{"meta":{"query_hash":"531a2dd68840","filters":{"topic":"Metaheuristic Optimization Algorithms Research"},"cohort_total":762,"direct_labels_cover":0,"predictions_cover":762,"exported":762,"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/531a2dd68840","api":"https://metacan.xera.ac/api/v1/cohort?topic=Metaheuristic+Optimization+Algorithms+Research"},"results":[{"id":"W1134540949","doi":"","title":"Investigating the Application of Opposition-Based Ideas to Ant Algorithms","year":2007,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Metaheuristic Optimization Algorithms Research","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Artificial intelligence; Ant colony optimization algorithms; Computer science; Machine learning; Opposition (politics); Ant colony; Algorithm; Mathematical optimization; Mathematics","score_opus":0.017446497090912904,"score_gpt":0.26927330726046056,"score_spread":0.25182681016954767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1134540949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093094096,0.00005779778,0.902699,0.0023495327,0.00025009984,0.0009688914,0.000037469705,0.00008822546,0.00045491825],"genre_scores_gemma":[0.03711277,0.000027473812,0.90435904,0.00018419477,0.00008005738,0.000006238505,0.0006141536,0.000045656576,0.0575704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979549,0.00014335656,0.00026486767,0.00047001563,0.00088467327,0.0002821932],"domain_scores_gemma":[0.99750125,0.00021891516,0.00045852867,0.00080210983,0.00083497714,0.00018421117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007631443,0.00020028173,0.0003387388,0.0005470635,0.00026109177,0.000049649305,0.0016391176,0.00018219757,0.00003633278],"category_scores_gemma":[0.000102484584,0.00019549874,0.00012658184,0.001132507,0.00012520578,0.00019423463,0.00015584988,0.0002605226,0.000042621385],"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":[0.00027023116,0.0009832451,0.0010803974,0.0028367175,0.0006743574,0.00009620698,0.3284876,0.09118707,0.06889304,0.040492944,0.00739425,0.45760393],"study_design_scores_gemma":[0.0007425738,0.00024384899,0.0040210523,0.00030328208,0.00009272698,0.0000026977873,0.01903699,0.9493243,0.024271747,0.00085428,0.00058975146,0.0005167339],"about_ca_topic_score_codex":0.012243724,"about_ca_topic_score_gemma":0.0030484088,"teacher_disagreement_score":0.85813725,"about_ca_system_score_codex":0.00009287177,"about_ca_system_score_gemma":0.0003241396,"threshold_uncertainty_score":0.9943338},"labels":[],"label_agreement":null},{"id":"W116939436","doi":"10.1007/978-3-642-37198-1_19","title":"Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University; Carleton University; University of Toronto","funders":"","keywords":"Crossover; Computer science; Fitness function; Genetic algorithm; Representation (politics); Cluster analysis; Operator (biology); Fitness landscape; Smoothness; Predictability; Vehicle routing problem; Mathematical optimization; Routing (electronic design automation); Algorithm; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.020595764676756556,"score_gpt":0.23648365873075455,"score_spread":0.215887894053998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W116939436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052777276,0.00009390701,0.9937341,0.00042117905,0.0006019763,0.00087225763,0.0000036096817,0.00013735391,0.0036078035],"genre_scores_gemma":[0.10053175,0.00006983623,0.89802617,0.0008183744,0.00036547985,0.000030239828,0.000004248421,0.000035946505,0.000117929165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955545,0.00012591647,0.0007144262,0.000821729,0.0020277612,0.00075568794],"domain_scores_gemma":[0.99675447,0.00076210557,0.00045044764,0.0012267389,0.0006512755,0.00015494564],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021717406,0.00045346457,0.00035687955,0.00071977713,0.00071342615,0.0015859343,0.0030968792,0.00021681249,0.000069583475],"category_scores_gemma":[0.00032784004,0.00031912272,0.0000807382,0.0008568664,0.0005157107,0.0011829176,0.0011349746,0.0010370145,0.00012922255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015976248,0.000008270064,0.000092517526,0.000027178057,0.000007968622,0.000004115311,0.0006402986,0.3369411,0.000008652683,0.0044955616,0.0000069347716,0.6577658],"study_design_scores_gemma":[0.00016424569,0.00012032309,0.00019152985,0.00034443615,0.00000644024,0.00004150854,7.9603984e-7,0.98798305,0.00031498913,0.010382719,0.00010060395,0.0003493493],"about_ca_topic_score_codex":0.000024035728,"about_ca_topic_score_gemma":0.0000014337426,"teacher_disagreement_score":0.65741646,"about_ca_system_score_codex":0.0002486841,"about_ca_system_score_gemma":0.0005665134,"threshold_uncertainty_score":0.9999261},"labels":[],"label_agreement":null},{"id":"W117128401","doi":"","title":"Parallel implementation of an ant colony optimization metaheuristic with OpenMP","year":2001,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":63,"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":"Metaheuristic; Computer science; Parallel metaheuristic; Ant colony optimization algorithms; Parallel computing; Scheduling (production processes); Job shop scheduling; Mathematical optimization; Graphics; Algorithm; Routing (electronic design automation); Embedded system; Meta-optimization; Mathematics","score_opus":0.021434303378045014,"score_gpt":0.2869342218692247,"score_spread":0.26549991849117965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117128401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040970976,0.0001988273,0.98497105,0.0028863796,0.00014448879,0.0011196006,0.00006147354,0.00022155687,0.0062995],"genre_scores_gemma":[0.072107755,0.0005737326,0.92442477,0.000058158093,0.000019771689,0.00017745805,0.00081696257,0.000055879438,0.0017655264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9897875,0.006419915,0.0009483953,0.00119297,0.0011707,0.00048052735],"domain_scores_gemma":[0.98959476,0.0009340005,0.0011027065,0.0032396088,0.0048136828,0.000315253],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006234899,0.00040826845,0.00060340716,0.0004803634,0.0003074621,0.00081213476,0.0030879083,0.00022919831,0.00035299917],"category_scores_gemma":[0.00082417857,0.0004149052,0.00013193442,0.0010975103,0.00024877157,0.00061247806,0.0018950804,0.0005448598,0.000015036315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096689335,0.0027476263,0.0019977982,0.00064367696,0.0006032226,0.00008883678,0.014278792,0.5689728,0.00043087997,0.21287821,0.00092200417,0.19633943],"study_design_scores_gemma":[0.0011559841,0.0000046289633,0.0015442134,0.000380984,0.00006784924,0.000027507884,0.00012792058,0.98857373,0.0054705516,0.0014636229,0.0006975807,0.00048541583],"about_ca_topic_score_codex":0.0019261732,"about_ca_topic_score_gemma":0.00072756625,"teacher_disagreement_score":0.4196009,"about_ca_system_score_codex":0.00015656717,"about_ca_system_score_gemma":0.00097197655,"threshold_uncertainty_score":0.9998303},"labels":[],"label_agreement":null},{"id":"W118921981","doi":"","title":"Topological order planner for POMDPs","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Université Laval","funders":"","keywords":"Domain (mathematical analysis); Computer science; Exploit; Partially observable Markov decision process; Topology (electrical circuits); Space (punctuation); Planner; Focus (optics); Mathematical optimization; Point (geometry); Order (exchange); Mathematics; Artificial intelligence; Markov chain; Combinatorics; Geometry","score_opus":0.03528534749597181,"score_gpt":0.33101782895301934,"score_spread":0.29573248145704756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118921981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006569165,0.000016421904,0.96871096,0.0080154175,0.000112812224,0.00018597113,8.504222e-7,0.00021786473,0.022674033],"genre_scores_gemma":[0.013021123,0.0000060951083,0.9783615,0.0018823552,0.00006269842,0.0000120646955,0.0000024683711,0.000002469026,0.0066492143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916893,0.000030690986,0.00012441551,0.00023803144,0.00020454249,0.00023337793],"domain_scores_gemma":[0.9993025,0.0001284511,0.000018182482,0.0002900705,0.00016957302,0.00009122562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002441687,0.000064227956,0.00008908021,0.0000617451,0.000074311305,0.00011564221,0.000550295,0.000043642252,0.00027862235],"category_scores_gemma":[0.00023799155,0.00004567105,0.000029061353,0.00027955556,0.000014050658,0.00014351675,0.00006198141,0.000056173296,0.00009230786],"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":[0.000005868995,0.00013466903,0.000018833656,0.0000023221146,0.0000064301175,0.0000072014873,0.000062669584,0.00044666626,0.00003447165,0.919646,0.023202628,0.056432188],"study_design_scores_gemma":[0.0003956507,0.00026341356,0.0005058493,0.0000010412996,0.000001483076,0.000010514966,0.0000056752183,0.93197507,0.00030823966,0.024717275,0.04169167,0.00012412609],"about_ca_topic_score_codex":0.000001559946,"about_ca_topic_score_gemma":2.6144124e-7,"teacher_disagreement_score":0.9315284,"about_ca_system_score_codex":0.000012910959,"about_ca_system_score_gemma":0.000044274104,"threshold_uncertainty_score":0.30507195},"labels":[],"label_agreement":null},{"id":"W129316467","doi":"10.1007/978-3-642-30353-1_37","title":"A Multiagent System to Solve JSSP Using a Multi-Population Cultural Algorithm","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Computer science; Population; Multi-agent system; Convergence (economics); Scheduling (production processes); Artificial intelligence; Distributed computing; Algorithm; Mathematical optimization; Mathematics","score_opus":0.05702662813677803,"score_gpt":0.32145490471459465,"score_spread":0.26442827657781665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W129316467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000031191015,0.0003941156,0.9951375,0.00012685177,0.002611828,0.001150648,0.000015394835,0.00026728777,0.0002651961],"genre_scores_gemma":[0.009015994,0.000014381668,0.98964375,0.0002917611,0.0006504745,0.00002255725,0.000014631945,0.00005225964,0.00029419732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941644,0.00008550591,0.0008513133,0.0017795855,0.0019766726,0.0011425326],"domain_scores_gemma":[0.9964167,0.0002812234,0.00037838303,0.0015542825,0.000761947,0.0006074931],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015680189,0.0006724665,0.0007232863,0.0018187815,0.0004496652,0.0010237162,0.003120528,0.00035928548,0.00003495155],"category_scores_gemma":[0.00021590342,0.0005978914,0.00017153486,0.0012388291,0.00026897073,0.0010285589,0.0021987418,0.00076925446,0.00015797133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003232807,0.00004532117,0.00003313223,0.00007007461,0.00001815979,0.00006648402,0.0011131532,0.20614497,0.00008560678,0.0030468102,0.0000065426143,0.7893665],"study_design_scores_gemma":[0.0003436573,0.00006108809,0.00014473619,0.0003873328,0.000013004014,0.00012997315,7.9934085e-7,0.99723756,0.00022974392,0.0003946255,0.00037673878,0.0006807235],"about_ca_topic_score_codex":0.00017954728,"about_ca_topic_score_gemma":0.000027302702,"teacher_disagreement_score":0.79109263,"about_ca_system_score_codex":0.0013956575,"about_ca_system_score_gemma":0.0003974414,"threshold_uncertainty_score":0.99964726},"labels":[],"label_agreement":null},{"id":"W133409354","doi":"10.1007/978-3-642-37959-8_8","title":"Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units","year":2013,"lang":"en","type":"book-chapter","venue":"Natural computing series","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Computer science; Particle swarm optimization; Memetic algorithm; Graphics; Scheduling (production processes); Multi-swarm optimization; Mathematical optimization; Computer graphics (images); Parallel computing; Artificial intelligence; Algorithm; Local search (optimization); Mathematics","score_opus":0.0388667624364467,"score_gpt":0.27135341513722666,"score_spread":0.23248665270077995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W133409354","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016899767,0.0015057132,0.9901532,0.00044206146,0.0005964324,0.0008117368,0.0000056906606,0.00063283043,0.0041623525],"genre_scores_gemma":[0.14976902,0.00006136324,0.83424926,0.00025151088,0.0002724954,0.0000056355093,0.000049611717,0.00011817411,0.0152229015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968225,0.000102643324,0.0004948423,0.0008615092,0.001152271,0.00056624244],"domain_scores_gemma":[0.9967406,0.00030848308,0.00049704895,0.00062356744,0.0016554228,0.0001749241],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00037962693,0.00055159704,0.00050154526,0.00038195553,0.0007543969,0.001112625,0.00090382976,0.00029072035,0.000022571467],"category_scores_gemma":[0.00041929036,0.0004567578,0.00007203997,0.0006754223,0.00022812054,0.00079183746,0.00042566497,0.0012075108,0.000018809502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003835326,0.000019470672,0.000017872066,0.00015148162,0.00006941869,0.000032329845,0.00036754893,0.9367361,0.000021620785,0.04830033,0.000055423243,0.014190051],"study_design_scores_gemma":[0.00036469483,0.00015133737,0.000023629918,0.0006820313,0.00003219453,0.000077638164,0.000025377312,0.9968045,0.0004095266,0.0005080896,0.00036082248,0.00056018936],"about_ca_topic_score_codex":0.000023491613,"about_ca_topic_score_gemma":0.000008010799,"teacher_disagreement_score":0.15590392,"about_ca_system_score_codex":0.00013135147,"about_ca_system_score_gemma":0.0003431942,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W136384337","doi":"","title":"A Kohonen-like decomposition method for the traveling salesman problem—KNIES_DECOMPOSE","year":2000,"lang":"en","type":"article","venue":"European Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Travelling salesman problem; Euclidean geometry; Partition (number theory); 2-opt; Heuristic; Self-organizing map; Computer science; Mathematical optimization; Bottleneck traveling salesman problem; Decomposition; Artificial neural network; Traveling purchaser problem; Euclidean distance; Mathematics; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.11076987322655765,"score_gpt":0.37623869320672243,"score_spread":0.2654688199801648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W136384337","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001364752,0.000058759597,0.9754575,0.003392072,0.00038188562,0.0008990545,0.000012828743,0.00023180156,0.019429611],"genre_scores_gemma":[0.17546797,0.00035896577,0.81849295,0.0014017173,0.0003477509,0.0001212099,0.000022714226,0.00006832259,0.0037183878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964073,0.0008543801,0.0007430866,0.0008402077,0.0005727174,0.0005823247],"domain_scores_gemma":[0.99725163,0.001043839,0.00015730182,0.0009925598,0.00035319218,0.00020149887],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0023666585,0.0002995902,0.00025532788,0.00013270347,0.00066994416,0.0009900723,0.0021963145,0.000051658284,0.0012210517],"category_scores_gemma":[0.00015796858,0.00023566654,0.00014629925,0.00062750647,0.00016039719,0.00030521053,0.00017294516,0.00038167683,0.0023157028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060458995,0.000118074066,5.131996e-7,0.000009397104,0.000021867903,0.000014087846,0.0007009565,0.0233527,0.0005791508,0.120523624,0.00026786546,0.8543513],"study_design_scores_gemma":[0.00007221663,0.0002555154,0.00003819004,0.00005479262,0.000012811468,0.000020281217,0.0000851205,0.9699511,0.0036279545,0.014045563,0.011538015,0.0002984543],"about_ca_topic_score_codex":0.000024271685,"about_ca_topic_score_gemma":0.000015661188,"teacher_disagreement_score":0.9465984,"about_ca_system_score_codex":0.00005031876,"about_ca_system_score_gemma":0.000115599876,"threshold_uncertainty_score":0.99969196},"labels":[],"label_agreement":null},{"id":"W141825768","doi":"10.1007/978-3-540-95974-8_4","title":"Ant Colony Optimization for Option Pricing","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Mathematical optimization; Binomial options pricing model; Dynamic pricing; Valuation of options; Heuristic; Optimization problem; Finance; Algorithm; Mathematics; Economics","score_opus":0.15000555853926176,"score_gpt":0.4058184447291009,"score_spread":0.2558128861898391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W141825768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.5650362e-7,0.001701458,0.9767028,0.0007543533,0.00075722474,0.0010466669,0.000018207267,0.000110148794,0.018908791],"genre_scores_gemma":[0.0005009023,0.002140654,0.9582651,0.00029275086,0.00021433482,0.00009378038,0.00010098783,0.000037353468,0.0383541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970134,0.00005704167,0.00091488793,0.0008556935,0.00079885806,0.00036010158],"domain_scores_gemma":[0.9959711,0.0015825493,0.00040100017,0.00038624305,0.0015778153,0.00008128698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008240795,0.00037765238,0.00055114564,0.0006389411,0.00023089306,0.0001401607,0.00089032727,0.00018957375,0.000045418336],"category_scores_gemma":[0.00070514245,0.0003952756,0.00012405337,0.0002937661,0.00021991048,0.0002994058,0.0003579891,0.00034116904,0.00004468938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008155136,0.000019847346,6.506669e-7,0.000054343673,0.00004206946,0.000010273215,0.00020952916,0.6090292,9.474764e-8,0.338965,0.00040286378,0.05125798],"study_design_scores_gemma":[0.00010440111,0.00014106143,0.000003899204,0.00023522387,0.000010409052,0.000011049068,0.000021404354,0.75033265,0.000007065796,0.24579217,0.0030618666,0.00027881193],"about_ca_topic_score_codex":0.000002434545,"about_ca_topic_score_gemma":0.0000039749198,"teacher_disagreement_score":0.14130346,"about_ca_system_score_codex":0.00051351095,"about_ca_system_score_gemma":0.00027568362,"threshold_uncertainty_score":0.9998499},"labels":[],"label_agreement":null},{"id":"W1485070158","doi":"10.1007/978-3-642-16493-4_10","title":"Diversity Analysis of Opposition-Based Differential Evolution—An Experimental Study","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Ode; Differential evolution; Opposition (politics); Computer science; Diversification (marketing strategy); Convergence (economics); Mathematical optimization; Diversity (politics); Applied mathematics; Mathematics; Algorithm; Sociology; Political science","score_opus":0.02656602512742368,"score_gpt":0.2934900456550681,"score_spread":0.2669240205276444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485070158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005546341,0.00002560867,0.99274224,0.0000590493,0.0008351195,0.0005142379,0.000017353666,0.00007595305,0.00018407256],"genre_scores_gemma":[0.7923482,6.69761e-7,0.20743714,0.000061139806,0.00008298084,0.000006158635,0.000014792594,0.000011724999,0.00003717447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953766,0.00012985813,0.0005308644,0.0014056271,0.002132444,0.00042462337],"domain_scores_gemma":[0.99684155,0.00036009582,0.00032438818,0.0017349711,0.0004853643,0.00025361974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084603275,0.00037923938,0.000673645,0.0027415983,0.00081599003,0.00028380874,0.003942667,0.00023273533,0.00024720744],"category_scores_gemma":[0.000071949704,0.00036376825,0.00021039718,0.0017915356,0.0007817123,0.0004696823,0.0036821608,0.000685321,0.0000065937033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007665809,0.0050565363,0.008887694,0.00005745023,0.000824751,0.0002369275,0.009327479,0.89848846,0.004939873,0.032594904,0.0000050277795,0.039504267],"study_design_scores_gemma":[0.0004002989,0.000503977,0.002086414,0.000019549052,0.00010719044,0.0000019479303,9.694223e-7,0.9908052,0.0021262406,0.0035917803,0.0000024195688,0.0003539882],"about_ca_topic_score_codex":0.0001512327,"about_ca_topic_score_gemma":0.00019239236,"teacher_disagreement_score":0.7868019,"about_ca_system_score_codex":0.00042379153,"about_ca_system_score_gemma":0.00053323444,"threshold_uncertainty_score":0.99988145},"labels":[],"label_agreement":null},{"id":"W1485502543","doi":"10.1007/s10898-016-0467-8","title":"Geodesic