720768,0.00070070574,0.00068737654,0.00048196196,0.00020674354],"domain_scores_gemma":[0.9986301,0.00016068232,0.00035091897,0.0005223509,0.00023374305,0.00010218253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005158003,0.00022731454,0.00048402918,0.00022665109,0.0000546002,0.00023423853,0.00048594308,0.00024267331,0.0000049180726],"category_scores_gemma":[0.00010338297,0.00023789036,0.000076850556,0.000113980655,0.000015784733,0.00016708128,0.0006065936,0.00018755267,0.000032589043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016539901,0.000013341364,0.0012818243,0.00007002066,0.00007220157,0.0000064142237,0.00020379743,0.990502,0.00040743497,0.0071115023,0.00011918942,0.00019575903],"study_design_scores_gemma":[0.00058996084,0.00002549364,0.008451125,0.0003353158,0.00003155432,0.0000028708441,0.000025009504,0.98470205,0.000037507245,0.0055730427,0.0000052347723,0.0002208102],"about_ca_topic_score_codex":0.0023404234,"about_ca_topic_score_gemma":0.000023443661,"teacher_disagreement_score":0.830935,"about_ca_system_score_codex":0.00006398738,"about_ca_system_score_gemma":0.000075889555,"threshold_uncertainty_score":0.9700887},"labels":[],"label_agreement":null},{"id":"W4386597136","doi":"10.1109/mmar58394.2023.10242582","title":"Arithmetic Billiard Paths Revisited: Escaping from a Rectangular Room","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Academy of Finland","keywords":"Robot; Rectangle; Dynamical billiards; Point (geometry); Line (geometry); Computer science; Reflection (computer programming); Mathematics; Geometry; Algorithm; Artificial intelligence","score_opus":0.025403297739261105,"score_gpt":0.25480378188934827,"score_spread":0.22940048415008715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386597136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010206338,0.00042275342,0.9812193,0.0013094777,0.00083561643,0.0001988588,0.0000101138285,0.0023536899,0.0034438819],"genre_scores_gemma":[0.04970462,0.00009212131,0.9465737,0.0009289038,0.00035538906,0.00003183213,0.0000438685,0.000039681338,0.0022298838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978696,0.00013369703,0.00031687578,0.0007007498,0.0004762206,0.00050284254],"domain_scores_gemma":[0.99829584,0.00036246039,0.00008288199,0.0010352443,0.00006634383,0.00015720296],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00061068183,0.00020131818,0.00027947174,0.00026576882,0.00013877072,0.00026168412,0.0010860588,0.00009998422,0.000044010427],"category_scores_gemma":[0.0002707878,0.00017556356,0.00009881677,0.0016584967,0.000020823773,0.0003272295,0.00043592794,0.00020099983,0.002260842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017115606,0.0002545655,0.01657857,0.00013490517,0.00044849826,0.005215688,0.01047323,0.016824141,0.015062601,0.027385965,0.15215307,0.7554516],"study_design_scores_gemma":[0.00030201563,0.00005637306,0.012443875,0.0001638773,0.000013372982,0.000021841732,0.000083102634,0.97070134,0.0010795662,0.010188275,0.004523151,0.00042319874],"about_ca_topic_score_codex":0.00016777533,"about_ca_topic_score_gemma":0.0000013714839,"teacher_disagreement_score":0.9538772,"about_ca_system_score_codex":0.000053016138,"about_ca_system_score_gemma":0.000052092655,"threshold_uncertainty_score":0.998516},"labels":[],"label_agreement":null},{"id":"W4386802537","doi":"10.23977/jaip.2023.060501","title":"Control System Design of Remote-controlled Floating Garbage Cleaning Robot Suitable for Small Water Area","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arduino; Remote control; Garbage; Microprocessor; Embedded system; Wireless; Computer science; Robot; Computer hardware; Robotic arm; Control (management); Real-time computing; Engineering; Operating system; Artificial intelligence","score_opus":0.10922860709562975,"score_gpt":0.32011374786507324,"score_spread":0.21088514076944348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802537","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014647279,0.00008971877,0.9945562,0.0013042311,0.0016181703,0.0006766969,0.0000022746663,0.00011432991,0.00017363798],"genre_scores_gemma":[0.31996155,0.000012463997,0.6795003,0.000104247025,0.00032195114,0.000006462366,7.8576136e-7,0.0000249378,0.00006735192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571055,0.00068126456,0.0019163935,0.00034768909,0.00067231455,0.00067176775],"domain_scores_gemma":[0.988654,0.0074262503,0.0018202685,0.00043587378,0.001492646,0.00017092169],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012391567,0.00024921028,0.0008486484,0.0004397332,0.0002766974,0.0003274846,0.0011445956,0.00014220762,0.0000051573115],"category_scores_gemma":[0.008709045,0.00018803708,0.00028411995,0.00059831096,0.000050575665,0.00129424,0.000121041114,0.000480956,0.00006236115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016193422,0.00012319992,0.0000085003685,0.00010054007,0.00029138787,0.00035476938,0.0035541821,0.89910996,0.04836117,0.0034300683,0.0001409007,0.042905986],"study_design_scores_gemma":[0.0005433483,0.000639033,0.0000037416003,0.00029621206,0.00016303099,0.00031688457,0.0029113167,0.93761736,0.05570277,0.0014866148,0.00012325752,0.00019643918],"about_ca_topic_score_codex":0.00005449792,"about_ca_topic_score_gemma":0.0000010981645,"teacher_disagreement_score":0.31849682,"about_ca_system_score_codex":0.00013096911,"about_ca_system_score_gemma":0.00022655618,"threshold_uncertainty_score":0.999641},"labels":[],"label_agreement":null},{"id":"W4386826669","doi":"10.1016/j.eswa.2023.121623","title":"Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Acceleration; Energy consumption; Kinematics; Motion planning; Range (aeronautics); Energy (signal processing); Motion (physics); Kinetic energy; Efficient energy use; Artificial neural network; Process (computing); Simulation; Control theory (sociology); Artificial intelligence; Engineering; Mathematics; Robot; Aerospace engineering","score_opus":0.029435436862763686,"score_gpt":0.27404676815523216,"score_spread":0.24461133129246848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386826669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012979958,0.00055481144,0.9855586,0.00003979835,0.0001068846,0.00036017734,0.0000025782601,0.00033427813,0.00006289568],"genre_scores_gemma":[0.89677,0.0000023889418,0.10272013,0.000025975383,0.00009356935,0.0003300405,0.000017815442,0.000019612333,0.000020492384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986733,0.000054480075,0.00030416087,0.00043871542,0.00023369877,0.00029565464],"domain_scores_gemma":[0.9989496,0.000065641936,0.00018104514,0.00058771786,0.00008268355,0.00013332785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019147497,0.00015660444,0.00020763729,0.00017437538,0.00022758316,0.00009694667,0.00038262914,0.00007084348,1.4182041e-7],"category_scores_gemma":[0.0000038447893,0.00013965227,0.00002204187,0.0006527722,0.000052732452,0.00016342953,0.00010269495,0.000076701,0.0000011178812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017024412,0.00002609182,0.000086075735,0.000010339299,0.000007708147,0.0000035977105,0.00093437545,0.99102795,0.00026435265,0.0035643245,0.000011293177,0.0040622097],"study_design_scores_gemma":[0.00015720465,0.000040950166,0.00024481805,0.000041971147,0.0000059467798,0.000073023286,0.00017984581,0.9989842,0.000036539237,0.000045099972,0.00003473424,0.00015563187],"about_ca_topic_score_codex":0.00016695606,"about_ca_topic_score_gemma":0.0000010868382,"teacher_disagreement_score":0.88379,"about_ca_system_score_codex":0.00004345797,"about_ca_system_score_gemma":0.000030712075,"threshold_uncertainty_score":0.5694854},"labels":[],"label_agreement":null},{"id":"W4386883446","doi":"10.1109/rcar58764.2023.10249227","title":"Focused Bidirectional Search Trees: A Bidirectional Optimal Fast Matching Method for Robot Path Planning","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Fast marching method; Motion planning; Computer science; Heuristic; Path (computing); Bidirectional search; Vertex (graph theory); Any-angle path planning; Matching (statistics); Robot; Mathematical optimization; Set (abstract data type); Beam search; Algorithm; Brute force; Search algorithm; Artificial intelligence; Incremental heuristic search; Theoretical computer science; Mathematics","score_opus":0.06730620531167672,"score_gpt":0.3483778810909097,"score_spread":0.281071675779233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386883446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028347806,0.000032775868,0.9918059,0.0010043596,0.0011394159,0.0003196935,0.000022649227,0.0016905825,0.0011498109],"genre_scores_gemma":[0.0051203915,0.0000024507558,0.9898942,0.00014559702,0.00039328015,0.00014577965,0.000040906994,0.00003735402,0.004220018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717206,0.00017875095,0.00037186936,0.00081841776,0.00070412096,0.0007547844],"domain_scores_gemma":[0.9978724,0.0012866758,0.00008707321,0.00040012505,0.0001578643,0.00019586917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017378452,0.0002633118,0.0003078262,0.00056549883,0.0005478076,0.0003057077,0.00076806895,0.00012387797,0.000026232243],"category_scores_gemma":[0.00011521979,0.0002476779,0.00017970629,0.0013865983,0.00003322961,0.00050439395,0.00031094957,0.0002859551,0.0001617117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003070485,0.00007349376,0.0005763862,0.000026993639,0.00010618913,0.000067642584,0.0023350576,0.9228339,0.003984155,0.004663548,0.009144835,0.056157064],"study_design_scores_gemma":[0.00060909335,0.00013554936,0.0059884395,0.00005623197,0.0000085568345,0.00009839652,0.00024058609,0.9887311,0.0015021862,0.0011208688,0.00118418,0.00032479962],"about_ca_topic_score_codex":0.00016268296,"about_ca_topic_score_gemma":0.0000023771665,"teacher_disagreement_score":0.06589718,"about_ca_system_score_codex":0.00011311286,"about_ca_system_score_gemma":0.00023226083,"threshold_uncertainty_score":0.99999756},"labels":[],"label_agreement":null},{"id":"W4386954977","doi":"10.1016/j.actaastro.2023.09.028","title":"Recovery policies for safe exploration of lunar permanently shadowed regions by a solar-powered rover","year":2023,"lang":"en","type":"article","venue":"Acta Astronautica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Computer science; Discretization; Stochastic programming; Fault (geology); Aerospace engineering; Real-time computing; Mathematical optimization; Engineering; Geology; Mathematics","score_opus":0.03649104438867337,"score_gpt":0.2819541405060571,"score_spread":0.24546309611738373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386954977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008856465,0.000052495925,0.9773288,0.011453123,0.0006137745,0.00051001273,0.00015250615,0.00036484687,0.0006680028],"genre_scores_gemma":[0.8021398,0.00005993173,0.19229548,0.0004336283,0.00016975106,0.00017041751,0.0004068683,0.000053449,0.00427068],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841714,0.000059178554,0.0003388872,0.00038126332,0.00032967547,0.000473856],"domain_scores_gemma":[0.9987189,0.00030495442,0.00017081504,0.0006042026,0.00009562706,0.000105457155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029730148,0.00017302687,0.00024578656,0.00017703066,0.00014554727,0.00008018184,0.0006735218,0.000084500585,0.0000096954955],"category_scores_gemma":[0.00019256985,0.00017475159,0.000111847134,0.0004830571,0.00006585255,0.00089687796,0.00020119146,0.00011255165,0.00005169041],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023615597,0.00062675774,0.0009245098,0.00020559912,0.00041461762,0.000030005125,0.013170023,0.00715808,0.07289198,0.03421199,0.82196736,0.048162904],"study_design_scores_gemma":[0.0034339805,0.0021266886,0.020143049,0.00032934727,0.00013048624,0.000035813326,0.0012191213,0.822453,0.011465465,0.018208604,0.11910806,0.0013463642],"about_ca_topic_score_codex":0.000027910155,"about_ca_topic_score_gemma":0.0000015337545,"teacher_disagreement_score":0.8152949,"about_ca_system_score_codex":0.00005662603,"about_ca_system_score_gemma":0.000111464215,"threshold_uncertainty_score":0.7126163},"labels":[],"label_agreement":null},{"id":"W4387099446","doi":"10.18280/jesa.560407","title":"Optimal Control Design for Propeller Pendulum Systems Using Gorilla Troops Optimization","year":2023,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gorilla; Propeller; Pendulum; Control theory (sociology); Computer science; Control (management); Engineering; Marine engineering; Biology; Mechanical engineering; Artificial intelligence","score_opus":0.0831111779519725,"score_gpt":0.29542987408558197,"score_spread":0.21231869613360949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387099446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013058491,0.0082034115,0.97178346,0.00088108133,0.014611645,0.0020899684,0.00008040591,0.00079273473,0.00025146207],"genre_scores_gemma":[0.0143733695,0.0003850498,0.96612626,0.00008778364,0.0027734626,0.000080421734,0.000014254126,0.00023642897,0.015922958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9917205,0.0024402093,0.0018827457,0.00089088816,0.0012349724,0.0018307002],"domain_scores_gemma":[0.9946049,0.0011308471,0.001562729,0.00072083005,0.0013203649,0.00066035526],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0050125835,0.0007578648,0.0011705437,0.0007332933,0.0015242739,0.0032929117,0.0014441696,0.00038728383,0.000058037887],"category_scores_gemma":[0.001191405,0.0007219264,0.0004176465,0.0018196775,0.00028315213,0.002095173,0.00025993751,0.0006706881,0.0004936842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004711453,0.00008724289,0.00008463464,0.0005977404,0.00035655228,0.0009780766,0.0014461249,0.95157146,0.0002415736,0.00095985254,0.014341006,0.029288642],"study_design_scores_gemma":[0.0023445406,0.0007824291,0.0014422144,0.0022702273,0.00030136993,0.008758663,0.00019217498,0.98100567,0.000034095247,0.00022201487,0.0018711825,0.00077539583],"about_ca_topic_score_codex":0.00009113127,"about_ca_topic_score_gemma":3.538771e-7,"teacher_disagreement_score":0.029434253,"about_ca_system_score_codex":0.0008908251,"about_ca_system_score_gemma":0.0011125974,"threshold_uncertainty_score":0.9997756},"labels":[],"label_agreement":null},{"id":"W4387171282","doi":"10.3233/faia230531","title":"Intractability of Optimal Multi-Agent Pathfinding on Directed Graphs","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Pathfinding; Vertex (graph theory); Directed graph; Computer science; Planar graph; Set (abstract data type); Digraph; Combinatorics; Bounded function; Mathematics; Theoretical computer science; Mathematical optimization; Shortest path problem; Graph","score_opus":0.07339087815767956,"score_gpt":0.2960417104497567,"score_spread":0.22265083229207713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387171282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000059080103,0.00016331505,0.9945285,0.00011211068,0.0005616857,0.00065406674,0.000053231834,0.00020116278,0.0036668733],"genre_scores_gemma":[0.016767992,0.0011879519,0.96401006,0.00009059873,0.00023358625,0.0005452879,0.00010100693,0.00010420027,0.016959324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980588,0.000028642033,0.00067053153,0.00070982956,0.0002673116,0.0002649121],"domain_scores_gemma":[0.99869287,0.0001832763,0.00027701326,0.00065916684,0.00008916774,0.000098484656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038410578,0.00026240904,0.00040413308,0.00047332636,0.00012436877,0.00005931426,0.00066540425,0.00023914353,0.0000051556462],"category_scores_gemma":[0.00005984872,0.0002785148,0.000095876574,0.00032083815,0.00023899594,0.00009396437,0.00016377345,0.00043154624,0.00005697652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000092100945,0.00013582363,0.000186239,0.000043079006,0.000037982245,0.000011591299,0.0006928734,0.0052659353,0.000034680477,0.5348609,0.00025628417,0.45846543],"study_design_scores_gemma":[0.000057504596,0.00014649978,0.000646722,0.0004049977,0.000040272836,0.0000042368038,0.00026980974,0.4617149,0.00182735,0.5313458,0.0027794049,0.0007625492],"about_ca_topic_score_codex":0.000021367483,"about_ca_topic_score_gemma":0.000006422836,"teacher_disagreement_score":0.45770288,"about_ca_system_score_codex":0.000074024516,"about_ca_system_score_gemma":0.00006561697,"threshold_uncertainty_score":0.9999667},"labels":[],"label_agreement":null},{"id":"W4387187284","doi":"10.3390/robotics12050134","title":"Three-Dimensional Flight Corridor: An Occupancy Checking Process for Unmanned Aerial Vehicle Motion Planning inside Confined Spaces","year":2023,"lang":"en","type":"article","venue":"Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Obstacle; Polyhedron; Computer science; Motion planning; Process (computing); Regular polygon; Collision avoidance; Bounded function; Trajectory; Obstacle avoidance; Path (computing); Maxima and minima; Occupancy; Computer vision; Artificial intelligence; Aerospace engineering; Collision; Robot; Mathematics; Mobile robot; Engineering; Geography; Geometry; Physics","score_opus":0.065493725807536,"score_gpt":0.3190001457847077,"score_spread":0.2535064199771717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387187284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.146036,0.00005061233,0.84897137,0.0008162786,0.002450727,0.0005196249,0.000016637263,0.001078062,0.00006069814],"genre_scores_gemma":[0.74894845,0.0000013721366,0.24957776,0.0002518825,0.00081594323,0.00006116029,0.00017615705,0.00006300123,0.00010428202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754554,0.000054934953,0.00042651215,0.00070829113,0.00054847053,0.00071627233],"domain_scores_gemma":[0.99847233,0.0002645499,0.00024165511,0.00055711204,0.00025608105,0.00020825618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005505546,0.0003016458,0.00036716473,0.00024529063,0.00044110412,0.00027619523,0.0007814749,0.00017737373,0.0000032208172],"category_scores_gemma":[0.00024264804,0.00030288697,0.00008388089,0.00078065024,0.00005939542,0.000665108,0.00019545507,0.00021718995,0.000066927045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051539046,0.00007660176,0.004242634,0.0000638298,0.000032471457,0.00008314591,0.0010728377,0.9871332,0.0015680861,0.002374968,0.0009398725,0.0023608066],"study_design_scores_gemma":[0.0009867892,0.0002254783,0.01060227,0.0001354028,0.000022225122,0.00002494943,0.000065034714,0.98205405,0.0024130577,0.003016451,0.00007025944,0.00038402618],"about_ca_topic_score_codex":0.000022724733,"about_ca_topic_score_gemma":0.000008684724,"teacher_disagreement_score":0.6029124,"about_ca_system_score_codex":0.00006435359,"about_ca_system_score_gemma":0.00019460899,"threshold_uncertainty_score":0.9999423},"labels":[],"label_agreement":null},{"id":"W4387228526","doi":"","title":"Path Planning and Trajectory Control of Collaborative Mobile Robots Using Hybrid Control Architecture","year":2008,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Mobile robot; Motion planning; Trajectory; Control (management); Architecture; Computer science; Path (computing); Robot; Artificial intelligence; Geography; Computer network; Physics","score_opus":0.015389689568034564,"score_gpt":0.24317647183585292,"score_spread":0.22778678226781834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387228526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10743343,0.001149472,0.8903797,0.000044837692,0.00016096258,0.00039891264,0.000030194798,0.000121009674,0.0002814712],"genre_scores_gemma":[0.73839927,0.0000042410416,0.26132488,0.00016915602,0.000044371736,0.00001457863,0.0000013130965,0.000010238048,0.000031924228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984075,0.00017385793,0.00032881147,0.00042264492,0.00032043076,0.00034679187],"domain_scores_gemma":[0.99878055,0.00037805887,0.0001958883,0.00035007612,0.00015397888,0.00014143625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024692866,0.00022006324,0.00045651747,0.00014880137,0.00018469132,0.000038071106,0.0003701253,0.000061285755,0.000004499167],"category_scores_gemma":[0.000058273876,0.00018766352,0.000052416213,0.00030151894,0.00017341672,0.00023247291,0.00005982024,0.00019470367,0.0000019921104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005771048,0.000094181196,0.010538297,0.00003507037,0.00012155057,0.0004911291,0.004884832,0.9670565,0.013527997,0.00025510477,0.00032695007,0.0026106637],"study_design_scores_gemma":[0.002750689,0.00037650295,0.008408563,0.00008396232,0.000023533872,0.0005783839,0.00014031249,0.9852186,0.0019170711,0.00011563136,0.00006369508,0.00032307793],"about_ca_topic_score_codex":0.000032685344,"about_ca_topic_score_gemma":2.698544e-7,"teacher_disagreement_score":0.6309658,"about_ca_system_score_codex":0.00003256925,"about_ca_system_score_gemma":0.00020960835,"threshold_uncertainty_score":0.76526964},"labels":[],"label_agreement":null},{"id":"W4387266213","doi":"10.32920/24231046","title":"A Multi-Objective Optimization Problem on Evacuating 2 Robots from the Disk in the Face-to-Face Model; Trade-Offs between Worst-Case and Average-Case Analysis","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Face (sociological concept); Computer science; Robot; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence","score_opus":0.08089326734878918,"score_gpt":0.3121876391158692,"score_spread":0.23129437176708004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387266213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033196017,0.000041105235,0.9607567,0.0033118178,0.0001328374,0.0019827457,0.00021560851,0.0003045224,0.000058649257],"genre_scores_gemma":[0.49696797,0.000008970615,0.5023537,0.00021926648,0.00006432891,0.00021046074,0.00008614764,0.0000294685,0.000059658614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995126,0.0010478063,0.0007744782,0.0017770962,0.0007117024,0.0005628905],"domain_scores_gemma":[0.99499995,0.00260143,0.00040795226,0.001753946,0.0000604091,0.00017633238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018268992,0.0006150674,0.0007612567,0.00052109954,0.00049449864,0.0009236702,0.0017451934,0.00032714327,0.0000026363502],"category_scores_gemma":[0.00037140286,0.00039322526,0.00022706317,0.002128345,0.00006132731,0.00026417503,0.0017477222,0.001327517,0.000013038096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005825149,0.000056678145,0.0019466294,0.000014074055,0.00036633614,0.0013134571,0.03548112,0.95760334,0.0000016285877,0.000019035922,0.000032148524,0.0031597598],"study_design_scores_gemma":[0.00039867428,0.0000503484,0.006189637,0.00016788776,0.00035610364,0.00010141343,0.0016159463,0.9902445,0.000009307333,0.00038906586,2.4528222e-7,0.0004768654],"about_ca_topic_score_codex":0.0034981011,"about_ca_topic_score_gemma":0.00039989644,"teacher_disagreement_score":0.46377197,"about_ca_system_score_codex":0.00016299095,"about_ca_system_score_gemma":0.00013274982,"threshold_uncertainty_score":0.99985194},"labels":[],"label_agreement":null},{"id":"W4387394760","doi":"10.1609/aiide.v19i1.27532","title":"Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Pathfinding; Set (abstract data type); Context (archaeology); Function (biology); Fitness function; Readability; Space (punctuation); Domain (mathematical analysis); Artificial intelligence; Mathematical optimization; Theoretical computer science; Machine learning; Genetic algorithm; Mathematics; Programming language","score_opus":0.13577454629137986,"score_gpt":0.34300468388273164,"score_spread":0.20723013759135178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387394760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43538436,0.000025820673,0.55740654,0.001757668,0.0011021668,0.0014710699,0.00005192343,0.00028193629,0.0025184823],"genre_scores_gemma":[0.99361336,0.000012396888,0.0059082294,0.00010213055,0.000043052096,0.00008756754,0.0000028871311,0.000015683205,0.00021467241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809027,0.000010641304,0.00050598284,0.0005608868,0.00037416333,0.00045806143],"domain_scores_gemma":[0.9987769,0.00028721298,0.00036029876,0.00019237322,0.0002771088,0.00010608769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022329,0.00026688012,0.00028956193,0.00020275124,0.00026237007,0.0006261931,0.0009398792,0.00006511546,0.0000027370932],"category_scores_gemma":[0.00063622557,0.00020360667,0.00015119716,0.00034064718,0.0001123445,0.001085593,0.00054976967,0.00021746068,0.000029209468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004102306,0.0008445451,0.006118831,0.00031352168,0.00026595933,0.00001226447,0.029578138,0.0011049907,0.035992723,0.32675028,0.0003376535,0.5982709],"study_design_scores_gemma":[0.00011795381,0.00053733704,0.0013710205,0.0011710067,0.000017344157,0.000010698189,0.0058620535,0.77931637,0.19427754,0.016725875,0.0001981407,0.00039464774],"about_ca_topic_score_codex":0.0000065608456,"about_ca_topic_score_gemma":2.982948e-7,"teacher_disagreement_score":0.7782114,"about_ca_system_score_codex":0.00010521005,"about_ca_system_score_gemma":0.00003928878,"threshold_uncertainty_score":0.83028394},"labels":[],"label_agreement":null},{"id":"W4387564952","doi":"10.20944/preprints202310.0386.v1","title":"Stream Function-Based Obstacle Avoidance Algorithm for Autonomous Underwater Vehicles","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Obstacle avoidance; Underwater; Robustness (evolution); Computer science; Adaptability; Collision avoidance; Function (biology); Curvature; Algorithm; Real-time computing; Artificial intelligence; Mobile robot; Robot; Mathematics; Collision; Geology","score_opus":0.13770184785120346,"score_gpt":0.3312111090500035,"score_spread":0.19350926119880005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387564952","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021442173,0.00009294328,0.9687352,0.0012587568,0.0043744408,0.001281657,0.00013499722,0.0024027298,0.00027710997],"genre_scores_gemma":[0.36400723,0.000020047562,0.6237799,0.0007278881,0.0008874576,0.0019147807,0.0003527713,0.00024047449,0.008069482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949186,0.00020121348,0.0008269224,0.0024307768,0.0006626452,0.0009598387],"domain_scores_gemma":[0.99512655,0.0006557262,0.00051702827,0.003073162,0.0003437326,0.00028379535],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012293521,0.0006824722,0.00070610555,0.00034891727,0.0003392792,0.00022482828,0.0028383967,0.00056025345,0.00004109294],"category_scores_gemma":[0.00023476964,0.00072921626,0.00042095914,0.00036009587,0.00012704359,0.0002719794,0.002671891,0.0010344523,0.0022227457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007245819,0.0008873939,0.08901207,0.0009929702,0.00094024796,0.0002432912,0.0022130194,0.6870744,0.0018679375,0.0014097872,0.0020085739,0.21327783],"study_design_scores_gemma":[0.0007814048,0.00007407308,0.078625225,0.00028419204,0.00006825032,0.000007264813,0.0000320727,0.8936037,0.010925911,0.012678826,0.0020146933,0.00090440834],"about_ca_topic_score_codex":0.00027639986,"about_ca_topic_score_gemma":0.00000740893,"teacher_disagreement_score":0.34495533,"about_ca_system_score_codex":0.00041280524,"about_ca_system_score_gemma":0.00075358595,"threshold_uncertainty_score":0.9995159},"labels":[],"label_agreement":null},{"id":"W4387640867","doi":"10.1007/s41315-023-00300-3","title":"Constraint-free discretized manifold-based path planner","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Robotics and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Any-angle path planning; Path (computing); Constraint (computer-aided design); Mathematical optimization; Discretization; Representation (politics); Homotopy; Local consistency; Fast path; Mathematics; Computer science; Robot; Topology (electrical circuits); Artificial intelligence; Constraint satisfaction problem; Probabilistic logic","score_opus":0.02240096966686959,"score_gpt":0.284589378431241,"score_spread":0.2621884087643714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387640867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038564633,0.00010193793,0.9902507,0.007960565,0.0005689891,0.00013317374,0.00002794126,0.00007208986,0.00049896375],"genre_scores_gemma":[0.37687162,0.000457832,0.62027115,0.00094343506,0.0008611486,0.00004599305,0.000061223174,0.000032161573,0.00045545204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985082,0.00003181953,0.0005223809,0.00020263468,0.000549394,0.00018553302],"domain_scores_gemma":[0.99843246,0.00027759417,0.00033364576,0.00034203075,0.00046359963,0.00015068759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040968414,0.00013353702,0.00017643333,0.00032494112,0.00008495863,0.00021746996,0.0015973373,0.000054260214,0.000011145737],"category_scores_gemma":[0.00011264193,0.00011428887,0.00010350213,0.00029645435,0.000068983536,0.0001655955,0.00023921941,0.00019435918,0.000051983458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029843886,0.00029969448,0.0019470204,0.000022124033,0.00031148476,0.00034420518,0.00042401906,0.24998085,0.0007490767,0.6732548,0.0103448555,0.062292017],"study_design_scores_gemma":[0.0014928267,0.0002216533,0.0034503893,0.00026237758,0.000056400037,0.00068797544,0.00030511644,0.89342916,0.0015721021,0.0701609,0.027853489,0.00050757633],"about_ca_topic_score_codex":0.000005125594,"about_ca_topic_score_gemma":5.894628e-7,"teacher_disagreement_score":0.64344835,"about_ca_system_score_codex":0.000050875675,"about_ca_system_score_gemma":0.000117833544,"threshold_uncertainty_score":0.4660565},"labels":[],"label_agreement":null},{"id":"W4387717526","doi":"10.1109/jiot.2023.3325234","title":"Efficient Path Planning and Dynamic Obstacle Avoidance in Edge for Safe Navigation of USV","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Huimin Technology Research and Development Projects of Ningbo","keywords":"Obstacle avoidance; Motion planning; Computer science; Collision avoidance; Enhanced Data Rates for GSM Evolution; Obstacle; Path (computing); Computer network; Mobile robot; Artificial intelligence; Computer security; Robot","score_opus":0.021082331404735855,"score_gpt":0.2908237582465823,"score_spread":0.26974142684184643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387717526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54739827,0.000113602255,0.4517167,0.00008565678,0.0005815823,0.00006637003,0.0000018958876,0.000018935967,0.000016973032],"genre_scores_gemma":[0.9217875,0.000004317329,0.078066334,0.000033392087,0.00002628993,0.0000033751853,0.0000015153406,0.000008516195,0.00006878624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987706,0.0000525899,0.0004821588,0.00017537552,0.0002798649,0.00023938993],"domain_scores_gemma":[0.99898726,0.00025425435,0.00041712186,0.00014446386,0.00013535001,0.000061559105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011433779,0.00010500543,0.00022608734,0.00025296232,0.000040835537,0.0000621984,0.00050907687,0.000056113247,7.165748e-7],"category_scores_gemma":[0.00011646835,0.00009917672,0.000058653277,0.0002723522,0.00004752175,0.00021907504,0.00008903233,0.00025623775,0.0000024048718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013790742,0.00020107976,0.01857865,0.00048443905,0.00010023871,0.00030117002,0.093165495,0.79307353,0.046345957,0.0007855184,0.0017312531,0.045094777],"study_design_scores_gemma":[0.0004876246,0.00015021006,0.009437649,0.0011031206,0.000004221961,0.00016696645,0.00022212768,0.9840957,0.0033806798,0.000844285,0.000013485767,0.000093931194],"about_ca_topic_score_codex":0.00003633393,"about_ca_topic_score_gemma":3.4784497e-7,"teacher_disagreement_score":0.3743892,"about_ca_system_score_codex":0.000065725115,"about_ca_system_score_gemma":0.000046456113,"threshold_uncertainty_score":0.40443096},"labels":[],"label_agreement":null},{"id":"W4387929144","doi":"10.1007/s10846-023-01996-y","title":"Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intelligent decision support system; Computer science; Field (mathematics); Intelligent agent; Robot; Systems engineering; Artificial intelligence; Data science; Engineering","score_opus":0.20727187018420937,"score_gpt":0.34449850890210815,"score_spread":0.13722663871789878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387929144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010919421,0.058204792,0.9368377,0.00029429825,0.0027351144,0.00067857286,0.0000068187046,0.00008174159,0.00006905986],"genre_scores_gemma":[0.819272,0.029038595,0.15067558,0.000020997259,0.00044488566,0.00008876122,0.000014197667,0.0000889457,0.0003560502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99362564,0.0016691614,0.0026718774,0.00048045063,0.0010612527,0.00049163826],"domain_scores_gemma":[0.991173,0.0037527517,0.0025281261,0.00094655633,0.0013451872,0.00025433965],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009554775,0.00036174216,0.0015843059,0.0009692857,0.00007624215,0.00011887407,0.0017287907,0.00021367136,0.0000021718308],"category_scores_gemma":[0.00133776,0.00029420902,0.00020816161,0.00136292,0.00016464347,0.0002906503,0.00038150244,0.0003543709,0.000027865874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003400297,0.00018244755,0.0032462336,0.0020874736,0.00057178014,0.00006714134,0.0018769613,0.9617723,0.00030876798,0.009414505,0.0005566496,0.019881723],"study_design_scores_gemma":[0.00061510195,0.0015965968,0.029672211,0.0041567087,0.0002510063,0.0019163651,0.0071757403,0.9503596,0.0018007061,0.0009294499,0.0007102629,0.00081628084],"about_ca_topic_score_codex":0.0004931938,"about_ca_topic_score_gemma":0.000007107753,"teacher_disagreement_score":0.81818,"about_ca_system_score_codex":0.00014397889,"about_ca_system_score_gemma":0.00027507998,"threshold_uncertainty_score":0.999951},"labels":[],"label_agreement":null},{"id":"W4387938727","doi":"10.5539/mas.v17n2p49","title":"Develop and Design Small Scale UAV","year":2023,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Teknikal Malaysia Melaka; Ministry of Education, India","keywords":"Quadcopter; Arduino; Computer science; Controller (irrigation); Scale (ratio); DC motor; Transmitter; Simulation; Computer hardware; Embedded system; Channel (broadcasting); Electrical engineering; Telecommunications; Engineering; Aerospace engineering","score_opus":0.051815872264754974,"score_gpt":0.24946083171062436,"score_spread":0.19764495944586938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387938727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010025257,0.000019305358,0.98589975,0.00034239845,0.00018898147,0.00019705824,5.565355e-7,0.0005864862,0.0027402102],"genre_scores_gemma":[0.3895083,0.000004167617,0.6100026,0.00019167407,0.000019433115,0.000029864877,4.754242e-7,0.000007675496,0.00023584843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787235,0.000022676337,0.0001658238,0.00079275225,0.0005292125,0.00061717205],"domain_scores_gemma":[0.9989763,0.0001297264,0.000052844993,0.00055554125,0.00007516959,0.00021043741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016861034,0.00014481466,0.00014243147,0.00026726507,0.00047806322,0.00035980472,0.0015851245,0.000045154004,9.15642e-7],"category_scores_gemma":[0.00006594544,0.0001346451,0.000011665907,0.0025820732,0.0003685798,0.00029397244,0.00078354386,0.000121658624,0.00025472988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010583096,0.000060976938,0.0011165224,0.000030883813,0.000010165539,0.00010248526,0.014387738,0.041985143,0.41223028,0.021912385,0.0012627409,0.50689006],"study_design_scores_gemma":[0.00013431022,0.000015064714,0.008295242,0.000008346694,0.000001378877,0.000018661247,0.00002527817,0.96781343,0.007956254,0.015476576,0.000060264287,0.00019517192],"about_ca_topic_score_codex":0.0000054484462,"about_ca_topic_score_gemma":6.7158015e-7,"teacher_disagreement_score":0.92582834,"about_ca_system_score_codex":0.0000466705,"about_ca_system_score_gemma":0.00030466073,"threshold_uncertainty_score":0.54906684},"labels":[],"label_agreement":null},{"id":"W4388036820","doi":"10.2316/j.2023.206-0927","title":"USING YUMI ROBOT AND RGB-D CAMERA WITH YOLOV5 FOR PICK-AND-PLACE APPLICATION, 1-10.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Royal Golden Jubilee (RGJ) Ph.D. Programme; Thailand Research Fund","keywords":"Computer science; RGB color model; Artificial intelligence; Robot; Computer vision; Computer graphics (images)","score_opus":0.026741235112367305,"score_gpt":0.30304265449781104,"score_spread":0.2763014193854437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388036820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0407494,0.000100979734,0.95553523,0.003196654,0.00025212544,0.00010483151,0.0000035148053,0.0000410117,0.000016223832],"genre_scores_gemma":[0.37865257,0.00007371374,0.62085754,0.00012080525,0.00017390047,0.000004350002,0.000008679385,0.000011035273,0.00009737172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912286,0.00002223364,0.0002820877,0.0001563214,0.0003029426,0.000113541406],"domain_scores_gemma":[0.9989345,0.00016677346,0.00033106515,0.00008303045,0.0004155255,0.00006910869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038409146,0.0000943609,0.00014016221,0.00021738112,0.0000928815,0.00021581288,0.00022568462,0.00004082406,7.1017234e-7],"category_scores_gemma":[0.000069406444,0.0000792166,0.000021253096,0.00014452578,0.000041852916,0.0004357186,0.0000768068,0.000075580545,0.0000016368031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004477643,0.000046159807,0.003400214,0.000045088196,0.00018603833,0.000037561735,0.0012274558,0.9218494,0.0022269497,0.013823538,0.0005737713,0.056539036],"study_design_scores_gemma":[0.0005810506,0.00010391904,0.00954194,0.00008339414,0.00001961801,0.00043355292,0.000048312413,0.98729426,0.000105065286,0.0014502701,0.0002443032,0.000094341405],"about_ca_topic_score_codex":0.000007856698,"about_ca_topic_score_gemma":9.3546856e-7,"teacher_disagreement_score":0.33790317,"about_ca_system_score_codex":0.000038210645,"about_ca_system_score_gemma":0.000062755724,"threshold_uncertainty_score":0.32303593},"labels":[],"label_agreement":null},{"id":"W4388080880","doi":"10.2316/j.2023.206-0837","title":"MOBILE ROBOT PATH PLANNING WITH TWO STAGES BASED ON HYBRID INTELLIGENT OPTIMISATION ALGORITHM, 416-429.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Mobile robot; Computer science; Path (computing); Artificial intelligence; Robot; Computer network","score_opus":0.020092507972515977,"score_gpt":0.29208274928490635,"score_spread":0.27199024131239036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388080880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011994159,0.00005550292,0.98582864,0.00080474844,0.0009897033,0.0001201502,0.000007573579,0.00012626492,0.00007324782],"genre_scores_gemma":[0.4693934,0.000039933537,0.5300941,0.00016626947,0.00020286826,0.0000061446517,0.000029828725,0.000015061942,0.00005243475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808156,0.000072408,0.00046860144,0.00022636564,0.00095640495,0.00019467517],"domain_scores_gemma":[0.99851966,0.00022153395,0.0005214402,0.00017315522,0.0004572036,0.000107034495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063928805,0.00017061876,0.00019920326,0.0005614364,0.00009584032,0.00034344796,0.00053841906,0.00003721765,0.000004086202],"category_scores_gemma":[0.000068941925,0.000140258,0.000060028746,0.0002645681,0.000034979195,0.0005358242,0.00007964006,0.0002102367,0.000013337753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014703646,0.000055683122,0.0005919643,0.0000059143304,0.000047822534,0.00025721232,0.0003788103,0.89931685,0.00010223061,0.00034531104,0.00025519604,0.09862833],"study_design_scores_gemma":[0.00074093795,0.00055590726,0.0035615298,0.00033224566,0.000013759388,0.00018821839,0.00007333698,0.9925277,0.001146708,0.00056217605,0.00013935012,0.00015809319],"about_ca_topic_score_codex":0.000005369482,"about_ca_topic_score_gemma":9.316145e-8,"teacher_disagreement_score":0.45739925,"about_ca_system_score_codex":0.00012225445,"about_ca_system_score_gemma":0.00011919806,"threshold_uncertainty_score":0.5719555},"labels":[],"label_agreement":null},{"id":"W4388280350","doi":"10.1109/safeprocess58597.2023.10295624","title":"Disturbance observer-based fault-tolerant control of UAV with obstacle avoidance","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Obstacle avoidance; Control theory (sociology); Computer science; Fault tolerance; Thrust; Observer (physics); Obstacle; Motion planning; Nonlinear system; Collision avoidance; Control engineering; Mobile robot; Control (management); Engineering; Robot; Artificial intelligence; Distributed computing; Aerospace engineering","score_opus":0.018052716811898212,"score_gpt":0.22997830497753566,"score_spread":0.21192558816563745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388280350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041719213,0.000085044485,0.95550483,0.0010385346,0.00024308088,0.00021204494,0.0000129435775,0.00058409886,0.00060017896],"genre_scores_gemma":[0.8701103,0.0000011970147,0.12862098,0.0003210191,0.000023902852,0.000025670053,0.000004469452,0.0000127238845,0.00087978225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983184,0.000068878806,0.00027794958,0.00044197598,0.0004870073,0.00040579747],"domain_scores_gemma":[0.9985007,0.00032706631,0.00014367604,0.0007961078,0.00012880375,0.00010364748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030273892,0.00017534182,0.0003011856,0.00008445066,0.00008808141,0.00005828043,0.0009333604,0.000048444057,0.0000064274873],"category_scores_gemma":[0.00006220173,0.0001321376,0.00006079683,0.00094091974,0.00008654276,0.00025545957,0.000069307076,0.00012724964,0.00009833821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115403804,0.00032481182,0.059953198,0.00020223373,0.00012252017,0.0005684043,0.0012905131,0.896597,0.0039841267,0.015847865,0.004278576,0.016715327],"study_design_scores_gemma":[0.0012604725,0.00016376801,0.07099508,0.00008760329,0.0000062412832,0.000006184229,0.000021734368,0.9238938,0.0028967333,0.00013787496,0.00032439153,0.0002061001],"about_ca_topic_score_codex":0.00007918507,"about_ca_topic_score_gemma":0.000005291185,"teacher_disagreement_score":0.828391,"about_ca_system_score_codex":0.000029977133,"about_ca_system_score_gemma":0.000120462566,"threshold_uncertainty_score":0.53884155},"labels":[],"label_agreement":null},{"id":"W4388429534","doi":"10.23977/acss.2023.070904","title":"Study on Flight Attitude Control of Four-rotor UAV","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Control engineering; Attitude control; Flexibility (engineering); MATLAB; Rotor (electric); Control (management); Electronic stability control; Field (mathematics); Control system; PID controller; Intelligent control; Controller (irrigation); Control theory (sociology); Computer science; Engineering; Stability (learning theory); Temperature control; Automotive engineering; Artificial intelligence","score_opus":0.042960971741786855,"score_gpt":0.3069068098092549,"score_spread":0.263945838067468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388429534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0477132,0.001387508,0.94763684,0.000094940566,0.0019824621,0.0008676824,0.000006138247,0.00016760212,0.00014362988],"genre_scores_gemma":[0.9909617,0.000040246916,0.008607802,0.00006224163,0.00019126588,0.00008465104,8.798534e-7,0.00001030637,0.000040954586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979659,0.00027552628,0.0005102179,0.0005033645,0.00043096353,0.000314081],"domain_scores_gemma":[0.99854547,0.000642574,0.00018940405,0.00047703716,0.00006640745,0.00007909964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009139216,0.0001885721,0.0004935829,0.00025442193,0.00006678301,0.00010528591,0.0006345046,0.00004841377,4.8994167e-7],"category_scores_gemma":[0.000017975177,0.00015481979,0.000043058826,0.0005324387,0.00003462144,0.00035495125,0.00015857047,0.000119662,0.000026129921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042754822,0.0005093149,0.09075932,0.0002682913,0.00013687827,0.0010707295,0.0047543454,0.8384734,0.00032178243,0.0048061325,0.0005474057,0.058309685],"study_design_scores_gemma":[0.0013773653,0.0011109404,0.019980643,0.00034993415,0.00000524834,0.000025646194,0.0000952376,0.9759572,0.00004013822,0.00023152347,0.00059395813,0.00023212482],"about_ca_topic_score_codex":0.000019026289,"about_ca_topic_score_gemma":0.0000016755413,"teacher_disagreement_score":0.94324845,"about_ca_system_score_codex":0.000020347776,"about_ca_system_score_gemma":0.00002189657,"threshold_uncertainty_score":0.6313368},"labels":[],"label_agreement":null},{"id":"W4388470044","doi":"10.1109/tits.2023.3324317","title":"Chance-Constrained Planning for Dynamically Stable Motion of Reconfigurable Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; McGill University","funders":"","keywords":"Motion planning; Computer science; Motion (physics); Vehicle dynamics; Control engineering; Engineering; Automotive engineering; Artificial intelligence; Robot","score_opus":0.04906395761715301,"score_gpt":0.2839794724246136,"score_spread":0.23491551480746056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388470044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016380327,0.000041584513,0.97966605,0.00010948607,0.0021076992,0.0008086083,0.00023181713,0.0005280083,0.0001264401],"genre_scores_gemma":[0.9862154,0.000027888736,0.012590988,0.000018245231,0.000035272944,0.00031950045,0.00007213192,0.00002805327,0.0006924879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978371,0.00006722205,0.0008179023,0.00047920668,0.00041316714,0.00038537392],"domain_scores_gemma":[0.9986298,0.0003700654,0.0002556988,0.00037626977,0.00025966574,0.0001084736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865537,0.00021974504,0.00035737664,0.0004764073,0.00019460024,0.000073473544,0.0004012013,0.00014066382,0.000008923957],"category_scores_gemma":[0.0000079338115,0.00022956825,0.00017458996,0.0008664315,0.000048310114,0.0003703568,2.5878913e-7,0.00016278138,0.000046633053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004143134,0.00009054955,0.000058353256,0.00022915435,0.00008187414,0.0000083327905,0.002266151,0.97609067,0.0061887056,0.0018975408,0.00008406658,0.012963183],"study_design_scores_gemma":[0.0004973511,0.0002513931,0.00055731816,0.00042202167,0.000035455567,0.0000068735144,0.00083915994,0.92542326,0.0711955,0.0002559394,0.00022820022,0.0002875417],"about_ca_topic_score_codex":0.000097637305,"about_ca_topic_score_gemma":0.0000074286017,"teacher_disagreement_score":0.9698351,"about_ca_system_score_codex":0.00007223816,"about_ca_system_score_gemma":0.00008297619,"threshold_uncertainty_score":0.93615216},"labels":[],"label_agreement":null},{"id":"W4388511483","doi":"10.18280/jesa.560505","title":"Navigation and Path Planning of Mobile Robots","year":2023,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mobile robot; Motion planning; Computer science; Path (computing); Mobile robot navigation; Robot; Human–computer interaction; Artificial intelligence; Robot control; Computer network","score_opus":0.0409347945004074,"score_gpt":0.2967729456780566,"score_spread":0.25583815117764924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388511483","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56918025,0.035421126,0.3852564,0.0008585605,0.006674137,0.00053400616,0.000049103957,0.00071760867,0.0013087818],"genre_scores_gemma":[0.59775454,0.0019827618,0.38882658,0.000109894965,0.001178574,0.00002927094,0.000022818615,0.00015321547,0.009942346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618566,0.0006647075,0.0011203235,0.0004440207,0.00083151937,0.0007537711],"domain_scores_gemma":[0.9973846,0.0004885381,0.00097242533,0.00042686885,0.0003597638,0.00036780062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019687787,0.00035304992,0.0006266626,0.00041773097,0.00049837935,0.0006293637,0.0006881618,0.0001851855,0.000023249831],"category_scores_gemma":[0.0003092142,0.00034858106,0.00014703076,0.0014070016,0.00033194138,0.0012702945,0.0004015578,0.00059426244,0.0001975739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008674246,0.00009293314,0.007745451,0.0011146462,0.00017607983,0.0034345041,0.015716149,0.20336215,0.0013026111,0.00223095,0.010276593,0.75453925],"study_design_scores_gemma":[0.00042439453,0.00044028036,0.3071471,0.0046184384,0.000053239346,0.005940804,0.00035118792,0.6756798,0.00012996496,0.004014071,0.0009092078,0.00029150784],"about_ca_topic_score_codex":0.000060735503,"about_ca_topic_score_gemma":1.9708564e-7,"teacher_disagreement_score":0.7542477,"about_ca_system_score_codex":0.00015825687,"about_ca_system_score_gemma":0.0002708993,"threshold_uncertainty_score":0.99989665},"labels":[],"label_agreement":null},{"id":"W4388579628","doi":"10.1109/jas.2023.123975","title":"Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios","year":2023,"lang":"en","type":"article","venue":"IEEE/CAA Journal of Automatica Sinica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Ideal (ethics); Motion planning; Control (management); Path (computing); Tracking (education); Computer science; Control theory (sociology); Artificial intelligence; Psychology; Robot; Epistemology; Philosophy","score_opus":0.03489914072935201,"score_gpt":0.3095191981650242,"score_spread":0.2746200574356722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388579628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04948537,0.00026639077,0.9461762,0.0020292813,0.0014635563,0.0002957672,0.0000062385366,0.00023526303,0.00004198769],"genre_scores_gemma":[0.8581199,0.000009164523,0.14098436,0.00044962924,0.0003676984,0.000009629208,0.0000011354767,0.0000359004,0.000022606853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968538,0.00016745183,0.0011711158,0.00038653042,0.00068278087,0.0007383067],"domain_scores_gemma":[0.9949198,0.0033081844,0.0007492124,0.0004015513,0.00028513503,0.00033615847],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022101486,0.0003035478,0.00074645743,0.00038653158,0.0003414756,0.00044927775,0.0009157438,0.00016263114,0.000003031088],"category_scores_gemma":[0.0006448262,0.00026818068,0.00022261289,0.00045223028,0.000111861096,0.00080323924,0.00009280431,0.00048984686,0.0000172917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006413967,0.0013895298,0.019721523,0.0029298116,0.0036089283,0.007782723,0.042772714,0.44987872,0.08795431,0.0032067157,0.04506975,0.33504388],"study_design_scores_gemma":[0.002247888,0.0004607382,0.01866254,0.0010771235,0.00009718784,0.0007484481,0.00016899823,0.972252,0.0002826876,0.0034812898,0.00018190517,0.0003391738],"about_ca_topic_score_codex":0.000002982622,"about_ca_topic_score_gemma":2.1113777e-7,"teacher_disagreement_score":0.8086345,"about_ca_system_score_codex":0.00008698802,"about_ca_system_score_gemma":0.00031215558,"threshold_uncertainty_score":0.99997705},"labels":[],"label_agreement":null},{"id":"W4388623424","doi":"10.1109/ro-man57019.2023.10309646","title":"Optimal Robot Path Planning In a Collaborative Human-Robot Team with Intermittent Human Availability","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Motion planning; Computer science; Supervisor; Path (computing); Baseline (sea); Human–robot interaction; Field (mathematics); Distributed computing; Human–computer interaction; Artificial intelligence; Computer network","score_opus":0.0334845610872379,"score_gpt":0.3083521722049768,"score_spread":0.2748676111177389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388623424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5295307,0.00004578277,0.45517468,0.00062680634,0.00036693335,0.00097083097,0.000009532118,0.0017274419,0.011547315],"genre_scores_gemma":[0.7724573,9.634069e-7,0.22531013,0.00013998238,0.000062328574,0.000110310626,0.000023561031,0.000028583763,0.0018668554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969257,0.00021564575,0.00054526917,0.0010040719,0.0005583938,0.0007509055],"domain_scores_gemma":[0.99839413,0.00017635484,0.0001750679,0.00090678956,0.00016656687,0.00018106523],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095735764,0.00035276334,0.00045839857,0.00040063373,0.00030931053,0.00027281634,0.0011843867,0.00011335238,0.000023722534],"category_scores_gemma":[0.00006997976,0.00029000652,0.0000559859,0.0020998954,0.00015441599,0.0005627761,0.00059551053,0.00044070688,0.00017137326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005730299,0.00066995237,0.19021904,0.00009983845,0.00013222639,0.0022434809,0.028782737,0.7479662,0.0064788847,0.0060326965,0.013983679,0.0033339702],"study_design_scores_gemma":[0.0022136162,0.0014420128,0.4882949,0.0004954536,0.000014689525,0.000067536166,0.0031515704,0.5010783,0.0014508908,0.00045577603,0.00025093104,0.0010843],"about_ca_topic_score_codex":0.00014891141,"about_ca_topic_score_gemma":0.000025778545,"teacher_disagreement_score":0.29807585,"about_ca_system_score_codex":0.00020548509,"about_ca_system_score_gemma":0.00017188315,"threshold_uncertainty_score":0.99995524},"labels":[],"label_agreement":null},{"id":"W4388709207","doi":"10.23977/jaip.2023.060705","title":"Research on Robot Path Planning Based on Simulated Annealing Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shortest path problem; Motion planning; Simulated annealing; Constrained Shortest Path First; Crossover; Computer science; K shortest path routing; Mathematical optimization; Correctness; Shortest Path Faster Algorithm; Any-angle path planning; Yen's algorithm; MATLAB; Path (computing); Algorithm; Robot; Mathematics; Dijkstra's algorithm; Artificial intelligence; Theoretical computer science; Graph","score_opus":0.2319650491379905,"score_gpt":0.46551648775397564,"score_spread":0.23355143861598515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388709207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022990196,0.00006350429,0.98770136,0.006523995,0.001974931,0.00017979475,0.0000028351508,0.00017260308,0.0010819692],"genre_scores_gemma":[0.45814344,0.000049761078,0.53890496,0.0014931037,0.0012350928,0.0000052494006,0.000004427498,0.00005733474,0.00010663838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941773,0.0011511064,0.0010697085,0.0005063234,0.0022975407,0.0007980391],"domain_scores_gemma":[0.98689055,0.009915227,0.00078324246,0.0006630966,0.0014384324,0.00030943556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011198339,0.00025078087,0.00036816043,0.0014496294,0.0005250798,0.00060305384,0.0015324734,0.00017085839,0.000014399608],"category_scores_gemma":[0.0067997756,0.000223129,0.00014953731,0.0034177278,0.000101492005,0.0012408949,0.00017012519,0.0019503352,0.0007337709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001657312,0.00021492378,0.000010005188,0.000006439892,0.000028423061,0.0022433584,0.0010153044,0.8423795,0.00028653315,0.0013210158,0.000933117,0.15139565],"study_design_scores_gemma":[0.00007946945,0.001619314,0.000083169914,0.0004074472,0.000014893213,0.00011043929,0.0017575264,0.9849939,0.006152567,0.0028207665,0.0017375626,0.00022297903],"about_ca_topic_score_codex":0.000038973165,"about_ca_topic_score_gemma":1.7504952e-7,"teacher_disagreement_score":0.45584443,"about_ca_system_score_codex":0.0002099725,"about_ca_system_score_gemma":0.0004207225,"threshold_uncertainty_score":0.9431385},"labels":[],"label_agreement":null},{"id":"W4388759117","doi":"10.1007/978-3-031-45321-2_4","title":"Onboard Trajectory Coordination of Multiple Unmanned Air Vehicles","year":2023,"lang":"en","type":"book-chapter","venue":"Sustainable aviation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Trajectory; Aerospace engineering; Reliability (semiconductor); Computer science; Engineering; Simulation; Aeronautics; Physics","score_opus":0.017277616153630958,"score_gpt":0.2294771530890562,"score_spread":0.21219953693542523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388759117","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000718239,0.0005849743,0.8497027,0.0018165946,0.0023585022,0.0032736512,0.00006140184,0.002373143,0.13911079],"genre_scores_gemma":[0.048399426,0.000031918302,0.02230196,0.000088780536,0.00022099406,0.00008206505,0.00021733955,0.0001044896,0.92855304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786353,0.000051606225,0.00050205394,0.00055257574,0.0006233111,0.0004069326],"domain_scores_gemma":[0.9976825,0.00031038318,0.0005728046,0.00066093984,0.00069737417,0.00007599902],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005917505,0.0003025019,0.0004027752,0.0006220592,0.00015478148,0.00005771683,0.0006610599,0.00036255276,0.0000066357975],"category_scores_gemma":[0.00027420183,0.00034056912,0.00014368544,0.0002490527,0.00005639736,0.00051133946,0.00023301365,0.00029371656,0.000115673916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002670466,0.00006289854,0.00024503746,0.0008239727,0.00014851216,0.00025356276,0.0015953543,0.019402254,0.00023255142,0.9491616,0.007495319,0.020552272],"study_design_scores_gemma":[0.0032934889,0.0010755609,0.013305523,0.0011798394,0.0002423807,0.000025775385,0.0011397023,0.55126476,0.0039240243,0.3804398,0.04137397,0.0027351612],"about_ca_topic_score_codex":0.00009900633,"about_ca_topic_score_gemma":0.000003258656,"teacher_disagreement_score":0.82740074,"about_ca_system_score_codex":0.00042002235,"about_ca_system_score_gemma":0.000321932,"threshold_uncertainty_score":0.99990463},"labels":[],"label_agreement":null},{"id":"W4388855982","doi":"10.1109/icus58632.2023.10318512","title":"Motion Planning and Control of Quadrotor Based on MPC in Frenet Frame","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Trajectory; Motion planning; Computer science; Control theory (sociology); Quadcopter; Frenet–Serret formulas; Path (computing); Kalman filter; Extended Kalman filter; Model predictive control; Process (computing); Probabilistic logic; Artificial intelligence; Control engineering; Robot; Engineering; Control (management); Mathematics; Aerospace engineering","score_opus":0.021410537792717607,"score_gpt":0.266147357206568,"score_spread":0.2447368194138504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388855982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041189063,0.000014454822,0.9573926,0.00043526597,0.00013887371,0.00015013166,0.0000028846896,0.0001589282,0.0005178079],"genre_scores_gemma":[0.9505085,3.680694e-7,0.04918901,0.00021879225,0.000015677153,0.0000124233,0.0000025283698,0.0000043591713,0.000048393606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991917,0.000059396356,0.00016348176,0.00022267953,0.00019187703,0.00017085717],"domain_scores_gemma":[0.9993537,0.00030339093,0.00005019818,0.0002312181,0.000017686249,0.00004378324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003836772,0.00007566089,0.00014063422,0.00023573198,0.000022678114,0.000027811655,0.00020825415,0.00005182383,0.0000039039855],"category_scores_gemma":[0.00009026037,0.00006780508,0.000016463639,0.00037245572,0.000018679375,0.00009683643,0.000035121404,0.00008848885,0.000019268933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028239341,0.000108316955,0.20320715,0.000054625023,0.0000131409815,0.00014116323,0.0012141293,0.7719328,0.0028003403,0.003859965,0.0007680099,0.015872128],"study_design_scores_gemma":[0.0005448474,0.00008841035,0.16045427,0.000053989468,8.7214767e-7,9.325078e-7,0.000013618982,0.8380299,0.00045766757,0.0002817978,0.000013103723,0.000060573075],"about_ca_topic_score_codex":0.0000669621,"about_ca_topic_score_gemma":8.6604837e-7,"teacher_disagreement_score":0.9093194,"about_ca_system_score_codex":0.000014200945,"about_ca_system_score_gemma":0.000021702457,"threshold_uncertainty_score":0.2765011},"labels":[],"label_agreement":null},{"id":"W4388872633","doi":"10.1109/icus58632.2023.10318423","title":"Vision-based Autonomous Landing of UAV on Dynamic Apron","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Global Positioning System; Position (finance); Odometry; Mobile robot; Robot","score_opus":0.01664642366041081,"score_gpt":0.2901581509042814,"score_spread":0.2735117272438706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388872633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025164235,0.0000070471615,0.96673757,0.0008774188,0.0004862232,0.00012241387,0.0000026830921,0.0008412332,0.0057611996],"genre_scores_gemma":[0.86984015,7.164487e-7,0.12875685,0.00015012619,0.000014307537,0.0000060137054,0.0000068344407,0.000008913595,0.001216074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907666,0.00003176927,0.00016249514,0.0002633713,0.00024732584,0.00021840284],"domain_scores_gemma":[0.99920046,0.00023325183,0.000059058726,0.00042837468,0.000027275057,0.00005158476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030009312,0.00008781389,0.00012807325,0.00021875236,0.000053652442,0.00004102152,0.00049426773,0.000041140363,0.000009508169],"category_scores_gemma":[0.00004847783,0.00007290061,0.000041594703,0.0005471179,0.000019387056,0.00009658887,0.00009131938,0.00007633452,0.00035400564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025039455,0.00034169934,0.0065732,0.00014213884,0.00005086904,0.00036218253,0.0012590577,0.78002614,0.011506342,0.025260562,0.007132683,0.16732012],"study_design_scores_gemma":[0.0001889235,0.00015419464,0.011955566,0.000044182572,0.0000013320104,0.000001590438,0.000007806387,0.98624176,0.0009003107,0.00034607068,0.00006962553,0.00008861515],"about_ca_topic_score_codex":0.00002502837,"about_ca_topic_score_gemma":0.0000012342591,"teacher_disagreement_score":0.84467596,"about_ca_system_score_codex":0.000045244484,"about_ca_system_score_gemma":0.000061856466,"threshold_uncertainty_score":0.45501444},"labels":[],"label_agreement":null},{"id":"W4388917367","doi":"10.23977/jeis.2023.080505","title":"UAV Path Planning Method Based on Improved Wolf Pack Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Terrain; Motion planning; Computer science; Convergence (economics); Algorithm; Path (computing); Genetic algorithm; Trajectory; Route planning; MATLAB; Drone; Mathematical optimization; Artificial intelligence; Mathematics; Geography; Robot; Machine learning","score_opus":0.014225721268012328,"score_gpt":0.2958162509884254,"score_spread":0.2815905297204131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388917367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027923402,0.00005420116,0.9951195,0.00085618626,0.0003840427,0.00007091726,0.000001674553,0.0000510791,0.00067006494],"genre_scores_gemma":[0.06793656,0.00010977243,0.93005896,0.0017580861,0.00009243083,0.0000036393174,0.000002693624,0.000006702556,0.00003118699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806637,0.00004084804,0.00048796934,0.0001397451,0.00082446606,0.00044058435],"domain_scores_gemma":[0.99847734,0.00019413017,0.0005276086,0.00023524508,0.00039344214,0.00017222347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004254593,0.00011442816,0.00017248947,0.00080789323,0.00029033507,0.0005084962,0.0007978214,0.000044088305,0.0000010871355],"category_scores_gemma":[0.00035651517,0.00008936927,0.000047972222,0.001907735,0.000062924,0.0053535705,0.00009816063,0.00032730866,0.000013308219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026477639,0.00004200813,0.0001919964,0.000024396873,0.000015600932,0.000029861087,0.0030887583,0.18797447,0.0023705226,0.014313622,0.0020248184,0.7898975],"study_design_scores_gemma":[0.00036784585,0.00052770064,0.002136391,0.00004351596,0.0000028927157,0.000076817494,0.00006570854,0.99067634,0.00094363786,0.0006264239,0.0044237943,0.00010892399],"about_ca_topic_score_codex":0.000001392258,"about_ca_topic_score_gemma":1.676021e-8,"teacher_disagreement_score":0.8027019,"about_ca_system_score_codex":0.00011656647,"about_ca_system_score_gemma":0.00077508495,"threshold_uncertainty_score":0.49034405},"labels":[],"label_agreement":null},{"id":"W4389299519","doi":"10.3390/electronics12234881","title":"Recent Advances in Motion Planning and Control of Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Intervention (counseling); Control (management); Motion (physics); Motion planning; Computer science; Motion control; Engineering; Transport engineering; Aeronautics; Artificial intelligence; Psychology; Robot","score_opus":0.013558406973763657,"score_gpt":0.26347728659318426,"score_spread":0.2499188796194206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389299519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12630925,0.118435554,0.7527127,0.0012560876,0.00031137816,0.00025156076,0.0000028119275,0.00037256497,0.00034808912],"genre_scores_gemma":[0.9821145,0.0062306398,0.011491336,0.000070389775,0.000028962228,0.000013163533,0.000004097739,0.000009407365,0.000037499805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918914,0.00004542222,0.00016238127,0.00019427929,0.000127358,0.00028139446],"domain_scores_gemma":[0.99961776,0.00011101301,0.0000689184,0.00014946269,0.000023752647,0.000029084476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035656552,0.000069730726,0.0001319111,0.00012815952,0.000033182605,0.000019670379,0.00020115066,0.000036772926,4.4946654e-7],"category_scores_gemma":[0.000055462733,0.00007048473,0.0000121805,0.00043812676,0.00001923951,0.00022495027,0.000044018347,0.00012367239,0.0000050014064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009606783,0.000027243359,0.0106343925,0.000021107862,0.000008968845,0.00002993569,0.00096074276,0.084251046,0.0014900452,0.004523495,0.000049808154,0.8979936],"study_design_scores_gemma":[0.00064325554,0.00017154444,0.02482751,0.000046577425,0.0000031690156,0.0000130666995,0.000030060659,0.9580327,0.0018979018,0.004523238,0.009681555,0.00012938248],"about_ca_topic_score_codex":0.0000011643633,"about_ca_topic_score_gemma":0.0000017035554,"teacher_disagreement_score":0.8978642,"about_ca_system_score_codex":0.000053006694,"about_ca_system_score_gemma":0.00006459507,"threshold_uncertainty_score":0.2874284},"labels":[],"label_agreement":null},{"id":"W4389482877","doi":"10.1007/978-3-031-49193-1_6","title":"A Sub-quadratic Time Algorithm for Computing the Beacon Kernel of Simple Polygons","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kernel (algebra); Simple (philosophy); Quadratic equation; Algorithm; Computer science; Combinatorics; Mathematics; Geometry","score_opus":0.02193965446037178,"score_gpt":0.26314301259475426,"score_spread":0.24120335813438248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389482877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004531873,0.00018427527,0.9959904,0.000654494,0.0016381991,0.00087818195,0.000040482933,0.00029028708,0.00027833934],"genre_scores_gemma":[0.0064718784,0.000010200169,0.9914282,0.0006953801,0.0006567052,0.000018781584,0.000024102234,0.00008309046,0.00061163877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955193,0.00006536594,0.00085392705,0.0014770984,0.0011360582,0.0009482725],"domain_scores_gemma":[0.9939988,0.0031890008,0.00062647735,0.0016766828,0.00035041966,0.00015859357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020364318,0.00059499114,0.0008182476,0.0006790196,0.00044976117,0.00039058845,0.0047383597,0.00031115778,0.0000032927596],"category_scores_gemma":[0.00026550872,0.00046920512,0.00026026636,0.000968588,0.00084417016,0.00030065214,0.0016509116,0.0007365496,0.00006669739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023064538,0.00002533522,0.000010810651,0.00005362492,0.000035548906,0.000047579866,0.001208263,0.08608217,0.00023862718,0.0029750208,0.00013451137,0.9091862],"study_design_scores_gemma":[0.00025935273,0.0001998773,0.00009415333,0.00036826928,0.000018892884,0.000058578364,4.4720002e-7,0.9360777,0.001103605,0.061236735,0.00010740856,0.0004749523],"about_ca_topic_score_codex":0.000040525505,"about_ca_topic_score_gemma":0.000008323119,"teacher_disagreement_score":0.90871125,"about_ca_system_score_codex":0.00018754818,"about_ca_system_score_gemma":0.0006816516,"threshold_uncertainty_score":0.99977595},"labels":[],"label_agreement":null},{"id":"W4389540751","doi":"10.17118/11143/21107","title":"Design and development of an autonomous mobile patrol robot","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Computer science; Robot; Human–computer interaction; Artificial intelligence","score_opus":0.040125291776141624,"score_gpt":0.27110563269354193,"score_spread":0.2309803409174003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389540751","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018513862,0.000017766179,0.9806936,0.000031811553,0.000102439095,0.00016022386,2.1712069e-7,0.00035300606,0.0001271001],"genre_scores_gemma":[0.06823611,0.0000012041385,0.93147737,0.000028554645,0.000008270236,0.000030479216,0.0000018019035,0.000005248401,0.00021096086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991677,0.000048109163,0.00018218554,0.00024909215,0.00015542525,0.00019750255],"domain_scores_gemma":[0.9994864,0.00008606101,0.00004388213,0.00027192113,0.000028552731,0.00008320244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046551306,0.00008089255,0.00011793114,0.00009972754,0.00006813195,0.000040399053,0.0003590893,0.00003311582,0.000005014693],"category_scores_gemma":[0.000008635013,0.00007050764,0.00000910563,0.00024367265,0.00001961463,0.00018784334,0.00015795109,0.000041381696,0.00006448829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035412352,0.00008279937,0.0003639938,0.000022199962,0.000028012197,0.00006193883,0.008876963,0.28558996,0.0046513123,0.0004848306,0.0003843175,0.69945014],"study_design_scores_gemma":[0.00016201207,0.000122004254,0.005342301,0.00000712375,0.0000012896262,0.000012004923,0.00007132163,0.9834554,0.010379898,0.0001499356,0.00017888758,0.00011780507],"about_ca_topic_score_codex":0.000007717939,"about_ca_topic_score_gemma":3.390507e-7,"teacher_disagreement_score":0.69933236,"about_ca_system_score_codex":0.000015841846,"about_ca_system_score_gemma":0.00013350489,"threshold_uncertainty_score":0.2875218},"labels":[],"label_agreement":null},{"id":"W4389665453","doi":"10.1109/iros55552.2023.10342150","title":"An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Obstacle avoidance; Computer science; Reinforcement learning; Collision avoidance; Robot; Obstacle; Artificial intelligence; Monte Carlo tree search; Mobile robot; Code (set theory); Process (computing); Tree (set theory); Monte Carlo method; Computer security; Operating system","score_opus":0.09578545611895606,"score_gpt":0.35001926753012386,"score_spread":0.2542338114111678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389665453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011012891,0.000068136986,0.98605305,0.0015281513,0.00018035903,0.00028531736,0.0000013768964,0.00052179815,0.00034893604],"genre_scores_gemma":[0.16100186,0.000006333994,0.83810234,0.00047297255,0.000029619843,0.00006654247,0.0000111084555,0.000011485509,0.00029772075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824375,0.00033517843,0.00024896118,0.00059204677,0.00019899418,0.00038106373],"domain_scores_gemma":[0.99826515,0.000789506,0.000059045695,0.00070878066,0.000043060485,0.0001344329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010356439,0.00013783932,0.00022457867,0.0002834975,0.0001484696,0.00007303271,0.00066522765,0.00009246312,0.000005576291],"category_scores_gemma":[0.00012591311,0.00013240098,0.000027615855,0.0012718082,0.0000522433,0.00030689198,0.00020645896,0.00015881856,0.00010462186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023337614,0.00044543343,0.038857687,0.00012805792,0.00002650628,0.00044746525,0.0045128916,0.67318505,0.025606928,0.052461497,0.0020173944,0.20228775],"study_design_scores_gemma":[0.0003626989,0.000064231965,0.043342356,0.00001584639,0.0000030254128,0.000016758768,0.00005957429,0.9516446,0.000527592,0.0036059066,0.00019222609,0.0001652033],"about_ca_topic_score_codex":0.00012263129,"about_ca_topic_score_gemma":0.000041695483,"teacher_disagreement_score":0.27845952,"about_ca_system_score_codex":0.000042997424,"about_ca_system_score_gemma":0.00008012921,"threshold_uncertainty_score":0.5399155},"labels":[],"label_agreement":null},{"id":"W4389666280","doi":"10.1109/iros55552.2023.10342010","title":"Spatial Reasoning via Deep Vision Models for Robotic Sequential Manipulation","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Heuristics; Computer science; Artificial intelligence; Solver; Heuristic; Motion planning; Scalability; Task (project management); Trajectory; Robot","score_opus":0.04610965168593926,"score_gpt":0.2971374358758373,"score_spread":0.251027784189898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389666280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048598746,0.000010210817,0.9962393,0.00034472355,0.0011768269,0.00029738678,4.868876e-7,0.0008305852,0.0006144793],"genre_scores_gemma":[0.4365517,0.0000018725871,0.5627377,0.00006463979,0.00015598365,0.000026113687,0.00003440728,0.000015866513,0.00041172135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854475,0.000045613255,0.0002454364,0.00044876206,0.00032904884,0.00038637407],"domain_scores_gemma":[0.9992474,0.00010565415,0.00008012804,0.00039526745,0.000083122126,0.00008847262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041034527,0.00013863092,0.00015637702,0.00017263842,0.00019725152,0.0001672543,0.0004701124,0.000083321414,0.000006485761],"category_scores_gemma":[0.00004462061,0.00013313263,0.000074476644,0.00041912185,0.000014786624,0.0006951359,0.0002018505,0.00007824728,0.00014258533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041640415,0.00001176786,0.00005295234,0.00001016867,0.0000085765305,0.000012864022,0.00024085544,0.9563102,0.0004126337,0.016175732,0.0003793175,0.026380746],"study_design_scores_gemma":[0.00029600566,0.00007962619,0.0012471237,0.000023659892,0.0000074569634,0.000014698305,0.000007812029,0.9775371,0.00028643062,0.020316677,0.0000149334965,0.0001684922],"about_ca_topic_score_codex":0.00013581183,"about_ca_topic_score_gemma":0.000010079991,"teacher_disagreement_score":0.4360657,"about_ca_system_score_codex":0.000058066747,"about_ca_system_score_gemma":0.000033722445,"threshold_uncertainty_score":0.54289913},"labels":[],"label_agreement":null},{"id":"W4389666466","doi":"10.1109/iros55552.2023.10342039","title":"Bidirectional Search Strategy for Incremental Search-based Path Planning","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Motion planning; Computer science; Path (computing); Artificial intelligence; Search algorithm; Robot; Robotics; Perception; Graph; Range (aeronautics); Theoretical computer science; Algorithm; Engineering","score_opus":0.1331988411523985,"score_gpt":0.35995228381742894,"score_spread":0.22675344266503045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389666466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020028329,0.000016521428,0.9749614,0.0006616563,0.00036779247,0.00029100408,0.000019101564,0.0009296039,0.002724575],"genre_scores_gemma":[0.5948429,0.0000012357657,0.40215003,0.00029304347,0.00021763578,0.00007799163,0.00010390952,0.0000254874,0.0022877539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826366,0.000073692376,0.00017818152,0.00042213392,0.0005348587,0.0005274701],"domain_scores_gemma":[0.9991114,0.0003587597,0.000023982046,0.0002796123,0.0000917202,0.00013452368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091388536,0.0001258004,0.0001253656,0.00027000607,0.00026083417,0.00018152178,0.0005695345,0.000055961118,0.000024951672],"category_scores_gemma":[0.000034798453,0.00011724824,0.00006323335,0.00074938923,0.000041817977,0.00023080292,0.00015816937,0.00014805577,0.00020929384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038198534,0.00013494043,0.008402249,0.000080732025,0.00006796122,0.0002016396,0.000856091,0.9080847,0.005527399,0.013197235,0.03525884,0.02815003],"study_design_scores_gemma":[0.00046331147,0.00018043141,0.00820784,0.000024258587,0.0000017445719,0.000008700789,0.00016383706,0.9858271,0.004425239,0.00022935217,0.00031290317,0.00015530614],"about_ca_topic_score_codex":0.00007044532,"about_ca_topic_score_gemma":5.388317e-7,"teacher_disagreement_score":0.57481456,"about_ca_system_score_codex":0.000070698065,"about_ca_system_score_gemma":0.00027364292,"threshold_uncertainty_score":0.47812444},"labels":[],"label_agreement":null},{"id":"W4389692243","doi":"10.1109/lra.2023.3342671","title":"Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Task (project management); Workspace; Robot; Artificial intelligence; Inverse kinematics; Engineering","score_opus":0.02463957206024244,"score_gpt":0.2684519002067864,"score_spread":0.24381232814654394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389692243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013812799,0.0000072789653,0.9809109,0.003529009,0.0006103201,0.0005360956,0.00003406426,0.0005469522,0.000012575103],"genre_scores_gemma":[0.7677254,0.0000035338364,0.23089531,0.0009480752,0.00016950081,0.00013726729,0.000059307316,0.000017437107,0.000044172786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998753,0.000052890708,0.00026578445,0.0003576654,0.0002714246,0.00029924655],"domain_scores_gemma":[0.9993368,0.00017679122,0.00011192645,0.00022843732,0.000057875874,0.00008821279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030534397,0.00014389138,0.00017165703,0.00017576413,0.00019949273,0.00016689222,0.00023207531,0.000077938006,4.0560946e-7],"category_scores_gemma":[0.000027563776,0.00014475599,0.000058715472,0.00023336007,0.0000484463,0.00036733676,0.000041183568,0.00010964945,0.00001570546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051796364,0.000012068481,0.000049776347,0.000018609777,0.00002246451,0.00000577468,0.00039335995,0.9870218,0.0060852356,0.0027638432,0.0019235158,0.0016983893],"study_design_scores_gemma":[0.0006944457,0.00006724155,0.0013858709,0.00002074637,0.000019542991,0.000007824608,0.0000066075295,0.9954743,0.00013406132,0.002005545,0.000026452033,0.00015738928],"about_ca_topic_score_codex":0.000002880746,"about_ca_topic_score_gemma":1.633327e-7,"teacher_disagreement_score":0.75391257,"about_ca_system_score_codex":0.00004695972,"about_ca_system_score_gemma":0.00003700917,"threshold_uncertainty_score":0.5902978},"labels":[],"label_agreement":null},{"id":"W4389763731","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-915-2023","title":"ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"GNSS applications; Computer science; Map matching; Global Positioning System; Real-time computing; Inertial navigation system; Simulation; Computer vision; Artificial intelligence; Orientation (vector space); Telecommunications","score_opus":0.02262234887210841,"score_gpt":0.2676258502196134,"score_spread":0.24500350134750498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389763731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010462698,0.000029458197,0.9758888,0.007028113,0.003007354,0.000571675,0.000041670486,0.00015879108,0.0028114743],"genre_scores_gemma":[0.96269417,0.00004853956,0.035810165,0.0010452728,0.0002516491,6.010663e-7,0.000039118513,0.000012550512,0.000097939126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941721,0.00043142796,0.0016780739,0.00059635274,0.0023704555,0.0007515956],"domain_scores_gemma":[0.9952606,0.0018188077,0.0017905534,0.00064508227,0.0003064354,0.0001785201],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002318283,0.00048074606,0.0004986562,0.0008139155,0.0021086526,0.0013678452,0.0028079264,0.00013013647,0.000003514494],"category_scores_gemma":[0.0009872594,0.0003110913,0.00040749714,0.0015788038,0.002519088,0.0010207179,0.0020971168,0.00068692537,0.000009813405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046909296,0.000014629113,0.00013875902,0.000055797238,0.00006648404,0.0000018873678,0.0068959426,0.015175463,0.0038989356,0.00004743361,0.000046790683,0.973611],"study_design_scores_gemma":[0.00057229336,0.00012929177,0.0019540035,0.00053381216,0.00003011677,0.00019731594,0.0022608836,0.9739885,0.00624049,0.012158556,0.0015772175,0.0003575333],"about_ca_topic_score_codex":0.5438973,"about_ca_topic_score_gemma":0.02290329,"teacher_disagreement_score":0.9732534,"about_ca_system_score_codex":0.00006850912,"about_ca_system_score_gemma":0.00029258998,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W4389782056","doi":"10.5194/isprs-annals-x-1-w1-2023-1165-2023","title":"Preface: Workshop “NGC of AV: Navigation, Guidance and Control of Autonomous Vehicles”","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"","keywords":"Control (management); Aeronautics; Computer science; Aerospace engineering; Engineering; Artificial intelligence","score_opus":0.04272114320154014,"score_gpt":0.30712777289911447,"score_spread":0.26440662969757434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389782056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14407805,0.0001123493,0.85346067,0.0016455959,0.00027633004,0.00020276263,0.00001282073,0.00004719821,0.0001642215],"genre_scores_gemma":[0.98701996,0.000038530383,0.012729049,0.00017580144,0.000013251317,1.6815376e-7,0.0000017083977,0.0000026750274,0.000018831623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832815,0.00010080554,0.000619057,0.00017339762,0.000538662,0.00023992355],"domain_scores_gemma":[0.9982413,0.00030756483,0.0007074834,0.00031561582,0.0003611986,0.000066807304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017828721,0.000120948076,0.0002691968,0.00025179898,0.00021392599,0.00012037387,0.00046219004,0.00006559727,3.3078206e-7],"category_scores_gemma":[0.0005252067,0.00008952788,0.00006440789,0.001342543,0.00056464993,0.00042214154,0.00018296568,0.000095557785,0.0000011863268],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009414689,0.000006473677,0.0005653734,0.00006493053,0.000015593005,4.1550066e-7,0.0028562504,0.012118552,0.0015246781,0.000034519107,0.0001443404,0.98265946],"study_design_scores_gemma":[0.0002014414,0.000084510044,0.008521046,0.00022989669,0.000005999753,0.000011898665,0.00028846355,0.9671296,0.02241457,0.0008600775,0.000159961,0.00009255184],"about_ca_topic_score_codex":0.0128719155,"about_ca_topic_score_gemma":0.00008979603,"teacher_disagreement_score":0.9825669,"about_ca_system_score_codex":0.000005425098,"about_ca_system_score_gemma":0.00009230333,"threshold_uncertainty_score":0.99370146},"labels":[],"label_agreement":null},{"id":"W4389782520","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-1993-2023","title":"Preface: Workshop “NGC of AV: Navigation, Guidance and Control of Autonomous Vehicles”","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Royal Military College of Canada","funders":"","keywords":"Aeronautics; Control (management); Computer science; Systems engineering; Artificial intelligence; Engineering","score_opus":0.018551978229755524,"score_gpt":0.26391215839966825,"score_spread":0.24536018016991273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389782520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018470272,0.000049788247,0.9734991,0.0037198463,0.0013438916,0.0005859943,0.00010058005,0.00006516213,0.0021653583],"genre_scores_gemma":[0.9864534,0.00010868012,0.012830244,0.00042611404,0.00007219166,4.5561418e-7,0.000023137945,0.000008985169,0.00007679885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952194,0.00032140134,0.0016797903,0.00042351423,0.001876432,0.00047945575],"domain_scores_gemma":[0.9948622,0.0017359016,0.0022351039,0.0005891786,0.00043332775,0.00014427194],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0020973284,0.0003660556,0.0005079918,0.0009928988,0.00084565824,0.0004947832,0.0022069558,0.00009517828,0.0000018280874],"category_scores_gemma":[0.0009797894,0.00024429997,0.00028092673,0.0015082313,0.003156722,0.0006512558,0.0011443861,0.00036482626,0.0000021319759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007673435,0.000016756465,0.0005099233,0.00006606991,0.000085178544,4.301957e-7,0.005564746,0.014762511,0.0034460474,0.000029347735,0.000034139615,0.97540814],"study_design_scores_gemma":[0.00080991077,0.00013581214,0.009060827,0.00056065334,0.000032430602,0.000104692226,0.001190705,0.9713996,0.009512402,0.0062376535,0.00070987426,0.0002454319],"about_ca_topic_score_codex":0.41264498,"about_ca_topic_score_gemma":0.014190351,"teacher_disagreement_score":0.9751627,"about_ca_system_score_codex":0.000033190496,"about_ca_system_score_gemma":0.0002861377,"threshold_uncertainty_score":0.9995561},"labels":[],"label_agreement":null},{"id":"W4389791303","doi":"10.1007/978-3-031-46363-1_5","title":"“Tortured and Long Delayed”—Fixed-Wing Search and Rescue Airplanes","year":2023,"lang":"en","type":"book-chapter","venue":"Canada and international affairs","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Fixed wing; Aeronautics; Government (linguistics); Search and rescue; Engineering; Wing; Operations research; Computer security; Political science; Aerospace engineering; Computer science; Artificial intelligence","score_opus":0.02278770133048505,"score_gpt":0.23378089015849762,"score_spread":0.21099318882801257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389791303","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024762513,0.021064494,0.098150015,0.036962982,0.036344822,0.0028390677,0.0026019334,0.0019529941,0.7753212],"genre_scores_gemma":[0.17545965,0.0016984489,0.013430807,0.0007787258,0.0012310097,0.000018939832,0.00036356613,0.00013467255,0.80688417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985465,0.000013248346,0.00021719215,0.000521073,0.00048538324,0.0002166081],"domain_scores_gemma":[0.99931127,0.00014178467,0.00004901216,0.0001994839,0.00010270997,0.00019576395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015778787,0.00023114351,0.00022126193,0.00016804667,0.00012923944,0.00019281197,0.000402659,0.00013312048,0.000010223744],"category_scores_gemma":[0.00003392333,0.00022601346,0.000023602588,0.000029660596,0.00008824762,0.00016785231,0.0003906634,0.00029919928,0.000003908241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009031396,0.000027937454,0.0055479724,0.00052620075,0.0021019946,0.010102142,0.0031064409,0.004150876,0.000095632895,0.6747489,0.08687128,0.2126303],"study_design_scores_gemma":[0.0029158944,0.0006217744,0.06480448,0.0033744094,0.00021406494,0.0047928654,0.0008991204,0.73957306,0.00009998487,0.023872081,0.153952,0.004880226],"about_ca_topic_score_codex":0.052320186,"about_ca_topic_score_gemma":0.12848602,"teacher_disagreement_score":0.7354222,"about_ca_system_score_codex":0.000108289074,"about_ca_system_score_gemma":0.00031674517,"threshold_uncertainty_score":0.9539905},"labels":[],"label_agreement":null},{"id":"W4389966182","doi":"10.1016/j.fmre.2023.10.015","title":"Developing inverse motion planning technique for autonomous vehicles using integral nonlinear constraints","year":2023,"lang":"en","type":"article","venue":"Fundamental Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Motion (physics); Inverse; Computer science; Motion planning; Control theory (sociology); Mathematics; Artificial intelligence; Physics; Robot; Geometry; Control (management)","score_opus":0.29935995752710354,"score_gpt":0.45273216062800925,"score_spread":0.1533722031009057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389966182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1128918,0.000018761591,0.884832,0.00048445963,0.0003432868,0.00085014815,0.00002365486,0.00040055052,0.0001553253],"genre_scores_gemma":[0.17580223,0.0000036176182,0.8235325,0.0000724568,0.0001370714,0.00015770143,0.00003973888,0.000027777769,0.00022686592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975042,0.00017926538,0.00028793127,0.0005275671,0.0006403063,0.0008607725],"domain_scores_gemma":[0.9987664,0.00050055946,0.00006353808,0.00032904596,0.00019289844,0.00014755591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026051933,0.00015888097,0.00018825226,0.0006669479,0.0005711679,0.00029241934,0.0008118755,0.0001224651,0.000004762196],"category_scores_gemma":[0.0003781176,0.00016316921,0.00006222958,0.0012495774,0.0002728013,0.0004012496,0.0006388247,0.00044408796,0.00011502896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024896202,0.000562314,0.054385234,0.0011429013,0.00039052218,0.0023473692,0.02086155,0.048986692,0.4006547,0.031102471,0.010149587,0.4291677],"study_design_scores_gemma":[0.00050677295,0.00018355032,0.0024789926,0.00037075966,0.00000288987,0.000091582275,0.0010090682,0.966309,0.025292384,0.0023805653,0.0010775188,0.0002969154],"about_ca_topic_score_codex":0.0000558873,"about_ca_topic_score_gemma":0.0000014554928,"teacher_disagreement_score":0.91732234,"about_ca_system_score_codex":0.00066801376,"about_ca_system_score_gemma":0.0005181279,"threshold_uncertainty_score":0.66538477},"labels":[],"label_agreement":null},{"id":"W4390022972","doi":"10.18280/mmep.100622","title":"Enhanced Obstacle Avoidance and Intelligent Navigation for Mobile Robots: An Integrated Approach Using Fuzzy Logic and an Optimized APF Method","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Obstacle avoidance; Fuzzy logic; Mobile robot; Computer science; Collision avoidance; Artificial intelligence; Robot; Obstacle; Control engineering; Mobile robot navigation; Human–computer interaction; Computer vision; Engineering; Robot control; Computer security; Geography","score_opus":0.07282447467198265,"score_gpt":0.3095756378501471,"score_spread":0.23675116317816441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390022972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031401377,0.00021581903,0.96726084,0.00001534641,0.000064565225,0.0005508436,0.0000035348232,0.0004783461,0.00000930282],"genre_scores_gemma":[0.072451785,0.00003527893,0.9272557,0.000007722209,0.000026667341,0.00015446426,0.000020295267,0.000030631276,0.000017482156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984781,0.000060174807,0.00035483472,0.00057369046,0.00015657062,0.00037659393],"domain_scores_gemma":[0.99908906,0.00029622976,0.00006989055,0.00028265722,0.000066526576,0.00019564782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011898864,0.00024544587,0.00036359165,0.00012664347,0.00014646769,0.00026331132,0.00020529043,0.00011928853,2.6406076e-7],"category_scores_gemma":[0.000057290974,0.00021637516,0.000028812421,0.00031121747,0.000037054167,0.00041435138,0.00009278107,0.00018925946,9.4041803e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043266405,0.00004127139,4.76519e-7,0.00044964012,0.000014232497,0.0000012502221,0.003088755,0.98128754,0.00224807,0.006497357,3.898946e-7,0.006366696],"study_design_scores_gemma":[0.0002648673,0.0001297704,9.735005e-7,0.00026414375,0.000017704542,0.00003785869,0.00016514552,0.97606504,0.00075236097,0.022034073,0.0000032621788,0.00026478147],"about_ca_topic_score_codex":0.000010351298,"about_ca_topic_score_gemma":3.4721385e-8,"teacher_disagreement_score":0.041050408,"about_ca_system_score_codex":0.000027723474,"about_ca_system_score_gemma":0.000014364543,"threshold_uncertainty_score":0.8823523},"labels":[],"label_agreement":null},{"id":"W4390071568","doi":"10.1109/cisct57197.2023.10351421","title":"Desired Track Guidance for Flight Navigation Technique","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Track (disk drive); Artificial intelligence; Computer vision","score_opus":0.04281310446827947,"score_gpt":0.30178244950589206,"score_spread":0.2589693450376126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390071568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058944814,0.000013061348,0.99501026,0.0009979896,0.0002877551,0.0004220523,0.000002365088,0.0013572472,0.0013198294],"genre_scores_gemma":[0.02066252,0.0000013522194,0.9760741,0.00012643963,0.00006316541,0.00027087843,0.000013847783,0.0000099568115,0.0027777255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990974,0.000025569832,0.00017251619,0.00029470355,0.00016125833,0.0002485368],"domain_scores_gemma":[0.99930835,0.00014021301,0.000049821985,0.00037910786,0.00007178443,0.00005071711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051733915,0.0000864788,0.000095970696,0.00008207748,0.00009842194,0.00007070841,0.00055745733,0.000060308354,0.0000020007342],"category_scores_gemma":[0.00007060729,0.00007828399,0.000042962838,0.00060534646,0.000015495267,0.0002652563,0.00006904284,0.000050536113,0.00015898902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023699631,0.00019160932,0.0012679403,0.00033865432,0.00007538203,0.00025446888,0.0030219557,0.019504605,0.18173932,0.27737,0.25665656,0.2595558],"study_design_scores_gemma":[0.00022885007,0.00007701634,0.0021605454,0.00006775217,0.0000026657503,0.000026369866,0.000010434626,0.87209046,0.10679869,0.014617815,0.0037052082,0.00021420368],"about_ca_topic_score_codex":0.0000053393364,"about_ca_topic_score_gemma":1.8358757e-7,"teacher_disagreement_score":0.85258585,"about_ca_system_score_codex":0.000027989407,"about_ca_system_score_gemma":0.000039079467,"threshold_uncertainty_score":0.31923285},"labels":[],"label_agreement":null},{"id":"W4390296083","doi":"10.1016/j.engappai.2023.107770","title":"A 3D-RRT-star algorithm for optimizing constrained mountain railway alignments","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Mathematical optimization; Discretization; Obstacle; Path (computing); Constraint (computer-aided design); A* search algorithm; Algorithm; Process (computing); Heuristic; Random tree; Sampling (signal processing); Set (abstract data type); Star (game theory); Tree (set theory); Motion planning; Mathematics; Artificial intelligence","score_opus":0.030182132103809708,"score_gpt":0.2856380456883223,"score_spread":0.2554559135845126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390296083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002412192,0.000043122698,0.99787754,0.0002058373,0.0002264783,0.0006956597,0.000048312864,0.0006102775,0.00005157316],"genre_scores_gemma":[0.021959918,0.0000120867235,0.9771369,0.000020416683,0.000108176,0.0006373045,0.000032123266,0.000024975885,0.00006810226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982786,0.000015587408,0.0005401975,0.00045966834,0.00027536406,0.00043054705],"domain_scores_gemma":[0.9985416,0.00042801962,0.00013483345,0.0006300989,0.00015356978,0.00011188662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057090103,0.0001934525,0.000236403,0.00033087167,0.00014162587,0.00008231953,0.00095264096,0.00008248892,0.0000048458205],"category_scores_gemma":[0.000117155956,0.00022356785,0.00008554686,0.0012683488,0.000069320544,0.00019037818,0.00014790868,0.00012274148,0.00009952734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019739994,0.00006431853,0.000003056539,0.0000408248,0.00004144627,0.0000036055383,0.00059586135,0.4888772,0.008791325,0.049724165,0.00009955042,0.45175666],"study_design_scores_gemma":[0.000033799104,0.000044431876,0.000011478549,0.000034123434,0.000008484648,0.0000050171366,0.00012652324,0.9672914,0.028467996,0.0029033588,0.00086481177,0.000208575],"about_ca_topic_score_codex":0.000018877183,"about_ca_topic_score_gemma":2.797514e-7,"teacher_disagreement_score":0.47841418,"about_ca_system_score_codex":0.00006004513,"about_ca_system_score_gemma":0.000067335684,"threshold_uncertainty_score":0.91168326},"labels":[],"label_agreement":null},{"id":"W4390403175","doi":"10.3390/machines12010023","title":"Design and Control of Autonomous Flying Excavator","year":2023,"lang":"en","type":"article","venue":"Machines","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Excavator; Drone; Software; Excavation; Digging; Engineering; Computer science; Simulation; Embedded system; Real-time computing; Mechanical engineering; Operating system; Geotechnical engineering","score_opus":0.024580295112144435,"score_gpt":0.258909988517694,"score_spread":0.23432969340554954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390403175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016462386,0.0001369619,0.9823304,0.00038702015,0.00022643343,0.000103580955,0.0000014734351,0.0002723234,0.00007943488],"genre_scores_gemma":[0.7645385,0.000004219029,0.23514575,0.000085507316,0.000034183617,0.000010432529,7.791356e-7,0.000007146918,0.00017345327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936116,0.000062147505,0.00013122948,0.00017760387,0.00011330208,0.00015453165],"domain_scores_gemma":[0.9994105,0.00025864522,0.00005321335,0.0002115646,0.000022628228,0.000043429984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038113273,0.00007591396,0.00013983579,0.00009434951,0.000072271425,0.000038578117,0.00028328045,0.000026140598,0.0000016348106],"category_scores_gemma":[0.00007099358,0.00006461322,0.000017654571,0.00025035537,0.000026096764,0.00010806921,0.00009225345,0.000049334096,0.00002539624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003407935,0.00009788559,0.056282412,0.00020097203,0.00016300735,0.00040436388,0.008943336,0.14123286,0.0502703,0.010583638,0.0030028187,0.7287843],"study_design_scores_gemma":[0.00025894522,0.000036396785,0.015890103,0.000012355728,0.0000034058082,0.000013354065,0.0000042937695,0.9821433,0.0004559591,0.0010464772,0.00006381312,0.000071642135],"about_ca_topic_score_codex":0.000024604702,"about_ca_topic_score_gemma":1.11483054e-7,"teacher_disagreement_score":0.8409104,"about_ca_system_score_codex":0.0000058794176,"about_ca_system_score_gemma":0.000024253788,"threshold_uncertainty_score":0.26348507},"labels":[],"label_agreement":null},{"id":"W4390448179","doi":"10.1177/02783649231216499","title":"Energy-optimal trajectories for skid-steer rovers","year":2023,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Turning radius; Trajectory; Skid (aerodynamics); Motion planning; Control theory (sociology); Path (computing); Energy (signal processing); Parameterized complexity; Energy consumption; Work (physics); Optimal control; Pontryagin's minimum principle; Mathematics; Mathematical optimization; Engineering; Computer science; Aerospace engineering; Robot; Algorithm; Physics; Artificial intelligence; Control (management)","score_opus":0.11273250681721328,"score_gpt":0.40078022692039944,"score_spread":0.28804772010318613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390448179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026089842,0.000107933294,0.9727582,0.02085161,0.003058133,0.00009537731,0.00000846207,0.000046325113,0.00046499135],"genre_scores_gemma":[0.53856236,0.00038795415,0.44647038,0.00057497167,0.0036986805,0.000030555882,0.00001583841,0.00006207637,0.010197205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967878,0.00015844377,0.00038056992,0.00016984626,0.0020944695,0.0004088713],"domain_scores_gemma":[0.9955092,0.002003442,0.00017419831,0.00029210356,0.0019103382,0.0001106994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003542712,0.00010065017,0.00015835275,0.0005338025,0.00019733995,0.0003858347,0.0036272954,0.0000544424,0.000005986295],"category_scores_gemma":[0.0008107499,0.00006955556,0.00014484527,0.0006325213,0.00013020767,0.000365595,0.00046315335,0.00039772116,0.00003333347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012920151,0.00008572392,0.00023434186,0.000009279151,0.00037300316,0.00031640983,0.001959263,0.786382,0.001556983,0.1067333,0.082935154,0.019285297],"study_design_scores_gemma":[0.0008586822,0.00039928456,0.0007761275,0.000083432344,0.000010644846,0.00030836515,0.00044841017,0.9591313,0.002747058,0.019574286,0.015502385,0.00016003614],"about_ca_topic_score_codex":0.00002399437,"about_ca_topic_score_gemma":0.0000020364082,"teacher_disagreement_score":0.53595334,"about_ca_system_score_codex":0.00017588183,"about_ca_system_score_gemma":0.0003966881,"threshold_uncertainty_score":0.67404777},"labels":[],"label_agreement":null},{"id":"W4390574149","doi":"10.2139/ssrn.4684037","title":"Automating Adaptive Scan Planning for Static Laser Scanning in Complex 3d Environments","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Laser scanning; Computer science; Laser; Optics; Physics","score_opus":0.03452964633277304,"score_gpt":0.2955597477995764,"score_spread":0.26103010146680333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390574149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023723401,0.0027887716,0.9707712,0.000556034,0.0009858215,0.0006537253,0.000021724005,0.00019638194,0.00030294483],"genre_scores_gemma":[0.61220306,0.00015342358,0.38600177,0.00015703635,0.0005244837,0.00013542784,0.000049472805,0.00011943752,0.0006559057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935396,0.00025535317,0.00092218607,0.0009766173,0.0007052393,0.0036010204],"domain_scores_gemma":[0.9982999,0.0002910607,0.0006490691,0.00054084836,0.000045612596,0.00017349405],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0035496408,0.0005653654,0.00067257293,0.00066017715,0.00029993898,0.00053789734,0.0017569307,0.00026052727,0.0000041409407],"category_scores_gemma":[0.000117314776,0.0005749624,0.00022571665,0.00032222376,0.0000627975,0.00027481237,0.0013899523,0.0063412357,0.000034215314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047943784,0.00013352891,0.0012366432,0.00023755996,0.0007856512,0.00029388093,0.006330186,0.87177926,0.00017634647,0.015614115,0.00046113154,0.10290377],"study_design_scores_gemma":[0.0006093059,0.0002581694,0.001124624,0.0011087473,0.00005314891,0.0003380839,0.00062481064,0.81407285,0.00002316227,0.18119074,0.00007504016,0.0005212827],"about_ca_topic_score_codex":0.000084724204,"about_ca_topic_score_gemma":0.000029240584,"teacher_disagreement_score":0.58847964,"about_ca_system_score_codex":0.0039233877,"about_ca_system_score_gemma":0.0035281323,"threshold_uncertainty_score":0.9999004},"labels":[],"label_agreement":null},{"id":"W4390576725","doi":"10.1109/lcsys.2024.3349989","title":"MR.CAP: Multi-Robot Joint Control and Planning for Object Transport","year":2024,"lang":"en","type":"article","venue":"IEEE Control Systems Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Vector Institute","keywords":"Motion planning; Computer science; Robot; Path (computing); Scalability; Graph; Mathematical optimization; Real-time computing; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.029580921163695248,"score_gpt":0.2561718505904332,"score_spread":0.22659092942673792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390576725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007621572,0.003604812,0.9766767,0.0029896167,0.006821859,0.0014551212,0.00007042691,0.0007328142,0.000027090042],"genre_scores_gemma":[0.9773352,0.0000029683008,0.019260403,0.0019381979,0.00084576616,0.00039026246,0.0000054185625,0.000051968327,0.00016977535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971379,0.00016231427,0.00066956005,0.00089985307,0.0003934135,0.00073693536],"domain_scores_gemma":[0.9984355,0.0006028104,0.00014968646,0.0005144946,0.000082259714,0.00021524668],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00111414,0.00039711935,0.0007155319,0.00028564266,0.00019098875,0.0004980685,0.00053644273,0.00014417131,6.353165e-7],"category_scores_gemma":[0.00005613539,0.00035354946,0.00020988112,0.00022349728,0.00007430509,0.00047806973,0.000015973932,0.0002984586,0.000027363496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011700933,0.0000942617,0.0034747152,0.0015618115,0.0013203133,0.0027561693,0.006131731,0.62673366,0.33400205,0.001222149,0.018731276,0.003854838],"study_design_scores_gemma":[0.0036226246,0.0001014735,0.0022259224,0.00060896244,0.00009115855,0.00021843049,0.000045428027,0.9909935,0.00014393605,0.000008744204,0.001488305,0.00045150498],"about_ca_topic_score_codex":0.0001305948,"about_ca_topic_score_gemma":0.0000015299361,"teacher_disagreement_score":0.9697137,"about_ca_system_score_codex":0.0001053846,"about_ca_system_score_gemma":0.0000842306,"threshold_uncertainty_score":0.99989164},"labels":[],"label_agreement":null},{"id":"W4390595017","doi":"10.5383/juspn.18.01.002","title":"Control of autonomous UAV using an onboard LSTM neural network","year":2023,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Embedding; Global Positioning System; Artificial intelligence; Artificial neural network; Control (management); Lift (data mining); Control engineering; Real-time computing; Engineering; Machine learning; Telecommunications","score_opus":0.03414590375742107,"score_gpt":0.2692528413358813,"score_spread":0.2351069375784602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390595017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28701273,0.0043099024,0.70175654,0.00018530471,0.006281611,0.00029438623,0.000005164993,0.000095995216,0.000058362544],"genre_scores_gemma":[0.9857604,0.00010590218,0.010921577,0.00010495412,0.0030397621,0.0000030294802,0.0000016091203,0.000025715997,0.000037064605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972486,0.00039984373,0.0010030619,0.0002906231,0.0004788688,0.0005789871],"domain_scores_gemma":[0.9973053,0.00041884204,0.0011376091,0.0004002771,0.00044046962,0.0002975074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016743169,0.00024710645,0.00081399875,0.00020306808,0.00020921187,0.00023064612,0.0007412466,0.00018669476,0.0000011787301],"category_scores_gemma":[0.000049940933,0.00020498932,0.00017154877,0.0005862772,0.00007523952,0.0005570556,0.00013296999,0.00042623567,0.0000016105561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026033835,0.000028914286,0.0037303744,0.00004179615,0.00010094346,0.00036912897,0.0006026028,0.9902232,0.00022009911,0.0003717006,0.000860271,0.003424932],"study_design_scores_gemma":[0.0007484216,0.000650778,0.005092449,0.0003559438,0.000055452067,0.0014611573,0.0001996311,0.9909209,0.000007185646,0.000074170726,0.00023314214,0.00020074812],"about_ca_topic_score_codex":0.000119244716,"about_ca_topic_score_gemma":0.0000038467706,"teacher_disagreement_score":0.69874763,"about_ca_system_score_codex":0.00006238421,"about_ca_system_score_gemma":0.00016256806,"threshold_uncertainty_score":0.8359222},"labels":[],"label_agreement":null},{"id":"W4390695490","doi":"10.3390/s24020408","title":"Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Fuel efficiency; Particle swarm optimization; Computer science; Speedup; Trajectory; Swarm behaviour; Smoothing; Simulation; Real-time computing; Mathematical optimization; Automotive engineering; Engineering; Algorithm; Artificial intelligence; Parallel computing; Mathematics","score_opus":0.05263922027544007,"score_gpt":0.29994070944944484,"score_spread":0.24730148917400477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390695490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27054256,0.00034364488,0.7269421,0.00033370772,0.0012102658,0.00021859155,0.000008761142,0.00038147508,0.00001889379],"genre_scores_gemma":[0.43548736,0.000020463796,0.5641084,0.000048619677,0.00021517575,0.000012141044,0.000010512241,0.000019896455,0.00007746024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986336,0.00009615204,0.00026618215,0.0004444392,0.00019750524,0.00036212686],"domain_scores_gemma":[0.9992068,0.00029578386,0.000067673915,0.00027763774,0.000070715345,0.00008142834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040184896,0.00014981901,0.00017051873,0.00007792536,0.00013875235,0.00031019293,0.00024140227,0.00007780592,0.000003165789],"category_scores_gemma":[0.000106065076,0.00015126543,0.00006967756,0.00026325276,0.000046452384,0.00030355257,0.00006219836,0.00008153488,0.000033482156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023217995,0.0000152441835,0.00041237232,0.00007037712,0.000022179713,0.000021612159,0.00067674124,0.99510825,0.0011297798,0.0011435645,0.00011545407,0.0012612272],"study_design_scores_gemma":[0.0003756472,0.00003927381,0.0005543375,0.000066899585,0.00000997156,0.000028185175,0.000029695422,0.99718267,0.0011003971,0.00023705637,0.00018041438,0.00019546432],"about_ca_topic_score_codex":0.00001735662,"about_ca_topic_score_gemma":0.0000015087007,"teacher_disagreement_score":0.16494481,"about_ca_system_score_codex":0.000067885056,"about_ca_system_score_gemma":0.00005963435,"threshold_uncertainty_score":0.6168425},"labels":[],"label_agreement":null},{"id":"W4390956158","doi":"10.1109/tce.2024.3355061","title":"AI-Enabled Trajectory Optimization of Logistics UAVs With Wind Impacts in Smart Cities","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Science Foundation of Sichuan Province; National Natural Science Foundation of China","keywords":"Payload (computing); Computer science; Trajectory; Energy consumption; Motion planning; Wind power; Process (computing); Genetic algorithm; Trajectory optimization; Real-time computing; Path (computing); Simulation; Engineering; Artificial intelligence","score_opus":0.013954069506367812,"score_gpt":0.24547393361039385,"score_spread":0.23151986410402603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390956158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028286425,0.0012951291,0.9944464,0.0003021792,0.00047405413,0.00022521547,0.000016514663,0.0002350678,0.00017681408],"genre_scores_gemma":[0.93577445,0.0003442505,0.06348027,0.00011718725,0.000014776728,0.000019475787,0.000004279309,0.000032976448,0.00021230412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983301,0.0000958296,0.00034131328,0.0004138078,0.00033193428,0.0004869801],"domain_scores_gemma":[0.99905163,0.00028335524,0.00006372249,0.0004163652,0.00010147963,0.00008346046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000269289,0.00023032971,0.00027106662,0.0004860423,0.000085270374,0.00012391745,0.00034533607,0.00012330827,0.000017633869],"category_scores_gemma":[0.00000858603,0.00021121415,0.00006701307,0.0010018649,0.00011595298,0.0004050246,0.000001997422,0.00059513794,0.000012811521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003226921,0.00010458057,0.00004123827,0.00006981093,0.000091350106,0.00004569963,0.0005040083,0.9941252,0.00029164707,0.0005444065,0.000076650154,0.0040731393],"study_design_scores_gemma":[0.0005044205,0.0004627225,0.00006574133,0.00023398505,0.00005957783,0.0000770885,0.000029901707,0.9898088,0.008092295,0.00018053818,0.0002141642,0.00027074656],"about_ca_topic_score_codex":0.00008172515,"about_ca_topic_score_gemma":0.0000787684,"teacher_disagreement_score":0.93294585,"about_ca_system_score_codex":0.00025474498,"about_ca_system_score_gemma":0.0009329933,"threshold_uncertainty_score":0.8613063},"labels":[],"label_agreement":null},{"id":"W4391054243","doi":"10.2316/j.2024.206-1055","title":"BEHAVIOUR-DEFINED NAVIGATION FRAMEWORK FOR DYNAMICAL OBSTACLE AVOIDANCE IN MULTI-ROBOT SYSTEMS CONSISTING OF HOLONOMIC ROBOTS, 379-390.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Holonomic; Obstacle avoidance; Robot; Obstacle; Computer science; Collision avoidance; Mobile robot; Artificial intelligence; Control engineering; Computer vision; Control theory (sociology); Engineering; Geography; Computer security; Control (management); Collision","score_opus":0.03261786131653167,"score_gpt":0.32186430714436737,"score_spread":0.2892464458278357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391054243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06836615,0.0008164243,0.9272832,0.0008812542,0.0024577493,0.00013737919,0.000015728974,0.000038238242,0.000003848308],"genre_scores_gemma":[0.5645891,0.000015675212,0.43528205,0.000012728756,0.000073067065,0.0000032913724,0.000009651376,0.0000071441314,0.0000072921325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984856,0.000059375394,0.0007774714,0.0001951148,0.00034149684,0.00014090369],"domain_scores_gemma":[0.9983881,0.00058750605,0.0004937379,0.00011217663,0.0003660912,0.00005240121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006744493,0.000119410906,0.00024512954,0.0003110741,0.000038058148,0.0002796446,0.0003963828,0.00010211539,5.063418e-7],"category_scores_gemma":[0.00032465038,0.000115524,0.00007826998,0.00016730701,0.000041040446,0.00052179745,0.00006450228,0.00023684751,0.0000015073208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013814625,0.00009296457,0.004607868,0.000109294044,0.00007240188,0.000074675634,0.0007140099,0.9163314,0.0014738647,0.06555129,0.00002521601,0.010933194],"study_design_scores_gemma":[0.00042644067,0.00007800813,0.021530949,0.0015212774,0.000018030352,0.00025292483,0.00005370984,0.97258055,0.00014287795,0.0032821351,0.000007597511,0.000105499596],"about_ca_topic_score_codex":0.000024897196,"about_ca_topic_score_gemma":0.0000011645244,"teacher_disagreement_score":0.49622297,"about_ca_system_score_codex":0.00017237065,"about_ca_system_score_gemma":0.00011654895,"threshold_uncertainty_score":0.4710932},"labels":[],"label_agreement":null},{"id":"W4391054288","doi":"10.2316/j.2024.201-0414","title":"A NEW HYBRID WHALE PARTICLE SWARM OPTIMISATION ALGORITHM FOR ROBOT TRAJECTORY PLANNING AND TRACKING CONTROL, 48-57.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tracking (education); Whale; Particle swarm optimization; Trajectory; Computer science; Swarm behaviour; Robot; Particle (ecology); Artificial intelligence; Control theory (sociology); Computer vision; Control (management); Algorithm; Physics; Geology; Biology","score_opus":0.01889071334022606,"score_gpt":0.2508368840327881,"score_spread":0.23194617069256201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391054288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017673778,0.039237067,0.9557687,0.00047872914,0.0014859815,0.0008597735,0.00002280136,0.0003562769,0.000023293378],"genre_scores_gemma":[0.94182044,0.000026895848,0.056906532,0.00010188875,0.00058574765,0.00018412195,0.0000043058667,0.000032835494,0.0003372357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791586,0.00011925633,0.00043428535,0.0006754586,0.00027816443,0.0005769587],"domain_scores_gemma":[0.9988624,0.00043233967,0.00011605443,0.00029502585,0.000059324226,0.00023488079],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000954399,0.00027304864,0.00046187747,0.000110571425,0.00022314402,0.0009324562,0.00026081168,0.000092407245,0.0000019182569],"category_scores_gemma":[0.00003503029,0.00024766682,0.00009479759,0.0001300026,0.000026820688,0.0006653476,0.00003673137,0.00018040289,0.0000075325083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060430015,0.000052779968,0.00023167973,0.00038520893,0.00055228226,0.0002225394,0.0029077947,0.08023896,0.0041162744,0.016043762,0.0012558277,0.89393246],"study_design_scores_gemma":[0.0029457717,0.00021381321,0.00013639312,0.00028740044,0.00009468659,0.00026556314,0.0001786486,0.993559,0.00041927915,0.0004077536,0.0012055212,0.0002861549],"about_ca_topic_score_codex":0.00014071687,"about_ca_topic_score_gemma":0.0000013055512,"teacher_disagreement_score":0.94005305,"about_ca_system_score_codex":0.00009415528,"about_ca_system_score_gemma":0.00018464428,"threshold_uncertainty_score":0.99999756},"labels":[],"label_agreement":null},{"id":"W4391054416","doi":"10.2316/j.2024.201-0392","title":"AN ADAPTIVE CONTROL ALGORITHM OF TETHERED UNMANNED AERIAL VEHICLE, 11-17.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Simulation; Control engineering; Control (management); Real-time computing; Aerospace engineering; Engineering; Marine engineering; Control theory (sociology); Artificial intelligence","score_opus":0.009512876633437868,"score_gpt":0.2305566577690741,"score_spread":0.22104378113563625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391054416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024836368,0.008471249,0.9853011,0.00025440173,0.0021802508,0.00073638314,0.00009334103,0.00035699105,0.00012264705],"genre_scores_gemma":[0.9896448,0.000013202936,0.009350544,0.00006829983,0.00056318555,0.000110279776,0.0000037880252,0.000029484738,0.00021637176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738663,0.000358685,0.0005580693,0.0006974412,0.00044932295,0.0005498305],"domain_scores_gemma":[0.9985513,0.00028547482,0.00017585361,0.0006583728,0.00012637372,0.0002026443],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001018565,0.00030532051,0.00066888356,0.0001771764,0.00011754122,0.000336521,0.00065953337,0.00017824084,0.0000065315835],"category_scores_gemma":[0.000020687807,0.0002553479,0.00012489242,0.0002721486,0.00007083235,0.0005033031,0.00005281749,0.0002039888,0.000016468037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005722038,0.00065309444,0.0006374081,0.00069096504,0.0028162259,0.0011300495,0.005647292,0.053235676,0.03689493,0.4357637,0.0024057787,0.45955268],"study_design_scores_gemma":[0.0028701136,0.0008057607,0.00015740326,0.00020129989,0.000076715456,0.000082114275,0.0002098922,0.9935005,0.00014973954,0.00071275193,0.0009543732,0.00027934802],"about_ca_topic_score_codex":0.0005501239,"about_ca_topic_score_gemma":0.0000071956365,"teacher_disagreement_score":0.9871612,"about_ca_system_score_codex":0.00008475966,"about_ca_system_score_gemma":0.00022241293,"threshold_uncertainty_score":0.99998987},"labels":[],"label_agreement":null},{"id":"W4391128583","doi":"10.1109/lra.2024.3357313","title":"Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristic; Maximization; Planner; Mathematical optimization; Motion planning; Computer science; Function (biology); Correctness; Range (aeronautics); Pareto principle; Path (computing); Simple (philosophy); Robot; Artificial intelligence; Mathematics; Algorithm; Engineering","score_opus":0.034383656741541695,"score_gpt":0.27343320200726107,"score_spread":0.23904954526571937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391128583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013100847,0.00028735117,0.9825581,0.0016287684,0.0013829448,0.00023687088,0.0000022155027,0.00078165764,0.000021288357],"genre_scores_gemma":[0.23428488,0.000005516823,0.7651598,0.00037722828,0.0001056054,0.000008309983,0.000009845587,0.000026629963,0.000022211472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984204,0.00008705299,0.0003396861,0.0005186881,0.0002984017,0.00033578114],"domain_scores_gemma":[0.9993803,0.00011469474,0.000114677074,0.00023859086,0.00005907602,0.00009269832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031113005,0.00022269477,0.00019780715,0.00034946424,0.00026614542,0.00079036335,0.0002492416,0.00009560972,0.0000010845348],"category_scores_gemma":[0.000020791684,0.00021740772,0.000050843817,0.00060714356,0.000048691163,0.0009788367,0.00007549726,0.00022799258,0.000015435833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.808367e-7,0.000015878364,0.00056625094,0.00007687654,0.000037877933,0.000053034975,0.0016090297,0.9761522,0.016977137,0.0007919265,0.00008251937,0.0036368],"study_design_scores_gemma":[0.00021422535,0.000018872377,0.0026893134,0.0004583238,0.000024237119,0.00006962106,0.000025032681,0.99542874,0.0005864725,0.00019220499,0.000021953585,0.00027099418],"about_ca_topic_score_codex":0.000021116639,"about_ca_topic_score_gemma":2.1679428e-7,"teacher_disagreement_score":0.22118403,"about_ca_system_score_codex":0.00011943424,"about_ca_system_score_gemma":0.00005426269,"threshold_uncertainty_score":0.88656294},"labels":[],"label_agreement":null},{"id":"W4391302817","doi":"10.2514/6.2024-0432","title":"Spacecraft Rendezvous and Docking with Obstacle Avoidance Using Bounded Buffered Voronoi Cells","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Rendezvous; Voronoi diagram; Spacecraft; Docking (animal); Obstacle avoidance; Bounded function; Computer science; Obstacle; Vehicle dynamics; Aerospace engineering; Collision avoidance; Engineering; Mathematics; Artificial intelligence; Mobile robot; Robot; Geography; Collision; Computer security","score_opus":0.014916896089771274,"score_gpt":0.23487164172084637,"score_spread":0.2199547456310751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391302817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072850585,0.00096520025,0.9230515,0.00042876785,0.0006149511,0.00013471331,0.0000015527796,0.0005905551,0.0013621805],"genre_scores_gemma":[0.46511313,0.000009144463,0.5335616,0.00009483447,0.00006978208,0.0000035802273,6.23286e-7,0.00001981937,0.0011275186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984153,0.00005451271,0.00017793957,0.0006344527,0.0003033117,0.0004144775],"domain_scores_gemma":[0.99915844,0.00015632117,0.000045311106,0.00046889595,0.000039536706,0.00013149176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024313785,0.00020654096,0.00019676812,0.000112466376,0.00017682678,0.00086242415,0.00037645706,0.00006204275,0.000008210208],"category_scores_gemma":[0.000013332185,0.00016334097,0.00002944226,0.00040706227,0.00007342975,0.0006311421,0.00020097614,0.00020764292,0.00002904305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012914451,0.0005015114,0.012425969,0.001815181,0.0011418894,0.012066775,0.04578297,0.22214065,0.19224784,0.17228714,0.0092265885,0.33023435],"study_design_scores_gemma":[0.0002046039,0.00007639495,0.00065682764,0.00021030559,0.000014267967,0.0002992583,0.000058973907,0.9907475,0.005834235,0.0007571297,0.00084251567,0.00029800102],"about_ca_topic_score_codex":0.00018911705,"about_ca_topic_score_gemma":0.000012225836,"teacher_disagreement_score":0.76860684,"about_ca_system_score_codex":0.000093016206,"about_ca_system_score_gemma":0.00013428855,"threshold_uncertainty_score":0.83163756},"labels":[],"label_agreement":null},{"id":"W4391552376","doi":"10.1007/978-3-031-49121-4_5","title":"Trajectory Control Using an Information Engine","year":2023,"lang":"en","type":"book-chapter","venue":"Springer theses","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Trajectory; Control (management); Computer science; Aeronautics; Engineering; Artificial intelligence; Physics","score_opus":0.053385505418274165,"score_gpt":0.25053021097648503,"score_spread":0.19714470555821087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391552376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011694597,0.00018144202,0.88835764,0.00009823158,0.0023330885,0.0004139322,0.00004116321,0.0015253038,0.10693228],"genre_scores_gemma":[0.053145893,0.00020655419,0.64850575,0.001858349,0.004248074,0.00008795346,0.00017679286,0.0006119689,0.29115865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851155,0.00002512427,0.0003475835,0.00031285125,0.00043722102,0.00036565366],"domain_scores_gemma":[0.9986186,0.00015544801,0.00023329185,0.00075664755,0.00009793796,0.00013805214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043988443,0.00030765758,0.0003516698,0.00037396597,0.00010517883,0.0002342838,0.0008536809,0.00021983118,0.000018205394],"category_scores_gemma":[0.00007180742,0.00029971954,0.000110718116,0.00006235923,0.00004411646,0.0010530276,0.00014544318,0.00033358627,0.00051957736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044167613,0.000099978184,0.0001579055,0.00048634273,0.00092488143,0.00060822937,0.008022196,0.21869265,0.0005826856,0.40957114,0.00090889505,0.35990092],"study_design_scores_gemma":[0.00081222376,0.0002311995,0.0010749588,0.00085063133,0.00012325493,0.00008977181,0.00003356786,0.9405557,0.0002145209,0.014420481,0.039986145,0.0016075723],"about_ca_topic_score_codex":0.00003372828,"about_ca_topic_score_gemma":0.0000012170902,"teacher_disagreement_score":0.72186303,"about_ca_system_score_codex":0.00010442821,"about_ca_system_score_gemma":0.00014033614,"threshold_uncertainty_score":0.9999455},"labels":[],"label_agreement":null},{"id":"W4391589833","doi":"10.1016/j.eswa.2024.123388","title":"An efficient indoor large map global path planning for robot navigation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Representation (politics); Computer science; Tile; Path (computing); Robot; Any-angle path planning; Artificial intelligence; Computer vision","score_opus":0.01691856003676576,"score_gpt":0.31515997401507034,"score_spread":0.29824141397830456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391589833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048045735,0.0024824128,0.99330455,0.00032964686,0.000651345,0.0015591999,0.00008650324,0.0009205227,0.00018536528],"genre_scores_gemma":[0.6618734,0.0000010806841,0.33248997,0.00008509513,0.000547376,0.0046877624,0.00018219456,0.000029205417,0.00010394103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981755,0.00005372874,0.00031131806,0.0007044251,0.0003501278,0.0004048977],"domain_scores_gemma":[0.9987254,0.00010354031,0.00009524779,0.00077749015,0.00012298304,0.00017532709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003841832,0.00020368991,0.0001955826,0.00007581639,0.0003468033,0.0005315987,0.0006757445,0.00009091545,7.9078325e-7],"category_scores_gemma":[0.0000057676616,0.00016808174,0.000047212012,0.0005809092,0.00002764383,0.00028147834,0.00005514713,0.000106397936,0.00008190368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025936932,0.0006028324,0.0012852928,0.0006114693,0.00017125273,0.00008723871,0.011019779,0.4778951,0.0015166025,0.48215613,0.014004632,0.010623736],"study_design_scores_gemma":[0.0002633059,0.00010450763,0.00021520791,0.00034383964,0.00000812039,0.00009416414,0.00036443718,0.9653581,0.000062482686,0.00015141,0.032789096,0.00024536424],"about_ca_topic_score_codex":0.00004843094,"about_ca_topic_score_gemma":3.8747916e-7,"teacher_disagreement_score":0.6613929,"about_ca_system_score_codex":0.00018498875,"about_ca_system_score_gemma":0.00015164373,"threshold_uncertainty_score":0.6854174},"labels":[],"label_agreement":null},{"id":"W4391654576","doi":"10.1007/s10462-023-10670-6","title":"Learning team-based navigation: a review of deep reinforcement learning techniques for multi-agent pathfinding","year":2024,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Victoria","funders":"","keywords":"Pathfinding; Reinforcement learning; Computer science; Artificial intelligence; Human–computer interaction; Shortest path problem","score_opus":0.16325681667852845,"score_gpt":0.42313002362440405,"score_spread":0.25987320694587557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391654576","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.1589466e-9,0.5089546,0.4878573,0.000073034964,0.0003015164,0.0025040868,0.000002456371,0.00024914884,0.00005788095],"genre_scores_gemma":[6.0113683e-7,0.82216865,0.17566457,0.0002394492,0.00014492394,0.0014136342,0.00018175383,0.00007606493,0.0001103819],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9930317,0.00076384784,0.0034895032,0.0012373959,0.0007728909,0.00070470833],"domain_scores_gemma":[0.9955437,0.0006598028,0.002072654,0.001052663,0.00048036268,0.00019077164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049836584,0.000869264,0.0033259448,0.0003672539,0.000270933,0.00017183574,0.0018270095,0.00028774698,0.00003759674],"category_scores_gemma":[0.0019508653,0.00070771284,0.0015991979,0.0020274585,0.00010488635,0.0002546534,0.00041503969,0.0012614516,0.00037309856],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.4226738e-7,0.000024165436,7.080504e-8,0.3637782,0.00005274355,0.000012944432,0.000047373866,0.00023347024,3.2588844e-7,0.0012166704,0.0001001932,0.6345335],"study_design_scores_gemma":[0.000006132587,0.00016331128,3.4255845e-9,0.49280316,0.0007896508,0.000028260109,0.0000063808034,0.07923237,0.000045651992,0.00006630417,0.42645425,0.00040453637],"about_ca_topic_score_codex":0.0000100464595,"about_ca_topic_score_gemma":3.9276867e-7,"teacher_disagreement_score":0.634129,"about_ca_system_score_codex":0.00035817132,"about_ca_system_score_gemma":0.0006171991,"threshold_uncertainty_score":0.9995374},"labels":[],"label_agreement":null},{"id":"W4391760000","doi":"10.2316/j.2024.201-0394","title":"AUTONOMOUS NAVIGATION PATH PLANNING OF SERVICE ROBOT BASED ON MULTI-SENSOR FUSION, 76-82.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Robot; Sensor fusion; Computer science; Service robot; Path (computing); Service (business); Mobile robot navigation; Real-time computing; Fusion; Mobile robot; Artificial intelligence; Systems engineering; Engineering; Robot control; Business; Computer network","score_opus":0.018180590892107128,"score_gpt":0.25116553987954443,"score_spread":0.2329849489874373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391760000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051605343,0.0037127484,0.98829246,0.0005869469,0.0013553791,0.0004804289,0.000021239737,0.00031558546,0.000074687676],"genre_scores_gemma":[0.9837411,0.0000024902733,0.015720954,0.00019555117,0.00013291677,0.00006230623,0.000009402424,0.000020767277,0.00011448101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981537,0.00016389065,0.00044607732,0.0005296934,0.00036034224,0.00034633392],"domain_scores_gemma":[0.9988792,0.00026814017,0.0001627235,0.00047506907,0.00010877413,0.00010612483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007260622,0.00022540941,0.00038762463,0.00015833067,0.00011683909,0.00022427052,0.0003546764,0.000121940044,0.0000019908737],"category_scores_gemma":[0.00002450737,0.00018884511,0.00006960091,0.00029740922,0.000016251939,0.00019495194,0.000049339058,0.00020056899,0.000024399147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053053387,0.00015269732,0.00078634656,0.0012200662,0.00017139244,0.00034974294,0.0026039782,0.94351435,0.011623114,0.017550165,0.00018763077,0.021787487],"study_design_scores_gemma":[0.0011433293,0.00019541696,0.00060209987,0.001172006,0.000028669687,0.000049077546,0.00011867961,0.9959506,0.00016559771,0.00004928095,0.0003309896,0.00019428135],"about_ca_topic_score_codex":0.00029751938,"about_ca_topic_score_gemma":9.901596e-7,"teacher_disagreement_score":0.9785806,"about_ca_system_score_codex":0.00009518445,"about_ca_system_score_gemma":0.00018145746,"threshold_uncertainty_score":0.770088},"labels":[],"label_agreement":null},{"id":"W4391768842","doi":"10.1109/itsc57777.2023.10420846","title":"Risk-Aware Navigation for Mobile Robots in Unknown 3D Environments","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"European Commission","keywords":"Computer science; Context (archaeology); Mobile robot; Robot; Mobile robot navigation; Collision avoidance; Key (lock); Function (biology); Plan (archaeology); Human–computer interaction; Harm; Energy (signal processing); Collision; Navigation system; Artificial intelligence; Real-time computing; Computer security; Robot control","score_opus":0.017532177474004946,"score_gpt":0.272625226971923,"score_spread":0.25509304949791806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391768842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025647856,0.000015665308,0.97293895,0.000105406085,0.000366896,0.00042466557,0.0000065796444,0.0002626143,0.00023135042],"genre_scores_gemma":[0.27055037,0.000035860034,0.7211452,0.00014674291,0.00011799634,0.00062239444,0.000130335,0.0000329063,0.007218189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890673,0.00004491064,0.00019062108,0.00035788215,0.00020198956,0.00029784103],"domain_scores_gemma":[0.99935174,0.00016300501,0.0000676207,0.0003517012,0.000009709895,0.000056235356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038764294,0.00010143766,0.000114517585,0.00011076745,0.000079626836,0.000048446796,0.00042997758,0.000060993618,0.000005608631],"category_scores_gemma":[0.000031602205,0.00009623479,0.000033608325,0.0004318942,0.000015735402,0.00026522472,0.00014504432,0.00009187872,0.00036544708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039772635,0.00009343373,0.010793014,0.000021922571,0.000014942366,0.00004280583,0.0012394553,0.8550089,0.00040568184,0.00087800226,0.002670947,0.12882692],"study_design_scores_gemma":[0.00035313595,0.00006721394,0.018479846,0.000021791364,0.000002230284,0.0000025085399,0.000029021072,0.97803026,0.00049409136,0.0008137355,0.0015748235,0.00013134464],"about_ca_topic_score_codex":0.00004287203,"about_ca_topic_score_gemma":0.0000023394152,"teacher_disagreement_score":0.25179374,"about_ca_system_score_codex":0.000062051724,"about_ca_system_score_gemma":0.000020307218,"threshold_uncertainty_score":0.46972048},"labels":[],"label_agreement":null},{"id":"W4391769526","doi":"10.1109/itsc57777.2023.10422113","title":"Velocity Planning for Multi-Vehicle Systems via Distributed Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Computer science","score_opus":0.0732405502485279,"score_gpt":0.30933188026173364,"score_spread":0.23609133001320576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391769526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004260307,0.000025096235,0.9968832,0.00021078314,0.0007470233,0.00032970522,0.000021585924,0.0012903222,0.000066220375],"genre_scores_gemma":[0.033945806,0.0000011369849,0.9651117,0.000052782165,0.00007668867,0.00008343852,0.0001539121,0.000014318311,0.0005602605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988466,0.00004120963,0.0002214849,0.00034305415,0.00019607368,0.00035158647],"domain_scores_gemma":[0.99923944,0.00016271188,0.0000790364,0.000318387,0.00011069401,0.00008970231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040702912,0.00011639033,0.00015137238,0.000098843506,0.00020316981,0.0001742647,0.00049061334,0.000073307114,0.0000014075795],"category_scores_gemma":[0.00010912045,0.000108700486,0.000040402818,0.00068291964,0.000015453517,0.00030004885,0.00013668626,0.00006795974,0.000062687854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017073044,0.000015034123,0.0004106264,0.000017504835,0.000010531,0.00001082034,0.00019069051,0.9950277,0.000068619214,0.00062100886,0.002659924,0.00096582714],"study_design_scores_gemma":[0.00041060374,0.000031404936,0.0021132627,0.000025152105,0.0000033250724,0.0000065322324,0.00004700944,0.9968434,0.00014091609,0.00003937036,0.00019031418,0.00014870544],"about_ca_topic_score_codex":0.00003767124,"about_ca_topic_score_gemma":1.4795411e-7,"teacher_disagreement_score":0.033519775,"about_ca_system_score_codex":0.00005133003,"about_ca_system_score_gemma":0.00003365469,"threshold_uncertainty_score":0.44326773},"labels":[],"label_agreement":null},{"id":"W4391780217","doi":"10.1017/s0263574724000109","title":"End-to-end deep learning-based framework for path planning and collision checking: bin-picking application","year":2024,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Path (computing); Bin; End-to-end principle; Collision; Computer science; Motion planning; Artificial intelligence; Collision avoidance; Simulation; Computer vision; Algorithm; Robot; Computer security; Computer network","score_opus":0.02145502148204609,"score_gpt":0.3009103410904062,"score_spread":0.2794553196083601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391780217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083507714,0.00074400054,0.994765,0.0018744164,0.0005331733,0.00049598515,0.0000027565973,0.00065117754,0.000098451244],"genre_scores_gemma":[0.43907693,0.0000027883946,0.56041884,0.0002012954,0.00014266674,0.00008004076,0.000011893879,0.00002691742,0.000038639213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979479,0.00007266183,0.00029917035,0.0008231011,0.0003735863,0.00048362644],"domain_scores_gemma":[0.9978716,0.0013048488,0.00008365284,0.0004529565,0.00008050968,0.00020646422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006951538,0.00023698878,0.00025626374,0.00025367853,0.0003356006,0.0005680981,0.0005100492,0.00017900052,0.000003480719],"category_scores_gemma":[0.00046178867,0.00023228349,0.000069291134,0.0007286325,0.00004198436,0.00022967094,0.00018311123,0.00043221022,0.000046843925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010907172,0.00003226717,0.0012993308,0.00014918562,0.000026573325,0.000046163335,0.0017402903,0.9062121,0.000581819,0.040946607,0.00015233357,0.048802417],"study_design_scores_gemma":[0.00016122352,0.00018031489,0.0021583636,0.0005540784,0.000022786384,0.000016972048,0.000040581865,0.989122,0.0005773344,0.0033294891,0.0035502247,0.00028668455],"about_ca_topic_score_codex":0.000008608832,"about_ca_topic_score_gemma":2.994713e-7,"teacher_disagreement_score":0.43824187,"about_ca_system_score_codex":0.000092721144,"about_ca_system_score_gemma":0.00010240017,"threshold_uncertainty_score":0.94722456},"labels":[],"label_agreement":null},{"id":"W4391843196","doi":"10.3390/aerospace11020157","title":"Online Deterministic 3D Trajectory Generation for Electric Vertical Take-Off and Landing Aircraft","year":2024,"lang":"en","type":"article","venue":"Aerospace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Trajectory; Aeronautics; Aerospace engineering; Computer science; Engineering; Physics; Astronomy","score_opus":0.03360371092403169,"score_gpt":0.2863341383812724,"score_spread":0.2527304274572407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391843196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10338507,0.0014674391,0.8930896,0.00078790635,0.0007444781,0.00017174253,0.0000053798276,0.00030848698,0.000039879596],"genre_scores_gemma":[0.7369811,0.000043463177,0.26176593,0.00020770282,0.0004666902,0.000029701918,0.000016684264,0.000022060096,0.00046670833],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897724,0.000031559055,0.00014181349,0.00041095156,0.00015358742,0.00028483913],"domain_scores_gemma":[0.9993843,0.00027076592,0.000015307876,0.00021124154,0.000027836148,0.00009058565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019681151,0.00012663874,0.00013512245,0.00008607617,0.00011089006,0.00024017772,0.00018830772,0.00006794944,0.0000013905171],"category_scores_gemma":[0.000116951625,0.000117158306,0.000035520596,0.0002501997,0.000021473417,0.00020464964,0.000047025813,0.00012656764,0.000015572516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039753428,0.0004531215,0.002384805,0.00058695814,0.00024301224,0.0007959473,0.006071384,0.008217445,0.4927506,0.022590023,0.031100376,0.43476656],"study_design_scores_gemma":[0.0001732787,0.0001732164,0.001344084,0.000038684848,0.000019499324,0.000074152296,0.000005599088,0.9958951,0.0010768756,0.00010628916,0.00094252656,0.00015072466],"about_ca_topic_score_codex":0.000004751264,"about_ca_topic_score_gemma":0.0000054445995,"teacher_disagreement_score":0.98767763,"about_ca_system_score_codex":0.00006022126,"about_ca_system_score_gemma":0.000083437415,"threshold_uncertainty_score":0.47775772},"labels":[],"label_agreement":null},{"id":"W4391871119","doi":"10.1002/adrr.202500072","title":"Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback","year":2025,"lang":"en","type":"preprint","venue":"Advanced Robotics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Tamkeen; York University; Army Research Office; New York University Abu Dhabi","keywords":"Task (project management); Closed loop; Robot; Feedback loop; Ground; Control theory (sociology); Loop (graph theory); State (computer science); Computer science; Process management; Control engineering; Engineering; Control (management); Artificial intelligence; Computer security; Systems engineering; Electrical engineering; Mathematics","score_opus":0.1467630464574985,"score_gpt":0.44018960726984147,"score_spread":0.293426560812343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391871119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001495095,0.0030693936,0.9889983,0.0003732039,0.0023616739,0.002148415,0.00026725157,0.00049733114,0.00078934344],"genre_scores_gemma":[0.03249203,0.00013195798,0.96326923,0.00011759876,0.00030392373,0.00023611,0.00020835246,0.00011026866,0.0031305263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99126554,0.00068160036,0.0010215389,0.0023503508,0.0018502397,0.0028307387],"domain_scores_gemma":[0.99316555,0.00209678,0.00043224508,0.0027239395,0.0011541574,0.00042734182],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004199362,0.00078353524,0.0011119548,0.0014218024,0.0011314589,0.0009981651,0.0044357586,0.0006007932,0.0000022137167],"category_scores_gemma":[0.0008081068,0.0008527831,0.00032588214,0.0014799485,0.00018413652,0.00091822597,0.006978743,0.003375596,0.000016167176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036856407,0.00012286316,0.00006383375,0.0006793286,0.00013648164,0.00020167243,0.0026869653,0.97552305,0.0007848899,0.0064451206,0.00016631503,0.013152625],"study_design_scores_gemma":[0.0010861824,0.000099197205,0.000038437887,0.0015739782,0.000030384246,0.0000163747,0.00048743072,0.9609612,0.00040871702,0.03442569,0.000092915325,0.0007794782],"about_ca_topic_score_codex":0.00013202913,"about_ca_topic_score_gemma":0.000006612719,"teacher_disagreement_score":0.030996934,"about_ca_system_score_codex":0.0010546535,"about_ca_system_score_gemma":0.0012488147,"threshold_uncertainty_score":0.9993923},"labels":[],"label_agreement":null},{"id":"W4392155899","doi":"10.3390/robotics13030037","title":"Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions","year":2024,"lang":"en","type":"article","venue":"Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Korea Institute of Industrial Technology","keywords":"Motion planning; Aeronautics; Path (computing); Computer science; Transport engineering; Operations research; Aerospace engineering; Simulation; Systems engineering; Engineering; Operations management; Robot; Artificial intelligence","score_opus":0.05224649104234477,"score_gpt":0.3297683855986352,"score_spread":0.2775218945562904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392155899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00070938707,0.00027693246,0.9939558,0.0021664663,0.0013534396,0.00031837242,0.00008669721,0.00059325364,0.00053967384],"genre_scores_gemma":[0.26795012,0.0000025462286,0.73034006,0.0006323186,0.00021557236,0.00005056203,0.00022398772,0.000028051832,0.0005568023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984386,0.000037133723,0.00031331187,0.00051211304,0.000285893,0.00041296432],"domain_scores_gemma":[0.9989029,0.0004061493,0.000049787213,0.00039180907,0.00011643542,0.0001329134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017929761,0.00021193961,0.00021922342,0.00013420438,0.00030698287,0.0003865507,0.0005225434,0.00009135031,0.000017898123],"category_scores_gemma":[0.00004518695,0.0002096135,0.00009379403,0.00027527998,0.00005459083,0.0004986984,0.00012784003,0.00019046267,0.00008001257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001281379,0.00003134493,0.00007598243,0.00002588608,0.000038133207,0.000039759994,0.00029196238,0.82519203,0.00031945857,0.1670344,0.005408036,0.001541723],"study_design_scores_gemma":[0.00030524383,0.00009594946,0.0022157026,0.00011038674,0.000019717803,0.000039923878,0.00007301376,0.9877726,0.00008972288,0.0055910414,0.0034039768,0.00028275934],"about_ca_topic_score_codex":0.000004506688,"about_ca_topic_score_gemma":0.000001140847,"teacher_disagreement_score":0.26724073,"about_ca_system_score_codex":0.00012910108,"about_ca_system_score_gemma":0.00027786783,"threshold_uncertainty_score":0.854779},"labels":[],"label_agreement":null},{"id":"W4392373047","doi":"10.18280/jesa.570122","title":"Vision-Based Autonomous Landing and Charging System for a Hexacopter Drone","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Aeronautics; Computer science; Artificial intelligence; Aerospace engineering; Computer vision; Engineering; Biology","score_opus":0.019054370295297645,"score_gpt":0.2706409371051083,"score_spread":0.2515865668098106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392373047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012690625,0.0030670674,0.9799116,0.00056197937,0.0018493261,0.00028882487,0.0000109034545,0.0011499187,0.00046974857],"genre_scores_gemma":[0.66009325,0.000014587616,0.33899543,0.000075762895,0.00038146757,0.000024422134,0.0000022378333,0.00005149988,0.00036135275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786776,0.00017233567,0.0005715502,0.0004638448,0.00039843508,0.00052605406],"domain_scores_gemma":[0.99863267,0.0004928735,0.00020225637,0.00031413487,0.00010736729,0.0002506707],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012749074,0.00027184008,0.00038320842,0.00043739064,0.00052301615,0.0021144366,0.00055435067,0.00008136792,0.0000055182472],"category_scores_gemma":[0.00009945922,0.00022011522,0.00014075974,0.0003704253,0.000058998674,0.00076621375,0.00013019185,0.00029249876,0.00006147737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018905286,0.00006765676,0.0009913528,0.0034692513,0.00028574682,0.0028541847,0.003003503,0.010590156,0.0030973144,0.013872929,0.00310111,0.9586479],"study_design_scores_gemma":[0.00043870386,0.00021018396,0.0097108,0.0027075985,0.000036239744,0.003697462,0.00004580102,0.9811359,0.00016757648,0.0005871424,0.0009972211,0.00026537944],"about_ca_topic_score_codex":0.000008257251,"about_ca_topic_score_gemma":3.0819183e-7,"teacher_disagreement_score":0.9705457,"about_ca_system_score_codex":0.00037008626,"about_ca_system_score_gemma":0.00020993984,"threshold_uncertainty_score":0.99892145},"labels":[],"label_agreement":null},{"id":"W4392456128","doi":"10.1061/9780784485224.076","title":"Self-Optimization of Robot Design for Navigating in Ceiling Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ceiling (cloud); Computer science; Robot; Human–computer interaction; Artificial intelligence; Engineering; Structural engineering","score_opus":0.034343775998508824,"score_gpt":0.28774649988930956,"score_spread":0.25340272389080076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392456128","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013214089,0.00039903075,0.9979668,0.00008485561,0.0005906616,0.00032329297,6.1982087e-7,0.00032272897,0.00017985037],"genre_scores_gemma":[0.09264657,0.0000035534056,0.90722257,0.000008461273,0.00003633216,0.000028412378,0.0000015671311,0.000007953719,0.000044558135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912965,0.000055773762,0.00027679835,0.00023912331,0.00013400093,0.00016466582],"domain_scores_gemma":[0.9992755,0.00043175044,0.00004685744,0.00016819894,0.000051534636,0.000026152376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007478017,0.00007429907,0.00012769389,0.000081468184,0.00002794181,0.00012688787,0.00026547612,0.000044987395,5.599009e-7],"category_scores_gemma":[0.000055133554,0.000067199,0.000026869433,0.00042465556,0.0000062088325,0.00028604423,0.00004138684,0.00007790085,0.0000031017332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.362856e-7,0.000009822035,0.000045792247,0.00015501681,0.0000066595194,0.000005836915,0.00069919997,0.99353373,0.00011603868,0.0035493998,0.00002827101,0.0018497104],"study_design_scores_gemma":[0.00009472317,0.000041623593,0.000009482877,0.0003763745,0.0000032943017,0.0000098802775,0.000046625617,0.9984138,0.0007970137,0.00012207057,0.0000089182995,0.00007620064],"about_ca_topic_score_codex":0.000024616593,"about_ca_topic_score_gemma":1.4880027e-7,"teacher_disagreement_score":0.09251443,"about_ca_system_score_codex":0.000047597056,"about_ca_system_score_gemma":0.00007742639,"threshold_uncertainty_score":0.27402955},"labels":[],"label_agreement":null},{"id":"W4392542940","doi":"10.32920/25365292","title":"Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Technology Planning Project of Guangdong Province; National Key Research and Development Program of China","keywords":"Manipulator (device); Object (grammar); Computer science; Control (management); Control theory (sociology); Artificial intelligence; Control engineering; Computer vision; Engineering; Robot","score_opus":0.04056716823509699,"score_gpt":0.2848639476878868,"score_spread":0.24429677945278983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392542940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41138,0.00014934523,0.5769068,0.00020788702,0.007934799,0.0013841639,0.00006404892,0.0002689712,0.0017039947],"genre_scores_gemma":[0.8964291,0.0000011964248,0.10267607,0.000046031222,0.00074991566,0.000022322965,0.000021472302,0.000024507504,0.000029347822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966633,0.00051944633,0.00084114284,0.00085428194,0.00070040906,0.0004214216],"domain_scores_gemma":[0.99767786,0.00015457883,0.00037953196,0.0016006003,0.00009291238,0.000094522045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015719646,0.00036404876,0.0007434245,0.0003054414,0.0000439892,0.0002678415,0.0028556394,0.0003157604,0.000016531769],"category_scores_gemma":[0.00007698092,0.00027443655,0.00017149806,0.00029479148,0.00009588936,0.00024448262,0.0010268565,0.0007753894,0.000007798468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076941746,0.0002599162,0.00015980446,0.0002658853,0.000121582314,0.00017130215,0.0040674703,0.97720474,0.0011986917,0.013345903,0.00008539176,0.0030423824],"study_design_scores_gemma":[0.00093445036,0.0003146739,0.0029417446,0.00030425924,0.000050844603,0.00003265711,0.00024129091,0.9914342,0.0007677145,0.0026447545,0.000005420193,0.00032794653],"about_ca_topic_score_codex":0.0017472215,"about_ca_topic_score_gemma":0.00007995851,"teacher_disagreement_score":0.48504913,"about_ca_system_score_codex":0.000072230294,"about_ca_system_score_gemma":0.00045362543,"threshold_uncertainty_score":0.9999708},"labels":[],"label_agreement":null},{"id":"W4392543142","doi":"10.32920/25365352.v1","title":"A Multi-Objective Trajectory Planning Method for Collaborative Robot","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Information Science and Technology University; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Robot; Motion planning; Human–computer interaction; Artificial intelligence; Physics","score_opus":0.06796582472776573,"score_gpt":0.3649779583139575,"score_spread":0.2970121335861918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392543142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026159001,0.0018251983,0.9868023,0.00061272987,0.004539063,0.0016871875,0.00014159811,0.0012854799,0.0030803261],"genre_scores_gemma":[0.00086680823,0.000005124043,0.9947724,0.0002725209,0.00033501617,0.00083446683,0.000036100224,0.00006648113,0.0028111236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961962,0.0003171495,0.00054671825,0.0018760661,0.00042910062,0.00063474115],"domain_scores_gemma":[0.9969292,0.0010918493,0.00027757834,0.0010547969,0.0004551943,0.00019134957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012237508,0.0006313426,0.0008051102,0.0004863209,0.00017277888,0.0006407967,0.0017269552,0.0005223258,0.0000056390254],"category_scores_gemma":[0.00027117602,0.0005730982,0.0003047295,0.0006670778,0.000053642878,0.0001599624,0.0024574709,0.0012647494,0.00006484003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044002056,0.00022213967,0.000041932057,0.0008781571,0.0011983957,0.00037884928,0.038198702,0.90490717,0.0013640095,0.009964271,0.01736443,0.025437934],"study_design_scores_gemma":[0.00045924293,0.0001331643,0.00024370695,0.00049581414,0.000078109624,0.000024503084,0.0005242658,0.98592794,0.0015706905,0.009343242,0.0004821508,0.00071717234],"about_ca_topic_score_codex":0.00009973327,"about_ca_topic_score_gemma":0.0000062829376,"teacher_disagreement_score":0.081020765,"about_ca_system_score_codex":0.00032745634,"about_ca_system_score_gemma":0.0012969202,"threshold_uncertainty_score":0.99967206},"labels":[],"label_agreement":null},{"id":"W4392543942","doi":"10.32920/25365292.v1","title":"Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Technology Planning Project of Guangdong Province; National Key Research and Development Program of China","keywords":"Manipulator (device); Object (grammar); Computer science; Control (management); Artificial intelligence; Aeronautics; Computer vision; Control engineering; Control theory (sociology); Engineering; Robot","score_opus":0.04056716823509699,"score_gpt":0.2848639476878868,"score_spread":0.24429677945278983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392543942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41138,0.00014934523,0.5769068,0.00020788702,0.007934799,0.0013841639,0.00006404892,0.0002689712,0.0017039947],"genre_scores_gemma":[0.8964291,0.0000011964248,0.10267607,0.000046031222,0.00074991566,0.000022322965,0.000021472302,0.000024507504,0.000029347822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966633,0.00051944633,0.00084114284,0.00085428194,0.00070040906,0.0004214216],"domain_scores_gemma":[0.99767786,0.00015457883,0.00037953196,0.0016006003,0.00009291238,0.000094522045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015719646,0.00036404876,0.0007434245,0.0003054414,0.0000439892,0.0002678415,0.0028556394,0.0003157604,0.000016531769],"category_scores_gemma":[0.00007698092,0.00027443655,0.00017149806,0.00029479148,0.00009588936,0.00024448262,0.0010268565,0.0007753894,0.000007798468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076941746,0.0002599162,0.00015980446,0.0002658853,0.000121582314,0.00017130215,0.0040674703,0.97720474,0.0011986917,0.013345903,0.00008539176,0.0030423824],"study_design_scores_gemma":[0.00093445036,0.0003146739,0.0029417446,0.00030425924,0.000050844603,0.00003265711,0.00024129091,0.9914342,0.0007677145,0.0026447545,0.000005420193,0.00032794653],"about_ca_topic_score_codex":0.0017472215,"about_ca_topic_score_gemma":0.00007995851,"teacher_disagreement_score":0.48504913,"about_ca_system_score_codex":0.000072230294,"about_ca_system_score_gemma":0.00045362543,"threshold_uncertainty_score":0.9999708},"labels":[],"label_agreement":null},{"id":"W4392562404","doi":"10.32920/25365352","title":"A Multi-Objective Trajectory Planning Method for Collaborative Robot","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Beijing Information Science and Technology University; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Robot; Motion planning; Human–computer interaction; Artificial intelligence; Physics","score_opus":0.06796582472776573,"score_gpt":0.3649779583139575,"score_spread":0.2970121335861918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392562404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026159001,0.0018251983,0.9868023,0.00061272987,0.004539063,0.0016871875,0.00014159811,0.0012854799,0.0030803261],"genre_scores_gemma":[0.00086680823,0.000005124043,0.9947724,0.0002725209,0.00033501617,0.00083446683,0.000036100224,0.00006648113,0.0028111236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961962,0.0003171495,0.00054671825,0.0018760661,0.00042910062,0.00063474115],"domain_scores_gemma":[0.9969292,0.0010918493,0.00027757834,0.0010547969,0.0004551943,0.00019134957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012237508,0.0006313426,0.0008051102,0.0004863209,0.00017277888,0.0006407967,0.0017269552,0.0005223258,0.0000056390254],"category_scores_gemma":[0.00027117602,0.0005730982,0.0003047295,0.0006670778,0.000053642878,0.0001599624,0.0024574709,0.0012647494,0.00006484003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044002056,0.00022213967,0.000041932057,0.0008781571,0.0011983957,0.00037884928,0.038198702,0.90490717,0.0013640095,0.009964271,0.01736443,0.025437934],"study_design_scores_gemma":[0.00045924293,0.0001331643,0.00024370695,0.00049581414,0.000078109624,0.000024503084,0.0005242658,0.98592794,0.0015706905,0.009343242,0.0004821508,0.00071717234],"about_ca_topic_score_codex":0.00009973327,"about_ca_topic_score_gemma":0.0000062829376,"teacher_disagreement_score":0.081020765,"about_ca_system_score_codex":0.00032745634,"about_ca_system_score_gemma":0.0012969202,"threshold_uncertainty_score":0.99967206},"labels":[],"label_agreement":null},{"id":"W4392816528","doi":"10.1177/09596518241233319","title":"A finite-time path-tracking control algorithm for nonholonomic mobile robots with unknown dynamics and subject to wheel slippage/skid disturbances","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Slippage; Skid (aerodynamics); Mobile robot; Nonholonomic system; Control theory (sociology); Path (computing); Tracking (education); Computer science; Control engineering; Robot; Algorithm; Simulation; Control (management); Engineering; Artificial intelligence; Mechanical engineering; Psychology; Structural engineering","score_opus":0.005112297311991162,"score_gpt":0.19523769032068874,"score_spread":0.19012539300869757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392816528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010631228,0.0021812518,0.9854786,0.00015840503,0.0009182113,0.00054557406,0.00003175567,0.000049106675,0.000005887286],"genre_scores_gemma":[0.95520145,0.00003367786,0.044497427,0.000011226087,0.00018038796,0.000044352408,4.224878e-7,0.000017603525,0.00001343301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985553,0.000008708632,0.00064157555,0.00023735045,0.0003066077,0.00025045624],"domain_scores_gemma":[0.99885416,0.00028805144,0.0003032128,0.00009622777,0.00031049742,0.0001478778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094092527,0.00022175863,0.0005967774,0.00019949199,0.00006528203,0.00016242836,0.00039399927,0.00009135349,1.7463684e-7],"category_scores_gemma":[0.00019132002,0.00015327735,0.00011097914,0.00024812436,0.000039936705,0.0005044798,0.00004959995,0.00023839991,2.3292857e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057838304,0.000030130483,0.00002515933,0.0006142809,0.00029318998,0.000008697988,0.0002567992,0.9650717,0.009263743,0.017338073,0.000026672633,0.007013744],"study_design_scores_gemma":[0.0011688818,0.00055432867,0.00003851108,0.0019447997,0.000097279946,0.00024357853,0.000058694746,0.99461985,0.0008423067,0.00002497362,0.00023727662,0.0001695322],"about_ca_topic_score_codex":0.0000053333356,"about_ca_topic_score_gemma":1.387689e-7,"teacher_disagreement_score":0.94457024,"about_ca_system_score_codex":0.00008798645,"about_ca_system_score_gemma":0.00007834235,"threshold_uncertainty_score":0.6250469},"labels":[],"label_agreement":null},{"id":"W4392822449","doi":"10.1109/tie.2024.3370939","title":"A Novel Feature Learning-Based Bio-Inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Feature (linguistics); Collision; Artificial intelligence; Collision avoidance; Computer security","score_opus":0.031176399769935855,"score_gpt":0.2616927844814378,"score_spread":0.23051638471150193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392822449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018090352,0.00036593797,0.9908589,0.0010725142,0.003910088,0.0010973132,0.00015176539,0.0007044979,0.000029917812],"genre_scores_gemma":[0.71028864,0.00017957998,0.27672115,0.00019564944,0.0032879754,0.0010842647,0.00015502701,0.00039470778,0.007692993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971442,0.00019845343,0.0005555077,0.0007308885,0.0005505129,0.0008204042],"domain_scores_gemma":[0.9975758,0.0010950926,0.0001650223,0.0008260686,0.00017480522,0.00016320674],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007990156,0.00036540205,0.0005168621,0.00032253482,0.00037091246,0.0002662674,0.0010314299,0.0005770128,0.000003111821],"category_scores_gemma":[0.00008270204,0.0003499648,0.00030524534,0.0015618837,0.00007116992,0.00025225963,0.00000680397,0.0013103236,0.000011281469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018401959,0.00012404555,0.0000012162427,0.00004324717,0.000112188594,0.000009362073,0.000071398696,0.98545516,0.003752102,0.00032973185,0.004327613,0.0055899257],"study_design_scores_gemma":[0.002215811,0.0014741189,0.0000033994868,0.00036297995,0.000096894386,0.000029782586,0.000008417176,0.98728883,0.0054052565,0.000028554072,0.0027568138,0.0003291574],"about_ca_topic_score_codex":0.00020873714,"about_ca_topic_score_gemma":0.000018541574,"teacher_disagreement_score":0.7141378,"about_ca_system_score_codex":0.00037114834,"about_ca_system_score_gemma":0.001069265,"threshold_uncertainty_score":0.9998952},"labels":[],"label_agreement":null},{"id":"W4392946413","doi":"10.1016/j.oceaneng.2024.117415","title":"An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers","keywords":"Ant colony optimization algorithms; Path (computing); Computer science; Motion planning; Algorithm; Artificial intelligence; ANT","score_opus":0.009984636503135374,"score_gpt":0.2503895549330217,"score_spread":0.2404049184298863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392946413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009385359,0.000552576,0.9884635,0.000020029525,0.0007929573,0.00015644038,0.00002096597,0.0005588646,0.000049293638],"genre_scores_gemma":[0.25973302,0.0000032237626,0.74008524,0.000009824223,0.00011576725,0.000006752423,0.00002002947,0.000019397676,0.0000067253186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987782,0.000016148737,0.00027044298,0.0003858497,0.00021044356,0.00033887208],"domain_scores_gemma":[0.9993677,0.00013773373,0.00005618792,0.0002687856,0.000066212524,0.00010334105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026920723,0.00017687108,0.00020639955,0.00011354274,0.000056866636,0.00015036529,0.0004360857,0.00007798547,8.105346e-7],"category_scores_gemma":[0.0000523938,0.00018876938,0.000058856338,0.00044401863,0.000014466171,0.0004695549,0.00006302869,0.00008532391,9.841848e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014819392,0.000014408306,0.00007360292,0.00007227079,0.000024622119,0.000029971336,0.00073964486,0.95310074,0.00073713803,0.00046921952,0.000052206728,0.04468471],"study_design_scores_gemma":[0.00017204607,0.00013905061,0.00027681506,0.00025851474,0.000012404675,0.00002163657,0.000049517643,0.9964257,0.0023565935,0.00003397251,0.00005046376,0.00020329255],"about_ca_topic_score_codex":0.0000029069129,"about_ca_topic_score_gemma":4.3964064e-8,"teacher_disagreement_score":0.25034764,"about_ca_system_score_codex":0.000090897876,"about_ca_system_score_gemma":0.000054085303,"threshold_uncertainty_score":0.7697792},"labels":[],"label_agreement":null},{"id":"W4392973354","doi":"10.1007/s41315-024-00330-5","title":"A state-of-the-art review on topology and differential geometry-based robotic path planning—part I: planning under static constraints","year":2024,"lang":"en","type":"review","venue":"International Journal of Intelligent Robotics and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Topology (electrical circuits); Path (computing); Differential (mechanical device); State (computer science); Differential geometry; Computer science; Geometry; Mathematics; Robot; Engineering; Artificial intelligence; Algorithm; Aerospace engineering; Combinatorics; Programming language","score_opus":0.05977028052270289,"score_gpt":0.36379504861856626,"score_spread":0.3040247680958634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392973354","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016997689,0.4746178,0.52357,0.00065355777,0.00076024776,0.00032384915,0.000032451375,0.000011310106,0.000029056051],"genre_scores_gemma":[0.0002427435,0.9768309,0.021997826,0.0004460241,0.00022126292,0.00004688021,0.000036962516,0.000034154502,0.00014321925],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971314,0.00014784603,0.0014174549,0.00040213505,0.0006640464,0.00023708136],"domain_scores_gemma":[0.9970987,0.00067495875,0.0013319873,0.00038653868,0.00034882853,0.00015898634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043053488,0.00038212963,0.0010611234,0.00048741183,0.000084139094,0.00018527199,0.00129223,0.00011606898,0.000011142466],"category_scores_gemma":[0.00011034445,0.0002573467,0.00034096595,0.0003746648,0.00027481414,0.00007925358,0.0003045688,0.0007315043,0.00001647162],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008590679,0.0003439573,0.0000342671,0.009244266,0.0013831387,0.0001450259,0.00015488626,0.113069706,0.0000014845302,0.03129074,0.004705642,0.83961827],"study_design_scores_gemma":[0.00073098124,0.00073478156,0.00007265429,0.2857784,0.0035795297,0.0033634526,0.000066131324,0.069872536,0.000020458461,0.013494094,0.62084794,0.0014390228],"about_ca_topic_score_codex":0.0000014276259,"about_ca_topic_score_gemma":1.2768908e-7,"teacher_disagreement_score":0.8381793,"about_ca_system_score_codex":0.00012872761,"about_ca_system_score_gemma":0.00042522245,"threshold_uncertainty_score":0.9999879},"labels":[],"label_agreement":null},{"id":"W4392982452","doi":"10.1109/uvs59630.2024.10467170","title":"Hierarchical Geometric Path-Following Control for UAV Slung Load Transport","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Path (computing); Computer science; Control (management); Control theory (sociology); Artificial intelligence; Computer network","score_opus":0.01582459513814993,"score_gpt":0.26299885723583466,"score_spread":0.24717426209768473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392982452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022331525,0.0009811624,0.9897842,0.0014601275,0.0019941372,0.0003575733,0.000011653567,0.000849908,0.0023280864],"genre_scores_gemma":[0.6785892,0.0000037338239,0.3194932,0.00035876257,0.00017928921,0.000057864683,0.000004860459,0.000018634335,0.0012944285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979866,0.00003347663,0.0003123494,0.0006139493,0.00053127884,0.000522355],"domain_scores_gemma":[0.9986581,0.0006926465,0.000023709086,0.00040058274,0.00005200138,0.00017294937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008625699,0.00018770047,0.0002733058,0.00031828426,0.000113917115,0.00020731975,0.0007286847,0.00009834549,0.000014377022],"category_scores_gemma":[0.000114445014,0.00015516247,0.00029048932,0.0011419798,0.00002378992,0.0004059233,0.00003780074,0.0002257758,0.0000894599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006622693,0.0004004306,0.011074967,0.0005813017,0.001058164,0.0046676085,0.003996229,0.01590793,0.0027190913,0.18960074,0.014854807,0.7550725],"study_design_scores_gemma":[0.0008038106,0.0001546125,0.003977528,0.0000865939,0.000045366047,0.00004556474,0.00000882538,0.9871843,0.00018910681,0.0018146554,0.005381514,0.00030812147],"about_ca_topic_score_codex":0.000026922666,"about_ca_topic_score_gemma":6.2204504e-7,"teacher_disagreement_score":0.97127634,"about_ca_system_score_codex":0.000098459466,"about_ca_system_score_gemma":0.0002606399,"threshold_uncertainty_score":0.63273424},"labels":[],"label_agreement":null},{"id":"W4392985035","doi":"10.23977/jaip.2024.070115","title":"Robot Trajectory Planning and Simulation Based on Matlab Robotics Toolbox","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toolbox; Robotics; Artificial intelligence; MATLAB; Computer science; Trajectory; Robot; Computer vision; Control engineering; Simulation; Human–computer interaction; Engineering; Programming language; Physics","score_opus":0.07610961322348701,"score_gpt":0.3674769715951302,"score_spread":0.2913673583716432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392985035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044942356,0.0006700155,0.9917274,0.0045946487,0.0019530498,0.00009093417,8.554095e-7,0.00008439927,0.000429271],"genre_scores_gemma":[0.58214515,0.000013233994,0.41698834,0.00045713817,0.00035940018,9.175499e-7,4.1248748e-7,0.000014283,0.000021133194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782586,0.00023807051,0.00071763544,0.00029978153,0.0006582649,0.000260411],"domain_scores_gemma":[0.9937382,0.005142229,0.00039586803,0.000264199,0.00030850054,0.0001510365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019058078,0.00018396303,0.00023510987,0.0004034308,0.00013748475,0.000810364,0.00045262178,0.00010017259,0.000008778724],"category_scores_gemma":[0.0024984938,0.00016155119,0.000088098896,0.00054531044,0.000056615532,0.0019345916,0.000043060198,0.00068985496,0.000053779746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000654957,0.00008451513,0.00001316194,0.00002207737,0.000027751345,0.0006023977,0.0011099325,0.8958161,0.0003509292,0.004475355,0.00005990783,0.09737239],"study_design_scores_gemma":[0.000028304354,0.00049163465,0.00008332389,0.00033329724,0.000050148785,0.00021544832,0.00035020898,0.9936284,0.0015295878,0.002190142,0.0009275847,0.00017191216],"about_ca_topic_score_codex":0.0000062730237,"about_ca_topic_score_gemma":2.2572036e-7,"teacher_disagreement_score":0.58169574,"about_ca_system_score_codex":0.00010341881,"about_ca_system_score_gemma":0.0002453285,"threshold_uncertainty_score":0.7814359},"labels":[],"label_agreement":null},{"id":"W4393121492","doi":"10.5267/j.ijiec.2024.1.002","title":"Truck-drone joint path planning for post-disaster emergency material deployment considering fairness","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Software deployment; Truck; Drone; Joint (building); Motion planning; Path (computing); Computer science; Aeronautics; Transport engineering; Engineering; Automotive engineering; Computer network; Robot; Structural engineering; Artificial intelligence","score_opus":0.05161699659116461,"score_gpt":0.29362337203353917,"score_spread":0.24200637544237455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393121492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04810183,0.00015168391,0.9277882,0.0014857755,0.022073636,0.00016617385,0.00005083015,0.00016268271,0.000019191957],"genre_scores_gemma":[0.7986374,0.000005910857,0.19826709,0.000044415036,0.0029272584,0.000018511477,0.000027664668,0.000039422142,0.000032328535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980701,0.000032853222,0.0008747736,0.00023601395,0.0005352288,0.0002510176],"domain_scores_gemma":[0.99873835,0.00026096214,0.00023919462,0.00012743475,0.00050581986,0.00012823273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004643675,0.00019962828,0.00025926594,0.00056678086,0.00006467591,0.00043672786,0.0006535026,0.00009691587,0.000019268093],"category_scores_gemma":[0.00031996815,0.00019337547,0.00018733778,0.00021730883,0.000014703587,0.0006811095,0.00013324138,0.00032362342,0.000006850564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030522213,0.000048452293,0.00008638552,0.00002644154,0.00041324287,0.00034983747,0.0014680017,0.9751635,0.0032845153,0.004819655,0.0024664274,0.0118430015],"study_design_scores_gemma":[0.0011250451,0.00023865236,0.00037030815,0.00090713025,0.000044333265,0.0008987164,0.00011196398,0.9907709,0.001514207,0.0009567195,0.0027548065,0.00030725953],"about_ca_topic_score_codex":0.000007660968,"about_ca_topic_score_gemma":1.06024736e-7,"teacher_disagreement_score":0.75053555,"about_ca_system_score_codex":0.00020141578,"about_ca_system_score_gemma":0.00025120168,"threshold_uncertainty_score":0.7885623},"labels":[],"label_agreement":null},{"id":"W4393155085","doi":"10.1007/s41315-024-00331-4","title":"A state-of-the-art review on topology and differential geometry-based robotic path planning—part II: planning under dynamic constraints","year":2024,"lang":"en","type":"review","venue":"International Journal of Intelligent Robotics and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Topology (electrical circuits); Differential (mechanical device); Path (computing); State (computer science); Computer science; Robot; Mathematics; Engineering; Artificial intelligence; Aerospace engineering; Algorithm; Combinatorics","score_opus":0.0458761840161373,"score_gpt":0.35804122018189577,"score_spread":0.31216503616575847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393155085","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003387226,0.501667,0.4963862,0.0007011664,0.0008549256,0.0003132363,0.00003239913,0.00001267433,0.000029006309],"genre_scores_gemma":[0.0004534371,0.98142576,0.017273363,0.00033800953,0.00017904157,0.00004308205,0.00004275369,0.000034060315,0.00021046535],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99723023,0.00012179019,0.001357596,0.00042416202,0.00062786095,0.00023838879],"domain_scores_gemma":[0.99736756,0.00046521545,0.0012836191,0.00040059938,0.00033225806,0.00015076852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040055986,0.00039118636,0.0010437716,0.0005038985,0.00012402899,0.00015504677,0.0013818927,0.00013583641,0.000010201939],"category_scores_gemma":[0.0000878264,0.0002667842,0.00037559055,0.00037673404,0.00027828696,0.00007720378,0.00043486457,0.0007768323,0.000012282472],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008206207,0.00036848802,0.000023063987,0.0062700287,0.0012816726,0.00011732182,0.00013586314,0.09671939,0.0000021732471,0.026169851,0.0026040077,0.8662999],"study_design_scores_gemma":[0.0007097248,0.0008564411,0.00008461325,0.26671007,0.0033513468,0.0040613757,0.00004904172,0.112667754,0.000015759932,0.011216935,0.5987782,0.0014987283],"about_ca_topic_score_codex":0.0000012093984,"about_ca_topic_score_gemma":1.955333e-7,"teacher_disagreement_score":0.8648012,"about_ca_system_score_codex":0.00015360139,"about_ca_system_score_gemma":0.00039073286,"threshold_uncertainty_score":0.9999784},"labels":[],"label_agreement":null},{"id":"W4393160136","doi":"10.1609/aaai.v38i12.29215","title":"Stitching Sub-trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Image stitching; Diffusion; Computer science; Applied mathematics; Artificial intelligence; Econometrics; Statistical physics; Mathematics; Physics; Thermodynamics","score_opus":0.06110543382956793,"score_gpt":0.2972952787388138,"score_spread":0.23618984490924586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053139508,0.000031159172,0.9423323,0.0026165647,0.00048687556,0.00050085573,0.000056081262,0.0002263576,0.00061024964],"genre_scores_gemma":[0.954075,0.00001078041,0.045270525,0.00011684253,0.00012469878,0.000088588735,0.0000089832465,0.000020269359,0.00028433808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979821,0.000010622201,0.00045812043,0.0006087024,0.0005844993,0.000355956],"domain_scores_gemma":[0.9986084,0.00023913052,0.00020809352,0.00023024403,0.00062401773,0.000090097165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042581683,0.00026341373,0.00026160904,0.00017458205,0.00031732811,0.0004613522,0.0011535713,0.00009112221,0.000009574257],"category_scores_gemma":[0.0002192081,0.00018387429,0.000113711234,0.00053960655,0.00025011844,0.00070150715,0.00017962561,0.00030277635,0.000028883589],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062608546,0.00009304273,0.000039848423,0.00009687661,0.000030468218,0.0000016222502,0.0018919274,0.009116088,0.041311156,0.9374845,0.00023217687,0.009639659],"study_design_scores_gemma":[0.000028924855,0.00014768937,0.00006792128,0.00038615306,0.00001604863,0.000009714819,0.00011120344,0.6702912,0.10600903,0.22274755,0.000015321422,0.0001692456],"about_ca_topic_score_codex":0.000013183349,"about_ca_topic_score_gemma":0.0000047336744,"teacher_disagreement_score":0.9009355,"about_ca_system_score_codex":0.000069193164,"about_ca_system_score_gemma":0.00024901913,"threshold_uncertainty_score":0.74981755},"labels":[],"label_agreement":null},{"id":"W4393160543","doi":"10.1609/aaai.v38i16.29707","title":"Large-Scale Multi-Robot Coverage Path Planning via Local Search","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Motion planning; Computer science; Path (computing); Robot; Artificial intelligence; Geography; Cartography; Computer network","score_opus":0.08629451453765642,"score_gpt":0.32782028875780855,"score_spread":0.24152577422015215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0131305205,0.00014419915,0.9813618,0.0012931238,0.0010540652,0.0003452969,0.000011697276,0.00032833056,0.002330975],"genre_scores_gemma":[0.96071285,0.000019023206,0.03850721,0.00017642729,0.00010898011,0.000023255046,0.0000012782295,0.000026443518,0.0004245625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969165,0.00003678083,0.00061457016,0.00086787465,0.00085312076,0.00071116415],"domain_scores_gemma":[0.99864596,0.00018393413,0.00015847589,0.0004176379,0.00042184078,0.00017216214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010560005,0.0003342644,0.00034386365,0.00022777313,0.0002917994,0.0006234395,0.0024796522,0.00015614723,0.000041223528],"category_scores_gemma":[0.00015019554,0.00025639328,0.00016928393,0.0010705785,0.00029083143,0.0006249116,0.0007279627,0.00076744106,0.00035423334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011009519,0.00081516267,0.0012945035,0.00052690186,0.00012729164,0.00008693399,0.03498571,0.03730251,0.06665853,0.5386712,0.0011600318,0.31826112],"study_design_scores_gemma":[0.00003238793,0.0001571463,0.00024794549,0.0007202319,0.000011244668,0.000026735624,0.00074104336,0.8877746,0.094195545,0.015695674,0.00011988507,0.00027758078],"about_ca_topic_score_codex":0.000046464236,"about_ca_topic_score_gemma":0.0000019200006,"teacher_disagreement_score":0.9475823,"about_ca_system_score_codex":0.000103891405,"about_ca_system_score_gemma":0.00019624623,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4393234702","doi":"10.3390/a17040139","title":"Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Drone; Computer science; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.024032155411139344,"score_gpt":0.265282071233722,"score_spread":0.24124991582258265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393234702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007771028,0.00041210107,0.98972404,0.00067791704,0.0003442622,0.00024970016,0.0000065622203,0.00050791714,0.0003064863],"genre_scores_gemma":[0.27636814,0.0000021818419,0.7232232,0.00010217871,0.000068675385,0.00006694944,0.00001632099,0.000023732086,0.00012861603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980154,0.00005302915,0.00036527996,0.0006973356,0.0004259889,0.00044296225],"domain_scores_gemma":[0.9989648,0.00031866337,0.000059041442,0.00051784154,0.000022692186,0.000116975054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005361005,0.00022012512,0.00020158265,0.00024344219,0.000090661386,0.00026216722,0.0005642369,0.00009928653,0.0000033827241],"category_scores_gemma":[0.00004647031,0.00021659677,0.00005778743,0.00061856135,0.000049753744,0.00048515128,0.00016596656,0.00029549372,0.00020149184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.840493e-7,0.000069358604,0.00034141686,0.000028331147,0.000006634755,0.00045728678,0.0004163424,0.82504773,0.00035842683,0.000626441,0.00007374632,0.17257331],"study_design_scores_gemma":[0.00025300233,0.00010723065,0.002255539,0.00028312363,0.0000060264547,0.000051859937,0.000006582253,0.9929148,0.00012764057,0.0035835372,0.00015818635,0.0002524465],"about_ca_topic_score_codex":0.000113900416,"about_ca_topic_score_gemma":0.0000013229961,"teacher_disagreement_score":0.26859713,"about_ca_system_score_codex":0.00029579634,"about_ca_system_score_gemma":0.00023850812,"threshold_uncertainty_score":0.883256},"labels":[],"label_agreement":null},{"id":"W4393275085","doi":"10.2316/j.2024.206-1099","title":"ADAPTIVE CONSTRAINT CONTROL OF ROBOTIC MANIPULATORS BASED ON BARRIER LYAPUNOV FUNCTION","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lyapunov function; Control theory (sociology); Constraint (computer-aided design); Lyapunov redesign; Computer science; Robot manipulator; Adaptive control; Function (biology); Control (management); Control engineering; Mathematics; Artificial intelligence; Engineering; Physics; Nonlinear system","score_opus":0.014302368082974545,"score_gpt":0.2503783437830337,"score_spread":0.23607597570005914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393275085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001716111,0.000168701,0.9939683,0.0012924061,0.0025716862,0.00006988677,0.0000053903254,0.00004233851,0.00016518506],"genre_scores_gemma":[0.91587263,0.000006904996,0.08380634,0.0001491194,0.0001393965,8.8363157e-7,0.0000023035707,0.0000071193776,0.000015324282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860656,0.000061452476,0.0004754478,0.00014849036,0.0006070542,0.00010098785],"domain_scores_gemma":[0.9988485,0.00027541828,0.00030780552,0.00010291908,0.00039608422,0.00006928176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045932207,0.00011412283,0.00018443189,0.00042547673,0.000031412677,0.0001646955,0.0002888651,0.000058091107,0.0000086818845],"category_scores_gemma":[0.00008902974,0.00009568325,0.00009592255,0.00013952925,0.00004870353,0.00040077628,0.00002562968,0.00016825013,0.0000051118955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028695003,0.000044025863,0.00032019403,0.000014495924,0.00013895854,0.000077358265,0.00012057898,0.92096144,0.00042584084,0.0518462,0.00008193291,0.025940282],"study_design_scores_gemma":[0.00051772187,0.00035714888,0.0060910536,0.00039384118,0.000037650152,0.00012777721,0.00002199861,0.98911524,0.00018938452,0.0030245762,0.0000383745,0.00008523438],"about_ca_topic_score_codex":0.000002989612,"about_ca_topic_score_gemma":1.4561054e-7,"teacher_disagreement_score":0.9141565,"about_ca_system_score_codex":0.00008583294,"about_ca_system_score_gemma":0.00015273616,"threshold_uncertainty_score":0.39018497},"labels":[],"label_agreement":null},{"id":"W4393619444","doi":"10.23952/jnva.8.2024.3.08","title":"A multi-step inertial asynchronous sequential algorithm for common fixed point problems","year":2024,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inertial frame of reference; Fixed point; Asynchronous communication; Algorithm; Computer science; Point (geometry); Mathematics; Telecommunications; Mathematical analysis; Physics; Geometry","score_opus":0.02547683667831869,"score_gpt":0.2964318379734412,"score_spread":0.2709550012951225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393619444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009923824,0.0004941423,0.9965887,0.0012366666,0.00048404883,0.00009734732,0.000064297,0.000033091932,0.000009302091],"genre_scores_gemma":[0.022710506,0.000022766511,0.9762921,0.00009658115,0.0007320783,0.0000053042477,0.000042501488,0.000009500423,0.00008868207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845916,0.000079422985,0.0006169666,0.00025199895,0.0004049879,0.0001874759],"domain_scores_gemma":[0.99880356,0.00034767896,0.0002605438,0.0001441179,0.00031802792,0.0001260662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009089549,0.00014538759,0.0003867508,0.0005482944,0.00011641562,0.0003219121,0.00032520993,0.000077491095,0.000015778498],"category_scores_gemma":[0.00007747244,0.00011416452,0.00036953145,0.0007689563,0.000025391779,0.00046498977,0.00007920277,0.0002082988,0.000004159098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009320694,0.0014764754,0.0042833695,0.00029547702,0.019317335,0.00064402167,0.0046698507,0.39141658,0.0040690796,0.005723256,0.0019108253,0.56610054],"study_design_scores_gemma":[0.00057292293,0.00017513511,0.003204928,0.00003932343,0.0006768355,0.00015785688,0.000011778448,0.9940028,0.000039472743,0.00028445123,0.00070113793,0.00013331322],"about_ca_topic_score_codex":0.000060405928,"about_ca_topic_score_gemma":0.000009011038,"teacher_disagreement_score":0.60258627,"about_ca_system_score_codex":0.00007275851,"about_ca_system_score_gemma":0.00022447413,"threshold_uncertainty_score":0.46554938},"labels":[],"label_agreement":null},{"id":"W4393945814","doi":"10.4271/10-08-02-0011","title":"An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications","year":2024,"lang":"en","type":"article","venue":"SAE International journal of vehicle dynamics, stability, and NVH","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Motion planning; Motion (physics); Artificial intelligence; Robot","score_opus":0.047493560592479146,"score_gpt":0.3379405709734532,"score_spread":0.29044701038097404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393945814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13849984,0.0020377024,0.8577034,0.0007930397,0.0005604325,0.00021160288,0.000099856275,0.000054964592,0.000039192266],"genre_scores_gemma":[0.87121916,0.00009247714,0.12836851,0.000049089642,0.00020755224,0.000014784985,0.000020479274,0.00001590457,0.000012053948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983634,0.00006270882,0.00058558554,0.00033069513,0.00047195936,0.00018568824],"domain_scores_gemma":[0.9982246,0.00037157163,0.00029184495,0.00026780536,0.00072141574,0.00012273368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008765852,0.00015613453,0.00027026082,0.00023882107,0.00008519063,0.0002954807,0.0008338515,0.00006701276,0.0000037727903],"category_scores_gemma":[0.000040186187,0.00013397563,0.0001000751,0.00019817612,0.00013104893,0.00082716247,0.00009600544,0.0002183342,9.729704e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021500525,0.0016624099,0.066934384,0.00090855785,0.0012343447,0.00026918927,0.007504399,0.025238106,0.0032556022,0.1550555,0.00007117839,0.73765135],"study_design_scores_gemma":[0.0005698041,0.0005583751,0.05570678,0.0002735833,0.000043521948,0.00035935687,0.00019992352,0.92251205,0.00015502103,0.019227175,0.00021486994,0.00017950732],"about_ca_topic_score_codex":0.000069715294,"about_ca_topic_score_gemma":0.000020144205,"teacher_disagreement_score":0.89727396,"about_ca_system_score_codex":0.00020340778,"about_ca_system_score_gemma":0.00021333095,"threshold_uncertainty_score":0.54633677},"labels":[],"label_agreement":null},{"id":"W4394629045","doi":"10.1109/csce60160.2023.00102","title":"Minimizing Turns in Watchman Robot Navigation: Strategies and Solutions","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robot; Computer science; Computer vision; Artificial intelligence","score_opus":0.04748865741209374,"score_gpt":0.2812053499562062,"score_spread":0.23371669254411248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394629045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07655864,0.00018325703,0.9086031,0.0067070955,0.0005424616,0.00017999123,0.0000013744608,0.0010311399,0.006192999],"genre_scores_gemma":[0.79607064,0.000009978678,0.20338847,0.000067868255,0.000037761132,0.0000124681,0.0000058463397,0.000005077996,0.0004018723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915147,0.00003480324,0.0001501386,0.00024615467,0.00013770466,0.0002797503],"domain_scores_gemma":[0.9995831,0.000101637204,0.000025442325,0.00021476785,0.00002072986,0.00005429842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002817366,0.000078799545,0.00009300054,0.00012661527,0.000087615204,0.000246359,0.0002623596,0.00003933925,0.0000027185863],"category_scores_gemma":[0.000016762879,0.000073426716,0.000015169484,0.000666969,0.000034007535,0.00050478056,0.00019142458,0.0001002181,0.00006300954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004327934,0.00013031314,0.0112156095,0.00015347775,0.000048392212,0.00093686394,0.03622459,0.4506925,0.00395501,0.43348104,0.00620629,0.05695159],"study_design_scores_gemma":[0.00022168928,0.000028571969,0.065966174,0.000057030164,0.0000017308175,0.00003887448,0.001599232,0.91978705,0.00009812375,0.011916411,0.00011683818,0.00016829892],"about_ca_topic_score_codex":0.000112298185,"about_ca_topic_score_gemma":0.0000189282,"teacher_disagreement_score":0.71951205,"about_ca_system_score_codex":0.000019022149,"about_ca_system_score_gemma":0.00005759533,"threshold_uncertainty_score":0.29942545},"labels":[],"label_agreement":null},{"id":"W4394730178","doi":"10.2139/ssrn.4791362","title":"A Proposed Adaptive Navigation Architecture to Improve Safety of Industrial Self-Guided Vehicles in Dynamic Environment","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Architecture; Computer science; Self driving; Computer architecture; Automotive engineering; Engineering; Geography","score_opus":0.012080616221228779,"score_gpt":0.24560096985034352,"score_spread":0.23352035362911475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394730178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17358282,0.0016765632,0.81852764,0.0026645649,0.0015605628,0.0016944244,0.000039268973,0.00016402217,0.000090118345],"genre_scores_gemma":[0.89497244,0.0003224732,0.103987195,0.00003474954,0.00038351203,0.00004534824,0.000020967864,0.000059521033,0.00017379696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952961,0.00036423813,0.0009316456,0.000838269,0.000783661,0.0017860401],"domain_scores_gemma":[0.998541,0.00008989782,0.00053722283,0.00061462476,0.00006645475,0.00015081336],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002773699,0.00043181414,0.00056185655,0.0005271579,0.00008232317,0.00013112396,0.0014751417,0.00044288023,0.0000013943774],"category_scores_gemma":[0.00005979565,0.00039285683,0.00020683235,0.00040083873,0.000043896023,0.00009595913,0.0013586886,0.008488641,0.000022893355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00035259247,0.00043052033,0.00022957359,0.00016954224,0.0011231741,0.00019313008,0.008985856,0.51874584,0.0058398284,0.02266473,0.000049109305,0.44121608],"study_design_scores_gemma":[0.001961316,0.0016494032,0.000411929,0.0017697471,0.00013336736,0.00073242374,0.00038234907,0.3900763,0.0011598361,0.6007501,0.00006911992,0.0009041032],"about_ca_topic_score_codex":0.00013328073,"about_ca_topic_score_gemma":0.00003388101,"teacher_disagreement_score":0.7213896,"about_ca_system_score_codex":0.004869758,"about_ca_system_score_gemma":0.006027418,"threshold_uncertainty_score":0.99985236},"labels":[],"label_agreement":null},{"id":"W4394862905","doi":"10.1109/jiot.2024.3389771","title":"SOScheduler: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Scheduling (production processes); Distributed computing; Real-time computing; Computer network; Mathematical optimization","score_opus":0.02890343332367779,"score_gpt":0.2840172100980529,"score_spread":0.25511377677437513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394862905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04822623,0.0018032333,0.9462797,0.0006600402,0.0025636242,0.00017875439,0.0000034245675,0.00012590324,0.00015910272],"genre_scores_gemma":[0.56654876,0.000035732435,0.43307853,0.00006601873,0.00012194403,0.0000046564855,4.6863457e-7,0.000015702102,0.00012817945],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799776,0.00017791183,0.0004869248,0.0004805154,0.0005105112,0.0003464003],"domain_scores_gemma":[0.9988168,0.00021365586,0.00031383373,0.00020205545,0.00024434793,0.0002093139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007476169,0.0002679394,0.00035345246,0.0002915754,0.000109949084,0.00055394025,0.0007892271,0.0001427742,0.000006298714],"category_scores_gemma":[0.0001235584,0.00021626119,0.00010587627,0.00036052588,0.0001410457,0.0020991273,0.00024426862,0.00097319967,0.00001578447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069825776,0.00094910903,0.0023197327,0.0005300246,0.003522108,0.004430815,0.45833725,0.015118877,0.12532957,0.0054416996,0.007572909,0.37574965],"study_design_scores_gemma":[0.00046143288,0.00040231456,0.0003214081,0.001303467,0.000035029076,0.0010286168,0.0006225876,0.9496013,0.04459084,0.0012756657,0.00010353025,0.00025378945],"about_ca_topic_score_codex":0.000047849855,"about_ca_topic_score_gemma":3.5003697e-7,"teacher_disagreement_score":0.93448246,"about_ca_system_score_codex":0.00013281821,"about_ca_system_score_gemma":0.00023589507,"threshold_uncertainty_score":0.88188756},"labels":[],"label_agreement":null},{"id":"W4394862936","doi":"10.1109/tiv.2024.3389640","title":"An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Planner; Trajectory; Computer science; Graph; Artificial intelligence; Theoretical computer science; Physics","score_opus":0.04757777007765936,"score_gpt":0.2944212223293077,"score_spread":0.2468434522516483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394862936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026325757,0.0003414725,0.9676367,0.0005489091,0.0030918317,0.00045645126,0.00025532933,0.0013141653,0.000029375857],"genre_scores_gemma":[0.8977498,0.00005416302,0.101055905,0.00024711172,0.00026104052,0.0001306263,0.000036469777,0.000060595,0.00040431757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757004,0.00011525065,0.00051779137,0.00087055017,0.0003863694,0.00054000143],"domain_scores_gemma":[0.99848604,0.00040004333,0.00006728118,0.0006954608,0.00009452202,0.00025662355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037335226,0.0003691751,0.00030705487,0.0005437476,0.00030352955,0.00037930865,0.0008944814,0.00018995334,0.00002706661],"category_scores_gemma":[0.000005407837,0.00034569323,0.00029809,0.0004580555,0.00010582244,0.00055290584,0.0000035383525,0.00044829524,0.000114491406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009286448,0.0010215043,0.000045376284,0.00013274558,0.00021709259,0.00011207781,0.0024253367,0.24400881,0.0041322242,0.0008520739,0.0008895061,0.7460704],"study_design_scores_gemma":[0.00024191476,0.00090753473,0.00024974498,0.00017447362,0.00005530526,0.000053009262,0.00013826533,0.93481874,0.058130533,0.0015104663,0.0032110363,0.0005089738],"about_ca_topic_score_codex":0.00031861925,"about_ca_topic_score_gemma":0.00012372859,"teacher_disagreement_score":0.871424,"about_ca_system_score_codex":0.00012922593,"about_ca_system_score_gemma":0.00019619969,"threshold_uncertainty_score":0.9998995},"labels":[],"label_agreement":null},{"id":"W4395095479","doi":"10.1017/9781108682404.025","title":"Index","year":2024,"lang":"en","type":"paratext","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Index (typography); Mathematics; Computer science; World Wide Web","score_opus":0.021556728641862487,"score_gpt":0.22598518522091768,"score_spread":0.2044284565790552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095479","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009164043,0.00047898,0.29149514,0.000039565813,0.00438579,0.00019334297,0.00013182146,0.00032537407,0.7029408],"genre_scores_gemma":[0.00021208751,0.00005226168,0.0066856975,0.000096509284,0.00034210176,0.0000014502111,0.00004875452,0.000036921356,0.9925242],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99774384,0.00013025268,0.00019170513,0.0009917555,0.000425223,0.00051721086],"domain_scores_gemma":[0.998206,0.00008645948,0.00014841571,0.0012104952,0.000116128664,0.00023245196],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001365974,0.00042611265,0.00043800584,0.0003837003,0.00016724138,0.00033634127,0.0027320422,0.0004649311,0.000003998714],"category_scores_gemma":[0.000009667931,0.0004855493,0.00020972014,0.00010032302,0.000120450764,0.000200024,0.0017327409,0.000947693,0.002843984],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005120191,0.000009747357,5.0431515e-7,0.00013912901,0.000104622646,0.0009808267,0.00007910713,0.00024113018,0.000010897675,0.06364959,0.93329984,0.001479508],"study_design_scores_gemma":[0.00022825172,0.00003284388,0.000009707705,0.00026688338,0.00005147285,0.00005744687,0.000013287666,0.028190298,0.00018432991,0.000001191087,0.9704445,0.0005198064],"about_ca_topic_score_codex":0.0002794493,"about_ca_topic_score_gemma":1.3078618e-7,"teacher_disagreement_score":0.28958338,"about_ca_system_score_codex":0.00026186003,"about_ca_system_score_gemma":0.00038049076,"threshold_uncertainty_score":0.9997596},"labels":[],"label_agreement":null},{"id":"W4395095482","doi":"10.1017/9781108682404.024","title":"Bibliography","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Bibliography; Computer science; Library science","score_opus":0.02396857245159101,"score_gpt":0.2055111913474761,"score_spread":0.1815426188958851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095482","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.681523e-7,0.00055577315,0.06314221,0.00004179825,0.0010936651,0.00021754458,0.000104993574,0.000874498,0.9339687],"genre_scores_gemma":[0.000015117246,0.00048628208,0.009907715,0.00007134454,0.00015581184,5.0689096e-7,0.000016427577,0.000045092977,0.9893017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809074,0.0000211532,0.0001863635,0.0008972841,0.00043536385,0.00036907615],"domain_scores_gemma":[0.9982794,0.00007534771,0.00014395767,0.0011450576,0.00012425164,0.0002319979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012190377,0.00043144348,0.00037879546,0.0071776994,0.00014607624,0.00025655358,0.0019404388,0.0003770642,0.0000013754213],"category_scores_gemma":[0.0000025365512,0.0004976993,0.00036465176,0.0003065035,0.0001702751,0.00019724568,0.0011944162,0.0006913376,0.00017593143],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029301868,0.0000028398144,4.957191e-7,0.00004790804,0.00011656679,0.0014761701,0.000016255206,0.000010314429,0.0000043538043,0.73006296,0.267624,0.0006352295],"study_design_scores_gemma":[0.00017329711,0.00004711432,0.0000070024776,0.00029364988,0.000120309756,0.00006582365,0.000003102836,0.0039755185,0.000047069534,0.00020551264,0.9945128,0.0005488134],"about_ca_topic_score_codex":0.000060214596,"about_ca_topic_score_gemma":1.09563906e-7,"teacher_disagreement_score":0.72985744,"about_ca_system_score_codex":0.00005086551,"about_ca_system_score_gemma":0.00007123507,"threshold_uncertainty_score":0.99974746},"labels":[],"label_agreement":null},{"id":"W4395095505","doi":"10.1017/9781108682404.013","title":"Mapping and Related Tasks","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Computer science; Psychology","score_opus":0.021314443365020336,"score_gpt":0.19533766750495177,"score_spread":0.17402322413993143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095505","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010635841,0.000651309,0.023011485,0.000061195635,0.00076054176,0.0002169581,0.000049793704,0.0005794924,0.9746586],"genre_scores_gemma":[0.00013481808,0.000058534646,0.005876343,0.00003583286,0.00005826504,3.0520684e-7,0.000013252806,0.00003386552,0.9937888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984347,0.000023104283,0.00018982615,0.00080060464,0.00026129046,0.00029047142],"domain_scores_gemma":[0.99882615,0.00007517668,0.00014141152,0.00069945527,0.0000714128,0.00018636904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013503127,0.0003503477,0.00034587714,0.00033828037,0.00015943806,0.00016306962,0.00088812766,0.00038414745,0.0000010183531],"category_scores_gemma":[0.000006308437,0.00041243446,0.00012398185,0.000020496658,0.00016915592,0.00014982605,0.0012326553,0.0006992782,0.00006563709],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002387352,0.0000018507128,4.1545033e-7,0.00008083427,0.00013496351,0.002527527,0.0002210832,0.00001195295,0.000013798512,0.9825471,0.011605679,0.00285239],"study_design_scores_gemma":[0.00031901826,0.00004142269,0.000018866776,0.000693917,0.0000961164,0.0003284714,0.000021259752,0.021482093,0.000027551581,0.0003855417,0.9759241,0.0006616747],"about_ca_topic_score_codex":0.000034429875,"about_ca_topic_score_gemma":6.462838e-8,"teacher_disagreement_score":0.9821616,"about_ca_system_score_codex":0.0001337785,"about_ca_system_score_gemma":0.00008516146,"threshold_uncertainty_score":0.99983275},"labels":[],"label_agreement":null},{"id":"W4395095506","doi":"10.1017/9781108682404.003","title":"Fundamental Problems","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Computer science","score_opus":0.02836678775937257,"score_gpt":0.20330683397821248,"score_spread":0.1749400462188399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095506","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000029676398,0.00036321848,0.03457429,0.000046328143,0.0011193777,0.00036057012,0.00012104042,0.00075193174,0.96266025],"genre_scores_gemma":[0.0000779386,0.000030533298,0.0065422235,0.00005728686,0.00015021353,8.7716694e-7,0.000026766409,0.00004783204,0.9930663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99804074,0.000020533813,0.00020419782,0.00090994884,0.00043742903,0.0003871803],"domain_scores_gemma":[0.99855083,0.000052979605,0.00015623946,0.0009364166,0.00008359322,0.00021993964],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000117281976,0.0004402453,0.0003758271,0.0002773114,0.00016248532,0.0002381165,0.0016642058,0.00033617212,0.000002146613],"category_scores_gemma":[0.000002844913,0.0005073306,0.0002340368,0.000017025512,0.00016622068,0.00019084594,0.0014139938,0.00069250807,0.00026029185],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036640367,0.0000057641623,4.524317e-7,0.000106144435,0.00012861616,0.00140747,0.00009518261,0.000042307383,0.000015500753,0.96169245,0.035116382,0.0013860341],"study_design_scores_gemma":[0.00024515437,0.00007061365,0.000003267767,0.00044808554,0.00010010598,0.00011419616,0.0000074704635,0.008764102,0.000050647217,0.0001467963,0.9894672,0.0005823685],"about_ca_topic_score_codex":0.0000558563,"about_ca_topic_score_gemma":2.4738154e-7,"teacher_disagreement_score":0.9615457,"about_ca_system_score_codex":0.00032586307,"about_ca_system_score_gemma":0.00014672722,"threshold_uncertainty_score":0.99973786},"labels":[],"label_agreement":null},{"id":"W4395095510","doi":"10.1017/9781108682404.008","title":"Representation and Planning","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Representation (politics); Computer science; Political science","score_opus":0.038087520773100667,"score_gpt":0.23644542787377765,"score_spread":0.19835790710067697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095510","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015827125,0.0005116973,0.06668902,0.000030839634,0.00070273527,0.0001777503,0.000030585892,0.00040110905,0.9314404],"genre_scores_gemma":[0.0001316595,0.000030133579,0.008406747,0.000034732515,0.00013283401,3.8436127e-7,0.000014840976,0.000026034135,0.9912226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986491,0.000020369604,0.00014104739,0.00071963825,0.00025679346,0.00021301907],"domain_scores_gemma":[0.9989845,0.000094805946,0.000119997036,0.00058647705,0.00007510149,0.00013911515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009432226,0.00026691594,0.0002764518,0.00024663677,0.00013045534,0.00018248236,0.0006313295,0.00023023445,3.4721066e-7],"category_scores_gemma":[0.0000096913745,0.00031677412,0.00009201939,0.000015556723,0.00010681184,0.0001835701,0.00084149203,0.00042567073,0.00002573032],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055899536,0.0000015181146,0.0000020674931,0.000057647158,0.00008026186,0.0023203322,0.00021009377,0.0000616218,0.000010621367,0.9767882,0.018041274,0.0024207681],"study_design_scores_gemma":[0.00045851615,0.00007291043,0.00006179704,0.00091013033,0.0002200957,0.0003180998,0.000056182478,0.04676395,0.00016467724,0.00043949057,0.94963396,0.0009001857],"about_ca_topic_score_codex":0.00003880533,"about_ca_topic_score_gemma":5.1659836e-8,"teacher_disagreement_score":0.9763487,"about_ca_system_score_codex":0.00009181642,"about_ca_system_score_gemma":0.000055932396,"threshold_uncertainty_score":0.9999284},"labels":[],"label_agreement":null},{"id":"W4395095516","doi":"10.1017/9781108682404.002","title":"Overview and Motivation","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Psychology; Computer science","score_opus":0.04279146761441957,"score_gpt":0.21593162438127958,"score_spread":0.17314015676686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095516","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007507334,0.0012223787,0.03302978,0.000050398903,0.0004248277,0.00019781494,0.000045846864,0.0003456409,0.9646758],"genre_scores_gemma":[0.00005700757,0.00020262807,0.0048157494,0.000061659724,0.00008023814,2.9859376e-7,0.0000124945145,0.000024032819,0.9947459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988044,0.000017821618,0.00013160279,0.0006095701,0.00024368372,0.00019290697],"domain_scores_gemma":[0.999056,0.000082867264,0.00010921483,0.00055394205,0.00007249274,0.00012550456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009509735,0.00027473018,0.00026924442,0.0001931632,0.000104440456,0.00013945483,0.0006360895,0.00023283686,6.0523234e-7],"category_scores_gemma":[0.000007506982,0.00031483,0.00009019886,0.000012702757,0.00010203968,0.00016883119,0.00084158056,0.00037461586,0.000030625568],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021052408,0.0000017373125,9.092253e-7,0.00011952229,0.00005631986,0.00043148772,0.000052810723,0.000005770801,0.000004838622,0.98818576,0.008072302,0.0030664057],"study_design_scores_gemma":[0.00021282956,0.000042288255,0.00005707772,0.00062691886,0.00010515614,0.000072773524,0.0000044426865,0.009770414,0.00003518649,0.00032146508,0.9882981,0.00045331163],"about_ca_topic_score_codex":0.00003106569,"about_ca_topic_score_gemma":1.04161145e-7,"teacher_disagreement_score":0.9878643,"about_ca_system_score_codex":0.00008705691,"about_ca_system_score_gemma":0.00006500261,"threshold_uncertainty_score":0.9999304},"labels":[],"label_agreement":null},{"id":"W4395095518","doi":"10.1017/9781108682404.011","title":"System Control","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Computer science","score_opus":0.016766785651080723,"score_gpt":0.1879318603279319,"score_spread":0.1711650746768512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095518","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.638382e-7,0.0002935533,0.19186726,0.000027147442,0.0012447928,0.00030157948,0.00015052727,0.0010155907,0.80509895],"genre_scores_gemma":[0.00047433435,0.000007890467,0.0036051555,0.00004815649,0.0002113179,8.387954e-7,0.000010518306,0.000046028537,0.99559575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980918,0.000035322217,0.00022208525,0.0008586033,0.00042280654,0.0003693555],"domain_scores_gemma":[0.9982323,0.00010328012,0.00018485416,0.0011139445,0.00013752804,0.0002280651],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015896544,0.0004358832,0.0005067856,0.0002819153,0.00015612022,0.0001965352,0.0017500133,0.00039158788,5.100381e-7],"category_scores_gemma":[0.000004244404,0.0004914386,0.0002647585,0.000014462584,0.00011793374,0.00013763217,0.0006861897,0.0006299465,0.00025870415],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007331472,0.0000022566217,1.7106956e-7,0.00017976762,0.00017459481,0.0034959747,0.000033565277,0.000053923995,0.0000054665775,0.97216594,0.02321022,0.000670806],"study_design_scores_gemma":[0.00052603736,0.00005453645,0.0000023210064,0.000828747,0.00024765095,0.00020511074,0.000013019323,0.053950425,0.00002907989,0.00002531502,0.94348574,0.0006320286],"about_ca_topic_score_codex":0.000049543924,"about_ca_topic_score_gemma":9.032596e-8,"teacher_disagreement_score":0.9721406,"about_ca_system_score_codex":0.00041249377,"about_ca_system_score_gemma":0.00015737275,"threshold_uncertainty_score":0.9997537},"labels":[],"label_agreement":null},{"id":"W4395095523","doi":"10.1017/9781108682404.005","title":"Mobile Robot Hardware","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Computer science; Mobile robot; Computer hardware; Embedded system; Robot; Human–computer interaction; Computer graphics (images); Artificial intelligence","score_opus":0.023180256202493088,"score_gpt":0.2104049327189792,"score_spread":0.1872246765164861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395095523","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.73882e-7,0.00067106675,0.10625968,0.000015723119,0.0012018653,0.00037453783,0.00014970194,0.000870556,0.8904559],"genre_scores_gemma":[0.000039641134,0.000059387185,0.013497294,0.000057667676,0.00019419668,0.0000019821894,0.000032861197,0.00005851453,0.9860585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997831,0.000026666326,0.00021944284,0.0010434786,0.00045710837,0.0004223282],"domain_scores_gemma":[0.99804115,0.00006613856,0.00016495855,0.0013234616,0.0001473781,0.00025690484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011753287,0.00049994595,0.00047173857,0.0003131342,0.00017955479,0.00022480047,0.0021005962,0.00043090942,0.0000027534015],"category_scores_gemma":[0.000005116345,0.00058295333,0.0002988316,0.000021303831,0.0001554934,0.00019728848,0.001586658,0.00080192846,0.00032502355],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005373076,0.000005683082,2.0773939e-7,0.00009907884,0.00013554457,0.0027558003,0.00012743114,0.00022368779,0.000012947419,0.9297526,0.063552156,0.0033295066],"study_design_scores_gemma":[0.0002130027,0.00009734882,0.0000017233245,0.0003894963,0.00012391806,0.00009376842,0.000010995624,0.00996534,0.000113139686,0.000078172285,0.9882748,0.00063829793],"about_ca_topic_score_codex":0.0000463143,"about_ca_topic_score_gemma":1.7056962e-7,"teacher_disagreement_score":0.9296744,"about_ca_system_score_codex":0.00028076017,"about_ca_system_score_gemma":0.00018465276,"threshold_uncertainty_score":0.99966216},"labels":[],"label_agreement":null},{"id":"W4395449399","doi":"10.1016/j.knosys.2024.111830","title":"Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Computer science; Population; Genetic algorithm; Encoding (memory); Task (project management); Motion planning; Real-time computing; Distributed computing; Artificial intelligence; Machine learning; Engineering; Robot","score_opus":0.053032747225832295,"score_gpt":0.29698082521081365,"score_spread":0.24394807798498136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395449399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004253146,0.006733965,0.9840227,0.000015816253,0.003672817,0.0007651026,0.000047183774,0.00043575055,0.00005356027],"genre_scores_gemma":[0.6460594,0.0000031380503,0.35325345,0.000018192273,0.00030635024,0.00008396862,0.000013686816,0.000054558914,0.00020721804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966043,0.00059267745,0.0008251756,0.0009852302,0.0004018982,0.0005906879],"domain_scores_gemma":[0.9979992,0.0002218261,0.00020605238,0.0009636835,0.0003392959,0.00026997612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007644105,0.0004183842,0.000574346,0.00041707853,0.00019224797,0.00046480936,0.000940791,0.0001778085,0.0000040170708],"category_scores_gemma":[0.00003781408,0.00038406928,0.00015323823,0.0008461726,0.00010849126,0.00030738357,0.00017259273,0.00024992364,0.0000697989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006059118,0.0006846282,0.00012215246,0.00094684586,0.00029731228,0.0004367789,0.006126541,0.8470944,0.098485224,0.00009293989,0.00011568099,0.04559143],"study_design_scores_gemma":[0.00055122026,0.00028591452,0.000050716964,0.0008030789,0.00003028509,0.00012365427,0.000117096846,0.9881813,0.00900274,0.000005472095,0.00042352482,0.0004250241],"about_ca_topic_score_codex":0.0001902345,"about_ca_topic_score_gemma":0.000004035192,"teacher_disagreement_score":0.6418063,"about_ca_system_score_codex":0.00038857604,"about_ca_system_score_gemma":0.00067950244,"threshold_uncertainty_score":0.9998611},"labels":[],"label_agreement":null},{"id":"W4395670013","doi":"10.1007/s10846-024-02090-7","title":"A Compact Aerial Manipulator: Design and Control for Dexterous Operations","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China; Beijing Nova Program; Major State Basic Research Development Program of China; National Natural Science Foundation of China","keywords":"Manipulator (device); Control (management); Computer science; Control engineering; Engineering; Artificial intelligence; Robot","score_opus":0.051104288091026656,"score_gpt":0.29715258912027115,"score_spread":0.2460483010292445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395670013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042301154,0.0055293352,0.9851935,0.00040551595,0.00774676,0.0006115397,0.0000032531243,0.00006473854,0.000022329617],"genre_scores_gemma":[0.81037295,0.00004673009,0.18813309,0.00007212116,0.0010041019,0.000014176869,7.6445053e-7,0.000025533856,0.00033055965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801946,0.00021868691,0.00085619785,0.00024999178,0.00036398828,0.00029168057],"domain_scores_gemma":[0.99838525,0.00073038135,0.00017921322,0.0002448533,0.00025917293,0.00020115451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001438594,0.00019836832,0.000490143,0.00026729726,0.00011511247,0.0009973806,0.00052287366,0.000082258906,0.0000037416746],"category_scores_gemma":[0.00013721712,0.00015045734,0.00015104222,0.00018408327,0.000033774606,0.00045835561,0.000029475035,0.00019007883,0.000030248706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002642424,0.00004123955,0.000035725527,0.0000989403,0.00021482389,0.0001431531,0.0007257071,0.9864068,0.0007015399,0.0070486045,0.0033879548,0.0011690996],"study_design_scores_gemma":[0.00041053785,0.000555788,0.00004591696,0.00057504163,0.00008055528,0.0028453227,0.000082782026,0.99402195,0.00027584104,0.00021238838,0.000736525,0.00015732886],"about_ca_topic_score_codex":0.000029920235,"about_ca_topic_score_gemma":4.2707111e-7,"teacher_disagreement_score":0.80994993,"about_ca_system_score_codex":0.00014959276,"about_ca_system_score_gemma":0.00022066051,"threshold_uncertainty_score":0.9617763},"labels":[],"label_agreement":null},{"id":"W4395671342","doi":"10.2316/j.2024.206-1087","title":"A NOVEL ROBOT PATH PLANNING ALGORITHM BASED ON THE IMPROVED WILD HORSE OPTIMISER WITH HYBRID STRATEGIES, 515-533.