and contour optimization using conformal mapping","year":2016,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","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":"Geodesic; Mathematics; Local optimum; Conformal map; Curse of dimensionality; Differentiable function; Function (biology); Jump; Mathematical optimization; Algorithm; Mathematical analysis","score_opus":0.02831613189250692,"score_gpt":0.28467594396953333,"score_spread":0.2563598120770264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485502543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014713629,0.00015470355,0.99642026,0.0009293904,0.00037783443,0.00013821508,0.0000048844026,0.000032139793,0.00047118415],"genre_scores_gemma":[0.0749232,0.00030908384,0.9244711,0.00011998678,0.00011496009,9.605392e-7,0.0000010686213,0.000009279625,0.000050351147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980877,0.00015250575,0.00068249763,0.00020146722,0.0006006565,0.00027516115],"domain_scores_gemma":[0.99777067,0.00010146236,0.00065070833,0.00021450373,0.0010446805,0.0002179711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078931195,0.0001507956,0.00026601125,0.00018791133,0.0001311759,0.00027580833,0.00044816243,0.000089666486,0.000070988586],"category_scores_gemma":[0.00053465046,0.00010826294,0.00006357467,0.00061727053,0.00007440724,0.0020881454,0.00015648027,0.00008616761,0.000003008448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019363693,0.000025808096,0.0009683262,0.000008135626,0.000031197436,0.00001521039,0.00003645977,0.9843761,0.00006934124,0.0034387233,0.00010681222,0.010904569],"study_design_scores_gemma":[0.0012132357,0.00009489556,0.00061866816,0.00008678258,0.000014666384,0.00035327475,0.000020739704,0.9970997,0.000031206593,0.0001946739,0.00013509222,0.00013705519],"about_ca_topic_score_codex":0.000005637293,"about_ca_topic_score_gemma":3.619555e-7,"teacher_disagreement_score":0.07345184,"about_ca_system_score_codex":0.0002640262,"about_ca_system_score_gemma":0.0002847804,"threshold_uncertainty_score":0.44148347},"labels":[],"label_agreement":null},{"id":"W1488040414","doi":"10.1007/978-3-642-13059-5_20","title":"The Ant Search Algorithm: An Ant Colony Optimization Algorithm for the Optimal Searcher Path Problem with Visibility","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Defence Research and Development Canada; Université Laval","funders":"","keywords":"Ant colony optimization algorithms; Visibility; Computer science; Path (computing); Solver; Mathematical optimization; Algorithm; Integer programming; Search algorithm; Object (grammar); Motion planning; Artificial intelligence; Mathematics; Robot","score_opus":0.02427545259411336,"score_gpt":0.29298416388863086,"score_spread":0.2687087112945175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488040414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043270225,0.00029362194,0.99280417,0.0021092847,0.00093941414,0.0034052194,0.00005432023,0.00017038059,0.00021927731],"genre_scores_gemma":[0.00020615404,0.00019951115,0.997603,0.0003411488,0.0006430206,0.00017766796,0.000031609434,0.00008499665,0.0007129132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9916563,0.0003081082,0.0008436082,0.002493386,0.0031451113,0.001553494],"domain_scores_gemma":[0.9900433,0.0034529832,0.0003829951,0.0032561682,0.0024015605,0.00046299075],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.0082814675,0.0007614828,0.00063607265,0.0005295425,0.002280093,0.003075215,0.0074833306,0.00047257828,0.000051545205],"category_scores_gemma":[0.00031943375,0.00043383604,0.00016302726,0.0013899883,0.0029638456,0.0009963241,0.0020054951,0.002365087,0.000013043069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016510097,0.00006102714,0.000003274432,0.00001785751,0.000024613457,0.000022362445,0.00041647724,0.34896562,0.000009759538,0.001302617,0.0000108974245,0.649149],"study_design_scores_gemma":[0.0005856905,0.000840485,0.00002576289,0.00008759455,0.000017486305,0.00009071449,0.000002316896,0.99045885,0.00037051397,0.005372281,0.0015561215,0.00059216353],"about_ca_topic_score_codex":0.00008374401,"about_ca_topic_score_gemma":0.00008887864,"teacher_disagreement_score":0.6485568,"about_ca_system_score_codex":0.000474107,"about_ca_system_score_gemma":0.0029342922,"threshold_uncertainty_score":0.9999365},"labels":[],"label_agreement":null},{"id":"W1490136960","doi":"10.1007/978-3-540-78761-7_60","title":"Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Computer science; Adaptation (eye); Recombination; Algorithm; Biology; Genetics; Neuroscience","score_opus":0.05761800603408946,"score_gpt":0.30842489990165806,"score_spread":0.2508068938675686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490136960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003567755,0.0001767119,0.9955435,0.00020848856,0.0013609275,0.0012572376,0.000012869634,0.00013180019,0.0012728209],"genre_scores_gemma":[0.035933074,0.000065536464,0.9632144,0.00008194071,0.0002835093,0.000027128954,0.000019744892,0.000037666687,0.0003369858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956207,0.000125253,0.0006855766,0.0015022133,0.0014486208,0.00061759277],"domain_scores_gemma":[0.9956118,0.001296783,0.00055986294,0.0008118539,0.0015649748,0.00015476387],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010523936,0.00049199595,0.0005091546,0.0014549937,0.0006222194,0.0006716966,0.0017152345,0.00032519022,0.000014868504],"category_scores_gemma":[0.00043991703,0.00047835294,0.00013319284,0.0009860296,0.00063269446,0.002044593,0.00048053608,0.0005855573,0.000013515883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014561531,0.00003830636,0.0000030528581,0.000037785605,0.000020731173,0.000013163774,0.0019335513,0.63964283,0.000018377943,0.083794236,0.00001769971,0.27446568],"study_design_scores_gemma":[0.0004168997,0.00018481698,0.000041818945,0.00014874889,0.00000753422,0.000022686936,0.0000019648212,0.8013845,0.00020358637,0.19704008,0.00014789254,0.00039948116],"about_ca_topic_score_codex":0.00008057506,"about_ca_topic_score_gemma":0.00005782959,"teacher_disagreement_score":0.2740662,"about_ca_system_score_codex":0.00113326,"about_ca_system_score_gemma":0.0020213916,"threshold_uncertainty_score":0.9997668},"labels":[],"label_agreement":null},{"id":"W1491708267","doi":"10.1007/3-540-45034-3_70","title":"Using Local Information to Guide Ant Based Search","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Travelling salesman problem; Computer science; Ant colony optimization algorithms; Ant colony; Mathematical optimization; Artificial intelligence; ANT; Local search (optimization); Local optimum; Theoretical computer science; Mathematics; Algorithm; Computer network","score_opus":0.061170187621450635,"score_gpt":0.3404723376348202,"score_spread":0.27930215001336955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491708267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005070136,0.000039761602,0.9933189,0.00068389514,0.0010341295,0.0006416468,0.000007085217,0.00013037071,0.0041391244],"genre_scores_gemma":[0.0029308163,0.000006305027,0.99354875,0.0030806246,0.00022029685,0.000004120543,0.000008581402,0.000024073674,0.00017640773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942904,0.00006473928,0.00084239925,0.0010659074,0.0027719333,0.00096462114],"domain_scores_gemma":[0.9961019,0.0005848827,0.00018853317,0.0015305354,0.0011243621,0.0004697424],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033636116,0.00044303449,0.00044724642,0.002978141,0.00030095785,0.0010310115,0.0037129815,0.00029628442,0.000075019234],"category_scores_gemma":[0.00031654438,0.00042214312,0.000096258635,0.0019038058,0.0005603797,0.0011387945,0.0017612781,0.0008529372,0.00021285505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035958556,0.000007983916,0.0000048812562,0.00001910833,0.0000024630738,0.000032446296,0.00016927025,0.51062715,0.000012229192,0.0026853476,0.000019327266,0.48641616],"study_design_scores_gemma":[0.00022049111,0.00011457081,0.000026310046,0.00018149502,0.0000027872914,0.000036647143,2.3397482e-7,0.99105906,0.0014165266,0.0028586425,0.0036289042,0.00045431816],"about_ca_topic_score_codex":0.00008762775,"about_ca_topic_score_gemma":0.000023201568,"teacher_disagreement_score":0.48596185,"about_ca_system_score_codex":0.0010675478,"about_ca_system_score_gemma":0.002243726,"threshold_uncertainty_score":0.99982303},"labels":[],"label_agreement":null},{"id":"W1493450895","doi":"10.1007/3-540-47922-8_15","title":"Genetic Algorithms for Continuous Problems","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Crossover; Computer science; Encoding (memory); Algorithm; Bit array; String (physics); Decoding methods; Floating point; Range (aeronautics); Genetic algorithm; Chromosome; Point (geometry); Theoretical computer science; Mathematics; Artificial intelligence; Type (biology); Machine learning","score_opus":0.03575385609887436,"score_gpt":0.2751730455615375,"score_spread":0.23941918946266313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493450895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015627191,0.00078492967,0.99202573,0.0006939045,0.0017474525,0.0015015742,0.000016662167,0.00020649111,0.0030216756],"genre_scores_gemma":[0.00069265044,0.00015995643,0.9936289,0.0005943713,0.0006026316,0.000078107965,0.0000062642957,0.00006411023,0.004173004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944125,0.00005014514,0.0007886336,0.0020908357,0.0015846591,0.0010732537],"domain_scores_gemma":[0.99589384,0.0008106294,0.0003491314,0.0018080944,0.0008185666,0.00031974027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011416496,0.00060845027,0.00073085004,0.001148969,0.00032199244,0.0010080735,0.0046964977,0.0003702292,0.00010731202],"category_scores_gemma":[0.00032209224,0.0005693126,0.0001872108,0.0008179936,0.00070758344,0.0004145597,0.0012691874,0.000723429,0.00011169937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002050481,0.000042133488,0.000009515695,0.000063944775,0.000018785686,0.000059479415,0.0002791464,0.08451658,0.000014507739,0.0066730124,0.00014732097,0.9081735],"study_design_scores_gemma":[0.00042657118,0.00024884418,0.00002332347,0.00013526097,0.000008384576,0.000080150654,3.8008054e-8,0.92497313,0.00012097639,0.065407194,0.007972351,0.0006037865],"about_ca_topic_score_codex":0.00001171529,"about_ca_topic_score_gemma":0.000008357678,"teacher_disagreement_score":0.90756977,"about_ca_system_score_codex":0.00029208403,"about_ca_system_score_gemma":0.0005015137,"threshold_uncertainty_score":0.9996758},"labels":[],"label_agreement":null},{"id":"W1495151533","doi":"","title":"Genetic Weighted K-means for Large-Scale Clustering Problems.","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Saskatchewan","funders":"","keywords":"Crossover; Cluster analysis; Genetic algorithm; Computer science; Operator (biology); Selection (genetic algorithm); Mutation; Algorithm; Mathematics; Artificial intelligence; Machine learning; Genetics; Biology","score_opus":0.022238352882183142,"score_gpt":0.27861319892262676,"score_spread":0.25637484604044364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495151533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011056405,0.000062786916,0.99112105,0.0020584073,0.00013069143,0.00052682974,0.000004799085,0.00022128868,0.0057635815],"genre_scores_gemma":[0.004741686,0.00002253934,0.98600686,0.00037885323,0.0001461269,0.00009420728,0.0000035042895,0.00001539327,0.0085908305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985038,0.000045344736,0.0002747153,0.0003944554,0.00032965574,0.00045205333],"domain_scores_gemma":[0.99900633,0.00009018829,0.000045695986,0.00051072496,0.00019708295,0.00014996306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038521193,0.00011361714,0.00013549135,0.00013412428,0.00014570709,0.00020063197,0.0007671908,0.000048843627,0.0003281027],"category_scores_gemma":[0.000044055847,0.00009944009,0.00005552474,0.00035612003,0.000015490734,0.00027701724,0.0002867813,0.00007855629,0.00018777493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025959524,0.0010115973,0.00083315664,0.000317441,0.00012345923,0.000013360058,0.0036822949,0.244141,0.00094228936,0.055554576,0.040666442,0.65268844],"study_design_scores_gemma":[0.0005060799,0.00003981702,0.00016621173,0.0000051602083,0.0000024275123,0.000006938545,0.000007899478,0.92327625,0.0003166658,0.00038662629,0.07516111,0.00012483398],"about_ca_topic_score_codex":0.000004834008,"about_ca_topic_score_gemma":0.00006292951,"teacher_disagreement_score":0.67913526,"about_ca_system_score_codex":0.000042811585,"about_ca_system_score_gemma":0.00006250853,"threshold_uncertainty_score":0.4055049},"labels":[],"label_agreement":null},{"id":"W1501269181","doi":"10.1109/cec.2015.7257044","title":"Evaluating landscape characteristics of dynamic benchmark functions","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Dynamism; Benchmark (surveying); Representation (politics); Computer science; Modal; Work (physics); Geography; Engineering; Cartography","score_opus":0.08016350475788461,"score_gpt":0.3685295591600608,"score_spread":0.2883660544021762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501269181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070589297,0.000031432974,0.97632295,0.00030286776,0.00036966708,0.00011807793,0.0000056768044,0.00007587476,0.015714543],"genre_scores_gemma":[0.3467135,0.0000075986018,0.64724326,0.00005388152,0.000035788926,0.000012348691,0.000024435803,0.000007955737,0.0059012207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986027,0.00011314767,0.0002910384,0.00021753447,0.0005989933,0.00017660313],"domain_scores_gemma":[0.998506,0.0001439036,0.00010105275,0.00048494904,0.00061170576,0.00015240413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010299452,0.00007548481,0.00014603251,0.00014970165,0.000047851914,0.00007445586,0.000484911,0.00003388234,0.0003266138],"category_scores_gemma":[0.0009023358,0.00006515914,0.00003126337,0.00054565864,0.000030791285,0.0002088303,0.00023164143,0.000088183304,0.00013428617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052956657,0.00056102464,0.0073292092,0.00009817525,0.00012915206,0.00002253236,0.001371185,0.0061197407,0.000878813,0.037498984,0.016317816,0.9296204],"study_design_scores_gemma":[0.00026568893,0.00013541571,0.003396887,0.0000048939137,0.000004480453,0.0000063330267,0.000043338918,0.9952155,0.00003089339,0.00047094794,0.00035173603,0.000073900555],"about_ca_topic_score_codex":0.000009780785,"about_ca_topic_score_gemma":0.0000016516964,"teacher_disagreement_score":0.98909575,"about_ca_system_score_codex":0.000028662094,"about_ca_system_score_gemma":0.00024071643,"threshold_uncertainty_score":0.35761923},"labels":[],"label_agreement":null},{"id":"W1502618581","doi":"10.1109/mwsym.2015.7167073","title":"Parallel gradient-based local search accelerating particle swarm optimization for training microwave neural network models","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Computer science; Message Passing Interface; Microwave; Multi-swarm optimization; Local search (optimization); Process (computing); Algorithm; Speedup; Mathematical optimization; Parallel computing; Message passing; Artificial intelligence; Mathematics","score_opus":0.20561438215051492,"score_gpt":0.3283289173993272,"score_spread":0.12271453524881226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502618581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011372205,0.000061554994,0.99530786,0.0013860533,0.0002559251,0.0006882712,0.000002514167,0.0002476483,0.0009129559],"genre_scores_gemma":[0.32709715,0.0000019538898,0.6720695,0.00039601632,0.00011015136,0.00008008414,0.000020259158,0.000021156286,0.00020370999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971121,0.00025656878,0.00046704634,0.0005955583,0.0006773636,0.00089136977],"domain_scores_gemma":[0.9978886,0.00035185248,0.00008728323,0.000500579,0.000677353,0.0004942873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016432108,0.00020397201,0.00025876032,0.000101042635,0.0002854503,0.0005559928,0.00080601126,0.00008241549,0.000027640817],"category_scores_gemma":[0.00018943989,0.00019051577,0.00008676719,0.0007940689,0.00009193299,0.00081811927,0.00022162378,0.00018573547,0.000013737242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031379932,0.000050296007,0.000021468428,0.000009712838,0.000011428069,0.000004872787,0.00060185115,0.95426637,0.00001056367,0.019959478,0.00076032954,0.024272246],"study_design_scores_gemma":[0.0015649125,0.00017937254,0.0000035819514,0.00000900599,0.0000057242924,0.0000065242652,0.00022048561,0.99571955,0.00048748674,0.0015261655,0.000052578267,0.00022463371],"about_ca_topic_score_codex":0.00002984856,"about_ca_topic_score_gemma":0.000009700466,"teacher_disagreement_score":0.32595995,"about_ca_system_score_codex":0.00011056608,"about_ca_system_score_gemma":0.000424656,"threshold_uncertainty_score":0.77690077},"labels":[],"label_agreement":null},{"id":"W1510216528","doi":"10.1007/11546245_4","title":"A Taxonomy of Cooperative Search Algorithms","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":88,"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":"Heuristics; Computer science; Implementation; Search algorithm; Incremental heuristic search; Heuristic; Taxonomy (biology); Best-first search; Meta heuristic; Theoretical computer science; Algorithm; Beam search; Artificial intelligence","score_opus":0.04515293231171504,"score_gpt":0.29364136654187756,"score_spread":0.2484884342301625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510216528","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000046353975,0.00039960438,0.9883062,0.00060253864,0.0006370548,0.00083785923,0.000014588949,0.000085654414,0.009111828],"genre_scores_gemma":[0.0046536564,0.000115933726,0.9923281,0.00036881198,0.00041423776,0.000027883947,0.000006665468,0.000035822803,0.0020488966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99462867,0.00010534069,0.0008034533,0.0016248035,0.002049104,0.00078860234],"domain_scores_gemma":[0.9957311,0.0007648059,0.00028392868,0.0017180515,0.0012144924,0.00028761802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001773269,0.00051255315,0.00075684686,0.0015386214,0.0002033784,0.0004501314,0.004663221,0.00030043448,0.00022253621],"category_scores_gemma":[0.00023204264,0.0004674606,0.00013833141,0.0013473453,0.0013437272,0.00066178164,0.0021638644,0.0010783848,0.00010430711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041946714,0.000047005506,0.00000971157,0.00003118558,0.000018328918,0.000050811188,0.0003771743,0.16416882,0.000036388054,0.017425233,0.00003214497,0.817799],"study_design_scores_gemma":[0.00033001718,0.00020257013,0.000019654872,0.00017082415,0.0000046408804,0.000043524316,2.1418224e-7,0.9879169,0.0024837335,0.004774885,0.0035782517,0.00047477233],"about_ca_topic_score_codex":0.000028359198,"about_ca_topic_score_gemma":0.000019222012,"teacher_disagreement_score":0.8237481,"about_ca_system_score_codex":0.0003589355,"about_ca_system_score_gemma":0.0018336877,"threshold_uncertainty_score":0.99977773},"labels":[],"label_agreement":null},{"id":"W1520561082","doi":"","title":"Towards a Guided Cooperative Search","year":2010,"lang":"en","type":"article","venue":"reroDoc Digital Library","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Montréal; Université de Montréal","funders":"","keywords":"Thread (computing); Metaheuristic; Computer science; Beam search; Robustness (evolution); Bidirectional search; Mathematical optimization; Search algorithm; Theoretical computer science; Algorithm; Best-first search; Mathematics; Programming language","score_opus":0.025233261105302027,"score_gpt":0.2798107181671884,"score_spread":0.25457745706188634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520561082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014927658,0.00006222156,0.5065268,0.0071415114,0.0007415873,0.0005682162,0.00016468046,0.0010916642,0.4687756],"genre_scores_gemma":[0.46882948,0.00004685198,0.4828598,0.001246737,0.0005127058,0.000049182752,0.0002165947,0.00009335463,0.046145312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806786,0.00007730783,0.00028662593,0.0005095985,0.00063613185,0.0004224985],"domain_scores_gemma":[0.99846524,0.00015074368,0.000039762046,0.0008539307,0.00014820811,0.0003421401],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021179128,0.00017041243,0.00018935947,0.0001984783,0.0001168725,0.0024977624,0.0016167805,0.00008064426,0.00080614665],"category_scores_gemma":[0.00037055026,0.00014496049,0.00007053763,0.0009861422,0.0001368827,0.005104047,0.0011734082,0.0004232128,0.0011481828],"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":[0.000024713247,0.0004181591,0.0016138689,0.000043229003,0.000075428616,0.0003026122,0.00045726128,0.00043653746,0.00090206985,0.51433635,0.11898608,0.3624037],"study_design_scores_gemma":[0.0014637497,0.00038744483,0.0041437694,0.00004041442,0.0000056091285,0.00032600827,0.000045022047,0.49304843,0.033372343,0.013011389,0.4529486,0.0012072286],"about_ca_topic_score_codex":0.0000026717323,"about_ca_topic_score_gemma":1.7101816e-7,"teacher_disagreement_score":0.50132495,"about_ca_system_score_codex":0.000009679017,"about_ca_system_score_gemma":0.0004834651,"threshold_uncertainty_score":0.99962956},"labels":[],"label_agreement":null},{"id":"W1522384369","doi":"10.1109/icec.1995.487445","title":"A fitness scaling method based on a span measure","year":2002,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Montréal","funders":"","keywords":"Scaling; Life span; Computer science; Span (engineering); Invariant (physics); Scale invariance; Transformation (genetics); Measure (data warehouse); Population; Scaling law; Artificial intelligence; Statistical physics; Theoretical computer science; Algorithm; Mathematics; Statistics; Data mining; Engineering; Physics; Geometry","score_opus":0.06681664661226051,"score_gpt":0.31897107459344504,"score_spread":0.25215442798118454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522384369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010459357,0.00001753029,0.9489656,0.003916439,0.00012458443,0.00014466136,8.457651e-7,0.00020588351,0.04661399],"genre_scores_gemma":[0.024919257,0.0000029332236,0.9696484,0.0013480105,0.0000432867,0.000017733786,7.55252e-7,0.000011316361,0.0040082666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979508,0.000316185,0.00018498133,0.00041710844,0.0008211535,0.00030979505],"domain_scores_gemma":[0.9984639,0.0003823295,0.00003926883,0.0007252319,0.00021150544,0.00017772602],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010341465,0.00011733614,0.00014742275,0.00023060033,0.00011732499,0.00023576699,0.0008041214,0.00005182058,0.0017200205],"category_scores_gemma":[0.00042210508,0.00009587793,0.000059770344,0.0008632047,0.000021189668,0.00016392095,0.00010697103,0.00016348524,0.00047031246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007802824,0.00033973317,0.00006914662,0.000024830279,0.000020316322,0.00005762335,0.00026843874,0.2132318,0.000045544803,0.02947538,0.008910486,0.7475489],"study_design_scores_gemma":[0.0002982846,0.000039466373,0.000053826436,0.000011766602,0.0000015946655,0.000004037338,0.000004037713,0.9962133,0.0008105914,0.00020792014,0.0022334058,0.00012176134],"about_ca_topic_score_codex":0.000013454034,"about_ca_topic_score_gemma":8.6436324e-7,"teacher_disagreement_score":0.7829815,"about_ca_system_score_codex":0.00002741376,"about_ca_system_score_gemma":0.000035209767,"threshold_uncertainty_score":0.99919254},"labels":[],"label_agreement":null},{"id":"W1523796837","doi":"10.1007/978-3-642-11842-5_73","title":"A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":23,"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":"Particle swarm optimization; Benchmark (surveying); Multi-swarm optimization; Computer science; Mathematical optimization; Transformation (genetics); Local optimum; Metaheuristic; Algorithm; Mathematics","score_opus":0.026336877777956808,"score_gpt":0.2683005472629652,"score_spread":0.2419636694850084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1523796837","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005966831,0.00003309332,0.9960283,0.0017101151,0.00033357673,0.0007443681,0.000017401226,0.00015181107,0.0009216717],"genre_scores_gemma":[0.042535994,0.000039722683,0.9565268,0.00059946836,0.00009489256,0.000019635463,0.000015951216,0.000033682412,0.00013384745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99558866,0.00008554126,0.000502316,0.0013417688,0.0018015745,0.0006801161],"domain_scores_gemma":[0.99745816,0.00042083376,0.00013784732,0.0011170187,0.0005302606,0.0003358994],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017267527,0.0004510254,0.00042901578,0.00084241555,0.00041052364,0.0008671596,0.001636355,0.00027000677,0.000020474963],"category_scores_gemma":[0.00016111415,0.00042823155,0.000078178025,0.00060313113,0.0006223087,0.0007483672,0.00044818246,0.0011981234,0.00001437272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007502899,0.00003262043,5.296522e-7,0.00002078082,0.0000029024022,0.000012926336,0.00031003295,0.7570735,0.000022749962,0.0038447038,0.0000010701634,0.23867066],"study_design_scores_gemma":[0.00055333547,0.0001492963,0.00000460977,0.00008850603,0.0000056493436,0.000019358082,1.0591235e-7,0.982623,0.004477621,0.011636533,0.00001744179,0.00042457733],"about_ca_topic_score_codex":0.000020662199,"about_ca_topic_score_gemma":0.0000075213406,"teacher_disagreement_score":0.2382461,"about_ca_system_score_codex":0.00022783184,"about_ca_system_score_gemma":0.0008676388,"threshold_uncertainty_score":0.99981695},"labels":[],"label_agreement":null},{"id":"W1524160437","doi":"10.1007/978-3-642-01085-9_17","title":"PSO_Bounds: A New Hybridization Technique of PSO and EDAs","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"EDAS; Estimation of distribution algorithm; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Computer science; Population; Artificial intelligence; Mathematics; Geography; Medicine","score_opus":0.1090581124548539,"score_gpt":0.3856699672446664,"score_spread":0.2766118547898125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524160437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.6221763e-7,0.0044540144,0.96495616,0.00049501134,0.00020229183,0.0005289142,0.000008523651,0.000056391764,0.029297804],"genre_scores_gemma":[0.0028257833,0.0061737797,0.93408525,0.00020185582,0.00013939149,0.000030865787,0.000028762734,0.00003504284,0.056479294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975138,0.000055678825,0.0008290147,0.0006306025,0.0007573874,0.00021354495],"domain_scores_gemma":[0.99753976,0.0007854212,0.00036465906,0.00036611196,0.0008532425,0.00009081808],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005608844,0.0003025612,0.00053220306,0.0006202056,0.000087504166,0.00007081276,0.000698616,0.00013650981,0.00003928617],"category_scores_gemma":[0.0004588816,0.0003117953,0.00006677953,0.00030362402,0.00040560114,0.00020966158,0.0005150171,0.00034200447,0.000020041989],"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":[0.000007800328,0.000027210017,0.0000058906717,0.00011298538,0.00007612095,0.000029916362,0.00058243796,0.063685544,0.0000014924859,0.76268375,0.0011813783,0.17160548],"study_design_scores_gemma":[0.00008911621,0.00014842999,0.000027324064,0.00049942423,0.0000111257195,0.00004762954,0.000022959966,0.16133776,0.00011643051,0.8333335,0.0040557743,0.00031049628],"about_ca_topic_score_codex":0.000015518466,"about_ca_topic_score_gemma":0.000009087121,"teacher_disagreement_score":0.17129499,"about_ca_system_score_codex":0.00014980428,"about_ca_system_score_gemma":0.00043365546,"threshold_uncertainty_score":0.9999334},"labels":[],"label_agreement":null},{"id":"W1529419995","doi":"10.1109/cec.2005.1555019","title":"Evolution Strategies with Adaptively Rescaled Mutation Vectors","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Mutation; Computer science; Artificial intelligence; Biology; Genetics","score_opus":0.015755516664548438,"score_gpt":0.263580395690554,"score_spread":0.24782487902600558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529419995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032492497,0.000027617323,0.97765297,0.0008101681,0.00004331705,0.00016257986,6.330465e-7,0.00022120809,0.017832266],"genre_scores_gemma":[0.5444656,0.000002392709,0.45392755,0.000033003675,0.00004358882,0.000011617228,0.000001956624,0.0000050664025,0.0015092503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987551,0.000091942085,0.00015870587,0.0002842183,0.0004953961,0.00021463487],"domain_scores_gemma":[0.9992319,0.00007413165,0.000051470462,0.00028965005,0.00026526928,0.000087618835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025634796,0.000093425806,0.00008970958,0.00015984302,0.000102325794,0.00025854702,0.0003668051,0.000034152363,0.00014534578],"category_scores_gemma":[0.000052134947,0.000070739705,0.000018614652,0.0005962117,0.00005176434,0.0011946602,0.00006207812,0.00009402805,0.00016746375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004043276,0.00014086487,0.00021789785,0.000011567622,0.00003242471,0.000025838046,0.00070878107,0.2077406,0.00034232243,0.7458008,0.0015130852,0.043425377],"study_design_scores_gemma":[0.00036639406,0.00013011809,0.005812652,0.000005913427,0.0000026545279,0.000019248311,0.00015134196,0.9910445,0.0006057004,0.0010427978,0.00069427036,0.00012436695],"about_ca_topic_score_codex":0.000050405713,"about_ca_topic_score_gemma":0.00006316176,"teacher_disagreement_score":0.7833039,"about_ca_system_score_codex":0.00010145687,"about_ca_system_score_gemma":0.00023432904,"threshold_uncertainty_score":0.28846815},"labels":[],"label_agreement":null},{"id":"W1531223525","doi":"10.1007/3-540-45105-6_11","title":"Revisiting Elitism in Ant Colony Optimization","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Travelling salesman problem; Ant colony optimization algorithms; Mathematical optimization; Computer science; Ant colony; Extremal optimization; Artificial intelligence; Metaheuristic; Mathematics; Meta-optimization","score_opus":0.02164736604405561,"score_gpt":0.2762496322912347,"score_spread":0.25460226624717913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531223525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043517325,0.00055507926,0.9904555,0.0015421774,0.00091884664,0.00056731,0.0000032179835,0.00010295642,0.005850548],"genre_scores_gemma":[0.0013629844,0.00027213217,0.99568665,0.0017239605,0.00026076008,0.000014846011,0.000009319886,0.000041201773,0.0006281303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949123,0.0001548861,0.0008550797,0.0017238387,0.0014941607,0.0008596887],"domain_scores_gemma":[0.9969535,0.00068161904,0.00033932604,0.001341988,0.00046401646,0.00021958564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024112863,0.00047841977,0.0006172439,0.0016582906,0.00021800105,0.0008111069,0.0028112885,0.0003358567,0.00011308717],"category_scores_gemma":[0.00071199634,0.00048668933,0.00008953035,0.0016502013,0.00044360713,0.00071012456,0.00096505333,0.0010146925,0.000041918967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018159122,0.00001596205,0.000027263053,0.000032854532,0.0000035549879,0.00015684158,0.00017752321,0.77054954,0.0000072516136,0.010638399,0.000020832917,0.21836817],"study_design_scores_gemma":[0.00030531234,0.000057064928,0.000045000546,0.00040338712,0.0000027009737,0.000055348675,1.0566324e-7,0.9805203,0.000081148304,0.016943019,0.0010982099,0.0004883966],"about_ca_topic_score_codex":0.000022455919,"about_ca_topic_score_gemma":0.000012980912,"teacher_disagreement_score":0.21787977,"about_ca_system_score_codex":0.0006087617,"about_ca_system_score_gemma":0.0007429732,"threshold_uncertainty_score":0.9997585},"labels":[],"label_agreement":null},{"id":"W1551741615","doi":"10.1007/3-540-45724-0_28","title":"Using Genetic Algorithms to Optimize ACS-TSP","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":96,"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; Genetic algorithm; Ant colony optimization algorithms; Algorithm; Variable (mathematics); Ant colony; Travelling salesman problem; Population-based incremental learning; Mathematical optimization; Machine learning; Mathematics","score_opus":0.05726925925110299,"score_gpt":0.3053369914652968,"score_spread":0.24806773221419381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551741615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010025979,0.00037836135,0.99276096,0.0007252121,0.0019314408,0.00081177114,0.000009273664,0.00020001433,0.0031729322],"genre_scores_gemma":[0.0006934068,0.00008289424,0.9954931,0.0013374083,0.00059620076,0.000014224769,0.0000025104555,0.00007084752,0.0017094014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992814,0.00009378877,0.00086141744,0.0025122538,0.0024504685,0.0012680542],"domain_scores_gemma":[0.99537057,0.00051056367,0.0002873224,0.0025169372,0.0006790526,0.0006355555],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0010894967,0.0007501487,0.0007921592,0.002067519,0.00039267816,0.0013289598,0.0060957647,0.00039160176,0.0002534532],"category_scores_gemma":[0.00035099467,0.0007342926,0.00015503602,0.0018994663,0.0005887554,0.0005554302,0.0031848163,0.0010237751,0.00033098905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021237151,0.000024199831,0.000005070081,0.00001700116,0.000010387566,0.000177605,0.00025659174,0.6024654,0.000034264092,0.0011930325,0.00004381068,0.39577052],"study_design_scores_gemma":[0.00026665593,0.00013903102,0.000030927284,0.00018895084,0.000008901411,0.00016513905,6.853014e-8,0.9865824,0.00040078547,0.0101582855,0.0012625661,0.0007962626],"about_ca_topic_score_codex":0.000032515563,"about_ca_topic_score_gemma":0.0000050390554,"teacher_disagreement_score":0.39497426,"about_ca_system_score_codex":0.0006251274,"about_ca_system_score_gemma":0.0007089811,"threshold_uncertainty_score":0.99970776},"labels":[],"label_agreement":null},{"id":"W1552110283","doi":"10.1007/978-3-642-13495-1_8","title":"A New Particle Swarm Optimization Algorithm and Its Numerical Analysis","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Science North","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Position (finance); Metaheuristic; Computer science; Mathematical optimization; Current (fluid); Algorithm; Swarm behaviour; Derivative-free optimization; Imperialist competitive algorithm; Particle (ecology); Mathematics; Physics","score_opus":0.018230710757319944,"score_gpt":0.2749722324822797,"score_spread":0.25674152172495973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552110283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003856894,0.00036123683,0.997212,0.0009782608,0.0005876058,0.00032968912,0.0000050084677,0.00013016358,0.0003921902],"genre_scores_gemma":[0.0018354749,0.00008990492,0.9964556,0.00037618657,0.00026185645,0.000006173911,0.0000076201372,0.00002521931,0.0009419423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958289,0.000059247577,0.00053081167,0.0015792192,0.0013697997,0.0006320132],"domain_scores_gemma":[0.9972785,0.0004390577,0.00023386483,0.0010915269,0.00044081963,0.00051623094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009225891,0.00041455307,0.0005950903,0.0011143124,0.00026605302,0.0009441224,0.0021285545,0.00032243205,0.00019182185],"category_scores_gemma":[0.0002760624,0.00038556833,0.00012310183,0.0024938209,0.00027144904,0.00063411135,0.0013529975,0.0009003605,0.000038517803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016574531,0.000015392494,0.000010865712,0.000006222841,0.00003622623,0.000032689106,0.00018917702,0.5371015,0.000014976286,0.0031284252,0.000003991565,0.4594589],"study_design_scores_gemma":[0.00023221332,0.00007465418,0.000036160236,0.000023324294,0.00004474882,0.00003065997,4.9787534e-8,0.9908392,0.00093790726,0.007083177,0.000294818,0.00040310936],"about_ca_topic_score_codex":0.000030322968,"about_ca_topic_score_gemma":0.000012300177,"teacher_disagreement_score":0.45905578,"about_ca_system_score_codex":0.00012066321,"about_ca_system_score_gemma":0.00065836817,"threshold_uncertainty_score":0.99985963},"labels":[],"label_agreement":null},{"id":"W1556372911","doi":"10.1007/978-3-540-72393-6_31","title":"Comparative Studies of Fuzzy Genetic Algorithms","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Toronto Metropolitan University","funders":"China Scholarship Council","keywords":"Crossover; Computer science; Fuzzy logic; Quality control and genetic algorithms; Genetic algorithm; Algorithm; Mutation; Artificial intelligence; Data mining; Machine learning; Meta-optimization","score_opus":0.09541663711369208,"score_gpt":0.36418204513654645,"score_spread":0.26876540802285437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556372911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021096123,0.0030495198,0.9889615,0.00019579395,0.001443043,0.00053549715,0.0000075061703,0.000087779255,0.005698244],"genre_scores_gemma":[0.006288129,0.00032896397,0.9920876,0.0002964523,0.00029032017,0.000008343376,0.0000028538968,0.000026977017,0.0006703759],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99446774,0.000083994004,0.0010024573,0.0015370678,0.0021429823,0.0007657691],"domain_scores_gemma":[0.9948081,0.0014072682,0.00051633385,0.0015919814,0.0014468642,0.00022949035],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017825257,0.0005593175,0.0011213756,0.0017395216,0.00020572316,0.0002276397,0.0038804426,0.00026182522,0.000032061238],"category_scores_gemma":[0.0002816265,0.000494869,0.00014679528,0.0014093411,0.0020666667,0.00036382512,0.002055712,0.0008064714,0.000056443012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008893937,0.00007325526,0.000028197015,0.00012828752,0.00009952035,0.00019747914,0.0030422234,0.13427536,0.00006480464,0.018043268,0.00006683015,0.84397185],"study_design_scores_gemma":[0.00035729172,0.00031450632,0.00014645682,0.0003402732,0.000014105082,0.0000677186,0.0000013084529,0.9023206,0.002314937,0.09286935,0.0005987117,0.0006547151],"about_ca_topic_score_codex":0.000013719384,"about_ca_topic_score_gemma":0.000026872072,"teacher_disagreement_score":0.84331715,"about_ca_system_score_codex":0.00031575,"about_ca_system_score_gemma":0.0007551709,"threshold_uncertainty_score":0.9997503},"labels":[],"label_agreement":null},{"id":"W1561620206","doi":"10.1007/978-3-642-13059-5_19","title":"Automatic