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Horse; Path (computing); Computer science; Robot; Algorithm; Artificial intelligence; Biology; Operating system; Paleontology","score_opus":0.015584726676932682,"score_gpt":0.25811946381208173,"score_spread":0.24253473713514906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395671342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013604871,0.00015848853,0.99138093,0.0056174165,0.0011366004,0.00010595718,0.000010294312,0.000080989696,0.00014881461],"genre_scores_gemma":[0.34466872,0.000012633908,0.65460366,0.0003708691,0.00027918283,0.000003915293,0.000008440028,0.000016340438,0.000036225687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984235,0.000047494665,0.0003969336,0.0002282977,0.00072573306,0.00017805338],"domain_scores_gemma":[0.99878806,0.00030845052,0.00029806636,0.00018066796,0.00033921067,0.00008554578],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00059305225,0.00018323006,0.00016939729,0.00026967446,0.000099566314,0.0011812986,0.0006577603,0.00004488451,0.00000414353],"category_scores_gemma":[0.000052995572,0.000115607036,0.00007063282,0.00018532677,0.000048285743,0.00080076855,0.000066122644,0.00034950094,0.000003313437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017389577,0.000072369505,0.000026899961,0.000011290178,0.000121487494,0.0003107442,0.00031092574,0.9656746,0.00059437164,0.005500741,0.0004163653,0.026942808],"study_design_scores_gemma":[0.0004660293,0.00029375518,0.00092775805,0.00065674534,0.000023690052,0.00071002287,0.000074669835,0.9956828,0.00018906426,0.0006416066,0.00018737513,0.00014651376],"about_ca_topic_score_codex":0.0000093847875,"about_ca_topic_score_gemma":1.8506712e-7,"teacher_disagreement_score":0.34330824,"about_ca_system_score_codex":0.00009347565,"about_ca_system_score_gemma":0.00032153138,"threshold_uncertainty_score":0.9998556},"labels":[],"label_agreement":null},{"id":"W4396214157","doi":"10.5539/jmr.v16n2p71","title":"Using Mathematical Models and Extensions to Solve a Robot’s Equidistant Returning Path","year":2024,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Equidistant; Mathematics; Path (computing); Robot; Calculus (dental); Mathematical optimization; Applied mathematics; Geometry; Artificial intelligence; Computer science; Programming language","score_opus":0.3732550173447792,"score_gpt":0.46529123841744224,"score_spread":0.09203622107266302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396214157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0149092935,0.0009783392,0.9818252,0.0012939922,0.0002175435,0.00019215487,0.0000019687009,0.00004705724,0.00053449336],"genre_scores_gemma":[0.071624905,0.000047631485,0.9280286,0.00002830487,0.00012788575,0.0000035085736,1.2006812e-7,0.000027768672,0.00011127772],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964987,0.00013580396,0.000773259,0.00029570417,0.0017221207,0.0005743961],"domain_scores_gemma":[0.9968855,0.0014600086,0.00013138793,0.0004812305,0.0006019375,0.0004399772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006691228,0.00016894775,0.000425105,0.00076239806,0.00021897983,0.00083535654,0.00086986244,0.00009228541,0.000009364987],"category_scores_gemma":[0.0010584826,0.00012392599,0.00010863751,0.00082152965,0.00009294641,0.0006472319,0.0007162755,0.0009008569,0.000037276495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027524375,0.00070407655,0.00000875903,0.0022935057,0.0003351371,0.0076270765,0.06171704,0.08025789,0.07276517,0.75686514,0.0061627864,0.011235856],"study_design_scores_gemma":[0.00009214337,0.00016741622,0.000003981964,0.0019631882,0.000014219898,0.0022284505,0.0006098071,0.8102831,0.00027523583,0.1842038,0.000047600835,0.00011100158],"about_ca_topic_score_codex":0.0000032301164,"about_ca_topic_score_gemma":1.6199046e-7,"teacher_disagreement_score":0.73002523,"about_ca_system_score_codex":0.00017769402,"about_ca_system_score_gemma":0.000405236,"threshold_uncertainty_score":0.80553627},"labels":[],"label_agreement":null},{"id":"W4396241134","doi":"10.2316/j.2024.201-0443","title":"APPLICATION OF PSO-ACO ALGORITHM IN PATH PLANNING OF INTELLIGENT ROBOTS","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Path (computing); Robot; Artificial intelligence; Ant colony optimization algorithms; Mathematical optimization; Algorithm; Mathematics; Computer network","score_opus":0.009833049298697527,"score_gpt":0.24767725809776053,"score_spread":0.237844208799063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396241134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00140438,0.01922913,0.9782825,0.00006766691,0.0005046936,0.00037770983,0.000008755601,0.00006568668,0.000059456503],"genre_scores_gemma":[0.9852103,0.00003188155,0.014566036,0.0000088696415,0.0000628115,0.000074684875,0.0000021642488,0.00000960925,0.000033621855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861234,0.00008381058,0.0004933065,0.00034001764,0.00024031237,0.00023021261],"domain_scores_gemma":[0.99930274,0.0001424305,0.00013861636,0.0003207336,0.000045162877,0.000050333816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000703744,0.00013185946,0.00036572778,0.00018933609,0.000021751703,0.000054601016,0.0003098664,0.000078228055,4.866371e-7],"category_scores_gemma":[0.0000126951145,0.00011478309,0.00005107362,0.00028505982,0.000023631925,0.00014894434,0.00005747724,0.00012234373,0.0000036014667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001580536,0.000105672545,0.0026822223,0.000841374,0.00017773407,0.0000788411,0.0031130572,0.13479017,0.0044497983,0.09542687,0.0000604537,0.758258],"study_design_scores_gemma":[0.00034515606,0.00010962719,0.0005484037,0.00045796446,0.000012678721,0.000030789066,0.00015758745,0.99705887,0.00026000378,0.0006485442,0.00026658142,0.00010382078],"about_ca_topic_score_codex":0.00035978953,"about_ca_topic_score_gemma":6.4399507e-7,"teacher_disagreement_score":0.98380595,"about_ca_system_score_codex":0.000056159246,"about_ca_system_score_gemma":0.00008638225,"threshold_uncertainty_score":0.46807188},"labels":[],"label_agreement":null},{"id":"W4396242229","doi":"10.3390/drones8050173","title":"VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments","year":2024,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Modular design; Computer science; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.00849846114815495,"score_gpt":0.28328163109362897,"score_spread":0.274783169945474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396242229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008088293,0.0003900754,0.98944294,0.0009849339,0.00043685656,0.0003767603,0.0000017098383,0.00022631734,0.000052119285],"genre_scores_gemma":[0.6619376,0.000009719229,0.337369,0.000107439235,0.00006901064,0.00011523046,0.000054029657,0.000023967548,0.0003140198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837416,0.000063610896,0.00033088992,0.0005204555,0.00031198552,0.00039890883],"domain_scores_gemma":[0.9991392,0.00029189314,0.000084761705,0.00039305477,0.000013354211,0.000077771285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369546,0.00019490918,0.0001890449,0.00028026334,0.00012971222,0.00019756611,0.00043838023,0.00013134014,0.000005704443],"category_scores_gemma":[0.00012348604,0.00020040157,0.00007143824,0.00041356814,0.000038092585,0.0003219371,0.000093320574,0.0002871997,0.0001000539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004509885,0.000030153571,0.00010993347,0.00005391952,0.00001283389,0.00003655354,0.0010525954,0.92456704,0.00041953064,0.0031487183,0.000008145339,0.07055609],"study_design_scores_gemma":[0.00024344603,0.00016804395,0.0011526655,0.00036795225,0.0000069966936,0.0000055631,0.000024901863,0.9922377,0.00019409307,0.0036832483,0.0017015672,0.0002138114],"about_ca_topic_score_codex":0.000044901648,"about_ca_topic_score_gemma":8.7609243e-7,"teacher_disagreement_score":0.6538493,"about_ca_system_score_codex":0.00040617428,"about_ca_system_score_gemma":0.00014280871,"threshold_uncertainty_score":0.8172139},"labels":[],"label_agreement":null},{"id":"W4396542517","doi":"10.1109/lra.2024.3396091","title":"Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Notation; Computer science; Mathematics; Discrete mathematics; Algorithm; Combinatorics; Arithmetic","score_opus":0.026578821108556628,"score_gpt":0.2746432342162779,"score_spread":0.24806441310772126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396542517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021218352,0.00012628654,0.9699753,0.0070817326,0.0009040587,0.0001416078,0.0000027907645,0.0004691757,0.00008072612],"genre_scores_gemma":[0.5591255,0.000005443295,0.43876663,0.0017917944,0.0001950418,0.00000942636,0.000033321583,0.00001739645,0.000055475273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858594,0.00008108475,0.0002708389,0.00044612054,0.00037400663,0.00024198489],"domain_scores_gemma":[0.99937457,0.00018368178,0.00006445728,0.00021671262,0.000060433293,0.000100118275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017970758,0.00017685119,0.00019487906,0.00021107409,0.00014435129,0.00069614593,0.00018254499,0.000070142705,0.0000026495145],"category_scores_gemma":[0.000013979045,0.00016133595,0.000049053604,0.0003585612,0.00007214199,0.0005056458,0.000048976177,0.00016949295,0.000047443235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001907814,0.00003578929,0.000025898129,0.00012059194,0.00008005503,0.000093872,0.0007687616,0.8771295,0.009028763,0.10241581,0.005267463,0.0050315824],"study_design_scores_gemma":[0.00016232708,0.00002760732,0.001000438,0.00009245033,0.00002129432,0.00007990965,0.000017283333,0.9977148,0.00024536037,0.00026575438,0.00017519311,0.00019754506],"about_ca_topic_score_codex":0.00001539951,"about_ca_topic_score_gemma":8.192896e-8,"teacher_disagreement_score":0.5379071,"about_ca_system_score_codex":0.000051578212,"about_ca_system_score_gemma":0.000044718345,"threshold_uncertainty_score":0.67129517},"labels":[],"label_agreement":null},{"id":"W4396855513","doi":"10.3390/s24103100","title":"Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shanghai","keywords":"Obstacle avoidance; Iterative closest point; Computer science; Robot; Computer vision; Point cloud; Obstacle; Artificial intelligence; Motion planning; Mobile robot; Overshoot (microwave communication); Path (computing); Collision avoidance; Trajectory; Collision; Geography","score_opus":0.013617329699053273,"score_gpt":0.2458808329737931,"score_spread":0.23226350327473982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396855513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4437105,0.00048472662,0.55412483,0.0003914679,0.00041914952,0.000083668965,0.0000033775927,0.00027952073,0.0005027556],"genre_scores_gemma":[0.82106847,0.0000061483865,0.17857699,0.00016603766,0.000059463793,0.0000039228225,0.000002904485,0.000011862161,0.0001042103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987407,0.00007459336,0.00016221711,0.000527598,0.00024223336,0.00025268647],"domain_scores_gemma":[0.9990937,0.00049276766,0.000039461425,0.00025863017,0.00002429186,0.00009113268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028187368,0.00015036287,0.00014549737,0.00017052864,0.00019486724,0.0003450577,0.00015551095,0.000060550075,0.0000020838388],"category_scores_gemma":[0.000076961565,0.00014153405,0.000021687234,0.00028443433,0.000036922498,0.00021449193,0.000082394065,0.0002058904,0.00001973018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060320803,0.000086206186,0.0013493885,0.00043330117,0.00008150253,0.0022852414,0.02120823,0.5701347,0.054309398,0.014956664,0.0011505446,0.3339445],"study_design_scores_gemma":[0.000187689,0.00008738388,0.008135592,0.0009839376,0.000003764446,0.00003444149,0.00008990313,0.9859737,0.0034819592,0.00074172526,0.000092579474,0.00018733654],"about_ca_topic_score_codex":0.000011789185,"about_ca_topic_score_gemma":8.0235864e-8,"teacher_disagreement_score":0.415839,"about_ca_system_score_codex":0.000030736323,"about_ca_system_score_gemma":0.000025349575,"threshold_uncertainty_score":0.5771591},"labels":[],"label_agreement":null},{"id":"W4396905391","doi":"10.21203/rs.3.rs-4343274/v1","title":"Field Robot Self-Exploration Based on Deep Reinforcement Learning and Obstacle Avoidance  Strategy","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Obstacle avoidance; Reinforcement learning; Field (mathematics); Artificial intelligence; Obstacle; Robot; Computer science; Collision avoidance; Human–computer interaction; Mobile robot; Geography; Mathematics; Computer security","score_opus":0.06583168310872474,"score_gpt":0.37425704844604335,"score_spread":0.3084253653373186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396905391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011711116,0.0011643051,0.9854016,0.0032931655,0.00059582666,0.0010535028,0.0000027846156,0.0007310989,0.006586649],"genre_scores_gemma":[0.9404347,0.00021726223,0.057359785,0.00010301549,0.00027287213,0.00030274995,0.000059528556,0.00004555984,0.0012045546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956609,0.0006079672,0.0003575156,0.0011046665,0.0015632774,0.0007056956],"domain_scores_gemma":[0.99746305,0.0009740672,0.00010426097,0.0008877787,0.0003410142,0.00022982091],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001972928,0.00030759265,0.00029022407,0.0005492948,0.00036675594,0.0011981701,0.0008709115,0.0003145089,0.000020936039],"category_scores_gemma":[0.0004926028,0.00029742645,0.00008452016,0.0005768401,0.000049466948,0.0002285472,0.0018930526,0.0032766107,0.00019143405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018174236,0.000049441307,0.00012880989,0.0009019819,0.000027674596,0.0001873313,0.0012232895,0.9707948,0.000036461945,0.0027226934,0.00053902203,0.023370327],"study_design_scores_gemma":[0.00018492634,0.0009657318,0.00020707592,0.0011963951,0.000006154303,0.0000037032223,0.0001582935,0.99180746,0.00045099104,0.0043267263,0.0004067144,0.00028582872],"about_ca_topic_score_codex":0.0001126739,"about_ca_topic_score_gemma":0.0000062071786,"teacher_disagreement_score":0.9392636,"about_ca_system_score_codex":0.000313863,"about_ca_system_score_gemma":0.00056086236,"threshold_uncertainty_score":0.9999478},"labels":[],"label_agreement":null},{"id":"W4396910037","doi":"10.1109/tro.2024.3400975","title":"Safe Set-Based Trajectory Planning for Robotic Manipulators","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast","keywords":"Trajectory; Robot manipulator; Set (abstract data type); Motion planning; Computer science; Control engineering; Control theory (sociology); Robot; Robotics; Artificial intelligence; Robot kinematics; Engineering; Mobile robot; Control (management)","score_opus":0.054269292859222065,"score_gpt":0.29969644732774886,"score_spread":0.2454271544685268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396910037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012437311,0.00021641431,0.991432,0.0008209716,0.0056965942,0.00042830117,0.00001654124,0.0010996972,0.00016508237],"genre_scores_gemma":[0.35840568,0.0000043530094,0.64014983,0.00037116837,0.00014423506,0.000081893,0.000007897931,0.000072271156,0.0007626627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774873,0.000071975635,0.00042072806,0.0007324781,0.00042672775,0.000599374],"domain_scores_gemma":[0.99824935,0.00073032564,0.000059701983,0.00068325974,0.00007080465,0.00020655346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031531905,0.0003570669,0.00032177547,0.00050193764,0.00034139946,0.0003327087,0.0006930051,0.00018854834,0.000012928123],"category_scores_gemma":[0.000010456179,0.0003605111,0.00028238,0.0007054595,0.00006207323,0.0003598381,0.0000027177389,0.00048349425,0.00014118203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010757902,0.00007855951,0.0000058523465,0.000115446004,0.00006967294,0.000083107196,0.0003415077,0.99144906,0.00014917091,0.0011198845,0.00067114277,0.005905866],"study_design_scores_gemma":[0.00037653878,0.00025346744,0.00005745449,0.00028644412,0.00008433035,0.000042595682,0.0000302206,0.9953316,0.0025527182,0.0003326735,0.00023998787,0.00041195485],"about_ca_topic_score_codex":0.000009090638,"about_ca_topic_score_gemma":0.0000016914571,"teacher_disagreement_score":0.3582813,"about_ca_system_score_codex":0.00020849386,"about_ca_system_score_gemma":0.00027234957,"threshold_uncertainty_score":0.99988467},"labels":[],"label_agreement":null},{"id":"W4398137232","doi":"10.1007/s40314-024-02756-w","title":"Hybrid search method for Zermelo’s navigation problem","year":2024,"lang":"en","type":"article","venue":"Computational and Applied Mathematics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Agencia Estatal de Investigación; European Regional Development Fund; Banco Bilbao Vizcaya Argentaria; Mitacs; Dalhousie University; Fundación BBVA","keywords":"Mathematics; Computer science; Applied mathematics; Mathematical economics; Mathematical optimization","score_opus":0.031132445975382027,"score_gpt":0.3155709476423339,"score_spread":0.2844385016669519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398137232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000716406,0.00008925809,0.99679697,0.0005412891,0.00009784645,0.00038079973,0.000012826702,0.0002469925,0.0011176167],"genre_scores_gemma":[0.014088827,0.0000013020149,0.9855156,0.00007152992,0.00007199351,0.00007395701,0.000042877913,0.000013205213,0.00012069815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999043,0.0000105698255,0.00022101217,0.00030460168,0.00025008735,0.00017076975],"domain_scores_gemma":[0.9989817,0.00073538214,0.00003595218,0.00011733051,0.000066043205,0.00006355472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049291435,0.000115368704,0.00014176944,0.000078209305,0.00012194336,0.00027577847,0.00021127089,0.000029861156,0.0000015558074],"category_scores_gemma":[0.000009104595,0.00010108877,0.00003397134,0.0001512528,0.000026860269,0.00011972996,0.00009789713,0.000094118106,0.000031565898],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001012658,0.000029698844,3.3625932e-7,0.00049981935,0.000022480566,0.0000046485807,0.0011121251,0.053939454,0.00023230743,0.84438574,0.0007782993,0.0989941],"study_design_scores_gemma":[0.00008283027,0.000014104956,0.000009076034,0.00005880286,0.0000057752386,0.00006611264,0.000015828948,0.59874207,0.00033045447,0.4003955,0.0002032871,0.00007613433],"about_ca_topic_score_codex":6.0787056e-7,"about_ca_topic_score_gemma":9.852698e-9,"teacher_disagreement_score":0.5448026,"about_ca_system_score_codex":0.000022532391,"about_ca_system_score_gemma":0.00007326165,"threshold_uncertainty_score":0.41222805},"labels":[],"label_agreement":null},{"id":"W4399175867","doi":"10.1609/icaps.v34i1.31520","title":"Multi-Robot Connected Fermat Spiral Coverage","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workspace; Computer science; Robot; Path (computing); Motion planning; Scalability; Distributed computing; Artificial intelligence; Mathematical optimization; Computer network; Mathematics","score_opus":0.054018082742686706,"score_gpt":0.30943663855518305,"score_spread":0.25541855581249634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399175867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9255389,0.0003941591,0.057954308,0.004261743,0.0025871093,0.0002725292,0.000023401913,0.0024985608,0.0064692656],"genre_scores_gemma":[0.9291569,0.00001116992,0.070375636,0.00017680207,0.000053792006,0.0000078609955,0.000003510706,0.000012376537,0.00020193718],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985742,0.000011173278,0.00031482326,0.00041694907,0.00045340185,0.00022943199],"domain_scores_gemma":[0.99929535,0.00011451451,0.00015310959,0.00011188075,0.00025502397,0.00007009073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003611477,0.00019623441,0.00018998711,0.0002083272,0.00013806761,0.00074328325,0.0010717128,0.000092838935,0.000007622826],"category_scores_gemma":[0.0002343634,0.00014756541,0.000063493506,0.00028518954,0.00006390744,0.00047170452,0.0003001226,0.0003610509,0.000015599991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012812979,0.00030259503,0.026936369,0.00072126254,0.0008355714,0.00018277104,0.012686552,0.059869207,0.47632706,0.40617958,0.0026097936,0.013221101],"study_design_scores_gemma":[0.00025247998,0.00004423568,0.0057923817,0.0014497336,0.000010021209,0.00009302853,0.000117070136,0.9836401,0.007496135,0.00088040484,0.000052525498,0.0001718922],"about_ca_topic_score_codex":0.000012224021,"about_ca_topic_score_gemma":8.769149e-8,"teacher_disagreement_score":0.9237709,"about_ca_system_score_codex":0.000048032918,"about_ca_system_score_gemma":0.00008025249,"threshold_uncertainty_score":0.7167497},"labels":[],"label_agreement":null},{"id":"W4399177038","doi":"10.1609/icaps.v34i1.31455","title":"Exact Multi-objective Path Finding with Negative Weights","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"State Government of Victoria; Australian Government","keywords":"Mathematical optimization; Shortest path problem; Computer science; Path (computing); Point (geometry); Graph; Enhanced Data Rates for GSM Evolution; Task (project management); Pareto optimal; Algorithm; Multi-objective optimization; Mathematics; Theoretical computer science; Artificial intelligence; Engineering","score_opus":0.03990089687618361,"score_gpt":0.29810617310350235,"score_spread":0.25820527622731876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399177038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92798495,0.00021107715,0.056624927,0.0020970064,0.0011379204,0.00032306963,0.00001893289,0.0015648485,0.010037247],"genre_scores_gemma":[0.8678323,0.000006770881,0.13188383,0.000053914802,0.000042567986,0.000011386503,0.0000014671366,0.000012002323,0.0001557386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859476,0.000012210449,0.00023364119,0.00045911636,0.0004812269,0.00021904986],"domain_scores_gemma":[0.9991723,0.00019008461,0.0001805005,0.00009393857,0.00029940758,0.0000637799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003396671,0.00020527994,0.00018480862,0.00023350611,0.00017563032,0.0006000745,0.0008083051,0.00007366974,0.000003876078],"category_scores_gemma":[0.0001730188,0.0001333236,0.000045729947,0.0003385922,0.00008161081,0.00054671575,0.00019716547,0.00038292698,0.000006267143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035699146,0.00038597686,0.05100264,0.0007253035,0.0018782973,0.00027938743,0.06893633,0.03725786,0.06138104,0.76372266,0.0009955465,0.0130779445],"study_design_scores_gemma":[0.0002516813,0.00009747575,0.0054380484,0.002926149,0.000013227418,0.000083585146,0.0006118459,0.97975945,0.009021834,0.0016072871,0.000011640429,0.00017776014],"about_ca_topic_score_codex":0.00001937311,"about_ca_topic_score_gemma":1.7530891e-7,"teacher_disagreement_score":0.9425016,"about_ca_system_score_codex":0.00006602656,"about_ca_system_score_gemma":0.000109391556,"threshold_uncertainty_score":0.5786532},"labels":[],"label_agreement":null},{"id":"W4399182933","doi":"10.3390/drones8060226","title":"UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic","year":2024,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Concordia University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Process (computing); Controller (irrigation); Fuzzy logic; Control engineering; Machine learning; Engineering","score_opus":0.038220064240781185,"score_gpt":0.33787657427233303,"score_spread":0.29965651003155186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399182933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002357643,0.0018406363,0.9940181,0.00026507056,0.00083713443,0.00015053598,5.042998e-7,0.0003557436,0.00017464373],"genre_scores_gemma":[0.22606318,0.00006770862,0.7730922,0.0000846401,0.000056223605,0.000010409249,0.000007669181,0.000015469202,0.0006024761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851197,0.00013178462,0.0002866044,0.00051996193,0.0002224914,0.00032717022],"domain_scores_gemma":[0.99944884,0.00011807131,0.000063668704,0.00023139671,0.000040648363,0.00009735003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056305825,0.00019787005,0.00020307935,0.00020480006,0.00015579614,0.00032345924,0.00032108862,0.00004898507,0.000029455687],"category_scores_gemma":[0.000054414515,0.00017920148,0.000062584266,0.00024417194,0.00004700746,0.0003436555,0.000382747,0.00028185354,0.00010365386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007960104,0.000048063117,0.0004141981,0.00026398024,0.00012158269,0.00047431796,0.008059671,0.7985349,0.0036498737,0.0040848446,0.000068951995,0.18427165],"study_design_scores_gemma":[0.000097582444,0.000085060165,0.00012524381,0.00015548813,0.000014528958,0.00025517578,0.00019554522,0.9964059,0.00034186835,0.0013426555,0.00076533895,0.00021564272],"about_ca_topic_score_codex":0.000032693093,"about_ca_topic_score_gemma":9.024236e-7,"teacher_disagreement_score":0.22370553,"about_ca_system_score_codex":0.0001323699,"about_ca_system_score_gemma":0.00003880973,"threshold_uncertainty_score":0.7307624},"labels":[],"label_agreement":null},{"id":"W4399261737","doi":"10.1609/socs.v17i1.31586","title":"Large-Scale Multi-Robot Coverage Path Planning via Local Search (Extended Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Motion planning; Computer science; Path (computing); Robot; Artificial intelligence; Geography; Computer network; Cartography","score_opus":0.02083594025571476,"score_gpt":0.29056882867554956,"score_spread":0.2697328884198348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399261737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39467764,0.000350182,0.5264731,0.013578836,0.039630044,0.0017171405,0.00011713846,0.001195755,0.022260139],"genre_scores_gemma":[0.99359906,0.000012502917,0.005099484,0.00011485471,0.00055955735,0.000026681031,0.000006505858,0.000038333023,0.0005430069],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99588495,0.000029249419,0.00049969135,0.0007718748,0.002201827,0.0006124077],"domain_scores_gemma":[0.9985055,0.0003051259,0.00011945566,0.00028805571,0.000603971,0.00017790284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015343999,0.0002899629,0.00028046066,0.00023225383,0.0002594048,0.0006686439,0.0032351045,0.00017243228,0.000022458635],"category_scores_gemma":[0.0000900791,0.00023284538,0.0002088417,0.0006211995,0.00013593643,0.0006902194,0.0012047545,0.0009631105,0.000101253296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084186235,0.004703726,0.0149316415,0.0011210674,0.0011881969,0.00036753554,0.02764535,0.07411933,0.30310592,0.5377501,0.014226965,0.019998275],"study_design_scores_gemma":[0.0015566661,0.00034936258,0.007018922,0.0008255695,0.000016683345,0.00007830678,0.00020394393,0.9192928,0.06184926,0.0063084452,0.0020719029,0.00042811464],"about_ca_topic_score_codex":0.000108771834,"about_ca_topic_score_gemma":3.7654493e-7,"teacher_disagreement_score":0.8451735,"about_ca_system_score_codex":0.0004467871,"about_ca_system_score_gemma":0.00017673349,"threshold_uncertainty_score":0.94951594},"labels":[],"label_agreement":null},{"id":"W4399261794","doi":"10.1609/socs.v17i1.31585","title":"Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics (Extended Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Integer programming; Mathematical optimization; Path (computing); Computer science; Motion planning; Integer (computer science); Robot; Mathematics; Artificial intelligence; Programming language","score_opus":0.02431336114452302,"score_gpt":0.2809545925918165,"score_spread":0.25664123144729345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399261794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3425655,0.00017109538,0.6158391,0.00728519,0.02395051,0.0037503943,0.00012132047,0.0011264627,0.0051904013],"genre_scores_gemma":[0.94342715,0.0000023390305,0.05554388,0.00003724538,0.00043012062,0.000096412274,0.000009214162,0.000044004664,0.00040964887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710125,0.000013770102,0.00042334784,0.00064469635,0.0013560198,0.00046088902],"domain_scores_gemma":[0.99828243,0.00045626095,0.00018279778,0.00019782079,0.000763457,0.00011723596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009940915,0.0002764962,0.00026122446,0.00017302614,0.00020931482,0.0007545582,0.0019729761,0.000110894194,0.000004063919],"category_scores_gemma":[0.00023349235,0.00019890117,0.0001544113,0.00041055778,0.00011112746,0.00027394647,0.00049065845,0.0005483614,0.000019810073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020350139,0.0039113313,0.00200641,0.0013642409,0.0014310059,0.00015193014,0.009068777,0.27710018,0.10053451,0.5707645,0.008278945,0.023353187],"study_design_scores_gemma":[0.0015876584,0.00073869876,0.00077457493,0.0010764094,0.00003078638,0.000049324237,0.0000635547,0.9642878,0.028121172,0.0008410758,0.0020778482,0.00035109153],"about_ca_topic_score_codex":0.000024986324,"about_ca_topic_score_gemma":6.665765e-8,"teacher_disagreement_score":0.6871876,"about_ca_system_score_codex":0.00030363124,"about_ca_system_score_gemma":0.00018143641,"threshold_uncertainty_score":0.8110954},"labels":[],"label_agreement":null},{"id":"W4399317118","doi":"10.48550/arxiv.2405.20502","title":"Reach-Avoid Control Synthesis for a Quadrotor UAV with Formal Safety Guarantees","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Control (management); Computer science; Formal synthesis; Aeronautics; Engineering; Artificial intelligence","score_opus":0.04359819816657217,"score_gpt":0.1833867908357771,"score_spread":0.13978859266920493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399317118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012270896,0.00012318927,0.98271257,0.00035034784,0.0007994384,0.0012927523,0.00022800383,0.0007193071,0.001503526],"genre_scores_gemma":[0.9513904,0.000032039123,0.046747603,0.00010463927,0.00018022761,0.00003152518,0.00001657062,0.000053922096,0.0014430942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99695605,0.00015494134,0.00033561737,0.0016030124,0.00018640228,0.0007639491],"domain_scores_gemma":[0.99702615,0.0007340419,0.0003039334,0.0015022854,0.00021803749,0.00021556481],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069139915,0.0005525876,0.0007016157,0.00038754224,0.00028048852,0.0002510324,0.002280293,0.00037209812,0.00000894882],"category_scores_gemma":[0.000114644725,0.00052233075,0.00037183234,0.0005070414,0.0001518392,0.0004262688,0.0013292511,0.0007819588,0.000104373474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009731682,0.000231614,0.0009096107,0.0011960895,0.001305849,0.0022707735,0.00105939,0.7817392,0.000066090164,0.20425557,0.0012063143,0.004786354],"study_design_scores_gemma":[0.0010477745,0.00020266428,0.00037089782,0.0007319475,0.00035388046,0.000043029886,0.00008107763,0.98261565,0.00012631461,0.013074215,0.0006125452,0.0007400098],"about_ca_topic_score_codex":0.00014516589,"about_ca_topic_score_gemma":0.000017968123,"teacher_disagreement_score":0.93911946,"about_ca_system_score_codex":0.00032857156,"about_ca_system_score_gemma":0.00049251254,"threshold_uncertainty_score":0.99972284},"labels":[],"label_agreement":null},{"id":"W4399368926","doi":"10.21428/d82e957c.ba48518e","title":"SLVVA: Scalable Land Viability via Vision-Language Architecture","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Architecture; Computer science; Scalability; Computer architecture; Geography; Archaeology; Operating system","score_opus":0.006263177157044097,"score_gpt":0.2598914936039353,"score_spread":0.2536283164468912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399368926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063200053,0.00042760288,0.9790277,0.0018237674,0.0008530893,0.00014285785,0.0000033150254,0.0010502379,0.010351465],"genre_scores_gemma":[0.3247759,0.000002346911,0.6699672,0.00045707152,0.00022999407,0.000014158635,0.0000067015853,0.000019842959,0.004526818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985012,0.000072614275,0.00018571228,0.0005816394,0.00031856206,0.00034031668],"domain_scores_gemma":[0.99885285,0.00024752304,0.000018329982,0.0007207547,0.000027848819,0.00013270683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004623063,0.00015839483,0.00015943669,0.00011644977,0.00008433306,0.00035304343,0.00068483705,0.00007684601,0.00010937647],"category_scores_gemma":[0.000050961793,0.00011490028,0.00007151421,0.000538352,0.000046365498,0.00029698454,0.00027410235,0.00026706117,0.0006281342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009647853,0.00022936868,0.0032931252,0.00033670024,0.00010391547,0.0013171885,0.0067410376,0.017941928,0.0061612395,0.006788826,0.01838324,0.93869376],"study_design_scores_gemma":[0.00013808056,0.00009389156,0.0036303957,0.000066816676,0.000006642432,0.0001646147,0.00001371607,0.98415214,0.0010374138,0.0060574096,0.0043704533,0.00026841514],"about_ca_topic_score_codex":0.00013910196,"about_ca_topic_score_gemma":0.000005959623,"teacher_disagreement_score":0.96621025,"about_ca_system_score_codex":0.000042647298,"about_ca_system_score_gemma":0.00006283063,"threshold_uncertainty_score":0.80736035},"labels":[],"label_agreement":null},{"id":"W4399376544","doi":"10.1061/jaeeez.aseng-5564","title":"Obstacle-Avoidance Safety-Guaranteed Rendezvous Control with a Velocity Safety Corridor and Prescribed Performance","year":2024,"lang":"en","type":"article","venue":"Journal of Aerospace Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Obstacle avoidance; Rendezvous; Collision avoidance; Obstacle; Computer science; Control theory (sociology); Control (management); Aeronautics; Vehicle safety; Engineering; Automotive engineering; Aerospace engineering; Computer security; Spacecraft; Robot; Mobile robot; Geography; Collision; Artificial intelligence","score_opus":0.004206870774862786,"score_gpt":0.17970925521223285,"score_spread":0.17550238443737007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399376544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08676335,0.002637843,0.90852994,0.00068433414,0.0010119407,0.00013035207,0.0000060585858,0.00017921442,0.000056972993],"genre_scores_gemma":[0.8260157,0.00013481216,0.17343284,0.000048317488,0.0001857067,0.0000027101078,3.37183e-7,0.000028259701,0.00015130916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841046,0.000033425615,0.0004384487,0.00027220658,0.0004633022,0.00038214118],"domain_scores_gemma":[0.9989696,0.00028354107,0.0001729619,0.000269859,0.00012425322,0.00017975428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006887062,0.0002451753,0.000405286,0.00016883595,0.00009730792,0.000255134,0.0004702791,0.00007095334,0.0000018919477],"category_scores_gemma":[0.00011162765,0.00019386543,0.00007023081,0.000427961,0.000039819803,0.0008360884,0.0000674423,0.0006277145,0.0000039709403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031257127,0.000044118362,0.0018292663,0.0004086994,0.0003336635,0.0011535998,0.003516026,0.96836567,0.005513751,0.00094129576,0.00055865134,0.01702269],"study_design_scores_gemma":[0.0011807359,0.00040173845,0.014450476,0.0011095672,0.000037163234,0.0024355964,0.000028860337,0.97758275,0.00071192445,0.0000061507617,0.001788679,0.00026634987],"about_ca_topic_score_codex":0.0000045974907,"about_ca_topic_score_gemma":5.5329457e-7,"teacher_disagreement_score":0.7392523,"about_ca_system_score_codex":0.00014695233,"about_ca_system_score_gemma":0.00014411575,"threshold_uncertainty_score":0.7905603},"labels":[],"label_agreement":null},{"id":"W4399382613","doi":"10.1139/dsa-2023-0093","title":"A systematic review on metaheuristic approaches for autonomous path planning of unmanned aerial vehicles","year":2024,"lang":"en","type":"review","venue":"Drone Systems and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Metaheuristic; Path (computing); Computer science; Artificial intelligence; Aeronautics; Systems engineering; Engineering; Robot","score_opus":0.09434154556006423,"score_gpt":0.33200703959001543,"score_spread":0.23766549402995119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399382613","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9801082e-8,0.74136394,0.2518546,0.000020035635,0.0001938525,0.0061994805,0.00015347634,0.00012380815,0.00009079705],"genre_scores_gemma":[0.000021289672,0.97311866,0.008920382,0.000014892316,0.00027253546,0.01710271,0.00014562397,0.000059188926,0.00034471162],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99705935,0.00022046604,0.0014287189,0.000758975,0.0002632885,0.00026920997],"domain_scores_gemma":[0.99720526,0.0005716347,0.0009922514,0.0010651823,0.000060276918,0.000105415915],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010300308,0.0004476786,0.0028799514,0.00020627149,0.00013715844,0.00018255679,0.00081336655,0.00017649689,1.5057859e-7],"category_scores_gemma":[0.000082224346,0.00031654793,0.0003452301,0.0004809043,0.000046782934,0.00006348251,0.0001415726,0.0002103532,0.000034754667],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.8359995e-7,0.00003329243,1.2870826e-8,0.93394,0.00023013014,0.0000027486617,0.00006232215,0.0000674849,6.7999e-8,0.047220595,0.00041985253,0.018023195],"study_design_scores_gemma":[0.00010644511,0.00010340684,8.5566256e-8,0.82614636,0.0044526774,0.00015197192,0.000028576927,0.034065437,1.4389352e-7,0.0002809302,0.13417909,0.00048486213],"about_ca_topic_score_codex":0.000009045994,"about_ca_topic_score_gemma":3.833e-8,"teacher_disagreement_score":0.24293423,"about_ca_system_score_codex":0.00007262997,"about_ca_system_score_gemma":0.00018838112,"threshold_uncertainty_score":0.99992865},"labels":[],"label_agreement":null},{"id":"W4399656344","doi":"","title":"An Optimal Transport Based hr-Adaptive Mesh Pursuit","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.015178011381848952,"score_gpt":0.23596217219307256,"score_spread":0.2207841608112236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399656344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068754507,0.00045590446,0.9699072,0.0072289957,0.00031786942,0.00022106747,0.000023035678,0.0010009537,0.013969509],"genre_scores_gemma":[0.4980041,0.000015126553,0.5000562,0.00012523001,0.000022184393,0.000029473664,0.00006887467,0.000026001982,0.0016528115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957763,0.0019521909,0.00036022335,0.0008695914,0.0006019352,0.000439754],"domain_scores_gemma":[0.9961757,0.000899207,0.00010400031,0.0018543696,0.0006922811,0.0002744413],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0044555343,0.00025532537,0.00023222697,0.00023241258,0.00028647564,0.00065651233,0.0023059244,0.0001259733,0.00008029109],"category_scores_gemma":[0.00017776838,0.00026083845,0.00014389356,0.0008872558,0.00015790078,0.00076150673,0.00016597529,0.00035776146,0.00012049114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030354386,0.0013347903,0.0012217169,0.00014749373,0.00016403574,0.00041261158,0.033292495,0.00905893,0.0088801775,0.5967432,0.0029611008,0.34575304],"study_design_scores_gemma":[0.00026400996,0.000001989661,0.0022848498,0.000484906,0.000018887866,0.000025485086,0.000052122854,0.9695176,0.021018757,0.0006615077,0.005340748,0.0003291219],"about_ca_topic_score_codex":0.00017670846,"about_ca_topic_score_gemma":0.00005321268,"teacher_disagreement_score":0.9604587,"about_ca_system_score_codex":0.00009032095,"about_ca_system_score_gemma":0.0003791406,"threshold_uncertainty_score":0.9999844},"labels":[],"label_agreement":null},{"id":"W4399781743","doi":"10.20944/preprints202406.1146.v1","title":"Three-Dimensional Unmanned Aerial Vehicle Path Planning in Simulated Rugged Mountainous Terrain Using Improved Enhanced Snake Optimizer (IESO)","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Terrain; Motion planning; Computer science; Path (computing); Aerospace engineering; Environmental science; Marine engineering; Remote sensing; Simulation; Engineering; Artificial intelligence; Geography; Cartography; Robot","score_opus":0.08110520473889593,"score_gpt":0.33554125268215157,"score_spread":0.25443604794325564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399781743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9021307,0.00024823812,0.09074433,0.00024307771,0.0039136293,0.0013823053,0.000033688717,0.0010233122,0.00028070915],"genre_scores_gemma":[0.9239715,0.000003152458,0.07501614,0.00017519602,0.0003990311,0.00010430808,0.00008147258,0.00012522166,0.00012399149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99272716,0.0003786593,0.0014948146,0.0030737866,0.0009603534,0.0013652294],"domain_scores_gemma":[0.9958475,0.00033998914,0.00064904464,0.002580546,0.0002512903,0.0003316325],"candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019211288,0.0010461272,0.0012367005,0.00069789705,0.00025497712,0.0003091304,0.0026084161,0.00094679696,0.000079344965],"category_scores_gemma":[0.00044368563,0.0011118179,0.0003586862,0.0007803603,0.000160447,0.00033102973,0.009731173,0.002921914,0.00038348415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018595599,0.00018853795,0.0056843203,0.0001687406,0.00019220503,0.00059099216,0.003755478,0.9177259,0.07094542,0.000060214403,0.000018953944,0.00048326945],"study_design_scores_gemma":[0.0014990657,0.000059909038,0.013186494,0.0013495294,0.000057768102,0.00003841228,0.000048109447,0.96523994,0.0129165575,0.004462703,0.000015169785,0.0011263225],"about_ca_topic_score_codex":0.0016112688,"about_ca_topic_score_gemma":0.000018895884,"teacher_disagreement_score":0.058028862,"about_ca_system_score_codex":0.00087887474,"about_ca_system_score_gemma":0.0009893656,"threshold_uncertainty_score":0.9993784},"labels":[],"label_agreement":null},{"id":"W4399801592","doi":"10.1109/icuas60882.2024.10556903","title":"ROS2-Gazebo Simulator for Drone Applications","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Drone; Computer science; Simulation","score_opus":0.015871620025108592,"score_gpt":0.2951241903622631,"score_spread":0.2792525703371545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399801592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035352354,0.0002582811,0.99446684,0.0014298224,0.0003628406,0.00034638002,0.0000056362014,0.0008482569,0.002246571],"genre_scores_gemma":[0.0628299,0.0000022223178,0.9287667,0.00030070223,0.00022591556,0.00027836836,0.0000065250756,0.000013524908,0.00757612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923074,0.0000074305376,0.00012492716,0.00032536912,0.00011969893,0.00019183835],"domain_scores_gemma":[0.99926794,0.00023030063,0.000013146915,0.00038909516,0.000030695966,0.000068822126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015438722,0.00007802627,0.00008010171,0.000066842345,0.00007600713,0.00021178786,0.0004634145,0.00003761972,0.000009027376],"category_scores_gemma":[0.000014260862,0.000066843946,0.000050175615,0.0002773067,0.000015168042,0.00020590304,0.000079755926,0.000057579524,0.00031320774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012997987,0.00006113544,0.000046799873,0.00009075979,0.000045902154,0.000012579227,0.00041580026,0.020614099,0.0008741284,0.7715461,0.03152548,0.17476587],"study_design_scores_gemma":[0.00006904261,0.000022896831,0.000039137303,0.000009985651,0.000004010241,0.000006361404,0.0000045091388,0.8904925,0.0004186773,0.007962309,0.10087891,0.00009167586],"about_ca_topic_score_codex":0.0000044317876,"about_ca_topic_score_gemma":2.47821e-7,"teacher_disagreement_score":0.8698784,"about_ca_system_score_codex":0.000027228203,"about_ca_system_score_gemma":0.000059620867,"threshold_uncertainty_score":0.4025756},"labels":[],"label_agreement":null},{"id":"W4400013623","doi":"10.18280/jesa.570315","title":"Comparative Analysis of Spiral Dynamic Algorithm and Artificial Bee Colony Optimization for Position Control of Flexible Link Manipulators","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Link (geometry); Position (finance); Spiral (railway); Artificial bee colony algorithm; Computer science; Artificial intelligence; Control theory (sociology); Algorithm; Engineering; Control (management); Mechanical engineering","score_opus":0.025058612931014364,"score_gpt":0.2970167001828719,"score_spread":0.27195808725185755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400013623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02544109,0.001336694,0.972105,0.00014727692,0.00042951235,0.00029465035,0.00007799915,0.00012822756,0.000039517105],"genre_scores_gemma":[0.5750389,0.000016470405,0.42479733,0.000015299947,0.000057306763,0.000008310819,0.00001188641,0.000011342306,0.00004316318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980518,0.0002051514,0.00082408363,0.0002948887,0.00037288002,0.00025116565],"domain_scores_gemma":[0.9985581,0.00031755384,0.0005027933,0.0001751278,0.00034055286,0.000105876265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006618088,0.00018806318,0.00065585645,0.00081418926,0.00016523404,0.00028714404,0.00030897392,0.00008338196,0.000010838261],"category_scores_gemma":[0.000045786757,0.00016968975,0.00021944173,0.0011957043,0.00011451272,0.0005425342,0.00005442561,0.00015775992,0.0000022954273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002458097,0.000070789385,0.00021625914,0.00017851785,0.0017707122,0.00005901263,0.0023077822,0.79173124,0.0015943508,0.004952204,0.00008045619,0.19701412],"study_design_scores_gemma":[0.00028331683,0.00043028928,0.04616344,0.0002452052,0.000694981,0.00016081367,0.000058359994,0.95016086,0.00030379684,0.0013476969,0.0000050081535,0.0001462337],"about_ca_topic_score_codex":0.000016466372,"about_ca_topic_score_gemma":0.0000012440498,"teacher_disagreement_score":0.5495978,"about_ca_system_score_codex":0.00012787494,"about_ca_system_score_gemma":0.000114613846,"threshold_uncertainty_score":0.6919747},"labels":[],"label_agreement":null},{"id":"W4400073990","doi":"10.1109/tvt.2024.3420159","title":"Hierarchical Trajectory Planning Based on Adaptive Motion Primitives and Bilevel Corridor","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Motion planning; Motion (physics); Bilevel optimization; Artificial intelligence; Algorithm; Physics","score_opus":0.021758685065468693,"score_gpt":0.25407009470081426,"score_spread":0.23231140963534558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400073990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012923013,0.0001994315,0.9824923,0.0017544626,0.0007668781,0.0002491385,0.00001779016,0.001488671,0.00010828032],"genre_scores_gemma":[0.9064166,0.000007686894,0.09321425,0.0001742633,0.00002655103,0.000083927254,0.0000017609256,0.000023661463,0.000051301875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983152,0.00010046662,0.00021495983,0.0007467103,0.00027803803,0.00034461395],"domain_scores_gemma":[0.99908245,0.00029522926,0.00003685475,0.00045748684,0.00004016972,0.00008781393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002020164,0.00024654748,0.00022760249,0.0011052482,0.0002212963,0.00008145543,0.0003756154,0.00032612673,0.000005632933],"category_scores_gemma":[0.000015075939,0.00022994642,0.00008704923,0.00080877834,0.0002340463,0.00018518827,0.000004844266,0.00092684315,0.000044702858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005645707,0.00035041285,0.000041099764,0.000057607904,0.0001258966,0.0009495069,0.00062776834,0.46528056,0.0054775537,0.006739344,0.00008536655,0.5202084],"study_design_scores_gemma":[0.00032706055,0.00068777543,0.00055588916,0.00028084582,0.00002512864,0.00014200697,0.000039284576,0.9802588,0.015894933,0.0013491281,0.00018916039,0.00024995976],"about_ca_topic_score_codex":0.0000050099334,"about_ca_topic_score_gemma":6.1286823e-7,"teacher_disagreement_score":0.8934936,"about_ca_system_score_codex":0.000104693594,"about_ca_system_score_gemma":0.00009982774,"threshold_uncertainty_score":0.9376943},"labels":[],"label_agreement":null},{"id":"W4400141077","doi":"10.1063/5.0219735","title":"A comprehensive study of path planning algorithms for autonomous robots","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Rheumatology Association","funders":"","keywords":"Motion planning; Computer science; Robot; Path (computing); Mobile robot; Artificial intelligence; Algorithm; Computer network","score_opus":0.07238661359973768,"score_gpt":0.3188400720410928,"score_spread":0.24645345844135513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400141077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061831392,0.0004139433,0.9342091,0.00031598564,0.0007950958,0.001038345,0.000009907408,0.0007065486,0.0006796683],"genre_scores_gemma":[0.83751607,0.0000043312007,0.16193955,0.00006335422,0.00010476767,0.00017400936,0.000003365327,0.000025206762,0.00016932102],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977043,0.000015537446,0.0004940211,0.00085507473,0.00042991297,0.000501111],"domain_scores_gemma":[0.9985746,0.00023601267,0.00016994626,0.00026617883,0.000616275,0.00013699791],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002780631,0.00030778724,0.00045536432,0.0002699983,0.00014633754,0.0005046782,0.0011149065,0.00010579904,0.0000037929624],"category_scores_gemma":[0.000073030846,0.0002863461,0.000090232796,0.0006028016,0.000064851054,0.0007460387,0.00031710468,0.00027367639,0.000017007267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016605598,0.002970585,0.030989325,0.004206935,0.001600948,0.00077523256,0.45360953,0.0054920204,0.04305939,0.12776223,0.018142229,0.31122553],"study_design_scores_gemma":[0.00060293195,0.0013420231,0.0043921545,0.00042318142,0.00004030845,0.000061465944,0.0032158664,0.9857234,0.00030794326,0.0025973246,0.000925478,0.0003679199],"about_ca_topic_score_codex":0.000045557936,"about_ca_topic_score_gemma":1.7822163e-7,"teacher_disagreement_score":0.9802314,"about_ca_system_score_codex":0.00006683318,"about_ca_system_score_gemma":0.00024033256,"threshold_uncertainty_score":0.9999589},"labels":[],"label_agreement":null},{"id":"W4400218313","doi":"10.1007/978-981-97-4393-3_19","title":"Safety Zone and Its Utilization in Collision Avoidance Control of Industrial Robot","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Collision avoidance; Control (management); Robot; Collision; Computer science; Simulation; Real-time computing; Environmental science; Computer security; Artificial intelligence","score_opus":0.07538811579882493,"score_gpt":0.30358851325907543,"score_spread":0.2282003974602505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400218313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010900059,0.002096277,0.9718201,0.00093981455,0.0004990086,0.000642533,0.00003700257,0.00006553363,0.023790743],"genre_scores_gemma":[0.6537418,0.01310506,0.32969967,0.000823605,0.0001272169,0.000069894166,0.00014223279,0.000034179582,0.0022563748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981212,0.000047008103,0.0009109815,0.0002968313,0.000435521,0.0001884601],"domain_scores_gemma":[0.997908,0.00035181013,0.00035826786,0.0010207226,0.00028598684,0.00007523892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016060851,0.00019204846,0.0003290063,0.0011666572,0.00018212118,0.00028635585,0.0017386351,0.00018172091,0.0000014073973],"category_scores_gemma":[0.00009456289,0.00019379256,0.000025373227,0.0007512138,0.00042827794,0.003829079,0.0013992935,0.0005055548,0.00001368815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001033292,0.000023166145,0.0001439819,0.000066816894,0.000007582168,0.0000015570783,0.0026910617,0.018194422,0.000010413999,0.7747306,0.00004692784,0.2040731],"study_design_scores_gemma":[0.00060105126,0.000056341712,0.00085304066,0.0008041025,0.000004988137,0.000017130573,0.000011680239,0.9876928,0.000016777796,0.0024096912,0.0073455335,0.00018689415],"about_ca_topic_score_codex":0.000012280016,"about_ca_topic_score_gemma":0.000004969775,"teacher_disagreement_score":0.96949834,"about_ca_system_score_codex":0.00013555292,"about_ca_system_score_gemma":0.00029651605,"threshold_uncertainty_score":0.7902631},"labels":[],"label_agreement":null},{"id":"W4400269320","doi":"10.1139/dsa-2023-0105","title":"Three-dimensional path planning and collision-free flight control for drone-assisted autonomous pollination systems","year":2024,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Office of Research and Engagement, University of Tennessee, Knoxville","keywords":"Drone; Free flight; Aerospace engineering; Motion planning; Collision; Aeronautics; Path (computing); Control (management); Computer science; Engineering; Artificial intelligence; Computer security; Biology; Robot; Air traffic control; Computer network","score_opus":0.017448476044640802,"score_gpt":0.2595420732688832,"score_spread":0.24209359722424237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400269320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012335947,0.011619268,0.9832867,0.00074076146,0.0006220394,0.0017820224,0.00021070043,0.00037073338,0.00013423113],"genre_scores_gemma":[0.9614418,0.0000081153585,0.034982372,0.000044043783,0.00046118227,0.002424027,0.00007652658,0.00003447241,0.00052744074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983158,0.000042629854,0.00044050175,0.00064315315,0.00027351035,0.00028437458],"domain_scores_gemma":[0.99859434,0.0004576909,0.0001399777,0.00052779046,0.00012622795,0.00015399537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005680404,0.00020611293,0.00031297782,0.00016547022,0.00045338037,0.00065110635,0.00033701793,0.00012659791,3.5039884e-7],"category_scores_gemma":[0.000023684004,0.00018253952,0.000043785705,0.0003078749,0.000053884298,0.00027196453,0.00010434264,0.00012110518,0.000010569293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017951063,0.0001667815,0.00056467176,0.0012400247,0.00025771253,0.000046126286,0.0007150047,0.07353059,0.0021958873,0.8903431,0.013915268,0.017006846],"study_design_scores_gemma":[0.0006189387,0.00007153373,0.0016511036,0.00026985505,0.0000382131,0.00018292296,0.00004527318,0.97931826,0.000010680768,0.001143945,0.016423862,0.00022538632],"about_ca_topic_score_codex":0.00017747334,"about_ca_topic_score_gemma":0.0000021874512,"teacher_disagreement_score":0.96020824,"about_ca_system_score_codex":0.00008096438,"about_ca_system_score_gemma":0.000111516645,"threshold_uncertainty_score":0.7443746},"labels":[],"label_agreement":null},{"id":"W4400777353","doi":"10.1002/rob.22395","title":"Autonomous multiple‐trolley collection system with nonholonomic robots: Design, control, and implementation","year":2024,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonholonomic system; Control engineering; Robot; Computer science; Control (management); Engineering; Control theory (sociology); Mobile robot; Artificial intelligence","score_opus":0.013400756032582815,"score_gpt":0.25472001833846664,"score_spread":0.24131926230588382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400777353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018666647,0.00060981413,0.9941538,0.0019456526,0.001090851,0.00020326403,9.96318e-7,0.0000795493,0.000049419414],"genre_scores_gemma":[0.5858277,0.00002784518,0.41385564,0.00008781684,0.00014989777,0.00000324404,3.1735925e-7,0.000009776747,0.000037723457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883574,0.00009170048,0.00045773148,0.00018942315,0.00022365316,0.00020176124],"domain_scores_gemma":[0.9987689,0.00058502285,0.00025114237,0.00015999484,0.00013132456,0.00010356501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000592527,0.00014104553,0.00027532902,0.0002377341,0.00011423431,0.00031474026,0.00025632812,0.00007912478,0.0000015695875],"category_scores_gemma":[0.00003869086,0.00010818424,0.00005779475,0.00023442313,0.000017840079,0.000474235,0.000033686207,0.00027427418,0.0000041294893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012082443,0.00003405332,0.001888492,0.00011853646,0.00028036462,0.00067191594,0.001081545,0.9788119,0.0007802971,0.0022424425,0.002752224,0.011217404],"study_design_scores_gemma":[0.0014262481,0.0012230286,0.0014881268,0.00022859496,0.00008142677,0.001993598,0.00019142212,0.9926396,0.00046348298,0.00006125129,0.000056877565,0.00014635072],"about_ca_topic_score_codex":0.000026451786,"about_ca_topic_score_gemma":0.0000042042893,"teacher_disagreement_score":0.58396107,"about_ca_system_score_codex":0.00016678525,"about_ca_system_score_gemma":0.0002615539,"threshold_uncertainty_score":0.44116253},"labels":[],"label_agreement":null},{"id":"W4400810875","doi":"10.1109/isie54533.2024.10595749","title":"Vision-Based Dexterous Motion Planning by Dynamic Movement Primitives with Human Hand Demonstration","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Nova","keywords":"Movement (music); Computer science; Computer vision; Motion (physics); Human motion; Artificial intelligence; Motion planning; Human–computer interaction; Motion capture; Robot; Aesthetics","score_opus":0.009698261669165101,"score_gpt":0.27509763342874055,"score_spread":0.26539937175957545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400810875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04453111,0.00016957932,0.95327336,0.00043147698,0.00017956347,0.00018234966,0.0000032444925,0.0004498567,0.00077945855],"genre_scores_gemma":[0.81124645,5.1608146e-7,0.18812433,0.0001866382,0.000015987653,0.000017571656,0.0000324839,0.000011913636,0.0003641303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987076,0.000049663828,0.00019125419,0.0004748253,0.00034079817,0.0002358859],"domain_scores_gemma":[0.9994994,0.00008366145,0.000046258512,0.00026012422,0.00003875223,0.000071794595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020668132,0.000161145,0.0001120599,0.00013465862,0.00022567238,0.00069293927,0.0002654562,0.000051650837,0.000007160533],"category_scores_gemma":[0.0000061977366,0.00012601212,0.000027927674,0.00025116082,0.000058392056,0.00050942216,0.000040560142,0.0001282074,0.00001992699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043139135,0.000867647,0.0082407445,0.00042005314,0.00029498982,0.001126404,0.006566962,0.3397913,0.31226495,0.021846404,0.006896537,0.30164087],"study_design_scores_gemma":[0.0002339757,0.00033475374,0.006083069,0.00026334773,0.000008514678,0.000011145804,0.00003293136,0.9847247,0.007623988,0.0004615306,0.000034676526,0.00018736576],"about_ca_topic_score_codex":0.000026366593,"about_ca_topic_score_gemma":0.0000040353043,"teacher_disagreement_score":0.7667153,"about_ca_system_score_codex":0.00009690628,"about_ca_system_score_gemma":0.00006591527,"threshold_uncertainty_score":0.66820294},"labels":[],"label_agreement":null},{"id":"W4400884904","doi":"10.23977/jaip.2024.070223","title":"Research on Path Planning Algorithm Based on Fast Target Detection","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Path (computing); Computer science; Motion planning; Algorithm; Artificial intelligence; Computer network; Robot","score_opus":0.12485595220064316,"score_gpt":0.4259096461640093,"score_spread":0.3010536939633661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400884904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028828825,0.00025682687,0.9890324,0.0041165566,0.0037932878,0.00013459919,0.0000026802247,0.0001056108,0.0022697505],"genre_scores_gemma":[0.41061524,0.000027724376,0.5870815,0.00064445165,0.0015051437,0.000008056917,9.2026613e-7,0.00003464272,0.000082318744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543256,0.00088424265,0.00080576463,0.00047835926,0.0018767572,0.0005223383],"domain_scores_gemma":[0.9928124,0.005278348,0.0003700979,0.00046065223,0.00085071934,0.00022776355],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007920823,0.00021525216,0.0002552039,0.0011413022,0.0003482639,0.0010525963,0.0009896241,0.00014319285,0.00002286052],"category_scores_gemma":[0.0027346115,0.00018250516,0.00014669506,0.001733248,0.00009031045,0.0016823423,0.00009153313,0.0022098962,0.00049619406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016747443,0.00031063418,0.0000020810073,0.00001736221,0.000040680316,0.0025003108,0.0013514878,0.29621834,0.0008371509,0.0040096105,0.0007918511,0.693753],"study_design_scores_gemma":[0.00002967381,0.0020674684,0.000015644926,0.0005173519,0.000015867186,0.00038706372,0.0012551787,0.9543221,0.02762691,0.0051964517,0.008385512,0.00018078633],"about_ca_topic_score_codex":0.00002290725,"about_ca_topic_score_gemma":2.4661293e-7,"teacher_disagreement_score":0.6935722,"about_ca_system_score_codex":0.00031013292,"about_ca_system_score_gemma":0.00045084677,"threshold_uncertainty_score":0.9999844},"labels":[],"label_agreement":null},{"id":"W4400946110","doi":"10.23919/ecc64448.2024.10590912","title":"Rigid Formation Geometry Based Station Keeping by Fixed Speed Vehicles without Self-Location Information","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Geometry; Fixed point; Mathematics","score_opus":0.01089694574171463,"score_gpt":0.2450165307323983,"score_spread":0.23411958499068367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400946110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0113835335,0.00007525252,0.98395354,0.0009376799,0.00048514377,0.00027307926,0.000008018288,0.0014926693,0.0013910986],"genre_scores_gemma":[0.6361235,0.000006064868,0.36265105,0.0006964723,0.000052512405,0.000017368957,0.00032121205,0.000010969001,0.00012085464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985586,0.000067901514,0.00041684252,0.00022616603,0.00048146848,0.00024903865],"domain_scores_gemma":[0.99917674,0.00013970138,0.00012485453,0.00029939009,0.00018285532,0.000076474964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058701646,0.00015221963,0.00012097975,0.0004257583,0.00014216383,0.00080539886,0.0003279597,0.00008592039,0.00000844107],"category_scores_gemma":[0.0000794909,0.00014476024,0.000035935234,0.0010877728,0.000013448357,0.0059038834,0.000047426645,0.00014199647,0.00041192747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004188292,0.00029058164,0.0038675272,0.0022961022,0.00019710262,0.000020569129,0.01971784,0.22071704,0.0099563,0.034775577,0.09871848,0.609401],"study_design_scores_gemma":[0.00025408226,0.00005842106,0.0013121066,0.00009898682,0.000009538803,0.000008741755,0.000059504484,0.9893868,0.005027966,0.00022675081,0.0033679183,0.0001892021],"about_ca_topic_score_codex":0.000063689404,"about_ca_topic_score_gemma":7.681884e-7,"teacher_disagreement_score":0.7686697,"about_ca_system_score_codex":0.00020205036,"about_ca_system_score_gemma":0.0001324851,"threshold_uncertainty_score":0.7766479},"labels":[],"label_agreement":null},{"id":"W4400971954","doi":"10.2316/j.2024.206-1164","title":"GLOBAL PATH PLANNING OF CLIMBING ROBOT FOR WELD QUALITY INSPECTION IN LARGE SCALE STORAGE TANKS","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Scale (ratio); Storage tank; Path (computing); Robot; Quality (philosophy); Climbing; Welding; Computer science; Marine engineering; Engineering; Artificial intelligence; Mechanical engineering; Structural engineering; Geography; Computer network","score_opus":0.023030506741832414,"score_gpt":0.34008021347227924,"score_spread":0.3170497067304468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400971954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071226835,0.00038042935,0.92509836,0.0007782502,0.0023251928,0.00006927974,0.000015881473,0.00004402872,0.00006172936],"genre_scores_gemma":[0.7902973,0.000017413726,0.20950358,0.000029240719,0.00013417285,0.0000012441968,0.0000050616345,0.00000417723,0.000007801043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986603,0.00005286734,0.000599992,0.00014943133,0.00041297392,0.00012443062],"domain_scores_gemma":[0.99910975,0.00016072903,0.00034761967,0.00007770011,0.00026256935,0.000041637828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010578602,0.000087261455,0.00018042972,0.00024800576,0.000038983024,0.00017116865,0.00029479712,0.00006181657,7.9060425e-7],"category_scores_gemma":[0.00012462889,0.00008231495,0.00007311123,0.00020738685,0.000017930317,0.0006378259,0.00006708938,0.00012421876,5.6065204e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003229546,0.00014550351,0.022758089,0.00011101065,0.000093426235,0.00008437062,0.0022085574,0.92254657,0.0006661913,0.031118747,0.00026052623,0.019974736],"study_design_scores_gemma":[0.00046420572,0.00009824953,0.09238156,0.0005193784,0.000008469244,0.00010960413,0.000106518324,0.9021424,0.00007737526,0.003964973,0.000049900398,0.000077331766],"about_ca_topic_score_codex":0.000012799104,"about_ca_topic_score_gemma":0.0000030114334,"teacher_disagreement_score":0.7190705,"about_ca_system_score_codex":0.00017419498,"about_ca_system_score_gemma":0.00008929506,"threshold_uncertainty_score":0.33567065},"labels":[],"label_agreement":null},{"id":"W4400971970","doi":"10.2316/j.2024.206-1098","title":"A NOVEL PATH PLANNING FOR AUV BASED ON DUNG BEETLE OPTIMISATION ALGORITHM WITH DEEP Q-NETWORK","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Path (computing); Computer science; Dung beetle; Artificial intelligence; Algorithm; Ecology; Computer network; Robot; Biology","score_opus":0.016568595776252967,"score_gpt":0.2732823468562783,"score_spread":0.2567137510800253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400971970","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004499623,0.00016055736,0.99594873,0.0017843427,0.0014129919,0.00010611145,0.0000059658096,0.00006602243,0.00006533966],"genre_scores_gemma":[0.10676809,0.000006577665,0.8925202,0.00017964085,0.00047212714,0.0000047255367,0.000014053898,0.000012300381,0.000022321172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988161,0.000021781116,0.0003267452,0.00017687114,0.00051636354,0.00014215741],"domain_scores_gemma":[0.9989345,0.0003321894,0.00024434878,0.00008981298,0.00033575128,0.00006341823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049785804,0.000120259305,0.00013804107,0.00023795772,0.00007440777,0.0004417036,0.0003042439,0.000052132487,0.0000013458487],"category_scores_gemma":[0.000055583074,0.00009626418,0.00005683333,0.00014962238,0.000018335319,0.00047460798,0.000029202503,0.0001430124,0.0000014325336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016152499,0.000035761517,0.000059040976,0.00001397586,0.00006458496,0.000057268626,0.00025992235,0.9356388,0.00008405368,0.004192902,0.0002188258,0.059358705],"study_design_scores_gemma":[0.0005781142,0.0003354684,0.0013994685,0.0007011662,0.000021856456,0.00024286465,0.000015809053,0.99513495,0.000065507615,0.001123554,0.00026797646,0.00011326538],"about_ca_topic_score_codex":0.0000017525705,"about_ca_topic_score_gemma":1.2977618e-7,"teacher_disagreement_score":0.10631812,"about_ca_system_score_codex":0.00009466651,"about_ca_system_score_gemma":0.00012164733,"threshold_uncertainty_score":0.4259358},"labels":[],"label_agreement":null},{"id":"W4400972116","doi":"10.2316/j.2024.206-1114","title":"OVERCOMING VALUE OVERESTIMATION FOR DISTRIBUTIONAL REINFORCEMENT LEARNING-BASED PATH PLANNING WITH CONSERVATIVE CONSTRAINTS","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Reinforcement; Path (computing); Value (mathematics); Computer science; Mathematical optimization; Operations research; Artificial intelligence; Psychology; Mathematics; Machine learning; Social psychology","score_opus":0.018973155249037844,"score_gpt":0.29280428513766765,"score_spread":0.2738311298886298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400972116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004313749,0.00009496692,0.99269956,0.0020444887,0.0006258136,0.00010707351,0.0000099880135,0.00005435135,0.000050024704],"genre_scores_gemma":[0.7165943,0.0000033509373,0.2831658,0.000079816,0.000093203154,0.0000032885812,0.00004048735,0.0000055240507,0.000014263991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988307,0.000036871235,0.00036791232,0.00014301403,0.0005040467,0.000117451586],"domain_scores_gemma":[0.99860895,0.00047454904,0.0003218427,0.00005440537,0.00048422161,0.00005605715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047543697,0.000109890236,0.00013166436,0.00018486952,0.00008998268,0.00039429884,0.00021751896,0.000041729192,0.000002895329],"category_scores_gemma":[0.00019743996,0.000090758615,0.000051041443,0.00010168942,0.000057597637,0.00061778666,0.00003157845,0.00015793527,0.0000011295383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025009445,0.00001646685,0.00076964725,0.000027437776,0.00010070879,0.0000562667,0.000264661,0.93560773,0.00012997212,0.058750685,0.00016486637,0.0040865634],"study_design_scores_gemma":[0.0006717693,0.00025993,0.003430339,0.00086151715,0.000022441282,0.00022192989,0.000031271637,0.991825,0.00024226331,0.0020709129,0.00025775225,0.00010491812],"about_ca_topic_score_codex":0.0000022753547,"about_ca_topic_score_gemma":4.6611387e-8,"teacher_disagreement_score":0.7122805,"about_ca_system_score_codex":0.00014712849,"about_ca_system_score_gemma":0.000273727,"threshold_uncertainty_score":0.38022327},"labels":[],"label_agreement":null},{"id":"W4400972601","doi":"10.2316/j.2024.206-1059","title":"VISION-BASED ROBOT INDOOR-POSITIONING AND NAVIGATION METHOD RESEARCH, 1-9.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Mobile robot navigation; Robot; Human–computer interaction; Mobile robot; Robot control","score_opus":0.028240259328945232,"score_gpt":0.3889500768446982,"score_spread":0.36070981751575293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400972601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048412057,0.0005706542,0.98554707,0.0077816043,0.0010593431,0.000057483416,0.0000020031755,0.000054784567,0.00008584607],"genre_scores_gemma":[0.43769,0.000029416136,0.5620058,0.00007682196,0.0001677537,0.0000011809175,0.0000053891413,0.0000061750266,0.000017456538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829406,0.00016944594,0.00037202687,0.00018292134,0.00085501035,0.00012655075],"domain_scores_gemma":[0.99840415,0.00058575923,0.00015634007,0.00009039104,0.0006807671,0.000082584265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957561,0.00008767062,0.00011941716,0.0005426412,0.0001121139,0.00094546954,0.00032280514,0.00006654502,0.0000021983806],"category_scores_gemma":[0.00014593569,0.00007693285,0.00004079529,0.00026381257,0.000047263136,0.0009153864,0.000096742704,0.00032799516,0.0000046161113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019108882,0.000087957844,0.00060344354,0.00009199971,0.00017186666,0.00050814735,0.0017363448,0.48137394,0.0074943216,0.08609348,0.0008744088,0.420945],"study_design_scores_gemma":[0.00023875767,0.00013525327,0.0039115255,0.0007234745,0.000009834503,0.00051759405,0.000025392867,0.9821503,0.0008304122,0.011180219,0.00019968567,0.00007751],"about_ca_topic_score_codex":0.000010205455,"about_ca_topic_score_gemma":2.1496935e-7,"teacher_disagreement_score":0.5007764,"about_ca_system_score_codex":0.00009572979,"about_ca_system_score_gemma":0.00014013858,"threshold_uncertainty_score":0.9117184},"labels":[],"label_agreement":null},{"id":"W4401044108","doi":"10.1016/j.swevo.2024.101680","title":"Two-stage knowledge-assisted coevolutionary NSGA-II for bi-objective path planning of multiple unmanned aerial vehicles","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Motion planning; Path (computing); Stage (stratigraphy); Artificial intelligence; Operations research; Real-time computing; Computer network; Robot; Geology","score_opus":0.028959557971983425,"score_gpt":0.29903018347065285,"score_spread":0.27007062549866945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401044108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12525217,0.0038658243,0.86772907,0.00022590205,0.0016837134,0.00046473433,0.00011746125,0.0004098211,0.00025129577],"genre_scores_gemma":[0.80658644,0.000009964312,0.1927264,0.000018663834,0.00025768892,0.000048308197,0.00016563048,0.000018100272,0.00016877598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998223,0.00014170537,0.000432703,0.00060145225,0.0002717319,0.0003293892],"domain_scores_gemma":[0.9984785,0.00083186507,0.00014039122,0.00018183138,0.0002647399,0.000102695034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041718833,0.00023047601,0.00027437546,0.00034638494,0.00047872044,0.000076233766,0.00025321209,0.00010783696,0.0000014045714],"category_scores_gemma":[0.00010540623,0.00023457578,0.000107124084,0.00057971117,0.00012767437,0.00059943675,0.0002261604,0.00015042225,0.000009423635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000513597,0.00089793233,0.005124211,0.0011414482,0.0004704882,0.000114655166,0.0171243,0.8498588,0.006876577,0.04014927,0.019731633,0.05799706],"study_design_scores_gemma":[0.0011215593,0.00037387034,0.035884075,0.00027323456,0.000026071124,0.00005799639,0.0002060704,0.953486,0.00019942808,0.007450764,0.00066306535,0.00025785895],"about_ca_topic_score_codex":0.00005434611,"about_ca_topic_score_gemma":0.0000014785026,"teacher_disagreement_score":0.6813343,"about_ca_system_score_codex":0.00015474031,"about_ca_system_score_gemma":0.00030908853,"threshold_uncertainty_score":0.9565723},"labels":[],"label_agreement":null},{"id":"W4401052399","doi":"10.1007/s11432-023-4087-4","title":"A path planning algorithm for a crop monitoring fixed-wing unmanned aerial system","year":2024,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fixed wing; Motion planning; Computer science; Path (computing); Wing; Algorithm; Artificial intelligence; Aerospace engineering; Engineering; Robot","score_opus":0.027172892646281564,"score_gpt":0.30868624347664975,"score_spread":0.28151335083036816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401052399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014506828,0.00016406589,0.9723496,0.00029046324,0.008578884,0.00044637165,0.000015239224,0.0010062719,0.0026423025],"genre_scores_gemma":[0.46945044,0.0000026127364,0.5299926,0.0000486249,0.00038375857,0.00007846248,0.0000030457377,0.0000063433768,0.000034112054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610025,0.000040376504,0.0006412069,0.00066772196,0.0016261424,0.0009242978],"domain_scores_gemma":[0.99867314,0.00020914576,0.00022381022,0.00042601628,0.00023415741,0.00023372965],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0050959755,0.00025880328,0.00023956088,0.0010532276,0.0021988414,0.0056749173,0.0027974907,0.00007359813,0.0000015245735],"category_scores_gemma":[0.0003250111,0.00020787679,0.00009934858,0.004187354,0.0006300757,0.015371696,0.00040516237,0.00019733513,0.00008986555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008688644,0.000033680095,0.0006340357,0.00058770896,0.000028257555,0.000051098094,0.049964424,0.08080855,0.0022776495,0.072391465,0.0011512603,0.7920632],"study_design_scores_gemma":[0.00017727278,0.00015233751,0.0011917696,0.0006013524,0.000005020586,0.00009836809,0.0014449318,0.9925271,0.0020938495,0.00049428106,0.0009177034,0.00029598866],"about_ca_topic_score_codex":0.0000442766,"about_ca_topic_score_gemma":5.619169e-8,"teacher_disagreement_score":0.91171855,"about_ca_system_score_codex":0.00030326008,"about_ca_system_score_gemma":0.0010599444,"threshold_uncertainty_score":0.99910015},"labels":[],"label_agreement":null},{"id":"W4401163911","doi":"10.1109/bmsb62888.2024.10608262","title":"Reinforcement Learning for Path Planning in Communication-Limited Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Motion planning; Path (computing); Artificial intelligence; Human–computer interaction; Robot; Computer network","score_opus":0.03162210386114098,"score_gpt":0.28460269427907653,"score_spread":0.2529805904179355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401163911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094197673,0.00043816585,0.9943843,0.00047896864,0.00015215007,0.00020341395,2.3804253e-7,0.00020942918,0.0031913426],"genre_scores_gemma":[0.67389834,0.000025616637,0.32234788,0.00016989566,0.000020284126,0.0000823544,0.000026844826,0.000012308912,0.0034164758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990492,0.000047911122,0.0002288654,0.00026423327,0.0001703395,0.00023945096],"domain_scores_gemma":[0.9992293,0.00031161224,0.000034375462,0.00037418242,0.0000064804767,0.00004404189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045009382,0.0000990941,0.00010091371,0.00014263121,0.00009511927,0.00015818386,0.00054118957,0.00004351415,0.000008008945],"category_scores_gemma":[0.000048532354,0.0000932478,0.000032888394,0.00022516825,0.000016260925,0.00032084453,0.00020275678,0.00018813566,0.00005611018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033846702,0.000024850622,0.0019965821,0.000031687323,0.000023760525,0.000032255368,0.003675554,0.95823973,0.0002840078,0.016596377,0.0018663898,0.01722543],"study_design_scores_gemma":[0.00019032415,0.000051313167,0.0014727019,0.00015144028,0.0000023300317,0.000004766688,0.00008798906,0.9855562,0.00013579003,0.00044603914,0.011783891,0.00011722299],"about_ca_topic_score_codex":0.000019120906,"about_ca_topic_score_gemma":1.917184e-7,"teacher_disagreement_score":0.67295635,"about_ca_system_score_codex":0.00008629807,"about_ca_system_score_gemma":0.000027609123,"threshold_uncertainty_score":0.3802535},"labels":[],"label_agreement":null},{"id":"W4401211849","doi":"10.1109/isca59077.2024.00048","title":"Collision Prediction for Robotics Accelerators","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Collision; Robot; Computer security","score_opus":0.028356656651579996,"score_gpt":0.2812047550641982,"score_spread":0.2528480984126182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023026881,0.00012865903,0.9935598,0.001048096,0.0025261294,0.00019287386,0.000006272447,0.00087499275,0.0014329235],"genre_scores_gemma":[0.03319329,0.000008008522,0.96410114,0.00014172685,0.00023348522,0.000030039539,0.000008349267,0.000011813235,0.0022721477],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921095,0.0000127195935,0.00014242936,0.00029216253,0.00016529395,0.00017646689],"domain_scores_gemma":[0.99948907,0.00014857008,0.000014026957,0.00023929543,0.000049374004,0.00005968458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021952418,0.00008058063,0.000075610704,0.000094046496,0.00008090241,0.00033846055,0.00032345913,0.00005513995,0.0000034753991],"category_scores_gemma":[0.00003297062,0.00006595214,0.000044983848,0.00033880808,0.000010189814,0.00042636454,0.0000705,0.00006709697,0.000066711895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056729245,0.000076625365,0.0004091628,0.00014443148,0.00006982379,0.000058654463,0.0009359083,0.28183773,0.0010003331,0.44602025,0.17229104,0.09715038],"study_design_scores_gemma":[0.00007917622,0.00007930394,0.00020771424,0.000033340697,0.0000049961477,0.000013538479,0.000007132693,0.9884823,0.0010211228,0.0022746483,0.007721012,0.000075721604],"about_ca_topic_score_codex":0.000003006287,"about_ca_topic_score_gemma":1.7479114e-7,"teacher_disagreement_score":0.7066446,"about_ca_system_score_codex":0.00004440549,"about_ca_system_score_gemma":0.000069444715,"threshold_uncertainty_score":0.3263783},"labels":[],"label_agreement":null},{"id":"W4401304652","doi":"10.2316/j.2024.201-0502","title":"APPLICATION OF IMPROVED ANT COLONY OPTIMISATION WITH HYBRID ADAPTIVE STRATEGY IN MOBILE ROBOT PATH PLANNING","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Motion planning; Computer science; Mobile robot; Path (computing); Artificial intelligence; ANT; Ant colony; Robot; Computer network","score_opus":0.009310001251328939,"score_gpt":0.2293450249573455,"score_spread":0.22003502370601655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401304652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012709025,0.007925289,0.9781223,0.000028343014,0.00015317964,0.00089352945,0.000010699517,0.00009895899,0.000058695485],"genre_scores_gemma":[0.9934845,0.000011795701,0.006000762,0.0000075054522,0.000051495757,0.00039621946,0.0000044459916,0.000011339018,0.000031949705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875516,0.0000842262,0.0003168143,0.00041000426,0.00018556748,0.00024822867],"domain_scores_gemma":[0.9993797,0.00011944209,0.00013809963,0.00025724797,0.00005282449,0.000052668187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005015191,0.00014913347,0.00029823743,0.00012210896,0.00004168327,0.00012312974,0.00020081332,0.000053702548,3.7007044e-7],"category_scores_gemma":[0.000004857261,0.00011873653,0.000026539734,0.00019405971,0.00002352406,0.00025781873,0.00002778263,0.00013503311,0.0000018486253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007548243,0.00005860893,0.00060276606,0.00020634131,0.00010816824,0.00008299275,0.0010332767,0.9257475,0.0073453817,0.018908018,0.000019749948,0.045811687],"study_design_scores_gemma":[0.0008187178,0.00062082167,0.00050874054,0.0002554475,0.00001689559,0.00006359337,0.000351958,0.99685127,0.00017857894,0.0001466921,0.000053664196,0.00013362983],"about_ca_topic_score_codex":0.00045240732,"about_ca_topic_score_gemma":0.0000036734784,"teacher_disagreement_score":0.9807755,"about_ca_system_score_codex":0.00009993672,"about_ca_system_score_gemma":0.00015686508,"threshold_uncertainty_score":0.4841935},"labels":[],"label_agreement":null},{"id":"W4401414774","doi":"10.1109/icra57147.2024.10610925","title":"Simulation and Experimental Validation of an Autonomous Perching and Takeoff Method for a Multirotor UAV on Vertical Surfaces using a Suction Cup","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multirotor; Suction; Takeoff; Suction cup; Computer science; Aerospace engineering; Takeoff and landing; Simulation; Marine engineering; Engineering; Mechanical engineering","score_opus":0.07022566914416448,"score_gpt":0.3987279942547191,"score_spread":0.32850232511055466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401414774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4845005,0.00003133966,0.5151214,0.000026428064,0.000095122436,0.00015841803,0.000001073248,0.000060318293,0.0000053857334],"genre_scores_gemma":[0.527189,1.2882653e-7,0.47276708,0.000007967919,0.00001913974,0.0000046967402,0.0000016809539,0.0000044426456,0.000005819226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991845,0.00009022953,0.00016093907,0.00032582373,0.0001267296,0.00011181618],"domain_scores_gemma":[0.999289,0.0004948668,0.000022963013,0.00011440495,0.000027292823,0.00005151787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040107878,0.000090916954,0.000112157315,0.0000951351,0.000088464534,0.00017741877,0.00006766727,0.000046337722,0.0000011559493],"category_scores_gemma":[0.00006831761,0.0000808321,0.000019381723,0.00008809999,0.000022406406,0.00061593315,0.000041295636,0.000051859635,3.8996677e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023237755,0.00005123425,0.000107536645,0.000044090248,0.000013161522,0.0000028596123,0.004459509,0.8971724,0.08144995,0.001309649,0.0000010083951,0.015365396],"study_design_scores_gemma":[0.00020947866,0.00023717909,0.0004231945,0.000042334483,0.000009155821,0.000014349191,0.00012087124,0.926853,0.071867116,0.00013230284,0.0000046016607,0.00008640441],"about_ca_topic_score_codex":0.000102872225,"about_ca_topic_score_gemma":5.492513e-7,"teacher_disagreement_score":0.042688556,"about_ca_system_score_codex":0.00004688154,"about_ca_system_score_gemma":0.00002661597,"threshold_uncertainty_score":0.32962373},"labels":[],"label_agreement":null},{"id":"W4401415703","doi":"10.1109/icra57147.2024.10611620","title":"Trajectory Optimization for Cooperatively Localizing Quadrotor UAVs","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Computer science; Trajectory optimization; Control theory (sociology); Artificial intelligence; Physics; Control (management)","score_opus":0.027132581884137853,"score_gpt":0.2849213999989907,"score_spread":0.2577888181148528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401415703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005362354,0.00025657352,0.99564296,0.0005364102,0.0008513966,0.0003202563,0.0000033936685,0.0007058159,0.0016295582],"genre_scores_gemma":[0.013396159,0.000004137301,0.98460424,0.00025376843,0.00012638846,0.00008152108,0.000006327864,0.000014235824,0.0015132176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910605,0.00003276897,0.00016047875,0.00035464537,0.00014160518,0.00020444454],"domain_scores_gemma":[0.9994685,0.00018633879,0.00001756765,0.00019778965,0.000068967514,0.000060842216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025956947,0.00010664333,0.00010554883,0.00009775139,0.0000941603,0.00032485713,0.00032919057,0.00004904378,0.000028107805],"category_scores_gemma":[0.000049690458,0.00008974867,0.00004777996,0.00031039584,0.000020109132,0.0004666229,0.00004580855,0.00007132609,0.00005104708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035632256,0.000029810717,0.000013372696,0.00008076631,0.00003559509,0.00002952613,0.0012314686,0.89425516,0.00044423487,0.06475859,0.010996482,0.028121438],"study_design_scores_gemma":[0.00011162469,0.00007377693,0.000015960439,0.000047496713,0.000004022475,0.000012228206,0.000022229326,0.9952176,0.00089512893,0.0001384042,0.003328671,0.00013283004],"about_ca_topic_score_codex":0.000009913362,"about_ca_topic_score_gemma":5.416294e-7,"teacher_disagreement_score":0.10096248,"about_ca_system_score_codex":0.00005036134,"about_ca_system_score_gemma":0.00012334799,"threshold_uncertainty_score":0.36598444},"labels":[],"label_agreement":null},{"id":"W4401416175","doi":"10.1109/icra57147.2024.10611172","title":"DRIVE: Data-driven Robot Input Vector Exploration","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robot; Artificial intelligence; Computer vision","score_opus":0.07456092153301755,"score_gpt":0.31449777250100186,"score_spread":0.2399368509679843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401416175","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000810184,0.00018134921,0.989959,0.003736455,0.0018224933,0.00012479356,0.0000056648255,0.0010848517,0.0030044005],"genre_scores_gemma":[0.083913274,0.000016226604,0.9126792,0.00032161232,0.00038556237,0.00002063792,0.00008253082,0.000017831198,0.0025631397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862224,0.000054926604,0.00020567962,0.00059176766,0.0003034218,0.0002219439],"domain_scores_gemma":[0.99859804,0.00012438798,0.000028153194,0.0011311878,0.00003667812,0.000081548016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028057487,0.00012517344,0.00014742103,0.00012164529,0.00006750608,0.0005434635,0.0014284973,0.000050077935,0.000028901419],"category_scores_gemma":[0.000055266595,0.00010102343,0.00003250731,0.00045595388,0.000021830223,0.0023052634,0.00045032564,0.00014287622,0.0011979942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007406234,0.0001668774,0.0003186072,0.0001473763,0.00026965846,0.0011131206,0.0072542294,0.12625805,0.0048057437,0.28422198,0.25404602,0.32139093],"study_design_scores_gemma":[0.000078287754,0.000028782415,0.00030079889,0.000042454547,0.0000069019397,0.000021792293,0.0000168734,0.992095,0.00029039918,0.00079010037,0.0061865645,0.00014208039],"about_ca_topic_score_codex":0.000021583703,"about_ca_topic_score_gemma":0.0000028240008,"teacher_disagreement_score":0.8658369,"about_ca_system_score_codex":0.000038003243,"about_ca_system_score_gemma":0.00011412461,"threshold_uncertainty_score":0.99957967},"labels":[],"label_agreement":null},{"id":"W4401416833","doi":"10.1109/icra57147.2024.10610148","title":"Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traverse; Drone; Planner; Computer science; Aeronautics; Motion planning; Simulation; Artificial intelligence; Robot; Engineering; Geology","score_opus":0.013921949503944408,"score_gpt":0.2561223273061458,"score_spread":0.2422003778022014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401416833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022803391,0.00016775411,0.9898963,0.00090598426,0.0009545218,0.0001759706,0.0000051154025,0.0010371828,0.004576805],"genre_scores_gemma":[0.056029353,0.0000011528731,0.9292552,0.00022440187,0.00024053347,0.000016078313,0.000011533478,0.000024173074,0.014197556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987319,0.000020982254,0.00019052933,0.00047270383,0.0001757911,0.00040806292],"domain_scores_gemma":[0.9992869,0.00025566915,0.000025870313,0.00030050855,0.000031130916,0.00009991534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035133417,0.00014984704,0.00015777192,0.00013988075,0.00011790753,0.00037501976,0.0004512245,0.000069196336,0.000035966],"category_scores_gemma":[0.000025074609,0.00013389632,0.00007862175,0.0002278995,0.000032666587,0.00048377665,0.00011788297,0.0001274059,0.0006174711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033955064,0.0001424708,0.00003864985,0.0003326113,0.00031566754,0.00081561576,0.011245935,0.59876394,0.011481387,0.044280488,0.11082015,0.22172916],"study_design_scores_gemma":[0.00014639155,0.00008691361,0.000027631315,0.000070820795,0.000010228388,0.00008407353,0.000027557075,0.99200743,0.0013997726,0.00031026255,0.005632218,0.00019670371],"about_ca_topic_score_codex":0.000012421293,"about_ca_topic_score_gemma":1.5658516e-7,"teacher_disagreement_score":0.39324352,"about_ca_system_score_codex":0.00008589868,"about_ca_system_score_gemma":0.00011415325,"threshold_uncertainty_score":0.79365474},"labels":[],"label_agreement":null},{"id":"W4401417303","doi":"10.1109/icra57147.2024.10610907","title":"MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Planner; Motion planning; Computer science; Path (computing); Real-time computing; Artificial intelligence; Robot; Computer network","score_opus":0.0153552514414539,"score_gpt":0.25567033618010887,"score_spread":0.24031508473865498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401417303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008351856,0.0003762027,0.9889982,0.00008039615,0.0004307047,0.00043201106,0.00005377054,0.000972338,0.00030455296],"genre_scores_gemma":[0.44389197,0.0000076953775,0.5556711,0.000054762328,0.0001161599,0.000058166028,0.000008321976,0.000021943633,0.0001698825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985343,0.000051851683,0.00016862736,0.0007019869,0.00021570848,0.00032751448],"domain_scores_gemma":[0.9991924,0.0002671579,0.00007963455,0.00025312146,0.00009500991,0.0001126496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019916904,0.00024813306,0.00021212011,0.00019620902,0.000267652,0.00028983533,0.0002546599,0.00011197098,0.0000018530873],"category_scores_gemma":[0.000042069947,0.00018014944,0.000040847724,0.000384128,0.00006410883,0.0005975442,0.00011218097,0.00033956507,0.00000725358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008030677,0.0003023868,0.056160416,0.0006437924,0.0018656083,0.0018651253,0.13543962,0.5110009,0.00046447988,0.040644806,0.036220133,0.21458967],"study_design_scores_gemma":[0.00057999184,0.0005711077,0.01724628,0.00019870432,0.000027215285,0.00017072781,0.0010764918,0.978445,0.00014619308,0.0009726911,0.00028391578,0.00028169435],"about_ca_topic_score_codex":0.00010697082,"about_ca_topic_score_gemma":0.0000067149986,"teacher_disagreement_score":0.4674441,"about_ca_system_score_codex":0.00009411441,"about_ca_system_score_gemma":0.00011762947,"threshold_uncertainty_score":0.7346281},"labels":[],"label_agreement":null},{"id":"W4401441818","doi":"10.1109/mits.2024.3430938","title":"Autonomous Airborne Transportation: Field Trials in Urban Water Landscapes","year":2024,"lang":"en","type":"article","venue":"IEEE Intelligent Transportation Systems Magazine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Field trial; Environmental science; Field (mathematics); Remote sensing; Transport engineering; Engineering; Marine engineering; Aeronautics; Geography","score_opus":0.03641116246291417,"score_gpt":0.28766572654515343,"score_spread":0.2512545640822393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401441818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019887019,0.0018363227,0.9686279,0.0008921525,0.0065444466,0.00082156644,0.00008778619,0.0007856062,0.00051719794],"genre_scores_gemma":[0.9915414,0.000082375096,0.0041461345,0.00011490873,0.00041754838,0.00018926665,0.00022406694,0.000039457034,0.0032448827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598855,0.00025745854,0.0019558722,0.0007657312,0.00054051494,0.000491869],"domain_scores_gemma":[0.9984966,0.00062352157,0.00015382053,0.00045841877,0.00011877274,0.00014885241],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022049341,0.00035496295,0.0007935696,0.00056155043,0.000057197904,0.00035931313,0.00056841876,0.0002033858,0.00012483931],"category_scores_gemma":[0.000034950663,0.00027322033,0.00026329502,0.00063366257,0.00002041836,0.00067200593,0.000002859309,0.00032693904,0.0010146676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000300252,0.00078530627,0.009749381,0.003903333,0.0009647132,0.008707274,0.10551749,0.678949,0.014522264,0.053128786,0.07465947,0.048812717],"study_design_scores_gemma":[0.0017649345,0.0006730413,0.012147867,0.0025309394,0.00023196563,0.0000951874,0.00043654113,0.6966973,0.06612117,0.000706749,0.21670973,0.0018845667],"about_ca_topic_score_codex":0.00020887624,"about_ca_topic_score_gemma":0.00013805635,"teacher_disagreement_score":0.97165436,"about_ca_system_score_codex":0.00008877261,"about_ca_system_score_gemma":0.00011051002,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W4401536896","doi":"10.1109/access.2024.3443196","title":"Socially Intelligent Path-Planning for Autonomous Vehicles Using Type-2 Fuzzy Estimated Social Psychology Models","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion planning; Fuzzy logic; Path (computing); Fuzzy set; Artificial