Parameter Settings for the PROAFTN Classifier Using Hybrid Particle Swarm Optimization","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Computer science; Metaheuristic; Classifier (UML); Artificial intelligence; Machine learning; Mathematical optimization; Data mining; Mathematics","score_opus":0.03951532335552556,"score_gpt":0.2955625530182069,"score_spread":0.25604722966268134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561620206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000078571225,0.00012683727,0.99450874,0.0019563993,0.0015515607,0.0014981502,0.000009614458,0.00016125839,0.00010889282],"genre_scores_gemma":[0.0076525393,0.000016080006,0.99052316,0.0010868692,0.00034483534,0.00006095263,0.000006221157,0.000049618335,0.000259708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582744,0.00006889327,0.00067439635,0.0013382756,0.0012791707,0.00081182126],"domain_scores_gemma":[0.9947745,0.0023084744,0.0004045316,0.0016813064,0.00063828455,0.00019289595],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021673464,0.00045123723,0.00044679537,0.00041962907,0.0006783123,0.0014140918,0.0033132872,0.00023785685,0.00007520049],"category_scores_gemma":[0.0010564036,0.00033261444,0.000148917,0.0006630576,0.00087297923,0.00064812356,0.0010656568,0.0009271383,0.000017850913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036083666,0.00002608251,0.0000057341335,0.000049414062,0.000017213128,0.000012427021,0.00026169053,0.6747792,0.000093849005,0.0021903676,0.00002305991,0.3225374],"study_design_scores_gemma":[0.000272925,0.000073348754,0.0000062418508,0.00010675137,0.000019957106,0.000060233586,1.6902501e-7,0.9776235,0.0030538016,0.017790569,0.0005981085,0.000394368],"about_ca_topic_score_codex":0.000007013614,"about_ca_topic_score_gemma":0.0000054886495,"teacher_disagreement_score":0.32214305,"about_ca_system_score_codex":0.00021611359,"about_ca_system_score_gemma":0.00077786174,"threshold_uncertainty_score":0.99991256},"labels":[],"label_agreement":null},{"id":"W1562612762","doi":"","title":"A New Dimension Division Scheme for Heterogeneous Multi-Population Cultural Algorithm","year":2014,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Dimension (graph theory); Benchmark (surveying); Division (mathematics); Schema (genetic algorithms); Computer science; Population; Convergence (economics); Algorithm; Division algorithm; Mathematical optimization; Mathematics; Theoretical computer science; Machine learning; Arithmetic; Combinatorics","score_opus":0.07994331202295402,"score_gpt":0.39861437697497754,"score_spread":0.3186710649520235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562612762","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018305688,0.0001448665,0.9920004,0.004157985,0.00052119925,0.0011160172,0.000007910137,0.00018630525,0.000034756973],"genre_scores_gemma":[0.039000582,0.000107176194,0.95624876,0.00038971534,0.00079478236,0.00016349339,0.0000619398,0.000043492746,0.0031900425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952941,0.0006977461,0.00038923253,0.00074531406,0.0018977299,0.00097589573],"domain_scores_gemma":[0.9961861,0.0010047772,0.00009135706,0.0011888682,0.001152769,0.00037614486],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005008303,0.00023316161,0.00026778664,0.0001025688,0.0013325262,0.00074313924,0.0017963895,0.0001539423,0.000042479187],"category_scores_gemma":[0.0009997257,0.0001562825,0.0002928626,0.0010988722,0.00017013992,0.0006026571,0.0011179039,0.0006849865,0.00012132855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056977846,0.0002797199,0.00034541189,0.0001507256,0.00023527085,0.0000063300868,0.006034565,0.016278915,0.0039361846,0.01322128,0.15393418,0.8055204],"study_design_scores_gemma":[0.0009666757,0.00017055696,0.00053299346,0.000021933633,0.000004282285,0.000006906407,0.000048090107,0.9847829,0.0013499518,0.002270108,0.009653475,0.0001921256],"about_ca_topic_score_codex":0.00027395898,"about_ca_topic_score_gemma":0.0000062691556,"teacher_disagreement_score":0.968504,"about_ca_system_score_codex":0.00021612445,"about_ca_system_score_gemma":0.00017140772,"threshold_uncertainty_score":0.9999676},"labels":[],"label_agreement":null},{"id":"W1563997662","doi":"10.1007/11513575_12","title":"Optimal Weighted Recombination","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":42,"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":"Recombination; Computer science; Truncation (statistics); Selection (genetic algorithm); Mathematical optimization; Algorithm; Adaptation (eye); Mathematics; Artificial intelligence; Physics; Genetics; Machine learning; Biology; Gene","score_opus":0.02024485215873753,"score_gpt":0.27399965642453433,"score_spread":0.2537548042657968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563997662","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010252035,0.00026349636,0.9848269,0.00185603,0.0015556498,0.00045669504,0.000003930191,0.0002284824,0.010798561],"genre_scores_gemma":[0.0029811258,0.00011585515,0.9920029,0.0006285874,0.0005092298,0.000012128851,0.000012489114,0.000040993807,0.0036966626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99490607,0.00007072264,0.0006617941,0.0017300976,0.0018652036,0.00076611206],"domain_scores_gemma":[0.99665964,0.0005041335,0.0003014266,0.0016089443,0.0006550984,0.00027075564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014965697,0.0005013294,0.00050837774,0.0016321383,0.0002757791,0.0008773175,0.0042630266,0.00035738468,0.000266861],"category_scores_gemma":[0.0002127803,0.00048065317,0.0001205505,0.0011195146,0.00054669345,0.0009089165,0.0015568549,0.0010074009,0.00028868893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028769864,0.000036807163,0.0000042703273,0.000014138579,0.00000854645,0.000053672884,0.00019329785,0.07978016,0.000013028224,0.02797738,0.0000663412,0.89184946],"study_design_scores_gemma":[0.00029750203,0.00011186311,0.00002665774,0.00010524134,0.0000040997265,0.00005315796,3.4096754e-8,0.95439553,0.0004672458,0.038454678,0.0055797435,0.00050426647],"about_ca_topic_score_codex":0.000007566883,"about_ca_topic_score_gemma":0.000011521853,"teacher_disagreement_score":0.8913452,"about_ca_system_score_codex":0.0005404003,"about_ca_system_score_gemma":0.0008923116,"threshold_uncertainty_score":0.9997645},"labels":[],"label_agreement":null},{"id":"W1568790063","doi":"10.1007/3-540-32391-0_103","title":"Multiple Cooperating Swarms for Non-Linear Function Optimization","year":2007,"lang":"en","type":"book-chapter","venue":"Advances in soft computing","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Particle swarm optimization; Swarm behaviour; Mathematical optimization; Synchronization (alternating current); Function (biology); Computer science; Mathematics; Computer network; Biology","score_opus":0.03660101500167154,"score_gpt":0.32551047250892634,"score_spread":0.28890945750725483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568790063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013185621,0.0012279458,0.96174484,0.000039476578,0.001331362,0.0009184274,0.0000066112857,0.00021536692,0.034514654],"genre_scores_gemma":[0.0006418521,0.00034863767,0.9856705,0.00019893568,0.0005938913,0.000013389406,0.00012458474,0.00008294967,0.012325227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969873,0.000034737106,0.0008401292,0.00096829067,0.00060815836,0.0005613849],"domain_scores_gemma":[0.9968345,0.0013139038,0.00048838434,0.00057706947,0.00066309725,0.0001230443],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013282108,0.00039627298,0.0005084377,0.00040352283,0.00036502592,0.00017836962,0.0008339132,0.0002852073,0.000057440066],"category_scores_gemma":[0.0006467227,0.00044272537,0.000118869975,0.0002518473,0.0000901147,0.00066452625,0.0004186088,0.0005575146,0.00004793961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000143542065,0.000015541113,0.000030180849,0.00007830396,0.00001179105,0.0000075256685,0.00011574149,0.7609304,0.0000018313597,0.005016465,0.000049372524,0.23372845],"study_design_scores_gemma":[0.00077485497,0.00010599429,0.0000041019707,0.00027737653,0.000009089351,0.0000069731723,0.000011170601,0.96399814,0.000013554817,0.0014121336,0.03296259,0.0004240389],"about_ca_topic_score_codex":0.0000057532457,"about_ca_topic_score_gemma":0.000019158146,"teacher_disagreement_score":0.23330443,"about_ca_system_score_codex":0.0001521568,"about_ca_system_score_gemma":0.00014912487,"threshold_uncertainty_score":0.9998025},"labels":[],"label_agreement":null},{"id":"W1576789214","doi":"10.1109/cec.2015.7257038","title":"A class of representations for evolving graphs","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Maximization; Benchmark (surveying); Representation (politics); Generative model; Mathematics; Function (biology); Graph; Enhanced Data Rates for GSM Evolution; Combinatorics; Parametrization (atmospheric modeling); Mathematical optimization; Theoretical computer science; Computer science; Discrete mathematics; Artificial intelligence; Generative grammar","score_opus":0.08648537132976239,"score_gpt":0.36113437954305805,"score_spread":0.27464900821329563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576789214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011154633,0.000031186322,0.97729814,0.00080577616,0.00013121052,0.00021159211,0.0000022163767,0.000054914643,0.02135343],"genre_scores_gemma":[0.062070347,0.0000029915611,0.9347467,0.00005575558,0.0000139363565,0.00003638605,0.0000027130372,0.0000044395633,0.0030667598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991576,0.000046112465,0.0001826357,0.00017557514,0.00030834106,0.00012972397],"domain_scores_gemma":[0.99849683,0.0002291164,0.0000515034,0.00040222198,0.00070961664,0.00011071969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005038533,0.000042406322,0.00008727913,0.00014939126,0.000033715598,0.000061451276,0.0004642863,0.000019985004,0.0000353029],"category_scores_gemma":[0.0010748759,0.000037139274,0.000038989358,0.0005007635,0.00003209645,0.00026892914,0.0001299059,0.000031603817,0.000012276215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004519733,0.000071557544,0.0005961303,0.000012542437,0.000019849347,0.0000012233832,0.00049888337,0.0032123413,0.0001692813,0.94445515,0.04486426,0.006094292],"study_design_scores_gemma":[0.0003712751,0.00005157671,0.00021556468,0.0000024301064,0.000002138312,0.00000206864,0.00007298296,0.97251064,0.0013175536,0.023579735,0.0018238451,0.000050165992],"about_ca_topic_score_codex":0.00003065524,"about_ca_topic_score_gemma":0.000003142724,"teacher_disagreement_score":0.9692983,"about_ca_system_score_codex":0.000013951954,"about_ca_system_score_gemma":0.0001386518,"threshold_uncertainty_score":0.15144956},"labels":[],"label_agreement":null},{"id":"W1580329799","doi":"10.1109/cec.2015.7256970","title":"Leaders and followers &amp;#x2014; A new metaheuristic to avoid the bias of accumulated information","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Metaheuristic; Particle swarm optimization; Differential evolution; Modal; Computer science; Mathematical optimization; Identification (biology); Random search; Range (aeronautics); Local search (optimization); Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.21876350847403217,"score_gpt":0.35251166408431184,"score_spread":0.13374815561027967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580329799","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027167683,0.000054344833,0.98831236,0.005080114,0.00019407451,0.00036988582,0.0000018282166,0.000077327066,0.0031933154],"genre_scores_gemma":[0.11346643,0.00002540811,0.878428,0.0017164107,0.000041025083,0.00001664356,0.000010574131,0.000012959089,0.006282591],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983683,0.00014787051,0.00038630815,0.00017945682,0.0006833229,0.00023471657],"domain_scores_gemma":[0.99825567,0.00031513572,0.00010582415,0.0005815552,0.00038381593,0.00035798867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011971279,0.00011844036,0.00018137605,0.00026364668,0.00006483298,0.00024757872,0.0007396553,0.000045211968,0.00005222098],"category_scores_gemma":[0.0028310544,0.00007761057,0.000037015234,0.0009796876,0.00007658042,0.0007498312,0.00032833545,0.00011211702,0.00025347754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002046945,0.00019747342,0.0013852187,0.00017890759,0.0004508694,0.0000063008474,0.05811785,0.15458684,0.00048920524,0.08104853,0.403242,0.3000921],"study_design_scores_gemma":[0.0018325509,0.0002666905,0.0010241042,0.000029413237,0.00003723727,0.00002804614,0.0011008111,0.8527728,0.0007292721,0.0031297412,0.13863476,0.00041460444],"about_ca_topic_score_codex":0.00036037757,"about_ca_topic_score_gemma":0.000024565263,"teacher_disagreement_score":0.6981859,"about_ca_system_score_codex":0.000029440496,"about_ca_system_score_gemma":0.00029260133,"threshold_uncertainty_score":0.3389241},"labels":[],"label_agreement":null},{"id":"W1581676142","doi":"10.1007/978-3-540-75396-4_13","title":"On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal","funders":"","keywords":"Memetic algorithm; Heuristic; Mathematical optimization; Computer science; Population; Convergence (economics); Scale (ratio); Local search (optimization); Combinatorial optimization; Algorithm; Mathematics","score_opus":0.18861212868658073,"score_gpt":0.384034373441075,"score_spread":0.19542224475449427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581676142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011867451,0.0035906208,0.993904,0.000052260893,0.0006474441,0.0010288102,0.000016093256,0.00004190323,0.0007070091],"genre_scores_gemma":[0.024487829,0.003427814,0.96531224,0.0001788947,0.0001718361,0.000112551665,0.000048358066,0.00007461902,0.0061858445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996466,0.00028719276,0.0010164598,0.00072320265,0.0011350471,0.0003721052],"domain_scores_gemma":[0.99441445,0.0034892117,0.00051264133,0.00059966336,0.00091929134,0.0000647149],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012806779,0.0004007704,0.000606585,0.00043522916,0.00031395108,0.00004878193,0.0011243558,0.00018092514,0.00007929626],"category_scores_gemma":[0.00036011508,0.00032655968,0.00014217265,0.00041898197,0.00043564825,0.00013148136,0.0006160178,0.0005815417,0.00003284118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007148242,0.00007019257,0.0000047944,0.000029979119,0.00009188259,0.000025826337,0.0007243351,0.9554132,4.0199552e-7,0.039839935,0.00017030867,0.0036219957],"study_design_scores_gemma":[0.00009856893,0.00010558263,0.0000102368285,0.0002616971,0.000014081859,0.000010493209,0.00007540056,0.97284466,0.000028386305,0.026158562,0.00011803817,0.00027430078],"about_ca_topic_score_codex":0.000014211902,"about_ca_topic_score_gemma":0.000003851549,"teacher_disagreement_score":0.028591737,"about_ca_system_score_codex":0.00027332795,"about_ca_system_score_gemma":0.0001471023,"threshold_uncertainty_score":0.99991864},"labels":[],"label_agreement":null},{"id":"W1583203589","doi":"10.3233/his-140198","title":"Recentering and restarting a genetic algorithm using a generative representation for an ordered gene problem1","year":2014,"lang":"en","type":"article","venue":"International Journal of Hybrid Intelligent Systems","topic":"Metaheuristic Optimization Algorithms Research","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 Guelph; Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Brock University","keywords":"Generative grammar; Computer science; Representation (politics); Genetic algorithm; Artificial intelligence; Generative model; Algorithm; Theoretical computer science; Machine learning","score_opus":0.07319674962700776,"score_gpt":0.3568253381792253,"score_spread":0.2836285885522175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583203589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032480553,0.00030311634,0.96468,0.00020375683,0.0018678906,0.00040292623,0.000010642849,0.000022354378,0.000028737546],"genre_scores_gemma":[0.19156194,0.00017041551,0.8072399,0.00006250069,0.0008461301,0.000020863337,0.0000097607335,0.000024309014,0.00006422366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971434,0.00037955245,0.0010528733,0.00034702886,0.0008373235,0.00023985718],"domain_scores_gemma":[0.99645156,0.00022612595,0.0008095191,0.00025771407,0.0020700535,0.00018503016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014896287,0.00016097791,0.00029308748,0.00044762416,0.000117023534,0.00070166396,0.00084286486,0.000027353077,0.0000058966475],"category_scores_gemma":[0.0006192576,0.00014802247,0.00008241442,0.00015990004,0.00004304261,0.0007344552,0.00017041466,0.00013648481,0.0000021187116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015834723,0.00030796186,0.00080089935,0.000080111044,0.00062474114,0.00016209143,0.0017674497,0.6002059,0.021465825,0.0030708436,0.0004464632,0.37090936],"study_design_scores_gemma":[0.0005175014,0.0002909193,0.0000410961,0.000115041854,0.000013454982,0.0015436796,0.0000943098,0.981298,0.014004113,0.00083563855,0.0011067385,0.00013948583],"about_ca_topic_score_codex":0.00007077406,"about_ca_topic_score_gemma":0.0000015406032,"teacher_disagreement_score":0.38109213,"about_ca_system_score_codex":0.00016546625,"about_ca_system_score_gemma":0.00012103939,"threshold_uncertainty_score":0.6766162},"labels":[],"label_agreement":null},{"id":"W1588619826","doi":"10.1007/978-3-540-70829-2_7","title":"Improving the Exploration Ability of Ant-Based Algorithms","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":24,"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":"Ant colony optimization algorithms; Travelling salesman problem; Opposition (politics); Mathematical optimization; Computer science; Algorithm; Local optimum; Artificial intelligence; Mathematics","score_opus":0.18745058216046437,"score_gpt":0.37816971902844815,"score_spread":0.19071913686798378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588619826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031243978,0.0023911053,0.99042916,0.0006753224,0.0006197313,0.0005619122,0.000021866103,0.000052964933,0.0052448385],"genre_scores_gemma":[0.026956668,0.003810219,0.95902646,0.00038508326,0.00032017348,0.00017073708,0.00007597914,0.00007385768,0.009180836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966197,0.0001444831,0.0010470725,0.00069739064,0.0012303704,0.00026101145],"domain_scores_gemma":[0.99416685,0.0028979713,0.0005382317,0.00069919945,0.0016435116,0.000054220716],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010862186,0.00032372767,0.00050873315,0.00036212237,0.00022057764,0.000052116742,0.0013150905,0.00012664222,0.00003801291],"category_scores_gemma":[0.0009644893,0.00025836242,0.00014244346,0.00034946724,0.001023774,0.00028476072,0.0005975275,0.0004890871,0.00004205802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010989039,0.000059451122,0.000007649498,0.00015961447,0.00008579271,0.000031460877,0.0014622554,0.7751518,0.0000016481597,0.11040635,0.00054324657,0.11207973],"study_design_scores_gemma":[0.00008411807,0.00008588808,0.000022951872,0.00013861046,0.000007732162,0.000008767132,0.000080505444,0.9250744,0.00010587647,0.073432155,0.0007256806,0.00023330262],"about_ca_topic_score_codex":0.000024774525,"about_ca_topic_score_gemma":0.000009171573,"teacher_disagreement_score":0.1499226,"about_ca_system_score_codex":0.00025289887,"about_ca_system_score_gemma":0.000542473,"threshold_uncertainty_score":0.9999869},"labels":[],"label_agreement":null},{"id":"W1592436817","doi":"10.1609/aaai.v24i1.7555","title":"Single-Frontier Bidirectional Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Israel Science Foundation","keywords":"Frontier; Node (physics); Computer science; Tree (set theory); Shortest path problem; Path (computing); Task (project management); Current (fluid); Search algorithm; Range (aeronautics); Bidirectional search; Mathematical optimization; Algorithm; Theoretical computer science; Incremental heuristic search; Mathematics; Beam search; Engineering; Graph; Computer network; Geography","score_opus":0.09925960070521146,"score_gpt":0.3189109092959158,"score_spread":0.21965130859070436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592436817","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":[0.16073547,0.000027352022,0.6836759,0.020086175,0.005178322,0.0013807727,0.000017429596,0.0004422259,0.12845637],"genre_scores_gemma":[0.9369345,0.000012456545,0.060827434,0.00011589521,0.00014102827,0.000021362857,4.5420327e-7,0.000014166367,0.0019327335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99757177,0.000026409994,0.0004587995,0.0005144001,0.0010348818,0.00039372488],"domain_scores_gemma":[0.9976195,0.00013253304,0.00018585884,0.0004120061,0.0015009962,0.00014906771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091123284,0.00018134265,0.0002044219,0.00023871407,0.0002456017,0.00043359102,0.002594032,0.0001049026,0.00048704568],"category_scores_gemma":[0.001037099,0.00013689633,0.000104065024,0.0010764416,0.00040917797,0.00042861985,0.0005519737,0.0006533229,0.00022718681],"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":[0.00001971038,0.0002069765,0.00016026254,0.000014119706,0.000011447493,4.2430935e-7,0.00032215452,0.00006192279,0.12178795,0.7572852,0.0004236361,0.11970619],"study_design_scores_gemma":[0.000023595288,0.00011414578,0.00021692883,0.000033034088,0.0000041571125,0.000009866648,0.00009254585,0.2769368,0.6676471,0.053949345,0.00079562794,0.00017686286],"about_ca_topic_score_codex":0.000029570167,"about_ca_topic_score_gemma":0.000008305734,"teacher_disagreement_score":0.776199,"about_ca_system_score_codex":0.000034313198,"about_ca_system_score_gemma":0.00018416722,"threshold_uncertainty_score":0.558247},"labels":[],"label_agreement":null},{"id":"W1605171964","doi":"","title":"Adapting heterogeneous ensembles with particle swarm optimization for video face recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Artificial intelligence; Particle swarm optimization; Machine learning; Facial recognition system; Biometrics; Artificial neural network; Hyperparameter; Process (computing); Matching (statistics); Pattern recognition (psychology)","score_opus":0.06262017676944999,"score_gpt":0.28945245454091584,"score_spread":0.22683227777146586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605171964","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066842,0.00007928263,0.9915516,0.00032568024,0.00012065764,0.00048737807,0.000003878432,0.00021666323,0.00053065945],"genre_scores_gemma":[0.28140783,0.000009289384,0.7179572,0.00018996597,0.00006868628,0.000092509355,0.000011121327,0.000015773863,0.000247581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855417,0.0000967703,0.00023299594,0.00029224262,0.00034012442,0.00048371393],"domain_scores_gemma":[0.9988194,0.00025668365,0.000093584364,0.00032578717,0.00030179432,0.00020272062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059023686,0.00012734307,0.0001299458,0.00007311937,0.00018432132,0.00019957928,0.00028896902,0.000038174014,0.00011606213],"category_scores_gemma":[0.00023907475,0.00010416192,0.000036484125,0.00037422558,0.0000276139,0.00085143285,0.00009399221,0.00006009163,0.000062766005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005871175,0.00024592318,0.00036342887,0.000044747372,0.000053845815,0.000004030611,0.00073980703,0.903056,0.00031890423,0.003180579,0.00039382203,0.091540165],"study_design_scores_gemma":[0.00044592848,0.00012194389,0.000017377792,0.000010203096,0.0000088090055,0.000033867673,0.000050454688,0.970965,0.027645092,0.00007998188,0.0004559701,0.00016534181],"about_ca_topic_score_codex":0.000010877226,"about_ca_topic_score_gemma":0.0000030175456,"teacher_disagreement_score":0.27472365,"about_ca_system_score_codex":0.000040723327,"about_ca_system_score_gemma":0.000046262892,"threshold_uncertainty_score":0.42475998},"labels":[],"label_agreement":null},{"id":"W1611901023","doi":"10.1609/aaai.v25i1.7823","title":"The Compressed Differential Heuristic","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Uncompressed video; Heuristic; Computer science; Differential (mechanical device); Compression (physics); State (computer science); Algorithm; Computer engineering; Parallel computing; Computer hardware; Artificial intelligence; Materials science; Engineering","score_opus":0.13452609741838098,"score_gpt":0.3040097206926812,"score_spread":0.1694836232743002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1611901023","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":[0.021945022,0.000075995675,0.90635943,0.005206162,0.0020517353,0.0014158767,0.000009087326,0.00029263177,0.06264405],"genre_scores_gemma":[0.9904695,0.000082932485,0.008533356,0.00009027202,0.000058455742,0.000040149826,2.747543e-7,0.000013721594,0.00071132206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997568,0.000053572658,0.00061146164,0.00046533672,0.00084809755,0.000453547],"domain_scores_gemma":[0.99763334,0.00025939502,0.00036708947,0.0005710892,0.0010383276,0.00013073832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060077524,0.00021889445,0.0002326476,0.000104593055,0.0005220468,0.0004333159,0.004429891,0.000071304465,0.00023894724],"category_scores_gemma":[0.00080749474,0.00012895174,0.00012095339,0.00070306804,0.000584877,0.00026300445,0.0007346034,0.00037630374,0.00016685495],"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":[0.000053819796,0.00014749478,0.00004753638,0.0000156295,0.000019569743,5.017623e-7,0.0009261434,0.000017334452,0.003592899,0.947091,0.0003354333,0.04775266],"study_design_scores_gemma":[0.00004889808,0.00020934366,0.00072050415,0.00008558903,0.000015724712,0.00000548095,0.00030344387,0.52034765,0.26235792,0.21529116,0.00034517059,0.00026908622],"about_ca_topic_score_codex":0.000047159596,"about_ca_topic_score_gemma":0.0000053390204,"teacher_disagreement_score":0.9685245,"about_ca_system_score_codex":0.000029193596,"about_ca_system_score_gemma":0.00010775629,"threshold_uncertainty_score":0.8231913},"labels":[],"label_agreement":null},{"id":"W1676876603","doi":"10.1109/ipdpsw.2015.92","title":"Differential Evolution on a GPGPU: The Influence of Parameters on Speedup and the Quality of Solutions","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Speedup; Computer science; Benchmark (surveying); Crossover; Differential evolution; Curse of dimensionality; Population; Parallel computing; Dimension (graph theory); Quality (philosophy); Function (biology); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Physics","score_opus":0.10246706759718062,"score_gpt":0.34245878983816525,"score_spread":0.23999172224098464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1676876603","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":[0.37089702,0.000017460196,0.6248811,0.0024187926,0.000095281874,0.00030846853,0.000004310467,0.000019471952,0.0013581143],"genre_scores_gemma":[0.9902084,0.000008756447,0.00948973,0.00009025979,0.000009548734,0.000009920045,5.178389e-7,0.0000024988644,0.00018040734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998023,0.0006192901,0.00031005472,0.00018396323,0.0007107412,0.00015296436],"domain_scores_gemma":[0.9978987,0.0009746686,0.00015228364,0.000622421,0.0002842574,0.00006764841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015261628,0.00007613886,0.00016245639,0.00007756596,0.000093410985,0.000048458838,0.0006106273,0.000029456753,0.000010200731],"category_scores_gemma":[0.0015241178,0.00003740097,0.000045389137,0.0003464743,0.0005122271,0.00010687589,0.00024763314,0.00012326732,0.000009170291],"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":[0.00014387962,0.00016527386,0.00032286468,0.000012548416,0.00003203832,2.1920577e-7,0.00086101063,0.049946047,0.00020068642,0.94545907,0.00040813227,0.0024482452],"study_design_scores_gemma":[0.0019864822,0.0003294378,0.061420746,0.000024245672,0.0000123254285,0.0000025165243,0.00019164341,0.91508996,0.00093302457,0.019856688,0.000036375528,0.00011656407],"about_ca_topic_score_codex":0.0005187497,"about_ca_topic_score_gemma":0.000012611657,"teacher_disagreement_score":0.9256024,"about_ca_system_score_codex":0.000031350937,"about_ca_system_score_gemma":0.00009571078,"threshold_uncertainty_score":0.18873222},"labels":[],"label_agreement":null},{"id":"W1690153029","doi":"10.1109/cec.2004.1330836","title":"An analysis of evolutionary gradient search","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Evolutionary computation; Evolutionary algorithm; Evolution strategy; Random search; Computer science; Function (biology); Human-based evolutionary computation; Evolutionary programming; Genetic algorithm; Gradient method; CMA-ES; Mathematical optimization; Mathematics; Artificial intelligence; Interactive evolutionary computation; Algorithm; Evolutionary biology; Biology","score_opus":0.02695156518853415,"score_gpt":0.3192879974002241,"score_spread":0.29233643221169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1690153029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012404576,0.00002444698,0.9850417,0.0004571806,0.000040163544,0.00007727577,0.000003566956,0.00006239529,0.0018886974],"genre_scores_gemma":[0.55310243,0.000009377282,0.44666234,0.000043421394,0.0000073722763,0.0000030646,0.000008797369,0.0000024172273,0.00016078891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986056,0.0000878073,0.00020381974,0.000280417,0.00062835676,0.00019396718],"domain_scores_gemma":[0.9987801,0.000046991423,0.000030117864,0.0006907479,0.0003040235,0.00014805986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036836488,0.000059195507,0.00015076801,0.0007113537,0.000058752383,0.000045341643,0.0007902629,0.000025886171,0.0002727705],"category_scores_gemma":[0.00004228005,0.00005108419,0.000075202435,0.0029136385,0.000060828563,0.00029279912,0.00013537004,0.00006357066,0.000033010063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000225446,0.00026060187,0.0017986728,0.0000039394363,0.00015881412,0.000007178333,0.00030969598,0.68489707,0.00015208816,0.3088664,0.0000610115,0.0034822687],"study_design_scores_gemma":[0.00014725256,0.00007579787,0.028574286,0.0000011689641,0.000023684619,0.0000016256697,0.000027977621,0.9693214,0.00071504107,0.0009978642,0.00005432619,0.000059559377],"about_ca_topic_score_codex":0.0002716086,"about_ca_topic_score_gemma":0.00001413504,"teacher_disagreement_score":0.5406978,"about_ca_system_score_codex":0.00007175769,"about_ca_system_score_gemma":0.00015186562,"threshold_uncertainty_score":0.29866457},"labels":[],"label_agreement":null},{"id":"W1728818732","doi":"10.5555/2343896.2343920","title":"MO-LOST: adaptive ant trail untangling in multi-objective multi-colony robot foraging","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Foraging; Robot; Computer science; Context (archaeology); Ant colony; Population; Set (abstract data type); Throughput; Dual (grammatical number); Ant colony optimization algorithms; Robot kinematics; Interference (communication); Artificial intelligence; Mobile robot; Ecology; Geography; Biology; Computer network; Telecommunications","score_opus":0.08703283696101467,"score_gpt":0.3321901935187764,"score_spread":0.24515735655776172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1728818732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020782654,0.00023765661,0.9946884,0.00018218126,0.0003337265,0.00055713725,0.000003874914,0.00014388599,0.0017748799],"genre_scores_gemma":[0.4030633,0.000013235734,0.5956126,0.0001253954,0.0000454317,0.000036646616,0.0000014650695,0.000016817363,0.0010851134],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973588,0.00028204467,0.00041476815,0.0005443565,0.0005203879,0.00087964255],"domain_scores_gemma":[0.998579,0.0003566929,0.00010626081,0.00041693938,0.00024790896,0.0002932023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012576621,0.00023500655,0.0003040058,0.00044765964,0.00013984674,0.00015174782,0.0007913266,0.000099033576,0.000097129036],"category_scores_gemma":[0.000448603,0.00021228172,0.0000732166,0.0011345409,0.00007227886,0.001212074,0.00044430833,0.0003711284,0.00023777904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023744242,0.009443717,0.106049776,0.000182884,0.00051328,0.0005150711,0.094731554,0.31845862,0.0053438954,0.11643312,0.0009560643,0.3471346],"study_design_scores_gemma":[0.0012310438,0.00005116486,0.017228656,0.000022829798,0.00000404819,0.000014144429,0.00055367645,0.97846913,0.0020178133,0.000080202524,0.000060509014,0.00026676658],"about_ca_topic_score_codex":0.0002927468,"about_ca_topic_score_gemma":0.00010688783,"teacher_disagreement_score":0.6600105,"about_ca_system_score_codex":0.0002788047,"about_ca_system_score_gemma":0.00012963172,"threshold_uncertainty_score":0.8656597},"labels":[],"label_agreement":null},{"id":"W173691115","doi":"","title":"A New Method For Constructing Nonlinear Modular n-Queens Solutions.","year":2006,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Metaheuristic Optimization Algorithms Research","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":"Modular design; Mathematics; Nonlinear system; Computer science; Programming language; Physics","score_opus":0.023402061188800352,"score_gpt":0.3087845100197513,"score_spread":0.285382448830951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W173691115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053239346,0.00010036191,0.99477243,0.0010110879,0.001384819,0.0004235042,0.000007758532,0.00021615996,0.0015514866],"genre_scores_gemma":[0.005458761,0.0000042103748,0.993042,0.0000720506,0.000056093984,0.000032207394,0.000014320883,0.00002114073,0.0012992487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980317,0.0001537173,0.00037407695,0.0004837677,0.0004335312,0.00052321213],"domain_scores_gemma":[0.99820524,0.00048648316,0.00012710292,0.0006266971,0.00038782335,0.0001666391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093674765,0.00016287592,0.00024384062,0.00017699595,0.000269039,0.0002428147,0.0007795408,0.00009100604,0.00003287187],"category_scores_gemma":[0.0004433107,0.00017191439,0.00010350664,0.00067668024,0.00004074071,0.00031671918,0.00027934022,0.00017007154,0.00004276042],"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":[0.0000030391018,0.000049361166,0.00007672266,0.000010786095,0.000017208826,0.000005393578,0.000042289495,0.0016701755,0.00032311957,0.97697705,0.0058506457,0.014974235],"study_design_scores_gemma":[0.0008985444,0.000043049236,0.00004746,0.0000072953426,0.000008096363,0.000017579734,0.0000109206185,0.6289757,0.001570612,0.36106536,0.0071943183,0.00016105783],"about_ca_topic_score_codex":0.00020835678,"about_ca_topic_score_gemma":0.0000034262978,"teacher_disagreement_score":0.62730557,"about_ca_system_score_codex":0.000078097655,"about_ca_system_score_gemma":0.00033241458,"threshold_uncertainty_score":0.7010466},"labels":[],"label_agreement":null},{"id":"W174767910","doi":"10.1007/978-81-322-2184-5_7","title":"Analyzing the Behaviour of Multi-recombinative Evolution Strategies Applied to a Conically Constrained Problem","year":2014,"lang":"en","type":"book-chapter","venue":"Infosys science foundation series","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.03022384821143987,"score_gpt":0.30133427765540166,"score_spread":0.2711104294439618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W174767910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036267254,0.000015085639,0.8996898,0.00070950587,0.00027666934,0.0010686665,0.000013077792,0.00010838222,0.09808254],"genre_scores_gemma":[0.36049777,0.00004363385,0.57163215,0.00014153482,0.000102742255,0.00023660697,0.00005319703,0.000055536453,0.06723682],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640256,0.000079268415,0.0009201725,0.0007845449,0.0013821216,0.0004313013],"domain_scores_gemma":[0.9953899,0.00028834675,0.00079956074,0.0009774456,0.0023490964,0.00019566322],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0027371775,0.0003380472,0.000472374,0.00076665706,0.00064765185,0.0013330058,0.0024582238,0.0001422171,0.00017652492],"category_scores_gemma":[0.00050799566,0.00026898843,0.00009220808,0.00089540833,0.002357619,0.0013560429,0.0007351463,0.00035522575,0.00016118531],"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":[0.000009388314,0.000016006843,0.00000986146,0.00002572977,0.000018643248,9.1339587e-7,0.0007077512,0.0029583995,0.00037373992,0.98374724,0.000037211266,0.012095101],"study_design_scores_gemma":[0.004213053,0.0028149255,0.0042157453,0.0012227348,0.0002712471,0.00017767254,0.0032661478,0.504871,0.0040001716,0.4260438,0.044174917,0.00472859],"about_ca_topic_score_codex":0.000037641297,"about_ca_topic_score_gemma":0.00005661717,"teacher_disagreement_score":0.55770344,"about_ca_system_score_codex":0.0003026612,"about_ca_system_score_gemma":0.0026839995,"threshold_uncertainty_score":0.9999762},"labels":[],"label_agreement":null},{"id":"W1764669605","doi":"10.5281/zenodo.7080765","title":"Invited paper: A Review of Thresheld Convergence","year":2015,"lang":"en","type":"review","venue":"eCite Digital Repository (University of Tasmania)","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Convergence (economics); Computer science; Applied