intelligence; Robot","score_opus":0.22967677248421153,"score_gpt":0.4425430234530261,"score_spread":0.21286625096881456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401536896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023006944,0.0006049791,0.96932286,0.00058520044,0.004949276,0.00027578426,0.000013641524,0.000729367,0.0005119614],"genre_scores_gemma":[0.69518435,0.000011146227,0.3029378,0.00065972196,0.0010006478,0.000042678734,0.0000141337405,0.000059553247,0.00008997325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981278,0.00006995363,0.00037059546,0.00065261644,0.00024017655,0.0005388582],"domain_scores_gemma":[0.999137,0.00018661359,0.00010723217,0.00029640397,0.0001868361,0.00008592747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042278203,0.00023260228,0.00028908838,0.00020526415,0.00032813798,0.0007814309,0.001359281,0.00019595418,0.0000028853574],"category_scores_gemma":[0.000038856047,0.0002350558,0.00010647463,0.00058124604,0.000070037284,0.0009703588,0.0001880091,0.00024851438,0.000032001008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005940305,0.00017679467,0.00037449863,0.00028757445,0.00031249746,0.0005398242,0.009614929,0.71563494,0.007088725,0.028186563,0.026296532,0.21142773],"study_design_scores_gemma":[0.00015277501,0.0000530509,0.00026984172,0.00010614929,0.000027032414,0.000032584543,0.000014591251,0.9522864,0.00070320355,0.045737073,0.00034143444,0.00027586467],"about_ca_topic_score_codex":0.000042395164,"about_ca_topic_score_gemma":6.491621e-7,"teacher_disagreement_score":0.67217743,"about_ca_system_score_codex":0.00013683194,"about_ca_system_score_gemma":0.0003975475,"threshold_uncertainty_score":0.9585298},"labels":[],"label_agreement":null},{"id":"W4401540744","doi":"10.1109/infocomwkshps61880.2024.10620813","title":"CPP-OTPO: A Cognitive Multi-UAV System for Adaptive Path and Trajectory Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Trajectory; Computer science; Path (computing); Trajectory optimization; Cognitive systems; Cognition; Computer network; Psychology","score_opus":0.03399542488675349,"score_gpt":0.26717708641780635,"score_spread":0.23318166153105285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401540744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014459928,0.00048686433,0.9966915,0.00009066445,0.0006028356,0.00047200554,0.00002585898,0.00065258454,0.0008330546],"genre_scores_gemma":[0.16286103,0.000005082485,0.8364043,0.00006634646,0.000063994,0.00008460781,0.000007826662,0.000014640299,0.00049216236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989427,0.00005343634,0.00016729759,0.00047376275,0.00014530375,0.00021754384],"domain_scores_gemma":[0.9992756,0.00036024177,0.000035507237,0.00013788404,0.00010617045,0.00008458203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025808386,0.00014190849,0.00015356267,0.00011136525,0.0000980829,0.00021924361,0.00017623749,0.00006707382,0.0000020407908],"category_scores_gemma":[0.000045044893,0.00012029889,0.00004448745,0.00022564377,0.000034314755,0.00036782346,0.000070170434,0.000084699495,0.000016219814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017980722,0.0005203808,0.00044635203,0.0026854896,0.0010962776,0.0011664585,0.038404614,0.58080965,0.00086993596,0.13272142,0.0057074483,0.23539214],"study_design_scores_gemma":[0.00041182752,0.00014460851,0.00014018588,0.000332593,0.000022076549,0.000050624167,0.0004713332,0.9979983,0.00020890152,0.000019784746,0.000035949404,0.00016379787],"about_ca_topic_score_codex":0.000017820581,"about_ca_topic_score_gemma":7.816036e-7,"teacher_disagreement_score":0.41718864,"about_ca_system_score_codex":0.000056533845,"about_ca_system_score_gemma":0.00008479581,"threshold_uncertainty_score":0.49056464},"labels":[],"label_agreement":null},{"id":"W4401701313","doi":"10.3390/app14167266","title":"Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Korea Institute of Machinery and Materials; National Research Foundation","keywords":"Path (computing); Computer science; Computer network","score_opus":0.01705734566672869,"score_gpt":0.24308049438515353,"score_spread":0.22602314871842483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401701313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64866304,0.0019809033,0.33719954,0.001245858,0.0007943773,0.0003389411,0.0000084060375,0.0009955475,0.008773409],"genre_scores_gemma":[0.93070734,0.00001704401,0.06871209,0.0003002006,0.000099810495,0.000022506056,0.0000021601575,0.000015559239,0.00012331305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735576,0.00006237638,0.00025735685,0.0010473403,0.0006615579,0.00061562454],"domain_scores_gemma":[0.99880934,0.0005301208,0.000089947804,0.0003852817,0.00003175395,0.00015356576],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001459116,0.00025169988,0.0002543892,0.00018170194,0.00061824074,0.0014450822,0.0009073384,0.000075876174,0.0000032401333],"category_scores_gemma":[0.00002784481,0.00019241666,0.000031864995,0.0012641905,0.00036812175,0.00096378225,0.0002748974,0.00026929996,0.000038364506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016920094,0.000312024,0.07684068,0.000681445,0.00032730287,0.0026351064,0.038302224,0.096085064,0.036225878,0.2721246,0.0066754096,0.4696211],"study_design_scores_gemma":[0.00054907345,0.00055622205,0.058520395,0.00047792474,0.000013657445,0.00022072303,0.0008252005,0.927837,0.00074947695,0.008128123,0.0011829333,0.0009392806],"about_ca_topic_score_codex":0.00008995213,"about_ca_topic_score_gemma":0.0000032470305,"teacher_disagreement_score":0.83175194,"about_ca_system_score_codex":0.00004909641,"about_ca_system_score_gemma":0.00018643345,"threshold_uncertainty_score":0.9995915},"labels":[],"label_agreement":null},{"id":"W4401703575","doi":"10.2139/ssrn.4928378","title":"Holistic Swarm Optimization: A Novel Metaphor-Less Algorithm Guided by Whole Population Information for Addressing Exploration-Exploitation Dilemma","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Dilemma; Metaphor; Swarm behaviour; Population; Computer science; Algorithm; Optimization algorithm; Artificial intelligence; Mathematical optimization; Theoretical computer science; Mathematics; Sociology; Philosophy","score_opus":0.06686891858667196,"score_gpt":0.3146110915971605,"score_spread":0.24774217301048854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401703575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039429645,0.0014218402,0.9916935,0.0019556792,0.0030550768,0.0009014039,0.00017042618,0.0003576158,0.000050138457],"genre_scores_gemma":[0.029762972,0.00057125685,0.9635885,0.0002334708,0.001205881,0.00045995627,0.0036191426,0.00011070201,0.000448152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950525,0.00014693342,0.0013507396,0.00068058586,0.00096753624,0.0018016694],"domain_scores_gemma":[0.99693686,0.0001570619,0.0012424474,0.00060478406,0.0008904536,0.00016839022],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002792008,0.0005591209,0.00055536715,0.0007382273,0.0005667246,0.0024066675,0.0012219673,0.00041709113,0.0000019016658],"category_scores_gemma":[0.00037898793,0.00057186495,0.00029150082,0.0006306862,0.00003593319,0.0033612538,0.0005088158,0.0027375426,0.00002536281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010346145,0.00005927359,0.0000054619013,0.000120885445,0.00024865803,0.0000019254257,0.001038098,0.8970641,0.000025194067,0.020060997,0.001576654,0.07978845],"study_design_scores_gemma":[0.0006883529,0.00012065984,0.00001325948,0.00034163275,0.00015518343,0.00018326385,0.000520583,0.84432703,0.000040580824,0.15269464,0.0003965691,0.0005182393],"about_ca_topic_score_codex":0.00016103452,"about_ca_topic_score_gemma":0.0000137036905,"teacher_disagreement_score":0.13263366,"about_ca_system_score_codex":0.0022986014,"about_ca_system_score_gemma":0.0027417797,"threshold_uncertainty_score":0.9996733},"labels":[],"label_agreement":null},{"id":"W4401863963","doi":"10.23977/jaip.2024.070307","title":"Research on path planning of patrol robot based on multi-algorithm fusion","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Path (computing); Fusion; Robot; Artificial intelligence; Algorithm; Computer vision; Computer network","score_opus":0.18942506317209146,"score_gpt":0.4519253544763342,"score_spread":0.26250029130424274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401863963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079121266,0.00042738565,0.99219745,0.0034202887,0.0023380439,0.00017221419,0.0000044011636,0.000059672468,0.00058932963],"genre_scores_gemma":[0.34988135,0.000048645114,0.64917403,0.00029621605,0.0005291862,0.000004287342,8.9016606e-7,0.000025658424,0.00003971267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949085,0.0009692798,0.0011230193,0.00046606737,0.0020351359,0.0004979884],"domain_scores_gemma":[0.99091023,0.0065631266,0.00061002496,0.0005615938,0.001134482,0.00022051705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008356362,0.00022773861,0.00036412498,0.0011369773,0.00022591211,0.00045676754,0.0012213892,0.00015454803,0.000021557766],"category_scores_gemma":[0.0032809346,0.00018614877,0.00019051839,0.0015651428,0.00012201719,0.0012023534,0.00014247574,0.0018711266,0.00018392784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027541057,0.0009100869,0.000014242192,0.000047725203,0.00005745628,0.002311669,0.0023471618,0.46814832,0.0034602012,0.0046514384,0.0006320536,0.5171442],"study_design_scores_gemma":[0.0000655126,0.0022915807,0.00006449475,0.0011451837,0.000026132799,0.00018245747,0.0012785569,0.96970195,0.0225988,0.0012564342,0.0012135324,0.00017538723],"about_ca_topic_score_codex":0.00004021966,"about_ca_topic_score_gemma":3.0710197e-7,"teacher_disagreement_score":0.51696885,"about_ca_system_score_codex":0.00019601974,"about_ca_system_score_gemma":0.0005828929,"threshold_uncertainty_score":0.81292176},"labels":[],"label_agreement":null},{"id":"W4401883993","doi":"10.2139/ssrn.4916155","title":"Fair and additive decomposition of metric components","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Decomposition; Metric (unit); Mathematics; Component (thermodynamics); Pure mathematics; Economics; Chemistry; Physics; Thermodynamics; Operations management; Organic chemistry","score_opus":0.012055476166276004,"score_gpt":0.27268871813315493,"score_spread":0.2606332419668789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401883993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04671997,0.016379237,0.9335112,0.0007147157,0.0016982376,0.00024015259,0.00003150596,0.00013014492,0.000574849],"genre_scores_gemma":[0.9581278,0.003149809,0.03820071,0.000029804725,0.0002544535,0.000008576836,0.000022890215,0.000026988706,0.00017897836],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99700814,0.0001786458,0.00047236116,0.00050567655,0.00050872925,0.0013264171],"domain_scores_gemma":[0.9988111,0.00014353124,0.00042377526,0.00035351943,0.0001553332,0.00011270321],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001522476,0.0002801023,0.00042846648,0.00071315974,0.00010555097,0.00020721472,0.0009824181,0.00019816919,0.0000021698213],"category_scores_gemma":[0.000049825256,0.0002601437,0.00016390072,0.00042534884,0.00005847183,0.00014918693,0.0013253121,0.004393395,0.000020917605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007383662,0.00045648898,0.0007347083,0.00050073466,0.0035291188,0.00035599232,0.0028359646,0.011515789,0.0015389987,0.48215312,0.001116366,0.4951889],"study_design_scores_gemma":[0.00042081004,0.00037879718,0.0020317154,0.00059572543,0.00013211498,0.002050061,0.00012708607,0.1301122,0.00036811808,0.86330193,0.00007038134,0.0004110806],"about_ca_topic_score_codex":0.000065041924,"about_ca_topic_score_gemma":0.0000065963022,"teacher_disagreement_score":0.9114078,"about_ca_system_score_codex":0.0008160907,"about_ca_system_score_gemma":0.0018279516,"threshold_uncertainty_score":0.9999851},"labels":[],"label_agreement":null},{"id":"W4401899761","doi":"10.4203/ccc.7.25.1","title":"Docking trains: A Comparison","year":2024,"lang":"en","type":"article","venue":"Civil-comp conferences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Engineering Link (Canada)","funders":"","keywords":"Train; NonStop; Computer science; Travel time; Simulation; Transport engineering; Engineering; Geography","score_opus":0.0516102404288199,"score_gpt":0.31124554348845374,"score_spread":0.25963530305963384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401899761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033261173,0.004121144,0.92904174,0.0012463216,0.0026987733,0.00016584131,0.0000059227286,0.0014014071,0.05799271],"genre_scores_gemma":[0.9622559,0.000030822444,0.037041828,0.00013150318,0.00020722284,0.000019884441,0.0000057843035,0.000011666431,0.0002954117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803776,0.00010329796,0.000360882,0.00063558324,0.00041425313,0.00044819972],"domain_scores_gemma":[0.9989408,0.00032362467,0.00006641753,0.00047159786,0.000062484534,0.00013506523],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00040296293,0.00023077898,0.00031967903,0.00016981582,0.00014908734,0.0011269553,0.0012864256,0.00008471437,0.000050944604],"category_scores_gemma":[0.00004787224,0.0001976595,0.00009588401,0.00043833713,0.000096749245,0.0005244855,0.0001912401,0.00032251628,0.00028585875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036861857,0.00013349955,0.0031624762,0.00019441557,0.00016641099,0.000452065,0.013930572,0.0035321068,0.00035430648,0.5569697,0.019045763,0.402055],"study_design_scores_gemma":[0.00012709282,0.00008711781,0.0023865486,0.00031452192,0.000012705091,0.000055772067,0.0001760418,0.9412081,0.0003027599,0.0067418665,0.048247598,0.00033984883],"about_ca_topic_score_codex":0.00010426605,"about_ca_topic_score_gemma":0.000022760187,"teacher_disagreement_score":0.9589298,"about_ca_system_score_codex":0.000037632417,"about_ca_system_score_gemma":0.00026879672,"threshold_uncertainty_score":0.99991},"labels":[],"label_agreement":null},{"id":"W4401915076","doi":"10.1111/mice.13319","title":"Autonomous post‐disaster indoor navigation and survivor detection using low‐cost micro aerial vehicles","year":2024,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Aeronautics; Real-time computing; Environmental science; Engineering","score_opus":0.005846805565486089,"score_gpt":0.20333727182062739,"score_spread":0.1974904662551413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401915076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43103734,0.00028079076,0.56645525,0.000031141797,0.0017894793,0.0001125181,0.000006647136,0.00028143267,0.0000054147517],"genre_scores_gemma":[0.8457146,0.00000806427,0.15341622,0.0000709695,0.00073668367,0.000006596138,0.000010947954,0.000030587064,0.0000052817786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985928,0.000037179714,0.00029473304,0.00055338076,0.00016757914,0.0003543497],"domain_scores_gemma":[0.9993869,0.00011054785,0.000056671248,0.00024385632,0.000054566106,0.00014746332],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017511276,0.00030390735,0.00025271025,0.00023692942,0.00013605892,0.0007425943,0.0002454532,0.00015447229,0.0000022300253],"category_scores_gemma":[0.00001761497,0.00029428178,0.00004818547,0.00032118452,0.00004051605,0.0007812019,0.000279173,0.00034199504,0.0000030448243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015836365,0.000017935734,0.00065042975,0.0007501962,0.00013400627,0.00019609186,0.004312871,0.37179154,0.22579809,0.00092002307,0.000090022324,0.39532295],"study_design_scores_gemma":[0.000257745,0.00005804426,0.011231079,0.00034117355,0.000015002913,0.00047939373,0.000010857229,0.98284835,0.0038442516,0.00023323532,0.00035667626,0.00032421743],"about_ca_topic_score_codex":0.00003064756,"about_ca_topic_score_gemma":0.000001452925,"teacher_disagreement_score":0.61105675,"about_ca_system_score_codex":0.00008601381,"about_ca_system_score_gemma":0.00005151134,"threshold_uncertainty_score":0.99995095},"labels":[],"label_agreement":null},{"id":"W4401943440","doi":"10.1109/cog60054.2024.10645669","title":"Efficient Octree-based 3D Pathfinding","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pathfinding; Octree; Computer science; Computer graphics (images); Computer vision; Theoretical computer science; Shortest path problem","score_opus":0.017561327283298553,"score_gpt":0.25482313230988785,"score_spread":0.23726180502658928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401943440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023753212,0.00023186803,0.9820071,0.00080337306,0.0013016345,0.00007154022,0.0000010535476,0.0011259154,0.012082205],"genre_scores_gemma":[0.4245432,4.9673207e-7,0.57428443,0.00032953755,0.00008139774,0.00000838448,0.0000014178908,0.000009993944,0.00074110355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988651,0.000030720825,0.00014031341,0.0003912809,0.0002873001,0.0002852499],"domain_scores_gemma":[0.9992999,0.0001870339,0.00001557469,0.0003938983,0.000020468004,0.00008312674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003288465,0.00010967061,0.00009063048,0.00016827857,0.000072457624,0.00030922616,0.0005087182,0.00004052738,0.00003177211],"category_scores_gemma":[0.000028269671,0.000088119115,0.00005266439,0.0005422799,0.000018915818,0.000064950604,0.000102413294,0.00012160511,0.00074126304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002515702,0.00013630443,0.00055093574,0.00008608694,0.00004203013,0.0015432944,0.0013858437,0.6548665,0.0018715598,0.10400125,0.008228332,0.22728531],"study_design_scores_gemma":[0.00007344585,0.000028409944,0.0003026231,0.00006433301,0.0000021467529,0.000017119151,0.0000051143875,0.996648,0.00077089545,0.000110168585,0.0018506689,0.00012707758],"about_ca_topic_score_codex":0.0000127352605,"about_ca_topic_score_gemma":2.0766657e-7,"teacher_disagreement_score":0.4221679,"about_ca_system_score_codex":0.00005646919,"about_ca_system_score_gemma":0.000111074456,"threshold_uncertainty_score":0.9527684},"labels":[],"label_agreement":null},{"id":"W4402036074","doi":"10.1016/j.eswa.2024.125238","title":"A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Residual; Artificial intelligence; Algorithm; Machine learning; Computer vision","score_opus":0.01574082428586497,"score_gpt":0.27792289904326045,"score_spread":0.26218207475739547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402036074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008351767,0.0006668125,0.9949586,0.00081004534,0.00037443358,0.0016819938,0.000007579238,0.0009829074,0.00050928065],"genre_scores_gemma":[0.26207492,0.0000050467347,0.72044903,0.00029137178,0.0009657368,0.014000225,0.00033499894,0.00008671709,0.0017919475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803996,0.00006815809,0.00037505612,0.00068331143,0.00044808193,0.0003854455],"domain_scores_gemma":[0.9984447,0.00048324539,0.00010337913,0.0006745338,0.00014968836,0.00014449877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037364816,0.00023817617,0.00021644584,0.00019472423,0.00041396712,0.00049803505,0.0005131496,0.0000944487,0.0000026499772],"category_scores_gemma":[0.000026371934,0.00020554963,0.000060381008,0.00048706183,0.000042168544,0.0002840723,0.000041582898,0.00021502706,0.000102858314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012025968,0.00009417638,0.000014705587,0.00027118015,0.00007338703,0.00002875551,0.002660513,0.8905368,0.00034650505,0.030702906,0.002954166,0.0723049],"study_design_scores_gemma":[0.0002801676,0.0002536459,0.000010286213,0.00034490228,0.000010771332,0.000038393588,0.00014893305,0.95149904,0.0001873965,0.00007474133,0.046896473,0.00025522395],"about_ca_topic_score_codex":0.000058646267,"about_ca_topic_score_gemma":4.656426e-7,"teacher_disagreement_score":0.27450955,"about_ca_system_score_codex":0.00026715614,"about_ca_system_score_gemma":0.00024872625,"threshold_uncertainty_score":0.83820707},"labels":[],"label_agreement":null},{"id":"W4402215436","doi":"10.1109/tro.2024.3454417","title":"Anytime Replanning of Robot Coverage Paths for Partially Unknown Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robot; Mobile robot; Computer science; Artificial intelligence; Computer vision","score_opus":0.02351335133019631,"score_gpt":0.2632614115300703,"score_spread":0.23974806019987402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402215436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019014679,0.00012910056,0.9962559,0.0004051098,0.0022328666,0.00033247296,0.000046376448,0.0002649454,0.0001431205],"genre_scores_gemma":[0.3671191,0.000055485718,0.6304316,0.00015152972,0.00008985472,0.000057672903,0.0000081099415,0.000050848634,0.0020358225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981403,0.00005883335,0.0004565581,0.0005634783,0.00038487007,0.00039599685],"domain_scores_gemma":[0.9986736,0.00041577037,0.00009109009,0.0006613777,0.000031885174,0.00012626385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031909856,0.00024306399,0.0002904729,0.00020582021,0.00016387209,0.00012655262,0.00052661914,0.00013155026,0.0000095613095],"category_scores_gemma":[0.000011740295,0.0002428396,0.0001971968,0.0003529789,0.00006276648,0.00034602193,0.0000055042065,0.0002750595,0.00007073047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012563191,0.00013831306,0.000004881967,0.00005855328,0.00008382075,0.000041903204,0.00037815055,0.9755468,0.0030068748,0.0011114875,0.00018947938,0.01942721],"study_design_scores_gemma":[0.0003633618,0.0003711929,0.00005867343,0.00020585168,0.00007202513,0.0000354861,0.000009399673,0.9736805,0.023490163,0.000525351,0.00091076083,0.00027723954],"about_ca_topic_score_codex":0.0000063348243,"about_ca_topic_score_gemma":9.3777317e-7,"teacher_disagreement_score":0.36692894,"about_ca_system_score_codex":0.00009462107,"about_ca_system_score_gemma":0.00011342965,"threshold_uncertainty_score":0.99027115},"labels":[],"label_agreement":null},{"id":"W4402261034","doi":"10.23919/acc60939.2024.10644838","title":"Weakly Coupled Systems of Eikonal Equations in Path-Planning Problems","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Science Foundation","keywords":"Eikonal equation; Computer science; Motion planning; Path (computing); Mathematical optimization; Applied mathematics; Physics; Mathematics; Mathematical analysis; Robot; Artificial intelligence","score_opus":0.037246143148192364,"score_gpt":0.2800242692721691,"score_spread":0.24277812612397676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402261034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037140078,0.0012108868,0.99059874,0.0002182882,0.00086089957,0.00020870254,0.000002600453,0.0002859855,0.002899905],"genre_scores_gemma":[0.9358553,0.0000030704043,0.06324277,0.000019996936,0.00004905689,0.000037266163,0.000005463858,0.000009523082,0.00077754905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863464,0.00005386724,0.00039529768,0.00032198534,0.000347343,0.00024688122],"domain_scores_gemma":[0.99916494,0.00037018277,0.000055349494,0.00029830096,0.00005560397,0.0000556026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062797824,0.000111416855,0.00018673198,0.00026124867,0.000033695596,0.00017074028,0.0004714428,0.000058681646,0.0000070691135],"category_scores_gemma":[0.000059235284,0.000095844916,0.00003813487,0.00075308233,0.0000251447,0.0003437428,0.00010068307,0.00015591232,0.00006410861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015609986,0.000084093364,0.0027993405,0.00033461978,0.000042394917,0.00024653823,0.0037445393,0.7258122,0.0040591927,0.25921893,0.001161159,0.002495433],"study_design_scores_gemma":[0.000099163015,0.000042691223,0.0015383685,0.0005414449,0.0000032037194,0.000027403134,0.000067557376,0.9967408,0.000043941287,0.000621326,0.00016040739,0.000113659386],"about_ca_topic_score_codex":0.0002853247,"about_ca_topic_score_gemma":0.0000019494933,"teacher_disagreement_score":0.9321413,"about_ca_system_score_codex":0.000053819425,"about_ca_system_score_gemma":0.00015959228,"threshold_uncertainty_score":0.39084423},"labels":[],"label_agreement":null},{"id":"W4402265497","doi":"10.23919/acc60939.2024.10644195","title":"Real-Time Sensor-Based Feedback Control for Obstacle Avoidance in Unknown Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Obstacle avoidance; Computer science; Collision avoidance; Obstacle; Feedback control; Control (management); Control theory (sociology); Real-time computing; Control engineering; Artificial intelligence; Mobile robot; Engineering; Robot; Geography; Computer security","score_opus":0.012562000338568469,"score_gpt":0.2437616160110939,"score_spread":0.23119961567252542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402265497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005504574,0.00009680144,0.98972404,0.0015918397,0.00041381066,0.0004416118,0.000015045275,0.00033874583,0.001873554],"genre_scores_gemma":[0.2286521,0.000009052662,0.7551292,0.0006799777,0.00012125738,0.0001255617,0.000013400182,0.000040410705,0.0152290305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984341,0.000070287584,0.00026668594,0.0005619745,0.00024163339,0.000425264],"domain_scores_gemma":[0.9987955,0.0005968553,0.000037018144,0.0004646207,0.00001171434,0.00009430994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004031181,0.00017183022,0.0002123312,0.00011985192,0.00005357194,0.0001580431,0.0004933439,0.000075638294,0.000025168178],"category_scores_gemma":[0.00004879731,0.0001556964,0.00007381385,0.00026324944,0.000038232,0.00026469445,0.000049237005,0.000120527235,0.0005915981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017774448,0.001036691,0.0055165202,0.0005310807,0.00027573577,0.001545561,0.002575648,0.59335285,0.18826185,0.052553684,0.03508856,0.11908408],"study_design_scores_gemma":[0.0009111196,0.00008060269,0.0021169605,0.00007289327,0.0000055065184,0.0000065557833,0.0000064574124,0.98977685,0.0018141012,0.00045883036,0.0045448407,0.00020529948],"about_ca_topic_score_codex":0.000041296753,"about_ca_topic_score_gemma":8.3006574e-7,"teacher_disagreement_score":0.396424,"about_ca_system_score_codex":0.00013031223,"about_ca_system_score_gemma":0.0000819286,"threshold_uncertainty_score":0.76039946},"labels":[],"label_agreement":null},{"id":"W4402265811","doi":"10.23919/acc60939.2024.10644387","title":"Hybrid Feedback Control for Global and Optimal Safe Navigation","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Control (management); Feedback control; Control theory (sociology); Control engineering; Engineering; Artificial intelligence","score_opus":0.012152918368347414,"score_gpt":0.2670697572926217,"score_spread":0.2549168389242743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402265811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036584341,0.00030509668,0.9920647,0.0015710595,0.00058865815,0.00019057219,0.000017735712,0.0002744148,0.0013293262],"genre_scores_gemma":[0.39710346,0.0000017243757,0.6020354,0.00022593727,0.00010516486,0.000017666705,0.000006834894,0.000004603565,0.0004992066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927086,0.000015691558,0.00011738453,0.0002944179,0.00012013166,0.00018151163],"domain_scores_gemma":[0.9996174,0.00011619993,0.00001582515,0.00014844468,0.000034598193,0.000067549365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020226226,0.00008367978,0.000093119386,0.000019528394,0.000056122928,0.0002993899,0.00020100585,0.000025043062,0.0000027597064],"category_scores_gemma":[0.000019394713,0.00007093801,0.00003222862,0.00009358429,0.000023287632,0.00030795753,0.000049510298,0.00004456944,0.000041438314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000287126,0.000049647257,0.00064257346,0.00018532286,0.00013317575,0.00022114895,0.00043635358,0.01786734,0.0004455881,0.5225081,0.027086256,0.43039578],"study_design_scores_gemma":[0.000329056,0.00007400548,0.00075632037,0.00003542572,0.0000076720635,0.0001401418,0.0000045606857,0.99142516,0.00015649974,0.0050238585,0.0019505467,0.00009676678],"about_ca_topic_score_codex":0.000008988468,"about_ca_topic_score_gemma":1.084235e-7,"teacher_disagreement_score":0.97355783,"about_ca_system_score_codex":0.000036349804,"about_ca_system_score_gemma":0.00004674053,"threshold_uncertainty_score":0.28927684},"labels":[],"label_agreement":null},{"id":"W4402266112","doi":"10.23919/acc60939.2024.10644232","title":"Obstacle-free Trajectory Planning of an Uncertain Space Manipulator: Learning from a Fixed-Based Manipulator","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Manipulator (device); Trajectory; Obstacle; Computer science; Motion planning; Mobile manipulator; Robot manipulator; Obstacle avoidance; Control theory (sociology); Robot; Artificial intelligence; Mobile robot; Control (management); Physics","score_opus":0.04409078947227674,"score_gpt":0.28046060896823627,"score_spread":0.23636981949595953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402266112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15412334,0.0009702978,0.84043646,0.00027766058,0.0009996357,0.00019176456,0.0000113422575,0.00144327,0.0015462373],"genre_scores_gemma":[0.6480154,7.967441e-7,0.35116518,0.0000980479,0.00014498753,0.000010356733,0.000026641808,0.000040678206,0.0004979274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725896,0.00020608104,0.0004567278,0.00089013326,0.00066857855,0.0005195263],"domain_scores_gemma":[0.99783784,0.000556654,0.00012365321,0.0011872363,0.000060685543,0.000233901],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004905294,0.00033204904,0.00038700027,0.00031347873,0.00013481466,0.00031068773,0.0015604398,0.00016562692,0.000058694837],"category_scores_gemma":[0.00013300648,0.00031346263,0.00014285742,0.0005987056,0.000062577616,0.0006705904,0.00024912658,0.0004939409,0.00006355585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031478234,0.0001488778,0.007886003,0.00019454092,0.00016163444,0.0014552006,0.0052191075,0.95193344,0.0075011337,0.011898885,0.0020852427,0.011484446],"study_design_scores_gemma":[0.00042646556,0.00020940333,0.006611613,0.00031944524,0.000024303981,0.000023825673,0.0002957869,0.9863564,0.003833044,0.0009358826,0.0005698146,0.0003940303],"about_ca_topic_score_codex":0.0008688302,"about_ca_topic_score_gemma":0.000012076762,"teacher_disagreement_score":0.49389204,"about_ca_system_score_codex":0.00012871005,"about_ca_system_score_gemma":0.0002510851,"threshold_uncertainty_score":0.99993175},"labels":[],"label_agreement":null},{"id":"W4402457162","doi":"10.1115/1.4066517","title":"Effective Map Merging for Long-Term Autonomous Navigation","year":2024,"lang":"en","type":"article","venue":"Journal of Autonomous Vehicles and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Term (time); Computer science; Artificial intelligence; Computer vision; Physics","score_opus":0.015645680101563607,"score_gpt":0.27495916501875367,"score_spread":0.2593134849171901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402457162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22208925,0.012052824,0.76021725,0.00064529735,0.004313923,0.00048471728,0.000006684251,0.00013526727,0.00005477027],"genre_scores_gemma":[0.9834902,0.00002344211,0.01543477,0.000024134766,0.00071926514,0.000022837583,0.0000022806155,0.000021165568,0.00026186902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849606,0.000102295206,0.00059782085,0.0002758112,0.00025782405,0.0002702107],"domain_scores_gemma":[0.9987823,0.00040283788,0.00032277693,0.00018478629,0.0001687376,0.00013859798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012541349,0.00017432185,0.00037674658,0.00021441698,0.00014680122,0.00076401385,0.0003664488,0.00009369996,4.827906e-7],"category_scores_gemma":[0.000024005269,0.00013959117,0.00014396003,0.00015487429,0.0000329511,0.00066000497,0.00006488401,0.00023126227,0.000008612327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008598458,0.00021594619,0.009133137,0.004726002,0.0012664843,0.0028256073,0.015342868,0.045185644,0.012055812,0.03210518,0.0031352637,0.87392205],"study_design_scores_gemma":[0.0005655659,0.00063504046,0.009054308,0.0017348141,0.00006934558,0.0025143535,0.000105018815,0.9812375,0.0006521619,0.000555711,0.0026050212,0.00027118754],"about_ca_topic_score_codex":0.000014821886,"about_ca_topic_score_gemma":2.435601e-7,"teacher_disagreement_score":0.93605185,"about_ca_system_score_codex":0.00017975282,"about_ca_system_score_gemma":0.00016489452,"threshold_uncertainty_score":0.7367403},"labels":[],"label_agreement":null},{"id":"W4402464086","doi":"10.11159/cist24.157","title":"Path Planning with RRT*M Algorithm in Simulated Human Respiratory Environment","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Sichuan Province","keywords":"Computer science; Path (computing); Motion planning; Algorithm; Artificial intelligence; Robot; Computer network","score_opus":0.009515496137336814,"score_gpt":0.21876369025700224,"score_spread":0.20924819411966541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7504822,0.0066554192,0.23693512,0.00039904722,0.0030836777,0.0011333947,0.0000035863682,0.00073309924,0.0005744722],"genre_scores_gemma":[0.99014944,0.0000056380572,0.009583687,0.000032114218,0.00008868285,0.000010606046,6.422036e-8,0.000011262253,0.00011851511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983872,0.00000821565,0.00025009117,0.00054158206,0.00046051075,0.00035238365],"domain_scores_gemma":[0.9995395,0.0000984057,0.000066780645,0.00014386578,0.000041828625,0.00010960183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005853969,0.00018172205,0.00023024839,0.0004437416,0.00014532817,0.0004648431,0.0006221048,0.00003537834,9.005274e-8],"category_scores_gemma":[0.00001100303,0.000120463876,0.000020997872,0.001248588,0.00012905702,0.00031529088,0.00022333568,0.00030230675,3.9392444e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024308936,0.00020693748,0.016263328,0.0010508356,0.00012137201,0.00023832326,0.0023005062,0.6943609,0.013390179,0.19563483,0.0007861369,0.07562231],"study_design_scores_gemma":[0.00016454676,0.00020750216,0.0048229718,0.0010269135,0.000003973714,0.00004077665,0.000003996634,0.99260896,0.00047267321,0.0000319361,0.0004494255,0.00016634013],"about_ca_topic_score_codex":0.000010663786,"about_ca_topic_score_gemma":5.642477e-8,"teacher_disagreement_score":0.29824802,"about_ca_system_score_codex":0.00007035175,"about_ca_system_score_gemma":0.000033824534,"threshold_uncertainty_score":0.49123743},"labels":[],"label_agreement":null},{"id":"W4402474280","doi":"10.1109/ccece59415.2024.10667258","title":"Towards a Deep Reinforcement Learning Solution to the Coverage Path Planning Problem","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Reinforcement learning; Computer science; Motion planning; Path (computing); Artificial intelligence; Mathematical optimization; Mathematics; Computer network; Robot","score_opus":0.02141267845718602,"score_gpt":0.2672183333223083,"score_spread":0.2458056548651223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016551078,0.00026027352,0.9733245,0.0032621722,0.00061463134,0.00027473268,2.5221158e-7,0.0007085081,0.021389429],"genre_scores_gemma":[0.6989118,0.000008467198,0.29505244,0.0009601862,0.00021091088,0.00007658549,0.0000062753024,0.000018218687,0.004755108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844563,0.00006936723,0.00022929472,0.00040401646,0.00044619644,0.00040549587],"domain_scores_gemma":[0.9993838,0.00009031638,0.000035257202,0.00034758807,0.000040316532,0.00010271299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007434126,0.00015079005,0.000114055445,0.000110408946,0.00026494265,0.00059203187,0.0006615417,0.000045807956,0.00001879955],"category_scores_gemma":[0.0000553257,0.00009988119,0.00005508953,0.000538618,0.000012823683,0.00036318228,0.00041033904,0.0002946296,0.00039842498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019819195,0.00000559296,0.000053343585,0.000021401913,0.000020267298,0.00007766155,0.005655441,0.9018571,0.00010531275,0.014668681,0.005424513,0.07210872],"study_design_scores_gemma":[0.000068716545,0.00014035318,0.00023098578,0.00013595689,0.0000052142414,0.00004133036,0.00007491877,0.9771328,0.000110995556,0.00048501918,0.021418072,0.00015566622],"about_ca_topic_score_codex":0.00009203154,"about_ca_topic_score_gemma":9.892011e-7,"teacher_disagreement_score":0.6987463,"about_ca_system_score_codex":0.00011898031,"about_ca_system_score_gemma":0.00010617613,"threshold_uncertainty_score":0.5708977},"labels":[],"label_agreement":null},{"id":"W4402474508","doi":"10.1109/ccece59415.2024.10667129","title":"Hierarchical Deep Reinforcement Learning with Cross-attention and Planning for Autonomous Roundabout Navigation","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Roundabout; Reinforcement learning; Computer science; Artificial intelligence; Motion planning; Human–computer interaction; Engineering; Transport engineering; Robot","score_opus":0.018097497057786873,"score_gpt":0.2966906387817514,"score_spread":0.27859314172396454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03195155,0.00015800796,0.9658479,0.00024423221,0.00031328047,0.00022298371,2.564557e-7,0.00050413597,0.00075764576],"genre_scores_gemma":[0.70417553,0.0000014137131,0.29369825,0.000052131,0.00009927924,0.000044080294,0.000026293517,0.000012755652,0.0018902259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988775,0.000025039448,0.0001985947,0.0004128785,0.00021683528,0.00026911093],"domain_scores_gemma":[0.9995018,0.0001617281,0.000043451524,0.00015045977,0.00006208817,0.000080477235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037734356,0.00012812349,0.00011228386,0.00009864951,0.0002675208,0.0010185195,0.0001756802,0.00005546596,0.0000027485305],"category_scores_gemma":[0.0000316699,0.00010187398,0.00003020512,0.00018358153,0.00005203371,0.00065794855,0.000082944665,0.00020331042,0.000011608409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003117177,0.000017699767,0.0069114766,0.00022820971,0.000081753824,0.000120466815,0.0047424603,0.8435575,0.00037062188,0.062230203,0.00009705735,0.08161138],"study_design_scores_gemma":[0.00027916452,0.00025746447,0.0039647953,0.00017497112,0.00000877268,0.00009689987,0.000050871313,0.99338365,0.000088604414,0.00079366367,0.00074454886,0.00015658294],"about_ca_topic_score_codex":0.000012857383,"about_ca_topic_score_gemma":2.856145e-7,"teacher_disagreement_score":0.672224,"about_ca_system_score_codex":0.00006515827,"about_ca_system_score_gemma":0.000065207314,"threshold_uncertainty_score":0.9821607},"labels":[],"label_agreement":null},{"id":"W4402551089","doi":"10.36602/ijeit.v12i1.479","title":"Designing and Implementing a 3DOF Robotic Arm for Color Sorting and Object Identification Using Computer Vision Technology","year":2024,"lang":"en","type":"article","venue":"المجلة الدولية للعلوم الهندسية وتقنية المعلومات.","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Sorting; Robotic arm; Computer vision; Identification (biology); Artificial intelligence; Object (grammar); Computer science; Computer graphics (images)","score_opus":0.03008092026826283,"score_gpt":0.32214682564014707,"score_spread":0.29206590537188426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402551089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06807,0.002021573,0.92574424,0.0010219001,0.0011112974,0.0010064021,0.000009286849,0.0009480766,0.000067250985],"genre_scores_gemma":[0.52672756,0.000023398852,0.47271422,0.00010408703,0.00023103246,0.00007009427,0.000011808865,0.00005569686,0.00006211992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99555504,0.0001574018,0.0010507099,0.0016303209,0.00045748512,0.0011490615],"domain_scores_gemma":[0.9977221,0.0007626691,0.00038891615,0.00070388836,0.00021261214,0.00020980867],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019966864,0.000520893,0.0006135134,0.0009619268,0.0009230659,0.0014555058,0.00073131965,0.00030490564,0.000004218977],"category_scores_gemma":[0.00023194816,0.00052901526,0.00012785383,0.0012653472,0.00023490319,0.0011970804,0.00091878074,0.0004484731,0.000018335126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070534865,0.0003070736,0.003644974,0.0019545807,0.000628625,0.00053909625,0.0070750234,0.026835555,0.3664448,0.08238574,0.00109606,0.50901794],"study_design_scores_gemma":[0.0006011939,0.00037649128,0.0005616632,0.00062264554,0.00011426147,0.0005772225,0.00023560233,0.9802745,0.0071843565,0.0085729435,0.000280464,0.00059863296],"about_ca_topic_score_codex":0.00004123412,"about_ca_topic_score_gemma":0.0000043430637,"teacher_disagreement_score":0.953439,"about_ca_system_score_codex":0.00018153239,"about_ca_system_score_gemma":0.00019547978,"threshold_uncertainty_score":0.99971616},"labels":[],"label_agreement":null},{"id":"W4402586310","doi":"10.1109/tits.2024.3453769","title":"Obstacle Avoidance for a Large-Scale High-Speed Underactuated AUV in Complex Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Obstacle avoidance; Underactuation; Scale (ratio); Computer science; Collision avoidance; Obstacle; Control theory (sociology); Control engineering; Engineering; Artificial intelligence; Mobile robot; Physics; Control (management); Robot; Geography","score_opus":0.04004288687547259,"score_gpt":0.2813557808184398,"score_spread":0.24131289394296723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402586310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0121146655,0.0001626273,0.98296756,0.00023011651,0.0027350849,0.0010252619,0.0003603154,0.0003774099,0.000026975806],"genre_scores_gemma":[0.9823682,0.00004281996,0.016042955,0.000077432334,0.000046201323,0.0002604386,0.00014865508,0.000042505744,0.00097083213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740666,0.000096829404,0.00080266746,0.0007435065,0.0004824343,0.00046791695],"domain_scores_gemma":[0.9989693,0.00032148103,0.00009699077,0.0004338593,0.000044973927,0.00013336104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041218413,0.00029485606,0.00034297424,0.0004109028,0.00017451984,0.00021742005,0.0004335202,0.00014625005,0.000042950458],"category_scores_gemma":[0.000002363383,0.0003042215,0.00016367836,0.0006629581,0.000030453992,0.00050309475,5.35452e-7,0.00030176307,0.00030849158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005002325,0.00042516517,0.00006643155,0.00024246283,0.00011169661,0.000060687275,0.0072154296,0.97981983,0.0034357938,0.0027725913,0.00028584735,0.0055140597],"study_design_scores_gemma":[0.0006386443,0.00016363367,0.0010749381,0.00033627276,0.00003504114,0.0000118941625,0.0005372193,0.979979,0.01084387,0.00016782527,0.005828712,0.00038298042],"about_ca_topic_score_codex":0.00019572745,"about_ca_topic_score_gemma":0.00009899678,"teacher_disagreement_score":0.97025347,"about_ca_system_score_codex":0.00027256034,"about_ca_system_score_gemma":0.000070911505,"threshold_uncertainty_score":0.999941},"labels":[],"label_agreement":null},{"id":"W4402626602","doi":"10.1109/tac.2024.3465245","title":"Safe and Quasi-Optimal Autonomous Navigation in Environments With Convex Obstacles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regular polygon; Computer science; Convex optimization; Control theory (sociology); Control engineering; Engineering; Mathematics; Artificial intelligence; Control (management); Geometry","score_opus":0.008496467678473205,"score_gpt":0.22544482606853727,"score_spread":0.21694835839006407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402626602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04069297,0.00013342527,0.957424,0.00064850145,0.00033118515,0.0003693734,0.000011637924,0.00033380216,0.000055108805],"genre_scores_gemma":[0.93363786,0.0000064830674,0.06600551,0.00009472282,0.000011672445,0.00006489415,0.0000012124156,0.000016082065,0.0001615518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986478,0.00009101515,0.00029591648,0.00041879292,0.0002815021,0.0002649728],"domain_scores_gemma":[0.99920267,0.0003396233,0.000048338206,0.00030752714,0.000009049491,0.0000928013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023387118,0.00019509775,0.00024478085,0.00021210071,0.000094664516,0.00019505115,0.00020960535,0.00007321595,0.000018037892],"category_scores_gemma":[0.000003396358,0.0001694231,0.00004134113,0.0002559028,0.000073216426,0.00041507775,0.0000015997664,0.00020389525,0.00009437786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030918476,0.0004030373,0.00008601727,0.00014777586,0.00019607523,0.00048506967,0.005218476,0.23092422,0.0015973913,0.00062630925,0.000022849117,0.76026183],"study_design_scores_gemma":[0.0010333889,0.0002630252,0.0016072663,0.00025563885,0.00003297982,0.00010843052,0.000044730303,0.9957648,0.00049334194,0.000121399855,0.000084399544,0.00019059697],"about_ca_topic_score_codex":0.00003384568,"about_ca_topic_score_gemma":0.000004372351,"teacher_disagreement_score":0.89294493,"about_ca_system_score_codex":0.00014758266,"about_ca_system_score_gemma":0.00008081271,"threshold_uncertainty_score":0.69088733},"labels":[],"label_agreement":null},{"id":"W4402677380","doi":"10.3390/a17090420","title":"An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Shortest path problem; Path (computing); Algorithm; Computer science; Shortest Path Faster Algorithm; K shortest path routing; Yen's algorithm; Mathematics; Combinatorics; Mathematical optimization; Dijkstra's algorithm; Graph","score_opus":0.01892442466466172,"score_gpt":0.2890340159810184,"score_spread":0.2701095913163567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402677380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020152895,0.0034269954,0.9715284,0.002861777,0.0006163089,0.0004575784,0.00011010075,0.00029757732,0.0005483799],"genre_scores_gemma":[0.15772758,0.00011374109,0.8410887,0.00066025485,0.00020620183,0.00006396926,0.00001771675,0.00003201221,0.00008981347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979813,0.000122433,0.00040478096,0.00067234464,0.00039263428,0.00042648712],"domain_scores_gemma":[0.99875796,0.00041481358,0.000049344606,0.00057656504,0.00005962535,0.00014169139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046052676,0.00024543775,0.00028711976,0.0001974571,0.00014162804,0.00031582074,0.00092799065,0.00009888778,0.000007865908],"category_scores_gemma":[0.00003227188,0.00018756615,0.00006380409,0.0010596327,0.00012884226,0.00090045884,0.00023964421,0.00033016512,0.000026870099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000554986,0.0004188486,0.000947081,0.00006277652,0.000120148456,0.0012271579,0.025494205,0.0050633647,0.002111587,0.0210556,0.0013899996,0.9421037],"study_design_scores_gemma":[0.0001646637,0.0003237092,0.030489007,0.00025250213,0.000020573341,0.00024716134,0.000383361,0.95788485,0.0005402084,0.00862139,0.00074548024,0.00032707758],"about_ca_topic_score_codex":0.00033220663,"about_ca_topic_score_gemma":0.000008142302,"teacher_disagreement_score":0.9528215,"about_ca_system_score_codex":0.00004149618,"about_ca_system_score_gemma":0.00012673515,"threshold_uncertainty_score":0.7648726},"labels":[],"label_agreement":null},{"id":"W4402686017","doi":"10.2514/6.2024-4168","title":"Enhancing Best-First Search Algorithms for Aircraft Ground Trajectory Optimization With Non-Additive Cost Functions","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Trajectory; Trajectory optimization; Computer science; Algorithm; Mathematical optimization; Mathematics; Physics","score_opus":0.028873560197770452,"score_gpt":0.27913181516107566,"score_spread":0.2502582549633052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402686017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016201915,0.000096713375,0.9946272,0.0006285362,0.0010951847,0.0008216476,0.000039611034,0.00058795395,0.0019411831],"genre_scores_gemma":[0.011811162,0.000015414762,0.98124105,0.00012303119,0.00039400245,0.00036049218,0.00007672399,0.00004699388,0.0059311455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797887,0.000040403047,0.0002630612,0.00076840736,0.00042517265,0.0005240974],"domain_scores_gemma":[0.9985184,0.0006027828,0.000037950937,0.00042210787,0.0002498377,0.00016889612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000435066,0.0002456966,0.00021323688,0.00029713963,0.0004319473,0.0005683212,0.00046055825,0.00009969495,0.000045497625],"category_scores_gemma":[0.00003511703,0.00020564889,0.000082860875,0.0008906349,0.00007795011,0.0010168136,0.00009921517,0.00025312282,0.00014749901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019078821,0.0001495561,0.00004194726,0.00014041216,0.00017237183,0.00011193384,0.0030456486,0.95033336,0.00015419182,0.0013245099,0.003980986,0.040526036],"study_design_scores_gemma":[0.00037672167,0.00044652127,0.00014453192,0.00020941994,0.00003361912,0.00008623447,0.00042501756,0.99535394,0.00096752675,0.000028132145,0.0016233881,0.00030492572],"about_ca_topic_score_codex":0.0001397912,"about_ca_topic_score_gemma":0.00004499873,"teacher_disagreement_score":0.045020632,"about_ca_system_score_codex":0.00022121317,"about_ca_system_score_gemma":0.00036569795,"threshold_uncertainty_score":0.83861184},"labels":[],"label_agreement":null},{"id":"W4403025265","doi":"10.2139/ssrn.4973345","title":"High-Performance Coupled Kinematics of Aerial Continuum Manipulation Systems for Control Applications","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kinematics; Control theory (sociology); Computer science; Control (management); Control engineering; Physics; Engineering; Artificial intelligence; Classical mechanics","score_opus":0.012915100974828906,"score_gpt":0.2516650012446911,"score_spread":0.2387499002698622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403025265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010904041,0.0038121769,0.979767,0.0003115193,0.003427612,0.0015740383,0.000032234322,0.0001368085,0.000034551653],"genre_scores_gemma":[0.95870876,0.0007628408,0.03779522,0.0000156755,0.0017652934,0.00043965445,0.0000444551,0.000049986083,0.00041808758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99649584,0.0000895091,0.0010369613,0.00046037108,0.00049989775,0.0014174238],"domain_scores_gemma":[0.9972414,0.00019194852,0.0013806195,0.00064824923,0.0004565984,0.000081186976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025469884,0.00032624308,0.0006892998,0.00027203967,0.00015499756,0.0003076444,0.0013313127,0.00028282343,8.021207e-7],"category_scores_gemma":[0.00006993776,0.00030140695,0.00019550949,0.00022057295,0.00004193078,0.0001325061,0.00027985175,0.0021465172,0.000015124405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045200464,0.00007794049,0.000059511145,0.0009497244,0.00065667386,0.0000023978505,0.00016511827,0.64936525,0.0003664011,0.34452564,0.00020728483,0.0035788473],"study_design_scores_gemma":[0.00090850226,0.00020568744,0.00006728745,0.00035359393,0.00016791165,0.00017203996,0.000034978526,0.85020643,0.0000473946,0.14752771,0.000056790723,0.00025167232],"about_ca_topic_score_codex":0.00005422183,"about_ca_topic_score_gemma":0.00000869432,"teacher_disagreement_score":0.94780475,"about_ca_system_score_codex":0.00077464635,"about_ca_system_score_gemma":0.0028053003,"threshold_uncertainty_score":0.9999438},"labels":[],"label_agreement":null},{"id":"W4403313824","doi":"10.1007/s13042-024-02393-z","title":"Improved D3QN with graph augmentation for enhanced multi-UAV cooperative path planning in urban environments","year":2024,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China","keywords":"Computational intelligence; Computer science; Motion planning; Path (computing); Graph; Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.010869670820580471,"score_gpt":0.28285369623278384,"score_spread":0.27198402541220335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403313824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07815561,0.001026931,0.919941,0.00028542872,0.00043211726,0.0000916801,0.0000048116767,0.000019687226,0.000042709835],"genre_scores_gemma":[0.86291873,0.00009495917,0.13644078,0.00005339554,0.00009586999,0.000006090246,0.000010403794,0.000012606826,0.00036714989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898976,0.00006766451,0.0003096684,0.00019823042,0.0002960127,0.00013866847],"domain_scores_gemma":[0.9994201,0.00020318566,0.00018650481,0.000050857525,0.00008123159,0.000058086076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003546597,0.00012636727,0.00014637958,0.00022174095,0.000046546873,0.00021005719,0.0002786349,0.000040335526,0.0000024267756],"category_scores_gemma":[0.00008010977,0.00010080966,0.00003888475,0.00010077439,0.000033250235,0.00027052485,0.000054049437,0.00035025712,0.0000010968669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007365256,0.0005935455,0.06681472,0.000102023194,0.0012376584,0.0012466112,0.04908444,0.62575966,0.055308443,0.003673425,0.00029994117,0.19514301],"study_design_scores_gemma":[0.0018957665,0.0006988371,0.0052254666,0.0004289984,0.000019128014,0.00015740404,0.00019194237,0.98781395,0.0020374975,0.00014417383,0.001223628,0.00016318694],"about_ca_topic_score_codex":0.000016602855,"about_ca_topic_score_gemma":0.000001814033,"teacher_disagreement_score":0.78476316,"about_ca_system_score_codex":0.00006872651,"about_ca_system_score_gemma":0.000047905272,"threshold_uncertainty_score":0.41108987},"labels":[],"label_agreement":null},{"id":"W4403422698","doi":"10.1109/icipca61593.2024.10709198","title":"Lake Water Flow Optimization Study Based on NCA Algorithm and SARIMAX Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Flow (mathematics); Algorithm; Optimization algorithm; Mathematical optimization; Mathematics","score_opus":0.015496717640696765,"score_gpt":0.24527213165630551,"score_spread":0.22977541401560875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403422698","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014548203,0.000017776834,0.99628973,0.0007648644,0.0003711938,0.00022632803,0.0000056549106,0.0005253494,0.0016536283],"genre_scores_gemma":[0.02047968,0.0000012135245,0.9779571,0.00039591274,0.00004616711,0.000017944836,0.0000141317405,0.000015208562,0.0010725985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986977,0.00005104172,0.00016218793,0.0005250108,0.00031541046,0.0002486736],"domain_scores_gemma":[0.99941015,0.0000613218,0.000011397147,0.00039664627,0.000037777027,0.00008271398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034372276,0.00015650982,0.0001302984,0.00016679578,0.00009529824,0.00043296002,0.00029705878,0.000049832444,0.000025496076],"category_scores_gemma":[0.000009467984,0.00010553082,0.000027351016,0.00018838789,0.000017639442,0.00033480726,0.00013329237,0.00013023756,0.00006656935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012147465,0.000077517965,0.000020682783,0.000006148065,0.00000954585,0.000062619794,0.0006168891,0.967728,0.000005631489,0.00010426421,0.00046799588,0.030899484],"study_design_scores_gemma":[0.00026136547,0.00017975067,0.00004829253,0.000020866937,0.000009349508,0.000007296869,0.000014442624,0.9989637,0.000094596326,0.00017057183,0.00006747415,0.00016227632],"about_ca_topic_score_codex":0.0000090786,"about_ca_topic_score_gemma":0.0000014124639,"teacher_disagreement_score":0.031235712,"about_ca_system_score_codex":0.00002497875,"about_ca_system_score_gemma":0.000046863617,"threshold_uncertainty_score":0.43034217},"labels":[],"label_agreement":null},{"id":"W4403487305","doi":"10.3233/faia240913","title":"Multi-Agent Path Finding with Task Assignment and Supporting Constraints","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Task (project management); Path (computing); Computer science; Distributed computing; Computer network; Engineering; Systems engineering","score_opus":0.048827334596339346,"score_gpt":0.2906728951649754,"score_spread":0.24184556056863607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403487305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000891563,0.0009252242,0.9902492,0.00026876616,0.00026821566,0.000631668,0.000036008914,0.00008974956,0.00752223],"genre_scores_gemma":[0.029601788,0.0007051072,0.9428843,0.00017046616,0.0002478718,0.00039796755,0.000056714664,0.00007379994,0.025861947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981727,0.000011398524,0.00049439725,0.0007891176,0.00022494275,0.00030744448],"domain_scores_gemma":[0.9992099,0.00006256052,0.00019241615,0.0003597802,0.000038922622,0.00013645658],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030678065,0.00028388025,0.00030753607,0.00025220704,0.00017063638,0.0002514775,0.00034593616,0.00016291557,0.000008039052],"category_scores_gemma":[0.000009947233,0.00026467,0.00003727512,0.00012281295,0.00036104105,0.00011673241,0.00018300218,0.00041157106,0.000043497443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003512021,0.00004570757,0.00019066405,0.00008158422,0.00006690309,0.000105729734,0.0013651302,0.0010771751,0.000053782278,0.37162852,0.0003958847,0.6249854],"study_design_scores_gemma":[0.000096560114,0.00019340825,0.00007079198,0.0013257442,0.00013340244,0.00012841617,0.0015309645,0.6004975,0.00045973403,0.36704534,0.027131835,0.0013863075],"about_ca_topic_score_codex":0.000008147951,"about_ca_topic_score_gemma":0.0000042846814,"teacher_disagreement_score":0.6235991,"about_ca_system_score_codex":0.000088272114,"about_ca_system_score_gemma":0.00008903403,"threshold_uncertainty_score":0.99998057},"labels":[],"label_agreement":null},{"id":"W4403510618","doi":"10.1109/tii.2024.3449990","title":"Factor Graph Optimization for Flexibly Modeled INS/GPS Navigation in Graphical State-Space","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province","keywords":"Global Positioning System; Computer science; Factor graph; Graph; State space; Factor (programming language); Theoretical computer science; Algorithm; Mathematics; Programming language; Operating system; Statistics","score_opus":0.05681800646831151,"score_gpt":0.28793424899119463,"score_spread":0.23111624252288313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403510618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010542602,0.000012193375,0.9852562,0.00040468297,0.0023805553,0.00077191176,0.00012587718,0.00044990276,0.000056058187],"genre_scores_gemma":[0.6506089,0.000039192255,0.34863904,0.00012632822,0.00012198424,0.00024079815,0.00005427959,0.000038870476,0.00013059074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981096,0.000063865715,0.00078281533,0.00024130382,0.00042182568,0.00038058715],"domain_scores_gemma":[0.99891645,0.00036210765,0.00012270712,0.00034935703,0.00011671725,0.00013266914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003897951,0.00024839377,0.00025694346,0.0008483459,0.00017107821,0.00044300713,0.0004215498,0.00032156633,0.0000072521125],"category_scores_gemma":[0.000025425197,0.00024200334,0.00013921321,0.0017260147,0.000049734947,0.0017217553,0.0000037771274,0.0007248,0.000019869733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033693817,0.00005901094,0.0000029463286,0.000045003962,0.000031114414,0.0000035474736,0.002582179,0.96847695,0.000014376146,0.00064575445,0.0001670061,0.027938418],"study_design_scores_gemma":[0.0010165994,0.00023673344,0.0000032373098,0.00023309632,0.000015744723,0.000010481438,0.00010244966,0.99515224,0.0018794928,0.0009500401,0.00014132822,0.00025853745],"about_ca_topic_score_codex":0.000037054237,"about_ca_topic_score_gemma":0.0000035009143,"teacher_disagreement_score":0.6400663,"about_ca_system_score_codex":0.00018041358,"about_ca_system_score_gemma":0.00026217723,"threshold_uncertainty_score":0.986861},"labels":[],"label_agreement":null},{"id":"W4403534786","doi":"10.1109/codit62066.2024.10708494","title":"Hybrid Framework for UAV Motion Planning and Obstacle Avoidance: Integrating Deep Reinforcement Learning with Fuzzy Logic","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Concordia University","funders":"National Research Council Canada","keywords":"Reinforcement learning; Obstacle avoidance; Computer science; Fuzzy logic; Motion planning; Artificial intelligence; Collision avoidance; Obstacle; Motion (physics); Control engineering; Engineering; Mobile robot; Robot; Computer security","score_opus":0.023312820881413555,"score_gpt":0.28041707339441235,"score_spread":0.2571042525129988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403534786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026410485,0.00079526874,0.9934027,0.0003314036,0.0003673693,0.0002605934,3.5751418e-7,0.00062429206,0.0015769704],"genre_scores_gemma":[0.42160037,0.000003813601,0.5778096,0.00012924407,0.00007949373,0.00003313753,0.0000057325096,0.000012848078,0.00032579058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855953,0.000039465926,0.00023976281,0.00052730914,0.00025310132,0.00038079722],"domain_scores_gemma":[0.99904096,0.0004954929,0.000075812066,0.00023450123,0.000061681065,0.00009155966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045765802,0.00019633287,0.00017843932,0.000119535856,0.00028393228,0.0005689652,0.00027785267,0.00006095533,0.000004902696],"category_scores_gemma":[0.00021321407,0.0001475348,0.000036853286,0.00024049722,0.000038168804,0.0005251797,0.00012569338,0.00042056927,0.000012639208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013763867,0.000012897579,0.0011834791,0.00017488483,0.00004912281,0.00012657986,0.0045622457,0.6234396,0.00013485891,0.28020397,0.00014002681,0.08995856],"study_design_scores_gemma":[0.00015081957,0.00031041476,0.00023289313,0.00053136254,0.000010441217,0.000103625636,0.00039587755,0.9796411,0.00042975723,0.017790534,0.0001836857,0.00021952884],"about_ca_topic_score_codex":0.000011275288,"about_ca_topic_score_gemma":3.118749e-7,"teacher_disagreement_score":0.41895932,"about_ca_system_score_codex":0.000068089714,"about_ca_system_score_gemma":0.00004205121,"threshold_uncertainty_score":0.60162944},"labels":[],"label_agreement":null},{"id":"W4403678466","doi":"10.1109/case59546.2024.10711838","title":"Hybrid Dynamic Modelling using FeedForward and Temporal Convolutional Networks (FNN+TCN) and Robust Control Scheme for Aerial Manipulators","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; Simon Fraser University","funders":"","keywords":"Feed forward; Scheme (mathematics); Computer science; Control theory (sociology); Robust control; Feedforward neural network; Control (management); Artificial intelligence; Control engineering; Control system; Artificial neural network; Engineering; Mathematics","score_opus":0.03358828489485824,"score_gpt":0.2539169354248478,"score_spread":0.22032865052998954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403678466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03763061,0.0010290459,0.95951355,0.00017890039,0.0010449034,0.00031533025,0.000016433658,0.0002460266,0.000025179366],"genre_scores_gemma":[0.5487038,0.0000070669876,0.45097077,0.00005622835,0.00016830143,0.000008389444,0.000011632606,0.000014480525,0.00005935148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854,0.000031120017,0.0002810858,0.0005611862,0.00018885524,0.0003977904],"domain_scores_gemma":[0.99938315,0.00018737068,0.00005273594,0.000182952,0.00005299261,0.00014077556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040536598,0.00020732325,0.00025073867,0.00011630818,0.00021056281,0.00045985752,0.0002066683,0.00009085139,0.000002896282],"category_scores_gemma":[0.0000123550535,0.00019276,0.000053305146,0.000115136936,0.00008085864,0.00050411,0.00011958829,0.00017192244,0.0000019446843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015123305,0.000008332427,0.00043038983,0.000037892994,0.000052903,0.000024544192,0.000041560408,0.98292166,0.00005746579,0.015356484,0.00013250035,0.0009211577],"study_design_scores_gemma":[0.00065911096,0.000047538277,0.00009731628,0.00007014589,0.000025708225,0.00018496848,0.000009143352,0.99712336,0.000006215866,0.0014593063,0.00007562869,0.00024153716],"about_ca_topic_score_codex":0.00006525561,"about_ca_topic_score_gemma":0.0000010097939,"teacher_disagreement_score":0.5110732,"about_ca_system_score_codex":0.000072527335,"about_ca_system_score_gemma":0.00009637673,"threshold_uncertainty_score":0.78605247},"labels":[],"label_agreement":null},{"id":"W4403823338","doi":"10.1007/s11370-024-00566-x","title":"R3T*-MOSafeRL($$\\lambda $$): path planning of mobile robots in unknown dynamic environments","year":2024,"lang":"en","type":"article","venue":"Intelligent Service Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Motion planning; Mobile robot; Lambda; Path (computing); Robot; Distributed computing; Human–computer interaction; Real-time computing; Artificial intelligence; Computer network","score_opus":0.018894601984861092,"score_gpt":0.28451215349957343,"score_spread":0.26561755151471234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403823338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009265283,0.003132927,0.98410314,0.0006262008,0.0017458816,0.0004754425,0.00001033958,0.00027461853,0.0003661829],"genre_scores_gemma":[0.6843548,0.00034802387,0.31353402,0.000458972,0.000104234015,0.000073998635,0.00006335482,0.00010497074,0.000957673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99669135,0.00012874854,0.0009431936,0.00085042906,0.00068084756,0.00070545025],"domain_scores_gemma":[0.9982465,0.0003521017,0.00017763495,0.0009870337,0.00005334768,0.00018340984],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053931866,0.00041869783,0.0005118133,0.00043530492,0.00006812807,0.00016465153,0.0015742168,0.00021703573,0.000018590736],"category_scores_gemma":[0.000030314111,0.00041960616,0.00012778769,0.00131775,0.00005633938,0.00049251673,0.0005797771,0.0005446776,0.00034320168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059327153,0.00017440226,0.00065409584,0.0002318278,0.000055330423,0.00035426964,0.002998238,0.98132634,0.0009137135,0.002480843,0.00007520396,0.010729808],"study_design_scores_gemma":[0.00016278416,0.00015555116,0.0013405359,0.0009599931,0.000026159707,0.000057326342,0.00021360525,0.99236125,0.0014967407,0.0012307096,0.0015696178,0.0004257411],"about_ca_topic_score_codex":0.000086111984,"about_ca_topic_score_gemma":0.00001091369,"teacher_disagreement_score":0.6750895,"about_ca_system_score_codex":0.0003128936,"about_ca_system_score_gemma":0.00014936105,"threshold_uncertainty_score":0.9998256},"labels":[],"label_agreement":null},{"id":"W4403871806","doi":"10.1155/2024/8171722","title":"Decision‐Making and Path Planning for Head‐On Collision Avoidance on Curved Roads","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Head (geology); Path (computing); Motion planning; Collision; Computer science; Operations research; Simulation; Transport engineering; Engineering; Computer security; Geology; Artificial intelligence; Computer network","score_opus":0.02289188737570673,"score_gpt":0.32954097670243754,"score_spread":0.3066490893267308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403871806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25442168,0.0007466877,0.74318135,0.00019534011,0.001256086,0.000131058,0.000007697732,0.000043487267,0.000016648884],"genre_scores_gemma":[0.6290233,0.000043568198,0.3707158,0.00008605789,0.00010076756,0.000004859773,0.000003089531,0.000011843621,0.000010685222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985961,0.00002759723,0.00049352704,0.00027645563,0.00042395663,0.00018238068],"domain_scores_gemma":[0.998561,0.000839135,0.00024475285,0.00014392819,0.0001345023,0.00007664999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051219936,0.00014669131,0.00022729102,0.00024818632,0.000116383264,0.00011997894,0.00022195475,0.000058555444,5.965771e-7],"category_scores_gemma":[0.00010295929,0.00012163566,0.00009121738,0.00027972262,0.000015368267,0.0007456314,0.0000032936314,0.00024745305,0.0000017417007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029025495,0.000039231785,0.00027660083,0.000060194703,0.000021333199,0.00023257332,0.0027833814,0.81664556,0.00088318926,0.0025761744,0.0001992205,0.17599227],"study_design_scores_gemma":[0.0044911434,0.005707117,0.30389088,0.019008873,0.00009208957,0.00021338755,0.00046115147,0.6228888,0.0032675425,0.032190736,0.0069485176,0.0008397723],"about_ca_topic_score_codex":4.2034e-7,"about_ca_topic_score_gemma":5.906686e-7,"teacher_disagreement_score":0.37460166,"about_ca_system_score_codex":0.00006135286,"about_ca_system_score_gemma":0.00006725741,"threshold_uncertainty_score":0.49601585},"labels":[],"label_agreement":null},{"id":"W4403890319","doi":"10.61527/appfe-2024.102-104","title":"TRACKING THE TRAJECTORY OF A UAV BY A SEQUENCE OF FRAMES FROM ITS CAMERA","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Trajectory; Computer vision; Computer science; Artificial intelligence; Sequence (biology); Tracking (education); Computer graphics (images); Physics","score_opus":0.03821355284626322,"score_gpt":0.2829713110576476,"score_spread":0.2447577582113844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403890319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08034223,0.0050042705,0.91115904,0.0009346899,0.00059269386,0.00015272552,0.00006098204,0.00023476155,0.0015186125],"genre_scores_gemma":[0.92245257,0.000020825879,0.07702466,0.00014254556,0.000036926023,0.0000056839735,0.0000023060093,0.000007681866,0.00030682996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897975,0.00006450859,0.00023648054,0.00026655168,0.00029637074,0.00015636109],"domain_scores_gemma":[0.9989514,0.0005224201,0.000061557635,0.00038064533,0.00004925736,0.000034731514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024124747,0.00009734893,0.00016194124,0.000050337,0.000029739238,0.000068325775,0.0008227473,0.000048635433,0.000024051791],"category_scores_gemma":[0.000060336577,0.000063407795,0.000063961794,0.00034681926,0.000072436305,0.00025184493,0.00008479382,0.00016398086,0.000016776517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008749997,0.0001812726,0.0008437878,0.0002692072,0.00044291507,0.00018283071,0.039655328,0.005996158,0.6748405,0.038269855,0.024917062,0.21439232],"study_design_scores_gemma":[0.00009689246,0.00007801802,0.0011539322,0.00028657715,0.000020327017,0.000015814738,0.00019560257,0.90115005,0.09470697,0.001362692,0.000767907,0.00016521761],"about_ca_topic_score_codex":0.00043170105,"about_ca_topic_score_gemma":0.0000030371054,"teacher_disagreement_score":0.8951539,"about_ca_system_score_codex":0.00001727434,"about_ca_system_score_gemma":0.000107256536,"threshold_uncertainty_score":0.25856948},"labels":[],"label_agreement":null},{"id":"W4403900380","doi":"10.18280/mmep.111030","title":"Design and Implementation Low Cost Smart Cleaner Mobile Robot in Complex Environment","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Human–computer interaction; Computer science; Mobile robot; Embedded system; Robot; Systems engineering; Engineering; Artificial intelligence","score_opus":0.03793608394669088,"score_gpt":0.248425930959718,"score_spread":0.21048984701302711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403900380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032605682,0.00034557373,0.995636,0.00009642521,0.000046020792,0.00041985372,8.892691e-7,0.00017798776,0.000016711288],"genre_scores_gemma":[0.30793002,0.00007340208,0.69180447,0.000010308151,0.000012764781,0.00012640304,0.0000034917077,0.000017454235,0.000021669619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989732,0.000021097,0.0002610893,0.00032420916,0.00015085551,0.00026956174],"domain_scores_gemma":[0.9995573,0.0001770729,0.000017008571,0.00015549184,0.0000051125025,0.000088011184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004641673,0.00015200904,0.00017228944,0.00010640276,0.000037861035,0.00020148016,0.00011894761,0.000047394868,0.000006956695],"category_scores_gemma":[0.0000033798817,0.00013830882,0.000016999467,0.00010805389,0.000019370957,0.00017143064,0.00007902202,0.00014626904,0.000018609839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.9623304e-7,0.000017819648,0.0000048724214,0.00029799543,0.000008005781,0.000011215191,0.0013931427,0.9863466,0.00030432723,0.003302756,0.000011738853,0.008301054],"study_design_scores_gemma":[0.00013259046,0.00004097773,0.00002132032,0.00022687121,0.0000058499522,0.000034125576,0.00001721747,0.9948322,0.000107087464,0.0042739566,0.00014738894,0.00016038347],"about_ca_topic_score_codex":0.000008262173,"about_ca_topic_score_gemma":6.205555e-8,"teacher_disagreement_score":0.30466944,"about_ca_system_score_codex":0.00003886288,"about_ca_system_score_gemma":0.0000082613315,"threshold_uncertainty_score":0.56400704},"labels":[],"label_agreement":null},{"id":"W4403919862","doi":"10.1109/sm63044.2024.10733396","title":"Design and Preliminary Testing of an Aerial Landing Dock for Mobile Robots for Multi-Modal Robotic Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"DOCK; Modal; Mobile robot; Computer science; Robot; Engineering; Marine engineering; Aeronautics; Real-time computing; Artificial intelligence","score_opus":0.09876765060755384,"score_gpt":0.32515020252008214,"score_spread":0.22638255191252832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403919862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036649092,0.00047316332,0.99260205,0.000019109011,0.0012889778,0.001598156,0.000005911639,0.00033136664,0.000016367747],"genre_scores_gemma":[0.14717169,9.2434067e-7,0.8521374,0.0000063757957,0.00016779924,0.00032088294,0.0000049858504,0.000020388556,0.00016957002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864745,0.000067409135,0.00031968366,0.0004921277,0.0001429592,0.00033038776],"domain_scores_gemma":[0.99812025,0.0013431944,0.00007683636,0.00026470234,0.000095199604,0.00009984665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007468714,0.00016421053,0.00027201662,0.00012763131,0.0001228282,0.00028614755,0.00036197194,0.00008185926,3.7506007e-7],"category_scores_gemma":[0.00015422527,0.00013983148,0.000042191215,0.00020500382,0.0000289606,0.0004653051,0.00010543177,0.00006512206,0.0000015062568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003082996,0.000055605484,0.000055807923,0.0005287137,0.000031081807,0.000016381682,0.0006984922,0.98216534,0.0052539376,0.001438392,0.00014689747,0.009578498],"study_design_scores_gemma":[0.00055964495,0.0013413555,0.000100671954,0.00026803548,0.000026891365,0.0001031293,0.00006123838,0.99589825,0.0012835889,0.00016451045,0.000013145091,0.00017953337],"about_ca_topic_score_codex":0.00004355873,"about_ca_topic_score_gemma":2.8117165e-7,"teacher_disagreement_score":0.14350678,"about_ca_system_score_codex":0.000033436583,"about_ca_system_score_gemma":0.000095433745,"threshold_uncertainty_score":0.57021624},"labels":[],"label_agreement":null},{"id":"W4403944426","doi":"10.18280/ijsse.140524","title":"A Hybrid Optimal Approach for Efficient Remote Monitoring and Avoidance of Train Collisions","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Computer science; Computer security; Collision","score_opus":0.010936394415353368,"score_gpt":0.2507523340125653,"score_spread":0.23981593959721192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403944426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07358354,0.001674438,0.9235481,0.00025004655,0.00083399314,0.00005379062,0.000017886612,0.000019660465,0.00001851576],"genre_scores_gemma":[0.648992,0.00012871396,0.35071528,0.000002751303,0.00015178345,4.8837103e-7,7.3135124e-7,0.000004452323,0.0000037869206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992038,0.000010308876,0.00030125835,0.00012756536,0.0002545375,0.000102542166],"domain_scores_gemma":[0.9994348,0.00023264979,0.000076418255,0.000058184767,0.00013308701,0.00006484738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000507407,0.0000808587,0.00014882395,0.00016879954,0.000029188805,0.00008715079,0.00027909118,0.000025965503,2.2071247e-7],"category_scores_gemma":[0.00010745087,0.00007482037,0.000057852398,0.00007343798,0.000019494939,0.00019101506,0.00007426594,0.00016968606,6.964649e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004031437,0.00003086743,0.000028097475,0.00012472559,0.00013796941,0.00008946966,0.002945077,0.9701169,0.0019362697,0.0074360743,0.000011298618,0.017102921],"study_design_scores_gemma":[0.00026072515,0.00005202458,0.00038149848,0.00036860543,0.000008289156,0.0005602318,0.00003870446,0.99645126,0.0013715621,0.00015517918,0.00028341738,0.00006850286],"about_ca_topic_score_codex":0.0000024443982,"about_ca_topic_score_gemma":1.0205538e-8,"teacher_disagreement_score":0.57540846,"about_ca_system_score_codex":0.000039525305,"about_ca_system_score_gemma":0.00003650588,"threshold_uncertainty_score":0.30510858},"labels":[],"label_agreement":null},{"id":"W4404062082","doi":"10.23977/acss.2024.080616","title":"Optimization of Bench Dancing Dragon Team Motion Based on Collision Detection Model and Numerical Simulation","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Guizhou Province","keywords":"Collision; Collision detection; Motion (physics); Computer science; Simulation; Aerospace engineering; Computer graphics (images); Artificial intelligence; Engineering; Computer security","score_opus":0.01634655167809281,"score_gpt":0.27519085198086685,"score_spread":0.25884430030277406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404062082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005093916,0.0015134375,0.9923042,0.00002873749,0.00070353935,0.00023567925,0.0000022333068,0.00008567451,0.00003258329],"genre_scores_gemma":[0.8530196,0.000033205386,0.14684187,0.000020741683,0.00006170279,0.000009897662,0.0000030145213,0.0000071943846,0.0000027806389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873126,0.00011774411,0.00034789255,0.00041071096,0.00025798692,0.00013440914],"domain_scores_gemma":[0.9992607,0.0003686838,0.000103279584,0.00016435393,0.00005909311,0.00004387888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004387631,0.0001287487,0.00021123828,0.00023321387,0.0000659428,0.00016224926,0.00010879094,0.0000695313,1.7610606e-7],"category_scores_gemma":[0.000016876318,0.000115573486,0.000022180233,0.0003617185,0.000019008343,0.00073193293,0.00004506363,0.00009860328,4.051898e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057668517,0.000018917624,0.00011913188,0.00012530162,0.000002282702,0.000003269947,0.0002716375,0.9428027,0.00012712083,0.00021298508,0.0000024924566,0.056308378],"study_design_scores_gemma":[0.00019134927,0.00020526924,0.00011590176,0.00056835945,0.0000035952894,0.000006966087,0.000007696107,0.9983839,0.00017490592,0.00021100458,0.000015122361,0.00011592305],"about_ca_topic_score_codex":0.000017656334,"about_ca_topic_score_gemma":3.5473374e-7,"teacher_disagreement_score":0.84792566,"about_ca_system_score_codex":0.00004986959,"about_ca_system_score_gemma":0.000020466827,"threshold_uncertainty_score":0.471295},"labels":[],"label_agreement":null},{"id":"W4404102113","doi":"10.1109/tfr.2024.3492151","title":"Off the Beaten Track: Laterally Weighted Motion Planning for Local Obstacle Avoidance","year":2024,"lang":"en","type":"article","venue":"IEEE transactions on field robotics.","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle avoidance; Track (disk drive); Computer science; Obstacle; Computer vision; Motion (physics); Artificial intelligence; Motion planning; Geology; Geography; Mobile robot; Robot","score_opus":0.02202060450837179,"score_gpt":0.2673618717728599,"score_spread":0.24534126726448813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027070288,0.00031438522,0.9875734,0.0074817254,0.0033061856,0.00033441006,0.000012425436,0.0005227448,0.00018403694],"genre_scores_gemma":[0.8638379,0.000025116251,0.13289566,0.0011160002,0.00018063522,0.00008590611,0.00000463162,0.00003499104,0.001819183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984687,0.00006528785,0.00030397554,0.0004930056,0.00027296905,0.00039608247],"domain_scores_gemma":[0.9985087,0.0007782141,0.00004422548,0.00051385467,0.000067251945,0.00008776998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002677035,0.00021869586,0.00018543935,0.00013987515,0.00038680003,0.0003013871,0.00063725736,0.00016294123,0.000013782625],"category_scores_gemma":[0.000008110643,0.00016684596,0.00017067703,0.00036986257,0.000051183764,0.0004760466,0.000004187446,0.0005253165,0.0000691271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016734575,0.000058132995,0.000002900769,0.00003989371,0.000061087776,0.000042119365,0.001028871,0.82001287,0.00013857412,0.0013716257,0.0011871054,0.17604007],"study_design_scores_gemma":[0.0002468314,0.00021572663,0.00006642881,0.0001630843,0.000039120518,0.000053786516,0.0000431873,0.98958653,0.0073278095,0.0011882097,0.00085224747,0.000217008],"about_ca_topic_score_codex":0.000026634205,"about_ca_topic_score_gemma":0.000004608767,"teacher_disagreement_score":0.8635672,"about_ca_system_score_codex":0.00006862161,"about_ca_system_score_gemma":0.00006902595,"threshold_uncertainty_score":0.6803781},"labels":[],"label_agreement":null},{"id":"W4404165606","doi":"10.1007/978-3-031-73151-8_8","title":"Beyond Traditional Motion Planning: A Proximal Policy Optimization Reinforcement Learning Approach for Robotics","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Artificial intelligence; Robotics; Motion (physics); Computer science; Robot","score_opus":0.033336279703458725,"score_gpt":0.24677833735523294,"score_spread":0.2134420576517742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404165606","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.6191805e-7,0.003613221,0.97779024,0.00017493266,0.0009850559,0.0010209106,0.000007142049,0.00015901025,0.016249219],"genre_scores_gemma":[0.14878245,0.00031563712,0.8052902,0.0003717402,0.011439955,0.00042097855,0.0029460706,0.00033354067,0.030099431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978722,0.00004015803,0.0005709984,0.00072783156,0.00037639376,0.00041241734],"domain_scores_gemma":[0.9989531,0.00029057584,0.000294232,0.0002750501,0.00008662537,0.00010043033],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048557573,0.00043131132,0.00051573484,0.0004022662,0.0001751962,0.0004625688,0.00031203902,0.00062987686,0.0000010879352],"category_scores_gemma":[0.000054618555,0.00039756962,0.000106855514,0.00016123847,0.000049502138,0.00014791798,0.00010580713,0.0008018553,9.582725e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007646754,0.000007300206,0.00000982456,0.000251492,0.00004996589,0.000014482187,0.00028747224,0.8402082,3.101826e-7,0.15745178,0.00006655953,0.0016450221],"study_design_scores_gemma":[0.00027295426,0.00017669763,0.0000024363346,0.000735503,0.000034458822,0.00011211864,0.0000031234624,0.9891195,3.2388974e-7,0.008868191,0.0002916814,0.0003829964],"about_ca_topic_score_codex":0.000012636577,"about_ca_topic_score_gemma":2.937337e-7,"teacher_disagreement_score":0.17250004,"about_ca_system_score_codex":0.0002144007,"about_ca_system_score_gemma":0.00009680985,"threshold_uncertainty_score":0.9998476},"labels":[],"label_agreement":null},{"id":"W4404200251","doi":"10.23977/acss.2024.080617","title":"Trajectory Planning of Rotor Welding Manipulator Based on an Improved Particle Swarm Optimization Algorithm","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Rotor (electric); Welding; Manipulator (device); Computer science; Swarm behaviour; Trajectory; Multi-swarm optimization; Algorithm; Mathematical optimization; Engineering; Mechanical engineering; Artificial intelligence; Mathematics; Robot; Physics","score_opus":0.023947126376509396,"score_gpt":0.2881181692155115,"score_spread":0.26417104283900206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404200251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006588607,0.004442914,0.9867888,0.000024981126,0.001621238,0.0003177178,0.000005506187,0.0001802558,0.000029998178],"genre_scores_gemma":[0.60665166,0.000019820425,0.3930075,0.000045680157,0.00021329339,0.000035729572,0.000004056695,0.000015895526,0.0000063513257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826646,0.00017351232,0.00046199118,0.00054661545,0.000270536,0.00028089373],"domain_scores_gemma":[0.9991011,0.00031415015,0.00011435448,0.000322434,0.000046161138,0.000101812126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063671876,0.00019094773,0.0003096652,0.00018929823,0.00006616648,0.00026338108,0.0003451846,0.000069527174,0.0000011440629],"category_scores_gemma":[0.000008656545,0.0001710385,0.00004104087,0.00035592614,0.0000306957,0.0009057747,0.000057292767,0.00014042699,0.000001370855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005206635,0.00004157248,0.0003861849,0.00013943539,0.000008017585,0.000053760727,0.00043846373,0.96194243,0.00015717553,0.0002462709,0.0000062533277,0.036575202],"study_design_scores_gemma":[0.00029323003,0.0004224752,0.00022181841,0.00083317864,0.000004340997,0.000015985164,0.000032654178,0.9973068,0.0005441339,0.000046618727,0.000079643614,0.00019910125],"about_ca_topic_score_codex":0.000028770268,"about_ca_topic_score_gemma":4.0307677e-7,"teacher_disagreement_score":0.6000631,"about_ca_system_score_codex":0.00004314533,"about_ca_system_score_gemma":0.000043704582,"threshold_uncertainty_score":0.6974747},"labels":[],"label_agreement":null},{"id":"W4404310614","doi":"10.1115/detc2024-142005","title":"Dynamics, Analysis and Experiments of Obstacle Negotiation for Wheeled Mobile Robots","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Obstacle; Mobile robot; Computer science; Dynamics (music); Robot; Negotiation; Human–computer interaction; Artificial intelligence; Psychology; Geography","score_opus":0.013578476815158922,"score_gpt":0.2899105397064654,"score_spread":0.27633206289130646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404310614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01664109,0.00036729008,0.982189,0.00011763192,0.00020371116,0.00017335304,0.000006342506,0.0001223761,0.00017918645],"genre_scores_gemma":[0.61297244,0.0000038688468,0.38646019,0.000016442804,0.000012312425,0.000031120373,0.000013532729,0.0000045100987,0.00048559584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992684,0.000017253777,0.00017694887,0.00028023752,0.00012901484,0.00012815217],"domain_scores_gemma":[0.9995186,0.000119778946,0.000040148207,0.00023309946,0.000046408888,0.000041990625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014673937,0.00007506359,0.00015546451,0.00020395503,0.00003339494,0.00007432328,0.00019436011,0.0000347772,0.0000061642268],"category_scores_gemma":[0.000016415506,0.00006665134,0.000067215864,0.00057206803,0.000016699383,0.00026619996,0.00006990249,0.000029659215,0.0000033781305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025784499,0.00048331212,0.026509255,0.0005304917,0.0035857817,0.000042037656,0.015488341,0.45347843,0.014923985,0.15984456,0.0020300339,0.323058],"study_design_scores_gemma":[0.00010330391,0.000062306775,0.002647081,0.000010719695,0.000053718835,0.0000015359763,0.00006034438,0.99317217,0.0032342381,0.0005608153,0.000019100678,0.000074646545],"about_ca_topic_score_codex":0.000059539863,"about_ca_topic_score_gemma":0.000006221044,"teacher_disagreement_score":0.59633136,"about_ca_system_score_codex":0.000038772374,"about_ca_system_score_gemma":0.000024157272,"threshold_uncertainty_score":0.27179626},"labels":[],"label_agreement":null},{"id":"W4404488682","doi":"10.1155/atr/6496007","title":"<i>B</i><sup>∗</sup> Algorithm: Multiobjective Path Planning for Flexible Buses Traffic System","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education, Fujian Province; Putian Science and Technology Bureau; Natural Science Foundation of Fujian Province","keywords":"Path (computing); Algorithm; Computer science; Motion planning; Transport engineering; Mathematical optimization; Engineering; Mathematics; Artificial intelligence; Computer network","score_opus":0.015911814480514857,"score_gpt":0.27740800996970993,"score_spread":0.26149619548919506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404488682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026966287,0.0017531838,0.96894586,0.00009803797,0.001511702,0.0003040027,0.000042007166,0.0003515402,0.000027381635],"genre_scores_gemma":[0.33001202,0.000025091744,0.6695187,0.000027869262,0.0003047304,0.000022963277,0.0000193818,0.000027247075,0.000041951578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806947,0.000042861288,0.0007476451,0.000346406,0.0004763232,0.00031727363],"domain_scores_gemma":[0.9986454,0.0003441153,0.00033449277,0.00018226646,0.00036329127,0.0001304026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004936996,0.00022186199,0.00036646554,0.00035013474,0.0001333689,0.00015509714,0.00040811702,0.000090401794,7.5524343e-7],"category_scores_gemma":[0.000030819418,0.00019752796,0.00022949303,0.0005235625,0.000025595764,0.001606222,0.0000040228,0.00029455175,0.0000048113984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004042075,0.000037314345,0.00002447745,0.0002130519,0.00008117605,0.00030377536,0.01006997,0.9081355,0.00031269068,0.0008111134,0.00014094834,0.07982959],"study_design_scores_gemma":[0.0014568644,0.0006427321,0.002213159,0.0018684481,0.0001095585,0.00025235367,0.0033017267,0.98646635,0.0011232087,0.0003422175,0.0018989904,0.00032436397],"about_ca_topic_score_codex":0.0000023105915,"about_ca_topic_score_gemma":2.0056031e-7,"teacher_disagreement_score":0.30304575,"about_ca_system_score_codex":0.00016805236,"about_ca_system_score_gemma":0.00021908783,"threshold_uncertainty_score":0.8054956},"labels":[],"label_agreement":null},{"id":"W4404652056","doi":"10.1016/j.oceaneng.2024.119819","title":"Path Re-planning method of unmanned underwater vehicles based on dynamic bayesian threat assessment","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Underwater; Motion planning; Marine engineering; Bayesian probability; Path (computing); Computer science; Dynamic Bayesian network; Environmental science; Aeronautics; Engineering; Artificial intelligence; Geology; Oceanography; Robot","score_opus":0.01281965228284382,"score_gpt":0.2860595439035842,"score_spread":0.27323989162074036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404652056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019440077,0.00013226205,0.99588716,0.00031073377,0.0005306861,0.00010477112,0.00000516422,0.0005907909,0.0004944393],"genre_scores_gemma":[0.50512946,0.0000011590046,0.49473366,0.00003310146,0.000025004238,0.000002498698,0.000005805916,0.00002474942,0.000044561064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998517,0.000052715826,0.00027037502,0.0004490386,0.00035618996,0.0003546787],"domain_scores_gemma":[0.99901056,0.00033811215,0.000042608313,0.0004888358,0.000025836107,0.00009405216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005520918,0.00023139364,0.0002461638,0.00032389225,0.000047650134,0.00014011718,0.00048202387,0.000077226854,0.000004949471],"category_scores_gemma":[0.00002066898,0.00020958629,0.000089098576,0.00042512827,0.000012139258,0.00021443538,0.00008528808,0.00027961438,0.000009649118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018350095,0.000020818698,0.00023431011,0.00019096358,0.000034658202,0.00020178065,0.0003689937,0.9898865,0.0019666571,0.0019005459,0.00014023727,0.005052692],"study_design_scores_gemma":[0.00014647566,0.00008388518,0.0015189729,0.0007249517,0.000013610392,0.000014717876,0.000022185548,0.9953484,0.0015750743,0.00024822005,0.00007993068,0.00022356585],"about_ca_topic_score_codex":0.0000055481423,"about_ca_topic_score_gemma":7.567469e-8,"teacher_disagreement_score":0.50318545,"about_ca_system_score_codex":0.0001426714,"about_ca_system_score_gemma":0.00006948273,"threshold_uncertainty_score":0.8546681},"labels":[],"label_agreement":null},{"id":"W4404688710","doi":"10.1109/oceans55160.2024.10754502","title":"A Hexagonal Grid Cell-inspired Collision Avoidance Motion Planning Algorithm for USVs in a Velocity Domain-based Marine Environment","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Collision avoidance; Hexagonal tiling; Hexagonal crystal system; Grid cell; Grid; Computer science; Domain (mathematical analysis); Motion (physics); Motion planning; Collision; Algorithm; Artificial intelligence; Computer vision; Geology; Mathematics; Geodesy; Chemistry; Robot; Crystallography; Mathematical analysis","score_opus":0.016094256363033785,"score_gpt":0.2452892554291696,"score_spread":0.22919499906613583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404688710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014168897,0.00021293049,0.9828056,0.0006678785,0.00087807636,0.00057229283,0.0000296521,0.00034107425,0.000323595],"genre_scores_gemma":[0.028289188,0.0000060012285,0.97067004,0.00023625484,0.00018457696,0.0001686291,0.00006264589,0.000025329866,0.0003573504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764496,0.00009157462,0.00041914106,0.0008446047,0.00048630917,0.0005133883],"domain_scores_gemma":[0.9989682,0.00037142882,0.000077612654,0.00042582912,0.000025977712,0.00013094276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008186003,0.0002691678,0.0002670478,0.00027229488,0.00014902394,0.00020361751,0.0005553013,0.00012281736,0.000018442646],"category_scores_gemma":[0.000020967202,0.0002545411,0.00010586864,0.00044941503,0.00005582728,0.0003230198,0.00022991635,0.0002548732,0.000060048078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005630045,0.00068391213,0.0020204762,0.00032525815,0.000046379184,0.00091405644,0.0021652046,0.5490389,0.0021059567,0.0019495968,0.0033703316,0.43732363],"study_design_scores_gemma":[0.00090219505,0.00017146021,0.004910779,0.00013554707,0.0000058316555,0.000015086391,0.000029605519,0.9873295,0.0027549835,0.0010465644,0.002396815,0.00030162485],"about_ca_topic_score_codex":0.00006763944,"about_ca_topic_score_gemma":0.0000017916891,"teacher_disagreement_score":0.4382906,"about_ca_system_score_codex":0.00037946366,"about_ca_system_score_gemma":0.00015130875,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W4404727836","doi":"10.54254/2755-2721/109/20241456","title":"Analysis of the Applications of Algorithm and Automatic Pathfinding","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Algorithm; Computer graphics (images); Artificial intelligence; Theoretical computer science; Shortest path problem; Graph","score_opus":0.0050638141270246675,"score_gpt":0.20821624536107827,"score_spread":0.2031524312340536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00817463,0.00029038338,0.9912568,0.000039020862,0.000037962025,0.00007729937,0.0000064360124,0.00006085106,0.000056636396],"genre_scores_gemma":[0.68936634,0.0000023232053,0.3105946,0.0000064397937,0.000008166183,0.0000133700905,0.0000027524632,0.0000029420255,0.0000030784631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949515,0.000004367037,0.00015514484,0.00014056005,0.000139821,0.0000649668],"domain_scores_gemma":[0.9996049,0.00021571317,0.00003541474,0.00010347686,0.00001770669,0.000022837326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107748,0.000060222257,0.00012262333,0.00018886158,0.000031932366,0.000032806944,0.00013673339,0.000019465113,5.902783e-7],"category_scores_gemma":[0.0000037732807,0.000048726404,0.000031823103,0.00089523045,0.000023574967,0.00004666377,0.00008346246,0.000052507366,3.52005e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.018423e-8,0.000006095857,0.00008897291,0.00007290558,0.00017949713,5.085846e-7,0.00047477748,0.7452753,0.00020034486,0.11172534,0.000002019723,0.14197418],"study_design_scores_gemma":[0.000034050758,0.0000031236157,0.017298447,0.000026045669,0.00006431608,0.0000045936417,0.0000112313755,0.9807012,0.000058917252,0.0017317283,0.000019126966,0.000047215734],"about_ca_topic_score_codex":0.0000027782821,"about_ca_topic_score_gemma":2.6940233e-8,"teacher_disagreement_score":0.6811917,"about_ca_system_score_codex":0.00000735705,"about_ca_system_score_gemma":0.000019535642,"threshold_uncertainty_score":0.19870052},"labels":[],"label_agreement":null},{"id":"W4404748902","doi":"10.2139/ssrn.5022693","title":"Obstacle-Avoiding Robotic Vehicle for Efficient Navigation in Hospital Environments","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Obstacle; Obstacle avoidance; Computer science; Human–computer interaction; Artificial intelligence; Mobile robot; Robot; Aeronautics; Computer vision; Engineering; Geography","score_opus":0.010636941966591306,"score_gpt":0.2492159428185949,"score_spread":0.2385790008520036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404748902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15109637,0.0043200953,0.8395466,0.0013510821,0.0029835282,0.0005775924,0.0000043328823,0.00009259605,0.000027766628],"genre_scores_gemma":[0.9798853,0.00024896237,0.019003527,0.000022927868,0.00037152576,0.000066622146,0.000019196836,0.000054493437,0.00032747647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952408,0.00016327051,0.0006208573,0.0008160408,0.00058456056,0.002574454],"domain_scores_gemma":[0.9988296,0.00015851794,0.0003446807,0.0005065842,0.00004057126,0.00012003702],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0029114594,0.0003745773,0.00039165947,0.00032783247,0.00021814687,0.00042245394,0.001372595,0.00026889282,7.931618e-7],"category_scores_gemma":[0.00011496975,0.000377997,0.00024397358,0.0002682428,0.000036399546,0.00014076155,0.0009320673,0.0061071417,0.000057664725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010387108,0.0002532344,0.00023394996,0.00010409875,0.00018670413,0.00008234815,0.0019370327,0.93350625,0.00033574866,0.04053374,0.000048467533,0.022768024],"study_design_scores_gemma":[0.00046740824,0.0002781346,0.00030204296,0.00048921985,0.000037538895,0.00017267569,0.00022689714,0.7222596,0.0001294829,0.27522266,0.000030382673,0.0003839952],"about_ca_topic_score_codex":0.000029896755,"about_ca_topic_score_gemma":0.00000510519,"teacher_disagreement_score":0.8287889,"about_ca_system_score_codex":0.0038924008,"about_ca_system_score_gemma":0.0018489104,"threshold_uncertainty_score":0.99993145},"labels":[],"label_agreement":null},{"id":"W4405309804","doi":"10.1016/j.rineng.2024.103701","title":"Aerial assistive payload transportation using quadrotor UAVs with nonsingular fast terminal SMC for human physical interaction","year":2024,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Higher Education and Scientific Research; University of Thi-Qar","keywords":"Payload (computing); Terminal (telecommunication); Drone; Computer science; Engineering; Aeronautics; Aerospace engineering; Computer network; Biology; Network packet","score_opus":0.019335595872681428,"score_gpt":0.292116467728112,"score_spread":0.2727808718554306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405309804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2317449,0.000011922691,0.76715875,0.000022572794,0.00062515785,0.00020975246,0.000021877902,0.0001820386,0.000023054414],"genre_scores_gemma":[0.83862174,2.493055e-7,0.16093647,0.0000025234294,0.00035477028,0.000029614432,0.000024540475,0.000019269633,0.000010844312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891245,0.000015199693,0.00024498103,0.0003920724,0.00018459915,0.00025071992],"domain_scores_gemma":[0.9995541,0.00013472578,0.000048891405,0.00017792398,0.000035416888,0.000048946564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017477952,0.00016433289,0.0001747179,0.00020198911,0.00005363623,0.00015535243,0.00017229766,0.00005121833,2.3030178e-7],"category_scores_gemma":[0.00003485064,0.00015350609,0.000051262792,0.00030899636,0.000014771613,0.0004362917,0.000011716699,0.00019650688,0.0000017328491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009601226,0.000064625965,0.000048623053,0.00020814779,0.000043299824,0.00032282955,0.0049018823,0.9473097,0.035115786,0.00094699615,0.000011593148,0.01093053],"study_design_scores_gemma":[0.0005498044,0.00013663528,0.0017811967,0.0007138923,0.000015801235,0.000032142994,0.00003851456,0.9927049,0.0036935348,0.00002703231,0.0001068505,0.00019968125],"about_ca_topic_score_codex":0.00005400716,"about_ca_topic_score_gemma":0.000006398691,"teacher_disagreement_score":0.6068768,"about_ca_system_score_codex":0.00016661957,"about_ca_system_score_gemma":0.000047889593,"threshold_uncertainty_score":0.62597966},"labels":[],"label_agreement":null},{"id":"W4405518954","doi":"10.2316/j.2025.201-0502","title":"APPLICATION OF IMPROVED ANT COLONY OPTIMISATION WITH HYBRID ADAPTIVE STRATEGY IN MOBILE ROBOT PATH PLANNING, 192-201.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Motion planning; Computer science; ANT; Mobile robot; Path (computing); Ant colony; Artificial intelligence; Robot; Computer network","score_opus":0.00875364846358866,"score_gpt":0.2287761138694838,"score_spread":0.22002246540589512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405518954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011665843,0.008789937,0.9780232,0.000034250323,0.00020315142,0.0010726012,0.000016985567,0.00011359893,0.00008044471],"genre_scores_gemma":[0.9942211,0.00001590033,0.005118747,0.000009444953,0.0000671075,0.00047948555,0.0000067708297,0.000013804445,0.000067671084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985599,0.00009855048,0.00036195942,0.00047496264,0.00021805907,0.0002865709],"domain_scores_gemma":[0.99926597,0.00011989541,0.00017100583,0.00031873301,0.00006355359,0.000060844275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005457775,0.00017832151,0.00034546456,0.00013703397,0.000046258276,0.00013647543,0.00024648252,0.0000669619,5.5669244e-7],"category_scores_gemma":[0.000005588329,0.00014133872,0.000032859494,0.00020223993,0.00003089136,0.00027355028,0.000033146574,0.00015480291,0.0000038814956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014398231,0.00012986509,0.0010694505,0.00035765787,0.00020997008,0.00013921903,0.0017638195,0.8998484,0.009188921,0.029557701,0.000076654156,0.057514377],"study_design_scores_gemma":[0.0009698775,0.0007812218,0.00057407626,0.0002535138,0.000021378011,0.00007120674,0.00030433174,0.996384,0.00019314486,0.00016231154,0.00012439089,0.00016050511],"about_ca_topic_score_codex":0.00067921815,"about_ca_topic_score_gemma":0.000006328773,"teacher_disagreement_score":0.9825552,"about_ca_system_score_codex":0.0001165767,"about_ca_system_score_gemma":0.00019431225,"threshold_uncertainty_score":0.57636255},"labels":[],"label_agreement":null},{"id":"W4405518995","doi":"10.2316/j.2025.201-0443","title":"APPLICATION OF PSO-ACO ALGORITHM IN PATH PLANNING OF INTELLIGENT ROBOTS, 42-53.