mathematics; Mathematics; Economics; Macroeconomics","score_opus":0.04705993929200671,"score_gpt":0.28210217302895435,"score_spread":0.23504223373694763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1764669605","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":[8.0201914e-7,0.9729146,0.01657547,0.000093524344,0.00033428895,0.0006511783,0.00010904973,0.00007365735,0.009247434],"genre_scores_gemma":[0.0000019860945,0.9892152,0.007960783,0.000058367474,0.000032554584,0.0000013735438,0.00007397953,0.000022013508,0.0026337453],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684113,0.00026504102,0.0007148878,0.00068420876,0.0011963956,0.00029830847],"domain_scores_gemma":[0.99581903,0.00024921203,0.0011662798,0.0014843587,0.0009792888,0.0003018555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046608032,0.0003514891,0.0018257622,0.00032906767,0.00010289631,0.000104742336,0.0029851466,0.00021503464,0.000057978352],"category_scores_gemma":[0.0003074539,0.00037640767,0.00062002084,0.0014574636,0.00028751453,0.0013642217,0.0012851596,0.0003323387,0.000081944105],"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":[0.000008064226,0.00020791881,0.000013193015,0.089726485,0.0003677971,0.0003650349,0.00012345305,0.0000067471533,0.0000010845987,0.0008784504,0.0525975,0.85570425],"study_design_scores_gemma":[0.00015296019,0.00010280671,0.000003745357,0.027370863,0.00018304645,0.00010246597,0.000021774833,0.00085572584,0.0000013552203,0.00004228439,0.9708353,0.00032763087],"about_ca_topic_score_codex":0.000026358572,"about_ca_topic_score_gemma":0.0000010198463,"teacher_disagreement_score":0.91823786,"about_ca_system_score_codex":0.00015850496,"about_ca_system_score_gemma":0.00059351965,"threshold_uncertainty_score":0.9998688},"labels":[],"label_agreement":null},{"id":"W1770820624","doi":"10.1109/cec.2004.1330933","title":"Enhancement of the shifting balance genetic algorithm for highly multimodal problems","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Knapsack problem; Genetic algorithm; Computer science; Mathematical optimization; Extension (predicate logic); Population; Balance (ability); Continuous knapsack problem; Function (biology); Mechanism (biology); Algorithm; Machine learning; Mathematics; Biology","score_opus":0.01811963696650311,"score_gpt":0.26752115022537576,"score_spread":0.24940151325887264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1770820624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00075949077,0.00006337119,0.99649394,0.0010558896,0.00027698025,0.0007902491,0.0000045198385,0.000046680365,0.0005088915],"genre_scores_gemma":[0.04530566,0.000015229667,0.9535801,0.00012511725,0.000042296226,0.00010168111,0.0000011558983,0.000009120084,0.0008196568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845964,0.00004634908,0.0003468062,0.00033142892,0.00050515827,0.00031063953],"domain_scores_gemma":[0.99886215,0.000100198165,0.00012961059,0.0005775536,0.0002632608,0.00006725642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037443519,0.00010933835,0.00014733983,0.00005736333,0.0001270144,0.000075635355,0.0011305327,0.000036670488,0.000027744667],"category_scores_gemma":[0.000113568574,0.000074162985,0.00007183669,0.00044204856,0.00006425244,0.00013394255,0.00031022407,0.00008022418,0.000013765658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000623282,0.00073103566,0.00039268125,0.00021263852,0.00010677964,0.0000040145774,0.0016433373,0.1720147,0.0077756844,0.05342478,0.0004681486,0.76321995],"study_design_scores_gemma":[0.0007687247,0.000080574275,0.0005367542,0.000025390978,0.0000032243881,0.0000025013899,0.000006673757,0.95879793,0.036719188,0.0025217286,0.00043570143,0.000101632286],"about_ca_topic_score_codex":0.0000568964,"about_ca_topic_score_gemma":0.0000030149358,"teacher_disagreement_score":0.7867832,"about_ca_system_score_codex":0.000058641504,"about_ca_system_score_gemma":0.00017733069,"threshold_uncertainty_score":0.30242786},"labels":[],"label_agreement":null},{"id":"W1801849579","doi":"10.1016/s0167-739x(00)00043-1","title":"– Ant System","year":2000,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2698,"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":"Travelling salesman problem; Ant colony optimization algorithms; Computer science; Benchmark (surveying); Quadratic assignment problem; Extremal optimization; Mathematical optimization; Parallel metaheuristic; Combinatorial optimization; Metaheuristic; Ant colony; Optimization problem; Artificial intelligence; Algorithm; Meta-optimization; Mathematics","score_opus":0.016814986928085477,"score_gpt":0.23204394409662635,"score_spread":0.21522895716854087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1801849579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026033865,0.0006261996,0.94760877,0.00054877874,0.047456227,0.00040797595,0.000005703159,0.0005265079,0.002559476],"genre_scores_gemma":[0.017726747,0.00010922702,0.71664166,0.0005827383,0.25533855,0.00019577521,0.00012045795,0.000060889713,0.009223957],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973883,0.0004242592,0.0005071988,0.0006093668,0.00072234054,0.00034854177],"domain_scores_gemma":[0.99841946,0.00003697474,0.00010038085,0.0009542519,0.0002891762,0.00019973093],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005721984,0.00021135331,0.00027480983,0.00017293473,0.00028593917,0.0010371237,0.0009341567,0.00011544947,0.000112338355],"category_scores_gemma":[0.0000043546925,0.00018364642,0.00006906155,0.0006132674,0.00001797905,0.0004742817,0.00010188952,0.00014578823,0.000879877],"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":[0.000004201399,0.00007210622,0.000025081543,0.000111350164,0.00006143469,0.00008224219,0.00052773533,0.13551621,0.00011123553,0.1178306,0.5913973,0.15426053],"study_design_scores_gemma":[0.00019814324,0.00003722067,0.00003727741,0.000013986332,0.0000022477534,0.00012209691,0.000010420521,0.7021792,0.00005095472,6.316396e-7,0.29720238,0.00014546794],"about_ca_topic_score_codex":0.00002250957,"about_ca_topic_score_gemma":0.0000016247915,"teacher_disagreement_score":0.56666297,"about_ca_system_score_codex":0.00011663521,"about_ca_system_score_gemma":0.00010109684,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W1802369074","doi":"10.1504/ijpse.2015.071426","title":"gpuMF: a framework for parallel hybrid metaheuristics on GPU with application to the minimisation of harmonics in multilevel inverters","year":2015,"lang":"en","type":"article","venue":"International Journal of Process Systems Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Royal Military College of Canada","funders":"","keywords":"Metaheuristic; Computer science; Speedup; Massively parallel; Parallel computing; Graphics processing unit; General-purpose computing on graphics processing units; Graphics; Algorithm","score_opus":0.038730280253787566,"score_gpt":0.31600884068880253,"score_spread":0.277278560435015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1802369074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024080926,0.000073886724,0.9954295,0.0010020845,0.00063436135,0.00041540383,0.000009583244,0.000012661489,0.000014401317],"genre_scores_gemma":[0.7527937,0.000006606635,0.24691777,0.00004645723,0.00013783417,0.000072949384,0.000002179265,0.000012136432,0.000010321932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981029,0.000039067734,0.00058278197,0.00015725727,0.0009771131,0.00014087456],"domain_scores_gemma":[0.9971124,0.00035944983,0.00040133655,0.00019551022,0.0018182226,0.000113098016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009324663,0.000113314556,0.00021428251,0.00040172174,0.000016133248,0.00012231668,0.0010819391,0.00003496204,4.3906297e-7],"category_scores_gemma":[0.0013245381,0.00008147341,0.000039081548,0.00027787327,0.000012234405,0.00026170842,0.00004774728,0.00018109685,0.0000017195376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008124369,0.00005248263,0.00008642978,0.000053205316,0.00006409056,0.0000070911515,0.00091410667,0.98431414,0.00003491372,0.010034173,0.00011983641,0.004238266],"study_design_scores_gemma":[0.0006171287,0.00015102081,0.00013871868,0.00025553486,0.0000071563495,0.00005422605,0.00011962462,0.99672717,0.0004753271,0.000494645,0.00087376643,0.00008569101],"about_ca_topic_score_codex":0.0000115068415,"about_ca_topic_score_gemma":0.0000010111876,"teacher_disagreement_score":0.75038564,"about_ca_system_score_codex":0.0001646337,"about_ca_system_score_gemma":0.00019937375,"threshold_uncertainty_score":0.3322389},"labels":[],"label_agreement":null},{"id":"W1803694054","doi":"10.1007/978-3-540-75396-4_15","title":"Particle Swarm Optimization with Mutation for High Dimensional Problems","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"SavvyDox (Canada)","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Mutation; Multi-swarm optimization; Computer science; Set (abstract data type); Optimization problem; Artificial neural network; Evolutionary algorithm; Metaheuristic; Operator (biology); Artificial intelligence; Mathematics","score_opus":0.11411252491759621,"score_gpt":0.34737800273483493,"score_spread":0.23326547781723872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1803694054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007757622,0.0015890313,0.9938234,0.0006909168,0.00039302232,0.0011079044,0.00002868394,0.00009985181,0.0022594517],"genre_scores_gemma":[0.010348563,0.0011590004,0.96547514,0.00022128447,0.00014233858,0.0003187168,0.00021998966,0.00006531272,0.022049623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969642,0.00005273667,0.00076963264,0.00084161834,0.001024982,0.00034680503],"domain_scores_gemma":[0.9959124,0.0013725936,0.00035758826,0.00034169503,0.0019242979,0.000091460715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042206515,0.00037071077,0.0004725924,0.00031149716,0.00027398497,0.00008277976,0.000576228,0.0001289871,0.000044206492],"category_scores_gemma":[0.00028309124,0.00033576434,0.00007149917,0.00030221572,0.00043130503,0.0003281861,0.000286762,0.000277556,0.000048285732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022629765,0.000042218755,0.0000026039877,0.000071188624,0.00010380247,0.00003890076,0.00048428946,0.82735693,6.784303e-8,0.16466916,0.00042189183,0.0067862966],"study_design_scores_gemma":[0.0002771872,0.00026977685,0.0000071065015,0.00023513328,0.000014536341,0.00006078577,0.000018759987,0.9211787,0.000036552738,0.07707535,0.0004693415,0.00035680342],"about_ca_topic_score_codex":0.0000077129525,"about_ca_topic_score_gemma":0.00001116364,"teacher_disagreement_score":0.09382172,"about_ca_system_score_codex":0.0002731661,"about_ca_system_score_gemma":0.00034045236,"threshold_uncertainty_score":0.99990946},"labels":[],"label_agreement":null},{"id":"W1805834402","doi":"10.1007/978-3-540-73007-1_124","title":"Hybrid Unsupervised/Supervised Virtual Reality Spaces for Visualizing Cancer Databases: An Evolutionary Computation Approach","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"National Research Council Canada","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Machine learning; Cluster analysis; Data mining; Knowledge extraction","score_opus":0.1003301340051558,"score_gpt":0.36815997418564805,"score_spread":0.2678298401804923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1805834402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004958598,0.0005616814,0.9948082,0.00039480135,0.0014197852,0.0014827306,0.00019018995,0.00029128615,0.0008017313],"genre_scores_gemma":[0.007577422,0.00012456329,0.9897533,0.00083845877,0.0008956719,0.00007964715,0.00044072498,0.00007890418,0.00021130318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925164,0.00018952353,0.0010006828,0.0028074756,0.002342865,0.0011430427],"domain_scores_gemma":[0.99490356,0.0012157797,0.0004166772,0.0016969423,0.0012731176,0.0004938997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033223247,0.00075516547,0.0008149204,0.0016301002,0.00069547497,0.0009278415,0.0036260837,0.00025998935,0.000040764164],"category_scores_gemma":[0.000343798,0.00074506743,0.00017888061,0.0010331689,0.00093131664,0.001868172,0.0014645974,0.0008479487,0.000012616759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033206037,0.00014297571,0.000025833353,0.00013613135,0.000028029828,0.00003128739,0.0005284896,0.4824291,0.0000408515,0.023206418,0.00012126473,0.49327645],"study_design_scores_gemma":[0.0006310903,0.00025614773,0.00006163182,0.00019887641,0.000017622291,0.00004766804,0.0000017215834,0.9833901,0.00023194702,0.013193277,0.0011545668,0.0008153801],"about_ca_topic_score_codex":0.0002293176,"about_ca_topic_score_gemma":0.00005265928,"teacher_disagreement_score":0.500961,"about_ca_system_score_codex":0.0007375144,"about_ca_system_score_gemma":0.001683467,"threshold_uncertainty_score":0.99950004},"labels":[],"label_agreement":null},{"id":"W1810526953","doi":"10.1007/978-3-540-76931-6_2","title":"Analyzing the Role of “Smart” Start Points in Coarse Search-Greedy Search","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Toronto; York University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Computer science; Memetic algorithm; Point (geometry); Greedy algorithm; Domain (mathematical analysis); Local search (optimization); Process (computing); Mathematics","score_opus":0.038195848875386566,"score_gpt":0.3025436831775436,"score_spread":0.264347834302157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1810526953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022893507,0.00067739526,0.99353147,0.00068535557,0.0004323096,0.0006642581,0.000007962123,0.000054159755,0.003718183],"genre_scores_gemma":[0.09979263,0.00021805889,0.8984443,0.0005033858,0.00030541173,0.00001100828,0.000007646806,0.00006846897,0.00064908405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935732,0.0002450879,0.0009335005,0.0014613107,0.0026469084,0.0011399968],"domain_scores_gemma":[0.9950092,0.0016460762,0.00023365671,0.0020551425,0.0007904069,0.00026555968],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.00714499,0.00046004675,0.0006542939,0.0024951354,0.00024585056,0.00048813532,0.0058089434,0.00028281467,0.000103275976],"category_scores_gemma":[0.00036546256,0.0003627269,0.0001428211,0.0026479624,0.0015920646,0.00051036273,0.002858888,0.0017608476,0.00005897208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020234256,0.00006808228,0.0007349916,0.000046053476,0.000019474166,0.00012493921,0.0017954593,0.12953931,0.00010545732,0.016279707,0.000008061755,0.8512582],"study_design_scores_gemma":[0.0002765836,0.00015029985,0.0002497095,0.00021603025,0.0000038998355,0.000027590044,0.0000020112245,0.9725279,0.0023370206,0.023437442,0.00042039124,0.00035110774],"about_ca_topic_score_codex":0.00024032543,"about_ca_topic_score_gemma":0.0003000205,"teacher_disagreement_score":0.8509071,"about_ca_system_score_codex":0.00038569627,"about_ca_system_score_gemma":0.0012468896,"threshold_uncertainty_score":0.99988246},"labels":[],"label_agreement":null},{"id":"W1820106783","doi":"10.1007/978-3-642-00267-0_3","title":"The Evolutionary-Gradient-Search Procedure in Theory and Practice","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Evolutionary algorithm; Computer science; Inverse; Mathematical optimization; Artificial intelligence; Mathematics; Algorithm","score_opus":0.08754761877130322,"score_gpt":0.40931156537023544,"score_spread":0.3217639465989322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1820106783","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000035978858,0.05387782,0.8015075,0.010933174,0.00068476505,0.0013759123,0.000013966068,0.00008830754,0.13151497],"genre_scores_gemma":[0.014094021,0.08675378,0.64751726,0.0026024058,0.0005394566,0.00028650966,0.00005453585,0.00012787033,0.24802415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99651504,0.00047042264,0.0007249473,0.00078146305,0.0010977472,0.00041038648],"domain_scores_gemma":[0.98484707,0.01341906,0.00020129704,0.00040798908,0.0010424362,0.0000821684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038531616,0.00031212304,0.00035411466,0.00045886022,0.00034733454,0.0001693587,0.0010804565,0.00013190055,0.000015413012],"category_scores_gemma":[0.004775797,0.0002505426,0.00005337611,0.00039335462,0.00092251535,0.00034130862,0.00080412836,0.00082473166,0.000060793263],"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":[0.000044940094,0.000034126293,0.0000059334134,0.000036034664,0.000054328833,0.00007415982,0.0011951958,0.060279198,3.5370913e-8,0.8504828,0.00056903705,0.08722421],"study_design_scores_gemma":[0.00009411506,0.00010386432,0.00015030679,0.00024337857,0.0000067335773,0.00012082266,0.0004930135,0.20755021,8.0740415e-7,0.7798355,0.011142071,0.00025913125],"about_ca_topic_score_codex":0.0000060563752,"about_ca_topic_score_gemma":0.000022926934,"teacher_disagreement_score":0.15399022,"about_ca_system_score_codex":0.0003577292,"about_ca_system_score_gemma":0.00041793316,"threshold_uncertainty_score":0.9999947},"labels":[],"label_agreement":null},{"id":"W1835894572","doi":"10.1109/ccece.2002.1013043","title":"Enhancing the particle swarm optimizer via proper parameters selection","year":2003,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":98,"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":"Particle swarm optimization; Range (aeronautics); Mathematical optimization; Selection (genetic algorithm); Swarm intelligence; Computer science; Swarm behaviour; Multi-swarm optimization; Process (computing); Metaheuristic; Algorithm; Mathematics; Artificial intelligence; Engineering","score_opus":0.025822136232708577,"score_gpt":0.26812461032439533,"score_spread":0.24230247409168676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1835894572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004337983,0.00002697906,0.98912644,0.0009854585,0.0001952598,0.00030113873,8.821186e-8,0.00014623854,0.0048803887],"genre_scores_gemma":[0.3198309,0.00000940457,0.6744447,0.00051845255,0.000017148666,0.00005697033,2.6519345e-7,0.000010737013,0.0051114233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982819,0.00034990828,0.00024301978,0.0003290371,0.0004303117,0.00036580762],"domain_scores_gemma":[0.99902374,0.00018772963,0.000047166755,0.00044965468,0.00018080883,0.00011087846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011322034,0.0001068826,0.00010461371,0.00003672369,0.0002494474,0.00030673077,0.0004859494,0.000035335135,0.00031379578],"category_scores_gemma":[0.0004124177,0.000064886124,0.00004419752,0.0007961054,0.000054167398,0.0003655626,0.00008851515,0.00015587032,0.00032708218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004222948,0.0008969784,0.0017676196,0.00005701972,0.00028720216,0.000036327016,0.003891235,0.5299599,0.03405006,0.31879783,0.008673845,0.10153975],"study_design_scores_gemma":[0.00019535665,0.000043087995,0.00005491573,0.0000022116155,0.000003835894,0.000024231804,0.000028884708,0.8009429,0.19631737,0.0005436531,0.0017348671,0.00010866516],"about_ca_topic_score_codex":0.000032491873,"about_ca_topic_score_gemma":0.000007918131,"teacher_disagreement_score":0.31825417,"about_ca_system_score_codex":0.000045848068,"about_ca_system_score_gemma":0.000099940786,"threshold_uncertainty_score":0.42040887},"labels":[],"label_agreement":null},{"id":"W1859892029","doi":"10.1023/a:1011409029226","title":"Keep–Best Reproduction: A Local Family Competition Selection Strategy and the Environment it Flourishes in","year":2001,"lang":"en","type":"article","venue":"Constraints","topic":"Metaheuristic Optimization Algorithms Research","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 Regina; Kwantlen Polytechnic University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Fitness proportionate selection; Population; Offspring; Computer science; Operator (biology); Genetic algorithm; Mathematical optimization; Evolutionary biology; Biology; Machine learning; Mathematics; Genetics; Demography; Sociology","score_opus":0.029087930865425562,"score_gpt":0.2622588816988172,"score_spread":0.23317095083339168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1859892029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008937826,0.00020626311,0.9767794,0.0034613658,0.00014183104,0.00035954855,0.000001332136,0.000040892635,0.010071558],"genre_scores_gemma":[0.98187274,0.00060745666,0.016326824,0.00017933686,0.00008333154,0.000043123182,0.0000038890807,0.000007228738,0.0008760446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847937,0.0002694756,0.0002634811,0.00044922772,0.00031810274,0.00022032323],"domain_scores_gemma":[0.9993727,0.00011421173,0.00006040926,0.00032478775,0.000056463075,0.0000713832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094034744,0.000105891115,0.00014121241,0.000106064734,0.00013640981,0.00016905156,0.0002520885,0.000050225626,0.00022589735],"category_scores_gemma":[0.00009569037,0.0000858216,0.000024148549,0.00035268257,0.0007222374,0.00023791805,0.000101490674,0.00020591001,0.00008790403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054205033,0.00023706912,0.0014455505,0.000018583516,0.00003296704,0.00008029766,0.0006999236,0.032569714,0.00020065752,0.06816476,0.0004389888,0.8960573],"study_design_scores_gemma":[0.0026391372,0.00013371654,0.0102801435,0.00004075026,0.000010122749,0.00048386268,0.00084148307,0.9715255,0.00017827957,0.0059077092,0.0076952125,0.00026408903],"about_ca_topic_score_codex":0.000058330334,"about_ca_topic_score_gemma":0.000013881848,"teacher_disagreement_score":0.97293496,"about_ca_system_score_codex":0.000069561196,"about_ca_system_score_gemma":0.00007707895,"threshold_uncertainty_score":0.3499703},"labels":[],"label_agreement":null},{"id":"W191909571","doi":"10.1007/978-3-642-27534-0_7","title":"Investigating a Measure of the Recombinational Distance Traversed by the Genetic Algorithm","year":2012,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Unary operation; Measure (data warehouse); Algorithm; Set (abstract data type); Operator (biology); Function (biology); Population; Genetic algorithm; Computer science; Replicate; Binary number; Recombination; Mathematics; Mathematical optimization; Theoretical computer science; Data mining; Discrete mathematics; Biology; Statistics; Genetics; Arithmetic","score_opus":0.10173530705018209,"score_gpt":0.33894018647456564,"score_spread":0.23720487942438356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W191909571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024900878,0.009025064,0.9780652,0.0013520819,0.0008942926,0.0005874582,0.00007044648,0.0000263498,0.009976624],"genre_scores_gemma":[0.024986185,0.002483357,0.9068727,0.00091792067,0.0003791662,0.00021254442,0.00006380584,0.00011056793,0.06397377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639827,0.00022544735,0.0008917558,0.000511617,0.0016605245,0.0003123587],"domain_scores_gemma":[0.9955893,0.0020556434,0.0005873079,0.0005795778,0.0011168548,0.000071317794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010511724,0.00032687295,0.00040448355,0.00014288725,0.00030090494,0.000062189676,0.0019629346,0.00012237171,0.00006612834],"category_scores_gemma":[0.0007488624,0.00022314474,0.00014463815,0.00041812737,0.0012456601,0.0001483271,0.0007580825,0.00063749566,0.000029066683],"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":[0.000002426745,0.00006616361,0.00006408877,0.00010763555,0.00025130488,0.0000038514395,0.0024311692,0.10786668,0.0000013509826,0.617406,0.003985026,0.26781428],"study_design_scores_gemma":[0.00013212461,0.000035616693,0.0002642815,0.00041690154,0.000024308032,0.000025166928,0.00012505478,0.59471244,0.000049611226,0.39900705,0.0048639495,0.00034350294],"about_ca_topic_score_codex":0.0000101625055,"about_ca_topic_score_gemma":0.0000072770727,"teacher_disagreement_score":0.48684573,"about_ca_system_score_codex":0.000252367,"about_ca_system_score_gemma":0.00034049092,"threshold_uncertainty_score":0.9099578},"labels":[],"label_agreement":null},{"id":"W194959742","doi":"10.1007/978-3-319-06483-3_36","title":"Heterogeneous Multi-Population Cultural Algorithm with a Dynamic Dimension Decomposition Strategy","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Benchmark (surveying); Dimension (graph theory); Computer science; Decomposition; Population; Mathematical optimization; Algorithm; Theoretical computer science; Mathematics","score_opus":0.01824106933648329,"score_gpt":0.2962817787374648,"score_spread":0.2780407094009815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W194959742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000105948115,0.00019083286,0.9978486,0.00017984082,0.0006537418,0.00065385795,0.000007058899,0.00021364348,0.00014648068],"genre_scores_gemma":[0.089595,0.00004429468,0.9094802,0.00023221703,0.00014127744,0.000015523876,0.00007478675,0.00004803504,0.00036863866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99514824,0.00011916735,0.00062235317,0.001799557,0.0015742057,0.00073647354],"domain_scores_gemma":[0.9971673,0.00025962124,0.00038885695,0.0012771324,0.00063028577,0.0002767872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072959263,0.000628641,0.00059720495,0.00090647984,0.000386998,0.00095911446,0.0020148284,0.00032313258,0.000030050143],"category_scores_gemma":[0.000051166637,0.0005051394,0.00010792397,0.00069196115,0.0004889052,0.00062398193,0.00075481227,0.00075207156,0.000061652776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052955224,0.000024807501,0.000007807858,0.000018923685,0.000010980891,0.000073052346,0.00009009146,0.43462464,0.00004750793,0.00053032814,0.0000018915874,0.56456465],"study_design_scores_gemma":[0.0004995451,0.0003631585,0.00017177641,0.00022857035,0.000010690915,0.00026326784,9.818648e-8,0.9931109,0.00028582418,0.0043900143,0.000053389667,0.00062276784],"about_ca_topic_score_codex":0.00005242681,"about_ca_topic_score_gemma":0.000099633595,"teacher_disagreement_score":0.5639419,"about_ca_system_score_codex":0.0005227737,"about_ca_system_score_gemma":0.00028839006,"threshold_uncertainty_score":0.99974},"labels":[],"label_agreement":null},{"id":"W1963521172","doi":"10.1115/ipc2008-64410","title":"Fuel Optimization Using Biologically-Inspired Computational Models","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"TransCanada (Canada); University of Calgary","funders":"","keywords":"Computer science; Fuel efficiency; Particle swarm optimization; Pipeline (software); Mathematical optimization; Set (abstract data type); Optimization problem; Engineering; Algorithm; Mathematics","score_opus":0.12207240953058614,"score_gpt":0.3034342312988801,"score_spread":0.18136182176829396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963521172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005646203,0.000042609445,0.99233323,0.00030123914,0.00009601618,0.00017334994,0.0000021234139,0.00026838802,0.0062183943],"genre_scores_gemma":[0.108478,0.000043712167,0.8907577,0.00029520853,0.000029742805,0.000006005035,0.000015591704,0.000007679791,0.00036638483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984132,0.00012533703,0.00030025782,0.00039358393,0.00050621096,0.00026142842],"domain_scores_gemma":[0.9989352,0.00012312597,0.00007594065,0.00031698623,0.00041241775,0.00013630048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024807156,0.00012244258,0.00015431955,0.00018617499,0.00023292027,0.0000977938,0.00061330927,0.0000694458,0.00023597082],"category_scores_gemma":[0.00009962506,0.000104002975,0.000048867994,0.00069674256,0.000096523785,0.00066141225,0.00025044155,0.000092750146,0.000044973865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020234952,0.000048305217,0.00006312915,0.0000025960885,0.0000071545637,0.000013208478,0.00006535649,0.96998346,0.000010773389,0.02917979,0.0001430104,0.00048121676],"study_design_scores_gemma":[0.00027398323,0.000029197217,0.00013817145,0.0000023351802,0.0000013525535,0.000067536195,0.0000032993833,0.99367446,0.000017710869,0.005617908,0.00003954647,0.0001345252],"about_ca_topic_score_codex":0.000017432169,"about_ca_topic_score_gemma":2.436372e-7,"teacher_disagreement_score":0.10791338,"about_ca_system_score_codex":0.000053110158,"about_ca_system_score_gemma":0.00018338881,"threshold_uncertainty_score":0.4241118},"labels":[],"label_agreement":null},{"id":"W1964465571","doi":"10.1016/j.asoc.2014.04.018","title":"Improved global-best particle swarm optimization algorithm with mixed-attribute data classification capability","year":2014,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":23,"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":"Particle swarm optimization; Computer science; Multi-swarm optimization; Algorithm; Metaheuristic; Data mining; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.0402902922792972,"score_gpt":0.29176492415386873,"score_spread":0.25147463187457153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964465571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017454438,0.000022438897,0.99569196,0.0005145491,0.00022599808,0.00059520605,0.000023384604,0.0004727626,0.0007082414],"genre_scores_gemma":[0.37385094,0.0000029860482,0.6257159,0.000120134326,0.0000975999,0.000016431628,0.00015956281,0.000017697463,0.000018751773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964416,0.00024989012,0.00059533137,0.001310625,0.00073243375,0.00067012326],"domain_scores_gemma":[0.99600345,0.00046006063,0.0003218224,0.002489531,0.00043870037,0.0002864258],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021636812,0.0002900503,0.00034285145,0.0000639727,0.00046553154,0.00055911846,0.0022243056,0.00011321128,0.000012192221],"category_scores_gemma":[0.00040764033,0.00027020345,0.000033444696,0.0013188655,0.00015909718,0.00051676464,0.0012411296,0.00026046447,0.00007050684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016097178,0.00033296715,0.0010171939,0.000047535214,0.000057133453,0.0000026605858,0.00016853647,0.4537178,0.00016197984,0.014354416,0.00027591258,0.52984774],"study_design_scores_gemma":[0.00078963407,0.00007355546,0.0009896905,0.000013176847,0.000019601732,0.000010759014,0.000060577964,0.9966984,0.00040312068,0.0002694488,0.00035537922,0.0003166987],"about_ca_topic_score_codex":0.00005768552,"about_ca_topic_score_gemma":0.00000934937,"teacher_disagreement_score":0.54298055,"about_ca_system_score_codex":0.00016525935,"about_ca_system_score_gemma":0.00019533107,"threshold_uncertainty_score":0.999975},"labels":[],"label_agreement":null},{"id":"W1964649011","doi":"10.1145/1068009.1068050","title":"Factors governing the behavior of multiple cooperating swarms","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Benchmark (surveying); Computer science; Swarm behaviour; Synchronization (alternating current); Distributed computing; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","score_opus":0.05847962861320025,"score_gpt":0.30615081089765783,"score_spread":0.24767118228445759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964649011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078635044,0.000028700662,0.9181516,0.00034142903,0.000088283974,0.00023403726,0.0000029694893,0.00006841481,0.0024494939],"genre_scores_gemma":[0.8090587,0.0000032084233,0.18904756,0.000067367924,0.00003158898,0.00001945469,0.0000016846049,0.0000056469817,0.0017647616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891555,0.000076130265,0.00022689936,0.00017545088,0.00042878476,0.00017716528],"domain_scores_gemma":[0.99896383,0.00036031933,0.00006975963,0.00039677322,0.00015271915,0.00005661444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036755126,0.00007736678,0.000101182384,0.000041432395,0.00013225204,0.000109624285,0.0007578491,0.000024663726,0.00027710077],"category_scores_gemma":[0.00046836617,0.00004401568,0.000037274862,0.00031433257,0.000044940705,0.00026357846,0.0002553652,0.00011497529,0.00004664499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071898053,0.00077080564,0.18237576,0.00003672579,0.00008650823,0.000009840004,0.007723467,0.1477854,0.01345984,0.033872712,0.0040926556,0.6097791],"study_design_scores_gemma":[0.00016005123,0.000020103305,0.0079801185,0.0000028182096,0.0000026555988,0.000001413817,0.00009899797,0.97445285,0.0150973145,0.0000027554288,0.0021101781,0.00007074525],"about_ca_topic_score_codex":0.000056214107,"about_ca_topic_score_gemma":0.000018423836,"teacher_disagreement_score":0.8266674,"about_ca_system_score_codex":0.000024178093,"about_ca_system_score_gemma":0.00005451572,"threshold_uncertainty_score":0.3034059},"labels":[],"label_agreement":null},{"id":"W1965210196","doi":"10.1108/17427371011066383","title":"Intelligent learning agent for collaborative virtual workspace","year":2010,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Computer science; Workspace; Reinforcement learning; Human–computer interaction; Architecture; Intelligent agent; Originality; Artificial intelligence; Robot","score_opus":0.04465470724219504,"score_gpt":0.36766452904383473,"score_spread":0.3230098218016397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965210196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054084486,0.0003541445,0.9832535,0.0094552105,0.0009654681,0.00013048176,0.000004192058,0.00002415745,0.00040441583],"genre_scores_gemma":[0.47195253,0.0006106729,0.5268947,0.00016789812,0.00017616415,0.000004852502,0.0000060210587,0.000007687257,0.00017950052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869514,0.0001838787,0.00043694745,0.00014650567,0.00040024097,0.00013726676],"domain_scores_gemma":[0.99444,0.0014365733,0.00043994276,0.000397013,0.0031819227,0.00010455296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009990741,0.00009864246,0.00016069556,0.00024847398,0.00029924975,0.00038072796,0.0023092576,0.00004787359,0.000011831811],"category_scores_gemma":[0.0016916074,0.000091916416,0.00008060594,0.00024594384,0.00014142986,0.00026053336,0.0007965861,0.00058693904,0.000004480319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050362378,0.0003938779,0.0016557719,0.000009521884,0.00045180065,0.000015741274,0.009032185,0.017291894,0.0016155307,0.33721194,0.0020950623,0.6301763],"study_design_scores_gemma":[0.00083702896,0.00028764608,0.00097648776,0.000094450486,0.00001911506,0.0001887925,0.0017843976,0.91812384,0.00046901414,0.0022738352,0.07474966,0.00019574321],"about_ca_topic_score_codex":0.0000048927786,"about_ca_topic_score_gemma":0.00000870806,"teacher_disagreement_score":0.90083194,"about_ca_system_score_codex":0.000043778437,"about_ca_system_score_gemma":0.00022731296,"threshold_uncertainty_score":0.42912138},"labels":[],"label_agreement":null},{"id":"W1966076063","doi":"10.5539/jmr.v4n4p81","title":"Similarity Form, Similarity Variational Element and Similarity Solution to (2+1) Dimensional Breaking Solition Equation","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematics; Similarity (geometry); Similarity solution; Partial differential equation; Element (criminal law); Mathematical analysis; Burgers' equation; Applied mathematics; Law; Computer science; Artificial intelligence; Physics","score_opus":0.17058280712304255,"score_gpt":0.40661304280702126,"score_spread":0.2360302356839787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966076063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014469844,0.000106430365,0.97929263,0.0049352585,0.0002955894,0.0004629264,0.000011943643,0.000023685243,0.00040168254],"genre_scores_gemma":[0.35498673,0.000041093088,0.64436287,0.00014850227,0.0003409159,0.0000166151,0.0000073338006,0.000016652777,0.00007926871],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993413,0.00073353015,0.0010146038,0.00028319046,0.0037316303,0.0008240661],"domain_scores_gemma":[0.99404687,0.0017202924,0.0004191431,0.00045276282,0.0027196691,0.00064125273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019313607,0.00018803166,0.0003639724,0.0008638444,0.0005431542,0.0004069671,0.0007348836,0.00014864866,0.0001482595],"category_scores_gemma":[0.004386352,0.00016654437,0.00009963709,0.0008450468,0.000098073,0.0015605235,0.0008272628,0.0008501999,0.000036937025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025961536,0.008399842,0.00672844,0.0013516911,0.0005939032,0.00007726131,0.017677391,0.019677138,0.009970549,0.86890304,0.022241462,0.044119656],"study_design_scores_gemma":[0.00070775265,0.00026161934,0.0077992454,0.000119820535,0.000021188036,0.00016708915,0.00011703424,0.92917705,0.0011689904,0.0592956,0.0009442026,0.00022043432],"about_ca_topic_score_codex":0.000023027655,"about_ca_topic_score_gemma":0.000005595874,"teacher_disagreement_score":0.9094999,"about_ca_system_score_codex":0.00047463976,"about_ca_system_score_gemma":0.0004194241,"threshold_uncertainty_score":0.6791482},"labels":[],"label_agreement":null},{"id":"W1966725634","doi":"10.1145/1370256.1370260","title":"A bioinspired algorithm to price options","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Binomial options pricing model; Ant colony optimization algorithms; Mathematical optimization; Heuristic; Valuation of options; Algorithm; Finance; Economics; Artificial intelligence; Mathematics","score_opus":0.04023007675289864,"score_gpt":0.2890739934956034,"score_spread":0.24884391674270478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966725634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015085645,0.000025686008,0.9766565,0.0025495181,0.00017979433,0.00024931284,0.0000028329953,0.00033370743,0.01985178],"genre_scores_gemma":[0.0014238722,0.000038352006,0.9838212,0.0007789259,0.000053244552,0.00004028978,0.0000021215271,0.000008593521,0.013833407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984939,0.00006720015,0.00020658877,0.00038498832,0.00051615894,0.00033116803],"domain_scores_gemma":[0.9986402,0.000079309066,0.000027208678,0.000663766,0.000259025,0.00033048584],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024020598,0.000102294944,0.00012199141,0.00028341744,0.00023353177,0.00010000826,0.0009262889,0.000036042467,0.00030307082],"category_scores_gemma":[0.00018150685,0.0000914025,0.000042541298,0.0015280416,0.00004041627,0.00030866175,0.0003801927,0.00008938551,0.0017321962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008404585,0.00095960323,0.00035590422,0.000019348574,0.00008701887,0.00052709505,0.002715004,0.010134851,0.001329214,0.22224273,0.1653156,0.59630525],"study_design_scores_gemma":[0.00024739446,0.00008228099,0.0015389593,0.0000029950363,0.0000011517996,0.0001117737,0.000008739265,0.9595138,0.000888966,0.00023867341,0.037167653,0.0001976212],"about_ca_topic_score_codex":0.000027577498,"about_ca_topic_score_gemma":0.0000015233234,"teacher_disagreement_score":0.94937897,"about_ca_system_score_codex":0.00004391475,"about_ca_system_score_gemma":0.00015955964,"threshold_uncertainty_score":0.9990451},"labels":[],"label_agreement":null},{"id":"W1967756118","doi":"10.1007/s10957-004-0943-z","title":"Analysis of Random Restart and Iterated Improvement for Global Optimization with Application to the Traveling Salesman Problem","year":2005,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Travelling salesman problem; Mathematics; Iterated function; Theory of computation; A priori and a posteriori; Convergence (economics); Speedup; Mathematical optimization; Combinatorics; Rate of convergence; Applied mathematics; Algorithm; Computer science; Mathematical