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Path (computing); Computer science; Robot; Artificial intelligence; Algorithm; Mathematical optimization; Mathematics","score_opus":0.009675539340154684,"score_gpt":0.24620826531829088,"score_spread":0.23653272597813618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405518995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013809337,0.022536747,0.97485006,0.00008430364,0.0005653858,0.000430434,0.000011995185,0.0000772845,0.00006286878],"genre_scores_gemma":[0.984537,0.000044610944,0.0151783945,0.000011538527,0.000081458056,0.00008970315,0.000003232196,0.000012014422,0.00004205629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839944,0.000099092766,0.00056488323,0.0003926374,0.00027631308,0.00026761432],"domain_scores_gemma":[0.99919355,0.00015824373,0.0001640066,0.00037126982,0.000052932533,0.000059975675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007913849,0.00015575244,0.00042171942,0.00020721495,0.00002687808,0.00006622247,0.00036270116,0.000092398965,6.395618e-7],"category_scores_gemma":[0.000015161621,0.00013621582,0.000060635775,0.0003244044,0.000028126558,0.00016711268,0.00006845088,0.00014741292,0.000004760815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020211199,0.0001605027,0.0027020432,0.0010604543,0.00023352736,0.000095848045,0.0035412798,0.14230168,0.0046504443,0.109128155,0.00009954219,0.7360063],"study_design_scores_gemma":[0.00040619253,0.00012378838,0.0005483194,0.00051346124,0.00001618323,0.000035078425,0.00019320994,0.9967851,0.00029590854,0.00053721457,0.00042075373,0.00012476822],"about_ca_topic_score_codex":0.00046445275,"about_ca_topic_score_gemma":9.0311767e-7,"teacher_disagreement_score":0.9831561,"about_ca_system_score_codex":0.00006854632,"about_ca_system_score_gemma":0.000103751256,"threshold_uncertainty_score":0.555472},"labels":[],"label_agreement":null},{"id":"W4405574717","doi":"10.3390/s24248089","title":"A Comprehensive Study of Recent Path-Planning Techniques in Dynamic Environments for Autonomous Robots","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"","keywords":"Motion planning; Obstacle; Key (lock); Computer science; Obstacle avoidance; Path (computing); Process (computing); Robot; Resource (disambiguation); Distributed computing; Systems engineering; Dynamic decision-making; Risk analysis (engineering); Human–computer interaction; Mobile robot; Engineering; Artificial intelligence; Computer security; Computer network","score_opus":0.03465704014129595,"score_gpt":0.3136110168898205,"score_spread":0.27895397674852457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405574717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71926206,0.0004705132,0.2771513,0.00026913636,0.0006791387,0.0015308032,0.000010715164,0.00042071097,0.0002056289],"genre_scores_gemma":[0.8086694,0.00002702712,0.19096069,0.00003720993,0.000022428585,0.00008321727,0.000005693496,0.000025754318,0.00016858809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985554,0.00009139772,0.00035162442,0.00048752528,0.00022729799,0.00028671726],"domain_scores_gemma":[0.9992534,0.00020471647,0.00007746005,0.00039467614,0.000021986005,0.000047796897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017209927,0.00016841583,0.00026428612,0.0002521975,0.000041424246,0.000051067775,0.00039205255,0.000065316,0.000001655317],"category_scores_gemma":[0.000022698325,0.00016381021,0.000043324293,0.00032496746,0.00002909526,0.00011761293,0.00013937861,0.0001622237,0.000008940462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005765314,0.00155065,0.0042764135,0.00036589647,0.00026370288,0.0018394924,0.046538398,0.6167747,0.017563848,0.0005235403,0.00052238774,0.30972335],"study_design_scores_gemma":[0.00039841706,0.0006262201,0.013125807,0.00020610282,0.00001362532,0.00002476654,0.0008384315,0.9804757,0.0011921042,0.0003247979,0.0025227496,0.00025125945],"about_ca_topic_score_codex":0.00003917971,"about_ca_topic_score_gemma":0.000001374666,"teacher_disagreement_score":0.36370105,"about_ca_system_score_codex":0.00014358968,"about_ca_system_score_gemma":0.00003725777,"threshold_uncertainty_score":0.6679987},"labels":[],"label_agreement":null},{"id":"W4405603038","doi":"10.2316/j.2025.206-1164","title":"GLOBAL PATH PLANNING OF CLIMBING ROBOT FOR WELD QUALITY INSPECTION IN LARGE SCALE STORAGE TANKS, 242-253.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Scale (ratio); Storage tank; Robot; Computer science; Path (computing); Marine engineering; Environmental science; Artificial intelligence; Engineering; Mechanical engineering; Geography","score_opus":0.022946404129995015,"score_gpt":0.33887407109252166,"score_spread":0.31592766696252667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405603038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07588348,0.00045392945,0.92002857,0.00087772706,0.0025432906,0.00007919689,0.000020748952,0.00005039221,0.000062642],"genre_scores_gemma":[0.8074419,0.000022711441,0.19231732,0.00003406923,0.00016101389,0.0000014959487,0.0000071498657,0.000005142104,0.0000091960555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850184,0.00006130366,0.00066189136,0.00016953083,0.00046378316,0.00014167627],"domain_scores_gemma":[0.9990944,0.0001743864,0.00030475468,0.00008858826,0.00028942336,0.000048441314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011355904,0.000100163226,0.00020260153,0.00027412394,0.00004563988,0.00019498676,0.00032921595,0.00006675792,9.714614e-7],"category_scores_gemma":[0.00014019349,0.00009487508,0.000081380196,0.00023712368,0.000020508705,0.00070905534,0.000076651784,0.00013739483,7.262091e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038192302,0.00017156047,0.02485991,0.00012871304,0.0001085161,0.00009806057,0.0022593564,0.92445487,0.0008427004,0.029390402,0.00036501372,0.017282683],"study_design_scores_gemma":[0.00053254154,0.000107364554,0.0922217,0.000577779,0.00001018766,0.00012286972,0.00011569394,0.902571,0.00009702253,0.0034854752,0.00006799294,0.00009037485],"about_ca_topic_score_codex":0.000016968917,"about_ca_topic_score_gemma":0.0000042671486,"teacher_disagreement_score":0.73155844,"about_ca_system_score_codex":0.00020390128,"about_ca_system_score_gemma":0.000105754116,"threshold_uncertainty_score":0.38688937},"labels":[],"label_agreement":null},{"id":"W4405603119","doi":"10.2316/j.2025.206-1114","title":"OVERCOMING VALUE OVERESTIMATION FOR DISTRIBUTIONAL REINFORCEMENT LEARNING-BASED PATH PLANNING WITH CONSERVATIVE CONSTRAINTS, 124-132.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Reinforcement; Path (computing); Computer science; Mathematical optimization; Motion planning; Value (mathematics); Artificial intelligence; Mathematics; Machine learning; Engineering; Structural engineering","score_opus":0.018899572378057224,"score_gpt":0.29031087367882,"score_spread":0.2714113013007628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405603119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039789705,0.00012304807,0.9927024,0.002260144,0.0006844731,0.00012143,0.000013341917,0.00006247007,0.000053689368],"genre_scores_gemma":[0.7125174,0.0000046197924,0.28718805,0.00009408245,0.00011300747,0.0000039862707,0.00005453003,0.0000066987022,0.000017618673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986862,0.000042970216,0.00041705288,0.00016296808,0.0005567305,0.00013409673],"domain_scores_gemma":[0.9984584,0.0005203752,0.00036671566,0.000063208485,0.00052753027,0.00006376888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005310319,0.00012632748,0.00015011034,0.00020446963,0.000104327955,0.00044787035,0.00024484695,0.000048369802,0.0000035996395],"category_scores_gemma":[0.00021765576,0.00010505177,0.00005112528,0.00011462159,0.000065753935,0.0006793597,0.000036710062,0.0001842528,0.0000013123253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028122608,0.000018887953,0.0007496745,0.000031989493,0.00011438467,0.00006119389,0.00028813985,0.92758876,0.00016220835,0.066498786,0.00020781464,0.0042500123],"study_design_scores_gemma":[0.0007210589,0.0003180893,0.0032160373,0.0009464952,0.000026848838,0.00024461237,0.000036718957,0.99163973,0.00024190343,0.002121355,0.00036536972,0.00012180216],"about_ca_topic_score_codex":0.0000029649007,"about_ca_topic_score_gemma":6.2594815e-8,"teacher_disagreement_score":0.7085384,"about_ca_system_score_codex":0.00016969001,"about_ca_system_score_gemma":0.00030463256,"threshold_uncertainty_score":0.4318824},"labels":[],"label_agreement":null},{"id":"W4405706067","doi":"10.3390/electronics13245041","title":"Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National University of Singapore","keywords":"Terminal (telecommunication); Trajectory; Computer science; Transport engineering; Automotive engineering; Simulation; Real-time computing; Engineering; Computer network; Physics","score_opus":0.026780885346169438,"score_gpt":0.28653442141532803,"score_spread":0.2597535360691586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405706067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01421572,0.015616298,0.966702,0.0006247245,0.001003942,0.00023169893,0.0000061407823,0.00080936006,0.0007901512],"genre_scores_gemma":[0.8601766,0.000015490898,0.13854823,0.00031288457,0.00031364438,0.00005390944,0.000011064178,0.000039392347,0.00052874297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982907,0.000043489727,0.00023379193,0.000514943,0.00021391772,0.00070315413],"domain_scores_gemma":[0.9991993,0.00033349427,0.00005098751,0.00029352476,0.000032466698,0.00009024308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045779327,0.00019443709,0.00018297836,0.00013756134,0.00018301293,0.00032594847,0.00057660864,0.0001015332,0.0000048855204],"category_scores_gemma":[0.00002960794,0.00019571699,0.000099376826,0.00023841189,0.00007553588,0.00028412373,0.00005757325,0.00035240632,0.000025115074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027121632,0.00011731031,0.0002800142,0.00031844794,0.00029864133,0.00089219265,0.0071770702,0.06676333,0.011483535,0.13613604,0.010151212,0.7663551],"study_design_scores_gemma":[0.00033795962,0.00031086986,0.00039271516,0.000188231,0.000028906148,0.0005673631,0.00007359487,0.953223,0.0031023265,0.009540562,0.03177772,0.00045673453],"about_ca_topic_score_codex":0.0000011922194,"about_ca_topic_score_gemma":7.198687e-7,"teacher_disagreement_score":0.8864597,"about_ca_system_score_codex":0.00024034538,"about_ca_system_score_gemma":0.0006818763,"threshold_uncertainty_score":0.7981107},"labels":[],"label_agreement":null},{"id":"W4405785130","doi":"10.1109/iros58592.2024.10802643","title":"The Effectiveness of State Representation Model in Multi-Agent Proximal Policy Optimization for Multi-Agent Path Finding","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Path (computing); Representation (politics); State (computer science); Multi-agent system; Distributed computing; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.08128902569009212,"score_gpt":0.3677429541943815,"score_spread":0.2864539285042894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405785130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059411637,0.00008506596,0.99232805,0.00013221093,0.00028611536,0.0010546404,0.000010503629,0.00012636578,0.000035907673],"genre_scores_gemma":[0.29261997,0.000019405936,0.7068719,0.000013113059,0.0000152415505,0.00019267226,0.000009553471,0.000014116592,0.0002439773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984916,0.00023020223,0.00033461652,0.00042470352,0.00022986339,0.00028905485],"domain_scores_gemma":[0.99883956,0.00061327376,0.0000799668,0.0003258825,0.0000944294,0.00004686227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014373591,0.00013420862,0.00015577405,0.00021606726,0.0001066702,0.0001729788,0.00038805866,0.000044197623,3.8030427e-7],"category_scores_gemma":[0.00030655335,0.00009788016,0.00006730888,0.00057414046,0.000031012332,0.00037843946,0.00014789317,0.00009450985,0.0000027096264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019597293,0.00006365194,0.00041434466,0.000112684196,0.000016311478,0.0000057169314,0.001277842,0.9877457,0.0007344242,0.002741455,0.000011956553,0.006856342],"study_design_scores_gemma":[0.00061716494,0.000052079347,0.002354373,0.00015051698,0.0000046431264,0.000002767579,0.0000366277,0.9922189,0.0039974535,0.000451535,0.0000026905643,0.00011126092],"about_ca_topic_score_codex":0.0001914932,"about_ca_topic_score_gemma":0.0000058433466,"teacher_disagreement_score":0.28667882,"about_ca_system_score_codex":0.00018843476,"about_ca_system_score_gemma":0.00019796274,"threshold_uncertainty_score":0.3991437},"labels":[],"label_agreement":null},{"id":"W4405795682","doi":"10.1007/978-981-97-4892-1_35","title":"Development of Path Optimization Using Quarter-Sweep Modified Successive Over-Relaxation Iterative Technique","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relaxation (psychology); Path (computing); Quarter (Canadian coin); Mathematical optimization; Materials science; Computer science; Algorithm; Mathematics; Medicine; Internal medicine; Geography","score_opus":0.020225968978691892,"score_gpt":0.25015789729393295,"score_spread":0.22993192831524106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405795682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023726989,0.003196076,0.99351734,0.000017381473,0.0009398305,0.0007285148,0.0000065862455,0.00007910923,0.0014914375],"genre_scores_gemma":[0.34113044,0.00008068757,0.6569388,0.000050058963,0.00059075136,0.000110932226,0.000129145,0.00009441061,0.0008747904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979719,0.00006692163,0.0007966056,0.0005968044,0.00032998022,0.00023780578],"domain_scores_gemma":[0.9985887,0.00027831446,0.0006270729,0.00032775343,0.00012347977,0.000054673186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004571463,0.00038089478,0.000585236,0.00029698262,0.00009417754,0.0002336919,0.00028792792,0.0006074924,0.0000016102883],"category_scores_gemma":[0.00002586033,0.00033126143,0.000057029047,0.00017495948,0.000040873623,0.00016457823,0.00013434119,0.00049551117,5.8314157e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004276017,0.0000059099434,0.000009895088,0.00015791251,0.000039805043,0.000033042594,0.0016257036,0.98566073,0.000021374992,0.008544001,0.000007281257,0.0038900662],"study_design_scores_gemma":[0.00012401625,0.00004339334,0.000007844491,0.0036370691,0.000019789279,0.000028706703,0.0000054233533,0.9942944,0.000019928057,0.0014247379,0.000064089945,0.00033057213],"about_ca_topic_score_codex":0.00005144366,"about_ca_topic_score_gemma":0.0000085758265,"teacher_disagreement_score":0.3411067,"about_ca_system_score_codex":0.00019678344,"about_ca_system_score_gemma":0.000119237346,"threshold_uncertainty_score":0.99991393},"labels":[],"label_agreement":null},{"id":"W4405873221","doi":"10.1007/978-3-031-72062-8","title":"Towards Autonomous Robotic Systems","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Roma Tor Vergata; Universidad Autónoma de Occidente; Ben-Gurion University of the Negev; UK Atomic Energy Authority; Coventry University; Indian Institute of Science; University College London; Trent University; Technische Universität Chemnitz; Pennsylvania State University; Université de Montpellier; Nottingham Trent University; University of Pennsylvania; University of Essex; Queen Mary University of London","keywords":"Computer science; Artificial intelligence; Human–computer interaction","score_opus":0.018836821396150907,"score_gpt":0.2563644305937264,"score_spread":0.23752760919757548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405873221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030412864,0.0034953142,0.97087497,0.0007733001,0.017515237,0.0005426881,0.000006135219,0.00085898396,0.005930317],"genre_scores_gemma":[0.0064724227,0.000027583605,0.98321134,0.0008657706,0.0020686437,0.000045084813,0.00001411277,0.00009676855,0.007198287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935389,0.00010480663,0.00079840457,0.0025945825,0.0016939703,0.0012693265],"domain_scores_gemma":[0.9963364,0.0004916216,0.0002814683,0.002328674,0.00023755059,0.00032427552],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0015970294,0.0008066319,0.0009420828,0.0014993813,0.00023906051,0.0024019992,0.0067857313,0.0005364194,0.000004423687],"category_scores_gemma":[0.00015021906,0.000717365,0.00019077168,0.0021651553,0.0006142769,0.000675072,0.0025247198,0.0016408385,0.0005133663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.0300583e-7,0.0000234952,0.000005937496,0.00019377733,0.000020505588,0.0008705069,0.000876139,0.76576483,0.000011024107,0.0052463003,0.00091909553,0.22606756],"study_design_scores_gemma":[0.000119533805,0.00014172557,0.00003777723,0.0011680718,0.000016528771,0.0004874224,1.376784e-7,0.9588217,0.000060326664,0.03629551,0.0020695147,0.00078176346],"about_ca_topic_score_codex":0.000070860995,"about_ca_topic_score_gemma":0.000005036172,"teacher_disagreement_score":0.2252858,"about_ca_system_score_codex":0.001587868,"about_ca_system_score_gemma":0.0046713417,"threshold_uncertainty_score":0.99952775},"labels":[],"label_agreement":null},{"id":"W4405873280","doi":"10.1007/978-3-031-72059-8","title":"Towards Autonomous Robotic Systems","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Roma Tor Vergata; Universidad Autónoma de Occidente; Ben-Gurion University of the Negev; UK Atomic Energy Authority; Coventry University; Indian Institute of Science; University College London; Trent University; Technische Universität Chemnitz; Pennsylvania State University; Université de Montpellier; Nottingham Trent University; University of Pennsylvania; University of Essex; Queen Mary University of London","keywords":"Computer science; Artificial intelligence; Human–computer interaction; Computer vision","score_opus":0.018836821396150907,"score_gpt":0.2563644305937264,"score_spread":0.23752760919757548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405873280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030412864,0.0034953142,0.97087497,0.0007733001,0.017515237,0.0005426881,0.000006135219,0.00085898396,0.005930317],"genre_scores_gemma":[0.0064724227,0.000027583605,0.98321134,0.0008657706,0.0020686437,0.000045084813,0.00001411277,0.00009676855,0.007198287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935389,0.00010480663,0.00079840457,0.0025945825,0.0016939703,0.0012693265],"domain_scores_gemma":[0.9963364,0.0004916216,0.0002814683,0.002328674,0.00023755059,0.00032427552],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0015970294,0.0008066319,0.0009420828,0.0014993813,0.00023906051,0.0024019992,0.0067857313,0.0005364194,0.000004423687],"category_scores_gemma":[0.00015021906,0.000717365,0.00019077168,0.0021651553,0.0006142769,0.000675072,0.0025247198,0.0016408385,0.0005133663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.0300583e-7,0.0000234952,0.000005937496,0.00019377733,0.000020505588,0.0008705069,0.000876139,0.76576483,0.000011024107,0.0052463003,0.00091909553,0.22606756],"study_design_scores_gemma":[0.000119533805,0.00014172557,0.00003777723,0.0011680718,0.000016528771,0.0004874224,1.376784e-7,0.9588217,0.000060326664,0.03629551,0.0020695147,0.00078176346],"about_ca_topic_score_codex":0.000070860995,"about_ca_topic_score_gemma":0.000005036172,"teacher_disagreement_score":0.2252858,"about_ca_system_score_codex":0.001587868,"about_ca_system_score_gemma":0.0046713417,"threshold_uncertainty_score":0.99952775},"labels":[],"label_agreement":null},{"id":"W4405908135","doi":"10.1109/mra.2024.3505772","title":"A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: A Guide for Creating a New Testbed [Survey]","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics & Automation Magazine","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Testbed; Robot; Computer science; Scale (ratio); Mobile robot; Embedded system; Human–computer interaction; Real-time computing; Artificial intelligence; Computer network; Geography","score_opus":0.04715011470927296,"score_gpt":0.309720069600917,"score_spread":0.2625699548916441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018886369,0.00046274147,0.9745176,0.00095665565,0.00077578146,0.0012845832,0.00018659282,0.0028743239,0.00005538836],"genre_scores_gemma":[0.06936611,0.000021700473,0.9283924,0.00026266775,0.00021092128,0.000113602204,0.00032250123,0.00009208837,0.0012179796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970497,0.0002743926,0.0007902956,0.00096659886,0.000322653,0.0005963621],"domain_scores_gemma":[0.9927386,0.0057841814,0.00023982654,0.0005150427,0.00043456547,0.0002877982],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002129964,0.0004414349,0.0005539882,0.00032050614,0.00029340354,0.0009324208,0.00044069954,0.00021374616,0.000001541475],"category_scores_gemma":[0.0022494732,0.00042587504,0.00007789985,0.0009045179,0.000080168975,0.000410403,0.00011306,0.00019466119,0.000027005804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023836561,0.00051520386,0.016453417,0.0017194925,0.00065958145,0.0000832078,0.0043857354,0.6562428,0.03690012,0.0035594974,0.13170324,0.14753936],"study_design_scores_gemma":[0.0010689985,0.0004590226,0.19228613,0.0003566096,0.00004829789,0.000037239384,0.000006376562,0.8043458,0.00041764512,0.00042257013,0.00015836382,0.00039292534],"about_ca_topic_score_codex":0.0002719765,"about_ca_topic_score_gemma":0.00022909873,"teacher_disagreement_score":0.17583272,"about_ca_system_score_codex":0.00010982548,"about_ca_system_score_gemma":0.00034868866,"threshold_uncertainty_score":0.9998193},"labels":[],"label_agreement":null},{"id":"W4405908369","doi":"10.1109/wf-iot62078.2024.10811432","title":"Multi-Objective Algorithms for Learning Open-Ended Robotic Problems","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Algorithm; Machine learning","score_opus":0.06561810539829267,"score_gpt":0.32612159920180805,"score_spread":0.2605034938035154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002873914,0.0002667762,0.9920442,0.0008202087,0.001508916,0.0010527424,0.0000024450667,0.001222034,0.0030539313],"genre_scores_gemma":[0.015839837,0.000006031191,0.96856314,0.00014426903,0.00013628107,0.00027329416,0.000010714439,0.00003706528,0.0149893565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977574,0.00009810844,0.00032463888,0.0009650492,0.0002747414,0.00058010145],"domain_scores_gemma":[0.9987692,0.0004289302,0.0000636809,0.0004565721,0.000131332,0.00015025321],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00079390133,0.00025906603,0.0003051838,0.0002001151,0.00027676384,0.0014754742,0.0016894974,0.0001053959,0.000013055079],"category_scores_gemma":[0.0001688453,0.00021997628,0.0001120963,0.0006777831,0.00004156214,0.0011599266,0.0006942414,0.00036376118,0.0002405402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014907445,0.00046705542,0.0002948853,0.00046174793,0.0005012504,0.00031982848,0.009968763,0.62397337,0.0044891723,0.076381,0.012190653,0.27093735],"study_design_scores_gemma":[0.0005634116,0.00023221613,0.00027456082,0.00014368792,0.000014998211,0.00006417529,0.00007330469,0.9923015,0.0004820929,0.0017136894,0.0037983796,0.00033797856],"about_ca_topic_score_codex":0.00012395486,"about_ca_topic_score_gemma":0.000004833464,"teacher_disagreement_score":0.36832812,"about_ca_system_score_codex":0.00011944163,"about_ca_system_score_gemma":0.00021876874,"threshold_uncertainty_score":0.9995611},"labels":[],"label_agreement":null},{"id":"W4405909329","doi":"10.1109/wf-iot62078.2024.10811216","title":"Green and Intelligent Planning of Drone Launch in Truck-Drone Collaborative Delivery","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Aeronautics; Truck; Computer science; Aerospace engineering; Engineering","score_opus":0.02080540519122958,"score_gpt":0.2728378272181806,"score_spread":0.25203242202695103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405909329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1588059,0.0059838556,0.82988137,0.0008497171,0.0004944286,0.00027402933,0.000010531573,0.00023547627,0.00346466],"genre_scores_gemma":[0.7853876,0.000065879016,0.21319084,0.000093243514,0.000043452124,0.000011511149,0.0000031101683,0.00001203352,0.0011923293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886066,0.000060662132,0.0002818151,0.00036435435,0.00022016978,0.00021237094],"domain_scores_gemma":[0.9993764,0.00023650602,0.000040526338,0.00022168669,0.000057492023,0.00006735177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035198612,0.00012693829,0.00021537194,0.00025602343,0.000027897222,0.00008466804,0.00030702632,0.000058891823,0.0000102818585],"category_scores_gemma":[0.000026440674,0.00011088592,0.000020038602,0.000829325,0.000058619924,0.00033011337,0.00021822682,0.00015076723,0.000017997143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010367547,0.00047322823,0.054060116,0.0011947161,0.00050371373,0.0038866145,0.13190174,0.05215762,0.009490475,0.11850329,0.008539573,0.61918527],"study_design_scores_gemma":[0.00026658011,0.00022918556,0.009521489,0.00045788716,0.000007972293,0.000047930083,0.00073143165,0.98111326,0.0044045527,0.0021089944,0.00085112517,0.00025957916],"about_ca_topic_score_codex":0.00026774718,"about_ca_topic_score_gemma":0.000014397626,"teacher_disagreement_score":0.9289557,"about_ca_system_score_codex":0.000050555267,"about_ca_system_score_gemma":0.00011554821,"threshold_uncertainty_score":0.45217967},"labels":[],"label_agreement":null},{"id":"W4405943879","doi":"10.3390/aerospace12010021","title":"Emergency Trajectory Structure for UAVs","year":2024,"lang":"en","type":"article","venue":"Aerospace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Context (archaeology); Workload; Key (lock); Benchmark (surveying); Architecture; Trajectory; Computational complexity theory; Representativeness heuristic; Constraint (computer-aided design); Focus (optics); Strengths and weaknesses; Operations research; Risk analysis (engineering); Engineering; Algorithm; Computer security","score_opus":0.017182287712654877,"score_gpt":0.2762173572241072,"score_spread":0.2590350695114523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405943879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037611213,0.0022317925,0.9863863,0.0016504899,0.00444723,0.00019500688,0.000019104018,0.0007019717,0.00060698774],"genre_scores_gemma":[0.23856352,0.000055758308,0.7509442,0.00018720006,0.0006220564,0.000043199187,0.00001222852,0.000042282183,0.00952957],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989884,0.000019291525,0.00012737076,0.00040279748,0.00017531897,0.0002868332],"domain_scores_gemma":[0.9993894,0.00007892421,0.000024753352,0.0003938343,0.000037072597,0.0000760255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009744646,0.00013009203,0.000112592425,0.00005695192,0.000082474544,0.000111994756,0.0005285326,0.00006924584,0.000028311873],"category_scores_gemma":[0.000037485483,0.00011539002,0.000077760036,0.00031951806,0.000015595795,0.00027314044,0.000068249625,0.00012971739,0.000066305845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009739079,0.00006461562,0.0011180475,0.0005083203,0.00020419786,0.00018874762,0.009585248,0.008557977,0.028899565,0.15920728,0.6939687,0.09768751],"study_design_scores_gemma":[0.00043976944,0.00030512133,0.0048823035,0.000153757,0.000040202154,0.00011038555,0.000086440574,0.85826427,0.00748277,0.018925784,0.108409226,0.0008999601],"about_ca_topic_score_codex":0.000009630004,"about_ca_topic_score_gemma":0.0000034072505,"teacher_disagreement_score":0.8497063,"about_ca_system_score_codex":0.000038617247,"about_ca_system_score_gemma":0.000066896166,"threshold_uncertainty_score":0.47054684},"labels":[],"label_agreement":null},{"id":"W4405975712","doi":"10.1109/pimrc59610.2024.10817272","title":"Joint Green and Deadline-Aware Path Planning for Rotary-Wing UAV-Assisted Internet-of-Things","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Joint (building); Wing; Internet of Things; Path (computing); The Internet; Motion planning; Computer network; Embedded system; Artificial intelligence; Aerospace engineering; Engineering; World Wide Web; Robot","score_opus":0.047056625822588204,"score_gpt":0.28256538955493166,"score_spread":0.23550876373234345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405975712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010889875,0.0005996061,0.9861197,0.0006565057,0.0005779579,0.00023748784,0.000003052351,0.00043629896,0.00047953945],"genre_scores_gemma":[0.34083843,0.000002420969,0.6576976,0.00025253394,0.00008159402,0.000023796516,0.000009390637,0.000021766215,0.0010724866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851733,0.00003363406,0.0003819927,0.0005245234,0.00022789704,0.00031461526],"domain_scores_gemma":[0.9991486,0.00029731696,0.00007733095,0.000297509,0.00007177925,0.0001074767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000481445,0.00018994333,0.00027192262,0.00019458708,0.000066810084,0.0002209505,0.0004393795,0.000085280175,0.0000047387584],"category_scores_gemma":[0.00005872177,0.00015857979,0.00008950159,0.0002540105,0.000037417307,0.0005741881,0.00031437154,0.00017956505,0.000007678867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041019084,0.00013030613,0.006155672,0.0023588424,0.00048649852,0.0010066748,0.04125689,0.0020102328,0.006127846,0.026769375,0.014284532,0.8993721],"study_design_scores_gemma":[0.00021660699,0.00015206366,0.002680151,0.00075819617,0.000017398077,0.00011323839,0.000091395406,0.99274725,0.0010111355,0.0012760363,0.0007217916,0.00021474433],"about_ca_topic_score_codex":0.0002752889,"about_ca_topic_score_gemma":6.4196297e-7,"teacher_disagreement_score":0.990737,"about_ca_system_score_codex":0.0000386584,"about_ca_system_score_gemma":0.0000668492,"threshold_uncertainty_score":0.6466696},"labels":[],"label_agreement":null},{"id":"W4406265834","doi":"10.1109/ismar62088.2024.00061","title":"Object Speed Control with a Signed Distance Field for Distant Mid-Air Object Manipulation in Virtual Reality","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Concordia University","funders":"","keywords":"Object (grammar); Virtual reality; Computer science; Computer vision; Virtual image; Field (mathematics); Computer graphics (images); Signed distance function; Artificial intelligence; Mathematics","score_opus":0.02122609946422102,"score_gpt":0.27426644617023904,"score_spread":0.25304034670601805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406265834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024321026,0.000081319944,0.9930762,0.0015332621,0.00047642138,0.0006377133,0.000016357551,0.00039263198,0.00135401],"genre_scores_gemma":[0.96499455,0.0000011682463,0.03403288,0.00027785217,0.000079266945,0.000045949506,0.000013295652,0.000015008613,0.00054003886],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982646,0.00008338058,0.00033573623,0.00062874873,0.00031283312,0.00037468658],"domain_scores_gemma":[0.9985314,0.0008123849,0.000060896276,0.00046323836,0.000054481163,0.00007759965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054852763,0.00020146463,0.0002720249,0.00012027898,0.00007303006,0.00019953086,0.00041448977,0.00007945139,0.000005143148],"category_scores_gemma":[0.00010897387,0.00015309849,0.00006980085,0.0005044023,0.000030420277,0.00043227337,0.000045312416,0.00015059943,0.000010641475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019883309,0.0007315816,0.014357338,0.00089656195,0.0004651826,0.0020966374,0.011002407,0.35911945,0.0071639884,0.51499057,0.009759849,0.07742814],"study_design_scores_gemma":[0.0010628488,0.0005106078,0.0039600874,0.00017303223,0.000013612691,0.000018478493,0.000066270884,0.9898251,0.0012804628,0.002445488,0.00036021764,0.00028378615],"about_ca_topic_score_codex":0.00026067346,"about_ca_topic_score_gemma":0.00023470387,"teacher_disagreement_score":0.96256244,"about_ca_system_score_codex":0.0001375156,"about_ca_system_score_gemma":0.00013753997,"threshold_uncertainty_score":0.6243175},"labels":[],"label_agreement":null},{"id":"W4406327655","doi":"10.3390/s25020439","title":"Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale","keywords":"Terrain; Robot; Computer science; Artificial intelligence; Remote sensing; Geology; Geography; Cartography","score_opus":0.02777160890793751,"score_gpt":0.28908107470258365,"score_spread":0.26130946579464615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406327655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17386663,0.000069476104,0.82120174,0.0021561438,0.001341168,0.00064400147,0.000080491736,0.0003845494,0.0002558162],"genre_scores_gemma":[0.4955771,0.0000014812936,0.5035679,0.00042515062,0.00008842599,0.0000086949685,0.00006178837,0.000014358981,0.0002551707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976665,0.00020280227,0.00039454186,0.0010024426,0.00025192235,0.00048182812],"domain_scores_gemma":[0.99726635,0.00040050424,0.00011346791,0.002014622,0.00009732122,0.000107707514],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005760344,0.00026587895,0.000347241,0.00013008069,0.00027846446,0.0002615551,0.0014639432,0.00013167995,0.0000024478625],"category_scores_gemma":[0.00046861966,0.00024818178,0.00007548166,0.00038525477,0.00009666488,0.00027313613,0.0005153877,0.00018121602,0.000008445516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000608363,0.00015971136,0.0023690797,0.00030256185,0.0004080205,0.00022761484,0.005492623,0.866712,0.027562844,0.002820799,0.0050455104,0.08883843],"study_design_scores_gemma":[0.0005612047,0.00005253344,0.0013919234,0.000049211652,0.000020557987,0.000050774328,0.00008162937,0.9885301,0.006840179,0.0011881508,0.00095423055,0.00027952963],"about_ca_topic_score_codex":0.00016845195,"about_ca_topic_score_gemma":0.000036645986,"teacher_disagreement_score":0.32171047,"about_ca_system_score_codex":0.00008151087,"about_ca_system_score_gemma":0.00014306315,"threshold_uncertainty_score":0.999997},"labels":[],"label_agreement":null},{"id":"W4406400369","doi":"10.1049/icp.2024.4516","title":"Path planning simulation of a differential wheel robot based on the RRT Algorithm","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Motion planning; Differential (mechanical device); Computer science; Path (computing); Robot; Mobile robot; Simulation; Algorithm; Artificial intelligence; Engineering; Aerospace engineering; Computer network","score_opus":0.029887910827546067,"score_gpt":0.27995287363790305,"score_spread":0.250064962810357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406400369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005975921,0.000020001211,0.98718077,0.0011044975,0.00044615474,0.00032490786,0.0000058224873,0.00019876364,0.0047431616],"genre_scores_gemma":[0.9235458,0.000001391771,0.07582447,0.0003479382,0.000057128284,0.000038163136,0.0000039040656,0.000009452572,0.00017179732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981359,0.00003450871,0.0003893695,0.00052260957,0.00054579094,0.00037180085],"domain_scores_gemma":[0.9985812,0.00030705077,0.00025152473,0.0003521947,0.00043775584,0.00007028882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039936684,0.0002583591,0.00030659014,0.00021920253,0.00018649266,0.00030611106,0.0012193922,0.00012659504,0.000018170578],"category_scores_gemma":[0.00027393433,0.00019132356,0.000088633016,0.0006435301,0.00009868711,0.00029335511,0.00020637889,0.00034141663,0.000010685562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022470998,0.0013441256,0.024927594,0.00079854886,0.00035354256,0.000055314256,0.015683858,0.4251089,0.013019318,0.23555467,0.009198044,0.27373135],"study_design_scores_gemma":[0.00038595792,0.00012805659,0.007003334,0.00054518395,0.000016624086,0.0000011658326,0.0001265823,0.9873536,0.0020334392,0.0021051276,0.00011272664,0.00018816873],"about_ca_topic_score_codex":0.000012202561,"about_ca_topic_score_gemma":7.657298e-8,"teacher_disagreement_score":0.9175698,"about_ca_system_score_codex":0.000052631833,"about_ca_system_score_gemma":0.0002126813,"threshold_uncertainty_score":0.7801949},"labels":[],"label_agreement":null},{"id":"W4406460200","doi":"10.1109/icdacai65086.2024.00129","title":"Research on Lake Water Level Control Based on Simulated Annealing Multi-Objective Planning Modeling","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Computer science; Annealing (glass); Control (management); Environmental science; Artificial intelligence; Materials science; Machine learning","score_opus":0.20034880207364023,"score_gpt":0.3974037558397634,"score_spread":0.19705495376612317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406460200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007798152,0.00004930728,0.98730046,0.00092660956,0.0006482933,0.00034798047,0.000016343312,0.00087774656,0.0020351296],"genre_scores_gemma":[0.9268788,3.8685036e-7,0.071761176,0.0005804276,0.00013153638,0.000017577673,0.000014577241,0.000036234807,0.0005792429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996458,0.00036246586,0.00033547918,0.0009845415,0.0009400246,0.00091947324],"domain_scores_gemma":[0.997641,0.0012693044,0.000019858358,0.0006428009,0.00025418637,0.00017279752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023302783,0.0002723649,0.00026543456,0.00079637213,0.00038091675,0.000598074,0.00076446077,0.00016269775,0.000021542774],"category_scores_gemma":[0.00014051568,0.00019019206,0.00009361906,0.0006382056,0.000042143456,0.00032630184,0.00013592145,0.0009547612,0.00049587624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037606038,0.000055561162,0.00004083516,0.000018147754,0.00003134164,0.00039398752,0.0012049678,0.9959479,0.00034949725,0.0004101753,0.00015848562,0.0013514713],"study_design_scores_gemma":[0.00090817583,0.00026746842,0.00008169204,0.0004604013,0.0000053571343,0.0000055339733,0.00009728737,0.9958708,0.0017548368,0.0002062641,0.00006969576,0.0002724352],"about_ca_topic_score_codex":0.000093961324,"about_ca_topic_score_gemma":0.000003584979,"teacher_disagreement_score":0.9190807,"about_ca_system_score_codex":0.00015049583,"about_ca_system_score_gemma":0.0001384649,"threshold_uncertainty_score":0.7755807},"labels":[],"label_agreement":null},{"id":"W4406495303","doi":"10.1016/0967-0653(96)80672-n","title":"10.1016/0967-0653(96)80672-n","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Commercialization; Environmental science; Business; Marketing","score_opus":0.008986980769850897,"score_gpt":0.18445077381715513,"score_spread":0.17546379304730425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406495303","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030600288,0.000049481812,0.0034823557,0.00063026295,0.00000849564,0.00015028857,0.0000048529814,0.0005165252,0.99512714],"genre_scores_gemma":[0.000013451786,7.9722064e-8,0.048811283,0.00010243022,0.00013125481,0.000014113017,0.0000053758495,0.00001647372,0.95090556],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984601,0.000063230065,0.00021597369,0.0004584584,0.00034685803,0.00045536857],"domain_scores_gemma":[0.99881315,0.000084327956,0.000038420552,0.0007716335,0.000048593873,0.00024387828],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002627943,0.00018185645,0.00019713677,0.00010521896,0.000114617695,0.0001560859,0.0012068718,0.000072036164,0.95798343],"category_scores_gemma":[0.000044080854,0.00017933796,0.000060052727,0.0004919438,0.000029268258,0.00029380457,0.00015269198,0.00015086318,0.9978474],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055699693,0.00003499013,1.3677185e-7,0.0000025839195,0.000009259048,0.00003938145,0.00006346478,0.0020584667,0.000009322855,0.000007307815,0.1903568,0.80741274],"study_design_scores_gemma":[0.00015798706,0.00011774401,0.000040884242,0.000020477593,0.0000047243516,0.0000399712,3.0143298e-7,0.061033845,0.00006328798,0.000029621833,0.93826,0.00023113782],"about_ca_topic_score_codex":0.000020433017,"about_ca_topic_score_gemma":2.0775278e-8,"teacher_disagreement_score":0.8071816,"about_ca_system_score_codex":0.000045792945,"about_ca_system_score_gemma":0.00005162962,"threshold_uncertainty_score":0.731319},"labels":[],"label_agreement":null},{"id":"W4406495713","doi":"10.1109/tsmc.2024.3523132","title":"Cooperative Multi-AAV Path Planning for Discovering and Tracking Multiple Radio-Tagged Targets","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Center for Research and Development; National Natural Science Foundation of China","keywords":"Computer science; Tracking (education); Path (computing); Motion planning; Computer network; Artificial intelligence; Psychology","score_opus":0.028242601812778965,"score_gpt":0.2679498222998861,"score_spread":0.23970722048710716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406495713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011629884,0.002818302,0.9806827,0.00005027888,0.0030974254,0.0013101834,0.00006686098,0.00022155444,0.00012281415],"genre_scores_gemma":[0.9861271,0.000048751528,0.0117064,0.000032631095,0.00007374169,0.0002975669,0.0000040980744,0.000030264338,0.0016794964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977664,0.0002004898,0.00058676035,0.00073298975,0.000258921,0.00045445477],"domain_scores_gemma":[0.99856776,0.00052180723,0.00017783712,0.00043622986,0.00013291244,0.00016347642],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005143236,0.0003623001,0.00054981734,0.00026804223,0.000576982,0.0007592139,0.00033457973,0.00017228744,2.428213e-7],"category_scores_gemma":[0.000022772421,0.00034061365,0.00007674797,0.00027510527,0.00007798238,0.00033357006,0.000009175897,0.00027164785,0.0000031187099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082253326,0.00030766687,0.0016347365,0.0015946728,0.0005274927,0.00007688394,0.013341831,0.9654425,0.0048217475,0.003959494,0.00047988395,0.0077308747],"study_design_scores_gemma":[0.0017599316,0.00016705555,0.0006783804,0.0013096487,0.000045834506,0.00008113315,0.0016497201,0.99214345,0.0009583684,0.000008337481,0.0008114035,0.00038672352],"about_ca_topic_score_codex":0.00030287146,"about_ca_topic_score_gemma":0.000009526606,"teacher_disagreement_score":0.97449714,"about_ca_system_score_codex":0.000103933555,"about_ca_system_score_gemma":0.00006457259,"threshold_uncertainty_score":0.9999046},"labels":[],"label_agreement":null},{"id":"W4406611247","doi":"10.1109/smc54092.2024.10831501","title":"Wheeled Mobile Robots on Rough Terrains as Stochastic Nonholonomic Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Terrain; Nonholonomic system; Computer science; Robot; Control engineering; Artificial intelligence; Engineering; Geography","score_opus":0.014982572908501945,"score_gpt":0.2732770169165298,"score_spread":0.2582944440080278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406611247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007428915,0.00045899345,0.97552973,0.00040672722,0.0040600244,0.0004469416,0.000003726861,0.001318421,0.010346536],"genre_scores_gemma":[0.96225435,0.0000028593554,0.027604267,0.0002854298,0.0003184106,0.00015509267,0.000005490027,0.000033439504,0.0093406495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817914,0.000071134826,0.00029069834,0.0007083446,0.00031661088,0.00043406643],"domain_scores_gemma":[0.9987657,0.0002629065,0.000044473385,0.00074810465,0.00003020388,0.0001486223],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031810123,0.00023291596,0.00024438777,0.00020901681,0.00009843625,0.0006430701,0.0008891355,0.000095060444,0.000023590923],"category_scores_gemma":[0.000035846086,0.00019336266,0.00008210501,0.00036171914,0.000034925586,0.00038518957,0.00018129504,0.00025016986,0.004197367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028928723,0.000047035148,0.0000056452236,0.000032788692,0.000040284252,0.0002309984,0.00096456247,0.96356285,0.0001694971,0.024508743,0.0042493213,0.006185411],"study_design_scores_gemma":[0.0001486996,0.00030777714,0.00008000978,0.00019272933,0.0000078879575,0.00017910756,0.00004045204,0.9974002,0.000058428224,0.00030411527,0.0010304343,0.00025011794],"about_ca_topic_score_codex":0.00015293894,"about_ca_topic_score_gemma":9.03297e-7,"teacher_disagreement_score":0.95482546,"about_ca_system_score_codex":0.0001614759,"about_ca_system_score_gemma":0.00014772531,"threshold_uncertainty_score":0.996578},"labels":[],"label_agreement":null},{"id":"W4406611348","doi":"10.1109/smc54092.2024.10831403","title":"Trajectory Planning for UAV Transportation Systems Using RRT*-Informed NMPC","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Computer science; Motion planning; Aeronautics; Transport engineering; Engineering; Robot; Artificial intelligence","score_opus":0.07018627824057515,"score_gpt":0.33149655076084084,"score_spread":0.2613102725202657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406611348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007747194,0.0006830423,0.98706615,0.000068271176,0.0024620183,0.00033739235,0.000011787032,0.0007706664,0.00085345923],"genre_scores_gemma":[0.387846,0.0000029830649,0.6106786,0.00008974002,0.0003074751,0.000060205868,0.000033522305,0.000029399269,0.0009520778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873596,0.000018221936,0.0003180645,0.0003510621,0.00025044507,0.00032621622],"domain_scores_gemma":[0.9993068,0.00027414266,0.000049148293,0.00023640611,0.00004964494,0.00008388601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030523606,0.00014992281,0.00017117332,0.00018926909,0.000115833725,0.00036129516,0.00035368002,0.00008091295,0.0000029301536],"category_scores_gemma":[0.000020427487,0.00013248462,0.00008322139,0.00031788077,0.000017106224,0.0007337421,0.000009433526,0.00010428005,0.000018088731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017481663,0.00004274931,0.000745833,0.0014477876,0.00018290879,0.00028478194,0.016017037,0.8955372,0.0036925997,0.057556093,0.007404898,0.01707066],"study_design_scores_gemma":[0.00016234182,0.000050940518,0.00056906766,0.00029178557,0.000016328775,0.00003106778,0.00019409144,0.99554706,0.0003209161,0.00012779745,0.0024969776,0.00019162218],"about_ca_topic_score_codex":0.0000678222,"about_ca_topic_score_gemma":0.0000016538713,"teacher_disagreement_score":0.3800988,"about_ca_system_score_codex":0.00009807188,"about_ca_system_score_gemma":0.00023632504,"threshold_uncertainty_score":0.5402566},"labels":[],"label_agreement":null},{"id":"W4406668368","doi":"10.23977/jaip.2024.070417","title":"Literature Review of Path Planning Algorithms for Mobile Robots","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Motion planning; Mobile robot; Path (computing); Artificial intelligence; Robot; Algorithm; Computer network","score_opus":0.07333587596648408,"score_gpt":0.39731690110884765,"score_spread":0.3239810251423636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406668368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024848621,0.1434528,0.8510718,0.0024741841,0.0024412381,0.00028273728,0.00000820718,0.000043497723,0.00020068356],"genre_scores_gemma":[0.002897318,0.019080386,0.9761168,0.0008676292,0.00090442464,0.00002270082,0.0000035265643,0.000026479025,0.00008076584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717027,0.00020854533,0.0013186622,0.0003200534,0.00067548035,0.000306968],"domain_scores_gemma":[0.99489915,0.0023363526,0.0009294819,0.0003762801,0.0013152357,0.00014350456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037840437,0.00020431202,0.00046607363,0.00028059527,0.00008766517,0.00038920884,0.0009776672,0.00010783568,0.000010131822],"category_scores_gemma":[0.003088342,0.00016516351,0.00028753528,0.0011000269,0.000052622716,0.002429286,0.00010215413,0.0006417581,0.000021335727],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011196162,0.00038689122,0.0000042838287,0.0056207688,0.0002895247,0.002032227,0.0073379874,0.030219866,0.0017310396,0.023135701,0.009871466,0.9192583],"study_design_scores_gemma":[0.00008059473,0.0024793914,0.0000068913264,0.07772955,0.00036513238,0.0058876793,0.0011415133,0.79742783,0.016967135,0.014943544,0.08235809,0.00061266957],"about_ca_topic_score_codex":0.0000028698278,"about_ca_topic_score_gemma":4.0213983e-8,"teacher_disagreement_score":0.9186456,"about_ca_system_score_codex":0.000067960194,"about_ca_system_score_gemma":0.00034830277,"threshold_uncertainty_score":0.6735172},"labels":[],"label_agreement":null},{"id":"W4406735451","doi":"","title":"Stream Function-Based Navigation for Dynamic Quadcopter Obstacle Avoidance","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadcopter; Obstacle avoidance; Function (biology); Obstacle; Computer science; Computer vision; Artificial intelligence; Geography; Engineering; Aerospace engineering; Mobile robot; Biology; Robot","score_opus":0.012644038029811426,"score_gpt":0.24548752330108806,"score_spread":0.23284348527127663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406735451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036897138,0.0005833109,0.981849,0.0065893726,0.001060833,0.00082644966,0.00016194921,0.0006356186,0.0046037636],"genre_scores_gemma":[0.22075069,0.000028720026,0.76822025,0.00026272808,0.000027049658,0.00037655229,0.0012164388,0.00003509577,0.009082502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949307,0.0022286337,0.0006253724,0.0012601223,0.00048982113,0.0004653738],"domain_scores_gemma":[0.99104285,0.0027327065,0.0006132136,0.0031580823,0.0022943728,0.00015879789],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036764983,0.00041845997,0.00041908593,0.00027033858,0.00047729746,0.000664482,0.002494957,0.00035956592,0.000013334113],"category_scores_gemma":[0.0010255856,0.00047662164,0.00027852168,0.00053198216,0.00013175159,0.00025317972,0.0010060706,0.00064749713,0.00003083079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082876184,0.0022866759,0.0036831065,0.002497025,0.00048103504,0.000021816842,0.013530181,0.09099764,0.0019530951,0.23427053,0.005971616,0.6442244],"study_design_scores_gemma":[0.00061064766,0.0000010266743,0.0020701308,0.0025487354,0.00004339371,0.000002628894,0.000023507928,0.977257,0.00524378,0.008916764,0.0028166384,0.00046572863],"about_ca_topic_score_codex":0.00038636837,"about_ca_topic_score_gemma":0.00013948172,"teacher_disagreement_score":0.8862594,"about_ca_system_score_codex":0.0002815035,"about_ca_system_score_gemma":0.0007953753,"threshold_uncertainty_score":0.99976856},"labels":[],"label_agreement":null},{"id":"W4406794018","doi":"10.1109/tevc.2025.3534026","title":"A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Genetic algorithm; Robot; Artificial intelligence; Path (computing); Algorithm; Machine learning","score_opus":0.03299743742900293,"score_gpt":0.2900253858983608,"score_spread":0.2570279484693579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406794018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028606458,0.0001242892,0.99742496,0.00027826583,0.0009067891,0.0006606449,0.00004587422,0.00018371484,0.000089387315],"genre_scores_gemma":[0.2617721,0.0000019263696,0.7376697,0.00016930346,0.000026624497,0.00017916133,0.000039376886,0.000013982056,0.00012783814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981665,0.00010571059,0.00046224892,0.00062344054,0.0002673391,0.00037478513],"domain_scores_gemma":[0.9990082,0.00044309912,0.00010703977,0.00030091486,0.00006424387,0.00007652032],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019889948,0.00025108526,0.0002451169,0.0006706358,0.0003444196,0.000057187888,0.0004129912,0.0001224217,0.0000030115923],"category_scores_gemma":[0.000006265135,0.00029845248,0.000114831775,0.00078014,0.00006383474,0.0002530434,0.000005495708,0.00022402382,0.000024982275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014626236,0.0005764326,0.00003565501,0.000018617977,0.000022766691,0.0000048910547,0.0001489393,0.8737019,0.0005916489,0.00006217051,0.00027367452,0.124548696],"study_design_scores_gemma":[0.0019141925,0.0001667337,0.029772462,0.00013394021,0.0000184208,0.000011632234,0.00001991482,0.9665422,0.00027285863,0.00064941595,0.00024897314,0.00024925874],"about_ca_topic_score_codex":0.000025542347,"about_ca_topic_score_gemma":0.000001405214,"teacher_disagreement_score":0.26148602,"about_ca_system_score_codex":0.00048256412,"about_ca_system_score_gemma":0.00024447532,"threshold_uncertainty_score":0.9999468},"labels":[],"label_agreement":null},{"id":"W4406809705","doi":"10.2316/j.2025.206-1119","title":"ROPE-RRT: EFFICIENT FRONT-END PATH PLANNING METHODS IN CLUTTERED MAZE ENVIRONMENTS, 270-278.","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rope; Front (military); Path (computing); Computer science; Motion planning; Front and back ends; Simulation; Aeronautics; Artificial intelligence; Operations research; Engineering; Mechanical engineering; Operating system; Robot; Algorithm","score_opus":0.01569463970983768,"score_gpt":0.32569486095278893,"score_spread":0.31000022124295123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406809705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017041584,0.00044686222,0.9787197,0.0018802194,0.0015959549,0.000070388276,0.000001919684,0.000018955838,0.00022441204],"genre_scores_gemma":[0.32536638,0.00003459653,0.67420995,0.00021475583,0.00007013104,0.0000014894408,0.0000034090308,0.0000053799545,0.000093897994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984331,0.00015095397,0.000608025,0.00019104182,0.00045464913,0.00016222331],"domain_scores_gemma":[0.99910146,0.00020129315,0.00040821623,0.00014329543,0.000087979206,0.000057748137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009314412,0.00012766007,0.00021056384,0.00046901766,0.000042862706,0.00020442072,0.00061313855,0.00006690814,0.000004434731],"category_scores_gemma":[0.00013732402,0.00011644791,0.000056309673,0.00013554354,0.000031701544,0.00030867825,0.00018858978,0.0002267014,0.0000041551666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014083562,0.00011912558,0.0053626862,0.000009974613,0.00008825164,0.00013809261,0.001100748,0.91395515,0.0015938601,0.0036679083,0.00026816933,0.07368194],"study_design_scores_gemma":[0.00069382024,0.000044015753,0.10402468,0.00032691454,0.000011700331,0.000082483886,0.000049731323,0.8922727,0.0004394356,0.0013999902,0.0005538229,0.00010069931],"about_ca_topic_score_codex":0.000010245863,"about_ca_topic_score_gemma":2.958059e-7,"teacher_disagreement_score":0.3083248,"about_ca_system_score_codex":0.00018405398,"about_ca_system_score_gemma":0.00007342378,"threshold_uncertainty_score":0.4748608},"labels":[],"label_agreement":null},{"id":"W4406809808","doi":"10.2316/j.2025.206-1136","title":"A NOVEL PATH PLANNING SCHEME BASED ON IMPROVED INFORMED RRT*-CONNECT FOR INDUSTRIAL ROBOTS, 279-288.","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scheme (mathematics); Robot; Motion planning; Path (computing); Computer science; Artificial intelligence; Computer network; Mathematics","score_opus":0.03446222297544785,"score_gpt":0.30905436580535667,"score_spread":0.27459214282990885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406809808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035434624,0.000035326688,0.9897004,0.0039417353,0.0023817765,0.00017593417,0.000008998413,0.00004537914,0.00016699111],"genre_scores_gemma":[0.23043272,0.000005299247,0.76823306,0.00088851317,0.0003500101,0.000008189099,0.000018056897,0.000009411719,0.000054737004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861765,0.000025302725,0.0005845306,0.00017614375,0.00042139806,0.00017495353],"domain_scores_gemma":[0.9981561,0.00055459025,0.000566308,0.00014221607,0.00051014294,0.00007067595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056224904,0.0001496295,0.00022024801,0.00049836084,0.00009268408,0.00033086922,0.0006014643,0.000113897484,0.0000011837906],"category_scores_gemma":[0.00081930455,0.0001334144,0.00009773458,0.0001687783,0.000024024714,0.00049291627,0.00007813448,0.00022648576,8.2244696e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001721593,0.00020127834,0.001166077,0.0000340167,0.00021398955,0.000021896487,0.00027430404,0.9156942,0.0027587381,0.026105037,0.0019268944,0.051431425],"study_design_scores_gemma":[0.0028954109,0.00025391745,0.002688009,0.0005618439,0.00001725665,0.000035860194,0.000021550028,0.9914801,0.00050822494,0.00092648313,0.0004814067,0.00012993233],"about_ca_topic_score_codex":0.0000051678758,"about_ca_topic_score_gemma":2.31025e-7,"teacher_disagreement_score":0.22688927,"about_ca_system_score_codex":0.0001478217,"about_ca_system_score_gemma":0.00042758568,"threshold_uncertainty_score":0.5440482},"labels":[],"label_agreement":null},{"id":"W4407017360","doi":"10.52202/078374-0046","title":"VESPER: Virtual-Reality Exploration with Simultaneous Prospecting Extravehicular Robots","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Robot; Prospecting; Virtual reality; Astrobiology; Computer science; Telerobotics; Engineering; Mobile robot; Human–computer interaction; Artificial intelligence; Physics; Mining engineering","score_opus":0.02695928084038285,"score_gpt":0.2695022357881423,"score_spread":0.24254295494775946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407017360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002636597,0.00012978834,0.9915935,0.0013585801,0.00041863386,0.00023388192,9.681936e-7,0.0014010885,0.0022269662],"genre_scores_gemma":[0.76409626,0.000004723745,0.23419212,0.000103866034,0.00014265858,0.000022569096,0.000005077809,0.000019421923,0.0014132742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830586,0.0000641331,0.00022794965,0.00063840684,0.00044179906,0.00032187434],"domain_scores_gemma":[0.9989851,0.00025948667,0.000041546537,0.00051962375,0.00008639578,0.00010784255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039102233,0.00018911595,0.00016003569,0.00009962073,0.00014416598,0.0006378134,0.000426517,0.000065193766,0.000009838589],"category_scores_gemma":[0.00008046666,0.00014035501,0.000040685856,0.000608392,0.00003728204,0.0009900478,0.00010237615,0.00021769771,0.00016724714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005976838,0.00005394458,0.00011028812,0.00003998227,0.000048047037,0.0014337242,0.003035438,0.86866486,0.0012136543,0.06002615,0.0003499585,0.06501796],"study_design_scores_gemma":[0.00011035145,0.00019596421,0.00008014963,0.00010183953,0.000008382515,0.00017024216,0.00009616273,0.9959912,0.0015846842,0.0009985458,0.00043088244,0.0002316459],"about_ca_topic_score_codex":0.000045540506,"about_ca_topic_score_gemma":0.000006573863,"teacher_disagreement_score":0.7614597,"about_ca_system_score_codex":0.00008439219,"about_ca_system_score_gemma":0.000106359825,"threshold_uncertainty_score":0.61504495},"labels":[],"label_agreement":null},{"id":"W4407389699","doi":"10.2139/ssrn.5134756","title":"Investigation of Stochastic Deep Learning Path Planning Methods for Mobile Robots","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University; University of Guelph","funders":"","keywords":"Computer science; Mobile robot; Motion planning; Artificial intelligence; Deep learning; Path (computing); Robot; Human–computer interaction; Computer network","score_opus":0.02800016227270666,"score_gpt":0.339964596450845,"score_spread":0.3119644341781383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407389699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012778157,0.008950653,0.98766154,0.000108512904,0.0012489842,0.000561081,0.0000030101241,0.00013275506,0.000055624943],"genre_scores_gemma":[0.04361179,0.00023623192,0.9550774,0.000040751132,0.00030396797,0.00014975246,0.000028135979,0.00003418562,0.0005178038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954983,0.00065157143,0.0008110004,0.0006558951,0.00038094757,0.0020022818],"domain_scores_gemma":[0.9969599,0.0008715132,0.001142299,0.00052147976,0.00037123906,0.00013353046],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006393336,0.00038936507,0.0006736235,0.00050709647,0.0003121185,0.00017540934,0.0017083924,0.00034835987,0.0000010014274],"category_scores_gemma":[0.0006517212,0.00039472472,0.0002919022,0.00037136907,0.000061387545,0.00023156327,0.0006871884,0.0050364863,0.0000014553968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001748787,0.000013331607,0.00013635817,0.00010360127,0.00023048055,0.0000019870297,0.0015989183,0.8802051,0.00018516365,0.0124552185,0.000013291289,0.105039075],"study_design_scores_gemma":[0.00037312674,0.00046384268,0.00007690824,0.0005693445,0.00008330478,0.000114418566,0.0003118787,0.72616595,0.00017166467,0.27134714,0.000026175228,0.0002962528],"about_ca_topic_score_codex":0.000024608229,"about_ca_topic_score_gemma":0.000001777866,"teacher_disagreement_score":0.2588919,"about_ca_system_score_codex":0.0009597848,"about_ca_system_score_gemma":0.005218728,"threshold_uncertainty_score":0.99985045},"labels":[],"label_agreement":null},{"id":"W4407414833","doi":"10.2514/6.2025-0453","title":"Dynamic Time Warping-Based Trajectory for Autonomous Dynamic Soaring","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Dynamic time warping; Trajectory; Computer science; Image warping; Vehicle dynamics; Real-time computing; Computer vision; Artificial intelligence; Engineering; Aerospace engineering","score_opus":0.007710442858802979,"score_gpt":0.25848020290976464,"score_spread":0.2507697600509617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407414833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002007234,0.0000852565,0.9913454,0.0011240419,0.00076590775,0.0003538646,0.0000041098547,0.00089433446,0.0034198475],"genre_scores_gemma":[0.13477756,6.2299983e-7,0.85158867,0.0007921131,0.000011554831,0.00007219161,0.000014501065,0.00001783713,0.012724952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852765,0.00003735244,0.00027844222,0.0005527015,0.00015465413,0.00044918925],"domain_scores_gemma":[0.99883014,0.00031825886,0.00006791307,0.00064878905,0.00006265806,0.00007223238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034231905,0.00019513653,0.00024023525,0.00027270522,0.00018081641,0.00014013628,0.0009998328,0.0000959019,0.000010776033],"category_scores_gemma":[0.00006424726,0.00019264434,0.00011970564,0.0003725469,0.000040170267,0.00019419641,0.00013233487,0.00013484953,0.00009450712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004238584,0.0004526158,0.00044060135,0.00040505134,0.0002567893,0.00008194173,0.00072589825,0.7363315,0.011709375,0.0167535,0.005514709,0.22728564],"study_design_scores_gemma":[0.0005255707,0.000059438276,0.0014407604,0.000059313053,0.0000117558875,0.000003540012,0.0000057454517,0.99584544,0.00037855536,0.0010264382,0.00042775806,0.00021566486],"about_ca_topic_score_codex":0.00002108655,"about_ca_topic_score_gemma":0.0000074799104,"teacher_disagreement_score":0.25951397,"about_ca_system_score_codex":0.00025610146,"about_ca_system_score_gemma":0.0003789628,"threshold_uncertainty_score":0.7855808},"labels":[],"label_agreement":null},{"id":"W4407599460","doi":"10.1016/j.jii.2025.100792","title":"End-to-end autonomous underwater vehicle path following control method based on improved soft actor–critic for deep space exploration","year":2025,"lang":"en","type":"article","venue":"Journal of Industrial Information Integration","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"Path (computing); Underwater; Marine engineering; Space (punctuation); Engineering; Computer science; End-to-end principle; Aerospace engineering; Artificial intelligence; Geology; Computer network","score_opus":0.033701704621806904,"score_gpt":0.3000915545765266,"score_spread":0.2663898499547197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407599460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013662276,0.0000052755213,0.98706055,0.008001847,0.0026543622,0.00067916187,0.000009946421,0.0000626814,0.00015994495],"genre_scores_gemma":[0.67768073,5.198568e-7,0.31985652,0.002012003,0.00030266112,0.00006262179,0.00003370988,0.000008932292,0.00004227389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782085,0.00021077596,0.0011053347,0.00015343715,0.00044765172,0.00026192018],"domain_scores_gemma":[0.9974725,0.00080148026,0.00069433596,0.00025235926,0.00065588887,0.00012341222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021153202,0.00020688746,0.00034683698,0.0007579966,0.00020959391,0.0006256397,0.0004318292,0.0002291819,0.0000052819933],"category_scores_gemma":[0.0018949361,0.00017066333,0.00021529736,0.0005086965,0.000012257571,0.0042538997,0.000025810577,0.0004359721,0.000010179068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006644394,0.00012283817,0.00010654439,0.00002549088,0.00013943725,0.00000498547,0.0032580404,0.4948637,0.005876208,0.007229613,0.0031073738,0.48460135],"study_design_scores_gemma":[0.003523541,0.00077235093,0.000060809773,0.0002262287,0.000050232105,0.0000041074086,0.00041037594,0.9802069,0.012207517,0.0008618163,0.0015167092,0.00015943291],"about_ca_topic_score_codex":0.00002222595,"about_ca_topic_score_gemma":0.0000026355222,"teacher_disagreement_score":0.67631453,"about_ca_system_score_codex":0.00049603404,"about_ca_system_score_gemma":0.0006142651,"threshold_uncertainty_score":0.69594485},"labels":[],"label_agreement":null},{"id":"W4407666459","doi":"10.1051/itmconf/20257301005","title":"Optimizing Robotic Arm Control Using Deep Deterministic Policy Gradient: An Exploration of Hyperparameter Tuning","year":2025,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperparameter; Robotic arm; Computer science; Control (management); Artificial intelligence","score_opus":0.07324712132754402,"score_gpt":0.32118784973263864,"score_spread":0.24794072840509462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407666459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040891178,0.00012839955,0.9564603,0.0001802033,0.00038763823,0.00017751919,0.0000022719878,0.000064691536,0.0017077901],"genre_scores_gemma":[0.8030068,0.000007250385,0.19685966,0.00006555304,0.0000325524,0.0000073852084,0.0000020880373,0.0000053149474,0.000013385126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834645,0.00017826898,0.0005413577,0.000354923,0.0002802279,0.00029877218],"domain_scores_gemma":[0.998508,0.00035271785,0.0003488854,0.0004743469,0.00023433776,0.00008175523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034828435,0.00018528703,0.00045521115,0.0005241165,0.00010387117,0.00012538803,0.0008157834,0.000080203616,0.000004063391],"category_scores_gemma":[0.00025666895,0.00017673128,0.00007250756,0.00055426016,0.00016615022,0.0010062346,0.00010105896,0.00011659957,0.0000016042767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004409406,0.00033916032,0.00581646,0.0001892934,0.00017681363,0.000030579668,0.0049931807,0.7099102,0.01429285,0.19908775,0.0000044624007,0.06511511],"study_design_scores_gemma":[0.0004755008,0.00022668358,0.0007562605,0.00025716197,0.00004426118,0.0000065602585,0.00025293598,0.99003685,0.0017357399,0.0060394784,0.00001115403,0.00015741574],"about_ca_topic_score_codex":0.00018222068,"about_ca_topic_score_gemma":0.00000965253,"teacher_disagreement_score":0.76211566,"about_ca_system_score_codex":0.000037144255,"about_ca_system_score_gemma":0.001102688,"threshold_uncertainty_score":0.72068924},"labels":[],"label_agreement":null},{"id":"W4407678122","doi":"10.2514/6.2024-2419.c1","title":"Correction: Enhancing Robotic Grasping of Free-Floating Targets with Soft Actor-Critic Algorithm and Tactile Sensors: a Focus on the Pre-Grasp Stage","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"GRASP; Focus (optics); Computer science; Tactile sensor; Robotic hand; Artificial intelligence; Human–computer interaction; Soft robotics; Stage (stratigraphy); Computer vision; Robot; Software engineering","score_opus":0.012431480250126338,"score_gpt":0.24131634138161134,"score_spread":0.228884861131485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407678122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004963133,0.00036224528,0.9906903,0.00082568725,0.001293538,0.00032628435,0.0000027807118,0.00047232356,0.0010637252],"genre_scores_gemma":[0.7523082,0.000008623436,0.245724,0.00009959786,0.0001837149,0.000022706448,0.0000016047406,0.00003920896,0.0016123825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780536,0.0001477034,0.00037323628,0.0006611035,0.00054659776,0.00046600742],"domain_scores_gemma":[0.99731123,0.0016392409,0.00011940486,0.0007048751,0.00010657335,0.0001186721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054480555,0.00029286495,0.00031611492,0.00019145891,0.00027797947,0.00041390487,0.0005567024,0.00008013254,0.000020117397],"category_scores_gemma":[0.00031692054,0.00019382116,0.000065190114,0.0006585697,0.00010848821,0.0005493212,0.00023429042,0.00046119184,0.000012464431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087219116,0.00063818356,0.001728461,0.0016640257,0.001263192,0.0021766215,0.05395224,0.5340807,0.025882425,0.03608951,0.011330095,0.33110735],"study_design_scores_gemma":[0.000172146,0.00036465752,0.00050328876,0.0008527466,0.000025835066,0.00015458054,0.0007015707,0.9771377,0.019286938,0.00048858556,0.000043579366,0.00026840414],"about_ca_topic_score_codex":0.00031363216,"about_ca_topic_score_gemma":0.000021543752,"teacher_disagreement_score":0.74734503,"about_ca_system_score_codex":0.000079394806,"about_ca_system_score_gemma":0.000140723,"threshold_uncertainty_score":0.79037976},"labels":[],"label_agreement":null},{"id":"W4407691655","doi":"10.1109/tac.2025.3543293","title":"Toward Fast and Safety-Guaranteed Trajectory Planning and Tracking for Time-Varying Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Australian Research Council","keywords":"Trajectory; Computer science; Tracking (education); Control theory (sociology); Control engineering; Control (management); Engineering; Artificial intelligence","score_opus":0.01837391292381705,"score_gpt":0.2570903579546154,"score_spread":0.23871644503079834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407691655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002418675,0.00052046817,0.994132,0.00043168262,0.0008290229,0.00092100265,0.000027647962,0.00050977385,0.00020974563],"genre_scores_gemma":[0.9668939,0.0000045118427,0.03239222,0.00025033098,0.000027323102,0.00016176357,8.0135635e-7,0.000016998638,0.00025212867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984426,0.00011526467,0.00044839724,0.00045159986,0.00019318664,0.0003489835],"domain_scores_gemma":[0.99822193,0.0012228588,0.00010981122,0.0003004911,0.000053614673,0.00009126242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048643607,0.00024070586,0.00045562646,0.00032475,0.0003979559,0.00029115693,0.0002748097,0.000107224296,0.000002597703],"category_scores_gemma":[0.000025281499,0.00023513411,0.0000815702,0.00024289258,0.000057077035,0.00027266124,0.0000025867573,0.00016869642,0.000005384282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020399604,0.00021245542,0.000081913175,0.0011041618,0.00069810916,0.00006134089,0.0072660674,0.43209985,0.006512062,0.000805398,0.00016895177,0.55078566],"study_design_scores_gemma":[0.0026890554,0.00012823594,0.0004315591,0.00065941276,0.00009404272,0.00007162001,0.0001215213,0.9951593,0.00030541522,0.00007325691,0.00005293407,0.00021359474],"about_ca_topic_score_codex":0.000016087057,"about_ca_topic_score_gemma":4.2759e-7,"teacher_disagreement_score":0.9644753,"about_ca_system_score_codex":0.000068156485,"about_ca_system_score_gemma":0.00008579447,"threshold_uncertainty_score":0.9588491},"labels":[],"label_agreement":null},{"id":"W4407779513","doi":"10.3390/electronics14050832","title":"Quantum Particle Swarm Optimisation Proportional–Derivative Control for Trajectory Tracking of a Car-like Mobile Robot","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Trajectory; Mobile robot; Particle swarm optimization; Tracking (education); Computer science; Control theory (sociology); Quantum; Control (management); Robot; Artificial intelligence; Physics; Algorithm; Psychology","score_opus":0.017366646497176442,"score_gpt":0.2776122714846757,"score_spread":0.26024562498749926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407779513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043704398,0.0018473923,0.952947,0.0003315818,0.00026514282,0.000738413,0.00000584914,0.000118925935,0.000041301835],"genre_scores_gemma":[0.91923106,0.000022286538,0.080082566,0.00015413525,0.00003356565,0.00028086896,0.000009351496,0.000011069876,0.00017508278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985555,0.00007223565,0.00037474048,0.00034619131,0.00023298727,0.00041836104],"domain_scores_gemma":[0.99891174,0.00028098383,0.00019878325,0.00032440395,0.00023966978,0.000044397322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048939243,0.00014085158,0.0002508091,0.00008082753,0.00012477694,0.000044753906,0.0004009688,0.0000714106,0.000001999005],"category_scores_gemma":[0.00011750257,0.0001414997,0.00009619061,0.000387978,0.000048699105,0.00025254325,0.00003240453,0.0001513833,0.000001985153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015223162,0.0006045582,0.0013855667,0.00017804463,0.00034701414,0.0000068869713,0.0035027093,0.7489471,0.07497683,0.11380194,0.0009973656,0.05509979],"study_design_scores_gemma":[0.0012272772,0.00050965144,0.0015541718,0.000046757592,0.00003574314,0.000004384562,0.00007322911,0.9236448,0.06716646,0.004310195,0.0012485507,0.000178766],"about_ca_topic_score_codex":0.0000065613,"about_ca_topic_score_gemma":0.0000032827502,"teacher_disagreement_score":0.87552667,"about_ca_system_score_codex":0.0001826705,"about_ca_system_score_gemma":0.0005794167,"threshold_uncertainty_score":0.57701904},"labels":[],"label_agreement":null},{"id":"W4407782981","doi":"10.1109/tsc.2025.3544124","title":"Joint Trajectory Optimization and Resource Allocation in UAV-MEC Systems: A Lyapunov-Assisted DRL Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Joint (building); Resource allocation; Trajectory optimization; Lyapunov optimization; Resource management (computing); Lyapunov function; Mathematical optimization; Distributed computing; Lyapunov equation; Lyapunov exponent; Computer network; Optimal control; Artificial intelligence; Engineering; Nonlinear system; Mathematics","score_opus":0.018640206498967433,"score_gpt":0.23726839522926302,"score_spread":0.21862818873029558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407782981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010808008,0.00016498685,0.9867635,0.00020356027,0.0005504135,0.00039649312,0.0000020307882,0.00037351507,0.0007375321],"genre_scores_gemma":[0.76430875,0.000006528707,0.23532923,0.00020919337,0.00002739479,0.000023722087,0.000005587353,0.00001371916,0.000075885815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796575,0.00027762004,0.00052428874,0.000644419,0.00027351195,0.0003143933],"domain_scores_gemma":[0.9990112,0.00019560862,0.00016409016,0.00047342555,0.00008506261,0.00007058646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006177153,0.00023123896,0.00030169313,0.0006088862,0.00030988833,0.00028390627,0.00045416263,0.00013997659,6.1524986e-7],"category_scores_gemma":[0.0000032406433,0.0002484701,0.00005199557,0.0012274305,0.000030581556,0.0003294314,0.0000120869145,0.00032587198,0.000003338802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066434513,0.00012846096,0.000043699263,0.00024781466,0.000027032436,0.00000437703,0.0016959561,0.9836165,0.00021533675,0.0001580674,0.000005886954,0.013850276],"study_design_scores_gemma":[0.0005786409,0.000037229205,0.0014585272,0.0005726313,0.0000190072,0.000030479265,0.00060555583,0.9961623,0.00029302383,0.000010663942,0.000027537637,0.0002043989],"about_ca_topic_score_codex":0.0002799334,"about_ca_topic_score_gemma":0.00001250912,"teacher_disagreement_score":0.75350076,"about_ca_system_score_codex":0.0001609136,"about_ca_system_score_gemma":0.00007427312,"threshold_uncertainty_score":0.9999968},"labels":[],"label_agreement":null},{"id":"W4407822325","doi":"10.3103/s0147688224700357","title":"STRL Robotics: Intelligent Control for Robotic Platform in Human-Oriented Environment","year":2024,"lang":"en","type":"article","venue":"Scientific and Technical Information Processing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Robotic paradigms; Human–computer interaction; Control (management); Systems engineering; Robot; Engineering","score_opus":0.023580798834014382,"score_gpt":0.2752037548387582,"score_spread":0.2516229560047438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407822325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050168,0.00036983332,0.9971852,0.00041258935,0.0004674538,0.0004530988,0.0000044795997,0.00024470413,0.0003610105],"genre_scores_gemma":[0.83803886,0.000004534377,0.16158774,0.000094365154,0.00002376379,0.000072500254,0.000037889244,0.0000068025574,0.00013350704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983858,0.0000120465975,0.000568111,0.00034616864,0.00034725014,0.00034062224],"domain_scores_gemma":[0.99940276,0.000089773304,0.000099840254,0.00024654742,0.000060374798,0.000100721445],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001056085,0.00014920301,0.00017324009,0.00040245216,0.00034040562,0.0013317217,0.00035448835,0.00009716762,0.0000034151121],"category_scores_gemma":[0.000064943284,0.0001295913,0.000045016383,0.0005199111,0.00016713928,0.0020363536,0.00013129515,0.00020662825,0.000026659034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017944094,0.00016513563,0.00024278481,0.0010162101,0.000026976186,0.00001621541,0.0027318387,0.29664055,0.0021678822,0.24712567,0.0013098647,0.44853893],"study_design_scores_gemma":[0.00029548112,0.000059434075,0.00030114336,0.0002470569,0.000009382566,0.0000249802,0.000068610374,0.9884406,0.00026982883,0.007494939,0.0026077235,0.00018085832],"about_ca_topic_score_codex":0.0000022496502,"about_ca_topic_score_gemma":5.674729e-7,"teacher_disagreement_score":0.8375372,"about_ca_system_score_codex":0.00013189386,"about_ca_system_score_gemma":0.0000970204,"threshold_uncertainty_score":0.999705},"labels":[],"label_agreement":null},{"id":"W4407826006","doi":"10.1109/lra.2025.3544491","title":"Adaptive Trajectory Learning With Obstacle Awareness for Motion Planning","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Beijing Nova Program; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Obstacle; Trajectory; Motion (physics); Computer science; Artificial intelligence; Computer vision; Physics; Geography","score_opus":0.021674991594780254,"score_gpt":0.26228597712077634,"score_spread":0.24061098552599608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407826006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031874236,0.00004650779,0.9655218,0.0015990488,0.0004245837,0.00020842509,0.0000012518661,0.00027560611,0.000048519032],"genre_scores_gemma":[0.5306388,0.0000012646251,0.46861938,0.00056883844,0.0000474144,0.000028565823,0.000007462414,0.000010311541,0.000077955316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907964,0.000053700052,0.00017709791,0.00031377852,0.00015379064,0.00022197932],"domain_scores_gemma":[0.9994137,0.00020757833,0.0001112064,0.0001592772,0.00006527993,0.000042941636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019843085,0.00013798974,0.00015798317,0.00016893086,0.00029481735,0.00016459744,0.00018392073,0.000050195413,2.2420764e-7],"category_scores_gemma":[0.000025492944,0.00012890548,0.000028903603,0.00025204,0.00003865248,0.0003416182,0.000025057243,0.00012459101,0.0000014118082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059976433,0.0000125127735,0.00157514,0.000034593137,0.00002722387,0.0000064534797,0.00060593686,0.98534083,0.00086284796,0.0020687168,0.00021649979,0.009243272],"study_design_scores_gemma":[0.0004722576,0.00006898199,0.012784909,0.00015780672,0.00001716848,0.000008318629,0.000072801515,0.98542565,0.0006109375,0.0001648042,0.00005839087,0.00015798754],"about_ca_topic_score_codex":0.000008207313,"about_ca_topic_score_gemma":4.144768e-7,"teacher_disagreement_score":0.49876457,"about_ca_system_score_codex":0.000052839943,"about_ca_system_score_gemma":0.000051028575,"threshold_uncertainty_score":0.5256613},"labels":[],"label_agreement":null},{"id":"W4408116646","doi":"10.1007/s12204-025-2807-7","title":"Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios","year":2025,"lang":"en","type":"article","venue":"Journal of Shanghai Jiaotong University (Science)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Obstacle; Haptic technology; Artificial intelligence; Computer vision; Computer science; Blind spot; Object (grammar); Object detection; Simulation; Human–computer interaction; Pattern recognition (psychology); Geography","score_opus":0.014926335486116868,"score_gpt":0.25100155933281854,"score_spread":0.23607522384670165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408116646","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79612213,0.000049848397,0.20265645,0.00026450984,0.0005342191,0.00012642717,6.1964244e-7,0.000025067979,0.00022073102],"genre_scores_gemma":[0.9813042,0.00001787588,0.01852746,0.000036769936,0.000024456152,1.526528e-7,1.9486262e-7,0.0000031229908,0.00008572824],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982732,0.000059784827,0.00032709105,0.0003517964,0.00063120597,0.00035692044],"domain_scores_gemma":[0.9988762,0.00009869503,0.00026534835,0.00022384789,0.00036958174,0.00016630485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016487074,0.00012922888,0.00023209854,0.0014352556,0.00045459668,0.00021091149,0.0009751686,0.00006847186,0.0000014828408],"category_scores_gemma":[0.000115287046,0.00013330068,0.000054938915,0.002909323,0.00034285913,0.002294574,0.00018449366,0.00035351695,0.000001787391],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034898502,0.00037835003,0.013046115,0.00014329211,0.00006948962,0.0013279539,0.023239013,0.027807295,0.7558052,0.002125751,0.000020423859,0.17568812],"study_design_scores_gemma":[0.0038524433,0.00041901215,0.27240303,0.0011463534,0.000061865,0.00030616514,0.004201881,0.5473879,0.16889226,0.0008091197,0.000103740225,0.00041625462],"about_ca_topic_score_codex":0.0001053701,"about_ca_topic_score_gemma":0.000026913585,"teacher_disagreement_score":0.58691293,"about_ca_system_score_codex":0.00078982476,"about_ca_system_score_gemma":0.00082088803,"threshold_uncertainty_score":0.54358435},"labels":[],"label_agreement":null},{"id":"W4408152573","doi":"10.1504/ijcse.2025.10069763","title":"IQ-RRT*: a path planning algorithm based on informed-RRT* and quick-RRT*","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Heritage College","funders":"","keywords":"Path (computing); Computer science; Motion planning; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Robot","score_opus":0.008047135771112176,"score_gpt":0.271768716260692,"score_spread":0.26372158048957983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408152573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01918516,0.00013254257,0.97751564,0.0013504281,0.0012193049,0.00005111099,0.0000032064686,0.00003722706,0.00050535577],"genre_scores_gemma":[0.6030467,0.000009045695,0.39613846,0.00066913536,0.00011515253,0.0000019569231,0.0000015599924,0.0000042232964,0.000013782487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800813,0.000013340485,0.00037919753,0.00022127388,0.0011551608,0.00022292258],"domain_scores_gemma":[0.9984834,0.0004590082,0.00016754883,0.00009316891,0.0006524686,0.00014440062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008448338,0.00013987267,0.00016350056,0.0008630904,0.00013710157,0.00042843312,0.00080106955,0.00003773941,0.0000016530258],"category_scores_gemma":[0.000398363,0.00012649196,0.000038276597,0.000537149,0.000108375105,0.00092568126,0.00016548633,0.00022024954,0.0000015256508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008917114,0.00002687077,0.0006856179,0.000010157742,0.000028429125,0.00008280959,0.00033958547,0.9250978,0.00012832879,0.006078546,0.00022075885,0.06729217],"study_design_scores_gemma":[0.00047678198,0.00009039371,0.028658299,0.00036418982,0.0000043259406,0.00016819523,0.00003092144,0.9674595,0.000087970075,0.0015885144,0.0009515613,0.00011932814],"about_ca_topic_score_codex":0.0000050759963,"about_ca_topic_score_gemma":5.5956082e-8,"teacher_disagreement_score":0.58386153,"about_ca_system_score_codex":0.0001702049,"about_ca_system_score_gemma":0.0006059814,"threshold_uncertainty_score":0.51581925},"labels":[],"label_agreement":null},{"id":"W4408362165","doi":"10.3390/drones9030203","title":"A Survey on Obstacle Detection and Avoidance Methods for UAVs","year":2025,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Obstacle avoidance; Obstacle; Computer science; Collision avoidance; Artificial intelligence; Computer vision; Aeronautics; Computer security; Engineering; Geography; Mobile robot; Robot; Archaeology","score_opus":0.03694572322264856,"score_gpt":0.36332212413338744,"score_spread":0.3263764009107389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408362165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005055417,0.0003243289,0.9932458,0.000260398,0.0006947642,0.00013794962,0.000003144194,0.00009882332,0.00017937264],"genre_scores_gemma":[0.14897993,0.000009364723,0.849677,0.00024342023,0.00002903493,0.000043380307,0.0000020651123,0.00000593879,0.0010098617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992807,0.00015777911,0.00009907031,0.0002650223,0.000053839372,0.00014360699],"domain_scores_gemma":[0.9987644,0.0008850225,0.000034512504,0.00024474575,0.00004365681,0.000027681088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006069082,0.00007400015,0.000110143636,0.00007460313,0.0001195336,0.00006811449,0.0002073683,0.000040700277,2.7080353e-7],"category_scores_gemma":[0.0003767472,0.00007025781,0.000019061272,0.0002266153,0.000020724394,0.00009976892,0.000061508086,0.000060244784,0.0000037554544],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001566782,0.000027232736,0.0014114239,0.000025544421,0.000016395017,0.0000012767348,0.00026575956,0.00053018314,0.0019599572,0.0032350325,0.0003520919,0.9921594],"study_design_scores_gemma":[0.00035513175,0.0001311853,0.25272757,0.000043992197,0.0000051889306,0.0000030301821,0.000010467432,0.7243013,0.013534922,0.0064631426,0.0022778274,0.00014628762],"about_ca_topic_score_codex":0.00007094677,"about_ca_topic_score_gemma":0.000013302421,"teacher_disagreement_score":0.99201316,"about_ca_system_score_codex":0.000021718894,"about_ca_system_score_gemma":0.000022204731,"threshold_uncertainty_score":0.28650305},"labels":[],"label_agreement":null},{"id":"W4408399703","doi":"10.1109/lra.2025.3551067","title":"Robust Nonprehensile Object Transportation With Uncertain Inertial Parameters","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Inertial frame of reference; Object (grammar); Computer science; Artificial intelligence; Physics; Classical mechanics","score_opus":0.018583426637891882,"score_gpt":0.2311739636981385,"score_spread":0.2125905370602466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408399703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07554798,0.000017695276,0.91603786,0.007456293,0.00046222037,0.00017460818,0.000002715299,0.0002444782,0.000056182478],"genre_scores_gemma":[0.40557858,0.000004417407,0.5916349,0.002667786,0.000028226874,0.000015732605,0.000016691725,0.000009469458,0.000044151602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887305,0.000051863328,0.00025209395,0.00035525532,0.00023143974,0.00023627157],"domain_scores_gemma":[0.99939704,0.00011455146,0.00010500521,0.00027233927,0.000052328967,0.000058735106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013492355,0.00016977698,0.00018227252,0.0001858526,0.00014421929,0.00018765769,0.00023549634,0.00005795906,6.0796066e-7],"category_scores_gemma":[0.000011292387,0.00014953851,0.00003424828,0.00044723254,0.00006441679,0.00035945178,0.000010956104,0.00012370486,0.000004756882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005624643,0.000018542729,0.00082384393,0.00003228689,0.000032943866,0.00002679558,0.00041749454,0.9902998,0.0020670516,0.0013293654,0.0013930526,0.0035531768],"study_design_scores_gemma":[0.0005761758,0.000054854896,0.020307558,0.00011924158,0.0000309376,0.000009643664,0.000024658382,0.97721684,0.001288097,0.00009553801,0.000057577516,0.00021890669],"about_ca_topic_score_codex":0.00006275877,"about_ca_topic_score_gemma":0.00000723195,"teacher_disagreement_score":0.33003062,"about_ca_system_score_codex":0.00004316904,"about_ca_system_score_gemma":0.000059623246,"threshold_uncertainty_score":0.60980034},"labels":[],"label_agreement":null},{"id":"W4408401213","doi":"10.1109/icftic64248.2024.10913347","title":"Deep Reinforcement Learning Based Coverage Path Planning in Unknown Environments","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Reinforcement learning; Computer science; Motion planning; Path (computing); Artificial intelligence; Reinforcement; Engineering; Computer network; Robot","score_opus":0.0148985819006222,"score_gpt":0.24483441482406357,"score_spread":0.22993583292344136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408401213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010123563,0.00026619012,0.989948,0.00017043148,0.00038449527,0.00011826475,1.0240969e-7,0.0002817382,0.007818429],"genre_scores_gemma":[0.91856706,0.000009599205,0.07776928,0.00034522187,0.000042290816,0.00002068274,0.0000114313025,0.000016627386,0.0032178313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842656,0.000073241106,0.00026994673,0.00045992548,0.00038196912,0.00038838832],"domain_scores_gemma":[0.99938947,0.00017407372,0.000037308248,0.00030648915,0.000004399061,0.00008826494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042317226,0.00016682902,0.00014533053,0.00020660704,0.00007557797,0.00023108041,0.00044221198,0.00006224933,0.00005480852],"category_scores_gemma":[0.000035680463,0.00015314171,0.000045138957,0.00034227342,0.000017684628,0.00039349886,0.00017788085,0.0003260447,0.00025363412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016295282,0.000014696589,0.0016080546,0.000017001277,0.0000075605653,0.0004593921,0.0006382447,0.98722786,0.00012612295,0.0016487616,0.00014549124,0.008105208],"study_design_scores_gemma":[0.00025335606,0.000078896235,0.0015812677,0.00016892175,0.0000025530414,0.000008558611,0.000017861566,0.98986673,0.00024398713,0.00012189499,0.007465127,0.00019085697],"about_ca_topic_score_codex":0.000028460261,"about_ca_topic_score_gemma":3.6278072e-7,"teacher_disagreement_score":0.9175547,"about_ca_system_score_codex":0.00016009268,"about_ca_system_score_gemma":0.00005731963,"threshold_uncertainty_score":0.6244937},"labels":[],"label_agreement":null},{"id":"W4408633802","doi":"10.5267/j.ijdns.2024.7.008","title":"Fuzzy logic in real-time decision making for autonomous drones","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Prince Sattam bin Abdulaziz University","keywords":"Drone; Fuzzy logic; Computer science; Artificial intelligence","score_opus":0.038177809595778885,"score_gpt":0.3661179609577974,"score_spread":0.32794015136201854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408633802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011026987,0.0002765107,0.98457956,0.0013293666,0.0018855529,0.00007097367,0.000008420827,0.000013282798,0.00080935523],"genre_scores_gemma":[0.21633038,0.00012077837,0.78302485,0.0002683243,0.00021023069,0.0000010486717,0.000002371745,0.000002077556,0.000039958944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986906,0.000022286094,0.0003693847,0.0002866044,0.00042824727,0.0002028584],"domain_scores_gemma":[0.99866956,0.00051399856,0.00022058564,0.00030092816,0.00024955583,0.00004538743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023013565,0.00007125055,0.00014219341,0.00032623956,0.000101696045,0.00031836054,0.0037132844,0.000026517362,0.0000011586613],"category_scores_gemma":[0.0003574664,0.00005893669,0.00002082719,0.0005010803,0.00011293597,0.0014748151,0.0009672692,0.000096505355,0.0000017028215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010555909,0.00008754526,0.008431016,0.0000068007316,0.000038328864,0.00016112768,0.00028024096,0.045399886,0.0006779088,0.049722116,0.006414666,0.8886748],"study_design_scores_gemma":[0.00047547574,0.000071496965,0.019899938,0.00050024193,0.0000053286144,0.00016159544,0.000013803396,0.8900572,0.000024456505,0.08762044,0.0010745615,0.000095430754],"about_ca_topic_score_codex":0.0000046197333,"about_ca_topic_score_gemma":0.000001941925,"teacher_disagreement_score":0.88857937,"about_ca_system_score_codex":0.000077175406,"about_ca_system_score_gemma":0.00031551174,"threshold_uncertainty_score":0.6900268},"labels":[],"label_agreement":null},{"id":"W4408636263","doi":"10.1017/s0263574725000268","title":"Mobile robot tracking control based on lightweight network","year":2025,"lang":"en","type":"article","venue":"Robotica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Mobile robot; Tracking (education); Artificial intelligence; Control (management); Robot; Robot control; Computer vision","score_opus":0.009773516511639803,"score_gpt":0.24931590752954094,"score_spread":0.23954239101790115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408636263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020758935,0.00027241433,0.98262465,0.0020016248,0.0015171554,0.00037139133,0.0000015274728,0.0004923002,0.012698154],"genre_scores_gemma":[0.57019633,0.0000041719245,0.4243251,0.004043694,0.00026107332,0.000080335456,0.0000041594476,0.000020176227,0.0010649549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977271,0.00019409925,0.0003643546,0.0006517013,0.00038743357,0.00067531917],"domain_scores_gemma":[0.9978841,0.00070401223,0.00010033447,0.0010673428,0.000099786994,0.00014445036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005387046,0.0002681414,0.00037834633,0.000179901,0.00028987034,0.0002471847,0.001161747,0.0001501927,0.000019263474],"category_scores_gemma":[0.00012363355,0.00024465256,0.00013246178,0.0008660735,0.000053204076,0.00019425279,0.00010867295,0.00036543864,0.00019068303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011779708,0.00009910814,0.00071022497,0.000013649202,0.00002764858,0.000058775055,0.000051013212,0.9695547,0.000050729766,0.015624674,0.0044976897,0.009299969],"study_design_scores_gemma":[0.0008503236,0.00019965909,0.0032265044,0.00022799917,0.00002532646,0.000004089604,0.0000028177515,0.9906751,0.00022953807,0.0015426412,0.0027621624,0.00025385173],"about_ca_topic_score_codex":0.000005233721,"about_ca_topic_score_gemma":5.3963583e-7,"teacher_disagreement_score":0.5701756,"about_ca_system_score_codex":0.00009433225,"about_ca_system_score_gemma":0.00019091635,"threshold_uncertainty_score":0.9976643},"labels":[],"label_agreement":null},{"id":"W4408712124","doi":"10.1109/itsc58415.2024.10919503","title":"Real-Time Optimization-Based Path Planning for Autonomous Semi-Trailer Trucks*","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Truck; Trailer; Motion planning; Computer science; Path (computing); Automotive engineering; Engineering; Artificial intelligence; Computer network; Robot","score_opus":0.01761553989583676,"score_gpt":0.26934153476672595,"score_spread":0.2517259948708892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010536828,0.000082484,0.9856197,0.0007611436,0.00071813137,0.00028844285,0.000015529049,0.0017603246,0.010648849],"genre_scores_gemma":[0.008810232,0.0000017364969,0.98629934,0.0002421067,0.00016486483,0.00007044206,0.000046481004,0.000033496643,0.00433133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984425,0.000036565052,0.0002875555,0.0005919669,0.00023845992,0.00040298028],"domain_scores_gemma":[0.99876314,0.00053649204,0.000049444574,0.00045617676,0.00006761899,0.00012709651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036772207,0.00020109666,0.0002025881,0.00018233669,0.00013698435,0.0004456011,0.00059717946,0.00010112786,0.00008070005],"category_scores_gemma":[0.0000532236,0.00017892693,0.000099479825,0.00040573714,0.000026866455,0.0003617983,0.000066023036,0.00011228191,0.00012298791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002681319,0.00001760932,0.000031761283,0.000035081746,0.000019708492,0.00006002191,0.00037601372,0.9830259,0.0002724492,0.0022006582,0.011139712,0.0028183707],"study_design_scores_gemma":[0.00024651102,0.000087827146,0.00006167722,0.000117704374,0.000011097634,0.000016620827,0.000009509216,0.9969287,0.00028185901,0.00027908562,0.0017083101,0.00025109787],"about_ca_topic_score_codex":0.00001739128,"about_ca_topic_score_gemma":6.2322314e-8,"teacher_disagreement_score":0.0139027685,"about_ca_system_score_codex":0.000086134976,"about_ca_system_score_gemma":0.00032734082,"threshold_uncertainty_score":0.72964287},"labels":[],"label_agreement":null},{"id":"W4408909487","doi":"10.18280/jesa.580216","title":"Computation of Feasible Controls and Feasible States for a Four-Wheeled Autonomous Vehicle Robot","year":2025,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Johannesburg","keywords":"Computation; Robot; Computer science; Control engineering; Simulation; Control theory (sociology); Engineering; Artificial intelligence; Control (management); Algorithm","score_opus":0.04047094826827163,"score_gpt":0.2963397158404917,"score_spread":0.25586876757222005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408909487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036114722,0.021891631,0.9333045,0.004074573,0.002627972,0.001012105,0.000062141036,0.00021338955,0.00069897337],"genre_scores_gemma":[0.4314571,0.0005717265,0.56036305,0.0003444572,0.00028281214,0.000042073945,0.000009278075,0.00006985143,0.00685964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568164,0.0007591138,0.0015187411,0.00059847935,0.0005417677,0.0009002475],"domain_scores_gemma":[0.99553996,0.001232299,0.0012097056,0.00041059306,0.001268326,0.00033911678],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001861966,0.00048031402,0.0010879713,0.00055367086,0.0006974416,0.0011540466,0.0007660022,0.00021473445,0.000011482364],"category_scores_gemma":[0.00080680475,0.00048203563,0.00024999378,0.0010694186,0.00039986533,0.0011352047,0.00027842104,0.00046730804,0.000019768291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012821579,0.00039521809,0.004142669,0.0024772442,0.0007607248,0.0003196662,0.0029804416,0.18586786,0.0030440313,0.012270882,0.006733211,0.78087986],"study_design_scores_gemma":[0.0031320252,0.0007596405,0.1294157,0.002735149,0.00021739089,0.0011748248,0.00011165626,0.8349564,0.00051490805,0.026047044,0.00058824284,0.0003470117],"about_ca_topic_score_codex":0.00018007544,"about_ca_topic_score_gemma":0.00000782461,"teacher_disagreement_score":0.78053284,"about_ca_system_score_codex":0.00041174778,"about_ca_system_score_gemma":0.0010660688,"threshold_uncertainty_score":0.9998829},"labels":[],"label_agreement":null},{"id":"W4408962266","doi":"10.1007/s11370-025-00588-z","title":"Local path planning scheme for car-like robots’ shortest turning motion using geometric analysis","year":2025,"lang":"en","type":"article","venue":"Intelligent Service Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Motion planning; Scheme (mathematics); Robot; Shortest path problem; Path (computing); Motion analysis; Motion (physics); Artificial intelligence; Computer vision; Computer network; Mathematics; Theoretical computer science; Graph","score_opus":0.04894749739158865,"score_gpt":0.31444355705669724,"score_spread":0.2654960596651086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408962266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004477955,0.0006632593,0.9920309,0.00046454047,0.0014343653,0.0004600127,0.000009092712,0.00036912158,0.000090761605],"genre_scores_gemma":[0.2771747,0.000012371373,0.72147,0.0009859708,0.000103207465,0.000023210636,0.000079492005,0.0000338957,0.0001171174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964423,0.00011248535,0.0008956672,0.0010273574,0.0006135266,0.000908652],"domain_scores_gemma":[0.99696267,0.00066125725,0.0003428919,0.0011053717,0.0007010361,0.00022675724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008165202,0.00047640223,0.00071288756,0.0018876126,0.00043303374,0.00040573807,0.0015775365,0.00028376517,0.0000058061823],"category_scores_gemma":[0.00017266006,0.0005077132,0.00034189754,0.009166985,0.00004729168,0.00048052802,0.0005526804,0.00044098555,0.000025739155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000833242,0.00010134378,0.011293321,0.00010354416,0.0005504591,0.00003136302,0.00045427252,0.9805458,0.00018623602,0.0010333579,0.000081477025,0.0056104553],"study_design_scores_gemma":[0.0002286506,0.000056031316,0.0036239352,0.00022222997,0.0005845446,0.000016551414,0.0003239916,0.9929716,0.0009834138,0.0003815939,0.00013544604,0.00047201163],"about_ca_topic_score_codex":0.0002573954,"about_ca_topic_score_gemma":0.000012417402,"teacher_disagreement_score":0.27269676,"about_ca_system_score_codex":0.0004773911,"about_ca_system_score_gemma":0.00020976539,"threshold_uncertainty_score":0.99973744},"labels":[],"label_agreement":null},{"id":"W4408971191","doi":"10.23977/acss.2025.090117","title":"Map-less Navigation Algorithm for Autonomous Vehicles Based on Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Reinforcement; Computer vision; Engineering; Structural engineering","score_opus":0.015416080050304459,"score_gpt":0.2745526422381468,"score_spread":0.2591365621878423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408971191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032565833,0.002286245,0.9947319,0.00013740356,0.0016999769,0.0005327008,0.0000013240796,0.00012803084,0.00015678487],"genre_scores_gemma":[0.41590095,0.000045047127,0.58289796,0.0004037847,0.00024458597,0.00025431643,0.000027574199,0.000016132128,0.000209628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983374,0.00014261503,0.0004543138,0.00051397964,0.00023316541,0.00031853394],"domain_scores_gemma":[0.9987899,0.00060359744,0.00017184226,0.00028745938,0.000088776054,0.00005839462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006624699,0.00019870231,0.0003210308,0.00020794723,0.00018326654,0.00025946661,0.0004132148,0.00007610575,3.0441558e-7],"category_scores_gemma":[0.00001032272,0.00018636716,0.00004812841,0.00022823515,0.000031385694,0.00038759198,0.000098356875,0.00015695818,0.0000033973038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033435058,0.00001740184,0.00022758264,0.00010323979,0.000006368433,0.000008584068,0.000120249715,0.6713612,0.0000075795024,0.0017940826,0.000026225951,0.32632414],"study_design_scores_gemma":[0.0007383258,0.00026767512,0.0001320701,0.00077781285,0.000004023496,0.000004196185,0.000033556247,0.9947136,0.00009755239,0.00056739646,0.0024684654,0.00019531325],"about_ca_topic_score_codex":0.000020903524,"about_ca_topic_score_gemma":6.0976254e-7,"teacher_disagreement_score":0.4155753,"about_ca_system_score_codex":0.00008503479,"about_ca_system_score_gemma":0.000051394363,"threshold_uncertainty_score":0.75998324},"labels":[],"label_agreement":null},{"id":"W4409018123","doi":"10.4271/2025-01-8032","title":"Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function","year":2025,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Function (biology); Computer science; Computer vision; Artificial intelligence","score_opus":0.010638973332929581,"score_gpt":0.2502346742156786,"score_spread":0.239595700882749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409018123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.800098,0.0014577216,0.010593877,0.024021054,0.0043758824,0.0089679,0.00027336265,0.018114991,0.13209723],"genre_scores_gemma":[0.96064353,0.000011774835,0.035375375,0.0030400206,0.00011833704,0.00041780598,0.000025432988,0.00006495294,0.00030278458],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9947223,0.00023178267,0.0008510889,0.0019838922,0.0011315494,0.0010794097],"domain_scores_gemma":[0.99609804,0.0013586129,0.00030010854,0.0016902926,0.00018244704,0.00037051569],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008195733,0.0009037011,0.0009748377,0.0004916672,0.0006820719,0.0003769125,0.0013328819,0.00063915673,0.000024144518],"category_scores_gemma":[0.00077086105,0.0007294831,0.0002373964,0.001427315,0.0009954799,0.0003444093,0.00056549575,0.0013504463,0.000041537496],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024078605,0.0008385495,0.0006364687,0.000092847404,0.00015214783,0.00038943623,0.00013543843,0.017591361,0.9404164,0.018735481,0.0035480931,0.015055908],"study_design_scores_gemma":[0.0021759998,0.0037250614,0.98660195,0.0009466029,0.00011452492,0.000081997314,0.00011867876,0.000066162436,0.0007054872,0.000889752,0.0036632298,0.00091054424],"about_ca_topic_score_codex":0.000043809505,"about_ca_topic_score_gemma":0.0012192306,"teacher_disagreement_score":0.9859655,"about_ca_system_score_codex":0.00045168708,"about_ca_system_score_gemma":0.0003490183,"threshold_uncertainty_score":0.99951565},"labels":[],"label_agreement":null},{"id":"W4409019746","doi":"10.1109/tits.2025.3550348","title":"Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CHIST-ERA; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Royal Irish Academy","keywords":"Reinforcement learning; Trajectory; Computer science; Artificial intelligence; Simulation; Aerospace engineering; Human–computer interaction; Engineering; Physics","score_opus":0.031003461039441398,"score_gpt":0.2549165590388694,"score_spread":0.223913097999428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409019746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008124061,0.000032731634,0.9955049,0.00007578354,0.0014693832,0.0017912,0.0000118349135,0.0002251039,0.00007661337],"genre_scores_gemma":[0.97515637,0.0000058348187,0.023102794,0.0001841956,0.000024847506,0.00095434894,0.00006376533,0.000032246586,0.00047562085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716616,0.00028730356,0.0008609253,0.00070891774,0.00053521746,0.0004414891],"domain_scores_gemma":[0.99829584,0.00082193327,0.0002096097,0.00049786846,0.00006851699,0.00010622621],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006146103,0.0003676569,0.00039036747,0.00060145836,0.00025718595,0.000096271346,0.0004521145,0.0001712243,0.000013481191],"category_scores_gemma":[0.000009688995,0.00039858813,0.00022149441,0.000730632,0.000041879808,0.00019503466,3.2550403e-7,0.0003755448,0.00003276613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016195519,0.0003117562,0.00005892523,0.000114734714,0.00005037852,0.000011174812,0.0006830376,0.9923941,0.0020392372,0.0003258348,0.000011106582,0.0038377405],"study_design_scores_gemma":[0.0013791147,0.00041906087,0.00019341828,0.00038951816,0.000040332347,2.2627732e-7,0.0001264029,0.9057815,0.09112668,0.000027909457,0.00020956408,0.00030627818],"about_ca_topic_score_codex":0.000074485884,"about_ca_topic_score_gemma":0.0000102847935,"teacher_disagreement_score":0.97434396,"about_ca_system_score_codex":0.00039464064,"about_ca_system_score_gemma":0.00017903029,"threshold_uncertainty_score":0.9998466},"labels":[],"label_agreement":null},{"id":"W4409084574","doi":"10.18280/mmep.120335","title":"New Meta-Heuristic Computer-Oriented Algorithms to Solve Unconstrained Optimization Problems","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Mosul","keywords":"Meta heuristic; Heuristic; Computer science; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.0293129112836424,"score_gpt":0.230087210027798,"score_spread":0.2007742987441556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409084574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003641928,0.00025348045,0.9972061,0.00065158005,0.0003554734,0.0005030615,0.0000025813363,0.00068421214,0.00030709716],"genre_scores_gemma":[0.0030177736,0.000010227202,0.9964115,0.00008458668,0.0000589304,0.000076333345,0.000005597197,0.000026150316,0.00030888943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979637,0.000019938505,0.00056984974,0.00063076767,0.00030169383,0.000514008],"domain_scores_gemma":[0.9987664,0.00026602566,0.000069440204,0.0004822252,0.00009806289,0.0003178133],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048051364,0.00036080895,0.0005766786,0.00029435984,0.00012013827,0.00027869327,0.00045701428,0.00012212689,0.0000066672083],"category_scores_gemma":[0.00006812793,0.0003180604,0.00010573049,0.00071395683,0.000023510258,0.00020779819,0.00023612897,0.0002504353,0.00001687157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.886107e-7,0.00003688876,8.1504754e-7,0.00026275494,0.00018030654,0.000004552065,0.000620865,0.93531436,0.000020298305,0.061255585,0.000112599446,0.0021899848],"study_design_scores_gemma":[0.00029095114,0.00007368615,0.000001151792,0.00040825095,0.00013944347,0.00002320607,0.0000041556304,0.9857636,0.000041351002,0.012718965,0.00022642614,0.00030883242],"about_ca_topic_score_codex":0.000020770443,"about_ca_topic_score_gemma":1.06693655e-7,"teacher_disagreement_score":0.05044922,"about_ca_system_score_codex":0.000047332014,"about_ca_system_score_gemma":0.00006720469,"threshold_uncertainty_score":0.99992716},"labels":[],"label_agreement":null},{"id":"W4409129143","doi":"10.1109/lra.2025.3557751","title":"Multi-Robot Reliable Navigation in Uncertain Topological Environments With Graph Attention Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Agency for Science, Technology and Research","keywords":"Computer science; Robot; Topology (electrical circuits); Human–computer interaction; Distributed computing; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.01341694836921523,"score_gpt":0.24448333265965158,"score_spread":0.23106638429043636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409129143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05151936,0.000039582737,0.9440681,0.003711235,0.00035397295,0.00019643056,4.4337548e-7,0.00009595494,0.000014972107],"genre_scores_gemma":[0.51144683,0.000017396218,0.4873654,0.001019199,0.000020812871,0.000020560543,0.000016375043,0.000006322421,0.00008714101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886066,0.000074514865,0.0002659972,0.00036586556,0.00018058946,0.0002523717],"domain_scores_gemma":[0.99953336,0.000060772767,0.000108894616,0.00023712435,0.000014766314,0.00004508415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025236647,0.00014747569,0.00016048121,0.00016405915,0.0001281431,0.00012507445,0.00021507418,0.00008761667,5.8290937e-7],"category_scores_gemma":[0.000009326081,0.00013013897,0.000025305171,0.000429609,0.000073175484,0.00031808243,0.000046946217,0.00017026851,0.0000042489087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002841871,0.000045458084,0.010209708,0.000012005172,0.000011029109,0.000023752697,0.00007232097,0.9851133,0.0022059386,0.0008252652,0.00010016288,0.0013781759],"study_design_scores_gemma":[0.0005534687,0.000030918036,0.11366999,0.00014766598,0.000007947932,0.000008282445,0.000011840946,0.8851838,0.00007522108,0.00016350705,0.000014346397,0.00013297777],"about_ca_topic_score_codex":0.000036673497,"about_ca_topic_score_gemma":0.000001795856,"teacher_disagreement_score":0.45992744,"about_ca_system_score_codex":0.000088649715,"about_ca_system_score_gemma":0.000014561874,"threshold_uncertainty_score":0.5306913},"labels":[],"label_agreement":null},{"id":"W4409167691","doi":"10.1016/j.ijar.2025.109440","title":"Prospect utility with hyperbolic tangent function","year":2025,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación","keywords":"Hyperbolic function; Tangent; Function (biology); Mathematics; Mathematical analysis; Geometry; Biology","score_opus":0.008977055214861645,"score_gpt":0.25637515529276883,"score_spread":0.2473981000779072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409167691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035441335,0.0003035307,0.9562339,0.0010201099,0.0014348832,0.000087656364,0.0000013518677,0.000063452455,0.0054137385],"genre_scores_gemma":[0.69388074,0.000022350465,0.30555755,0.00018248691,0.00018243186,0.000004685328,0.000001610183,0.0000073428664,0.00016078333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985744,0.00004829567,0.00036381156,0.00020315923,0.0006297659,0.00018056204],"domain_scores_gemma":[0.99868906,0.000069597656,0.00035782653,0.00021113,0.0006065836,0.00006580402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005913621,0.0001257418,0.0001928998,0.00026405192,0.0000673614,0.00022787463,0.00093237276,0.000042107335,0.0000075760568],"category_scores_gemma":[0.00012742731,0.00009604318,0.00007673673,0.00029810073,0.00003752466,0.00054705754,0.0001367405,0.00024126946,0.00000501787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012465501,0.001116368,0.14287692,0.000115969495,0.0028766037,0.0016658271,0.002612341,0.016310597,0.0032359972,0.2933269,0.0034294976,0.5311864],"study_design_scores_gemma":[0.003818245,0.0006664365,0.1555983,0.002273578,0.00013377648,0.002826071,0.0002855107,0.80263376,0.006625932,0.016278053,0.008284156,0.00057619927],"about_ca_topic_score_codex":0.000010051901,"about_ca_topic_score_gemma":5.285682e-7,"teacher_disagreement_score":0.78632313,"about_ca_system_score_codex":0.00014865163,"about_ca_system_score_gemma":0.0002100216,"threshold_uncertainty_score":0.3916527},"labels":[],"label_agreement":null},{"id":"W4409262355","doi":"10.1109/wacv61041.2025.00671","title":"DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Drone; Computer science; Artificial intelligence; Computer vision","score_opus":0.025788411130527353,"score_gpt":0.28597006515775,"score_spread":0.2601816540272226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024656546,0.00032709632,0.98972285,0.0015196576,0.00011583336,0.00032710208,0.0005346152,0.0002989969,0.0046881847],"genre_scores_gemma":[0.053678792,0.00034596265,0.94043225,0.0008613255,0.000025041347,0.000035022444,0.0020006115,0.000022124983,0.002598873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984921,0.00006497077,0.00023965082,0.00073008006,0.00016085399,0.00031231326],"domain_scores_gemma":[0.99847364,0.00014853926,0.00004509168,0.0011775536,0.000027548842,0.00012760577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043006445,0.00020298427,0.00027646363,0.00014356189,0.0001565421,0.00025862685,0.00087188074,0.000065311135,0.000008388238],"category_scores_gemma":[0.000024045918,0.0001448735,0.000008777214,0.0004157953,0.00017162092,0.00067951693,0.00033451337,0.00016561778,0.000008616901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023165015,0.00070323213,0.0045635803,0.0005883153,0.00069009897,0.00073571684,0.0046503535,0.00070257235,0.0013914643,0.110757284,0.13882248,0.73616326],"study_design_scores_gemma":[0.0021444217,0.00037893583,0.007102577,0.00034989725,0.00011245798,0.00032143714,0.00022833647,0.9431463,0.0011116684,0.0011996638,0.04325151,0.00065280974],"about_ca_topic_score_codex":0.0005735133,"about_ca_topic_score_gemma":0.00014940106,"teacher_disagreement_score":0.9424437,"about_ca_system_score_codex":0.000028862913,"about_ca_system_score_gemma":0.00020519689,"threshold_uncertainty_score":0.590777},"labels":[],"label_agreement":null},{"id":"W4409363599","doi":"10.1609/aaai.v39i25.34873","title":"Inapproximability of Optimal Multi-Agent Pathfinding Problems","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Pathfinding; Computer science; Theoretical computer science; Shortest path problem","score_opus":0.09230795298380508,"score_gpt":0.3135191796714521,"score_spread":0.22121122668764703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18438147,0.00005509119,0.8060506,0.002029625,0.0009163314,0.0009976833,0.000008233383,0.00015715443,0.005403785],"genre_scores_gemma":[0.9025081,0.000010710679,0.09714217,0.000059725106,0.00001584988,0.000036589445,3.0276314e-7,0.000007391689,0.00021919108],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976179,0.00003258868,0.00083813095,0.0006195518,0.0004986267,0.00039321405],"domain_scores_gemma":[0.99800533,0.00014064992,0.00052534963,0.0004916991,0.00076660706,0.000070348804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010499475,0.00025070732,0.00039292936,0.00021401492,0.00017249679,0.0001381757,0.0027033614,0.00011782177,0.000010956529],"category_scores_gemma":[0.0007804161,0.00019358663,0.00015230011,0.0011441293,0.0003874037,0.0003277521,0.0007530882,0.00035924366,0.00001792339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004708881,0.00072302745,0.0031605037,0.00033297428,0.00004958779,8.318165e-7,0.0041641546,0.0038076108,0.06527005,0.8321493,0.000077800614,0.090217024],"study_design_scores_gemma":[0.00005214963,0.0001561737,0.0013425509,0.00063132093,0.000014606373,0.0000022881748,0.00045265068,0.49308074,0.4671392,0.036906425,0.000025711313,0.00019619688],"about_ca_topic_score_codex":0.000050187176,"about_ca_topic_score_gemma":0.0000015995748,"teacher_disagreement_score":0.7952429,"about_ca_system_score_codex":0.00008111367,"about_ca_system_score_gemma":0.000200105,"threshold_uncertainty_score":0.7894234},"labels":[],"label_agreement":null},{"id":"W4409535950","doi":"10.3390/sym17040609","title":"Path Planning of Mobile Robot Based on Dual-Layer Fuzzy Control and Improved Genetic Algorithm","year":2025,"lang":"en","type":"article","venue":"Symmetry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Mobile robot; Motion planning; Genetic algorithm; Dual (grammatical number); Layer (electronics); Fuzzy control system; Fuzzy logic; Path (computing); Dual layer; Control (management); Algorithm; Artificial intelligence; Robot; Computer network; Machine learning; Materials science","score_opus":0.00835808988260999,"score_gpt":0.2531884367773958,"score_spread":0.24483034689478578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409535950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056452076,0.00082883093,0.99073076,0.00015340876,0.0006184827,0.00036884125,0.00001870255,0.0001367979,0.0014989807],"genre_scores_gemma":[0.57679373,0.0000037625741,0.4223511,0.00061982195,0.000048440736,0.00004157716,0.0000031027225,0.000011942683,0.00012651568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983283,0.000116083465,0.0003756262,0.0005323095,0.00027998764,0.0003676484],"domain_scores_gemma":[0.9985414,0.00043069883,0.00015349507,0.0006844734,0.00008482743,0.00010508977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038024958,0.00022265308,0.00037893502,0.00032587055,0.000100977624,0.000079066056,0.0004439181,0.0001248734,0.0000023860239],"category_scores_gemma":[0.00009434635,0.0002069446,0.00007101625,0.00055128074,0.00006590569,0.000102401005,0.00013186644,0.0002158725,0.000006846119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011312949,0.0008780919,0.026588203,0.0006457887,0.00038396206,0.00047546462,0.0012115021,0.14603256,0.022924537,0.0036141386,0.0040225806,0.7931101],"study_design_scores_gemma":[0.0014617544,0.00033629438,0.027573239,0.000211492,0.000024718076,0.000009230279,0.00003845349,0.96773255,0.0018157576,0.00048554016,0.00010277975,0.00020821106],"about_ca_topic_score_codex":0.000042756557,"about_ca_topic_score_gemma":1.1196762e-7,"teacher_disagreement_score":0.8217,"about_ca_system_score_codex":0.000040839972,"about_ca_system_score_gemma":0.0001338247,"threshold_uncertainty_score":0.8438956},"labels":[],"label_agreement":null},{"id":"W4409588435","doi":"10.20944/preprints202504.1156.v1","title":"Inverse Dynamics-Based Motion Planning for Autonomous Vehicles: Simultaneous Trajectory and Speed Optimization with Kinematic Continuity","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Trajectory; Dynamics (music); Inverse kinematics; Inverse; Motion (physics); Inverse dynamics; Computer science; Control theory (sociology); Motion planning; Mathematics; Artificial intelligence; Classical mechanics; Physics; Geometry; Robot; Acoustics","score_opus":0.05999296531364521,"score_gpt":0.3127522676364065,"score_spread":0.2527593023227613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409588435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12845093,0.000033001772,0.8682886,0.00017824784,0.0004626583,0.0015530135,0.000046453366,0.00057398353,0.00041315547],"genre_scores_gemma":[0.6144793,0.0000035044884,0.38462847,0.0001316387,0.000053769985,0.0001186155,0.00014233138,0.000030237616,0.0004121003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699473,0.00019180807,0.00060199085,0.0014109617,0.0003594996,0.0004409961],"domain_scores_gemma":[0.9969434,0.00072174607,0.00058412226,0.0012767925,0.00031168212,0.00016221637],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008183406,0.0004942461,0.00064972095,0.00035128224,0.00019323941,0.00014146525,0.0010123699,0.00037787802,0.000008063103],"category_scores_gemma":[0.0006071538,0.0005115948,0.00011717587,0.00024985924,0.00011755688,0.00019761978,0.00085374987,0.00063784415,0.000010961421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054011372,0.000090225534,0.029093793,0.0007004027,0.000083673935,0.00004453214,0.0014312998,0.9671954,0.000052959844,0.00009722128,0.000005883056,0.0011506132],"study_design_scores_gemma":[0.0011016617,0.00006242663,0.009304482,0.0009516466,0.0000996958,0.0000122837255,0.00007602094,0.98717666,0.00046276883,0.00025021948,0.000009738092,0.00049239537],"about_ca_topic_score_codex":0.000103741,"about_ca_topic_score_gemma":0.000013536883,"teacher_disagreement_score":0.4860284,"about_ca_system_score_codex":0.0004370014,"about_ca_system_score_gemma":0.00047430256,"threshold_uncertainty_score":0.99973357},"labels":[],"label_agreement":null},{"id":"W4409602093","doi":"10.1007/978-3-031-85859-8_26","title":"Event-Based Vision for Robot Soccer","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Event (particle physics); Machine vision; Robot vision; Robot; Computer graphics (images); Mobile robot","score_opus":0.017522709072272928,"score_gpt":0.2865013538051415,"score_spread":0.2689786447328686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409602093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015260578,0.00033009186,0.9915677,0.0021830506,0.0037176183,0.00074184063,0.000013368091,0.00026413804,0.0011806737],"genre_scores_gemma":[0.0039272048,0.000005503028,0.99132335,0.0028809654,0.000378091,0.00003096083,0.00001535294,0.000031719126,0.0014068611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556524,0.000038548373,0.00060791464,0.0019575772,0.0009947994,0.000835906],"domain_scores_gemma":[0.9962177,0.0012082391,0.00031304665,0.0017209098,0.00036208172,0.00017801485],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011613508,0.0006099133,0.00063785946,0.0010665677,0.000362247,0.0006070517,0.004190464,0.00043536667,0.000007499329],"category_scores_gemma":[0.00020217252,0.0005680094,0.0002480382,0.00067804847,0.00042717712,0.00044497667,0.000998427,0.00068647723,0.000030273835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006956551,0.000032776337,0.0000137349125,0.00007423465,0.0000102007125,0.000041723026,0.00011201098,0.41917813,0.000090844005,0.007212968,0.00031839652,0.57290804],"study_design_scores_gemma":[0.0004344571,0.0002391168,0.00006869164,0.0008111453,0.000011042164,0.000012108924,3.3067582e-8,0.9439633,0.00077644916,0.051255696,0.0018483081,0.0005796916],"about_ca_topic_score_codex":0.000011152547,"about_ca_topic_score_gemma":0.000008532957,"teacher_disagreement_score":0.5723283,"about_ca_system_score_codex":0.00040337525,"about_ca_system_score_gemma":0.0014382123,"threshold_uncertainty_score":0.9996771},"labels":[],"label_agreement":null},{"id":"W4409603827","doi":"10.61091/jcmcc127b-167","title":"Research on Approval and Global Optimal Path Planning Strategy for Bulk Transportation Based on Greedy Algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Department of Guangxi Zhuang Autonomous","keywords":"Greedy algorithm; Path (computing); Algorithm; Mathematical optimization; Computer science; Motion planning; Operations research; Mathematics; Artificial intelligence","score_opus":0.04370584523504462,"score_gpt":0.34387748795589107,"score_spread":0.30017164272084645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409603827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097761646,0.0001474091,0.89180183,0.00031531596,0.00873439,0.00057472306,0.0000115871535,0.00006603693,0.00058703707],"genre_scores_gemma":[0.7931495,0.0000043375117,0.20614047,0.000048471342,0.00062197616,0.000007550365,0.0000034961377,0.000018756891,0.0000054638667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967871,0.00019983167,0.00097298046,0.0004510909,0.0010624934,0.00052655244],"domain_scores_gemma":[0.9960747,0.0020285407,0.0005893351,0.00032790343,0.0007667719,0.00021276085],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003591092,0.00032922582,0.00068967993,0.00040500672,0.0005432222,0.0005409126,0.0007293078,0.00022700474,4.1312532e-7],"category_scores_gemma":[0.00031849212,0.0003004851,0.00013971278,0.00070302305,0.000111244706,0.00023187722,0.000095718184,0.0007120631,5.9264545e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026453924,0.00071612844,0.00010731158,0.00025454178,0.00009111481,0.00008227745,0.00051872415,0.027847487,0.000026796297,0.9499419,0.00051408366,0.019635126],"study_design_scores_gemma":[0.0052184183,0.0034601723,0.0004454399,0.0010053914,0.00004210388,0.000019844725,0.00027387906,0.7521701,0.0001273965,0.23685224,0.0001348142,0.00025018922],"about_ca_topic_score_codex":0.000007160683,"about_ca_topic_score_gemma":3.0500864e-8,"teacher_disagreement_score":0.7243226,"about_ca_system_score_codex":0.00016682499,"about_ca_system_score_gemma":0.00040547474,"threshold_uncertainty_score":0.99994475},"labels":[],"label_agreement":null},{"id":"W4409605317","doi":"10.7210/jrsj.43.275","title":"Development of Water Hydraulics Components for Robotics","year":2025,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nalco (Canada)","funders":"","keywords":"Hydraulics; Artificial intelligence; Robotics; Computer science; Engineering; Systems engineering; Control engineering; Robot; Aerospace engineering","score_opus":0.030432082271934762,"score_gpt":0.26785298197964647,"score_spread":0.2374208997077117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409605317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012870589,0.00010107015,0.98068565,0.0046925703,0.0013616934,0.00018290116,0.000001458755,0.00001148905,0.000092579045],"genre_scores_gemma":[0.10291789,0.000012483154,0.8965052,0.0002302471,0.000050940263,0.0000010318039,0.0000010612986,0.000009676929,0.00027149348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979168,0.00005457419,0.001037904,0.00014999828,0.00053804496,0.00030266863],"domain_scores_gemma":[0.99798936,0.00020040221,0.0007737425,0.00046247573,0.0005044956,0.000069537215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010875746,0.0001675463,0.00048870716,0.00007474195,0.00017818726,0.000036104826,0.0017921225,0.000116518626,5.128287e-7],"category_scores_gemma":[0.000061963045,0.00010701286,0.00052818074,0.000256957,0.00011910413,0.0001533041,0.00046593626,0.00023689245,8.1125347e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018036022,0.00036488584,0.001680898,0.00033807324,0.0006356251,4.82603e-7,0.007867097,0.9556862,0.022174617,0.0022878,0.005231026,0.003715235],"study_design_scores_gemma":[0.0023310466,0.0002102511,0.00894831,0.0011387133,0.00024137169,0.000045697238,0.0006306909,0.87412393,0.103562176,0.006094411,0.0022926095,0.0003807895],"about_ca_topic_score_codex":0.0000017219827,"about_ca_topic_score_gemma":4.9746455e-7,"teacher_disagreement_score":0.09004729,"about_ca_system_score_codex":0.00012905258,"about_ca_system_score_gemma":0.00032241835,"threshold_uncertainty_score":0.43638575},"labels":[],"label_agreement":null},{"id":"W4409794809","doi":"10.61091/jcmcc127b-525","title":"Research on Constructing Task Scheduling and Processing Models for Complex Data Sets Using Multi-Objective Optimization Algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scheduling (production processes); Task (project management); Optimization algorithm; Algorithm; Mathematical optimization; Mathematics; Engineering; Systems engineering","score_opus":0.1269105992066066,"score_gpt":0.3887798224770101,"score_spread":0.2618692232704035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409794809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011823614,0.00034800824,0.9818817,0.00011575666,0.0051270486,0.00053284527,0.000007863916,0.00005443763,0.00010873295],"genre_scores_gemma":[0.27787223,0.000011569041,0.72171354,0.000019582976,0.00035426466,0.0000020127748,0.0000034402788,0.000022255033,0.0000011093707],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661195,0.00028735452,0.0011542939,0.0006010577,0.0008065496,0.0005387807],"domain_scores_gemma":[0.9945183,0.0021339965,0.0010104625,0.0005330143,0.0016271652,0.00017710128],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005403347,0.00032450288,0.00080195797,0.0006427264,0.0010313086,0.00094909524,0.0012352123,0.00020572962,2.1583854e-7],"category_scores_gemma":[0.0010513765,0.00031383298,0.0000731234,0.00083885324,0.00018692874,0.00093966717,0.0011897539,0.00077552546,1.4126452e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001233654,0.0009506731,0.00006612277,0.0009125979,0.0003297156,0.00004462256,0.0037504921,0.29011127,0.0005175487,0.62963194,0.00007097163,0.07349067],"study_design_scores_gemma":[0.0034139866,0.00027803335,0.000007872094,0.0012185356,0.000055194745,0.0000957825,0.0009358496,0.8281271,0.00009244579,0.16553906,0.000010012061,0.00022616971],"about_ca_topic_score_codex":0.00001073953,"about_ca_topic_score_gemma":7.075488e-8,"teacher_disagreement_score":0.5380158,"about_ca_system_score_codex":0.00017920471,"about_ca_system_score_gemma":0.00055414013,"threshold_uncertainty_score":0.9999314},"labels":[],"label_agreement":null},{"id":"W4409992882","doi":"10.1109/icarce63054.2024.00016","title":"A Dynamic Control System for Robotic Goalkeeping Using YOLOv9 and Path Planning","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Motion planning; Computer science; Control (management); Path (computing); Control system; Control engineering; Robot; Control theory (sociology); Artificial intelligence; Engineering","score_opus":0.022307139281137527,"score_gpt":0.28224387898887265,"score_spread":0.2599367397077351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409992882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031458326,0.0019107229,0.9922138,0.00020715993,0.0011660514,0.00035208534,0.0000031731413,0.0007996797,0.00020149136],"genre_scores_gemma":[0.5772726,8.954208e-7,0.42251256,0.000063314365,0.000042450512,0.000016125017,0.0000011971114,0.0000148609415,0.00007601275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857265,0.000048907295,0.00026599853,0.0005327554,0.0001724203,0.00040728357],"domain_scores_gemma":[0.99911433,0.0004139052,0.00005110166,0.00027663494,0.000039697465,0.000104305516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048169467,0.00017771522,0.00026980002,0.0001679733,0.00017477518,0.0005032604,0.00032675322,0.000074584284,5.4000475e-7],"category_scores_gemma":[0.00003954522,0.00015657836,0.000061992585,0.00024278245,0.00002730977,0.0003941991,0.00009938901,0.00012478641,0.000010055572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019062913,0.00003348911,0.0009288863,0.0020908955,0.0003193029,0.0009834192,0.003261714,0.8915624,0.006065397,0.06344292,0.00035053244,0.03094199],"study_design_scores_gemma":[0.0003471895,0.0000576306,0.0002285315,0.0007799013,0.000033664648,0.00037642813,0.00013016068,0.9975276,0.000025119138,0.0002562861,0.000035978137,0.00020153851],"about_ca_topic_score_codex":0.000022103548,"about_ca_topic_score_gemma":2.0963797e-7,"teacher_disagreement_score":0.5741267,"about_ca_system_score_codex":0.000135558,"about_ca_system_score_gemma":0.00008454059,"threshold_uncertainty_score":0.638508},"labels":[],"label_agreement":null},{"id":"W4410114573","doi":"10.1109/tro.2025.3567476","title":"Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Path (computing); Robot; Computer science; Scale (ratio); Any-angle path planning; Mobile robot; Artificial intelligence; Geography; Computer network","score_opus":0.015246344997580328,"score_gpt":0.26157700513755894,"score_spread":0.24633066013997862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410114573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022305294,0.000028101455,0.9926484,0.0005191589,0.002139299,0.00034855105,0.00003130251,0.00059043634,0.0014642131],"genre_scores_gemma":[0.5737954,0.000035602643,0.4216112,0.0016788461,0.00009497632,0.00007637125,0.000012631857,0.00005595867,0.002639045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975823,0.00011712351,0.00040004004,0.00077327224,0.00050566264,0.00062158384],"domain_scores_gemma":[0.99832064,0.00030139214,0.00012944112,0.0009588025,0.00011659808,0.00017313274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002747251,0.00039635884,0.00036465435,0.0004110231,0.00059888465,0.00020968808,0.00064018567,0.00020170442,0.000008656371],"category_scores_gemma":[0.000008668805,0.000362837,0.00012904039,0.00086368766,0.000058843856,0.00036093243,0.000007177761,0.00071326055,0.00008617991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005584268,0.00044635884,0.00018965818,0.000024514728,0.00006937093,0.00007568423,0.0010681737,0.99176794,0.00009458713,0.00075250934,0.00038278353,0.0050725844],"study_design_scores_gemma":[0.0016747563,0.0006343592,0.0014883731,0.00047252164,0.0000666356,0.000039322167,0.00015298113,0.9918295,0.0025693432,0.00016270964,0.00043004437,0.0004794397],"about_ca_topic_score_codex":0.000018729088,"about_ca_topic_score_gemma":0.0000063014004,"teacher_disagreement_score":0.57156485,"about_ca_system_score_codex":0.00020578223,"about_ca_system_score_gemma":0.00015885905,"threshold_uncertainty_score":0.99988234},"labels":[],"label_agreement":null},{"id":"W4410153305","doi":"10.1109/jsen.2025.3566035","title":"High-Traversability and Precise Navigation for Mobile Robots in Constrained Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Mobile robot; Robot; Computer science; Artificial intelligence; Computer vision","score_opus":0.010437968266049693,"score_gpt":0.26287053482882167,"score_spread":0.25243256656277197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410153305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49600458,0.00004662501,0.50259346,0.00023566332,0.00076725654,0.00028167057,0.000006607107,0.000018379415,0.000045762466],"genre_scores_gemma":[0.7764987,0.000018751603,0.22304443,0.00005487189,0.00006294797,0.000016618163,0.0000022335194,0.000005450425,0.00029602743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887913,0.00010848695,0.00032008658,0.00027380767,0.00017288784,0.00024562454],"domain_scores_gemma":[0.999327,0.00024258983,0.00011108273,0.00020311482,0.00002780194,0.00008839107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006198639,0.00011656022,0.0001819721,0.00012200414,0.00012530248,0.000111354595,0.00024796659,0.00007495704,0.0000026363384],"category_scores_gemma":[0.00006823511,0.00011309187,0.00004213258,0.00015315453,0.000083707826,0.00026159768,0.00003331228,0.00022542116,0.0000034083355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011220516,0.00040462444,0.014124052,0.00009485728,0.000111032154,0.00021298579,0.0031685182,0.8236059,0.024707861,0.0012164103,0.001410176,0.1308314],"study_design_scores_gemma":[0.0059505226,0.00045013652,0.083278865,0.00050126493,0.000042801817,0.000595275,0.00027240586,0.86601824,0.022393843,0.019202268,0.0007174843,0.0005769276],"about_ca_topic_score_codex":0.000008092613,"about_ca_topic_score_gemma":5.636456e-7,"teacher_disagreement_score":0.2804941,"about_ca_system_score_codex":0.00012066939,"about_ca_system_score_gemma":0.0000640209,"threshold_uncertainty_score":0.46117523},"labels":[],"label_agreement":null},{"id":"W4410182943","doi":"10.2316/j.2025.201-0498","title":"ROBOT ARM TRAJECTORY GENERATION BASED ON IMPROVED CMA-ES ALGORITHM, 294-303.","year":2025,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trajectory; Computer science; Robotic arm; Algorithm; Artificial intelligence; Computer vision; Physics","score_opus":0.010373825432995896,"score_gpt":0.22316932891311556,"score_spread":0.21279550348011966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410182943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055493537,0.002283124,0.9927824,0.00063743396,0.002489895,0.00068047375,0.000010789172,0.00023891641,0.00032202437],"genre_scores_gemma":[0.97075444,0.000011182522,0.027122311,0.0006770514,0.000404739,0.00024008908,0.000008546708,0.000017279039,0.00076435885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790454,0.00024630027,0.00041354698,0.00066533464,0.0003025363,0.00046776547],"domain_scores_gemma":[0.99878424,0.0001789474,0.0001572174,0.00067294226,0.000094825744,0.00011183818],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076647894,0.00028346942,0.00043671398,0.00022680822,0.00024491112,0.00034145536,0.0004722765,0.00015549922,0.0000032685857],"category_scores_gemma":[0.000040962022,0.00024738497,0.0000965671,0.00025269494,0.000028479773,0.00019430896,0.000045428107,0.00021227573,0.000012648314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091953705,0.00064385874,0.00036925846,0.00032186866,0.0005534255,0.000105476305,0.00050306396,0.2743771,0.043653138,0.10228055,0.0025008896,0.5745994],"study_design_scores_gemma":[0.0023838007,0.0002991399,0.0002845654,0.000102781494,0.00003420504,0.000008870618,0.00003401054,0.995195,0.0005529101,0.00010518412,0.0007633856,0.00023615232],"about_ca_topic_score_codex":0.00015978816,"about_ca_topic_score_gemma":0.00000600841,"teacher_disagreement_score":0.9701995,"about_ca_system_score_codex":0.00015855623,"about_ca_system_score_gemma":0.00027225958,"threshold_uncertainty_score":0.99999785},"labels":[],"label_agreement":null},{"id":"W4410237108","doi":"10.1007/s10845-025-02612-6","title":"A proposed adaptive navigation architecture to improve safety of self-guided vehicles in dynamic manufacturing environments","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Architecture; Engineering; Computer science; Systems engineering; Manufacturing engineering; Geography","score_opus":0.00999067057834677,"score_gpt":0.2540874847875332,"score_spread":0.2440968142091864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410237108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43198314,0.00007485573,0.5667965,0.00030390444,0.00041528055,0.00031119311,0.0000021287233,0.000021732296,0.00009128274],"genre_scores_gemma":[0.8214857,0.000028018645,0.17825705,0.00007965253,0.000031166725,0.000003591375,0.0000010793691,0.000012432494,0.00010129784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99741477,0.00014022752,0.0011584881,0.00036254484,0.0005480158,0.00037596846],"domain_scores_gemma":[0.99857074,0.00019790007,0.0006409004,0.000409612,0.000049516217,0.00013132908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076903164,0.00026939786,0.00047096823,0.00075091986,0.00007155358,0.00006314529,0.001036416,0.00010922403,0.0000031888264],"category_scores_gemma":[0.000055456385,0.00023338609,0.00016225467,0.0002078137,0.000031980937,0.00032089057,0.00033203923,0.00058450043,0.000007708206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002996587,0.00038603804,0.0005643218,0.00024642682,0.0003515507,0.0002708026,0.006400344,0.69471115,0.04326924,0.00022253589,0.000038161827,0.25323975],"study_design_scores_gemma":[0.00059405953,0.00026026898,0.016764449,0.0010392038,0.000027861308,0.00007998917,0.00018266543,0.031016676,0.9477728,0.001774008,0.00025344934,0.00023456539],"about_ca_topic_score_codex":0.00002778777,"about_ca_topic_score_gemma":0.00000268792,"teacher_disagreement_score":0.9045036,"about_ca_system_score_codex":0.00063580985,"about_ca_system_score_gemma":0.000118434145,"threshold_uncertainty_score":0.95172083},"labels":[],"label_agreement":null},{"id":"W4410297950","doi":"10.1109/hri61500.2025.10974179","title":"ImageInThat: Manipulating Images to Convey User Instructions to Robots","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Computer science; Robot; Human–computer interaction; Computer graphics (images); Computer vision; Artificial intelligence","score_opus":0.015706421794205224,"score_gpt":0.2879207109934149,"score_spread":0.27221428919920965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410297950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003995984,0.000009138911,0.9721376,0.005032227,0.001482032,0.00025250306,0.0000017581094,0.00045833984,0.016630474],"genre_scores_gemma":[0.056971077,3.9377082e-7,0.9280558,0.0029021737,0.00005238903,0.00003119822,0.0000011683279,0.0000085452175,0.011977214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986312,0.00004309043,0.00025781017,0.0005053339,0.00020196204,0.00036063348],"domain_scores_gemma":[0.99888545,0.00009998139,0.00003363108,0.000680795,0.00012256562,0.00017759932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019745284,0.00015396255,0.0001745184,0.0003152072,0.00019105445,0.00030365138,0.00084804924,0.000044683176,0.000025442076],"category_scores_gemma":[0.00018296682,0.00014894913,0.00004355091,0.0010863045,0.000018493984,0.0003854582,0.0006368272,0.00012179636,0.00044813362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013443138,0.00018789753,0.022376833,0.00008960923,0.00013737465,0.00021853927,0.0022244293,0.38824895,0.021379331,0.16663595,0.17356257,0.22492507],"study_design_scores_gemma":[0.0012022216,0.00024552678,0.41979784,0.0006459662,0.00003482905,0.00012618455,0.00045094514,0.50448143,0.056361217,0.003939905,0.011043603,0.001670345],"about_ca_topic_score_codex":0.00018668099,"about_ca_topic_score_gemma":0.0000074097397,"teacher_disagreement_score":0.397421,"about_ca_system_score_codex":0.00006664344,"about_ca_system_score_gemma":0.000071775896,"threshold_uncertainty_score":0.6073969},"labels":[],"label_agreement":null},{"id":"W4410360368","doi":"10.3390/wevj16050272","title":"Inverse Dynamics-Based Motion Planning for Autonomous Vehicles: Simultaneous Trajectory and Speed Optimization with Kinematic Continuity","year":2025,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Trajectory; Dynamics (music); Motion (physics); Inverse; Inverse dynamics; Computer science; Inverse kinematics; Control theory (sociology); Mathematics; Classical mechanics; Physics; Artificial intelligence; Geometry; Acoustics","score_opus":0.009286542075830248,"score_gpt":0.24225231652112597,"score_spread":0.23296577444529573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410360368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043950036,0.00017982873,0.9542561,0.00063023745,0.00020391501,0.00038747065,0.0000016505019,0.00017018603,0.00022054023],"genre_scores_gemma":[0.70748645,0.000003973291,0.2915397,0.00037201267,0.00006902844,0.000009196964,0.0000055900464,0.000018096362,0.00049591175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983455,0.00012647515,0.0004242496,0.000364672,0.0002772394,0.00046187537],"domain_scores_gemma":[0.9984319,0.0006425525,0.00031697148,0.00024263236,0.00021925035,0.00014670029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063753687,0.00022508121,0.0003189155,0.0006974801,0.00041840537,0.0003538501,0.00040339606,0.00007514721,0.0000027159726],"category_scores_gemma":[0.0002023237,0.00020529953,0.000062989515,0.0012230789,0.00004096031,0.00033703932,0.000034284476,0.00042969847,0.0000011908589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000709647,0.00006746086,0.00249977,0.000050525723,0.000046028672,0.00007706828,0.0002127807,0.97320735,0.00032887046,0.00020318896,0.00019242121,0.023043552],"study_design_scores_gemma":[0.0019867208,0.0002548403,0.0020900902,0.00020689557,0.000056678826,0.00011120054,0.00003281971,0.994483,0.0002647388,0.00026151122,0.00002978117,0.00022175466],"about_ca_topic_score_codex":0.000011692709,"about_ca_topic_score_gemma":0.000017135224,"teacher_disagreement_score":0.6635364,"about_ca_system_score_codex":0.00042113662,"about_ca_system_score_gemma":0.0003336971,"threshold_uncertainty_score":0.8371872},"labels":[],"label_agreement":null},{"id":"W4410501619","doi":"10.3390/drones9050380","title":"Collision Detection and Recovery Control of Drones Using Onboard Inertial Measurement Unit","year":2025,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drone; Inertial measurement unit; Computer science; Collision; Aeronautics; Aerospace engineering; Real-time computing; Engineering; Computer security; Biology","score_opus":0.028629494188969793,"score_gpt":0.2545705690277474,"score_spread":0.2259410748387776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410501619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24223234,0.00031457059,0.75646657,0.00014435033,0.0006012592,0.00012102588,0.0000014670751,0.00004645438,0.00007194273],"genre_scores_gemma":[0.96726835,0.000010857097,0.03258966,0.00005291033,0.000033893153,0.000005266732,3.6443853e-7,0.0000042437346,0.00003442991],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904394,0.00008715757,0.00022274084,0.00022792455,0.00027324958,0.00014500273],"domain_scores_gemma":[0.9994336,0.000079021156,0.000072748764,0.00023347,0.00014765972,0.00003351535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042530426,0.000099661025,0.000189463,0.00017066856,0.00010432367,0.000050258262,0.00019171136,0.00006001428,6.761047e-7],"category_scores_gemma":[0.0001148972,0.00009483005,0.000030669675,0.00029945816,0.000039485116,0.000209698,0.00008610227,0.000067726585,0.0000014545647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020146483,0.0001947267,0.008682908,0.00020039137,0.00020745772,0.000022086173,0.0007197298,0.09370627,0.74499536,0.002334707,0.000111838184,0.14862308],"study_design_scores_gemma":[0.0010424937,0.00016000232,0.015795348,0.00022869835,0.000041995943,0.000011354623,0.000041790685,0.89594764,0.08544529,0.000947057,0.00017730673,0.00016102333],"about_ca_topic_score_codex":0.00019275345,"about_ca_topic_score_gemma":0.000010968701,"teacher_disagreement_score":0.8022414,"about_ca_system_score_codex":0.000060109283,"about_ca_system_score_gemma":0.000079641555,"threshold_uncertainty_score":0.38670573},"labels":[],"label_agreement":null},{"id":"W4410536395","doi":"10.1109/tac.2025.3571641","title":"Virtual Nonholonomic Constraints-Based Motion Control for Underactuated UAV Slung-Payload Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Underactuation; Nonholonomic system; Payload (computing); Control theory (sociology); Computer science; Motion control; Control engineering; Engineering; Robot; Control (management); Mobile robot; Artificial intelligence","score_opus":0.012798076333989975,"score_gpt":0.2558602112995154,"score_spread":0.24306213496552545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410536395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013053078,0.000046958157,0.98962384,0.0018275212,0.003482211,0.0023314136,0.00015965768,0.0010871395,0.00013596311],"genre_scores_gemma":[0.98064446,0.0000011110944,0.017182259,0.0010763826,0.00003994971,0.0007225942,0.000008526297,0.00002559565,0.00029912865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728805,0.00030300082,0.00081681606,0.00067543646,0.00033926184,0.00057740766],"domain_scores_gemma":[0.99630255,0.0022264787,0.00027206563,0.00082930777,0.00020965117,0.00015993248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007507888,0.00039453988,0.00067288737,0.00053670397,0.00041992817,0.00033574534,0.0007224718,0.00022148664,0.00003357577],"category_scores_gemma":[0.00006999858,0.00039191113,0.0002873805,0.00044911078,0.0001475149,0.00030358965,0.0000016450635,0.00026438164,0.00019115624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118356715,0.00039694115,0.000010028541,0.00010134331,0.00041680195,0.000014320349,0.000107098385,0.8630739,0.0016031937,0.0019906724,0.0005731994,0.13159417],"study_design_scores_gemma":[0.009183936,0.00037574198,0.00018233815,0.00023211943,0.00020334414,0.000015658612,0.000039094044,0.98840517,0.0007772811,0.0001546491,0.000104789564,0.00032589317],"about_ca_topic_score_codex":0.000038128124,"about_ca_topic_score_gemma":0.0000032245084,"teacher_disagreement_score":0.9793391,"about_ca_system_score_codex":0.00037787296,"about_ca_system_score_gemma":0.0004846585,"threshold_uncertainty_score":0.99985325},"labels":[],"label_agreement":null},{"id":"W4410610232","doi":"10.37934/ard.132.1.114","title":"Harmonic Path Planning using Quarter-Sweep Boosted TOR Iterative Method","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Research Design","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Path (computing); Harmonic; Computer science; Iterative method; Mathematical optimization; Algorithm; Mathematics; Acoustics; Physics; Geography","score_opus":0.1587910249451556,"score_gpt":0.46234518933085605,"score_spread":0.3035541643857005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410610232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018966529,0.0015950549,0.99439293,0.0009264935,0.0005247199,0.00030653665,0.0000013077199,0.000042564963,0.00031374584],"genre_scores_gemma":[0.021365514,0.000026200441,0.97790253,0.00013495173,0.00013537753,0.000007616182,3.462214e-7,0.000014480793,0.0004130059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99439377,0.0024409974,0.00078227243,0.00040247105,0.0012278188,0.00075265026],"domain_scores_gemma":[0.994523,0.0030462379,0.00042090914,0.000503505,0.0012596118,0.00024673718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075317374,0.00021177641,0.0004899902,0.0010663813,0.0004248454,0.0004034621,0.0015054885,0.00010564098,0.0000061725964],"category_scores_gemma":[0.0015313228,0.0001765114,0.00013206358,0.0016674884,0.00008629085,0.0014039256,0.00025142383,0.0012669784,0.0000071542286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045409086,0.0002461583,0.0001492201,0.00007899623,0.00025751413,0.00266597,0.006187124,0.7220112,0.16443491,0.0017239363,0.014862369,0.08692852],"study_design_scores_gemma":[0.0017074033,0.001344085,0.00087050453,0.0014915834,0.000020266523,0.00039667744,0.0009944583,0.9507466,0.032693688,0.007964248,0.0014804999,0.00028998702],"about_ca_topic_score_codex":0.000006819282,"about_ca_topic_score_gemma":5.7917145e-8,"teacher_disagreement_score":0.2287354,"about_ca_system_score_codex":0.0004615223,"about_ca_system_score_gemma":0.0010525116,"threshold_uncertainty_score":0.7197926},"labels":[],"label_agreement":null},{"id":"W4410704540","doi":"10.1016/j.oceaneng.2025.121580","title":"Path planning algorithm for unmanned surface vessels based on COLREGs and Meta-TD3","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Algorithm; Unmanned surface vehicle; Path (computing); Motion planning; Computer science; Engineering; Operations research; Marine engineering; Artificial intelligence","score_opus":0.017090815863471026,"score_gpt":0.24551683813684133,"score_spread":0.2284260222733703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410704540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018015392,0.0003838355,0.99616987,0.00036971067,0.00042403696,0.00027530108,0.00001667024,0.0004017804,0.00015723116],"genre_scores_gemma":[0.08733791,0.0000023020298,0.9118599,0.00033006482,0.00004148712,0.000017745722,0.000009038529,0.000027632424,0.00037388457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987251,0.000022279944,0.00021003485,0.00046486376,0.00018717124,0.00039053004],"domain_scores_gemma":[0.9989078,0.00048819432,0.000050976832,0.00040406917,0.000050097977,0.00009887923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003910093,0.00024929256,0.00035563298,0.00016957034,0.00011623046,0.00014888037,0.00043027854,0.00007877614,0.0000010017081],"category_scores_gemma":[0.000087433444,0.00023994167,0.00008390303,0.0003696232,0.00001526707,0.00018154814,0.00009052511,0.00016185509,0.000001940709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035927046,0.000026214486,0.000083411935,0.000101541096,0.00019409078,0.000034496858,0.00016004231,0.9927787,0.00015429464,0.0016902123,0.0014401021,0.0033332736],"study_design_scores_gemma":[0.000463973,0.00007883267,0.0004195405,0.000088322646,0.00012194875,0.0000036398367,0.000009957533,0.9958328,0.0014011646,0.000082957406,0.0012659252,0.00023093152],"about_ca_topic_score_codex":0.000002853449,"about_ca_topic_score_gemma":1.6423767e-8,"teacher_disagreement_score":0.08553637,"about_ca_system_score_codex":0.00004814208,"about_ca_system_score_gemma":0.00005046778,"threshold_uncertainty_score":0.97845376},"labels":[],"label_agreement":null},{"id":"W4410773203","doi":"10.3390/wevj16060295","title":"Three-Dimensional Unmanned Aerial Vehicle Path Planning in Simulated Rugged Mountainous Terrain Using Improved Enhanced Snake Optimizer (IESO)","year":2025,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"Natural Science Foundation of Hunan Province","keywords":"Terrain; Motion planning; Environmental science; Path (computing); Computer science; Remote sensing; Marine engineering; Geography; Engineering; Artificial intelligence; Cartography; Robot","score_opus":0.013419294378863803,"score_gpt":0.26650973046949195,"score_spread":0.25309043609062815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410773203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6541668,0.0005907387,0.34268814,0.0005917183,0.0011646738,0.00037766315,0.0000016442663,0.00021433322,0.0002042594],"genre_scores_gemma":[0.9290684,0.0000044240774,0.06964334,0.00068454305,0.0003028733,0.000008902256,0.000004516049,0.000038759463,0.00024423713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568015,0.00032660767,0.001116401,0.0007774147,0.0006899618,0.0014094686],"domain_scores_gemma":[0.9979977,0.00042713044,0.00048304812,0.00057363935,0.00024518694,0.00027331515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014503063,0.00048274203,0.0006838547,0.001437058,0.00069796934,0.00050638855,0.0012493698,0.00020823037,0.000023284374],"category_scores_gemma":[0.00023088853,0.00047510007,0.0001856176,0.0037935069,0.000057710982,0.00071445346,0.00031142234,0.0015640663,0.000012167811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043328476,0.00027037296,0.002308028,0.000015908838,0.00013537776,0.0005723219,0.00062247564,0.78239626,0.1984561,0.00013642779,0.0006437328,0.014009734],"study_design_scores_gemma":[0.0039300406,0.00020851502,0.007057279,0.00029299583,0.000030162355,0.00014207613,0.000020675998,0.98003304,0.0063176122,0.0014228424,0.000054799002,0.00048997736],"about_ca_topic_score_codex":0.00024528298,"about_ca_topic_score_gemma":0.00003311604,"teacher_disagreement_score":0.27490157,"about_ca_system_score_codex":0.0008763388,"about_ca_system_score_gemma":0.00088649505,"threshold_uncertainty_score":0.99977005},"labels":[],"label_agreement":null},{"id":"W4410866321","doi":"10.3390/sym17060836","title":"Research on Intelligent Path Planning of Mobile Robot Based on Hybrid Symmetric Bio-Inspired Neural Network Algorithm in Complex Road Environments","year":2025,"lang":"en","type":"article","venue":"Symmetry","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Motion planning; Mobile robot; Artificial neural network; Path (computing); Artificial intelligence; Robot; Algorithm; Computer network","score_opus":0.056627792490961344,"score_gpt":0.34005848933187677,"score_spread":0.28343069684091543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410866321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05168808,0.00093878026,0.9384358,0.00033692247,0.0021244597,0.0014093316,0.000037468046,0.00022446718,0.0048046876],"genre_scores_gemma":[0.90618753,0.00001445257,0.092845485,0.0005147676,0.0001247649,0.00008431954,0.00004727638,0.00002771802,0.00015368451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952963,0.0007284086,0.0007500014,0.0009886115,0.0012227488,0.0010139128],"domain_scores_gemma":[0.9971906,0.0011160147,0.00019783019,0.0012554452,0.00006984275,0.00017027308],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002155369,0.00034978727,0.0005634682,0.0021186494,0.00021732936,0.00010240558,0.0016229323,0.00014028988,0.00001131292],"category_scores_gemma":[0.00021819495,0.00034439555,0.00013090034,0.0044878977,0.00015210066,0.00014196175,0.0005211336,0.00087885483,0.000068257505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003949864,0.00056256587,0.0064994586,0.00003821758,0.00003081223,0.00020587757,0.0000907336,0.64566976,0.00006800681,0.0005398063,0.002625407,0.34362984],"study_design_scores_gemma":[0.000668997,0.00075082725,0.06731802,0.00041688446,0.000006039023,0.0000030668853,0.00007644182,0.9277468,0.0018777881,0.00033005714,0.0005450659,0.00026002526],"about_ca_topic_score_codex":0.00016662116,"about_ca_topic_score_gemma":4.771939e-7,"teacher_disagreement_score":0.85449946,"about_ca_system_score_codex":0.0004145056,"about_ca_system_score_gemma":0.00013068938,"threshold_uncertainty_score":0.9999008},"labels":[],"label_agreement":null},{"id":"W4410887720","doi":"10.1109/syscon64521.2025.11014789","title":"Enhancing Robot Navigation in Crowded Spaces Through Systematic Strategy Selection","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; Queen's University","funders":"","keywords":"Selection (genetic algorithm); Computer science; Robot; Artificial intelligence; Mobile robot; Computer vision; Human–computer interaction","score_opus":0.017671725761202437,"score_gpt":0.288334335868557,"score_spread":0.27066261010735454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410887720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011704592,0.00011962749,0.98338854,0.00029384615,0.00035631465,0.00032470017,9.356927e-8,0.00025580154,0.0035564972],"genre_scores_gemma":[0.75474834,0.0000018200276,0.24400151,0.000092868846,0.000017592323,0.00003589764,0.0000015267756,0.0000040282694,0.0010964192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987422,0.0001445037,0.00036709665,0.0003196732,0.00019305316,0.00023345905],"domain_scores_gemma":[0.99938816,0.00016142875,0.000096163145,0.00025766197,0.000074060306,0.000022553528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046172977,0.000120335084,0.00023087018,0.00014096053,0.000085282736,0.00024818568,0.00035162797,0.00007245202,0.0000029545713],"category_scores_gemma":[0.00008627686,0.000107224834,0.000029070468,0.0010807646,0.000012520062,0.000675043,0.00006340501,0.00013717252,0.00003436519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001153206,0.00030957258,0.00516921,0.016681496,0.00014341615,0.00009896456,0.009235127,0.60005593,0.049653552,0.31515977,0.0006989795,0.0027824522],"study_design_scores_gemma":[0.0003082867,0.00005516742,0.002641558,0.0046284827,0.000011162454,0.000023668332,0.00046767626,0.9503679,0.0342954,0.0069936877,0.000003049493,0.00020392095],"about_ca_topic_score_codex":0.0003826376,"about_ca_topic_score_gemma":0.00005858218,"teacher_disagreement_score":0.7430437,"about_ca_system_score_codex":0.00013150724,"about_ca_system_score_gemma":0.00011395266,"threshold_uncertainty_score":0.43725017},"labels":[],"label_agreement":null},{"id":"W4410907225","doi":"10.21428/d82e957c.409284ce","title":"aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Free space; Lattice (music); Computer science; Space (punctuation); Control (management); Artificial intelligence; Physics","score_opus":0.026437713245267884,"score_gpt":0.2741005589380829,"score_spread":0.247662845692815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410907225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15896074,0.00048521307,0.8391134,0.0007054006,0.00010305567,0.00033164275,0.000008038591,0.000058017275,0.00023449886],"genre_scores_gemma":[0.7691754,0.000003770105,0.22996113,0.0002624931,0.000019039031,0.000012691027,0.000001556068,0.000008503805,0.0005554293],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871314,0.00008729232,0.00021439261,0.0004704649,0.00014459997,0.0003701004],"domain_scores_gemma":[0.99900323,0.0005011245,0.00004596684,0.0003505953,0.000013114304,0.00008598497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685785,0.00015261392,0.00023076597,0.00019313181,0.0001546622,0.00016119628,0.0003752987,0.000056399065,6.0938123e-7],"category_scores_gemma":[0.00008418987,0.00015249875,0.00001888455,0.00014790113,0.00007418575,0.00027060334,0.00029937524,0.00015053402,9.288895e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030718624,0.00058063946,0.57912433,0.00062726456,0.00037659163,0.00091765996,0.013643192,0.19895785,0.05662714,0.0953561,0.0020745618,0.051407482],"study_design_scores_gemma":[0.001667055,0.000054529624,0.037126772,0.000081043596,0.000011945361,0.000018083208,0.00007304005,0.95770293,0.0018191752,0.0010509223,0.00023841331,0.00015608473],"about_ca_topic_score_codex":0.0001239018,"about_ca_topic_score_gemma":0.0000012938215,"teacher_disagreement_score":0.7587451,"about_ca_system_score_codex":0.000076858145,"about_ca_system_score_gemma":0.00007617294,"threshold_uncertainty_score":0.6218718},"labels":[],"label_agreement":null},{"id":"W4410989508","doi":"10.1016/j.fss.2025.109485","title":"A fuzzy set-based methodology for autonomous navigation","year":2025,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Telefonaktiebolaget LM Ericsson","keywords":"Mathematics; Fuzzy set; Fuzzy logic; Set (abstract data type); Artificial intelligence; Data mining; Computer science; Programming language","score_opus":0.07576563705982475,"score_gpt":0.3380018149058238,"score_spread":0.26223617784599906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410989508","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056734807,0.0007479787,0.9873334,0.0009328496,0.00242044,0.0005621905,0.000024757566,0.00020448152,0.0021003808],"genre_scores_gemma":[0.41425386,0.0000028975392,0.58342224,0.0005332934,0.00015334028,0.00026537458,0.000060750455,0.000015300917,0.0012929426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986422,0.00026452477,0.00029685075,0.00040431097,0.00012097586,0.00027110893],"domain_scores_gemma":[0.9986647,0.0006738912,0.00011427435,0.00038236665,0.000095149626,0.00006962148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011835479,0.00013731647,0.0002931657,0.00013420418,0.00017051313,0.00018287596,0.00033825202,0.00012030573,2.097662e-7],"category_scores_gemma":[0.000106087005,0.000121187106,0.00004866637,0.00022867694,0.000033183653,0.00011982001,0.00006529291,0.00008787314,0.0000071820723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091089096,0.00017582416,0.0052314014,0.0025388862,0.00034693716,0.00013580208,0.0054929294,0.056940895,0.0030277763,0.6981891,0.036122613,0.19170675],"study_design_scores_gemma":[0.00086743035,0.00015289047,0.0015575711,0.00022743987,0.00002209483,0.00004270289,0.00011633533,0.97926515,0.00017877543,0.008650041,0.008690526,0.00022903825],"about_ca_topic_score_codex":0.00013627076,"about_ca_topic_score_gemma":0.0000012295194,"teacher_disagreement_score":0.92232424,"about_ca_system_score_codex":0.00004792406,"about_ca_system_score_gemma":0.00014220865,"threshold_uncertainty_score":0.49418667},"labels":[],"label_agreement":null},{"id":"W4411031703","doi":"10.3390/biomimetics10060366","title":"Decentralized Multi-Robot Navigation Based on Deep Reinforcement Learning and Trajectory Optimization","year":2025,"lang":"en","type":"article","venue":"Biomimetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"British Columbia Innovation Council","keywords":"Reinforcement learning; Obstacle; Computer science; Scalability; Obstacle avoidance; Robot; Trajectory; Artificial intelligence; Collision avoidance; Nonlinear system; Mathematical optimization; Mobile robot; Mathematics; Collision","score_opus":0.01644412939596442,"score_gpt":0.27633880432197366,"score_spread":0.25989467492600926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411031703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005748519,0.00010061625,0.99813616,0.00022991882,0.0003533849,0.00019685226,2.658789e-7,0.00016480457,0.00024312889],"genre_scores_gemma":[0.18073174,0.000020513226,0.8188186,0.00021333086,0.000010918312,0.000009976846,0.000023954033,0.0000074407844,0.0001634784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989763,0.00010080611,0.00022354974,0.00029378157,0.00019589797,0.00020970397],"domain_scores_gemma":[0.99940985,0.00013733358,0.000092464354,0.00022949706,0.00006539488,0.00006545971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025615498,0.00013372456,0.00013129208,0.00018153599,0.00015383241,0.00011821015,0.00021198702,0.00008057431,0.0000043691284],"category_scores_gemma":[0.00012103039,0.0001347311,0.000031270232,0.0003895606,0.000037688045,0.0000981069,0.000061522274,0.000116343406,0.000006426955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008821135,0.000034566132,0.0006090638,0.000018048599,0.0000100956395,0.0000042233996,0.0001077082,0.99178976,0.00050535414,0.00048215291,0.00002269934,0.0064074798],"study_design_scores_gemma":[0.0010486123,0.00010070629,0.0009823875,0.000094701456,0.000014036063,0.0000012222458,0.000011141818,0.9953495,0.002051954,0.000014188863,0.00020045637,0.00013107902],"about_ca_topic_score_codex":0.000009033801,"about_ca_topic_score_gemma":1.9139642e-7,"teacher_disagreement_score":0.18015689,"about_ca_system_score_codex":0.00008425198,"about_ca_system_score_gemma":0.00006438641,"threshold_uncertainty_score":0.5494175},"labels":[],"label_agreement":null},{"id":"W4411086218","doi":"10.1109/lra.2025.3577456","title":"Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computer vision; Artificial intelligence; Mobile robot; Mobile robot navigation; Robot; Human–computer interaction; Robot control","score_opus":0.014094567093177877,"score_gpt":0.27743394928512494,"score_spread":0.2633393821919471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411086218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049120978,0.000117206946,0.9492437,0.0005589426,0.00037646422,0.00025671927,0.0000035205644,0.0002297906,0.000092633185],"genre_scores_gemma":[0.29868495,0.0000027661463,0.7004181,0.0007256648,0.0000382394,0.000014835505,0.000022159653,0.000009898822,0.00008339246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869436,0.00006761067,0.00029950603,0.00041412291,0.00026645206,0.0002579544],"domain_scores_gemma":[0.9993304,0.000039913488,0.00013348785,0.00038033066,0.000051564955,0.00006427291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027125707,0.0001820628,0.00019940868,0.00021888228,0.00037159122,0.00063331163,0.00031596445,0.00008998953,3.0724874e-7],"category_scores_gemma":[0.000011817766,0.00018021461,0.00004900473,0.00042137067,0.00008860799,0.000546322,0.00009573938,0.00014990752,0.000005112528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012055632,0.000033575943,0.000030599123,0.000054856268,0.0000134197835,0.0000065224967,0.0016793513,0.9560839,0.03677041,0.0012183866,0.0004511935,0.0036565778],"study_design_scores_gemma":[0.0003273432,0.000020935224,0.00014697958,0.00014711094,0.00001815922,0.000017744562,0.00004718974,0.9970015,0.0017707163,0.00030431736,0.000013622281,0.00018435236],"about_ca_topic_score_codex":0.000023117696,"about_ca_topic_score_gemma":1.6963824e-7,"teacher_disagreement_score":0.24956398,"about_ca_system_score_codex":0.00008568169,"about_ca_system_score_gemma":0.00007230394,"threshold_uncertainty_score":0.7348939},"labels":[],"label_agreement":null},{"id":"W4411203455","doi":"10.1109/tmech.2025.3572522","title":"EMPC-Based Flight Control and Collision-Free Path Planning for a Quadrotor With Unbalanced Payload","year":2025,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Payload (computing); Path (computing); Collision; Control (management); Motion planning; Aerospace engineering; Collision avoidance; Computer science; Free flight; Control theory (sociology); Aeronautics; Engineering; Robot; Computer security; Artificial intelligence; Computer network; Air traffic control","score_opus":0.008509268697177792,"score_gpt":0.248141562772495,"score_spread":0.2396322940753172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411203455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027453871,0.00021504064,0.9923152,0.0021074207,0.00076704053,0.0012299189,0.00016083197,0.0003838376,0.00007533927],"genre_scores_gemma":[0.48942304,0.000014635643,0.5083328,0.0010203423,0.00004456935,0.0007388829,0.000008994968,0.000039604423,0.00037713608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780405,0.000098161545,0.00037175984,0.0007447139,0.000396825,0.0005844765],"domain_scores_gemma":[0.99782157,0.0007000035,0.00014042649,0.0010113152,0.00016291799,0.00016377536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004300934,0.0003555113,0.00044264283,0.00032159337,0.0005292163,0.00019994017,0.0008180193,0.00018134416,0.0000027273686],"category_scores_gemma":[0.00003243443,0.00030663933,0.00012348875,0.0005962727,0.000069947404,0.00028300466,0.00000795444,0.0003774634,0.0000064186706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009820102,0.00054066634,0.00019357153,0.00023761243,0.0003897167,0.00005953204,0.0007043208,0.96485686,0.0019772067,0.010696563,0.0017446765,0.017617278],"study_design_scores_gemma":[0.0072569097,0.00094450585,0.0001312619,0.00040413416,0.00010701935,0.0000129599575,0.00007560064,0.98201555,0.005060846,0.0013795807,0.0021770431,0.0004345993],"about_ca_topic_score_codex":0.00002109868,"about_ca_topic_score_gemma":0.000008213796,"teacher_disagreement_score":0.48667765,"about_ca_system_score_codex":0.00017717006,"about_ca_system_score_gemma":0.00048170838,"threshold_uncertainty_score":0.99993855},"labels":[],"label_agreement":null},{"id":"W4411228436","doi":"10.1139/dsa-2025-0002","title":"Performance evaluation of path planning algorithms for autonomous UAV deployment using two urban scenarios","year":2025,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Software deployment; Motion planning; Computer science; Path (computing); Algorithm; Artificial intelligence; Computer network; Robot; Software engineering","score_opus":0.055965582999848086,"score_gpt":0.3349501152493193,"score_spread":0.2789845322494712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411228436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040596236,0.0011259603,0.95613635,0.0000496254,0.0002055716,0.0014806442,0.000013243486,0.000080267506,0.00031208975],"genre_scores_gemma":[0.8966018,0.0000037144375,0.10226138,0.000014833523,0.000091465445,0.0008954768,0.000013470526,0.000008506159,0.0001093531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879086,0.000049434642,0.00036348007,0.00034539908,0.00025490936,0.00019589867],"domain_scores_gemma":[0.9990278,0.00007662048,0.00019903308,0.00041765807,0.00022905062,0.000049855524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008918454,0.00012015636,0.00020410231,0.00012406545,0.00027232242,0.00007734305,0.00027837584,0.00004689047,2.778595e-7],"category_scores_gemma":[0.000012949313,0.00011911702,0.000030455025,0.00030513803,0.000033117605,0.00015789732,0.0000784926,0.00006109485,0.0000012842785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005172441,0.00017109142,0.009837156,0.0004121731,0.00010228999,6.079401e-7,0.0014160806,0.82706225,0.0025782317,0.032358527,0.00042595703,0.12563047],"study_design_scores_gemma":[0.00054148264,0.000030860403,0.0023629004,0.00016882474,0.00005295562,0.000007775724,0.00008893127,0.9952233,0.00019616827,0.00012327495,0.0010895068,0.00011405801],"about_ca_topic_score_codex":0.000099850375,"about_ca_topic_score_gemma":2.798441e-7,"teacher_disagreement_score":0.85600555,"about_ca_system_score_codex":0.00011662053,"about_ca_system_score_gemma":0.00020526655,"threshold_uncertainty_score":0.4857451},"labels":[],"label_agreement":null},{"id":"W4411329761","doi":"10.3390/drones9060442","title":"UAV Autonomous Navigation System Based on Air–Ground Collaboration in GPS-Denied Environments","year":2025,"lang":"en","type":"article","venue":"Drones","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Global Positioning System; Computer science; Aeronautics; Air navigation; Navigation system; Real-time computing; Human–computer interaction; Systems engineering; Environmental science; Engineering; Telecommunications","score_opus":0.007872441620471597,"score_gpt":0.24763043143377178,"score_spread":0.23975798981330018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411329761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12530205,0.000058256006,0.8689678,0.0013585635,0.0012671169,0.0004625078,0.000006049987,0.00025540692,0.0023222896],"genre_scores_gemma":[0.97095096,4.6718506e-7,0.028308494,0.00015795101,0.000026479416,0.000051698196,0.000025538675,0.000006458722,0.00047193936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881923,0.00012127125,0.00025026503,0.00036466427,0.0002535985,0.00019095109],"domain_scores_gemma":[0.9993265,0.0001016348,0.00009008101,0.00042931832,0.000017047416,0.000035376543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002804691,0.00012755525,0.00015340596,0.00019622724,0.00009780552,0.000096435746,0.00035655784,0.00008126023,0.0000010279643],"category_scores_gemma":[0.00002293135,0.00013436994,0.000022002729,0.0005489422,0.000021898943,0.000266494,0.000052663014,0.00009813905,0.00008412479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005489979,0.0004813452,0.011736261,0.00024057535,0.00003805854,0.0003128127,0.002299444,0.9064344,0.0032894094,0.053267855,0.00078064983,0.021064254],"study_design_scores_gemma":[0.0005593538,0.000056623,0.030478569,0.000242541,0.0000049268147,0.0000028565091,0.00010909837,0.9663226,0.001409272,0.00020142767,0.0004761555,0.00013654836],"about_ca_topic_score_codex":0.00005679451,"about_ca_topic_score_gemma":0.000003203292,"teacher_disagreement_score":0.84564894,"about_ca_system_score_codex":0.00045427427,"about_ca_system_score_gemma":0.00011292258,"threshold_uncertainty_score":0.54794466},"labels":[],"label_agreement":null},{"id":"W4411430879","doi":"10.1051/epjconf/202532801006","title":"Autonomous navigation system for remote campus exploration","year":2025,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Mortgage and Housing Corporation","funders":"","keywords":"Obstacle avoidance; Interface (matter); Human–computer interaction; Robotics; Computer science; Robot; Software; Mobile robot; Mobile robot navigation; Obstacle; Remote control; Path (computing); Motion planning; Artificial intelligence; Robot control; Operating system","score_opus":0.03033521833510352,"score_gpt":0.28450394741554924,"score_spread":0.25416872908044574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411430879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007652383,0.00009618606,0.98127043,0.00058096315,0.0012011896,0.00027446388,0.000004696728,0.00018214776,0.008737524],"genre_scores_gemma":[0.86240524,0.000002283667,0.13721141,0.000016427504,0.000034341494,0.00001629771,0.000011515045,0.0000024455037,0.00030001602],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990852,0.000054722568,0.00030110893,0.00024980475,0.00016273835,0.0001464131],"domain_scores_gemma":[0.999099,0.00018205678,0.00019128504,0.00029718244,0.00020063967,0.000029842155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036336805,0.00009686459,0.00019281475,0.00012428878,0.00008739358,0.00009395802,0.0005041347,0.00006489148,0.0000010224306],"category_scores_gemma":[0.000065188346,0.00008965013,0.00004447806,0.00025260472,0.000038634942,0.0003400653,0.000054024527,0.00005472861,0.0000061099745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001749326,0.000039006063,0.00088294497,0.00041801535,0.00006647842,0.000008371744,0.0011663216,0.004730173,0.0023440935,0.75426716,0.0009423512,0.23511757],"study_design_scores_gemma":[0.00033944484,0.00009533789,0.00053594337,0.0004345179,0.0000133675085,0.0000045679103,0.00026804485,0.97486705,0.011781056,0.009774467,0.0017669876,0.00011921877],"about_ca_topic_score_codex":0.000067133304,"about_ca_topic_score_gemma":0.000002150458,"teacher_disagreement_score":0.9701369,"about_ca_system_score_codex":0.000040006988,"about_ca_system_score_gemma":0.0009102956,"threshold_uncertainty_score":0.36558262},"labels":[],"label_agreement":null},{"id":"W4411446975","doi":"10.1007/978-3-031-88634-8_2","title":"An A*-Aware Coevolutionary Algorithm Solution for Finding Collision-Free Paths","year":2025,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Collision; Computer science; Algorithm; Mathematical optimization; Mathematics; Computer security","score_opus":0.02182875567877301,"score_gpt":0.2768213417315326,"score_spread":0.2549925860527596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411446975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024961441,0.007265127,0.98026973,0.004337711,0.00019474806,0.0006699293,0.000204181,0.0006672699,0.006388822],"genre_scores_gemma":[0.001316025,0.0021356905,0.9676062,0.00028877935,0.000059066115,0.0002856598,0.00045555187,0.000042129283,0.027810887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982079,0.00006568469,0.0005121309,0.00066061167,0.00023454761,0.00031912795],"domain_scores_gemma":[0.99571306,0.00040798134,0.0004198811,0.002986241,0.0003962425,0.000076615506],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052489946,0.0003362974,0.00046607995,0.00072208425,0.00070104917,0.0001040994,0.0032100189,0.00092269987,0.000008848586],"category_scores_gemma":[0.00009373977,0.00036971454,0.00009255174,0.00020292583,0.00028164746,0.000272349,0.0013840377,0.0005690717,0.000009689902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048739766,0.000045303197,0.000005367238,0.000039058137,0.000059497153,0.000004601231,0.00007398796,0.00011340785,0.00007450851,0.6326493,0.008905464,0.3580246],"study_design_scores_gemma":[0.00042221017,0.0002623744,0.000013032176,0.00043081053,0.000039135997,0.000035332436,0.000030192026,0.44631165,0.00009629041,0.52397317,0.02799406,0.00039174163],"about_ca_topic_score_codex":0.00001437494,"about_ca_topic_score_gemma":0.0000040132795,"teacher_disagreement_score":0.44619825,"about_ca_system_score_codex":0.00013363225,"about_ca_system_score_gemma":0.0002372145,"threshold_uncertainty_score":0.9998755},"labels":[],"label_agreement":null},{"id":"W4411469645","doi":"10.1007/978-3-031-96522-7_4","title":"Swarms of Thermal and Optical Drones for Searching for Missing Persons","year":2025,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Society for Digital Humanities","funders":"","keywords":"Drone; Computer science; Thermal; Artificial intelligence; Geography; Meteorology; Biology","score_opus":0.015452771158386287,"score_gpt":0.29291343716141294,"score_spread":0.27746066600302666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411469645","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000046381556,0.0063441037,0.96518713,0.0027117368,0.000071427916,0.0005574378,0.00002032059,0.00010628452,0.024955202],"genre_scores_gemma":[0.0071737515,0.0032616635,0.9875059,0.000142691,0.000008124225,0.00014541358,0.00006117044,0.000008884823,0.0016924109],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992379,0.000011211109,0.00040798518,0.00013076601,0.00008107186,0.00013110053],"domain_scores_gemma":[0.99863213,0.000477549,0.00027731125,0.00047788987,0.000113889764,0.000021234688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032048128,0.00012453494,0.0002407057,0.0005256233,0.00014167577,0.000048288926,0.00060153205,0.00023222432,5.06046e-7],"category_scores_gemma":[0.00016103756,0.00012629713,0.000028867827,0.000074185366,0.00025195186,0.0007353698,0.0003105338,0.00024474575,4.1196134e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005663123,0.0000030226481,0.000011225462,0.00011316321,0.000005270422,4.13365e-8,0.00036968128,0.00008970504,0.0000059740805,0.5230309,0.000011973224,0.4763534],"study_design_scores_gemma":[0.0013801722,0.00025166987,0.000055210512,0.0012594392,0.000026184696,0.00002544603,0.00068550004,0.34767047,0.0005606273,0.47309244,0.17455207,0.00044075603],"about_ca_topic_score_codex":0.000001707193,"about_ca_topic_score_gemma":0.0000016594353,"teacher_disagreement_score":0.47591266,"about_ca_system_score_codex":0.000028684552,"about_ca_system_score_gemma":0.00006177013,"threshold_uncertainty_score":0.5150247},"labels":[],"label_agreement":null},{"id":"W4411472236","doi":"10.1109/access.2025.3581807","title":"Adaptive Multi-Robot Exploration for Unknown Environments Using Edge-Weighted Path Planning","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"National Research Foundation of Korea; Korea Institute of Machinery and Materials","keywords":"Motion planning; Computer science; Enhanced Data Rates for GSM Evolution; Robot; Path (computing); Mobile robot; Artificial intelligence; Computer network","score_opus":0.14516529258692523,"score_gpt":0.3694397766897154,"score_spread":0.22427448410279016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411472236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004548726,0.00013607477,0.9926449,0.00011201252,0.0018156805,0.0004927337,0.000006499746,0.00011956652,0.000123781],"genre_scores_gemma":[0.3098196,0.0000059878703,0.68907785,0.0003293783,0.0001549068,0.00012209336,0.000014899271,0.000021242147,0.000454053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984147,0.00007933531,0.00031441537,0.00057431724,0.00023285642,0.00038438485],"domain_scores_gemma":[0.99903613,0.00017757606,0.0001771623,0.00047958235,0.00005477557,0.00007478365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025139973,0.00021530916,0.00023272897,0.00020883896,0.00030545716,0.00029645322,0.0011587557,0.00010732528,0.0000011930704],"category_scores_gemma":[0.000041524472,0.0002150073,0.000063085055,0.0004348931,0.0000415596,0.0017960408,0.00023961635,0.0001435954,0.000010893077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000752013,0.00043923192,0.0037145333,0.000084784224,0.00021457495,0.00009297691,0.0032640856,0.93289757,0.014122162,0.0021109008,0.002691587,0.040292382],"study_design_scores_gemma":[0.00077473396,0.000045108893,0.001757895,0.0001720357,0.000021763775,0.0000026345808,0.000034045774,0.9816402,0.013598139,0.0011704691,0.00054508727,0.00023786516],"about_ca_topic_score_codex":0.000028797089,"about_ca_topic_score_gemma":6.787001e-7,"teacher_disagreement_score":0.30527088,"about_ca_system_score_codex":0.00014870064,"about_ca_system_score_gemma":0.00009930706,"threshold_uncertainty_score":0.8767743},"labels":[],"label_agreement":null},{"id":"W4411569951","doi":"10.2316/j.2025.206-1258","title":"TIME-OPTIMAL TRAJECTORY PLANNING FOR TOOL-CHANGING ROBOTS BASED ON THE HYBRID SEARCH ALGORITHM OF SPARROW-OSPREY, 415-424.","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sparrow; Trajectory; Robot; Computer science; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Biology; Ecology; Physics","score_opus":0.017827548967886638,"score_gpt":0.28184239682058787,"score_spread":0.26401484785270124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411569951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016449206,0.00008218023,0.9795947,0.002726319,0.000864162,0.00014391598,0.000009991185,0.000026381913,0.00010311403],"genre_scores_gemma":[0.36913934,0.0000065273102,0.63020134,0.0003295477,0.00017554154,0.000004686321,0.00000875922,0.000009965695,0.00012431276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985263,0.0000770119,0.00047301955,0.00015928352,0.0005836201,0.00018077523],"domain_scores_gemma":[0.99813306,0.0007791483,0.00036025254,0.00015536476,0.00053328247,0.00003887639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011323523,0.00012441906,0.00020031781,0.0005093594,0.00011281996,0.00016560096,0.00064599176,0.000043386688,0.0000036806214],"category_scores_gemma":[0.00016576778,0.00009748055,0.00010775482,0.00018444154,0.00005106983,0.00027495844,0.00008812655,0.00017759229,0.0000014994653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021940512,0.000045988738,0.00011592823,0.000014947446,0.0000865165,0.000019485838,0.00030625568,0.9649589,0.00049793324,0.0027531667,0.0003712752,0.03080763],"study_design_scores_gemma":[0.0005702579,0.00015782674,0.0016064257,0.00043363587,0.000019013056,0.00004090117,0.000048676837,0.99369055,0.002746685,0.00054971816,0.000049411603,0.00008690999],"about_ca_topic_score_codex":0.0000019463696,"about_ca_topic_score_gemma":3.299741e-8,"teacher_disagreement_score":0.35269013,"about_ca_system_score_codex":0.00008072014,"about_ca_system_score_gemma":0.00017606778,"threshold_uncertainty_score":0.39751413},"labels":[],"label_agreement":null},{"id":"W4411573252","doi":"10.1007/978-3-031-95489-4_20","title":"Design of an Electro-Permanent Magnet Based Aerial Landing Dock for Mobile Robots in Multi-Modal Robotic Systems","year":2025,"lang":"en","type":"book-chapter","venue":"Mechanisms and machine science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"DOCK; Modal; Magnet; Robot; Computer science; Mobile robot; Engineering; Marine engineering; Aerospace engineering; Mechanical engineering; Artificial intelligence; Materials science","score_opus":0.03072809842712763,"score_gpt":0.26915800215481106,"score_spread":0.23842990372768344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411573252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023332563,0.00034595083,0.99624664,0.000025569165,0.0011808416,0.00148766,0.000030489218,0.00008400919,0.00057552906],"genre_scores_gemma":[0.017262222,0.000024633298,0.974782,0.00007200891,0.000060835937,0.00016587382,0.000031342774,0.000033160228,0.0075679426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718106,0.00006531969,0.0005271809,0.0010627212,0.00057592895,0.0005877953],"domain_scores_gemma":[0.9985627,0.00017651834,0.00030070826,0.0006416456,0.00013897644,0.0001794324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017383709,0.00040283956,0.00064766995,0.0007083225,0.0002319753,0.00024936176,0.0013662908,0.00018443425,0.0000046344567],"category_scores_gemma":[0.00005106245,0.00036584897,0.00006481122,0.00024665665,0.00013618445,0.0003623531,0.00028262573,0.00026000632,0.0000017466178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005160083,0.00010942078,0.000002853021,0.00028322387,0.0000146134125,0.00005851099,0.00022293364,0.7827602,0.02742513,0.18253705,0.000029559731,0.0065048696],"study_design_scores_gemma":[0.00091570406,0.0008769024,0.00001135374,0.0003227436,0.000022758753,0.000029259956,0.0000059823296,0.9918165,0.0015456028,0.0040398138,0.00004067402,0.00037266535],"about_ca_topic_score_codex":0.00014613787,"about_ca_topic_score_gemma":0.000006288291,"teacher_disagreement_score":0.20905632,"about_ca_system_score_codex":0.00014005486,"about_ca_system_score_gemma":0.0005332879,"threshold_uncertainty_score":0.99987936},"labels":[],"label_agreement":null},{"id":"W4411617945","doi":"10.51847/82wydoie1q","title":"10.51847/82wydOIe1Q","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Degrees of freedom (physics and chemistry); Robot; Computer vision; Computer science; Artificial intelligence; Human–computer interaction; Physics; Acoustics","score_opus":0.008603299166053496,"score_gpt":0.19143776606367047,"score_spread":0.18283446689761698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411617945","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000081302846,0.000032268432,0.0037340238,0.0006415222,0.000010290936,0.00011269931,0.0000025166528,0.00044471963,0.99494064],"genre_scores_gemma":[0.000035993406,7.955943e-8,0.06421996,0.00014652038,0.00008375904,0.000009037838,0.0000023529904,0.000010166032,0.93549216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998906,0.00004056323,0.00014657949,0.00033220157,0.00024565283,0.00032898018],"domain_scores_gemma":[0.9990987,0.00005683417,0.000023322584,0.00061134365,0.00003255493,0.00017720283],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017935589,0.00012451592,0.00013767478,0.0000722651,0.00008237981,0.00011159549,0.00093073724,0.000046187677,0.86715555],"category_scores_gemma":[0.000028335906,0.00012260111,0.000039933853,0.00035907162,0.000018102408,0.00021191388,0.00010856152,0.00008818722,0.98810214],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035914352,0.000021746291,2.4028506e-7,0.0000012999974,0.000005621169,0.000028978102,0.000039237704,0.0021676673,0.000018270448,0.0000090061885,0.028278384,0.969426],"study_design_scores_gemma":[0.000114755974,0.00009135657,0.000111859175,0.0000129654345,0.0000027402712,0.00003059622,1.385705e-7,0.059050247,0.000023324965,0.000010100304,0.94038665,0.0001652932],"about_ca_topic_score_codex":0.000011134976,"about_ca_topic_score_gemma":6.9376647e-9,"teacher_disagreement_score":0.9692607,"about_ca_system_score_codex":0.000028592429,"about_ca_system_score_gemma":0.00003555846,"threshold_uncertainty_score":0.4999528},"labels":[],"label_agreement":null},{"id":"W4412431385","doi":"10.1109/med64031.2025.11073302","title":"Asymptotically Optimal Solutions for 3D Reach-Avoid Games with Exclusion Zones Using Informed-Expansive-Spaces-Tree","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Expansive; Asymptotically optimal algorithm; Tree (set theory); Computer science; Mathematical optimization; Mathematics; Combinatorics; Materials science","score_opus":0.02940767786424832,"score_gpt":0.2897010697518528,"score_spread":0.2602933918876045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412431385","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040127244,0.00008215113,0.98739755,0.0020743746,0.00038223696,0.00043942855,0.0000046863115,0.0003980672,0.0052087633],"genre_scores_gemma":[0.0127428,0.0000074100562,0.98487395,0.0004929163,0.00006109464,0.00003739781,0.00000826394,0.000013867255,0.001762321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980082,0.000044893135,0.00033701726,0.000522559,0.00035908032,0.0007282839],"domain_scores_gemma":[0.9982802,0.0005010968,0.00011093239,0.00065290346,0.0003213445,0.000133497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039061764,0.0002580831,0.00029741743,0.00028653443,0.00059568876,0.0002918611,0.00077983347,0.00012519858,0.0000053696003],"category_scores_gemma":[0.00026279324,0.00020272443,0.000082447266,0.00066451204,0.00013903677,0.00075466384,0.0005872495,0.00017902706,0.000013294046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002288314,0.00060351624,0.0022290747,0.0004056294,0.00045963863,0.000101913196,0.005932299,0.5297344,0.0032425295,0.30408362,0.013015178,0.13996343],"study_design_scores_gemma":[0.0007500223,0.00025511108,0.0015512109,0.00024780174,0.000037636964,0.000045943278,0.00023792862,0.99442005,0.00028454553,0.0004801549,0.0013919243,0.00029764388],"about_ca_topic_score_codex":0.00008899886,"about_ca_topic_score_gemma":0.000030040324,"teacher_disagreement_score":0.46468574,"about_ca_system_score_codex":0.00014200262,"about_ca_system_score_gemma":0.0007102848,"threshold_uncertainty_score":0.82668626},"labels":[],"label_agreement":null},{"id":"W4412486683","doi":"10.2514/6.2025-3412","title":"Hybrid Framework for UAV Mission Planning Using Logic-Based Imaging and Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence","score_opus":0.037976267248256534,"score_gpt":0.3299879116264972,"score_spread":0.2920116443782407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412486683","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018542707,0.00028136704,0.9951446,0.0009876414,0.00037426266,0.00021893557,2.574562e-7,0.00027441897,0.00086421747],"genre_scores_gemma":[0.22330232,0.0000011424136,0.7753763,0.0010164557,0.00003295691,0.0000055534283,0.0000030203294,0.0000070147275,0.00025521338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987471,0.000043483986,0.00024637065,0.00042612586,0.00017267137,0.0003642412],"domain_scores_gemma":[0.9990439,0.00042737508,0.00010122561,0.00028034538,0.00006521026,0.00008195729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042801572,0.00016277097,0.00018033362,0.00018782153,0.00040510617,0.00024506694,0.0003407191,0.000044667857,0.0000035862627],"category_scores_gemma":[0.00032173205,0.00014770766,0.000045115838,0.00019438042,0.000026052485,0.00022379145,0.0002069162,0.00021274199,0.0000017369673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001238159,0.000012634242,0.0044421353,0.000063000836,0.000012758761,0.00003015564,0.00019934715,0.9558606,0.0007100457,0.02289053,0.00047914265,0.015287223],"study_design_scores_gemma":[0.00034612478,0.00003843567,0.00024951095,0.0004749683,0.000009856701,0.000009660039,0.00007287157,0.98684406,0.0024859635,0.008668928,0.0006266806,0.00017296025],"about_ca_topic_score_codex":0.000016694845,"about_ca_topic_score_gemma":1.3676158e-8,"teacher_disagreement_score":0.22144805,"about_ca_system_score_codex":0.00006427567,"about_ca_system_score_gemma":0.00010146264,"threshold_uncertainty_score":0.6023344},"labels":[],"label_agreement":null},{"id":"W4412508506","doi":"10.1609/socs.v18i1.35995","title":"Augmenting Exploration with Locally Greedy Probes","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Greedy algorithm; Computer science; Environmental science; Algorithm","score_opus":0.015624956764000884,"score_gpt":0.2568409622711752,"score_spread":0.2412160055071743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412508506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.563085,0.000054732216,0.11445266,0.09787976,0.024413226,0.0030446826,0.000010345589,0.00075158203,0.19630799],"genre_scores_gemma":[0.99099916,0.0000044680705,0.0076703606,0.00016003214,0.00018406726,0.000049242946,0.000001280605,0.000010564202,0.0009208007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979847,0.000015592297,0.000283224,0.00037072974,0.001114891,0.00023083428],"domain_scores_gemma":[0.9986541,0.000103853214,0.00016491518,0.00018052311,0.0008534035,0.00004318489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006278274,0.00014502127,0.00014871222,0.00014883606,0.00018497217,0.00031265308,0.002262259,0.00006016359,0.000002347942],"category_scores_gemma":[0.000121580546,0.00010342405,0.00005862788,0.0006047085,0.000079823585,0.00064321427,0.0006304975,0.00025813127,0.000008182022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001600769,0.00031492542,0.008564089,0.000063578416,0.00012593876,0.0000018529062,0.0006977543,0.002241402,0.025407478,0.959027,0.0014141601,0.0019817313],"study_design_scores_gemma":[0.0043298723,0.0011059744,0.0056728255,0.001982793,0.000043659984,0.000022939828,0.00030927928,0.19317675,0.70651335,0.083358854,0.0028831803,0.00060056493],"about_ca_topic_score_codex":0.000053859454,"about_ca_topic_score_gemma":5.3616054e-7,"teacher_disagreement_score":0.87566817,"about_ca_system_score_codex":0.00018543021,"about_ca_system_score_gemma":0.00012443967,"threshold_uncertainty_score":0.42175102},"labels":[],"label_agreement":null},{"id":"W4412591489","doi":"10.1145/3721250.3743019","title":"Interactive Object Insertion with Differentiable Rendering","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rendering (computer graphics); Differentiable function; Computer science; Computer graphics (images); Mathematics; Pure mathematics","score_opus":0.007581811263194594,"score_gpt":0.23676955666743044,"score_spread":0.22918774540423584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412591489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013122546,0.000014871228,0.95527697,0.0003301594,0.00021368379,0.00006738485,1.3436386e-7,0.00026162798,0.030712638],"genre_scores_gemma":[0.7558132,0.0000011202202,0.23911652,0.0002050931,0.000010532383,0.000011047804,0.0000010445985,0.0000032718087,0.0048381696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993424,0.000027635946,0.00009628501,0.00025330385,0.00011718446,0.0001632142],"domain_scores_gemma":[0.999524,0.00006433154,0.000032160035,0.00030938757,0.0000417304,0.00002840416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000681533,0.00008568098,0.0000959941,0.000116328854,0.00007339616,0.000121764155,0.00033699375,0.000024808553,0.000007688428],"category_scores_gemma":[0.000019504125,0.00006360289,0.000017602357,0.00035898158,0.000013010353,0.0003732829,0.0001441729,0.00010212307,0.000021108988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037396705,0.0014818707,0.10308936,0.0003891159,0.0014923884,0.0004893543,0.013588087,0.08183952,0.03110479,0.27948064,0.03237835,0.45429257],"study_design_scores_gemma":[0.0006490043,0.00014741039,0.067764334,0.00025415514,0.000013139397,0.000024531637,0.0001485146,0.9054766,0.02175095,0.002887635,0.0006152694,0.000268402],"about_ca_topic_score_codex":0.00007113653,"about_ca_topic_score_gemma":0.000005520375,"teacher_disagreement_score":0.8236371,"about_ca_system_score_codex":0.000057823883,"about_ca_system_score_gemma":0.00005805061,"threshold_uncertainty_score":0.25936508},"labels":[],"label_agreement":null},{"id":"W4412940723","doi":"10.3390/wevj16080434","title":"Trajectory Optimization for Autonomous Highway Driving Using Quintic Splines","year":2025,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy","funders":"","keywords":"Quintic function; Trajectory; Computer science; Automotive engineering; Engineering; Physics","score_opus":0.016367297076065945,"score_gpt":0.2719496253238111,"score_spread":0.2555823282477452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412940723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01562526,0.0005740701,0.98090297,0.00091771554,0.0013218587,0.00019312685,3.265564e-7,0.00018982182,0.00027484525],"genre_scores_gemma":[0.28725612,0.00001791174,0.71070564,0.0003196341,0.00042756318,0.000009746444,0.0000010123658,0.000020680558,0.0012417214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831396,0.00009685872,0.00047234388,0.00032650208,0.00024360126,0.00054671324],"domain_scores_gemma":[0.9988971,0.00026306647,0.000245262,0.0002794058,0.00019847497,0.00011671217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007041377,0.00018339427,0.00025739896,0.00086849736,0.0005587722,0.00040249614,0.0007196112,0.00006292185,0.000007973116],"category_scores_gemma":[0.0001446252,0.00018163637,0.00012614549,0.0018181155,0.000018015939,0.0004924549,0.00007060396,0.00037899092,0.0000034365848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009946223,0.000074155876,0.0015982883,0.000021482268,0.000066743516,0.000027377304,0.00012500912,0.93937385,0.0062687504,0.0013382874,0.0017820939,0.049314037],"study_design_scores_gemma":[0.00053859194,0.000060108574,0.0018515439,0.00009861952,0.00003519234,0.00012165295,0.000004753265,0.9943117,0.0015749868,0.000790644,0.00042409627,0.00018810414],"about_ca_topic_score_codex":0.000008209425,"about_ca_topic_score_gemma":0.0000027973563,"teacher_disagreement_score":0.27163085,"about_ca_system_score_codex":0.0003920787,"about_ca_system_score_gemma":0.00051896117,"threshold_uncertainty_score":0.7406916},"labels":[],"label_agreement":null},{"id":"W4413041227","doi":"10.1007/s10514-025-10203-w","title":"Es-cbf: an energy sufficiency extension for sample based path planners to enable long term autonomy","year":2025,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Autonomy; Extension (predicate logic); Term (time); Path (computing); Sample (material); Energy (signal processing); Operations research; Industrial engineering; Simulation; Statistics; Programming language; Mathematics","score_opus":0.02058892619092877,"score_gpt":0.28058533008909325,"score_spread":0.2599964038981645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413041227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065000965,0.00010508577,0.98915344,0.0009833217,0.0012790455,0.0005483166,0.000024926878,0.00069591915,0.00070986757],"genre_scores_gemma":[0.3202002,0.0000016320785,0.6751064,0.0030344096,0.000120467135,0.00023805638,0.00010470867,0.00003848089,0.0011556091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968117,0.00010608882,0.00052990357,0.001274049,0.00029860056,0.0009796873],"domain_scores_gemma":[0.99734485,0.0005032757,0.00015878268,0.0014653774,0.00019134367,0.0003363667],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005778131,0.00039966058,0.00045983688,0.00049437676,0.00047180767,0.00035410037,0.0016481343,0.00016984694,0.0000105714535],"category_scores_gemma":[0.00020580876,0.0004028035,0.00014297594,0.000825768,0.000052556927,0.0005455638,0.0003225814,0.00015425579,0.000026030777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004773292,0.0006199335,0.002755964,0.000075928605,0.00005122711,0.00011350902,0.0009841385,0.7872419,0.0020460417,0.037122548,0.0028243,0.16611682],"study_design_scores_gemma":[0.00080980605,0.00051936216,0.012334194,0.00013450519,0.000024138757,0.000011856938,0.000023415627,0.97621053,0.0027296722,0.0011908647,0.0054202224,0.0005914107],"about_ca_topic_score_codex":0.00045706696,"about_ca_topic_score_gemma":0.000026757545,"teacher_disagreement_score":0.314047,"about_ca_system_score_codex":0.0003871712,"about_ca_system_score_gemma":0.001223173,"threshold_uncertainty_score":0.9998424},"labels":[],"label_agreement":null},{"id":"W4413097117","doi":"10.1016/j.jrst.2025.07.004","title":"Optimizing mountain railway alignments with a potential field guided 3D-RRT-star algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Railway Science and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for Central Universities of the Central South University; China Scholarship Council; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Algorithm; Field (mathematics); Star (game theory); Computer science; Mathematics","score_opus":0.007573736935442239,"score_gpt":0.2530745373715286,"score_spread":0.24550080043608632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413097117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036142644,0.0004005239,0.95168734,0.010265158,0.0006795494,0.00014454442,9.787805e-7,0.00009927628,0.000580011],"genre_scores_gemma":[0.17926201,0.000059055885,0.81972265,0.0006841921,0.000046157544,0.000004416365,1.5392503e-7,0.0000061364785,0.00021518847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976606,0.000037712223,0.0004999895,0.00045851577,0.00079143065,0.0005517544],"domain_scores_gemma":[0.99819434,0.00008904007,0.00038512136,0.0004957781,0.00069950864,0.00013620248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015659234,0.00019885266,0.0003702732,0.0015648063,0.00040888882,0.00026488325,0.0020062202,0.00016167988,0.0000041854837],"category_scores_gemma":[0.00036368906,0.00015058914,0.000040975985,0.0027784507,0.00064273906,0.0009855491,0.0005299066,0.00046045266,0.0000034276623],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060793154,0.00033551824,0.003025571,0.000048313992,0.00027832322,0.0025377823,0.0015790988,0.003770026,0.044066124,0.021584872,0.005568804,0.9171448],"study_design_scores_gemma":[0.006841204,0.0047771162,0.0019681577,0.0011157552,0.00018006621,0.010401224,0.0022989924,0.8413635,0.096263394,0.023408784,0.0100575285,0.0013242798],"about_ca_topic_score_codex":0.000014729683,"about_ca_topic_score_gemma":8.186363e-7,"teacher_disagreement_score":0.9158205,"about_ca_system_score_codex":0.00013474244,"about_ca_system_score_gemma":0.00079844287,"threshold_uncertainty_score":0.6140847},"labels":[],"label_agreement":null},{"id":"W4413157072","doi":"10.1109/isscs66034.2025.11105597","title":"Limit Cycle-Based Artificial Fields for Obstacle Avoidance in Robot Path Planning","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Motion planning; Obstacle avoidance; Obstacle; Robot; Limit (mathematics); Limit cycle; Collision avoidance; Computer science; Path (computing); Mobile robot; Artificial intelligence; Mathematics; Geography; Mathematical analysis","score_opus":0.030306855634248616,"score_gpt":0.2985111664963809,"score_spread":0.26820431086213226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006995743,0.00009351731,0.9868309,0.0026415605,0.0007329547,0.00027836915,0.0000022707588,0.00023285336,0.0021918337],"genre_scores_gemma":[0.5256259,4.3809254e-7,0.47225794,0.0014128152,0.000051449584,0.00006772098,0.000004100433,0.000007089438,0.00057255727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856544,0.000049429513,0.0003282364,0.00046648772,0.00016721888,0.00042317295],"domain_scores_gemma":[0.9987743,0.00057791686,0.00006259756,0.00047603226,0.000052215997,0.00005694344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039036848,0.00015202121,0.00021312461,0.00019987076,0.00012239022,0.0001418427,0.0007112951,0.0001158452,0.000003894741],"category_scores_gemma":[0.00019982796,0.00015049227,0.00006642389,0.00052284455,0.00002456627,0.00020328084,0.000103610975,0.0001925118,0.0000135174605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004571904,0.00020488231,0.006234144,0.0000737988,0.000017355487,0.00008146198,0.0007403742,0.88474077,0.0006343156,0.040137634,0.0043677134,0.06272184],"study_design_scores_gemma":[0.00047340864,0.00006786945,0.0057917787,0.00013781215,0.000003252823,0.0000012665051,0.000037452617,0.9826679,0.0033143524,0.0069144387,0.00040720275,0.00018324624],"about_ca_topic_score_codex":0.000052585267,"about_ca_topic_score_gemma":0.000010182202,"teacher_disagreement_score":0.51863015,"about_ca_system_score_codex":0.000062513216,"about_ca_system_score_gemma":0.00017610718,"threshold_uncertainty_score":0.61368966},"labels":[],"label_agreement":null},{"id":"W4413243979","doi":"10.1145/3747862","title":"Real-Time Triangle-SDF Continuous Collision Detection 49","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Computer Graphics and Interactive Techniques","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collision detection; Collision; Robustness (evolution); Polygon mesh; Computer science; Algorithm; Isosurface; Mathematics; Mathematical optimization; Artificial intelligence; Computer graphics (images); Visualization","score_opus":0.009298603840715038,"score_gpt":0.2574468140334266,"score_spread":0.24814821019271158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413243979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5356186,0.00008836422,0.44207984,0.0052507073,0.0016559373,0.0020317272,0.000011371706,0.0016952442,0.011568219],"genre_scores_gemma":[0.685911,0.00016630889,0.31268874,0.00071321864,0.00012121778,0.00007226193,8.2120687e-7,0.000022083761,0.0003043321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987192,0.000030492542,0.00034469448,0.00045961508,0.00024633727,0.0001996388],"domain_scores_gemma":[0.99831337,0.0003304051,0.00035421606,0.0004966299,0.00046184624,0.000043541633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217146,0.00021377257,0.0003462091,0.00043941336,0.00018746973,0.0002148044,0.001871204,0.00012077469,5.4919883e-7],"category_scores_gemma":[0.00030366203,0.00015739466,0.00013380666,0.0007543564,0.00011027579,0.0004541344,0.0014683752,0.00032801693,0.0000014266383],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059589074,0.0008981393,0.0049401512,0.0003349207,0.0005153423,0.000009302407,0.0027461087,0.000015167457,0.30177358,0.200631,0.045409035,0.44213137],"study_design_scores_gemma":[0.0005560947,0.001319083,0.0109506715,0.0015557098,0.000041014016,0.0000376738,0.000036275476,0.12362685,0.80227005,0.056825545,0.0023907672,0.0003902619],"about_ca_topic_score_codex":0.00006089145,"about_ca_topic_score_gemma":3.1816842e-7,"teacher_disagreement_score":0.50049645,"about_ca_system_score_codex":0.00005467077,"about_ca_system_score_gemma":0.000023064194,"threshold_uncertainty_score":0.6418368},"labels":[],"label_agreement":null},{"id":"W4413330532","doi":"10.1155/atr/3152748","title":"A Multiaircraft Path Distributive Planning Method via Autonomous Self‐Separation Operation Mode","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Tianjin Science and Technology Program; National Natural Science Foundation of China","keywords":"Distributive property; Separation (statistics); Path (computing); Mode (computer interface); Computer science; Self driving; Engineering; Automotive engineering; Mathematics; Human–computer interaction","score_opus":0.009618315359631894,"score_gpt":0.3284699504666082,"score_spread":0.31885163510697634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413330532","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022768576,0.00017505322,0.97563773,0.00034349057,0.00074058515,0.0001871957,0.0000092311475,0.00009249512,0.00004564457],"genre_scores_gemma":[0.40217778,0.000009034473,0.5976486,0.00006453966,0.000039706087,0.0000066804005,0.00002990821,0.000005206484,0.000018497105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984031,0.00010447535,0.00071507343,0.00025138335,0.00032684475,0.00019912908],"domain_scores_gemma":[0.9987229,0.00014708296,0.00050068705,0.00018789132,0.0003659877,0.00007543328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043712225,0.00016538671,0.0002854045,0.000224007,0.00014544402,0.00007409181,0.00033269927,0.00008558642,0.0000011886632],"category_scores_gemma":[0.00003929033,0.00015731853,0.0001042347,0.00040307347,0.000012618601,0.0015347525,0.000006038192,0.00026471275,0.0000021841313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048181522,0.000085927175,0.00067132665,0.000020447987,0.00005582595,0.000064679145,0.005318822,0.96098405,0.0070611215,0.0014437897,0.000045072804,0.024200734],"study_design_scores_gemma":[0.0016724941,0.0002841166,0.09247825,0.00022166705,0.0000850339,0.00003793509,0.0002362688,0.89182276,0.009294309,0.0032129216,0.00041780816,0.00023641471],"about_ca_topic_score_codex":0.0000097603815,"about_ca_topic_score_gemma":0.0000027007,"teacher_disagreement_score":0.3794092,"about_ca_system_score_codex":0.0001833889,"about_ca_system_score_gemma":0.00022860081,"threshold_uncertainty_score":0.64152634},"labels":[],"label_agreement":null},{"id":"W4413353340","doi":"10.1016/j.oceaneng.2025.122483","title":"A formation rendezvous method for multiple unmanned surface vehicles based on GAT-modMADDPG","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Ministry of Natural Resources","keywords":"Rendezvous; Unmanned surface vehicle; Surface (topology); Marine engineering; Aerospace engineering; Computer science; Engineering; Mathematics; Geometry; Spacecraft","score_opus":0.012348811237101704,"score_gpt":0.2535555314263013,"score_spread":0.24120672018919956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413353340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025437213,0.000039729384,0.99553704,0.0005138151,0.00044142775,0.00026006936,0.000007634937,0.0005090502,0.00014750686],"genre_scores_gemma":[0.26852423,6.208343e-7,0.73116136,0.00013891893,0.000025609546,0.000008418766,0.000009795804,0.000012715851,0.00011833574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897283,0.000030047346,0.00021156816,0.00029879026,0.00016862515,0.0003181607],"domain_scores_gemma":[0.99875975,0.000653749,0.000052225707,0.0004181773,0.000055800945,0.000060313323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045081298,0.000166497,0.00017583316,0.0001740268,0.00009282112,0.000092661954,0.00044635544,0.00006965648,4.2619402e-7],"category_scores_gemma":[0.00027060867,0.00017239936,0.00007128016,0.00038845817,0.0000053205918,0.00024658398,0.00005919749,0.00012501355,0.0000051148254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075895764,0.00001920212,0.00005830258,0.00007273778,0.0000103394395,0.000003826061,0.00012911884,0.9895646,0.0018378978,0.0016746351,0.00090182613,0.0057199355],"study_design_scores_gemma":[0.0005942769,0.00005552067,0.00046602308,0.00013529959,0.0000069735647,0.0000023497403,0.000009117908,0.979059,0.0183602,0.00012307626,0.0010217823,0.00016640656],"about_ca_topic_score_codex":0.0000056640465,"about_ca_topic_score_gemma":2.2967014e-7,"teacher_disagreement_score":0.2659805,"about_ca_system_score_codex":0.000102531,"about_ca_system_score_gemma":0.00003869676,"threshold_uncertainty_score":0.7030242},"labels":[],"label_agreement":null},{"id":"W4413396484","doi":"10.1088/1742-6596/3077/1/012012","title":"Direction-Guided Model Predictive Planning for High-Speed Obstacle Avoidance of Extra-Large AUVs","year":2025,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Obstacle avoidance; Obstacle; Computer science; Artificial intelligence; Geography; Mobile robot","score_opus":0.041307568976672954,"score_gpt":0.29710996542334434,"score_spread":0.25580239644667135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413396484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016298575,0.00014518469,0.9815481,0.00036561492,0.0008470754,0.00015439873,0.00003511461,0.00004453426,0.00056144997],"genre_scores_gemma":[0.813492,0.000016857935,0.1858488,0.000050013707,0.00010848662,0.0000060132443,0.0000022798815,0.000008586486,0.00046699354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984564,0.000059773625,0.0006053163,0.00023413119,0.00035335618,0.0002909925],"domain_scores_gemma":[0.99757,0.00023611684,0.0007090517,0.0003096369,0.0011034781,0.00007176376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004224364,0.0001860829,0.00047691388,0.00013018942,0.00016501456,0.000107612024,0.0007569007,0.00007286084,0.0000012423795],"category_scores_gemma":[0.00020014668,0.00017318348,0.00012840184,0.0003624125,0.00009245454,0.0012992116,0.000116278934,0.00026020996,6.229893e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024667475,0.00034230863,0.0012800103,0.00022991971,0.00038327096,0.000020358411,0.008950549,0.43186277,0.03836279,0.49497363,0.0023701575,0.020977557],"study_design_scores_gemma":[0.0010187961,0.00038332675,0.0027669703,0.00063421763,0.000054077398,0.000019547975,0.0004464166,0.7321587,0.13166548,0.13056707,0.00007151618,0.0002138581],"about_ca_topic_score_codex":0.000006672898,"about_ca_topic_score_gemma":4.6439976e-7,"teacher_disagreement_score":0.7971934,"about_ca_system_score_codex":0.00006889764,"about_ca_system_score_gemma":0.0006373958,"threshold_uncertainty_score":0.7062217},"labels":[],"label_agreement":null},{"id":"W4413401494","doi":"10.54097/989mtm33","title":"Multi-Robot Cooperative Path Planning: Advances, Challenges, and Future Trends","year":2025,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Path (computing); Robot; Computer science; Artificial intelligence; Computer network","score_opus":0.01197584224120374,"score_gpt":0.2556811699091025,"score_spread":0.24370532766789876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413401494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030110545,0.18294257,0.73042804,0.044762153,0.0053045703,0.00050093245,0.000007329775,0.0030305954,0.0029132683],"genre_scores_gemma":[0.43092325,0.0060834917,0.56259257,0.000054072607,0.00007835553,0.000052685926,9.2141596e-7,0.000011844626,0.00020280726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850994,0.000012800916,0.00019092433,0.00069665856,0.00015478666,0.00043490287],"domain_scores_gemma":[0.999373,0.000058037687,0.000039164097,0.0003993598,0.000057360117,0.00007308018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003248784,0.00019397704,0.00024395561,0.0015027187,0.00016465038,0.00007409795,0.0007258136,0.00015831078,2.1859665e-7],"category_scores_gemma":[0.00007244753,0.00016445472,0.0000097235625,0.0022777594,0.0003395387,0.00046151792,0.00034897978,0.00028424474,0.0000025331592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002571413,0.00006411343,0.00044690561,0.00004368675,0.0000112803145,0.00017082968,0.0017699059,0.0075748092,0.004389716,0.7577813,0.00005090493,0.22769397],"study_design_scores_gemma":[0.0013650168,0.00024641238,0.031973794,0.00044392652,0.0000078849225,0.00018943593,0.00047531765,0.8672536,0.0045713345,0.0012060166,0.09153232,0.0007349377],"about_ca_topic_score_codex":0.000002222363,"about_ca_topic_score_gemma":0.0000012010754,"teacher_disagreement_score":0.8596788,"about_ca_system_score_codex":0.000052124968,"about_ca_system_score_gemma":0.00006462227,"threshold_uncertainty_score":0.6706269},"labels":[],"label_agreement":null},{"id":"W4413437201","doi":"10.3389/fpls.2025.1650007","title":"Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance","year":2025,"lang":"en","type":"article","venue":"Frontiers in Plant Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Department of Science and Technology of Jilin Province; Education Department of Jilin Province","keywords":"Stage (stratigraphy); Path (computing); Domain (mathematical analysis); Computer science; Algorithm; Motion planning; Artificial intelligence; Mathematics; Biology; Computer network","score_opus":0.0309130548208899,"score_gpt":0.3069557904761197,"score_spread":0.2760427356552298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413437201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005494875,0.000106865904,0.9921334,0.00031359907,0.0046960437,0.00061231956,0.00013321,0.00031276763,0.0011423158],"genre_scores_gemma":[0.0022038952,0.000005361638,0.9964916,0.0005954731,0.000052145122,0.00014704814,0.00002062884,0.0000105860245,0.0004732358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99595404,0.00021380732,0.00057764567,0.0011893081,0.001145784,0.00091942225],"domain_scores_gemma":[0.998423,0.00026883272,0.0002597815,0.0007572268,0.00010168034,0.00018947535],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032576844,0.00035776984,0.0004028204,0.0017681763,0.0006455889,0.0003345316,0.0019283011,0.00015621945,0.0000018385078],"category_scores_gemma":[0.00051344204,0.0003352686,0.000076426026,0.0033745565,0.0002941281,0.0009428036,0.00026675433,0.00058815506,0.000010854509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029736172,0.0008192049,0.04228658,0.0002389373,0.00007290678,0.0011669919,0.0044493056,0.7502493,0.003466029,0.0037365928,0.030132093,0.16308467],"study_design_scores_gemma":[0.0008840052,0.000115869385,0.015232971,0.0006055887,0.0000034716024,0.000040795203,0.00016325698,0.97530407,0.0016318917,0.00041982572,0.005227639,0.00037062628],"about_ca_topic_score_codex":0.00012399758,"about_ca_topic_score_gemma":0.00000394789,"teacher_disagreement_score":0.22505473,"about_ca_system_score_codex":0.0007742182,"about_ca_system_score_gemma":0.0013144284,"threshold_uncertainty_score":0.99990994},"labels":[],"label_agreement":null},{"id":"W4413558763","doi":"10.1109/3dv66043.2025.00022","title":"SceneMotifCoder: Example-Driven Visual Program Learning for Generating 3D Object Arrangements","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object (grammar); Artificial intelligence; Human–computer interaction; Computer vision; Computer graphics (images)","score_opus":0.031086380774382447,"score_gpt":0.3264087158457227,"score_spread":0.2953223350713402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413558763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047712023,0.000046960216,0.9898634,0.00016930811,0.00063191034,0.00072122074,4.909811e-7,0.00091271044,0.0028828098],"genre_scores_gemma":[0.034369487,0.0000017410201,0.9621609,0.0003229468,0.00011902587,0.00019708912,0.000014936597,0.000014160405,0.0027997072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824774,0.00009625175,0.00030042673,0.00060079945,0.0002433134,0.0005114727],"domain_scores_gemma":[0.9991763,0.00020299837,0.000093265495,0.00032784048,0.00012438768,0.00007516529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040569567,0.0001938862,0.00022195453,0.00016638872,0.00041068718,0.00037586899,0.0006436072,0.000075359414,0.0000069264875],"category_scores_gemma":[0.00014428158,0.00018347787,0.00007934801,0.00045792895,0.000026867638,0.00026421723,0.0002842786,0.0001715013,0.00002139347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005442375,0.00018444529,0.0045448407,0.000072441224,0.00009643132,0.000010642791,0.0005855011,0.089042015,0.0013076271,0.0036481293,0.0014560701,0.8990464],"study_design_scores_gemma":[0.0005331743,0.00020488932,0.00082920253,0.00006389895,0.000011101778,0.0000031299885,0.00004423166,0.9936505,0.00082665775,0.000076944365,0.0035623831,0.00019389116],"about_ca_topic_score_codex":0.00006089386,"about_ca_topic_score_gemma":0.000005886209,"teacher_disagreement_score":0.9046085,"about_ca_system_score_codex":0.00006156124,"about_ca_system_score_gemma":0.00012154768,"threshold_uncertainty_score":0.748201},"labels":[],"label_agreement":null},{"id":"W4413636720","doi":"10.1016/j.actaastro.2025.07.059","title":"Path planning algorithm for a South Pole lunar rover mission","year":2025,"lang":"en","type":"article","venue":"Acta Astronautica","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Canadian Space Agency","funders":"","keywords":"Aerospace engineering; Motion planning; Path (computing); Computer science; Astrobiology; Algorithm; Geodesy; Geology; Remote sensing; Engineering; Physics; Artificial intelligence; Robot","score_opus":0.016902097559570995,"score_gpt":0.2826112014729088,"score_spread":0.2657091039133378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413636720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009895955,0.00013461208,0.9943463,0.0017742057,0.0007273568,0.00030520136,0.000024753872,0.0002789837,0.0014189964],"genre_scores_gemma":[0.12255094,7.3211686e-7,0.874499,0.00050950685,0.00011887767,0.000027707083,0.000020256439,0.000017391249,0.002255586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981605,0.00005097007,0.00029168394,0.00058748125,0.00028754974,0.00062184274],"domain_scores_gemma":[0.99877006,0.00023683347,0.000105548395,0.0006587047,0.000074357464,0.00015450282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030601406,0.00023318092,0.00026237275,0.00016773179,0.0002617427,0.00016370973,0.0009400405,0.00010913145,0.000008156636],"category_scores_gemma":[0.00011722982,0.00021691943,0.000106954714,0.00033933183,0.00004603986,0.00027604922,0.0003339626,0.0002148449,0.000023911405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006265381,0.00043304038,0.0038999987,0.00014529946,0.0002884586,0.0001105491,0.0046483004,0.0030707838,0.0025605366,0.017749099,0.072179794,0.8948515],"study_design_scores_gemma":[0.00094364164,0.0001688652,0.0058977916,0.0002286133,0.00002934226,0.000009578702,0.00012441869,0.9588089,0.0006693963,0.0013990545,0.031442378,0.0002780304],"about_ca_topic_score_codex":0.0000066888997,"about_ca_topic_score_gemma":3.1762994e-8,"teacher_disagreement_score":0.9557381,"about_ca_system_score_codex":0.00008154785,"about_ca_system_score_gemma":0.00022906526,"threshold_uncertainty_score":0.8845718},"labels":[],"label_agreement":null},{"id":"W4413735309","doi":"10.1017/pds.2025.10136","title":"Design of a Reconfigurable Crawler based on Waterbomb Origami","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Design Society","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Web crawler; Computer science; World Wide Web","score_opus":0.026901353701910827,"score_gpt":0.24026176264084012,"score_spread":0.2133604089389293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413735309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010335776,0.000038189602,0.993681,0.00144925,0.00032260467,0.00053049397,0.0000014181182,0.00011381175,0.0028296635],"genre_scores_gemma":[0.21620111,0.000008957652,0.7813965,0.0008014811,0.000023954408,0.00004706882,2.5217653e-7,0.000015334072,0.0015053534],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848825,0.00003944457,0.0003638666,0.0003829061,0.00041046168,0.0003150659],"domain_scores_gemma":[0.99858737,0.00036892522,0.00029793163,0.0003389447,0.0003636027,0.00004324002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015752169,0.00019111838,0.0003103236,0.00006525255,0.0001474391,0.000070899696,0.0017067092,0.00012931976,0.0000039155143],"category_scores_gemma":[0.00018422528,0.00013391169,0.00021202242,0.00083730434,0.00012491603,0.00022251735,0.000119656856,0.00022160605,0.000004818023],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019384961,0.0007644853,0.0018813063,0.0009788298,0.00042562746,0.0000016731083,0.0091236355,0.14019914,0.63859826,0.016343664,0.1839213,0.0075682187],"study_design_scores_gemma":[0.00038486905,0.000118833574,0.0002994294,0.00032368864,0.000022299662,0.0000012474221,0.000058824673,0.59611034,0.39851278,0.003951095,0.00009621965,0.0001203641],"about_ca_topic_score_codex":0.000009787828,"about_ca_topic_score_gemma":5.9597713e-9,"teacher_disagreement_score":0.4559112,"about_ca_system_score_codex":0.00007824781,"about_ca_system_score_gemma":0.00019860374,"threshold_uncertainty_score":0.54607606},"labels":[],"label_agreement":null},{"id":"W4413925110","doi":"10.1109/icra55743.2025.11128481","title":"Designing Experimental Setup Emulating Log-Loader Manipulator and Implementing Anti-Sway Trajectory Planner","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Loader; Trajectory; Manipulator (device); Planner; Computer science; Control engineering; Automotive engineering; Simulation; Robot; Engineering; Artificial intelligence; Operating system; Physics","score_opus":0.027381571655099333,"score_gpt":0.29478511251168416,"score_spread":0.2674035408565848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413925110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25874317,0.00038138777,0.7363457,0.00013699774,0.0004202163,0.00020048137,0.0000010440316,0.00035584444,0.0034151298],"genre_scores_gemma":[0.6817445,5.60757e-7,0.3175995,0.00025896804,0.000036515725,0.000009419064,0.000002528357,0.000008964999,0.00033902132],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825203,0.000078271594,0.00034062826,0.0005565092,0.00022022743,0.0005523529],"domain_scores_gemma":[0.9992957,0.00016652132,0.00008555959,0.00033207788,0.000027887025,0.00009221894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043337085,0.00021427835,0.00022837809,0.00018169021,0.00034918243,0.00026411246,0.00041318985,0.00007228313,0.000023875025],"category_scores_gemma":[0.00002893032,0.00020202255,0.000045219127,0.0003253366,0.000037086385,0.00041704674,0.00044505377,0.00015938953,0.000012023621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028177219,0.00041250544,0.49327722,0.00024708206,0.0005229341,0.00051398523,0.017879495,0.019626945,0.37718117,0.030476095,0.01081787,0.049016517],"study_design_scores_gemma":[0.0019505005,0.00013102102,0.094667934,0.00031154737,0.00003444411,0.00009040615,0.0023852366,0.69073814,0.20780022,0.00030307323,0.0005180282,0.001069472],"about_ca_topic_score_codex":0.000055621298,"about_ca_topic_score_gemma":6.292832e-7,"teacher_disagreement_score":0.67111117,"about_ca_system_score_codex":0.000053148302,"about_ca_system_score_gemma":0.000047556066,"threshold_uncertainty_score":0.82382405},"labels":[],"label_agreement":null},{"id":"W4413925460","doi":"10.1109/lra.2025.3604732","title":"Minimum-Length Coverage Path Planning for Grid Environments With Approximation Guarantees","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Grid; Path length; Path (computing); Computer science; Motion planning; Mathematical optimization; Mathematics; Computer network; Artificial intelligence; Geometry","score_opus":0.010804377827114938,"score_gpt":0.23438241739813143,"score_spread":0.2235780395710165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413925460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025113383,0.000044515502,0.97109896,0.0026647279,0.00051994936,0.00035603627,0.000006993664,0.00013478192,0.000060634557],"genre_scores_gemma":[0.34019932,0.000011127473,0.6571668,0.0022812863,0.00010112352,0.00006368575,0.000039174367,0.000016646442,0.000120885314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891573,0.00003752011,0.0002452279,0.00034794115,0.00021467856,0.00023888743],"domain_scores_gemma":[0.9993763,0.00015892384,0.00014659198,0.000257773,0.000017997998,0.000042414744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019832345,0.00016912217,0.00017908675,0.00014378467,0.00020421889,0.00020307829,0.00024085997,0.000054106775,3.2924208e-7],"category_scores_gemma":[0.000017953786,0.00015133958,0.000031579395,0.00016625701,0.000042306336,0.00039965467,0.00003565241,0.000086325934,0.000003234354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014698871,0.000059209906,0.0012466139,0.00011994742,0.000074589094,0.000016514923,0.0011100181,0.97698617,0.0055015306,0.0032016563,0.0033046503,0.008364407],"study_design_scores_gemma":[0.000817321,0.000065212975,0.0052347835,0.00017403757,0.00002217664,0.000009183806,0.000021363798,0.99197817,0.00083832437,0.00029509253,0.00035359644,0.00019076133],"about_ca_topic_score_codex":0.0000027640283,"about_ca_topic_score_gemma":7.805342e-8,"teacher_disagreement_score":0.31508595,"about_ca_system_score_codex":0.000053154818,"about_ca_system_score_gemma":0.000025358846,"threshold_uncertainty_score":0.6171449},"labels":[],"label_agreement":null},{"id":"W4414117888","doi":"10.1109/ccta53793.2025.11151389","title":"Drive-by-Logic: Trajectory Generation for Nonholonomic Ground Robots with Signal Temporal Logic Objectives","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robot; Kinematics; Trajectory; Nonholonomic system; Construct (python library); Control theory (sociology); Mobile robot; Robotics","score_opus":0.027877417912432197,"score_gpt":0.2694409622294261,"score_spread":0.2415635443169939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414117888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012371777,0.00014005686,0.98370945,0.0007026931,0.0003950728,0.00047831418,0.000005019635,0.00025956583,0.0019380377],"genre_scores_gemma":[0.39940757,0.000002173966,0.59744,0.0006977155,0.00009897391,0.00009417777,0.000027413087,0.00000915738,0.0022228314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850684,0.00008290608,0.00024097669,0.00066338456,0.00016010531,0.00034579303],"domain_scores_gemma":[0.9992102,0.00015691925,0.00010070722,0.00036779145,0.00009683529,0.000067567984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030176438,0.00021530868,0.0002390212,0.0001477557,0.0002462947,0.00022077655,0.00059188803,0.00009781533,0.000007653711],"category_scores_gemma":[0.00001997221,0.00016662451,0.00006312311,0.00029377293,0.00007987623,0.00045795948,0.00008417722,0.00012205307,0.000013131392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002985495,0.0014626115,0.020036165,0.00018847367,0.00075725524,0.00013781272,0.0036511219,0.4347458,0.14511012,0.23423222,0.09735138,0.062028524],"study_design_scores_gemma":[0.0013059392,0.00079962885,0.007337883,0.000033170676,0.000025651709,0.00002309897,0.000080203856,0.97860044,0.0061249137,0.004921677,0.0002710598,0.00047631943],"about_ca_topic_score_codex":0.000064082786,"about_ca_topic_score_gemma":0.000021213804,"teacher_disagreement_score":0.54385465,"about_ca_system_score_codex":0.00012882608,"about_ca_system_score_gemma":0.00021462612,"threshold_uncertainty_score":0.67947507},"labels":[],"label_agreement":null},{"id":"W4414346042","doi":"10.1115/1.4069825","title":"Dynamics, Analysis, and Experiments of Obstacle Negotiation for Wheeled Mobile Robots","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Nonlinear Dynamics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Obstacle; Robot; Mobile robot; Moment (physics); Trajectory; Accelerometer; Work (physics); Energy (signal processing); Test suite; Humanoid robot","score_opus":0.00805641358555623,"score_gpt":0.2875379015035849,"score_spread":0.2794814879180286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414346042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1603848,0.0002793001,0.83868504,0.00035236983,0.0001624986,0.00009427277,0.000020142958,0.000007201839,0.0000143602765],"genre_scores_gemma":[0.3888895,0.000016611566,0.6109428,0.000046846777,0.000025875592,0.0000019613155,0.000033228873,0.0000033208298,0.00003987096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990368,0.000036967962,0.00046973367,0.00014252088,0.00021262598,0.00010134304],"domain_scores_gemma":[0.9987114,0.00032313057,0.00040451204,0.00008838485,0.00041789314,0.00005466614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028971236,0.00009417403,0.00029459418,0.00035986482,0.00006983087,0.00004545209,0.00019278232,0.000048397793,4.5570894e-7],"category_scores_gemma":[0.000050134604,0.00008670963,0.00009066746,0.00041839096,0.000042378255,0.00022899962,0.00006757993,0.00008299141,1.0570351e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004075726,0.00015737659,0.013455442,0.000049362003,0.00047861703,0.000003841216,0.00028969574,0.94793105,0.000046290796,0.015102739,0.00002248169,0.022422342],"study_design_scores_gemma":[0.00063564733,0.0001482176,0.028920235,0.000035101933,0.00009526512,0.000016582733,0.00006618272,0.96174514,0.000050961924,0.008210529,0.0000072057587,0.00006892723],"about_ca_topic_score_codex":0.0000075775583,"about_ca_topic_score_gemma":0.0000055596515,"teacher_disagreement_score":0.22850469,"about_ca_system_score_codex":0.000064989814,"about_ca_system_score_gemma":0.00011750697,"threshold_uncertainty_score":0.35359162},"labels":[],"label_agreement":null},{"id":"W4414431462","doi":"10.1007/s10015-025-01049-7","title":"Centralization vs. decentralization in multi-robot sweep coverage with ground robots and UAVs","year":2025,"lang":"en","type":"article","venue":"Artificial Life and Robotics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Decentralization; Decentralised system; Task (project management); Robot; Scalability; Control (management); Fault tolerance","score_opus":0.023965389200177916,"score_gpt":0.2625274812271466,"score_spread":0.23856209202696868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414431462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02759958,0.00023621693,0.9698876,0.0015364366,0.0003213362,0.00025902002,0.0000015778414,0.00009027065,0.00006796701],"genre_scores_gemma":[0.8406355,0.0002228383,0.15824465,0.000734709,0.00003959074,0.0000063420334,0.000018577495,0.000012993613,0.00008484086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985721,0.000093586554,0.00034325573,0.00043983196,0.0001902585,0.00036092885],"domain_scores_gemma":[0.9993497,0.000099826066,0.000093136994,0.00025149027,0.000071465074,0.00013437196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020142517,0.00018892839,0.00024138544,0.00018217455,0.0002013967,0.00029273855,0.0001951354,0.00011045347,0.0000014058312],"category_scores_gemma":[0.00009825614,0.00017315592,0.000017330149,0.0006385583,0.00008135429,0.00044155066,0.00011900422,0.0001322454,0.0000033790889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036741367,0.00016530685,0.074148536,0.000054644035,0.000025965164,0.00003845467,0.0009861807,0.8267112,0.0001714375,0.090403624,0.00007805558,0.007179856],"study_design_scores_gemma":[0.0006511434,0.00007347057,0.18235414,0.00011344548,0.00001944986,0.000007831645,0.000052322368,0.814552,0.0001590355,0.0017456893,0.000045398763,0.00022606665],"about_ca_topic_score_codex":0.00014344692,"about_ca_topic_score_gemma":0.00011140202,"teacher_disagreement_score":0.81303585,"about_ca_system_score_codex":0.000043715463,"about_ca_system_score_gemma":0.000109296634,"threshold_uncertainty_score":0.7061093},"labels":[],"label_agreement":null},{"id":"W4414432324","doi":"10.1109/case58245.2025.11163822","title":"Interaction-Minimizing Roadmap Optimization for High-Density Multi-Agent Path Finding","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ford Motor Company (Canada)","funders":"","keywords":"Workflow; Controller (irrigation); Path (computing); Position (finance); SMT placement equipment; Work (physics); Control (management); Key (lock); Automation","score_opus":0.036645463063368196,"score_gpt":0.3049478438618115,"score_spread":0.2683023807984433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414432324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016562672,0.0000151307,0.9945128,0.000746713,0.002084834,0.00027301643,0.0000015427535,0.0003025278,0.00040719175],"genre_scores_gemma":[0.14123435,0.000002926548,0.85694396,0.00034686175,0.000041170722,0.00002670555,0.000009726581,0.000006132766,0.001388182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892557,0.00004259215,0.00025210675,0.00040635315,0.00012990538,0.00024346341],"domain_scores_gemma":[0.99912375,0.0002506427,0.00009774573,0.0003548802,0.00012039427,0.000052555813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028296362,0.00013036864,0.00016005737,0.0001766117,0.0002375201,0.00018897385,0.00041591146,0.00006556224,0.000010261946],"category_scores_gemma":[0.00019721998,0.00012559447,0.00005601117,0.0003098286,0.000010189106,0.0004261511,0.00019230714,0.00010078106,0.000016506845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013301475,0.00016316702,0.0011460739,0.000052283795,0.000070234084,0.000015465539,0.0008470271,0.9283055,0.00043901335,0.017575378,0.004816674,0.046555895],"study_design_scores_gemma":[0.00044839497,0.000027604707,0.0016973161,0.000068776106,0.000009217675,0.0000036446759,0.00007187947,0.99502534,0.002168595,0.00019627536,0.00015002373,0.00013291955],"about_ca_topic_score_codex":0.00007888568,"about_ca_topic_score_gemma":0.0000028119096,"teacher_disagreement_score":0.13957807,"about_ca_system_score_codex":0.00012364503,"about_ca_system_score_gemma":0.000066742636,"threshold_uncertainty_score":0.51215935},"labels":[],"label_agreement":null},{"id":"W4414520187","doi":"10.46298/dmtcs.13421","title":"The Fagnano Triangle Patrolling Problem","year":2025,"lang":"en","type":"article","venue":"Discrete Mathematics & Theoretical Computer Science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Patrolling; Trajectory; Property (philosophy); Optimization problem; Inscribed figure; Dynamical billiards; Artifact (error)","score_opus":0.011503274239149344,"score_gpt":0.2645188535859136,"score_spread":0.2530155793467643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414520187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047345945,0.00012441966,0.9776165,0.0033322023,0.0009164224,0.0004396142,0.0000015536381,0.00032668235,0.01676917],"genre_scores_gemma":[0.18743876,0.000008972373,0.8119312,0.00035417828,0.000072208655,0.000037371414,6.2837756e-7,0.000011556616,0.00014515559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637246,0.00015206252,0.00067685446,0.0008222093,0.0010116099,0.0009648271],"domain_scores_gemma":[0.9960585,0.0015540777,0.00018273403,0.0017225537,0.0002645167,0.00021761993],"candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.003989664,0.00030599386,0.00038428564,0.00022554309,0.0016088111,0.0018589079,0.005473241,0.00007618837,0.0000057899797],"category_scores_gemma":[0.0004236465,0.00018997479,0.0001610553,0.0020709275,0.0023897847,0.0004998558,0.0018016342,0.00033485447,0.00007572231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027056803,0.000044436936,0.000011398701,0.000023216635,0.000013631911,0.000009351609,0.0007106798,0.00054250436,0.00013196157,0.9790546,0.00015670153,0.019298833],"study_design_scores_gemma":[0.00018126184,0.000054642023,0.00002630948,0.000116013296,0.0000091240945,0.000013709055,0.0000331848,0.6073071,0.00051820045,0.39138207,0.00020264427,0.00015575967],"about_ca_topic_score_codex":0.0000017921773,"about_ca_topic_score_gemma":2.253449e-7,"teacher_disagreement_score":0.6067646,"about_ca_system_score_codex":0.00007855584,"about_ca_system_score_gemma":0.00030441154,"threshold_uncertainty_score":0.9999076},"labels":[],"label_agreement":null},{"id":"W4414603570","doi":"10.1109/tmech.2025.3605527","title":"Fixed-Time Distributed Position Estimation of Multiagent Systems Based on Local Bearing Measurement","year":2025,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Shanghai Aerospace Science and Technology Innovation Foundation; National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Orientation (vector space); Convergence (economics); Position (finance); Estimation; Multi-agent system; Bearing (navigation)","score_opus":0.015239983299458104,"score_gpt":0.23923104495167807,"score_spread":0.22399106165221996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414603570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061060913,0.00005305348,0.9968713,0.00046583498,0.0010887634,0.00048228307,0.00006719941,0.00027563883,0.00008534644],"genre_scores_gemma":[0.923184,0.0000034582083,0.07657215,0.00006634866,0.000012016877,0.000069475704,0.00002816612,0.000013994858,0.000050412793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976464,0.00018495695,0.0004922051,0.00047396534,0.00086074905,0.00034174568],"domain_scores_gemma":[0.9986405,0.00015621378,0.00016431125,0.0007276004,0.00021893869,0.00009248579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007117833,0.00024601826,0.00030200122,0.00036987255,0.00024374347,0.00009249975,0.0004737973,0.00014963807,0.0000063595853],"category_scores_gemma":[0.00002177124,0.00025538064,0.0001475138,0.0006326373,0.000038273025,0.00021457054,0.00000626411,0.00032038405,0.000056420093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030461426,0.00030210044,0.000001281067,0.00006793537,0.00005598178,0.0000039172783,0.000049534723,0.97922456,0.0016152095,0.0011699495,0.000066863984,0.017412214],"study_design_scores_gemma":[0.0007497033,0.00027315464,0.000056783494,0.00050502946,0.0000629057,0.0000031909838,0.000023641698,0.96842253,0.029570457,0.00010503063,0.000041392184,0.00018617955],"about_ca_topic_score_codex":0.00007180201,"about_ca_topic_score_gemma":0.0000022790387,"teacher_disagreement_score":0.9225734,"about_ca_system_score_codex":0.0010238866,"about_ca_system_score_gemma":0.00028593588,"threshold_uncertainty_score":0.99998987},"labels":[],"label_agreement":null},{"id":"W4414772366","doi":"10.1007/978-3-032-00986-9_14","title":"Online Collaborative UAV Path Planning for Mapping and Spraying Missions","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Planner; Motion planning; Plan (archaeology); Path (computing); Autonomy; Swarm behaviour","score_opus":0.06272485010864773,"score_gpt":0.33314161328541086,"score_spread":0.2704167631767631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414772366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001971302,0.0009479545,0.9600156,0.0015715256,0.0002913238,0.00054872554,0.00006327727,0.00009354067,0.036448356],"genre_scores_gemma":[0.0010352051,0.0008231571,0.9959983,0.00085783936,0.000029297964,0.00003361725,0.00009119285,0.0000057264415,0.001125624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984747,0.000029882525,0.00063127343,0.00033422478,0.00028273588,0.00024718675],"domain_scores_gemma":[0.99707997,0.0006998278,0.00035106088,0.0012537244,0.0005043381,0.000111085756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095479895,0.00022401166,0.00029066365,0.001062838,0.00086001714,0.00063951715,0.0020678572,0.00012595775,5.328149e-7],"category_scores_gemma":[0.00017021282,0.00022646043,0.000028992084,0.0005858812,0.00047853272,0.0035964053,0.0021634302,0.00037510667,0.0000024483777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030711349,0.000029885681,0.00013919808,0.00012244386,0.000017770803,0.0000013659985,0.010765111,0.0021370193,0.000005964983,0.57166564,0.0016360483,0.4134765],"study_design_scores_gemma":[0.00029593124,0.00004116347,0.0008985798,0.001034108,0.0000053114823,0.000013685124,0.00013863562,0.8990102,0.0000023574391,0.004982063,0.09331692,0.0002610749],"about_ca_topic_score_codex":0.0000040525774,"about_ca_topic_score_gemma":6.091666e-7,"teacher_disagreement_score":0.8968732,"about_ca_system_score_codex":0.00010518173,"about_ca_system_score_gemma":0.0005457676,"threshold_uncertainty_score":0.92347884},"labels":[],"label_agreement":null},{"id":"W4414780620","doi":"10.1038/s41586-025-09515-9","title":"Kirigami-inspired parachutes with programmable reconfiguration","year":2025,"lang":"en","type":"article","venue":"Nature","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Control reconfiguration; Flexibility (engineering); Limit (mathematics); Metre; Porosity","score_opus":0.006271997149368192,"score_gpt":0.2516769089421078,"score_spread":0.24540491179273963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414780620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003864618,0.0012903711,0.95168144,0.011041129,0.0012462634,0.0005510209,0.000002965218,0.00096574816,0.029356474],"genre_scores_gemma":[0.23668404,0.0000070092437,0.75430423,0.0023112204,0.00009359191,0.000060857477,0.00002495408,0.000010834041,0.0065032844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907255,0.000035831625,0.00011936863,0.0003379698,0.00020660854,0.0002276973],"domain_scores_gemma":[0.9992807,0.000056324425,0.000060464357,0.0004445647,0.000115148774,0.000042834956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016357581,0.00011754466,0.00012410434,0.00009929436,0.00011363039,0.00019301174,0.00053214724,0.00044674025,0.0000031409888],"category_scores_gemma":[0.000058472036,0.00008993572,0.00002575192,0.000674995,0.000026034064,0.00033923477,0.000046019664,0.0008996742,0.000023110319],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007859957,0.0003239659,0.012360887,0.00017230203,0.00023204614,0.00030143018,0.001171553,0.0059651555,0.0016431996,0.20387225,0.08136037,0.69251823],"study_design_scores_gemma":[0.0027516654,0.0006382379,0.054031346,0.00077785534,0.00007120553,0.00014301506,0.00008937197,0.66909504,0.022929009,0.016209278,0.23206602,0.0011979417],"about_ca_topic_score_codex":0.000009225962,"about_ca_topic_score_gemma":0.0000042601364,"teacher_disagreement_score":0.6913203,"about_ca_system_score_codex":0.000036110658,"about_ca_system_score_gemma":0.00012880554,"threshold_uncertainty_score":0.39086866},"labels":[],"label_agreement":null},{"id":"W4414804222","doi":"10.48550/arxiv.2510.00044","title":"Optimized Fish Locomotion using Design-by-Morphing and Bayesian Optimization","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Fish locomotion; Wake; Propulsion; Kinematics; Parametric statistics; Benchmark (surveying); Transshipment (information security); Drag; Evolutionary algorithm; Propulsive efficiency","score_opus":0.07949615798264409,"score_gpt":0.201555479317539,"score_spread":0.12205932133489492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414804222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015469706,0.00004697677,0.99677104,0.00012259601,0.00046776247,0.00042713317,0.000018944598,0.00031626623,0.0002823255],"genre_scores_gemma":[0.0913262,0.00012852818,0.9076524,0.00012643645,0.00004423739,0.000001131678,0.000041310544,0.000017824597,0.0006619749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779004,0.00034971256,0.00024382079,0.0011614794,0.0001080065,0.00034693853],"domain_scores_gemma":[0.9984176,0.00018133385,0.0003065421,0.0007984411,0.00014149435,0.0001545551],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004353527,0.00034781185,0.0003876142,0.0003776176,0.00027423445,0.00028629485,0.001070343,0.00035569989,0.000007854066],"category_scores_gemma":[0.00006316682,0.00044653108,0.00009512279,0.0006120937,0.000077388955,0.000555971,0.0014241808,0.00047849497,0.0000022736128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001194681,0.0000342188,0.00011364075,0.000048245864,0.000051513056,0.00009190044,0.00013798045,0.9979887,0.00001494366,0.0007856062,0.0003311896,0.00039014098],"study_design_scores_gemma":[0.00058680796,0.000029523613,0.000023946042,0.00028239016,0.00008565956,0.000009525085,0.000018975705,0.9969832,0.00009684952,0.0014782633,0.000009395991,0.0003954756],"about_ca_topic_score_codex":0.00016031128,"about_ca_topic_score_gemma":5.962379e-7,"teacher_disagreement_score":0.08977923,"about_ca_system_score_codex":0.00028132202,"about_ca_system_score_gemma":0.00023574092,"threshold_uncertainty_score":0.99979866},"labels":[],"label_agreement":null},{"id":"W4415016764","doi":"10.1016/j.atech.2025.101518","title":"Path planning for orchard mobile robots based on an improved ant colony algorithm and the dynamic window approach","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Chongqing University of Science and Technology","keywords":"Motion planning; Ant colony optimization algorithms; Path (computing); Obstacle avoidance; Heuristic; Mobile robot; Node (physics); Window (computing)","score_opus":0.0064515941853115245,"score_gpt":0.2448642071704955,"score_spread":0.23841261298518399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415016764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023345457,0.0003858301,0.96866786,0.0036448492,0.00061494973,0.0021069301,0.000020976022,0.00092530076,0.00028785918],"genre_scores_gemma":[0.3138522,0.000010788219,0.6836241,0.0005417287,0.00006120127,0.0014300126,0.00007860025,0.000015587693,0.00038580826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981067,0.00010729547,0.00030101318,0.00077514286,0.00016638706,0.0005434713],"domain_scores_gemma":[0.9986978,0.00029035748,0.000147896,0.0006670482,0.0001298125,0.000067079185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046008025,0.00030839266,0.00042498682,0.00023985152,0.00044369,0.00014328834,0.001152553,0.00031049544,3.6491537e-7],"category_scores_gemma":[0.00010407527,0.00017075556,0.00008425465,0.0008843277,0.00028740673,0.0001864057,0.00029035858,0.00042801912,0.0000023454402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006035259,0.0016125808,0.0028193723,0.0004416552,0.00063301134,0.00013673164,0.0036662654,0.09939362,0.025645176,0.06677525,0.0052045975,0.79306823],"study_design_scores_gemma":[0.0021709937,0.00060782203,0.004279593,0.00006428132,0.000025965364,0.000047674035,0.00041660303,0.9898962,0.0005409124,0.0013109212,0.00036963986,0.00026942938],"about_ca_topic_score_codex":0.000017709455,"about_ca_topic_score_gemma":0.0000013878637,"teacher_disagreement_score":0.8905026,"about_ca_system_score_codex":0.00009515569,"about_ca_system_score_gemma":0.000067657595,"threshold_uncertainty_score":0.696321},"labels":[],"label_agreement":null},{"id":"W4415180737","doi":"10.23919/ecc65951.2025.11186852","title":"Improved Approximation Accuracy for Nonconvex Trajectory Optimization via Trajectory Sensitivities","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Trajectory optimization; Solver; Convergence (economics); Sequential quadratic programming; Range (aeronautics); Nonlinear programming; Quadratic equation; Quadratic programming","score_opus":0.014756845568992077,"score_gpt":0.2574788996706106,"score_spread":0.24272205410161854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415180737","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037514133,0.00004829127,0.99429965,0.00050488353,0.00094058545,0.00065291265,0.00000436348,0.0005357095,0.002638487],"genre_scores_gemma":[0.05058537,0.0000037667808,0.9470494,0.00060338824,0.00008783176,0.000109962966,0.00002898759,0.000012941899,0.0015183907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986893,0.000081333266,0.0003144526,0.00045090352,0.00015732349,0.00030670819],"domain_scores_gemma":[0.99860877,0.00060213474,0.00012165377,0.00044393158,0.00016866953,0.00005481478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041803022,0.0001877161,0.0002218588,0.00023611421,0.00018146544,0.00016441348,0.00040105125,0.00011336175,0.000006689407],"category_scores_gemma":[0.00017570757,0.000182924,0.00009246269,0.00036867225,0.000042622287,0.0007629812,0.00007567252,0.00010858256,0.000004958452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006646998,0.00031512126,0.00011974566,0.0004269045,0.00019343599,0.000011160279,0.003819775,0.72082025,0.023415573,0.01543374,0.005793181,0.22958463],"study_design_scores_gemma":[0.00058280776,0.000059865248,0.00024031418,0.000028343684,0.000013534631,0.000006081058,0.000093255476,0.9879402,0.010272181,0.00043927456,0.00012482783,0.00019934785],"about_ca_topic_score_codex":0.00003000354,"about_ca_topic_score_gemma":0.0000022961017,"teacher_disagreement_score":0.2671199,"about_ca_system_score_codex":0.00010157018,"about_ca_system_score_gemma":0.00021764908,"threshold_uncertainty_score":0.7459425},"labels":[],"label_agreement":null},{"id":"W4415250295","doi":"10.1007/978-3-032-00385-0_40","title":"Parallel Meta-heuristic Search","year":2025,"lang":"fr","type":"book-chapter","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Decomposition; Parallelism (grammar); Presentation (obstetrics); Parallel processing; Decomposition method (queueing theory)","score_opus":0.1584978026418641,"score_gpt":0.31302512480697586,"score_spread":0.15452732216511175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415250295","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.4951297e-7,0.002348507,0.5171512,0.0030114262,0.0014776838,0.00029365168,0.000028543027,0.00020071732,0.47548813],"genre_scores_gemma":[0.00007567799,0.00016741049,0.4335341,0.0007001627,0.00018138415,0.000017459335,0.000017119513,0.000028721177,0.565278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99505246,0.00021255066,0.0009058918,0.0016527504,0.0011583845,0.0010179756],"domain_scores_gemma":[0.995532,0.0012884856,0.00021534343,0.0020791576,0.00046934097,0.00041567182],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010458786,0.0009463736,0.0015004136,0.00044568744,0.000350455,0.0004736871,0.002702545,0.0006453466,0.0030833231],"category_scores_gemma":[0.00017498138,0.0008381535,0.0008240996,0.00022771486,0.00046184158,0.0003046559,0.0015009107,0.0013194792,0.005613099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004604325,0.000039519175,0.000012863302,0.00014966902,0.0020539544,0.000778683,0.00011417331,0.008515527,0.0000013853238,0.95634687,0.0096304035,0.022352362],"study_design_scores_gemma":[0.00048081728,0.0002454828,0.00017277917,0.00043860762,0.002776212,0.00024180523,0.0000074978257,0.6614921,0.000019348441,0.04619979,0.28649208,0.0014334496],"about_ca_topic_score_codex":0.00049216824,"about_ca_topic_score_gemma":0.0000065609916,"teacher_disagreement_score":0.9101471,"about_ca_system_score_codex":0.00016301037,"about_ca_system_score_gemma":0.0008109338,"threshold_uncertainty_score":0.99940693},"labels":[],"label_agreement":null},{"id":"W4415457220","doi":"10.1108/ijicc-05-2025-0307","title":"Application of an improved ACO integrating BFS and Laplacian smoothing strategy in mobile robot path planning","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Motion planning; Heuristic; Mobile robot; Convergence (economics); Path (computing); Smoothing; Premature convergence; Ant colony optimization algorithms","score_opus":0.011047827954380439,"score_gpt":0.3050556662817677,"score_spread":0.2940078383273873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415457220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2883344,0.00061401207,0.7104301,0.00007661434,0.000266217,0.00007541495,0.0000010281909,0.000014348346,0.00018789158],"genre_scores_gemma":[0.8846267,0.000054301872,0.11515391,0.00006937775,0.000072571274,0.0000012427738,0.0000021434903,0.0000058120345,0.000013977099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984658,0.000084850166,0.0007783778,0.00023022016,0.00027893257,0.0001618322],"domain_scores_gemma":[0.99860185,0.00028833243,0.0005333847,0.00014228196,0.00036353178,0.00007060629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081039785,0.00014276252,0.000252957,0.00036508928,0.00004773249,0.00018290676,0.0006630181,0.0000754525,4.1481923e-7],"category_scores_gemma":[0.00012540654,0.00013367205,0.0000414864,0.00019661926,0.0000557329,0.00023050605,0.00021378516,0.00035233985,2.1495016e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031593132,0.00014869992,0.019208599,0.00003165336,0.00008387493,0.000048212853,0.0044023986,0.22739267,0.0036555897,0.005522237,0.000011013012,0.73946345],"study_design_scores_gemma":[0.00033085473,0.00023766866,0.009245823,0.0006018877,0.000009165103,0.00010842427,0.0009798349,0.9834732,0.0026480625,0.002184822,0.000066034925,0.00011424551],"about_ca_topic_score_codex":0.00008449955,"about_ca_topic_score_gemma":0.00000351268,"teacher_disagreement_score":0.7560805,"about_ca_system_score_codex":0.00006850003,"about_ca_system_score_gemma":0.00009019282,"threshold_uncertainty_score":0.5450988},"labels":[],"label_agreement":null},{"id":"W4415660325","doi":"10.1007/s42154-024-00346-4","title":"Trajectory Planning for Autonomous Vehicles in Narrow Parking Lot with Improved Hybrid A*","year":2025,"lang":"en","type":"article","venue":"Automotive Innovation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Anhui Provincial Department of Education; National Natural Science Foundation of China","keywords":"Waypoint; Trajectory; Heading (navigation); Motion planning; Constraint (computer-aided design); Sequence (biology); Path (computing); Point (geometry); Smoothness","score_opus":0.020116892195269288,"score_gpt":0.2789236465314052,"score_spread":0.2588067543361359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415660325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21090624,0.0000517035,0.78694665,0.00059904484,0.00033986743,0.0004162407,0.000003493081,0.00027569727,0.00046108547],"genre_scores_gemma":[0.8280372,1.6807208e-7,0.17116196,0.0004792188,0.000030408852,0.00010021213,0.000019912297,0.000011860733,0.00015906851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985621,0.000051437688,0.0004004124,0.0004884311,0.00015004029,0.00034755917],"domain_scores_gemma":[0.9989075,0.00023953967,0.0002022579,0.00029638864,0.00033455275,0.00001976247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006476387,0.00018141145,0.0002169631,0.00067795196,0.00013728777,0.00011736397,0.00041966006,0.00006221831,5.7912916e-7],"category_scores_gemma":[0.00020739327,0.0001760325,0.000023869867,0.0017796564,0.00004521994,0.00048502992,0.00006686783,0.00021116833,0.0000019604136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003859729,0.00082112657,0.10262749,0.00045434586,0.00037598205,0.00032305656,0.016533036,0.20551328,0.054312374,0.09849815,0.004430853,0.5157243],"study_design_scores_gemma":[0.0012858306,0.00022570978,0.10540442,0.00027784426,0.000007511033,0.0000113729575,0.0001146294,0.86372733,0.025904082,0.0024377734,0.0003253343,0.000278164],"about_ca_topic_score_codex":0.000034152683,"about_ca_topic_score_gemma":0.0000026123378,"teacher_disagreement_score":0.65821403,"about_ca_system_score_codex":0.00021468328,"about_ca_system_score_gemma":0.00030694818,"threshold_uncertainty_score":0.71783966},"labels":[],"label_agreement":null},{"id":"W4415666448","doi":"10.1016/j.engappai.2025.112795","title":"Investigation of stochastic deep learning path planning methods for mobile robots","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University; University of Guelph","funders":"National Research Council Canada","keywords":"Randomness; Dropout (neural networks); Motion planning; Path (computing); Mobile robot; Artificial neural network; Deep learning; Planner","score_opus":0.033703135695211564,"score_gpt":0.343660735234275,"score_spread":0.30995759953906343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415666448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079082535,0.000436385,0.9978766,0.000035290777,0.00015743964,0.0005307877,0.0000017066973,0.00014861849,0.0000223531],"genre_scores_gemma":[0.25798818,0.0000019280917,0.7415429,0.0000050895933,0.000021450356,0.00041305847,0.0000050689528,0.000007726333,0.000014628215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988512,0.000035925088,0.0005185594,0.0002931787,0.000111561276,0.00018962249],"domain_scores_gemma":[0.99820733,0.00095272565,0.0001877053,0.00039677648,0.00020716096,0.00004828275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006446904,0.00012456646,0.00021950461,0.0002943954,0.000089289286,0.000029666428,0.00060447404,0.0000692389,9.1722336e-7],"category_scores_gemma":[0.00037090512,0.00014375077,0.00006191974,0.00092981075,0.000055391323,0.00012178893,0.00009503185,0.00014870898,0.0000024427775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022692482,0.000011582342,0.000016709553,0.00006130152,0.000013333423,6.037096e-8,0.0005565809,0.8023265,0.013318889,0.046585966,0.0000024909498,0.1371043],"study_design_scores_gemma":[0.000014765211,0.00005856876,0.000067059234,0.00010284376,0.000011999896,7.874167e-7,0.000103834944,0.9072373,0.08255276,0.009689668,0.000060776652,0.000099671524],"about_ca_topic_score_codex":0.000009995554,"about_ca_topic_score_gemma":8.2894466e-8,"teacher_disagreement_score":0.25719735,"about_ca_system_score_codex":0.00003333543,"about_ca_system_score_gemma":0.000058705427,"threshold_uncertainty_score":0.5861987},"labels":[],"label_agreement":null},{"id":"W4415937960","doi":"10.36227/techrxiv.176238017.78871027/v1","title":"Safe Urban Delivery by a Mobile Robot via CLF-CBF-QP Control Under Dynamic Obstacles","year":2025,"lang":"","type":"preprint","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Obstacle avoidance; Controller (irrigation); Kinematics; Mobile robot; Task (project management); Control (management); Obstacle; Function (biology); Robot; Jerk","score_opus":0.008868418700471314,"score_gpt":0.24973792883077134,"score_spread":0.24086951013030003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415937960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008559408,0.008772622,0.9720233,0.0012889676,0.0076652486,0.003282321,0.0004897067,0.0009876386,0.0046342374],"genre_scores_gemma":[0.45265284,0.0012010011,0.47457454,0.0051146755,0.00043878477,0.0011802099,0.00045443868,0.00018655148,0.06419698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98675245,0.0011670544,0.002710791,0.0048859515,0.0018982932,0.00258547],"domain_scores_gemma":[0.9899809,0.0020336502,0.001260162,0.0049081645,0.0008746112,0.0009425085],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0015193614,0.0022728296,0.0026917676,0.00082406803,0.0008682831,0.0014671413,0.0072545344,0.0017885,0.0003829578],"category_scores_gemma":[0.00012817967,0.0023678914,0.0010705133,0.0013140964,0.0006124472,0.0008251366,0.004841196,0.0030284913,0.0009711706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010299085,0.0009842659,0.0005578645,0.00042702316,0.0013444049,0.00024908606,0.0013705434,0.87274396,0.0012853992,0.0010927673,0.012622226,0.10721947],"study_design_scores_gemma":[0.0025725013,0.00039511579,0.0012234636,0.00089174946,0.00037503007,0.00007976009,0.00021753888,0.9867268,0.0002705939,0.002607143,0.002292766,0.0023475282],"about_ca_topic_score_codex":0.0018870508,"about_ca_topic_score_gemma":0.000048469858,"teacher_disagreement_score":0.4974488,"about_ca_system_score_codex":0.0015399641,"about_ca_system_score_gemma":0.0021750308,"threshold_uncertainty_score":0.9998067},"labels":[],"label_agreement":null},{"id":"W4416211508","doi":"10.1007/978-3-032-07872-8_8","title":"A Codebook-Based DRL Approach for UAV Deployment and Trajectory Design","year":2025,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Software deployment; Trajectory; Reduction (mathematics); Transfer (computing); Collision avoidance","score_opus":0.03159841408839415,"score_gpt":0.23235680472132228,"score_spread":0.20075839063292814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416211508","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.847467e-7,0.0021872204,0.97091204,0.00007703036,0.00061938434,0.0013933603,0.000015536805,0.00039851188,0.02439646],"genre_scores_gemma":[0.00082378264,0.00011988358,0.9052852,0.0007626874,0.00044897973,0.0003980154,0.0000930072,0.00011418012,0.09195425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973694,0.000074097814,0.0004738341,0.001143145,0.00035891298,0.0005805983],"domain_scores_gemma":[0.9977863,0.0007181427,0.00028274307,0.00090434396,0.000112370784,0.00019613515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005533395,0.0006102027,0.0007442086,0.00022521598,0.0002286984,0.0001996495,0.0010243121,0.00064595125,0.0000028988334],"category_scores_gemma":[0.000011852405,0.00061443745,0.00019415526,0.00007042045,0.00013262412,0.00008706687,0.00021599067,0.00057187484,0.0000029849068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010118646,0.0000899854,0.00001586777,0.00037751242,0.00033345152,0.00008425208,0.0001827859,0.66820663,0.0000051531338,0.082137436,0.029235197,0.21923056],"study_design_scores_gemma":[0.0007800995,0.00014680094,0.000008206699,0.00047484747,0.00008660732,0.000011155487,0.0000017966062,0.9915143,0.000013671507,0.0010161706,0.005341562,0.0006047547],"about_ca_topic_score_codex":0.0000065610234,"about_ca_topic_score_gemma":0.0000010963334,"teacher_disagreement_score":0.32330772,"about_ca_system_score_codex":0.00013745633,"about_ca_system_score_gemma":0.0003419406,"threshold_uncertainty_score":0.9996307},"labels":[],"label_agreement":null},{"id":"W4416249446","doi":"10.1109/ijcnn64981.2025.11227361","title":"Collaborative Management for Spatial Safety for Unmanned Intelligent Vehicles in Human-Machine Coexistence Environment","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Occupancy; Space (punctuation); Constraint (computer-aided design); Unexpected events; Motion (physics); Object (grammar)","score_opus":0.01893839103832021,"score_gpt":0.2944002265426592,"score_spread":0.27546183550433895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416249446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032771192,0.00080861803,0.98394495,0.0031094614,0.0013710038,0.0065014325,0.00019537007,0.000087103974,0.0036543491],"genre_scores_gemma":[0.25590256,0.0005266075,0.7257645,0.001020181,0.0001279863,0.0016010159,0.00011956724,0.000046837344,0.014890765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99511147,0.00022873827,0.0014713892,0.0016465472,0.00050642027,0.0010354369],"domain_scores_gemma":[0.9974333,0.0008197128,0.00035958583,0.0010456012,0.00017068075,0.00017113934],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015970924,0.0006273858,0.0007991779,0.000503506,0.00065435946,0.0002891672,0.0016430691,0.00020883506,0.000038292557],"category_scores_gemma":[0.000091810136,0.00064231927,0.00021730651,0.00081934565,0.00024902733,0.00028008455,0.00095738407,0.00025482255,0.000026113936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016391642,0.002333499,0.0014203006,0.0014714383,0.00086596666,0.00009291011,0.006112083,0.28826115,0.00033576274,0.28604117,0.003113352,0.4083132],"study_design_scores_gemma":[0.004036736,0.0011171357,0.0038749403,0.00064122543,0.00012058407,0.0000012880554,0.0010126304,0.9647794,0.0035346209,0.0065926923,0.013565728,0.0007230134],"about_ca_topic_score_codex":0.00017977048,"about_ca_topic_score_gemma":0.00010324856,"teacher_disagreement_score":0.67651826,"about_ca_system_score_codex":0.0008687948,"about_ca_system_score_gemma":0.0001899877,"threshold_uncertainty_score":0.9996028},"labels":[],"label_agreement":null},{"id":"W4416272762","doi":"10.18280/jesa.580916","title":"Efficient Trajectory Generation of Mobile Robot Based on Q-learning Algorithm","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trajectory; Mobile robot; Robot; Tracking (education); Algorithm design","score_opus":0.023702473357970877,"score_gpt":0.2704277244282495,"score_spread":0.24672525107027865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416272762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030582681,0.002510613,0.95842564,0.00013199597,0.005552502,0.0006338293,0.000018899369,0.0002835255,0.001860288],"genre_scores_gemma":[0.54330087,0.000079555146,0.45395318,0.00020626676,0.0007777532,0.000029229595,0.0000076942515,0.00007423507,0.0015712171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99136394,0.0024889018,0.0023359195,0.0010051387,0.0017497267,0.0010563835],"domain_scores_gemma":[0.9949523,0.00070561917,0.0018348456,0.0010081686,0.0011024666,0.0003965955],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0033867469,0.0007560102,0.001155274,0.0015301394,0.0013699871,0.0009534966,0.0016280774,0.00031831957,0.00013669254],"category_scores_gemma":[0.00076377485,0.0007288228,0.0005333957,0.0023221553,0.00045360855,0.00028983975,0.00029890693,0.001384895,0.00013659541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013332642,0.00033349317,0.0001642474,0.00017491925,0.00011145588,0.00016341225,0.0009778951,0.5923754,0.0016892133,0.00007776947,0.00075599994,0.40316284],"study_design_scores_gemma":[0.0013155849,0.002020947,0.032753695,0.0032332626,0.00015127324,0.00029661294,0.00012478049,0.95702404,0.002266782,0.00007586323,0.00019746792,0.0005396922],"about_ca_topic_score_codex":0.00002300957,"about_ca_topic_score_gemma":7.8394106e-7,"teacher_disagreement_score":0.5127182,"about_ca_system_score_codex":0.000870439,"about_ca_system_score_gemma":0.00152201,"threshold_uncertainty_score":0.9999301},"labels":[],"label_agreement":null},{"id":"W4416308516","doi":"10.1007/s13177-025-00565-x","title":"Intelligent Local Path Planning for Autonomous Driving: Integrating Quintic Splines with Predictive Control","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy","funders":"","keywords":"Model predictive control; Motion planning; Obstacle avoidance; Obstacle; Quintic function; Trajectory; Path (computing); Sensor fusion","score_opus":0.04652107124168261,"score_gpt":0.3741213579875442,"score_spread":0.3276002867458616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416308516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008809103,0.0006666858,0.9860952,0.0008738972,0.002511754,0.0007449993,0.000028787112,0.0000618719,0.00020771576],"genre_scores_gemma":[0.96819097,0.000035909983,0.030967727,0.00005910894,0.00032245886,0.00009754883,0.000021159025,0.000021423979,0.0002836752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99547076,0.00027746687,0.0015253195,0.00044314362,0.0018137969,0.0004694858],"domain_scores_gemma":[0.9914292,0.0016257708,0.0006220842,0.00028410816,0.005856831,0.00018202099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003178066,0.0002581908,0.00048373087,0.00133239,0.00018725668,0.0004858985,0.0018639524,0.00013000207,0.00000496714],"category_scores_gemma":[0.00043484615,0.00020154512,0.00020021705,0.0006314797,0.00017233464,0.0006216688,0.000032939293,0.0007148009,0.000006566683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009810659,0.00040078873,0.022780305,0.00020287157,0.0016049382,0.00061481964,0.008252895,0.890481,0.0006208883,0.043387722,0.0015083804,0.029164331],"study_design_scores_gemma":[0.001725992,0.0013379758,0.0049344315,0.0042974013,0.00006301653,0.00018281388,0.007593791,0.97005284,0.0038080423,0.0013106803,0.004383481,0.0003095573],"about_ca_topic_score_codex":0.000100697274,"about_ca_topic_score_gemma":0.000010602437,"teacher_disagreement_score":0.9593819,"about_ca_system_score_codex":0.0005976097,"about_ca_system_score_gemma":0.0009081418,"threshold_uncertainty_score":0.8218771},"labels":[],"label_agreement":null},{"id":"W4416371854","doi":"10.2316/j.2026.206-1262","title":"RLH-MAPPING: REAL-TIME DENSE MAPPING FOR ROBOTS USING LOW-LIGHT FIELD AND HYBRID REPRESENTATIONS. 162-170","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Robot; Mobile robot; Noise (video); Tracking (education)","score_opus":0.01864981537205624,"score_gpt":0.29735832350108704,"score_spread":0.2787085081290308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034061756,0.00012031325,0.9582598,0.0061486135,0.0010403792,0.00012452452,0.000002518203,0.000035321777,0.00020675173],"genre_scores_gemma":[0.16965978,0.000107455955,0.8293784,0.00033890182,0.00022501689,0.000003153691,0.000006694955,0.000008892535,0.0002716849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987719,0.000047714115,0.00053434307,0.00019705977,0.0003101645,0.00013880285],"domain_scores_gemma":[0.99841183,0.00040272964,0.0004455717,0.00013453401,0.000542669,0.00006263859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045680528,0.000116331525,0.0001971191,0.00044926978,0.00013057911,0.0003473316,0.000356536,0.000052675437,0.0000019659235],"category_scores_gemma":[0.00034468295,0.00011365358,0.0000647435,0.00015530326,0.000023455512,0.0005798909,0.00013229257,0.000109737506,0.0000010659687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000745128,0.0002366401,0.0077484264,0.00021164688,0.0008358446,0.00028680664,0.0028705269,0.79281175,0.03955959,0.043780386,0.0074185664,0.10416531],"study_design_scores_gemma":[0.00062566245,0.0000387625,0.0052566836,0.00044923832,0.000022051587,0.0002995438,0.000043044987,0.9860361,0.0014059923,0.0055272607,0.00018285526,0.00011279616],"about_ca_topic_score_codex":0.000013713774,"about_ca_topic_score_gemma":2.8717736e-7,"teacher_disagreement_score":0.19322436,"about_ca_system_score_codex":0.00007879674,"about_ca_system_score_gemma":0.00012274389,"threshold_uncertainty_score":0.46346587},"labels":[],"label_agreement":null},{"id":"W4416477724","doi":"10.1145/3680207.3765590","title":"Demo: Task Cooperation for Urban Unmanned Sanitation Vehicles","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sanitation; Software deployment; Task (project management); Markov decision process; Markov process; Path (computing); Public transport","score_opus":0.017873658215029627,"score_gpt":0.2833359968813166,"score_spread":0.265462338666287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416477724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001916255,0.0006273039,0.98436785,0.005114196,0.0026727538,0.0010385908,0.000015390602,0.00023831373,0.004009323],"genre_scores_gemma":[0.4655421,0.000025097657,0.5111143,0.0022498907,0.00029032945,0.00012460802,0.000062013474,0.000022220269,0.02056946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975347,0.00017334662,0.0006444238,0.0008211706,0.00028538454,0.00054095685],"domain_scores_gemma":[0.9979117,0.0005915311,0.00016881195,0.000632805,0.0005808593,0.00011429767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007586569,0.00030409978,0.0003220369,0.0002880177,0.0006390292,0.00069956895,0.00079145597,0.00021585423,0.000013516027],"category_scores_gemma":[0.000483682,0.00031494358,0.000115457166,0.0010202808,0.000085960324,0.0007867257,0.00017317185,0.00017606554,0.00011542811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013308412,0.00047402666,0.0015541328,0.00038128003,0.00028848252,0.000015698433,0.005120906,0.054071393,0.008337248,0.33913347,0.19855131,0.39193898],"study_design_scores_gemma":[0.0011293216,0.00028785737,0.0021375457,0.00018947909,0.00006199685,0.0000030681747,0.00010476956,0.9835324,0.0035405129,0.0030844468,0.00559968,0.00032888004],"about_ca_topic_score_codex":0.00003888202,"about_ca_topic_score_gemma":0.0000063366483,"teacher_disagreement_score":0.92946106,"about_ca_system_score_codex":0.00014949888,"about_ca_system_score_gemma":0.0004907519,"threshold_uncertainty_score":0.99993026},"labels":[],"label_agreement":null},{"id":"W4416642639","doi":"10.1016/j.engappai.2025.113308","title":"A hybrid deep reinforcement learning approach for target allocation and routing of multiple nonholonomic vehicles","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration; Korea University; Korea Research Institute for Defense Technology Planning and Advancement","keywords":"Routing (electronic design automation); Travelling salesman problem; Scalability; Reinforcement learning; Constraint (computer-aided design); 2-opt; Vehicle routing problem; Artificial neural network","score_opus":0.01768247485987009,"score_gpt":0.25336647309916693,"score_spread":0.23568399823929684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416642639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032567368,0.00011418908,0.9959724,0.00004654849,0.00004240845,0.00043301476,0.0000012369037,0.00007655558,0.0000569143],"genre_scores_gemma":[0.5502282,0.0000038296444,0.44961122,0.0000029002103,0.000010664313,0.00012355234,0.000006771132,0.0000036929398,0.0000091764105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991055,0.000010209183,0.0004074061,0.00024707735,0.00008209004,0.00014771239],"domain_scores_gemma":[0.999189,0.00025594022,0.00013892699,0.00027737187,0.00010948751,0.000029266614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034497477,0.00009426978,0.0001530871,0.000165542,0.00008243223,0.000027630256,0.00036336758,0.000032912598,2.8256943e-7],"category_scores_gemma":[0.00019335547,0.00010988513,0.000035558933,0.00026889192,0.000038603084,0.00010024091,0.00009645783,0.00008795835,9.307434e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029974346,0.000025098218,0.00008240234,0.00006890428,0.0000134443335,3.6109626e-8,0.00023898674,0.89791226,0.0069775227,0.055307236,0.0000017158975,0.0393694],"study_design_scores_gemma":[0.000022875245,0.000024952957,0.000091113674,0.000027346954,0.000006321472,7.790255e-7,0.000052773874,0.8952878,0.10333158,0.0010407373,0.000038663682,0.0000750389],"about_ca_topic_score_codex":0.000022514627,"about_ca_topic_score_gemma":1.401147e-7,"teacher_disagreement_score":0.54697144,"about_ca_system_score_codex":0.000028697748,"about_ca_system_score_gemma":0.000032312386,"threshold_uncertainty_score":0.44809854},"labels":[],"label_agreement":null},{"id":"W4416706990","doi":"10.1109/tase.2025.3637392","title":"An Adaptive Coordination Exploration Approach for Multi-UAV Based on Entropy-Guided Local Planning","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China","keywords":"Task (project management); Complementarity (molecular biology); Motion planning; Workload; Robustness (evolution); Selection (genetic algorithm); Prioritization; Path (computing)","score_opus":0.059735399950324425,"score_gpt":0.30720940694735566,"score_spread":0.24747400699703123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416706990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010290807,0.00002009409,0.99552625,0.00034246952,0.0015901343,0.0009395649,0.000018100023,0.0004751169,0.00005919528],"genre_scores_gemma":[0.6419094,0.0000020722398,0.35769507,0.00012207663,0.000026383534,0.00018347306,0.0000057313255,0.000013844224,0.000041956322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688345,0.00009458663,0.0005672626,0.0010466516,0.0008365976,0.00057144574],"domain_scores_gemma":[0.99830157,0.00024557934,0.00016031467,0.00052807905,0.00053683156,0.0002276468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019250286,0.0003834519,0.0003082246,0.0016369532,0.0011010896,0.0007448208,0.00059493695,0.00017778816,0.0000017748196],"category_scores_gemma":[0.0000860375,0.00042124916,0.000078958015,0.002414164,0.00022864205,0.0029565853,0.0000045417933,0.00033522735,0.0000044996464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002619939,0.00023007227,0.0000024127141,0.00006788877,0.000013940965,0.0000012710226,0.0010542482,0.9266747,0.0038035149,0.0007662471,0.00002176424,0.06733772],"study_design_scores_gemma":[0.0012208311,0.00053373526,0.00045284317,0.00033655332,0.000038686325,0.000004305281,0.0003643759,0.9800565,0.016580261,0.000018897414,0.00001803862,0.0003749214],"about_ca_topic_score_codex":0.000016604004,"about_ca_topic_score_gemma":4.4960797e-7,"teacher_disagreement_score":0.64088035,"about_ca_system_score_codex":0.0005587944,"about_ca_system_score_gemma":0.0004945001,"threshold_uncertainty_score":0.9998239},"labels":[],"label_agreement":null},{"id":"W4416749187","doi":"10.1109/iros60139.2025.11246429","title":"Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Motion planning; Regular polygon; Convex set; Convex optimization; Proper convex function; Convex analysis; Subderivative; Convex combination","score_opus":0.05507588190981718,"score_gpt":0.33116338657913147,"score_spread":0.2760875046693143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018844121,0.00072305644,0.9902585,0.001085495,0.0025594407,0.0011414742,0.00003429738,0.00023108559,0.002082235],"genre_scores_gemma":[0.12883861,0.0000124674625,0.8623835,0.0003045606,0.00004292409,0.000044138735,0.000020281199,0.000026322865,0.008327236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638975,0.00019159919,0.0010078604,0.0010944776,0.0004593958,0.00085693714],"domain_scores_gemma":[0.9971466,0.0006437719,0.0004898801,0.0010371886,0.000487429,0.00019513165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011138859,0.0004885978,0.0008583203,0.00070579705,0.00032359644,0.00019999614,0.0012979995,0.00034826648,0.000042884792],"category_scores_gemma":[0.00037790943,0.0005053607,0.0003317052,0.001330526,0.00023891639,0.0005103062,0.00045133557,0.0003046735,0.00006773743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003721577,0.0029958289,0.027531972,0.003295389,0.002082821,0.00016208245,0.015195503,0.5142894,0.028985003,0.11123862,0.03479745,0.2590538],"study_design_scores_gemma":[0.0021152464,0.00027252838,0.0107518425,0.0007914081,0.00009648842,0.000010653143,0.00011420708,0.97411567,0.00950606,0.0016401645,0.00015405065,0.0004316661],"about_ca_topic_score_codex":0.00007736259,"about_ca_topic_score_gemma":7.020389e-7,"teacher_disagreement_score":0.4598263,"about_ca_system_score_codex":0.00009074604,"about_ca_system_score_gemma":0.0003400075,"threshold_uncertainty_score":0.9997398},"labels":[],"label_agreement":null},{"id":"W4416749934","doi":"10.1109/iros60139.2025.11245826","title":"Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Roundabout; Deadlock; Deadlock prevention algorithms; Robot; Control (management); Controller (irrigation); Decentralised system; Trajectory","score_opus":0.02460993014620093,"score_gpt":0.29132934375384095,"score_spread":0.26671941360764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013411035,0.004267843,0.98641354,0.0006527586,0.0029264758,0.0021991972,0.000017630464,0.00021158601,0.0019698688],"genre_scores_gemma":[0.6377701,0.000099164135,0.34666163,0.00015617453,0.00005125772,0.00007654948,0.0000075749053,0.0000287814,0.015148764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939115,0.0008917412,0.0017266886,0.0013044581,0.00102185,0.001143777],"domain_scores_gemma":[0.9959262,0.00047748562,0.00094468397,0.0015747069,0.00075637066,0.00032054007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013351056,0.0006932029,0.0014103438,0.0004523539,0.00030671124,0.00048718907,0.0017360711,0.00035584878,0.000049546514],"category_scores_gemma":[0.00017159793,0.00058903906,0.00028377614,0.0015763205,0.0002928642,0.00072116696,0.00028176428,0.00042341914,0.00009215686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012269758,0.004176925,0.02739056,0.0024770591,0.004351801,0.0002615514,0.002586869,0.8244687,0.0036494592,0.056360736,0.0025560488,0.070493326],"study_design_scores_gemma":[0.010040778,0.00077974197,0.006528384,0.0022942203,0.00041804946,0.00006501853,0.0001959652,0.9777795,0.00053820317,0.000096716554,0.0006735732,0.0005898679],"about_ca_topic_score_codex":0.00095701875,"about_ca_topic_score_gemma":0.000041071882,"teacher_disagreement_score":0.6397519,"about_ca_system_score_codex":0.00030216866,"about_ca_system_score_gemma":0.0011489354,"threshold_uncertainty_score":0.9996561},"labels":[],"label_agreement":null},{"id":"W4416750224","doi":"10.1109/iros60139.2025.11246634","title":"FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Robot; Prior probability; Matching (statistics); Task (project management); Reliability (semiconductor); Action (physics)","score_opus":0.016794462004543,"score_gpt":0.28290268848059397,"score_spread":0.266108226476051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416750224","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06337113,0.00052680646,0.9286417,0.0012403976,0.0032801896,0.000941332,0.0000067883143,0.00022770822,0.0017639825],"genre_scores_gemma":[0.41848877,0.000007716656,0.58011705,0.0003268853,0.000060566384,0.0000352569,0.00001051418,0.000015519763,0.00093770697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701816,0.00012694584,0.0006929367,0.0010389059,0.00039992388,0.000723153],"domain_scores_gemma":[0.9980823,0.0007173398,0.00018933836,0.0006262518,0.0001980407,0.00018673582],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012804675,0.0003831038,0.00045214975,0.0002667948,0.0007917827,0.00078298326,0.0007059344,0.00019420765,0.000004558167],"category_scores_gemma":[0.000154544,0.00038549918,0.000116523355,0.0007449284,0.00011463197,0.00030703816,0.0005661459,0.00032492023,0.000018075421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003434065,0.00019837456,0.000875007,0.0005197331,0.0001272269,0.000024940802,0.010231475,0.53173125,0.00053001515,0.03923345,0.00047459133,0.4160196],"study_design_scores_gemma":[0.0012953747,0.00014541548,0.0024744642,0.0010964734,0.00005908005,0.000024601757,0.00039280002,0.9897586,0.0007867406,0.0034312622,0.00015263172,0.00038255117],"about_ca_topic_score_codex":0.00006177961,"about_ca_topic_score_gemma":0.0000024043477,"teacher_disagreement_score":0.45802736,"about_ca_system_score_codex":0.00013605015,"about_ca_system_score_gemma":0.00029864293,"threshold_uncertainty_score":0.9998597},"labels":[],"label_agreement":null},{"id":"W50071543","doi":"10.1007/978-3-642-27552-4_82","title":"Bayesian Network Based Cooperative Area Coverage Searching for UAVs","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Bayesian network; Computer science; Bayesian probability; Artificial intelligence","score_opus":0.028763477113107862,"score_gpt":0.28653297812762585,"score_spread":0.257769501014518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W50071543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008747807,0.012996672,0.96628565,0.00007411385,0.0011779414,0.00056633627,0.000009168462,0.00013493035,0.018746436],"genre_scores_gemma":[0.049530577,0.0023861043,0.9338656,0.0008812683,0.0019470108,0.000039963008,0.00011994894,0.0001566408,0.011072888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974946,0.000063999316,0.0006038028,0.0007698696,0.00032524558,0.0007425327],"domain_scores_gemma":[0.9968312,0.0021313152,0.00032354542,0.0004118554,0.00013144674,0.00017067898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075271755,0.00047940193,0.0006190909,0.00019610328,0.00034939122,0.00020177786,0.00068487023,0.00021057461,0.000011098843],"category_scores_gemma":[0.00012090913,0.00047509727,0.0001233158,0.00010441781,0.00011189997,0.00043308674,0.00037188432,0.0006460406,0.000010024827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016447735,0.000018922143,0.0004903986,0.000112506656,0.00003083778,0.00003141035,0.0008631725,0.43143907,9.2921726e-7,0.06998956,0.00009785447,0.49690887],"study_design_scores_gemma":[0.00024476432,0.00010621656,0.000017282346,0.0011896996,0.00001386353,0.000014093345,0.000013700957,0.9329233,0.000022851986,0.023025796,0.0418536,0.00057484047],"about_ca_topic_score_codex":0.0000042707616,"about_ca_topic_score_gemma":0.0000061192595,"teacher_disagreement_score":0.5014842,"about_ca_system_score_codex":0.00011996735,"about_ca_system_score_gemma":0.00010257531,"threshold_uncertainty_score":0.99977005},"labels":[],"label_agreement":null},{"id":"W57898296","doi":"","title":"The Triangle Intersection Problem for Kite Systems.","year":2005,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kite; Mathematics; Intersection (aeronautics); Geometry; Aerospace engineering; Engineering","score_opus":0.015447424318495684,"score_gpt":0.2481110705169714,"score_spread":0.2326636461984757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W57898296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025435062,0.0019960597,0.9174635,0.0087089185,0.03317845,0.0031182664,0.00000823755,0.0014113843,0.008680122],"genre_scores_gemma":[0.93125004,0.000011569422,0.06548152,0.00013906589,0.000120809535,0.00032609387,0.0000042734573,0.00002544822,0.0026411866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881613,0.00008741302,0.00027563996,0.00028220777,0.00021124334,0.00032735147],"domain_scores_gemma":[0.9988047,0.00036613282,0.00013735282,0.0005022144,0.00011847586,0.00007115977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083023706,0.000122014164,0.00015156475,0.000055638982,0.00034798568,0.0003222169,0.0007938874,0.00006182544,5.123931e-7],"category_scores_gemma":[0.00009962815,0.000091213806,0.00007338062,0.0002423377,0.00002890426,0.0003533828,0.000106521875,0.00011879953,0.00006610384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011737177,0.000056856614,0.00008626483,0.00001585653,0.00003290463,0.0000025596696,0.00058429094,0.0016799278,0.0000816154,0.9520639,0.019318003,0.026066069],"study_design_scores_gemma":[0.0022328012,0.00040023305,0.0003929533,0.000092928865,0.00001754851,0.000054779986,0.00011854064,0.7477433,0.00061466685,0.12491477,0.123056784,0.00036067615],"about_ca_topic_score_codex":0.000019670993,"about_ca_topic_score_gemma":0.000001645256,"teacher_disagreement_score":0.90581495,"about_ca_system_score_codex":0.000136342,"about_ca_system_score_gemma":0.00005684409,"threshold_uncertainty_score":0.3719591},"labels":[],"label_agreement":null},{"id":"W62353344","doi":"10.5220/0002745403160323","title":"ARCHITECTURE FOR HUMAN-ROBOT COLLABORATIVE NAVIGATION","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Architecture; Robot; Human–computer interaction; Human–robot interaction; Mobile robot navigation; Mobile robot; Artificial intelligence; Robot control; Geography","score_opus":0.01468893389225829,"score_gpt":0.29579659532265956,"score_spread":0.2811076614304013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W62353344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066764606,0.000005535341,0.9881523,0.0011809393,0.0006116652,0.000289796,0.0000045909646,0.00023757675,0.0028410985],"genre_scores_gemma":[0.110682674,8.067091e-8,0.8881675,0.0001889123,0.00013722398,0.00004440678,0.000013835954,0.000006698159,0.0007586818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924034,0.00002015227,0.000126339,0.00027393215,0.00014660064,0.00019262428],"domain_scores_gemma":[0.99923915,0.00011993511,0.00005908463,0.00036133503,0.00015400555,0.00006649278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020040186,0.000096828895,0.00009922793,0.000053203363,0.0001743619,0.00012052675,0.00050237303,0.00006968114,0.000007517117],"category_scores_gemma":[0.000054557397,0.00008073859,0.00003121437,0.0002699903,0.000036502606,0.00017384122,0.00006319775,0.0001777711,0.00002512396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008981252,0.0001327256,0.0005043925,0.0000381831,0.000045860266,0.000021815407,0.005013235,0.009389001,0.27908844,0.6048707,0.009160048,0.09172664],"study_design_scores_gemma":[0.0029486455,0.00095070165,0.010759342,0.00008622843,0.000027585214,0.00015828927,0.0002059775,0.5435748,0.15666126,0.24431442,0.03880382,0.0015089145],"about_ca_topic_score_codex":0.000011804428,"about_ca_topic_score_gemma":0.000010694378,"teacher_disagreement_score":0.5341858,"about_ca_system_score_codex":0.0000095087125,"about_ca_system_score_gemma":0.00005734855,"threshold_uncertainty_score":0.3292424},"labels":[],"label_agreement":null},{"id":"W648617424","doi":"","title":"PATH TRACKING, OBSTACLE AVOIDANCE AND DEAD RECKONING BY AN AUTONOMOUS PLANETARY ROVER","year":2014,"lang":"en","type":"article","venue":"International Journal of Heavy Vehicle Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Dead reckoning; Obstacle avoidance; Computer vision; Odometry; Trajectory; Inertial measurement unit; Sensor fusion; Articulated vehicle; Obstacle; Computer science; Artificial intelligence; Collision avoidance; Path (computing); Engineering; Mobile robot; Global Positioning System; Aerospace engineering; Robot; Geography; Collision","score_opus":0.013556724493490286,"score_gpt":0.25101995372803015,"score_spread":0.23746322923453986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W648617424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47984844,0.0017469252,0.51319265,0.0007692172,0.0038807264,0.000102830985,0.000015269372,0.00007791984,0.00036600145],"genre_scores_gemma":[0.97997963,0.000018618035,0.019090971,0.00022023343,0.00056870753,0.0000018359705,0.000005046081,0.00001388811,0.0001010583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979185,0.00023795737,0.0006193681,0.0002660436,0.00071676704,0.0002414032],"domain_scores_gemma":[0.9984565,0.00023328091,0.0005565784,0.0002402161,0.00031646213,0.00019697247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011242816,0.00015557847,0.0002737618,0.0001366801,0.000089126705,0.00046000362,0.0010667777,0.00007669978,0.0000023644889],"category_scores_gemma":[0.000120222314,0.00014112532,0.000052285224,0.00007659348,0.000038305585,0.001159079,0.00008352773,0.0002963276,0.000011741914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042539887,0.0012218923,0.20052479,0.00026293987,0.0011438715,0.0025710203,0.012755246,0.17539082,0.045830067,0.017280111,0.027439682,0.5151542],"study_design_scores_gemma":[0.0009339694,0.00044910426,0.017985126,0.0003804699,0.000009568478,0.0026046888,0.00009191263,0.96712583,0.0009962397,0.00029999082,0.008860358,0.00026275782],"about_ca_topic_score_codex":0.00017162986,"about_ca_topic_score_gemma":0.0000020017067,"teacher_disagreement_score":0.791735,"about_ca_system_score_codex":0.00010992119,"about_ca_system_score_gemma":0.00007472852,"threshold_uncertainty_score":0.5754923},"labels":[],"label_agreement":null},{"id":"W6888821828","doi":"10.23645/epacomptox.22280740","title":"Trophic Niche Overlap Of Lake Superior Nearshore Fishes","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Trophic level; Niche; Ecological niche; Commission; Bycatch","score_opus":0.05074712949703659,"score_gpt":0.3032152719577168,"score_spread":0.2524681424606802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888821828","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010669813,0.0006146724,0.013728786,0.00085274066,0.004105218,0.0017808672,0.00058448105,0.00020578681,0.97706044],"genre_scores_gemma":[0.00012463077,0.000026708987,0.30525866,0.000031072315,0.00019908047,0.000019800911,0.000023629083,0.00034374997,0.69397265],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983776,0.00006730745,0.00027545716,0.0006158032,0.0003593758,0.0003044249],"domain_scores_gemma":[0.9984903,0.00009602918,0.00021472946,0.0010798867,0.00002912061,0.0000899832],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00027508033,0.00024977935,0.00050140027,0.00016814441,0.0000448924,0.00033573352,0.0028628097,0.00025534097,0.0017941378],"category_scores_gemma":[0.00008295483,0.00023134444,0.000088874716,0.00039750015,0.000097558215,0.0001605452,0.00091500615,0.00022118291,0.0017213067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011285235,0.0001515773,0.0027217478,0.00008990374,0.00030888835,0.000810671,0.002281319,0.00022617662,0.000098844226,0.000114238006,0.5386745,0.45451087],"study_design_scores_gemma":[0.000395342,0.00011795291,0.0018876547,0.0006204541,0.000030664443,0.000025591464,0.00003843616,0.005493559,0.00014562866,0.000033202818,0.9907538,0.000457694],"about_ca_topic_score_codex":0.00019483612,"about_ca_topic_score_gemma":0.00044924673,"teacher_disagreement_score":0.45405316,"about_ca_system_score_codex":0.00001970347,"about_ca_system_score_gemma":0.0002487081,"threshold_uncertainty_score":0.9991183},"labels":[],"label_agreement":null},{"id":"W6889009589","doi":"10.25384/sage.21971601","title":"sj-docx-1-trd-10.1177_26330040221150269 – Supplemental material for Novel insights into the phenotype and long-term D-gal treatment in PGM1-CDG: a case series","year":2023,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto; University of Alberta","funders":"","keywords":"Phenotype; Series (stratigraphy); Disease; Clinical phenotype","score_opus":0.06982461927324866,"score_gpt":0.33472340665976485,"score_spread":0.2648987873865162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889009589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8600955,0.005197291,0.11012547,0.00844429,0.0037150271,0.0027169173,0.009189855,0.000436557,0.00007912653],"genre_scores_gemma":[0.7754029,0.0045487876,0.2069339,0.0006313428,0.0028023436,0.00049323565,0.0064268,0.00019797905,0.002562724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978223,0.00012645718,0.00051231834,0.0006557584,0.00034227263,0.000540886],"domain_scores_gemma":[0.99808073,0.00024359599,0.00019755402,0.0012669379,0.000047656307,0.00016355148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008167627,0.00029848085,0.0003386201,0.00022725151,0.0004699872,0.0007546723,0.0014403469,0.00007954559,0.00030922593],"category_scores_gemma":[0.0000804403,0.00020688934,0.000050059658,0.00047912615,0.00009838681,0.0014091695,0.0013768786,0.00016077822,0.0000338293],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008647888,0.0018751835,0.026518218,0.00037627917,0.0012263117,0.054424718,0.0678889,0.0012617594,0.031175008,0.0005309122,0.027830226,0.78602767],"study_design_scores_gemma":[0.037376374,0.00699681,0.19744727,0.0033294416,0.0008288767,0.065743245,0.008393159,0.5936291,0.01368234,0.0065891347,0.060393464,0.0055907676],"about_ca_topic_score_codex":0.0003629513,"about_ca_topic_score_gemma":0.0009316651,"teacher_disagreement_score":0.78043693,"about_ca_system_score_codex":0.00012775164,"about_ca_system_score_gemma":0.00015325403,"threshold_uncertainty_score":0.84367025},"labels":[],"label_agreement":null},{"id":"W6889338293","doi":"10.25549/pcra-c14-181571","title":"Yevanhelʹsʹkyy holos = Evangelical Voice, 1998, no. 3 (1998 July September), whole no. 123","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Slavic languages; Christianity; Period (music); Interpretation (philosophy)","score_opus":0.008568896309947723,"score_gpt":0.17917772330307985,"score_spread":0.17060882699313212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889338293","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006410593,0.000232172,0.0018418991,0.00014915319,0.00048058384,0.00023857248,0.99287695,0.00037862387,0.0037379686],"genre_scores_gemma":[0.000009154009,0.000029201568,0.011711147,0.00030340994,0.00041344762,7.8521316e-7,0.9807467,0.000059518065,0.0067266715],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967386,0.00013753875,0.00046851666,0.0011187141,0.000874717,0.0006618712],"domain_scores_gemma":[0.9971452,0.00026115024,0.00047408757,0.0015250035,0.00013492745,0.0004596403],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000112260714,0.00062452554,0.0008122703,0.00032011885,0.00021305529,0.0005144503,0.0030667875,0.00061551796,0.000016308077],"category_scores_gemma":[0.000097987344,0.00066048035,0.00047769299,0.0005629398,0.0003343572,0.0010906031,0.0023995724,0.00084567367,0.95598733],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039403778,0.00022097386,0.00013313808,0.00020088858,0.00013435326,0.0010899802,0.000053932778,0.00005057414,0.000003176221,1.6824487e-8,0.99761766,0.00045587803],"study_design_scores_gemma":[0.0005396114,0.000101642654,0.000006931094,0.00039366484,0.0000677446,0.00004626779,0.00013520767,0.002667892,0.0000059173035,0.000056685625,0.995285,0.0006934468],"about_ca_topic_score_codex":0.000010025937,"about_ca_topic_score_gemma":0.0000016736528,"teacher_disagreement_score":0.955971,"about_ca_system_score_codex":0.00007549642,"about_ca_system_score_gemma":0.00050428597,"threshold_uncertainty_score":0.9995846},"labels":[],"label_agreement":null},{"id":"W6892412100","doi":"10.5281/zenodo.10166448","title":"Coccus strobus Hartig 1839","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Confusion; Coccus (insect); Mistake; White (mutation); Synonym (taxonomy)","score_opus":0.05494133877836483,"score_gpt":0.2597482587739387,"score_spread":0.20480691999557388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892412100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011410048,0.000089819674,0.8266617,0.004843004,0.00094713055,0.0007165326,0.00015696474,0.01367549,0.14149931],"genre_scores_gemma":[0.8801155,0.00018680007,0.09143452,0.0013308295,0.0011361261,3.147517e-7,0.0034583947,0.0054526906,0.01688479],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99806255,0.0002421291,0.00021402113,0.0004912371,0.00047531247,0.00051475805],"domain_scores_gemma":[0.99862486,0.00004010316,0.000077870245,0.00074509537,0.0003067373,0.00020532626],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008026523,0.0001299464,0.00012891294,0.0003130863,0.0016602929,0.0011473994,0.0022364596,0.000052138388,0.0011219522],"category_scores_gemma":[0.0005093478,0.00013843176,0.0000449036,0.0014695422,0.000089371526,0.00042047774,0.001975389,0.00023351921,0.042764045],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012107459,0.00009343215,0.000012932397,0.000038957933,0.00004398573,0.00021258062,0.0021844262,0.0040638614,0.0035582685,0.018793982,0.7216757,0.24930976],"study_design_scores_gemma":[0.00040393995,0.00017429235,0.0022091214,0.0000274341,0.0000048650304,0.0001370878,0.000105263265,0.09395816,0.00044452827,0.0005367947,0.9017609,0.00023765622],"about_ca_topic_score_codex":0.000006910636,"about_ca_topic_score_gemma":2.7356842e-8,"teacher_disagreement_score":0.8687055,"about_ca_system_score_codex":0.00008529129,"about_ca_system_score_gemma":0.000005250258,"threshold_uncertainty_score":0.9998895},"labels":[],"label_agreement":null},{"id":"W6892604641","doi":"10.5281/zenodo.10982914","title":"Collision Prediction for Robotics Accelerators","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collision; Robotics; Code (set theory); Work (physics); Robot","score_opus":0.043116014702449156,"score_gpt":0.262593782476346,"score_spread":0.21947776777389685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892604641","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011962935,0.00032801836,0.64236516,0.00046256182,0.0011815625,0.0007961525,0.00059993326,0.0040456983,0.35021976],"genre_scores_gemma":[0.00042790495,0.00036508447,0.21461362,0.00029408556,0.002536673,6.764828e-7,0.005900168,0.026954358,0.74890745],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782836,0.00016184524,0.00029327133,0.00080358284,0.00049141806,0.00042151645],"domain_scores_gemma":[0.9985061,0.00002762734,0.0001668343,0.0008221534,0.0002952026,0.00018206598],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005889788,0.0002573321,0.00023943788,0.00057627924,0.00074210233,0.0017554372,0.0020590771,0.00023522315,0.0012140789],"category_scores_gemma":[0.0003186047,0.00026656658,0.00008771555,0.0007367663,0.00007788002,0.00020054358,0.0014301313,0.00035284154,0.01158458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047854423,0.000042516152,1.2590905e-7,0.00016241086,0.000055478045,0.000015450803,0.00024288213,0.0006438633,0.000048082948,0.0061521404,0.97237,0.020262213],"study_design_scores_gemma":[0.00025866626,0.00020947818,0.000005589319,0.00023547585,0.000025767207,0.00006473112,0.000022959422,0.034079712,0.000036714613,0.0003874949,0.96443725,0.00023613877],"about_ca_topic_score_codex":0.000007992122,"about_ca_topic_score_gemma":7.7350286e-8,"teacher_disagreement_score":0.4277515,"about_ca_system_score_codex":0.00018423688,"about_ca_system_score_gemma":0.00000988985,"threshold_uncertainty_score":0.99997866},"labels":[],"label_agreement":null},{"id":"W6892697016","doi":"10.5281/zenodo.12232787","title":"role dun directeur pedagogique pdf","year":2024,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Filter (signal processing); Government (linguistics); Context (archaeology); Work (physics)","score_opus":0.039474742447059605,"score_gpt":0.2604125989987245,"score_spread":0.22093785655166487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892697016","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000393369,0.0020705254,0.086298496,0.0017950896,0.0013021318,0.00064246,0.00021287179,0.002470532,0.90516853],"genre_scores_gemma":[0.0009877732,0.0002884728,0.013018984,0.00025847636,0.0012692577,1.17243964e-7,0.0007802522,0.008331316,0.97506535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950515,0.0010833459,0.0005297292,0.0015134359,0.0008455579,0.00097645726],"domain_scores_gemma":[0.99714005,0.0000644552,0.0002618606,0.0014134138,0.00063735317,0.00048284043],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012720453,0.0005423529,0.0004618831,0.000651872,0.0014375742,0.0041430616,0.0038986239,0.00039101957,0.161445],"category_scores_gemma":[0.0010484622,0.0005959764,0.00017945685,0.0013391139,0.00033532054,0.00040216424,0.0043398584,0.0012383809,0.6362625],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011896107,0.00018139086,0.0000012115737,0.00034718652,0.00017032165,0.00037995286,0.004574918,0.00023480748,0.00068676774,0.026009742,0.71786475,0.24953705],"study_design_scores_gemma":[0.0002621392,0.00028324497,0.000181745,0.00052081404,0.00005183819,0.0007261434,0.0001394932,0.019933138,0.000086897446,0.0009812044,0.97624034,0.00059297564],"about_ca_topic_score_codex":0.00009487338,"about_ca_topic_score_gemma":8.3035434e-7,"teacher_disagreement_score":0.47481745,"about_ca_system_score_codex":0.00037855242,"about_ca_system_score_gemma":0.000027148957,"threshold_uncertainty_score":0.99986243},"labels":[],"label_agreement":null},{"id":"W6901581631","doi":"10.60692/180n0-mg005","title":"Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subgradient method; Robot; Mobile robot; Trajectory; Set (abstract data type); Motion planning; Optimization problem; Motion (physics); Decomposition","score_opus":0.01861076997965622,"score_gpt":0.19580276618575773,"score_spread":0.17719199620610152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901581631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010035959,0.0000013440007,0.98881006,0.000023497207,0.00026621128,0.00022587291,0.000042738753,0.0002876931,0.00030660853],"genre_scores_gemma":[0.8483283,3.7763954e-8,0.15142088,0.000028929144,0.000013508512,0.000014902206,0.00009939137,0.000006739776,0.00008731938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998663,0.0000740092,0.00050974,0.00018585773,0.00036676053,0.00020063313],"domain_scores_gemma":[0.9986145,0.000007954211,0.00046502956,0.0005299492,0.00031747945,0.00006508685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023441622,0.0001632353,0.00023846456,0.00023984772,0.000060983024,0.00016039633,0.00035349367,0.00009198889,0.000008053312],"category_scores_gemma":[0.000009432111,0.00013049995,0.000040605133,0.00044030146,0.00001737305,0.0016588639,0.000079805315,0.000054882243,0.0002717424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018127528,0.000004829586,0.017748507,0.00016322463,0.000025543348,0.00000191601,0.008582783,0.9725811,0.0000107384,0.00023265289,0.000020848784,0.00060971914],"study_design_scores_gemma":[0.00086115533,0.000072353694,0.0130799115,0.00018213868,0.000007988718,0.000040901825,0.00038566501,0.98468804,0.0005143764,2.0596585e-7,0.000010871874,0.0001563963],"about_ca_topic_score_codex":0.0000097039965,"about_ca_topic_score_gemma":1.4716397e-8,"teacher_disagreement_score":0.83829236,"about_ca_system_score_codex":0.000090893576,"about_ca_system_score_gemma":0.000033598964,"threshold_uncertainty_score":0.5321633},"labels":[],"label_agreement":null},{"id":"W6901762660","doi":"10.60692/v3xf2-xzh60","title":"Fuzzy-Based Distributed Behavioral Control With Wall-Following Strategy for Swarm Navigation in Arbitrary-Shaped Environments","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Swarm behaviour; Swarm robotics; Maxima and minima; Control (management); Obstacle avoidance; Robot; Obstacle","score_opus":0.0344379826306917,"score_gpt":0.23817455153287057,"score_spread":0.20373656890217887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901762660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2758292,0.0000023202542,0.72314894,0.000055990524,0.00016936724,0.00048586907,0.00012438979,0.00012956701,0.000054334618],"genre_scores_gemma":[0.97315794,1.06995435e-8,0.026061838,0.00011782796,0.000019214718,0.00020265665,0.00041616205,0.000010364603,0.000013985986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981329,0.000097396914,0.00064632145,0.00029172923,0.000451641,0.00038000938],"domain_scores_gemma":[0.9990267,0.000023566818,0.00030560562,0.00044841357,0.000086628024,0.00010909482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036232427,0.00022938641,0.00030795552,0.00015360667,0.000131238,0.00035724707,0.00032460343,0.00012118756,9.198227e-7],"category_scores_gemma":[0.000009729924,0.00020516283,0.000091901784,0.0003721882,0.000017014267,0.0012981835,0.000032916145,0.00013252522,0.000054174852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036574344,0.00011849537,0.4167959,0.00089975935,0.00026126692,0.0006912592,0.04527459,0.53184485,0.00017582998,0.0014779884,0.000038383096,0.002055916],"study_design_scores_gemma":[0.007603658,0.00021599754,0.052824434,0.00047067384,0.000053053995,0.00005940229,0.0019903362,0.93457365,0.0017133792,0.000009156911,0.000027829361,0.00045841842],"about_ca_topic_score_codex":0.000009772755,"about_ca_topic_score_gemma":2.1876592e-7,"teacher_disagreement_score":0.69732875,"about_ca_system_score_codex":0.00024646393,"about_ca_system_score_gemma":0.00015856203,"threshold_uncertainty_score":0.8366297},"labels":[],"label_agreement":null},{"id":"W6906205500","doi":"10.1594/pangaea.942255","title":"Snow profiles from Hansbreen (Hans Glacier, Svalbard) in 1994","year":2022,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Diafiltration; Liquation; Emperipolesis; Dysgeusia; Durvalumab; Fusible alloy","score_opus":0.07011455768553868,"score_gpt":0.29163079849907997,"score_spread":0.2215162408135413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906205500","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026088167,0.0025092347,0.019091103,0.00059120834,0.0035317144,0.001829237,0.97207475,0.000072785035,0.000039114355],"genre_scores_gemma":[0.000052114563,0.0015775777,0.03993885,0.00013685794,0.00088503805,0.00038984753,0.95586544,0.00005951785,0.001094766],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99293005,0.00039088368,0.00077104423,0.002695896,0.0014593186,0.001752792],"domain_scores_gemma":[0.9954627,0.0007298897,0.00030135078,0.0029078843,0.00003327429,0.00056489],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0056351405,0.00063643727,0.0007433448,0.00068891497,0.0020671561,0.00331513,0.0055562025,0.00043462645,0.00027322752],"category_scores_gemma":[0.0006027564,0.00064401596,0.00014395762,0.0008028307,0.0008591081,0.004145553,0.011765446,0.0014672442,0.0000071208747],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005926496,0.00019859783,0.00059871806,0.00014560064,0.00011054774,0.00004105865,0.00010190531,0.0004650529,0.000015840307,0.00021177,0.97852427,0.019527396],"study_design_scores_gemma":[0.0010278497,0.00017237382,0.0010986681,0.00010658934,0.000074029005,0.000031021915,0.00010461728,0.026997576,0.0000076966435,0.0011473057,0.9685584,0.0006738942],"about_ca_topic_score_codex":0.6007054,"about_ca_topic_score_gemma":0.1449092,"teacher_disagreement_score":0.45579618,"about_ca_system_score_codex":0.00033021992,"about_ca_system_score_gemma":0.00027628313,"threshold_uncertainty_score":0.9998242},"labels":[],"label_agreement":null},{"id":"W6907073358","doi":"10.20944/preprints202503.0300.v1","title":"Deep Reinforcement Learning Based Coverage Path Planning in Unknown Environments","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sherbrooke O.E.M (Canada); Université de Sherbrooke","funders":"","keywords":"Motion planning; Adaptability; Redundancy (engineering); Path (computing); Robot; Smoothing; Reinforcement learning","score_opus":0.06671034790712617,"score_gpt":0.31405842362152847,"score_spread":0.24734807571440232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6907073358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030184017,0.00017664555,0.95553356,0.00019378602,0.0011898574,0.0007920299,0.0000022997647,0.00035684192,0.011570962],"genre_scores_gemma":[0.9712135,0.00006294571,0.023924755,0.00038693784,0.00008298656,0.0002622067,0.00009886204,0.000034723576,0.003933075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949676,0.00045713782,0.00096599275,0.0019068404,0.0008653504,0.0008370868],"domain_scores_gemma":[0.99663836,0.00029815582,0.0005697209,0.0022503962,0.00003973095,0.00020363493],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001487383,0.00063982996,0.000707009,0.0005415533,0.00018885538,0.0001115664,0.0026784902,0.00047976355,0.00010024161],"category_scores_gemma":[0.00040851138,0.00074290007,0.0002085405,0.00037595036,0.00006411703,0.0002568306,0.0060106525,0.002299587,0.00048801186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001203939,0.00007756817,0.23313658,0.000114120056,0.00004303682,0.00017029892,0.0011993408,0.76399267,0.0001209961,0.00016564911,0.000010905441,0.000956799],"study_design_scores_gemma":[0.00073658844,0.000029035005,0.11916215,0.0010998365,0.000019783438,0.0000036096562,0.000019925197,0.87386304,0.0015441987,0.0004203638,0.0024826801,0.00061875954],"about_ca_topic_score_codex":0.0001754885,"about_ca_topic_score_gemma":0.000001250743,"teacher_disagreement_score":0.9410295,"about_ca_system_score_codex":0.0007104084,"about_ca_system_score_gemma":0.00038971903,"threshold_uncertainty_score":0.9995022},"labels":[],"label_agreement":null},{"id":"W6910739996","doi":"10.48550/arxiv.2501.06639","title":"Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada","keywords":"Representation (politics); Process (computing); Path (computing); Probabilistic logic; Encoding (memory); Motion planning; Tree (set theory); Code (set theory); Space (punctuation); Generative model; Feature learning","score_opus":0.05346106400745814,"score_gpt":0.2247518463148889,"score_spread":0.17129078230743075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6910739996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33978954,0.00003866125,0.657918,0.000036768633,0.0005111399,0.0001557211,0.000005340662,0.00015682996,0.001388034],"genre_scores_gemma":[0.9225945,0.000049945585,0.07599072,0.00002507369,0.000053623102,0.0000012060723,0.00006250982,0.0000093320195,0.00121311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979513,0.00023119025,0.00034647243,0.0009704487,0.00020025448,0.00030028398],"domain_scores_gemma":[0.99802554,0.00027060564,0.00063465955,0.00070465705,0.000303273,0.000061292376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034295057,0.00028624007,0.00041598175,0.00025844682,0.00024095653,0.00010359775,0.0008650834,0.00022890167,0.000009991121],"category_scores_gemma":[0.00011744233,0.00034146095,0.00011503416,0.0005958901,0.00009589087,0.00086930516,0.0009764938,0.0006847712,0.000014269225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023879255,0.000029798184,0.0035172934,0.00015923078,0.000060060363,0.000107457236,0.0012263233,0.98193455,0.0008858013,0.011655294,0.00006593385,0.00033440525],"study_design_scores_gemma":[0.0003923626,0.00007267625,0.0053799134,0.0008594743,0.000052509644,0.000005180017,0.00019698573,0.9776107,0.012191422,0.0028885582,0.000022379465,0.00032781973],"about_ca_topic_score_codex":0.00031871535,"about_ca_topic_score_gemma":0.0000010928048,"teacher_disagreement_score":0.582805,"about_ca_system_score_codex":0.00014292022,"about_ca_system_score_gemma":0.00029884672,"threshold_uncertainty_score":0.99990374},"labels":[],"label_agreement":null},{"id":"W6910798914","doi":"10.48550/arxiv.2104.05933","title":"Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Crowds; Mobile robot navigation; Pedestrian; Robot; Mobile robot; Collision avoidance; Navigation system; Motion (physics)","score_opus":0.09859877733485992,"score_gpt":0.19832371879534164,"score_spread":0.09972494146048172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6910798914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07560208,0.000047823094,0.92090636,0.00013593871,0.0008502399,0.0004646631,0.000009937599,0.00047748905,0.0015054957],"genre_scores_gemma":[0.7791524,0.000005279749,0.22004847,0.00016479981,0.00008920176,0.000003846764,0.00015011877,0.000027220614,0.00035868087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964379,0.0003557019,0.00032439982,0.0020451876,0.00025828322,0.00057852326],"domain_scores_gemma":[0.9968252,0.00020818629,0.00031051884,0.0019953935,0.00026616728,0.00039453185],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005421346,0.00048100413,0.00053628546,0.00039104512,0.00021580936,0.00045452846,0.0024961233,0.00042016065,0.0000059470476],"category_scores_gemma":[0.00011157178,0.00060816674,0.00028422507,0.0015325563,0.00006685371,0.00038611697,0.0016927135,0.00082628604,0.000067291905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012230857,0.00019714766,0.0011319075,0.00012050343,0.00005180123,0.0006751779,0.00034674158,0.9896449,0.000091555106,0.007143384,0.000117586,0.000467051],"study_design_scores_gemma":[0.00053235097,0.00006362083,0.0018135364,0.00028586088,0.00005943939,0.000015263595,0.00011595605,0.99444836,0.00016513442,0.0017352197,0.00007102822,0.0006942522],"about_ca_topic_score_codex":0.0004941401,"about_ca_topic_score_gemma":0.000008549464,"teacher_disagreement_score":0.7035503,"about_ca_system_score_codex":0.00041459082,"about_ca_system_score_gemma":0.0005639398,"threshold_uncertainty_score":0.99963695},"labels":[],"label_agreement":null},{"id":"W6917376496","doi":"10.57745/y7zcqf","title":"SOC storage potential subsoil percentile 0.95 upper limit of 90PI.tif","year":2022,"lang":"en","type":"dataset","venue":"Recherche Data Gouv France","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society; International Development Research Centre","funders":"","keywords":"Subsoil; Limit (mathematics); Percentile; Asymptote; Hydrology (agriculture)","score_opus":0.14579312068403105,"score_gpt":0.34681222011995,"score_spread":0.20101909943591895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6917376496","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036964986,0.0035146559,0.068127856,0.0002714658,0.0029549496,0.00039971617,0.9244361,0.00018600153,0.000072278155],"genre_scores_gemma":[0.0000064226915,0.002144775,0.11007387,0.00030940014,0.0003702954,0.000050154464,0.886023,0.00005700381,0.0009650853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926904,0.0016801849,0.0009506376,0.0021748084,0.0015755233,0.00092845206],"domain_scores_gemma":[0.98883134,0.00081330206,0.0008079391,0.009119921,0.00018238762,0.0002450867],"candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0042627645,0.00071442436,0.0010626647,0.00026783196,0.00028928267,0.0002459337,0.015078749,0.0010061115,0.0029318482],"category_scores_gemma":[0.001160478,0.0007703118,0.00023266682,0.0012457048,0.00020442113,0.0010320196,0.0064784293,0.0035632662,0.00035886664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012615101,0.0002501128,0.000012437004,0.0002647001,0.00011688496,0.00025342277,0.00014411032,0.0011070452,0.000045474695,0.000013523432,0.9886068,0.009172877],"study_design_scores_gemma":[0.00044630852,0.0000986603,0.00022750745,0.0001674672,0.000106104584,0.00010094867,0.00003845028,0.047686674,0.000029872346,0.000080862446,0.9502349,0.0007822333],"about_ca_topic_score_codex":0.0010035101,"about_ca_topic_score_gemma":0.000013381854,"teacher_disagreement_score":0.04657963,"about_ca_system_score_codex":0.00037359504,"about_ca_system_score_gemma":0.0009424777,"threshold_uncertainty_score":0.99947476},"labels":[],"label_agreement":null},{"id":"W6926677633","doi":"10.25384/sage.19750491","title":"sj-docx-1-mso-10.1177_20552173221086662 - Supplemental material for Fecal microbiota transplantation is safe and tolerable in patients with multiple sclerosis: A pilot randomized controlled trial","year":2022,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute","funders":"","keywords":"Randomized controlled trial; Fecal bacteriotherapy; Transplantation; Feces; Clinical trial; Gut flora","score_opus":0.036548188306375656,"score_gpt":0.26402944257301747,"score_spread":0.2274812542666418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926677633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62868845,0.0015506173,0.2880185,0.0021766522,0.003882926,0.02194061,0.05351376,0.00016373336,0.00006474032],"genre_scores_gemma":[0.84879655,0.00038882953,0.13936405,0.0008297186,0.00041565715,0.0017377114,0.0081055155,0.00007958632,0.0002823748],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.9964199,0.00077200553,0.0010082036,0.00068616186,0.00060590194,0.0005077925],"domain_scores_gemma":[0.9979341,0.00068697694,0.00050925073,0.0006654713,0.00007499342,0.00012918463],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032351764,0.00029263544,0.0011120326,0.00024749464,0.00045850055,0.0004777604,0.0012515103,0.000048537,0.0018445293],"category_scores_gemma":[0.0001453945,0.00023482721,0.000096177035,0.00027110393,0.00006123826,0.0009474379,0.0004581826,0.00026925848,0.0000038269145],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.9886877,0.0008144503,0.000832584,0.000030826002,0.0002600224,0.000033487322,0.00085910363,0.00039691414,0.001174618,0.0000043846435,0.004536704,0.0023692155],"study_design_scores_gemma":[0.94727373,0.0011889478,0.0014723622,0.00020615445,0.00014818402,0.00003396242,0.00004963694,0.04883337,0.00017930772,0.000038575472,0.00026886,0.0003069107],"about_ca_topic_score_codex":0.00013097767,"about_ca_topic_score_gemma":0.000014229653,"teacher_disagreement_score":0.2201081,"about_ca_system_score_codex":0.00009529185,"about_ca_system_score_gemma":0.00015024148,"threshold_uncertainty_score":0.9990679},"labels":[],"label_agreement":null},{"id":"W6927375872","doi":"10.26008/1912/bco-dmo.840626.1","title":"Physiological observations of Euphausia pacifica sampled in Puget Sound, WA aboard R/V Clifford A. Barnes during cruises CB1073 and CB1078 in 2017.","year":2021,"lang":"en","type":"dataset","venue":"Open Access Server of the Woods Hole Scientific Community (Woods Hole Scientific Community)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Division of Ocean Sciences","keywords":"Nucleofection; Gestational period; TSG101; Diafiltration; Liquation; Dysgeusia; Triacetin; Proteogenomics; Fusible alloy","score_opus":0.2233431844287285,"score_gpt":0.3822491566887705,"score_spread":0.15890597226004202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6927375872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82889175,0.00039767878,0.00014374893,0.0005229086,0.003497974,0.0020715287,0.16417104,0.00008453016,0.00021881197],"genre_scores_gemma":[0.68200654,0.00022309723,0.00984155,0.00022105778,0.00008455196,0.00038363694,0.3052108,0.000118437296,0.0019103137],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9801229,0.01157489,0.0028608122,0.0018150754,0.0020802743,0.0015460224],"domain_scores_gemma":[0.97776467,0.0020304306,0.0023037787,0.016362634,0.0011258916,0.00041261222],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["sts","open_science"],"category_scores_codex":[0.014741696,0.0011853984,0.0024353429,0.0023743426,0.005920556,0.010528742,0.04819241,0.00069158507,0.00009948308],"category_scores_gemma":[0.0025753705,0.0010537504,0.0005264433,0.012711313,0.0046730107,0.0048393556,0.059380583,0.005460036,0.000017086968],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040852153,0.02396621,0.03792523,0.0076621985,0.00084224745,0.00021250025,0.02660418,0.051440693,0.013946881,0.0015749424,0.8329626,0.0024538133],"study_design_scores_gemma":[0.013705178,0.00088607916,0.72127265,0.015100353,0.00068301766,0.0001597757,0.02022391,0.026696842,0.011228026,0.03956831,0.14218435,0.0082915],"about_ca_topic_score_codex":0.017388247,"about_ca_topic_score_gemma":0.028683834,"teacher_disagreement_score":0.69077826,"about_ca_system_score_codex":0.0003745408,"about_ca_system_score_gemma":0.0011570378,"threshold_uncertainty_score":0.9991913},"labels":[],"label_agreement":null},{"id":"W6948979899","doi":"10.5281/zenodo.10896048","title":"Collision Prediction for Robotics Accelerators","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collision; Robotics; Code (set theory); Work (physics); Robot","score_opus":0.043116014702449156,"score_gpt":0.262593782476346,"score_spread":0.21947776777389685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948979899","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011962935,0.00032801836,0.64236516,0.00046256182,0.0011815625,0.0007961525,0.00059993326,0.0040456983,0.35021976],"genre_scores_gemma":[0.00042790495,0.00036508447,0.21461362,0.00029408556,0.002536673,6.764828e-7,0.005900168,0.026954358,0.74890745],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782836,0.00016184524,0.00029327133,0.00080358284,0.00049141806,0.00042151645],"domain_scores_gemma":[0.9985061,0.00002762734,0.0001668343,0.0008221534,0.0002952026,0.00018206598],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005889788,0.0002573321,0.00023943788,0.00057627924,0.00074210233,0.0017554372,0.0020590771,0.00023522315,0.0012140789],"category_scores_gemma":[0.0003186047,0.00026656658,0.00008771555,0.0007367663,0.00007788002,0.00020054358,0.0014301313,0.00035284154,0.01158458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047854423,0.000042516152,1.2590905e-7,0.00016241086,0.000055478045,0.000015450803,0.00024288213,0.0006438633,0.000048082948,0.0061521404,0.97237,0.020262213],"study_design_scores_gemma":[0.00025866626,0.00020947818,0.000005589319,0.00023547585,0.000025767207,0.00006473112,0.000022959422,0.034079712,0.000036714613,0.0003874949,0.96443725,0.00023613877],"about_ca_topic_score_codex":0.000007992122,"about_ca_topic_score_gemma":7.7350286e-8,"teacher_disagreement_score":0.4277515,"about_ca_system_score_codex":0.00018423688,"about_ca_system_score_gemma":0.00000988985,"threshold_uncertainty_score":0.99997866},"labels":[],"label_agreement":null},{"id":"W6958155216","doi":"10.60692/51y90-7hc96","title":"Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion","year":2019,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subgradient method; Robot; Mobile robot; Trajectory; Set (abstract data type); Motion planning; Optimization problem; Motion (physics); Decomposition","score_opus":0.01861076997965622,"score_gpt":0.19580276618575773,"score_spread":0.17719199620610152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958155216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010035959,0.0000013440007,0.98881006,0.000023497207,0.00026621128,0.00022587291,0.000042738753,0.0002876931,0.00030660853],"genre_scores_gemma":[0.8483283,3.7763954e-8,0.15142088,0.000028929144,0.000013508512,0.000014902206,0.00009939137,0.000006739776,0.00008731938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998663,0.0000740092,0.00050974,0.00018585773,0.00036676053,0.00020063313],"domain_scores_gemma":[0.9986145,0.000007954211,0.00046502956,0.0005299492,0.00031747945,0.00006508685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023441622,0.0001632353,0.00023846456,0.00023984772,0.000060983024,0.00016039633,0.00035349367,0.00009198889,0.000008053312],"category_scores_gemma":[0.000009432111,0.00013049995,0.000040605133,0.00044030146,0.00001737305,0.0016588639,0.000079805315,0.000054882243,0.0002717424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018127528,0.000004829586,0.017748507,0.00016322463,0.000025543348,0.00000191601,0.008582783,0.9725811,0.0000107384,0.00023265289,0.000020848784,0.00060971914],"study_design_scores_gemma":[0.00086115533,0.000072353694,0.0130799115,0.00018213868,0.000007988718,0.000040901825,0.00038566501,0.98468804,0.0005143764,2.0596585e-7,0.000010871874,0.0001563963],"about_ca_topic_score_codex":0.0000097039965,"about_ca_topic_score_gemma":1.4716397e-8,"teacher_disagreement_score":0.83829236,"about_ca_system_score_codex":0.000090893576,"about_ca_system_score_gemma":0.000033598964,"threshold_uncertainty_score":0.5321633},"labels":[],"label_agreement":null},{"id":"W6966725921","doi":"10.48550/arxiv.2404.07781","title":"Estimating Visibility from Alternate Perspectives for Motion Planning with Occlusions","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visibility; Perspective (graphical); Motion planning; Trajectory; Motion (physics); Planner; Aggregate (composite)","score_opus":0.07683513523845469,"score_gpt":0.23341059938656028,"score_spread":0.15657546414810558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6966725921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19567119,0.00012689548,0.8016994,0.00013472006,0.00088560954,0.0004009775,0.00008040727,0.0005447004,0.00045612673],"genre_scores_gemma":[0.7117917,0.0000028593133,0.2877483,0.00001640124,0.00016544624,0.0000026594264,0.00003061971,0.000022645972,0.00021940448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731827,0.00010246772,0.00021305708,0.0018445925,0.00013728472,0.00038431332],"domain_scores_gemma":[0.9979423,0.00029994946,0.00025544214,0.0011466295,0.00020013026,0.00015550251],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003274666,0.00037366402,0.0003713263,0.00025430287,0.00028736156,0.00028447286,0.001400884,0.00020969265,0.0000058657884],"category_scores_gemma":[0.000094772324,0.00037722566,0.0001760918,0.00039675526,0.00012071545,0.0003001219,0.0023609921,0.00074415014,0.00004406602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026268475,0.000055159275,0.0016943392,0.00008640559,0.00015078748,0.00043218432,0.0031267942,0.9792588,0.000021119411,0.014523947,0.00006765527,0.00055654347],"study_design_scores_gemma":[0.00031500924,0.000065308755,0.001234717,0.0005501822,0.00010437338,0.000007805757,0.0003532741,0.8840339,0.000033644355,0.11292091,0.0000059421227,0.0003749011],"about_ca_topic_score_codex":0.0003732877,"about_ca_topic_score_gemma":0.000008783585,"teacher_disagreement_score":0.5161205,"about_ca_system_score_codex":0.00038290347,"about_ca_system_score_gemma":0.00021767833,"threshold_uncertainty_score":0.999868},"labels":[],"label_agreement":null},{"id":"W6990349806","doi":"","title":"Development of Lunar Rover Steering Systems based on Synthetic Computer Vision and Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Electronic Theses and Dissertations Repository (University of Pisa)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Traverse; Teleoperation; Synthetic data; Obstacle avoidance; Lidar; Convolutional neural network; Terrain; State (computer science)","score_opus":0.004679539101099271,"score_gpt":0.20566467764815835,"score_spread":0.20098513854705907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990349806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37526375,0.00012561216,0.62355727,0.000035594534,0.000068155365,0.00009159183,1.2019302e-7,0.000028758617,0.0008291667],"genre_scores_gemma":[0.9776785,0.0000049123337,0.021680152,0.000003651699,0.0000025137585,4.837208e-7,0.0000014934207,0.0000023986418,0.0006258961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993979,0.00004302588,0.00012284049,0.00018671757,0.00011919914,0.00013034124],"domain_scores_gemma":[0.99953777,0.0001309692,0.000102774684,0.00014882827,0.00004639014,0.00003327741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119507844,0.00007926074,0.0001343485,0.00011574829,0.0003066822,0.000032878284,0.00015765133,0.000030770596,8.091674e-7],"category_scores_gemma":[0.000007287628,0.000082903265,0.000023629635,0.00013552424,0.000035163175,0.00012558913,0.000056444533,0.000075742035,3.9343837e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013791025,0.00020356657,0.0025205298,0.0005007117,0.00021834353,0.00001999449,0.013890792,0.8560925,0.019791463,0.07680511,0.000030240219,0.029788831],"study_design_scores_gemma":[0.0002694442,0.000220937,0.011795886,0.00032911447,0.00001835312,0.0000038572934,0.0014025737,0.98437524,0.0012882712,0.000018383254,0.00018697821,0.00009097212],"about_ca_topic_score_codex":0.000064600215,"about_ca_topic_score_gemma":0.0000047632784,"teacher_disagreement_score":0.6024147,"about_ca_system_score_codex":0.000057477708,"about_ca_system_score_gemma":0.00013284635,"threshold_uncertainty_score":0.3380697},"labels":[],"label_agreement":null},{"id":"W6992918040","doi":"","title":"Motion planning for multi-link robots with artificial potential fields and modified simulated annealing","year":2011,"lang":"en","type":"dissertation","venue":"e-scholar@UOIT (University of Ontario Institute of Technology)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Ontario Institute of Technology","keywords":"Robot; Motion planning; Simulated annealing; Gradient descent; Control theory (sociology); Convergence (economics); Mobile robot; Path (computing)","score_opus":0.039995051740768006,"score_gpt":0.24490312831699843,"score_spread":0.20490807657623042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992918040","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6407522,0.00008458442,0.357842,0.000047652728,0.00055309944,0.00038426218,0.0000133411495,0.00015546837,0.0001673971],"genre_scores_gemma":[0.09680765,0.000007640457,0.90214056,0.0000065047943,0.000022429533,0.0000013863331,0.00024332057,0.000018397128,0.0007521312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833363,0.0000073385927,0.00033404096,0.0007123057,0.00026631655,0.00034633913],"domain_scores_gemma":[0.99827224,0.0000036652525,0.00066157576,0.00056661695,0.00041061602,0.000085263964],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027668494,0.0003362456,0.0006197373,0.0007671659,0.0005424686,0.000046456735,0.0011096265,0.0010482572,0.000005719877],"category_scores_gemma":[0.00006028592,0.0004019636,0.00012380148,0.0004389197,0.00013714327,0.0010283091,0.00018451436,0.0011123705,0.0000022443924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018574445,0.00061491696,0.0008695491,0.0008697762,0.001371268,0.0009638744,0.025621722,0.3857848,0.004099599,0.0070884624,0.00008519308,0.57077336],"study_design_scores_gemma":[0.010326121,0.0027413198,0.037798632,0.0033256672,0.0014027506,0.00016163348,0.0030326229,0.90803236,0.009809786,0.006157748,0.01420274,0.003008628],"about_ca_topic_score_codex":0.005431548,"about_ca_topic_score_gemma":0.0036843324,"teacher_disagreement_score":0.56776476,"about_ca_system_score_codex":0.00015736822,"about_ca_system_score_gemma":0.0004802583,"threshold_uncertainty_score":0.99984324},"labels":[],"label_agreement":null},{"id":"W6996980110","doi":"","title":"On transmission of structure borne power from wood studs to gypsum board mounted on resilient metal channels. Part 1: Force and moment transmission","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gypsum; Moment (physics); Power (physics); Transmission (telecommunications); Power transmission; Position (finance); Function (biology)","score_opus":0.01076370176741347,"score_gpt":0.24474141433188187,"score_spread":0.2339777125644684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996980110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4254824,0.000100115394,0.56934345,0.0033965246,0.0004491839,0.00046995492,0.000038582795,0.000100309575,0.00061951624],"genre_scores_gemma":[0.898188,0.000011923159,0.10114515,0.00036155005,0.000043277127,0.000012655887,0.000011441847,0.00001970509,0.00020632257],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99764115,0.000083256295,0.00035837735,0.0007177284,0.0008139249,0.00038557657],"domain_scores_gemma":[0.99887246,0.000079583864,0.000095108335,0.0005645971,0.000059613525,0.0003286413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019428137,0.00029322802,0.0003779129,0.00017952992,0.00012786381,0.00005964849,0.00052182126,0.00012887796,0.000054100266],"category_scores_gemma":[0.000019105733,0.00023021773,0.0000800217,0.00033673158,0.000042276464,0.00015492701,0.0000736838,0.00024401795,0.000008836589],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012030328,0.001535202,0.00005868768,0.00011451327,0.00030812778,0.00023712555,0.03002718,0.3202618,0.47252753,0.019809525,0.0031816429,0.1507356],"study_design_scores_gemma":[0.009129752,0.009605017,0.009062083,0.0025162357,0.00012616247,0.000029703127,0.00029928502,0.119861715,0.7601201,0.08142326,0.006212504,0.0016141854],"about_ca_topic_score_codex":0.00002516727,"about_ca_topic_score_gemma":6.3738446e-7,"teacher_disagreement_score":0.4727056,"about_ca_system_score_codex":0.0000978504,"about_ca_system_score_gemma":0.000058179834,"threshold_uncertainty_score":0.93880063},"labels":[],"label_agreement":null},{"id":"W7006794854","doi":"","title":"View Planning with a Registration Component","year":2001,"lang":"en","type":"article","venue":"NPARC","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Component (thermodynamics); Integer programming; Set (abstract data type); Object (grammar); Greedy algorithm; Integer (computer science)","score_opus":0.032944605536919726,"score_gpt":0.26437755236647953,"score_spread":0.2314329468295598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7006794854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011300506,0.000054796023,0.9507316,0.001453621,0.000119694276,0.00009256018,2.8712077e-7,0.00021567145,0.0360313],"genre_scores_gemma":[0.41192254,0.000006284352,0.58721125,0.00029512995,0.00006878488,0.000011800272,0.00000375455,0.000007126973,0.0004733349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991151,0.00003573898,0.00012758099,0.00024405173,0.00027030357,0.0002072112],"domain_scores_gemma":[0.999411,0.00004186247,0.00007021934,0.0003699236,0.00003546044,0.00007155283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017932527,0.00009187415,0.00010733209,0.000047082234,0.00009040728,0.00009523288,0.0003789839,0.000027758444,0.000011899014],"category_scores_gemma":[0.000012056351,0.000074902186,0.000016844993,0.00023851337,0.00002493593,0.0002341911,0.000046327834,0.00009813294,0.000053841755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018208068,0.00082670664,0.059638266,0.00017700714,0.00023516949,0.00863054,0.013208814,0.089512356,0.038488347,0.22166942,0.04431323,0.5231181],"study_design_scores_gemma":[0.0010763855,0.00047252627,0.033938486,0.0004476877,0.000015439742,0.0015555378,0.00006833286,0.92215466,0.0008021382,0.0071821674,0.031683546,0.00060311327],"about_ca_topic_score_codex":0.000006931849,"about_ca_topic_score_gemma":4.6933718e-7,"teacher_disagreement_score":0.83264226,"about_ca_system_score_codex":0.000027773289,"about_ca_system_score_gemma":0.000040582865,"threshold_uncertainty_score":0.30544224},"labels":[],"label_agreement":null},{"id":"W7014163805","doi":"","title":"Optimaal padvolgen voor robots snelle oplossingsmethoden voor praktische niet-convexe toepassingen","year":2015,"lang":"en","type":"article","venue":"Lirias (KU Leuven)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Motion planning; Path (computing); Robot; Convexity; Collision avoidance; Convex optimization; Optimization problem; Regular polygon","score_opus":0.08760100654053746,"score_gpt":0.2904779816983541,"score_spread":0.20287697515781664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014163805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010437881,0.0010571533,0.96567905,0.0055072815,0.004387468,0.00057536917,0.000015197446,0.0011295279,0.011211083],"genre_scores_gemma":[0.035230435,0.0000072953144,0.9558826,0.0011044631,0.0010311163,0.00007006982,0.000023353277,0.00009239933,0.0065582697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99502146,0.0005543813,0.00076522894,0.0013091831,0.001158883,0.0011908596],"domain_scores_gemma":[0.99629724,0.00036640995,0.00041830598,0.0017494786,0.00034474646,0.00082381675],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019808316,0.0005973026,0.0007372806,0.00028034134,0.00036959804,0.0008278759,0.0025312458,0.00038091006,0.00003420122],"category_scores_gemma":[0.0007719856,0.0005699452,0.00022326161,0.00095459307,0.00019938966,0.0015852164,0.0010274431,0.00064361776,0.0012801194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003536775,0.0018319889,0.009224524,0.00029490437,0.001223826,0.00373702,0.04583992,0.23204824,0.006903482,0.01855328,0.3308981,0.34909102],"study_design_scores_gemma":[0.0034259472,0.000726684,0.004118238,0.0002742356,0.00013485942,0.0005314054,0.00040424842,0.9231594,0.00436802,0.0069062663,0.053956315,0.0019943903],"about_ca_topic_score_codex":0.00023066177,"about_ca_topic_score_gemma":0.0000048072807,"teacher_disagreement_score":0.69111115,"about_ca_system_score_codex":0.00031242933,"about_ca_system_score_gemma":0.0008419186,"threshold_uncertainty_score":0.9996752},"labels":[],"label_agreement":null},{"id":"W7022496049","doi":"","title":"Soscience, keynote","year":2022,"lang":"fr","type":"other","venue":"Industrias Culturais (Universidade de Coimbra)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Identification (biology); Table (database); Cover (algebra)","score_opus":0.041412500509857494,"score_gpt":0.2516045462960211,"score_spread":0.21019204578616357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022496049","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005186371,0.0008575103,0.05425819,0.027350811,0.0063626748,0.0010690314,0.00034514457,0.000890054,0.90834796],"genre_scores_gemma":[0.0010609654,0.000060460017,0.038961757,0.0013004582,0.0010707533,0.000032284952,0.0001511964,0.00021887005,0.95714325],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929129,0.0008985452,0.00058493245,0.002067818,0.0015942154,0.0019416033],"domain_scores_gemma":[0.9958031,0.00042898237,0.0009010064,0.0017224888,0.00022362407,0.0009208036],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity","insufficient_payload"],"category_scores_codex":[0.0009496866,0.0010647675,0.0010070256,0.0010109447,0.0013131957,0.00090533745,0.0056231897,0.0015059202,0.041019462],"category_scores_gemma":[0.00061553135,0.0011958486,0.00048302903,0.0040369607,0.0007497768,0.0011862647,0.0021540544,0.0032182247,0.0026057826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034553217,0.00035988993,0.00086356845,0.00007859652,0.00041483928,0.0052629146,0.0063309697,0.011116454,0.0006882183,0.08445879,0.8331447,0.05724647],"study_design_scores_gemma":[0.0016518605,0.00033476963,0.020713612,0.0002983094,0.00017634842,0.0008827158,0.0015722085,0.03739452,0.00007307458,0.00026957784,0.9350257,0.0016072989],"about_ca_topic_score_codex":0.0016681135,"about_ca_topic_score_gemma":0.000047160444,"teacher_disagreement_score":0.10188097,"about_ca_system_score_codex":0.0021159737,"about_ca_system_score_gemma":0.001927957,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W7023864550","doi":"","title":"Practical Implementation of Multi-UAV Flocking Path Planning","year":2021,"lang":"","type":"dissertation","venue":"TSpace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Downwash; Flocking (texture); Motion planning; Aerodynamics; Control theory (sociology); Robot","score_opus":0.09558843130220168,"score_gpt":0.46335064554078986,"score_spread":0.3677622142385882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023864550","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06935722,0.001366685,0.9230485,0.00054134335,0.0040999753,0.0007282536,0.000016397209,0.00014626431,0.0006953833],"genre_scores_gemma":[0.18179132,0.000088382745,0.8142546,0.00009218759,0.00032855436,0.00005520674,0.00068995735,0.00009476542,0.0026050184],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99446565,0.00054684776,0.0012801902,0.0014042385,0.0013789396,0.0009241108],"domain_scores_gemma":[0.9953589,0.0006459779,0.001830616,0.0010749482,0.0008072356,0.0002823476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010567841,0.00070513436,0.0009909611,0.00037816976,0.0003784678,0.0003850903,0.0008435115,0.00051200733,0.00020799291],"category_scores_gemma":[0.0004816982,0.0008293566,0.00028394745,0.0010666564,0.000072605624,0.0006400961,0.00033175712,0.0010505143,0.000052939307],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018621332,0.0015604486,0.021637596,0.003202825,0.001498773,0.0045057987,0.76338744,0.0401819,0.036883313,0.0036444678,0.005837414,0.11747382],"study_design_scores_gemma":[0.0022357202,0.0004770047,0.03604599,0.0031826377,0.00038491195,0.0002336056,0.1775833,0.74303514,0.034770973,0.00003259925,0.00049773685,0.0015203872],"about_ca_topic_score_codex":0.0009781723,"about_ca_topic_score_gemma":0.000024058865,"teacher_disagreement_score":0.7028532,"about_ca_system_score_codex":0.00020321806,"about_ca_system_score_gemma":0.0018309104,"threshold_uncertainty_score":0.99941576},"labels":[],"label_agreement":null},{"id":"W7038720579","doi":"","title":"Investor Calendar Invites You to the U.S. Geothermal Inc. Fourth Quarter and Fiscal Year 2015 Earnings Conference Call and Webcast Live on Friday, March 11, 2016 - Earnings News (EARNINGS) News","year":2016,"lang":"en","type":"other","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Webcast; Earnings; Quarter (Canadian coin); Fiscal year; Government (linguistics)","score_opus":0.01666846153979082,"score_gpt":0.24840735824312754,"score_spread":0.23173889670333672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038720579","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014877813,0.0018948055,0.58731467,0.12677473,0.005182604,0.0072351326,0.0014373141,0.0037928151,0.25149012],"genre_scores_gemma":[0.001270811,0.0004043157,0.095148094,0.0046748556,0.0017259464,0.00014438629,0.00003779115,0.0005858612,0.89600796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953025,0.00055440137,0.0005338383,0.0016681064,0.0008807357,0.0010604085],"domain_scores_gemma":[0.99690616,0.0005069959,0.0005438987,0.0011941458,0.000121652425,0.00072714733],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00070910936,0.00089195935,0.0008419967,0.00054753275,0.00028887336,0.00050872733,0.0016281335,0.00057456753,0.0009989882],"category_scores_gemma":[0.00026656166,0.0005735097,0.0001002779,0.00023774002,0.00050185213,0.00036775277,0.0011224871,0.0010469806,0.0032654575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026570167,0.000034740595,0.0077243843,0.00004223339,0.00007133857,0.000059605423,0.0031893365,0.000029318126,0.000047206937,0.00026004144,0.9781184,0.010396805],"study_design_scores_gemma":[0.0010036868,0.000704228,0.012742978,0.00081000855,0.00003751351,0.00008854466,0.0003347462,0.005830112,0.0000099546805,0.00004789254,0.9772669,0.0011234087],"about_ca_topic_score_codex":0.005723404,"about_ca_topic_score_gemma":0.0007350423,"teacher_disagreement_score":0.6445178,"about_ca_system_score_codex":0.00008123767,"about_ca_system_score_gemma":0.00028321947,"threshold_uncertainty_score":0.9999142},"labels":[],"label_agreement":null},{"id":"W7043413241","doi":"","title":"Single-Query Robot Motion Planning using Rapidly Exploring Random Trees (RRTs)","year":2014,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Nucleofection; Diafiltration; TSG101; Gestational period; Demotion; Articular cartilage damage","score_opus":0.05987785442472843,"score_gpt":0.2324038827384386,"score_spread":0.17252602831371017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043413241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28167865,0.00024164756,0.714199,0.00010572565,0.0019096911,0.00025492464,0.000004288825,0.00029487498,0.001311163],"genre_scores_gemma":[0.63001657,0.00007920639,0.36783314,0.00003467428,0.00051956816,0.0000022481215,0.00029429962,0.00010949072,0.0011108462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976394,0.00017761694,0.0002538309,0.000769794,0.0006759257,0.00048342903],"domain_scores_gemma":[0.9979702,0.00021254414,0.0007240842,0.0007218284,0.00021991563,0.00015138646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000484355,0.00041976137,0.0007228642,0.0007094753,0.00047899003,0.00012691742,0.0013160165,0.00033793083,0.0000029023809],"category_scores_gemma":[0.000075566364,0.000570168,0.00026247886,0.00048165416,0.000066571156,0.0011354599,0.00020198815,0.00045100617,0.00003207118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011957749,0.0008459537,0.025694758,0.0020931994,0.0012347138,0.0018365416,0.028915811,0.73847747,0.05085967,0.0018263052,0.003494553,0.14352527],"study_design_scores_gemma":[0.0051586204,0.0004568027,0.14027292,0.003999999,0.0005265381,0.00007146235,0.031283308,0.8091787,0.005093628,0.00034906477,0.0013481906,0.0022607292],"about_ca_topic_score_codex":0.00094640657,"about_ca_topic_score_gemma":0.00402073,"teacher_disagreement_score":0.3483379,"about_ca_system_score_codex":0.0002677004,"about_ca_system_score_gemma":0.0001105722,"threshold_uncertainty_score":0.999675},"labels":[],"label_agreement":null},{"id":"W7071683681","doi":"","title":"Stochastic path planning in the presence of disambiguation and neutralization capabilities","year":2020,"lang":"tr","type":"other","venue":"Marmara University Open Access System","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Obstacle; Path (computing); Obstacle problem; Quality (philosophy); Line (geometry)","score_opus":0.06030684811469459,"score_gpt":0.30505394057309637,"score_spread":0.2447470924584018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071683681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024183165,0.00056010793,0.87067616,0.001289817,0.0016532601,0.0060689864,0.0002507606,0.00019223568,0.0951255],"genre_scores_gemma":[0.9898516,0.000020832256,0.004439716,0.00005687034,0.00008233202,0.000008762752,0.000047827907,0.00005001818,0.0054420396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970962,0.00088843383,0.00040823017,0.0007613877,0.00053598377,0.00030976706],"domain_scores_gemma":[0.9980813,0.0004519721,0.0007118178,0.00054915034,0.00010786073,0.00009787932],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008526122,0.00033244002,0.0006191615,0.0003193716,0.00023221379,0.001197657,0.0058615664,0.00018612374,0.000023577217],"category_scores_gemma":[0.000086868036,0.00030784184,0.000052535543,0.0010063823,0.00019078814,0.0019179011,0.0026082385,0.00031498837,0.0000101833175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000718656,0.000640943,0.17656477,0.016631557,0.0006083599,0.001873927,0.18149514,0.433101,0.000051052364,0.14960985,0.032628473,0.006076263],"study_design_scores_gemma":[0.0013361917,0.00014605494,0.081814736,0.005312574,0.00013109123,0.000047079102,0.021721182,0.887284,0.0000040641503,0.00005517222,0.001530435,0.000617406],"about_ca_topic_score_codex":0.0047924784,"about_ca_topic_score_gemma":0.00004975346,"teacher_disagreement_score":0.96566844,"about_ca_system_score_codex":0.00020191175,"about_ca_system_score_gemma":0.00019556409,"threshold_uncertainty_score":0.99993736},"labels":[],"label_agreement":null},{"id":"W7084114121","doi":"10.11896/jsjkx.221100057","title":"Mixed Path HMC Sampling Methods for Molecular Tree Spaces","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Tree traversal; Markov chain; Markov chain Monte Carlo; Robustness (evolution); Tree (set theory); Slice sampling; Metropolis–Hastings algorithm; Importance sampling; Random tree","score_opus":0.4566633430019507,"score_gpt":0.6092704970498022,"score_spread":0.15260715404785147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084114121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027333297,0.005797174,0.96270496,0.00052361237,0.0019006998,0.0006486987,0.00002359324,0.0002828752,0.00078509696],"genre_scores_gemma":[0.03163092,0.0011694832,0.9660333,0.00032947026,0.00021933544,0.00015306614,0.000019708756,0.00008910825,0.00035562387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99605405,0.0006169744,0.0009107922,0.0008584525,0.00076419266,0.0007955128],"domain_scores_gemma":[0.9954864,0.0018505405,0.0008524725,0.0010680491,0.00037630677,0.00036627104],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0050162654,0.000413809,0.0009340683,0.0011716904,0.00038557113,0.002719123,0.007168212,0.00015052296,0.00014302412],"category_scores_gemma":[0.0013669168,0.00038743913,0.00033076777,0.0026358622,0.00009247326,0.002364645,0.002182428,0.00038268548,0.000029437533],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008281552,0.00023984298,0.022540705,0.00023819588,0.0005393399,0.00026735922,0.0009406254,0.02885747,0.3133487,0.0030094427,0.044481855,0.5854536],"study_design_scores_gemma":[0.002203242,0.00008912871,0.29446068,0.0013680487,0.0002247402,0.000098697084,0.00025019993,0.39031574,0.16332047,0.118585445,0.026811726,0.0022718806],"about_ca_topic_score_codex":0.00015821554,"about_ca_topic_score_gemma":0.0000026088842,"teacher_disagreement_score":0.58318174,"about_ca_system_score_codex":0.000088697896,"about_ca_system_score_gemma":0.00021555708,"threshold_uncertainty_score":0.9998577},"labels":[],"label_agreement":null},{"id":"W7095215784","doi":"","title":"Executive summary","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Executive summary; State (computer science); Adaptation (eye); Path (computing); Executive committee","score_opus":0.015246345480817842,"score_gpt":0.24702716736877925,"score_spread":0.2317808218879614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095215784","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000120566554,0.00005891268,0.80795515,0.0019202214,0.00014685636,0.00003365098,2.465653e-7,0.0002808425,0.18948357],"genre_scores_gemma":[0.014644162,0.0000030728413,0.96368575,0.0011910825,0.00011146518,0.0000033424117,6.6428436e-7,0.0000034235188,0.020357028],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994046,0.000017662538,0.00008387945,0.00018512427,0.00013587423,0.00017283806],"domain_scores_gemma":[0.9995493,0.000045801346,0.000018932724,0.00030294267,0.000022479908,0.000060541217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010709753,0.00005968178,0.00006167091,0.000041388786,0.000042004947,0.00004715394,0.00049706816,0.00002462799,0.000013361112],"category_scores_gemma":[0.000014192329,0.000050343137,0.000022222286,0.00014532998,0.000014120692,0.00033770403,0.00012757695,0.000060010992,0.0004808212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027622204,0.00014840628,0.0017173435,0.0000047616873,0.00003154016,0.00010949864,0.002117936,0.0069018253,0.00058541377,0.21176426,0.4114373,0.36517894],"study_design_scores_gemma":[0.00027696704,0.000049273964,0.0033192902,0.000014187555,0.0000022278002,0.00005785304,0.000026007334,0.86825794,0.0037134096,0.0016095365,0.12240364,0.0002696992],"about_ca_topic_score_codex":0.000008688097,"about_ca_topic_score_gemma":7.8131484e-7,"teacher_disagreement_score":0.8613561,"about_ca_system_score_codex":0.000015903335,"about_ca_system_score_gemma":0.000025272237,"threshold_uncertainty_score":0.6180144},"labels":[],"label_agreement":null},{"id":"W7096102939","doi":"","title":"Path tracking for unmanned ground vehicle navigation","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Path (computing); State (computer science); Tracking (education); Adaptation (eye); Unmanned ground vehicle; Context (archaeology)","score_opus":0.029657816287376102,"score_gpt":0.2802035244376085,"score_spread":0.25054570815023236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096102939","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03095272,0.000035714264,0.96590704,0.0014797247,0.00022804955,0.00016426675,0.000001120169,0.00029321632,0.0009381455],"genre_scores_gemma":[0.4489949,5.300905e-7,0.550044,0.00030786116,0.00014889592,0.000014070545,0.0000050506296,0.000005921763,0.0004787859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915296,0.000019458132,0.00016174394,0.0002608314,0.00016425131,0.000240775],"domain_scores_gemma":[0.9994517,0.000107703825,0.000052906576,0.00026456357,0.000064153355,0.000058978698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027950318,0.000084586645,0.00008817579,0.000037755693,0.00012523695,0.00015088051,0.0003934288,0.000043796113,0.000004047231],"category_scores_gemma":[0.000027511307,0.00007795971,0.00004165818,0.00015601561,0.000012387717,0.0006956147,0.00004670868,0.000060303857,0.00007202646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011777728,0.00020619911,0.0011710463,0.000054424127,0.000033134842,0.000020585354,0.0034085608,0.019134639,0.006494151,0.09333543,0.0051022796,0.87102777],"study_design_scores_gemma":[0.00037232795,0.000065163535,0.00390908,0.000023995046,0.0000029665096,0.000013342244,0.00002399929,0.9884548,0.0029578928,0.002124208,0.0019123275,0.00013986687],"about_ca_topic_score_codex":0.000012879335,"about_ca_topic_score_gemma":9.690176e-7,"teacher_disagreement_score":0.9693202,"about_ca_system_score_codex":0.000047399222,"about_ca_system_score_gemma":0.000024932277,"threshold_uncertainty_score":0.31791046},"labels":[],"label_agreement":null},{"id":"W7096381609","doi":"","title":"Toward autonomous long range navigation","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terrain; Mars Exploration Program; Planetary exploration; Robotics; Exploration of Mars; Mobile robot; Mars rover; Motion planning; Key (lock)","score_opus":0.029446554990605077,"score_gpt":0.26334126940524666,"score_spread":0.23389471441464157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096381609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005208403,0.000069410686,0.97944075,0.002713771,0.00027294812,0.000077771496,3.3808968e-7,0.0004996084,0.011716971],"genre_scores_gemma":[0.3987613,0.0000011148912,0.59920645,0.00042134846,0.00014162203,0.000005620097,0.0000023227685,0.0000046271357,0.0014556311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920833,0.000023697223,0.00014134015,0.00023646865,0.00018469796,0.00020546585],"domain_scores_gemma":[0.9994985,0.000035167304,0.000038639286,0.0003205184,0.000034308563,0.00007290724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001839967,0.00008222828,0.00008483541,0.000046394623,0.000053288346,0.00010101114,0.00049291045,0.00004170236,0.00002571197],"category_scores_gemma":[0.000012613314,0.0000739776,0.000031660424,0.00016991606,0.000014771069,0.0005034751,0.00009316311,0.00008166612,0.0007467957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031703885,0.00013791508,0.0058891173,0.00002287007,0.000028159935,0.00020196881,0.004896127,0.027677987,0.00021931084,0.038942527,0.0070863524,0.9148945],"study_design_scores_gemma":[0.00028809882,0.000034017605,0.013934547,0.000017141365,0.0000028476777,0.00011226096,0.000010673036,0.98056334,0.0011825152,0.00040978487,0.0032512057,0.0001935883],"about_ca_topic_score_codex":0.000025436848,"about_ca_topic_score_gemma":9.389022e-7,"teacher_disagreement_score":0.9528853,"about_ca_system_score_codex":0.000059399026,"about_ca_system_score_gemma":0.000036887322,"threshold_uncertainty_score":0.9598797},"labels":[],"label_agreement":null},{"id":"W7100666172","doi":"","title":"Intelligent Mobility Algorithm Research &amp;amp; Development Creating Intelligent Algorithms for Robotic Locomotion in Complex Terrains","year":2005,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mobile robot; Terrain; Robot; Focus (optics); Intelligent control; Robotics; Intelligent transportation system; Intelligent decision support system","score_opus":0.2439783968056342,"score_gpt":0.40924638495840326,"score_spread":0.16526798815276905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100666172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018399302,0.00011554535,0.9932532,0.0011280545,0.00036088284,0.0017890255,0.0000063662833,0.00035815476,0.0011488437],"genre_scores_gemma":[0.017383633,0.000018142893,0.97891885,0.00021245245,0.0002674633,0.0004283589,0.00015902807,0.00003923217,0.0025728173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99389714,0.00048794635,0.0014194824,0.001429773,0.0012247853,0.0015409017],"domain_scores_gemma":[0.9964837,0.0010218557,0.00019831223,0.0012984021,0.0006058666,0.00039186695],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0059020007,0.00047376845,0.0005950558,0.00085493695,0.00056566973,0.00039339004,0.0018691749,0.00021656304,0.00010570526],"category_scores_gemma":[0.0005712989,0.00045338826,0.00015776404,0.0013250472,0.00020509765,0.0006098081,0.0008869874,0.0006990703,0.00067666644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011688358,0.0012044861,0.00061110716,0.00007685076,0.00004950071,0.000009736312,0.009655395,0.09765227,0.00027804144,0.0012064645,0.0029228772,0.8863216],"study_design_scores_gemma":[0.0004935161,0.00011155922,0.0034929735,0.00012911833,0.0000051081943,0.000038292575,0.00029121913,0.9576562,0.0026552037,0.0011664798,0.033390958,0.000569362],"about_ca_topic_score_codex":0.00037155533,"about_ca_topic_score_gemma":0.0006474906,"teacher_disagreement_score":0.8857522,"about_ca_system_score_codex":0.0015860213,"about_ca_system_score_gemma":0.0004085886,"threshold_uncertainty_score":0.9997918},"labels":[],"label_agreement":null},{"id":"W7105593118","doi":"10.1109/tii.2025.3627632","title":"Game-Theoretic Safe Multiagent Motion Planning With Reachability Analysis for Dynamic and Uncertain Environments","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China; Chinese Academy of Engineering; National Science Foundation","keywords":"Reachability; Scalability; Convergence (economics); Multi-agent system; Collision avoidance; Nash equilibrium; Motion planning; Iterated function; Set (abstract data type)","score_opus":0.04080694557400321,"score_gpt":0.2872776888509805,"score_spread":0.24647074327697727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105593118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032207448,0.00003498358,0.9640605,0.00027692312,0.0012771051,0.0017139679,0.0002205412,0.00008132535,0.00012720194],"genre_scores_gemma":[0.925668,0.00004456794,0.07330275,0.00014343623,0.000033800035,0.00016607637,0.000041316605,0.000020146354,0.0005798764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634767,0.00025204947,0.001504538,0.0005802737,0.0006197989,0.00069565076],"domain_scores_gemma":[0.9971375,0.00089611224,0.00059533137,0.0010165771,0.000093305236,0.00026120341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013208389,0.0005804062,0.00080512476,0.0010844733,0.00068792904,0.00040071,0.0005883669,0.0005727739,0.000015551166],"category_scores_gemma":[0.00005892781,0.0005508292,0.00027495396,0.0018802203,0.0004462491,0.0007779586,0.000013396633,0.0009616084,0.00000966382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002899087,0.00027098058,0.0003657698,0.00013558281,0.0011538533,0.0000031407817,0.004824492,0.8844533,0.000007524701,0.00008075791,0.000012931869,0.108401775],"study_design_scores_gemma":[0.0034366613,0.0007445878,0.0005607948,0.0004920693,0.0019323858,0.000008450253,0.0011349091,0.9902359,0.00065741636,0.00015812053,0.00015395091,0.00048473492],"about_ca_topic_score_codex":0.000076606666,"about_ca_topic_score_gemma":0.0000064489163,"teacher_disagreement_score":0.8934606,"about_ca_system_score_codex":0.00068703346,"about_ca_system_score_gemma":0.00029201363,"threshold_uncertainty_score":0.99969435},"labels":[],"label_agreement":null},{"id":"W7113906092","doi":"10.1145/3761668.3761692","title":"Path Smoothing Optimization for Mobile Robots in Intelligent Chemistry Laboratory Based on Improved A* Algorithm","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Path (computing); Motion planning; Smoothing; Automation; Mobile robot; Any-angle path planning; MATLAB","score_opus":0.009370230431587743,"score_gpt":0.2630055090295661,"score_spread":0.25363527859797835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113906092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000113818256,0.00041672672,0.9939866,0.00056960055,0.0019335763,0.0017638695,0.00005614988,0.00024295133,0.00091668224],"genre_scores_gemma":[0.008721901,0.00004158782,0.9875805,0.0015264917,0.00014631775,0.000577534,0.000059481354,0.000047131387,0.0012990971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99573606,0.0001766653,0.001117406,0.0015654806,0.00045672824,0.000947634],"domain_scores_gemma":[0.99696374,0.00069081294,0.00033887787,0.0012840321,0.0005008886,0.00022166084],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014741784,0.0006133165,0.00063376507,0.0004008627,0.0002784185,0.00050010707,0.0013102823,0.0004846465,0.00008834994],"category_scores_gemma":[0.00042120545,0.00066314486,0.0001819703,0.0019545911,0.000098010445,0.000427125,0.00030000336,0.00060991815,0.000011135739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003163077,0.00047844814,0.000056559165,0.0002907057,0.000025233894,0.000018869694,0.00030110468,0.86343586,0.0004574196,0.0000643799,0.00029995383,0.13453984],"study_design_scores_gemma":[0.0015609993,0.00029566413,0.000042690735,0.0009385988,0.000027959059,9.583297e-7,0.000155184,0.97851425,0.017518036,0.00006818059,0.00028496937,0.00059247727],"about_ca_topic_score_codex":0.00006318453,"about_ca_topic_score_gemma":0.0000010957608,"teacher_disagreement_score":0.13394736,"about_ca_system_score_codex":0.0007259555,"about_ca_system_score_gemma":0.0014678155,"threshold_uncertainty_score":0.999582},"labels":[],"label_agreement":null},{"id":"W7115607610","doi":"","title":"Bench-Push: Benchmarking Pushing-based Navigation and Manipulation Tasks for Mobile Robots","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Benchmarking; Mobile robot; Robotics; Robot; Modular design; Python (programming language); Implementation; Obstacle","score_opus":0.03979216649602045,"score_gpt":0.3016447239538219,"score_spread":0.26185255745780145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115607610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.335666,0.00090535835,0.6585806,0.00079042686,0.002571482,0.0011817339,0.000014019247,0.00014634691,0.00014404443],"genre_scores_gemma":[0.9042796,0.000020260559,0.09407402,0.00060910685,0.0003191907,0.00023776584,0.00015581869,0.000033038443,0.00027121202],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996162,0.00020300843,0.00091742916,0.0014192109,0.0004640411,0.0008343049],"domain_scores_gemma":[0.99722487,0.00074483606,0.0005027293,0.0009674712,0.0003538261,0.0002062886],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011866919,0.00052379386,0.0005342252,0.000379358,0.00093266944,0.00054568384,0.0007971836,0.00037641806,0.000014436568],"category_scores_gemma":[0.0002379344,0.00060098857,0.00017400677,0.0010517706,0.00016976436,0.0010185067,0.00033844297,0.00044625474,0.000026392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065593085,0.00028095912,0.43504193,0.0007156799,0.00014589475,0.000046419264,0.0017574888,0.43088728,0.002452544,0.0018058547,0.0011015533,0.1256988],"study_design_scores_gemma":[0.0010240673,0.0002902367,0.25294745,0.000836978,0.00009582944,0.000008000215,0.00004809988,0.741762,0.0013387458,0.00064904214,0.00057122426,0.0004283125],"about_ca_topic_score_codex":0.00012808193,"about_ca_topic_score_gemma":0.0000053176777,"teacher_disagreement_score":0.5686136,"about_ca_system_score_codex":0.00025626368,"about_ca_system_score_gemma":0.0003537803,"threshold_uncertainty_score":0.99964416},"labels":[],"label_agreement":null},{"id":"W7123352465","doi":"10.1109/cdc57313.2025.11312695","title":"Path Efficiency Enhanced Exploration Method for Mobile Robot in Unknown Unstructured Environments","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Adaptability; Mobile robot; Robot; Path (computing); Motion planning","score_opus":0.016820109893072317,"score_gpt":0.30707254846919113,"score_spread":0.2902524385761188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123352465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009306757,0.0004100892,0.99116576,0.000411683,0.0030427238,0.002659272,0.000010497005,0.000106057785,0.0012632458],"genre_scores_gemma":[0.15471387,0.00006548687,0.839343,0.00032958735,0.00007313771,0.00065574306,0.000022716893,0.000023406677,0.004773037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99517703,0.00045472555,0.0011611916,0.001669229,0.0005431841,0.0009946596],"domain_scores_gemma":[0.9976556,0.0006072258,0.00033015473,0.0011747257,0.000073632524,0.00015872001],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014056218,0.0005576129,0.0007001421,0.0005300655,0.0003145303,0.00029024197,0.0014322478,0.00035030468,0.00003959222],"category_scores_gemma":[0.00023024669,0.00055537885,0.00018397439,0.0015953678,0.00011058561,0.0009332227,0.0004559596,0.00036823406,0.00004315016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044130527,0.00040819668,0.000049391965,0.00011793893,0.000043411816,0.000009383874,0.0036486553,0.61287177,0.023503179,0.006026986,0.00017969363,0.35309726],"study_design_scores_gemma":[0.0019451567,0.00040798355,0.00061999075,0.00022622982,0.00003494335,0.0000026116218,0.00026863106,0.930278,0.060282387,0.0046613314,0.0007703621,0.0005023473],"about_ca_topic_score_codex":0.000059595288,"about_ca_topic_score_gemma":0.000007936823,"teacher_disagreement_score":0.3525949,"about_ca_system_score_codex":0.00039519754,"about_ca_system_score_gemma":0.0003729712,"threshold_uncertainty_score":0.99968976},"labels":[],"label_agreement":null},{"id":"W7123359294","doi":"10.1109/cdc57313.2025.11312902","title":"Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Control theory (sociology); Obstacle avoidance; Dissipative system; Controller (irrigation); Heading (navigation); Trajectory; Robot; Differentiable function; Position (finance); Collision avoidance","score_opus":0.01651482638487664,"score_gpt":0.29755888497757965,"score_spread":0.281044058592703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123359294","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037916792,0.00022869404,0.96348107,0.0048887385,0.0035049177,0.0014253851,0.0001358782,0.00020647116,0.022337155],"genre_scores_gemma":[0.2957789,0.0000144325595,0.6123259,0.001787478,0.00019059838,0.0002528075,0.00016859073,0.000051326555,0.08942994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961728,0.00022648346,0.00080669596,0.0014667424,0.00043848285,0.0008888096],"domain_scores_gemma":[0.99623954,0.0009919079,0.0004575787,0.0010757439,0.0010436429,0.00019160869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069636607,0.00057067815,0.00060275756,0.0002471086,0.0006424489,0.00051587215,0.001235655,0.00036857108,0.0000693507],"category_scores_gemma":[0.00028593178,0.0005831285,0.00020094322,0.0017360499,0.00028169816,0.0012205282,0.0004437582,0.0005989578,0.000098329074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024284933,0.00088405435,0.0013125389,0.00074029184,0.0009768466,0.000029300005,0.008268771,0.05486607,0.000703348,0.68526345,0.008864341,0.23784816],"study_design_scores_gemma":[0.0012338876,0.00020493679,0.0069734138,0.00061458227,0.00006962474,0.0000090556305,0.0010881625,0.9479627,0.0015895038,0.039135177,0.00051540567,0.0006035689],"about_ca_topic_score_codex":0.0000887845,"about_ca_topic_score_gemma":0.00005433334,"teacher_disagreement_score":0.8930966,"about_ca_system_score_codex":0.0009481413,"about_ca_system_score_gemma":0.00095045386,"threshold_uncertainty_score":0.99966204},"labels":[],"label_agreement":null},{"id":"W7124138604","doi":"10.65109/ajme3407","title":"A new approach to cooperative pathfinding","year":2008,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Plan (archaeology); Path (computing); Fidelity; Motion planning","score_opus":0.07326770196131503,"score_gpt":0.27391682953273305,"score_spread":0.200649127571418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124138604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061704975,0.00012103156,0.87012565,0.0007856225,0.0011521046,0.00054020656,0.0000028267198,0.00022563622,0.12642986],"genre_scores_gemma":[0.048546754,0.00001643908,0.88245636,0.0013124188,0.00041819215,0.000016618946,0.000002554668,0.0000260837,0.06720459],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960229,0.00019237348,0.0005608514,0.0014267814,0.0007875021,0.0010096072],"domain_scores_gemma":[0.99732125,0.00014241131,0.00011558466,0.0011419685,0.00019254973,0.0010862158],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046611423,0.00047077407,0.00053418416,0.0002950674,0.0007169228,0.00034505437,0.0018151037,0.00017868809,0.00018727005],"category_scores_gemma":[0.00021218242,0.00044333556,0.000122011064,0.0020295077,0.00008384124,0.00067525933,0.000806599,0.0004282325,0.004311739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044830773,0.0009219327,0.0017698286,0.000047682846,0.00024964174,0.0011327281,0.1532715,0.23122184,0.0006088997,0.055679835,0.38625592,0.16879538],"study_design_scores_gemma":[0.00077259773,0.0003473677,0.0027451196,0.000085108506,0.00001484157,0.0009907802,0.00025757588,0.989689,0.0008223342,0.000072815594,0.0033035448,0.00089891476],"about_ca_topic_score_codex":0.00026955846,"about_ca_topic_score_gemma":6.520214e-7,"teacher_disagreement_score":0.75846714,"about_ca_system_score_codex":0.00016236096,"about_ca_system_score_gemma":0.0011472055,"threshold_uncertainty_score":0.9998018},"labels":[],"label_agreement":null},{"id":"W7124165735","doi":"10.65109/dczu1006","title":"MO-LOST: adaptive ant trail untangling in multi-objective multi-colony robot foraging","year":2012,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Context (archaeology); Set (abstract data type); Dual (grammatical number); Throughput; Foraging","score_opus":0.08068120963942885,"score_gpt":0.3054454687226414,"score_spread":0.22476425908321254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124165735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015031725,0.0026828742,0.9755265,0.00026297383,0.003474223,0.0014449682,0.000022581291,0.00029831752,0.0012558389],"genre_scores_gemma":[0.5247732,0.000025155576,0.47374475,0.00026052445,0.00023469621,0.000045287106,0.0000025698678,0.00005551215,0.000858311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918806,0.0007323495,0.0014363015,0.0018226018,0.00095576904,0.0031723862],"domain_scores_gemma":[0.99633014,0.0008240065,0.00059613166,0.0010252971,0.00035415788,0.0008702456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026604692,0.0010844788,0.0011961878,0.0008584748,0.00051060296,0.0003871298,0.0018149824,0.0005272214,0.000057830413],"category_scores_gemma":[0.0004921792,0.001098029,0.00032289056,0.0021567997,0.00031279915,0.003202181,0.0010757328,0.0014984444,0.00067146844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025303746,0.0077991853,0.15413633,0.00018948219,0.0006239036,0.0012447181,0.2475409,0.41870087,0.0029212108,0.004563812,0.00019627708,0.16183028],"study_design_scores_gemma":[0.0033997274,0.00030477275,0.105884224,0.0005126318,0.000054432512,0.00013906253,0.0039592343,0.88222075,0.0022218842,0.00006281066,0.000021867423,0.0012186171],"about_ca_topic_score_codex":0.0015653514,"about_ca_topic_score_gemma":0.00015836052,"teacher_disagreement_score":0.5097415,"about_ca_system_score_codex":0.0012071886,"about_ca_system_score_gemma":0.00045346827,"threshold_uncertainty_score":0.999147},"labels":[],"label_agreement":null},{"id":"W7125504284","doi":"10.1109/icar65334.2025.11338748","title":"Scalable real-time multi-UAV coverage path planning for large geographical areas","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scalability; Flexibility (engineering); Route planning; Voronoi diagram; State (computer science); Motion planning","score_opus":0.016708971047552878,"score_gpt":0.2824771204539224,"score_spread":0.26576814940636956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125504284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031594706,0.000084354986,0.9853723,0.0005058232,0.00045675933,0.0003805961,0.000022721222,0.00066019216,0.009357797],"genre_scores_gemma":[0.052513346,0.000014501045,0.9330769,0.0010020004,0.00007972783,0.00009901396,0.000043096785,0.000022110438,0.013149273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979907,0.000067170506,0.0003263026,0.00066177483,0.0002587875,0.00069522095],"domain_scores_gemma":[0.9985249,0.00044162903,0.00007809068,0.00069093134,0.0001156798,0.00014874605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066869473,0.00022518668,0.0003165997,0.00024266461,0.0003082937,0.00023471961,0.0009112437,0.00015266976,0.000020093206],"category_scores_gemma":[0.00016496534,0.00020357672,0.00013638713,0.0006570731,0.000029808445,0.0003200864,0.0003496702,0.00017857151,0.00007318289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017651888,0.0026718704,0.27220494,0.00045921674,0.0006912902,0.00059132435,0.0023664653,0.028494842,0.005689507,0.36938208,0.28813612,0.029135805],"study_design_scores_gemma":[0.0014002611,0.000075602264,0.014189972,0.00012239785,0.000014112127,0.000009396172,0.000017222112,0.976769,0.0003866975,0.001728675,0.005010833,0.00027587288],"about_ca_topic_score_codex":0.00008039363,"about_ca_topic_score_gemma":9.385021e-7,"teacher_disagreement_score":0.94827414,"about_ca_system_score_codex":0.000043591474,"about_ca_system_score_gemma":0.00012352642,"threshold_uncertainty_score":0.8301618},"labels":[],"label_agreement":null},{"id":"W7125512333","doi":"10.18280/jesa.581214","title":"AI-Driven Visual Navigation for Smart Lab Tour Guide Robot","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Mobile robot; Visualization; Robotics; Mobile robot navigation","score_opus":0.02208078699328675,"score_gpt":0.31398042560118283,"score_spread":0.2918996386078961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125512333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006503444,0.0025226485,0.9748007,0.0033365844,0.009052287,0.0011641203,0.00004290162,0.0005328626,0.0020444165],"genre_scores_gemma":[0.18927003,0.00019779205,0.78437203,0.002330374,0.0018762293,0.00009894308,0.00003568022,0.00018119458,0.0216377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99153364,0.0012629231,0.0029042973,0.0012205736,0.001384482,0.0016940997],"domain_scores_gemma":[0.99390715,0.0008912984,0.0016887104,0.001050365,0.001826473,0.00063602737],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029796609,0.0009435914,0.0013291403,0.0009418404,0.002123575,0.0034556682,0.0026793813,0.00044553867,0.00006711062],"category_scores_gemma":[0.0012761874,0.0009366499,0.00071460585,0.001960443,0.00033542886,0.0021179384,0.00082636747,0.0013069152,0.00027964622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007629255,0.0004904246,0.002403435,0.0012486174,0.0008967905,0.00091642456,0.002386626,0.055797726,0.0014964364,0.010810405,0.06185707,0.8616198],"study_design_scores_gemma":[0.0019842046,0.0008855324,0.07744672,0.005296465,0.00030166362,0.0021394682,0.00013511222,0.895606,0.00052063476,0.00913237,0.005732614,0.0008192034],"about_ca_topic_score_codex":0.0000764767,"about_ca_topic_score_gemma":0.0000071749237,"teacher_disagreement_score":0.86080056,"about_ca_system_score_codex":0.0013063959,"about_ca_system_score_gemma":0.002047263,"threshold_uncertainty_score":0.9993084},"labels":[],"label_agreement":null},{"id":"W7125645782","doi":"10.1109/cascon66301.2025.00089","title":"Towards a Small Behavioral Model (SBM) for Theomorphic Robots","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Korea Creative Content Agency; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Ministry of Culture, Sports and Tourism; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Robot; Behavioral modeling; Behavioral analysis; Robotics; Field (mathematics)","score_opus":0.08822757981586213,"score_gpt":0.3348622586356596,"score_spread":0.24663467881979748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125645782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013285046,0.0005354603,0.9793331,0.0042595854,0.0030465028,0.0013939524,0.000050901963,0.00039244044,0.00965957],"genre_scores_gemma":[0.11821189,0.000023107803,0.8448003,0.0015126665,0.00012641081,0.0002090052,0.000013376716,0.000037327813,0.035065915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99561524,0.00013055647,0.0009106868,0.0015261169,0.00048328083,0.0013341071],"domain_scores_gemma":[0.9970449,0.00021260946,0.00022397499,0.0016962704,0.0005010478,0.0003211661],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010813426,0.0006488594,0.00076509104,0.0004225242,0.000517817,0.0007138691,0.002844891,0.00042806793,0.000037421247],"category_scores_gemma":[0.00012396948,0.0006298698,0.00043256968,0.0010243615,0.00021436116,0.0004355682,0.001084672,0.00047069497,0.000070425274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006684823,0.00085086375,0.00022166192,0.00017541696,0.00013077237,0.00005454032,0.0017549649,0.61034316,0.0003063822,0.15873794,0.0070980676,0.2202594],"study_design_scores_gemma":[0.0014142533,0.0003677095,0.00026645375,0.0002563868,0.00017534333,0.000018483219,0.000053316835,0.9733371,0.0009630476,0.022289753,0.00021779114,0.0006403633],"about_ca_topic_score_codex":0.0002466802,"about_ca_topic_score_gemma":0.000017096807,"teacher_disagreement_score":0.36299396,"about_ca_system_score_codex":0.00021901331,"about_ca_system_score_gemma":0.0018485397,"threshold_uncertainty_score":0.99961525},"labels":[],"label_agreement":null},{"id":"W7126389267","doi":"10.21428/594757db.50990c68","title":"A Minimalist Swarm Robotic Controller for Foraging","year":2025,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Controller (irrigation); Robot; Control theory (sociology); Cluster analysis; Swarm behaviour; Open-loop controller; Sample (material)","score_opus":0.01670206738247316,"score_gpt":0.28472880111356713,"score_spread":0.268026733731094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126389267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000100997095,0.00010974559,0.98368925,0.0040223645,0.00064014696,0.0003326625,8.6014035e-7,0.00028164193,0.0108223315],"genre_scores_gemma":[0.13119286,9.3026057e-7,0.85470515,0.002270275,0.000051195537,0.000053939566,0.0000017591161,0.000006970832,0.011716909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896806,0.000022875383,0.00021318413,0.00034976692,0.00012013653,0.00032594916],"domain_scores_gemma":[0.99911875,0.00035383683,0.000045618497,0.0003403227,0.00008641362,0.000055084503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002884018,0.00011949271,0.00021247454,0.00013391998,0.00013317158,0.00017268554,0.000682759,0.000044642136,0.0000024907313],"category_scores_gemma":[0.00013462717,0.0001034894,0.00008475242,0.00030516752,0.000023636007,0.00019051901,0.00013817925,0.000063930784,0.00002351342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040471594,0.00017926352,0.0011099806,0.00012523457,0.00020134033,0.000032833723,0.0008252086,0.052263882,0.00068753527,0.7916446,0.075239375,0.077650286],"study_design_scores_gemma":[0.0009852169,0.00003921755,0.0005207851,0.00003517575,0.000012171639,0.000005037838,0.000029135115,0.9870524,0.0005261414,0.007751929,0.0029134513,0.00012935212],"about_ca_topic_score_codex":0.000019915973,"about_ca_topic_score_gemma":0.0000010732069,"teacher_disagreement_score":0.9347885,"about_ca_system_score_codex":0.00004011774,"about_ca_system_score_gemma":0.000082789615,"threshold_uncertainty_score":0.4220175},"labels":[],"label_agreement":null},{"id":"W7127302687","doi":"10.1109/ccece64018.2025.11364381","title":"Towards Smarter Robotics: AI-Powered Potential Fields for Obstacle Avoidance","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Obstacle avoidance; Potential field; Motion planning; Obstacle; Construct (python library); Path (computing); Action (physics); Field (mathematics)","score_opus":0.014913671081813707,"score_gpt":0.2817909990189327,"score_spread":0.266877327937119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127302687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012643433,0.00094682106,0.9456249,0.027520426,0.014273959,0.0010449965,0.00003113553,0.00032540938,0.010105955],"genre_scores_gemma":[0.27132112,0.000045315,0.6860122,0.0076963473,0.00056896656,0.00009384346,0.000015378886,0.000039187256,0.03420764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99507964,0.00018148297,0.0010886791,0.0015834423,0.00062377914,0.00144296],"domain_scores_gemma":[0.99674726,0.00031240223,0.00024149599,0.0017856184,0.00060555025,0.00030768217],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007740028,0.0006493716,0.0007883294,0.00033638728,0.00061240175,0.0010542974,0.0024713215,0.00057614804,0.00013955797],"category_scores_gemma":[0.00031910834,0.0006615026,0.00048771946,0.0009215401,0.00020466362,0.0007931592,0.0008492312,0.00067543576,0.00017044539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024176578,0.0009883504,0.0005285976,0.00077781477,0.00081713614,0.00025883465,0.0026518167,0.25407097,0.0004716761,0.13027981,0.13281843,0.47609478],"study_design_scores_gemma":[0.0018561452,0.00039790073,0.0017356243,0.00029704167,0.00012243522,0.000029141283,0.00007795422,0.97409254,0.0013658502,0.015615448,0.0036772322,0.0007326809],"about_ca_topic_score_codex":0.00012606553,"about_ca_topic_score_gemma":0.0000103219145,"teacher_disagreement_score":0.72002155,"about_ca_system_score_codex":0.00018469577,"about_ca_system_score_gemma":0.0010042334,"threshold_uncertainty_score":0.9999827},"labels":[],"label_agreement":null},{"id":"W7127428264","doi":"10.5753/ssv.2025.39497","title":"Waypoint-Based Path Planning for Autonomous Robots with PSO","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade Federal do ABC; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Lakehead University","keywords":"Motion planning; Adaptability; Obstacle; Robot; Path (computing); Heuristic; Range (aeronautics); Particle swarm optimization","score_opus":0.02236168049739602,"score_gpt":0.2830658051483547,"score_spread":0.2607041246509587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127428264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041862438,0.0006447544,0.98059577,0.0042148908,0.0021929026,0.0013413174,0.000020214018,0.00063892966,0.0099325795],"genre_scores_gemma":[0.087204196,0.0000021870792,0.90021247,0.0028664905,0.00015653553,0.00017684225,0.000019464518,0.00004796636,0.009313829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99526536,0.00014444886,0.0008702782,0.0017039841,0.0005660389,0.0014499148],"domain_scores_gemma":[0.9963671,0.0009440875,0.00034827125,0.0016077076,0.00039964303,0.00033320748],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009396816,0.00073400384,0.00082353875,0.0005238222,0.0007408906,0.00084470847,0.0018652605,0.00030436087,0.000031109055],"category_scores_gemma":[0.00017947075,0.00063517404,0.00023399503,0.0013267566,0.00021703882,0.0005220075,0.00033842705,0.00048005805,0.00006217113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017911827,0.00034192798,0.0019578156,0.00033759561,0.0002145287,0.00023108213,0.00087423983,0.9073174,0.00008870503,0.013775462,0.012485141,0.062196966],"study_design_scores_gemma":[0.0030525927,0.0010694403,0.0023437976,0.0013546518,0.000116101895,0.000028735078,0.00010733491,0.9853088,0.0014842818,0.00069328345,0.0036365,0.0008044246],"about_ca_topic_score_codex":0.000082723906,"about_ca_topic_score_gemma":0.000002357063,"teacher_disagreement_score":0.08678557,"about_ca_system_score_codex":0.00029505213,"about_ca_system_score_gemma":0.0023583167,"threshold_uncertainty_score":0.99960995},"labels":[],"label_agreement":null},{"id":"W7131136634","doi":"10.1109/roboticcc68732.2025.00016","title":"A Route Planning Approach with RAG-based LLM and FAISS for Robotic Guide","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian University Press","keywords":"Geospatial analysis; Graph; Route planning; Automated planning and scheduling; Natural language; Motion planning; Path (computing); Point (geometry)","score_opus":0.024915102257059243,"score_gpt":0.2876241209121913,"score_spread":0.2627090186551321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131136634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003067967,0.0010716986,0.980837,0.002119361,0.00071069424,0.0014846703,0.0000067923056,0.000312632,0.013150389],"genre_scores_gemma":[0.11047818,0.0000034483717,0.8772282,0.0014277293,0.00009640577,0.00015601702,0.000018435403,0.000037241938,0.010554317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99565977,0.0001584313,0.0007872221,0.001716682,0.0005205309,0.0011573873],"domain_scores_gemma":[0.9970954,0.00081393484,0.0002708596,0.0012041804,0.00029290473,0.0003227005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010561821,0.00066783594,0.000849222,0.000474551,0.0006538421,0.00097373995,0.0012880523,0.00025830462,0.000004800413],"category_scores_gemma":[0.00019961428,0.00056498626,0.00013103712,0.0011906043,0.0002851844,0.0005105977,0.00039513316,0.0003907121,0.0000064408473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013824199,0.00032193365,0.0045896457,0.000608976,0.0002515741,0.00007826797,0.0011839698,0.9612947,0.00003704585,0.009646395,0.0047185947,0.017130617],"study_design_scores_gemma":[0.0032139835,0.0006853863,0.0024959086,0.00087094953,0.00017928165,0.000058111553,0.00028265512,0.9899184,0.00028395973,0.00029025224,0.001032041,0.00068904797],"about_ca_topic_score_codex":0.00012908131,"about_ca_topic_score_gemma":0.0000023860769,"teacher_disagreement_score":0.110171385,"about_ca_system_score_codex":0.00015456557,"about_ca_system_score_gemma":0.0011013863,"threshold_uncertainty_score":0.99968016},"labels":[],"label_agreement":null},{"id":"W7133073552","doi":"","title":"Prediction based guidance for real-time navigation of mobile robots in dynamic cluttered environments","year":2008,"lang":"","type":"dissertation","venue":"TSpace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Rendezvous; Terrain; Trajectory; A priori and a posteriori; Mobile robot; Motion planning; Vehicle dynamics; Path (computing)","score_opus":0.019383823399990174,"score_gpt":0.30781225495531833,"score_spread":0.28842843155532816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133073552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34420037,0.00044715035,0.649724,0.00007715374,0.0015699494,0.0034203744,0.0001477399,0.00012237884,0.00029084942],"genre_scores_gemma":[0.46654412,0.0005715613,0.51021636,0.00004283353,0.00017406736,0.001726575,0.006933296,0.0002126401,0.013578537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951499,0.00030720225,0.0014492992,0.0013974154,0.00096392614,0.0007322645],"domain_scores_gemma":[0.9964567,0.00037804196,0.0016725382,0.0011742642,0.000155086,0.00016341556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007673569,0.0006672103,0.0009417766,0.0004903659,0.00022217906,0.000057559704,0.0010126375,0.0006658704,0.000023624578],"category_scores_gemma":[0.00011793001,0.0008304643,0.00025290644,0.0007476271,0.00013223989,0.00044521072,0.000076185956,0.00040864988,0.00005949815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002691572,0.0005920696,0.0010859148,0.0006745428,0.00006401749,0.000037221907,0.015274062,0.86650544,0.10773111,0.000008487745,0.0002474969,0.007510487],"study_design_scores_gemma":[0.0018855678,0.0007440498,0.035716098,0.0020808934,0.000058197114,0.000011744663,0.0003514247,0.9486982,0.009826785,0.000037962862,0.000046168327,0.00054287177],"about_ca_topic_score_codex":0.0003773425,"about_ca_topic_score_gemma":0.000008976063,"teacher_disagreement_score":0.13950767,"about_ca_system_score_codex":0.00069655804,"about_ca_system_score_gemma":0.00045962495,"threshold_uncertainty_score":0.9994146},"labels":[],"label_agreement":null},{"id":"W7133116688","doi":"","title":"Least restrictive collision avoidance control","year":2004,"lang":"","type":"dissertation","venue":"TSpace","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Collision; Control theory (sociology); Controller (irrigation); Collision avoidance; Robot; Set (abstract data type); Task (project management)","score_opus":0.01883205515210801,"score_gpt":0.32276868276544074,"score_spread":0.30393662761333273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133116688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021548577,0.0033733675,0.95095205,0.0015973205,0.008080786,0.0019060209,0.000041567906,0.0004568408,0.012043483],"genre_scores_gemma":[0.76690376,0.0005547067,0.16584125,0.0006129322,0.0014057385,0.00027368774,0.0003276129,0.00025472225,0.06382559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99339646,0.00046876847,0.0009644064,0.0021323645,0.0017097784,0.0013282406],"domain_scores_gemma":[0.99488854,0.00061592524,0.0013593134,0.0017015216,0.0009016972,0.000532995],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007673074,0.0011087683,0.0012333996,0.0005161184,0.0008908488,0.0006475531,0.0024290811,0.0009319186,0.000047143236],"category_scores_gemma":[0.0007121316,0.0012094802,0.00035368322,0.0018750905,0.00018005051,0.0006551446,0.00017338253,0.0016854737,0.001406603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011225143,0.001228104,0.0007735602,0.00078265683,0.0006769521,0.0023042252,0.14536911,0.79841185,0.004323722,0.01218658,0.004006699,0.028814012],"study_design_scores_gemma":[0.010606012,0.0022336047,0.04593818,0.007314332,0.0005338489,0.0002686458,0.011811763,0.90550196,0.0064468523,0.0022444609,0.00265801,0.004442301],"about_ca_topic_score_codex":0.0011406905,"about_ca_topic_score_gemma":0.000026475718,"teacher_disagreement_score":0.7851108,"about_ca_system_score_codex":0.00093719113,"about_ca_system_score_gemma":0.002400815,"threshold_uncertainty_score":0.99937093},"labels":[],"label_agreement":null},{"id":"W7133839506","doi":"10.1109/prai67447.2025.11412595","title":"An Improved Bat Algorithm with Multi-Strategy for Multi-Robot Path Planning*","year":2025,"lang":"","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Path (computing); Bat algorithm; Sequence (biology); Minification","score_opus":0.06943515986427945,"score_gpt":0.3496480988255362,"score_spread":0.28021293896125676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133839506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022815014,0.0007970196,0.9921031,0.00042000227,0.0023720704,0.0026973772,0.00013621585,0.000829126,0.00041696688],"genre_scores_gemma":[0.02070083,0.000017956421,0.9675135,0.00091728487,0.0002580971,0.0003459365,0.00007613934,0.000098398225,0.010071876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923348,0.00033075942,0.0013623124,0.0030769259,0.0006434524,0.002251788],"domain_scores_gemma":[0.99468696,0.0004655182,0.00055047654,0.0026169,0.0008583996,0.0008217487],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0012484529,0.001312276,0.0012757771,0.0005858612,0.0010204818,0.0015358083,0.0033559026,0.0006651706,0.000027938439],"category_scores_gemma":[0.00014772452,0.001123585,0.00029900635,0.0015633754,0.00042890653,0.0015502921,0.0004916864,0.00090007123,0.00004571886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032246351,0.004308469,0.0027487122,0.0004750305,0.00088526274,0.0005062379,0.0039510326,0.17874698,0.0027346502,0.0017308682,0.0019214351,0.8016689],"study_design_scores_gemma":[0.008056439,0.0029182157,0.0071931677,0.0005754086,0.00017427566,0.000068444744,0.00068920344,0.97670305,0.0016789313,0.00008394858,0.00042736417,0.0014315578],"about_ca_topic_score_codex":0.00041475613,"about_ca_topic_score_gemma":0.000018933255,"teacher_disagreement_score":0.8002373,"about_ca_system_score_codex":0.00026099538,"about_ca_system_score_gemma":0.0016535972,"threshold_uncertainty_score":0.99996287},"labels":[],"label_agreement":null},{"id":"W7162115541","doi":"10.13182/t140-48700","title":"Learning from Demonstration for Robotic Nuclear Waste Management: A DMP-Based Approach","year":2025,"lang":"","type":"article","venue":"Transactions of the American Nuclear Society","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Radioactive waste; Nuclear decommissioning; Nuclear power; Waste disposal; Robot","score_opus":0.014226675638854318,"score_gpt":0.23499690441070106,"score_spread":0.22077022877184674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162115541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009264724,0.00008081342,0.98560715,0.0021031362,0.0007009366,0.0010051552,0.000024480521,0.00022479906,0.0009887803],"genre_scores_gemma":[0.57097983,0.000059032092,0.42782623,0.0005534453,0.000035898734,0.000023970493,0.0000047633935,0.00004747427,0.00046936746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968664,0.0003557529,0.0007047366,0.00092499354,0.00052624324,0.00062188803],"domain_scores_gemma":[0.9973327,0.00034733553,0.00075211126,0.001289894,0.00016259566,0.00011535659],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00045244975,0.00043197977,0.0006950171,0.00013207505,0.0015115358,0.00025468515,0.001932043,0.00015163988,0.000019306806],"category_scores_gemma":[0.000027608718,0.0004205302,0.0012521571,0.0020545383,0.0011093114,0.00030561045,0.000107983724,0.0007749692,0.000013153553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088763234,0.00046399847,0.000069047666,0.00027737426,0.0009548133,6.7923946e-7,0.0040342095,0.94148266,0.0010014973,0.0010713144,0.00080353906,0.049752127],"study_design_scores_gemma":[0.0010457367,0.0002592791,0.0012102054,0.000310164,0.000761833,0.0000019668587,0.008013342,0.987226,0.00014219857,0.0002192603,0.00045438,0.00035561135],"about_ca_topic_score_codex":0.0003763159,"about_ca_topic_score_gemma":0.0000014382774,"teacher_disagreement_score":0.5617151,"about_ca_system_score_codex":0.00024178652,"about_ca_system_score_gemma":0.00018256165,"threshold_uncertainty_score":0.99982464},"labels":[],"label_agreement":null},{"id":"W7164739653","doi":"10.13705/j.issn.1671-6833.2021.03.007","title":"Research Research Research Research Research Research on UAV Trails","year":2021,"lang":"zh","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Motion planning; Any-angle path planning; Path (computing); Ant colony optimization algorithms; Terrain; Key (lock)","score_opus":0.8729177208930546,"score_gpt":0.7434539157474783,"score_spread":0.12946380514557632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164739653","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33307442,0.1623743,0.001503054,0.050990686,0.008861534,0.019442001,0.0008642995,0.00062533893,0.42226437],"genre_scores_gemma":[0.79474837,0.08900197,0.009111489,0.00032929593,0.009093108,0.0028335347,0.00016995567,0.0013512655,0.093361035],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.6481909,0.20973271,0.007346873,0.012151677,0.097720034,0.024857787],"domain_scores_gemma":[0.78367597,0.105794646,0.0010358407,0.014290605,0.088417806,0.006785131],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","open_science","research_integrity","insufficient_payload"],"category_scores_codex":[0.5479874,0.0023000934,0.005028519,0.054329954,0.02562878,0.05405882,0.07372848,0.0032574574,0.018288586],"category_scores_gemma":[0.09898906,0.0023405969,0.001283492,0.11727354,0.020344298,0.011132439,0.07269105,0.08052372,0.009788336],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.002911208,0.007230393,0.015520555,0.0017472316,0.0009915645,0.018236186,0.009681785,0.0014764395,0.068421,0.022189163,0.7863574,0.065237075],"study_design_scores_gemma":[0.008491261,0.0044233347,0.20601328,0.03629773,0.000109413195,0.0011413028,0.034558438,0.022512088,0.10893721,0.32957917,0.2417536,0.006183167],"about_ca_topic_score_codex":0.024217369,"about_ca_topic_score_gemma":0.00093276263,"teacher_disagreement_score":0.5446038,"about_ca_system_score_codex":0.009398466,"about_ca_system_score_gemma":0.03324276,"threshold_uncertainty_score":0.9989738},"labels":[],"label_agreement":null},{"id":"W771571003","doi":"10.1184/r1/6552599","title":"Developing a Framework for Reliable Autonomous Surface Mobility","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ames Research Center; Carnegie Mellon University; National Aeronautics and Space Administration","keywords":"Computer science; Environmental science","score_opus":0.08432370225040367,"score_gpt":0.3228691486973077,"score_spread":0.23854544644690404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W771571003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021220636,0.000095417105,0.98836064,0.0005226556,0.0004525368,0.0004446087,0.008634973,0.00059461594,0.00068233657],"genre_scores_gemma":[0.0060049985,1.9135894e-7,0.99142575,0.00046293688,0.00021014448,0.00010832531,0.0015893527,0.000014608542,0.00018371905],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875194,0.000021103406,0.00018697843,0.00047169815,0.0001624328,0.00040584907],"domain_scores_gemma":[0.99849063,0.00038581315,0.00009522873,0.0006783591,0.00026230206,0.00008767193],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015305591,0.00013732321,0.00015513357,0.000028904418,0.0002216385,0.00016045685,0.00095929933,0.00012713902,0.0021031834],"category_scores_gemma":[0.0015887414,0.00013691858,0.00005125954,0.00032083745,0.000010382286,0.00027447814,0.00030606083,0.00013147635,0.0018838418],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023476043,0.00017696679,0.00042914026,0.0007260598,0.0000875588,0.00005601178,0.003262205,0.0075331703,0.00006729167,0.0593778,0.8987678,0.029492494],"study_design_scores_gemma":[0.00035477572,0.00027272408,0.0037009486,0.0026085153,0.0000049340474,0.000029208015,0.000022907396,0.7255724,0.0059646578,0.045185465,0.21551244,0.00077100575],"about_ca_topic_score_codex":0.0000060989505,"about_ca_topic_score_gemma":6.2180015e-7,"teacher_disagreement_score":0.7180393,"about_ca_system_score_codex":0.000109622866,"about_ca_system_score_gemma":0.00028744043,"threshold_uncertainty_score":0.9988933},"labels":[],"label_agreement":null},{"id":"W780523570","doi":"","title":"NEW APPROACH FOR ROBOTIC GRASPING OF UNKNOWN THREE DIMENSIONAL OBJECTS","year":2013,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Artificial intelligence; Computer vision; Computer science; Human–computer interaction","score_opus":0.02242496731552069,"score_gpt":0.21920527063610254,"score_spread":0.19678030332058186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W780523570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013537065,0.00013939218,0.8833303,0.000021713831,0.000871948,0.00065987464,0.00000218255,0.00012533653,0.11471389],"genre_scores_gemma":[0.0011133383,0.0000025967706,0.5188631,0.000022716578,0.000095344745,0.0000032519092,0.00019145265,0.00003732143,0.47967088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978671,0.00006188041,0.00031830635,0.00084167573,0.0004399302,0.0004711092],"domain_scores_gemma":[0.9983401,0.00014417048,0.000445068,0.0006140867,0.0002452026,0.00021141794],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010860527,0.00041711365,0.00058941694,0.00032931005,0.00015810308,0.00009873391,0.001599104,0.00036402303,0.0016317823],"category_scores_gemma":[0.000029936853,0.0004542952,0.00025270117,0.00070690934,0.000033908887,0.000497759,0.00022433186,0.0003071676,0.0000470317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020320425,0.0003084509,0.00038318234,0.0015380643,0.0007597407,0.00010781399,0.0026446986,0.26384062,0.0010221676,0.032743316,0.010072945,0.6863758],"study_design_scores_gemma":[0.002460838,0.0003485671,0.0019065931,0.00081671623,0.000334082,0.000020238042,0.0007137602,0.9842421,0.0010428444,0.0015713483,0.0051630083,0.0013799302],"about_ca_topic_score_codex":0.00027374478,"about_ca_topic_score_gemma":0.00007088456,"teacher_disagreement_score":0.72040147,"about_ca_system_score_codex":0.00012901484,"about_ca_system_score_gemma":0.00045890672,"threshold_uncertainty_score":0.9997909},"labels":[],"label_agreement":null},{"id":"W92902385","doi":"10.1609/aiide.v2i1.18750","title":"Improving Collaborative Pathfinding Using Map Abstraction","year":2006,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Abstraction; Construct (python library); Plan (archaeology); Space (punctuation); Theoretical computer science; Artificial intelligence; Programming language; Operating system; Geography","score_opus":0.034068287816034194,"score_gpt":0.27213444256292296,"score_spread":0.23806615474688878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W92902385","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6925505,0.00004021226,0.29224047,0.0015325989,0.0011952125,0.00086131075,0.000035500187,0.00011821229,0.0114260325],"genre_scores_gemma":[0.99526185,0.0000036704776,0.0044213836,0.00006543768,0.000066585206,0.000015306627,0.0000017718812,0.000010790993,0.00015320104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982984,0.000012129789,0.00050235813,0.00048182323,0.0003972438,0.00030804277],"domain_scores_gemma":[0.99871904,0.0000945928,0.00054692524,0.00015491984,0.00042133662,0.00006319007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018722341,0.00024648375,0.00023199922,0.00014430168,0.00020126521,0.00080633274,0.0005963091,0.00006220809,0.00000757422],"category_scores_gemma":[0.00012202974,0.00019108129,0.000084118576,0.00028632325,0.00015176571,0.0016907929,0.0003162013,0.00024346478,0.000017838402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033016957,0.0011374311,0.003202166,0.00014291996,0.0001301343,0.000011557318,0.0060879844,0.0023357107,0.24013533,0.47095308,0.00021091849,0.2753226],"study_design_scores_gemma":[0.000087883935,0.0004665736,0.0007146983,0.00062777835,0.000020195945,0.0000242892,0.0044009956,0.44905144,0.51230836,0.03178435,0.00008900894,0.0004244357],"about_ca_topic_score_codex":0.00008885154,"about_ca_topic_score_gemma":0.0000015364769,"teacher_disagreement_score":0.4467157,"about_ca_system_score_codex":0.00016052151,"about_ca_system_score_gemma":0.000057801335,"threshold_uncertainty_score":0.7792069},"labels":[],"label_agreement":null},{"id":"W9358471","doi":"10.1016/s1357-4310(97)01111-8","title":"OpenCL Implimentation of LiDAR Data Processing","year":2014,"lang":"en","type":"article","venue":"Molecular Medicine Today","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Obstacle; Obstacle avoidance; Data processing; Lidar; Architecture; Collision avoidance; Product (mathematics); Real-time computing; Embedded system; Artificial intelligence; Robot; Database; Computer security; Collision","score_opus":0.03390733785117129,"score_gpt":0.3185166103100542,"score_spread":0.2846092724588829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9358471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003652626,0.00039108837,0.99065363,0.0022645409,0.00020790231,0.00011600504,0.0000011666697,0.000071140996,0.002641918],"genre_scores_gemma":[0.63568956,0.0000019604472,0.36331084,0.00079634157,0.00008227848,0.000004298627,0.000040577666,0.000011600399,0.000062530824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985966,0.000100869176,0.00028590282,0.00038756852,0.00043951647,0.0001895483],"domain_scores_gemma":[0.9984677,0.000057563324,0.00016317405,0.0011502587,0.00008100447,0.00008032141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008804852,0.00011124287,0.00020887933,0.00009678175,0.000050729876,0.000020645053,0.0013787635,0.00003599475,0.000009195787],"category_scores_gemma":[0.00026000818,0.00009085572,0.000014643607,0.0003281538,0.00007157433,0.00029461118,0.00038534167,0.00009093131,0.000015011801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009357452,0.00012332204,0.0014492322,0.00021104672,0.000059694044,0.00012372005,0.0033451212,0.0011927307,0.3470735,0.013395124,0.003670763,0.6293464],"study_design_scores_gemma":[0.0019811974,0.00066199794,0.012190273,0.00070919504,0.000080486265,0.000105623236,0.00011574652,0.91885245,0.05618267,0.0027875225,0.005885059,0.00044780367],"about_ca_topic_score_codex":0.000057402314,"about_ca_topic_score_gemma":3.3112008e-7,"teacher_disagreement_score":0.9176597,"about_ca_system_score_codex":0.000013793061,"about_ca_system_score_gemma":0.000056633693,"threshold_uncertainty_score":0.37049887},"labels":[],"label_agreement":null},{"id":"W94728611","doi":"","title":"HOMER: Human Oriented MEssenger Robot","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Human–robot interaction; Computer science; Robot; Artificial intelligence; Mobile robot; Gesture; Computer vision; Mobile robot navigation; Obstacle avoidance; Human–computer interaction; Task (project management); Social robot; Planner; Modular design; Hidden Markov model; Obstacle; Scalability; Robot control; Engineering; Geography","score_opus":0.01763044967629639,"score_gpt":0.25973333372929025,"score_spread":0.24210288405299385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94728611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033949977,0.000059760416,0.93949205,0.00026862178,0.0005803042,0.00008677982,2.7365093e-7,0.0003463453,0.05577089],"genre_scores_gemma":[0.30487588,0.0000016130628,0.68788147,0.00035053285,0.000036751208,0.000009025734,0.0000014691759,0.000011444444,0.0068318383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987792,0.00008225502,0.00017852247,0.00036418525,0.00026535528,0.00033049897],"domain_scores_gemma":[0.99908805,0.00004852951,0.000047186273,0.0006347798,0.00006137157,0.000120098695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002839175,0.00012839261,0.00014196702,0.00009113254,0.00014774204,0.00008194739,0.00050845224,0.00005158573,0.00009072016],"category_scores_gemma":[0.000057311692,0.00011031307,0.000043213495,0.00043323144,0.000029969307,0.0001888392,0.00008049595,0.000118241514,0.00030144013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.3351335e-7,0.0001958204,0.0030344462,0.000010276171,0.00004399885,0.00015988342,0.00083014026,0.0025245326,0.012904427,0.9642647,0.01226419,0.003766764],"study_design_scores_gemma":[0.0070611876,0.0012245635,0.09026205,0.00020406314,0.00008072187,0.0010686734,0.00061000756,0.38711306,0.13614568,0.028829362,0.34192878,0.005471833],"about_ca_topic_score_codex":0.000016664988,"about_ca_topic_score_gemma":8.3848823e-7,"teacher_disagreement_score":0.9354353,"about_ca_system_score_codex":0.000027726333,"about_ca_system_score_gemma":0.000039769322,"threshold_uncertainty_score":0.44984365},"labels":[],"label_agreement":null}]}