analysis","score_opus":0.007913086695719185,"score_gpt":0.2766126644019262,"score_spread":0.26869957770620706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967756118","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041342532,0.00011871387,0.996365,0.0018095557,0.000010105464,0.0011617056,0.000018939081,0.000015012818,0.00008752316],"genre_scores_gemma":[0.11148808,0.00019221108,0.8876968,0.0002099817,0.00006798308,0.00024497707,0.000033487733,0.000009485937,0.00005701234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986264,0.00013740754,0.0005933196,0.00023496874,0.00027646174,0.00013143693],"domain_scores_gemma":[0.99780536,0.00032362205,0.00051769573,0.00028615238,0.0009641639,0.000103033395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016984332,0.00011514802,0.0002554863,0.00023265029,0.00023323714,0.0001548046,0.00033557674,0.000042116764,0.000007941871],"category_scores_gemma":[0.000092702234,0.00007949607,0.000054559245,0.0016273973,0.000068724585,0.0003265472,0.000053755466,0.000071323906,2.645639e-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":[0.00014130199,0.0000662918,0.00006179482,0.000012850146,0.00017605066,6.627467e-8,0.0002341673,0.9171121,0.00004413338,0.051409245,0.000014507953,0.03072749],"study_design_scores_gemma":[0.00092675176,0.00015285226,0.000108985354,0.0000128868,0.00029116572,0.000009565962,0.00007935119,0.99644417,0.00014850311,0.001037653,0.0006995642,0.00008857049],"about_ca_topic_score_codex":0.0000012950824,"about_ca_topic_score_gemma":0.0000038432504,"teacher_disagreement_score":0.111074656,"about_ca_system_score_codex":0.000041850988,"about_ca_system_score_gemma":0.000082320934,"threshold_uncertainty_score":0.3241756},"labels":[],"label_agreement":null},{"id":"W1970542049","doi":"10.1109/ccece.2012.6334976","title":"Center-point-based Simulated Annealing","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Simulated annealing; Computer science; Algorithm; Mathematical optimization; Metaheuristic; Local optimum; Adaptive simulated annealing; Benchmark (surveying); Curse of dimensionality; Center (category theory); Convergence (economics); Point (geometry); Mathematics; Artificial intelligence","score_opus":0.032179011542871755,"score_gpt":0.30372800627111723,"score_spread":0.27154899472824545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970542049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095651427,0.000037504953,0.99012005,0.0007094855,0.00031383807,0.00011618014,0.0000013558089,0.00026599976,0.007479076],"genre_scores_gemma":[0.60676605,0.0000017185349,0.39130697,0.00067016407,0.00006928874,0.000002775177,0.000006809323,0.00000954243,0.0011667161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987086,0.00010669389,0.0001875989,0.00019426325,0.00036396438,0.00043889473],"domain_scores_gemma":[0.99894637,0.00015889267,0.000038569597,0.00048485587,0.00012335197,0.0002479682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005681561,0.00009063063,0.00010063713,0.0001413517,0.00008383567,0.00013587944,0.0005504385,0.00003826483,0.0005891624],"category_scores_gemma":[0.00018185069,0.00007700861,0.000039645463,0.0005040296,0.000022446558,0.0005137955,0.00015719514,0.00009558153,0.0004803433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049559916,0.002586896,0.06261524,0.00015781204,0.00016513585,0.000071939416,0.0015157157,0.4455554,0.001818076,0.16734308,0.027814765,0.29030642],"study_design_scores_gemma":[0.0003432446,0.00001578484,0.00039781828,0.000003917583,0.0000010574262,0.0000029038474,0.0000041882663,0.99159557,0.00280277,0.000043127824,0.004690379,0.000099221936],"about_ca_topic_score_codex":0.000017373632,"about_ca_topic_score_gemma":4.729086e-7,"teacher_disagreement_score":0.6058095,"about_ca_system_score_codex":0.00003121435,"about_ca_system_score_gemma":0.000045144418,"threshold_uncertainty_score":0.64509153},"labels":[],"label_agreement":null},{"id":"W1971035609","doi":"10.1109/cec.2007.4424846","title":"Cryptanalysis of Pointcheval&amp;#x2019;s identification scheme using ant colony optimization","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ant colony optimization algorithms; Cryptanalysis; Scheme (mathematics); Identification (biology); Identification scheme; Computer science; Binary number; Ant colony; Cryptography; Algorithm; Data mining; Mathematics; Arithmetic","score_opus":0.05457023489829093,"score_gpt":0.3553378603244654,"score_spread":0.30076762542617447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971035609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054119346,0.000047009802,0.99278533,0.00019275196,0.00014928558,0.00020961072,0.000001959345,0.00008970108,0.0011124087],"genre_scores_gemma":[0.025656492,0.00002624972,0.97308856,0.000046471487,0.000034494227,0.0000026828047,0.000014753665,0.000011700791,0.0011185932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759954,0.00011562958,0.000733455,0.0004133727,0.0008321366,0.00030587017],"domain_scores_gemma":[0.997595,0.00018474853,0.0003312709,0.0008089956,0.0009459378,0.00013405325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024311314,0.00012615032,0.00022930697,0.00058343314,0.00011964184,0.00015214428,0.0007116305,0.00008237046,0.00042767465],"category_scores_gemma":[0.00064181763,0.0001221127,0.00008767916,0.0020592415,0.00007313396,0.0005421049,0.00020374081,0.00010192276,0.000049928858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038363974,0.0004853178,0.001943717,0.00008434478,0.00021738473,0.000004520592,0.000624341,0.7461806,0.19645946,0.045214195,0.00035047645,0.008397279],"study_design_scores_gemma":[0.00018943074,0.000015311576,0.0005234884,0.000007387293,0.000017660697,0.0000063035027,0.000029946805,0.9906858,0.00790599,0.00022956211,0.0002600642,0.00012900554],"about_ca_topic_score_codex":0.00008377484,"about_ca_topic_score_gemma":0.000011404742,"teacher_disagreement_score":0.24450526,"about_ca_system_score_codex":0.00012291726,"about_ca_system_score_gemma":0.00013767785,"threshold_uncertainty_score":0.49796113},"labels":[],"label_agreement":null},{"id":"W1971967548","doi":"10.1109/syscon.2012.6189539","title":"Overview of Artificial Bee Colony (ABC) algorithm and its applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Artificial bee colony algorithm; Computer science; Metaheuristic; Heuristic; Mathematical optimization; Algorithm; Key (lock); Optimization algorithm; Population; Meta heuristic; Artificial intelligence; Mathematics","score_opus":0.07954983294595744,"score_gpt":0.35052592406468414,"score_spread":0.27097609111872667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971967548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011281446,0.0014625106,0.99489355,0.00042957033,0.00006861853,0.00032510087,0.000007617434,0.000053766275,0.0026464674],"genre_scores_gemma":[0.017373854,0.0005006984,0.9801373,0.00020490117,0.00014577968,0.00011358909,0.0000043632417,0.000010577741,0.0015089626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989281,0.00007032378,0.0002444968,0.0001897466,0.00032459575,0.00024269578],"domain_scores_gemma":[0.9991343,0.00016842615,0.00006879127,0.0002625955,0.00019336992,0.00017253717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051440136,0.000077141,0.00014227594,0.000104862476,0.00008385227,0.000051270334,0.00038173475,0.000042170024,0.00020949279],"category_scores_gemma":[0.00008495252,0.00006934671,0.000025757225,0.00053399673,0.000045015477,0.00035796757,0.00027246185,0.00007069853,0.000119789605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010346477,0.00020076486,0.00006826673,0.00004739559,0.000017910248,5.316124e-7,0.00017136653,0.00002850223,0.0005283634,0.52207327,0.0009394099,0.47592318],"study_design_scores_gemma":[0.00019257748,0.00004825747,0.0011360798,0.000008552917,0.000011038071,0.000015681408,0.000021287164,0.9543893,0.0069715343,0.002285822,0.034729864,0.0001900456],"about_ca_topic_score_codex":0.000009955607,"about_ca_topic_score_gemma":7.6732607e-7,"teacher_disagreement_score":0.9543608,"about_ca_system_score_codex":0.000013544419,"about_ca_system_score_gemma":0.00004997727,"threshold_uncertainty_score":0.28278768},"labels":[],"label_agreement":null},{"id":"W1977528467","doi":"10.1145/2463372.2463426","title":"On the behaviour of the (1, λ)-es for a conically constrained problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Mathematical optimization; Applied mathematics; Mathematics; Computer science; Rate of convergence; Invariant (physics); Algorithm; Key (lock)","score_opus":0.028158495147658162,"score_gpt":0.27669647974194406,"score_spread":0.2485379845942859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977528467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003310115,0.000004491539,0.965228,0.020140214,0.00008353612,0.001582726,0.0000048438983,0.000039161037,0.009606873],"genre_scores_gemma":[0.66198653,0.000001919221,0.33094487,0.0010640662,0.000013396362,0.00029749426,6.54958e-7,0.000007274082,0.0056837825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999027,0.000103773855,0.00020967131,0.00016599803,0.00032387237,0.00016967098],"domain_scores_gemma":[0.9980305,0.00093967246,0.000076121534,0.0005550832,0.00035401675,0.000044608947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048651083,0.00006591649,0.000092460075,0.000026473048,0.000102231796,0.000121184705,0.0011584214,0.000028254648,0.00048425468],"category_scores_gemma":[0.0005238068,0.000028738828,0.00006274351,0.00021840548,0.00015806309,0.00008120034,0.00019319351,0.000085165164,0.000037810903],"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":[0.0000014902948,0.000059311194,0.00007427091,0.0000059203985,0.0000104444125,1.5147414e-7,0.00006599546,0.00018488069,0.00021912323,0.9846815,0.010329796,0.0043671355],"study_design_scores_gemma":[0.0006978234,0.00024898097,0.0025615087,0.000025348549,0.000007834537,0.000007328114,0.00006416996,0.9432594,0.0044728187,0.047744792,0.0007655555,0.00014444019],"about_ca_topic_score_codex":0.000026156966,"about_ca_topic_score_gemma":0.000002857992,"teacher_disagreement_score":0.9430745,"about_ca_system_score_codex":0.000009257987,"about_ca_system_score_gemma":0.0001353426,"threshold_uncertainty_score":0.5302249},"labels":[],"label_agreement":null},{"id":"W1979173729","doi":"10.1007/s10288-009-0108-x","title":"Attraction probabilities in variable neighborhood search","year":2009,"lang":"en","type":"article","venue":"4OR","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal; Royal Military College of Canada","funders":"","keywords":"Attraction; Variable (mathematics); Context (archaeology); Mathematical optimization; Operator (biology); Variable neighborhood search; Mathematics; Local search (optimization); Convergence (economics); Current (fluid); Global optimization; Computer science; Metaheuristic; Geography; Mathematical analysis; Physics; Economics","score_opus":0.023488047299865797,"score_gpt":0.28500180224200083,"score_spread":0.261513754942135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979173729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014192014,0.000053393804,0.9709004,0.0024761518,0.00015055948,0.00028162354,0.0000014503429,0.00011313212,0.024604093],"genre_scores_gemma":[0.43190354,0.000036674668,0.56226426,0.0004000226,0.00011693742,0.000031081945,0.0000062763656,0.000009898932,0.005231339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987147,0.00012814596,0.00018769022,0.0002828739,0.00038986458,0.000296723],"domain_scores_gemma":[0.99927855,0.00010210017,0.000023440787,0.00039387966,0.00012437398,0.00007765726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071099174,0.00007398715,0.00010760438,0.0001787066,0.00005771174,0.00017216842,0.00046439288,0.000046016106,0.00017876331],"category_scores_gemma":[0.00019320997,0.00006994485,0.00001899287,0.00078993203,0.000020454374,0.0005562751,0.00006916651,0.00018323706,0.00011804619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024560597,0.000735686,0.0017306913,0.000057604637,0.000011920771,0.00004338007,0.0014643971,0.025328316,0.0009277734,0.69339526,0.002042175,0.27423823],"study_design_scores_gemma":[0.00042304245,0.00015405445,0.009637134,0.000017559292,0.0000011326041,0.00001265293,0.000039892737,0.9634698,0.0006316169,0.02279631,0.002664646,0.00015213265],"about_ca_topic_score_codex":0.000029355791,"about_ca_topic_score_gemma":0.0000013713532,"teacher_disagreement_score":0.9381415,"about_ca_system_score_codex":0.00007816572,"about_ca_system_score_gemma":0.00015467867,"threshold_uncertainty_score":0.28522682},"labels":[],"label_agreement":null},{"id":"W1982970548","doi":"10.1007/s00500-010-0591-1","title":"DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization","year":2010,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":367,"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":"China University of Geosciences; China Scholarship Council","keywords":"Differential evolution; Benchmark (surveying); Evolutionary algorithm; Computer science; Global optimization; Curse of dimensionality; Mathematical optimization; Convergence (economics); Range (aeronautics); Evolutionary computation; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.009130652461568212,"score_gpt":0.26138213911071934,"score_spread":0.25225148664915115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982970548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026059104,0.000012968692,0.9953242,0.00035280143,0.0005379639,0.0005837202,0.000016609154,0.0004585114,0.00010733503],"genre_scores_gemma":[0.40515715,4.3365003e-7,0.5945538,0.000059037087,0.00012965662,0.000020309793,0.00005722384,0.000016308213,0.0000060209068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997669,0.00013541157,0.0003727297,0.0006515619,0.00056357775,0.0006077197],"domain_scores_gemma":[0.99815387,0.00025958085,0.00023010653,0.0005030737,0.00061717984,0.00023618562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004860194,0.00024625365,0.00024616875,0.00022612077,0.00046524603,0.00053791585,0.0007465448,0.00010337111,0.000051979423],"category_scores_gemma":[0.00037416938,0.00023169734,0.00011640747,0.0011529817,0.00010651281,0.0003059901,0.00016801411,0.00024777654,0.000003946445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033861947,0.00011983899,0.002481636,0.000024255633,0.000022564098,0.0000034791842,0.000028219843,0.9820101,0.000035254478,0.0055630524,0.00006451423,0.009613175],"study_design_scores_gemma":[0.0011246676,0.00015573404,0.0006369616,0.000022497074,0.000019743418,0.000028606784,0.0000063662765,0.9972147,0.00013126973,0.0003257791,0.000059868427,0.00027382595],"about_ca_topic_score_codex":0.0000315973,"about_ca_topic_score_gemma":0.0000019071219,"teacher_disagreement_score":0.40255126,"about_ca_system_score_codex":0.00012065864,"about_ca_system_score_gemma":0.0004029349,"threshold_uncertainty_score":0.94483435},"labels":[],"label_agreement":null},{"id":"W1985951128","doi":"10.1007/s00500-015-1630-8","title":"Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies","year":2015,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"","keywords":"Crossover; Benchmark (surveying); Computer science; Probabilistic logic; Operator (biology); Evolutionary computation; Evolutionary algorithm; Variable (mathematics); Computation; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.04493490173541932,"score_gpt":0.3110365270932806,"score_spread":0.2661016253578613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985951128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030325532,0.000018650386,0.9642772,0.000057314617,0.00039019488,0.0002800609,0.0000026417931,0.00019723258,0.004451161],"genre_scores_gemma":[0.687492,1.0571226e-7,0.3123345,0.00005744893,0.000055913628,0.0000036303982,0.000003429467,0.000014018587,0.00003894693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743766,0.00025978393,0.00044328326,0.0005376555,0.00086257706,0.0004590564],"domain_scores_gemma":[0.9976979,0.00046358875,0.00019088699,0.00081820926,0.0005703991,0.0002589998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016689603,0.00018810036,0.00027236165,0.00019695888,0.0001491302,0.0004924659,0.00090233074,0.000062805164,0.000015293683],"category_scores_gemma":[0.0008506881,0.00017865465,0.000044615972,0.0007344672,0.00009402885,0.000245608,0.000363415,0.00019655391,0.000022263794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020164225,0.00017747768,0.000083619714,0.000057102996,0.0000056543117,0.000009619139,0.00053753005,0.97322965,0.00018491504,0.021422943,0.000036417394,0.004234905],"study_design_scores_gemma":[0.0007021055,0.00024237862,0.0002001219,0.00007537054,0.0000040765467,0.0000053671374,0.00008209352,0.9964171,0.00035780433,0.0016456478,0.00009001265,0.0001779152],"about_ca_topic_score_codex":0.00004123025,"about_ca_topic_score_gemma":0.0000010694583,"teacher_disagreement_score":0.6571665,"about_ca_system_score_codex":0.000098885976,"about_ca_system_score_gemma":0.00069898757,"threshold_uncertainty_score":0.7285325},"labels":[],"label_agreement":null},{"id":"W1989327351","doi":"10.1109/ictai.2010.21","title":"Particle Swarm Classification for High Dimensional Data Sets","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Curse of dimensionality; Cardinality (data modeling); Computer science; Set (abstract data type); Data set; Swarm behaviour; Statistical classification; Class (philosophy); Dispersion (optics); Data mining; Algorithm; Artificial intelligence; Physics","score_opus":0.11288058756019513,"score_gpt":0.3645696285704929,"score_spread":0.25168904101029776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989327351","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070330827,0.000005618858,0.9865392,0.004990491,0.0005297853,0.0002642993,0.000023554028,0.00012560753,0.00048840465],"genre_scores_gemma":[0.34599325,0.0000015854052,0.65236104,0.00021725091,0.00005827838,0.00002738811,0.00008525048,0.0000062166873,0.0012497278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987612,0.000035186724,0.00018644673,0.00044022055,0.00035704477,0.00021990549],"domain_scores_gemma":[0.99786633,0.000264126,0.000045711302,0.0014604571,0.00023459217,0.00012877505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073512737,0.000068670044,0.00007915254,0.00004233252,0.00012471793,0.00017280359,0.0013668502,0.000043466534,0.00021224219],"category_scores_gemma":[0.0005436363,0.000057114314,0.000014758088,0.00024325437,0.000043191576,0.0005240286,0.00050353864,0.000111281675,0.00018220031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104046885,0.00022832479,0.00031515543,0.000010857746,0.000019126692,0.0000023507694,0.000044415978,0.00035553525,0.010420036,0.8149997,0.029118227,0.14447585],"study_design_scores_gemma":[0.00028046232,0.000020481997,0.0024718584,7.748553e-7,0.0000023923433,0.0000035026378,0.0000021061142,0.9840428,0.004548758,0.0033565615,0.005191357,0.00007889852],"about_ca_topic_score_codex":0.00001653912,"about_ca_topic_score_gemma":0.000013447877,"teacher_disagreement_score":0.9836873,"about_ca_system_score_codex":0.000008146952,"about_ca_system_score_gemma":0.000108939974,"threshold_uncertainty_score":0.25399706},"labels":[],"label_agreement":null},{"id":"W1989361868","doi":"10.1109/cec.2010.5586139","title":"Fighting noise with noise: DE with individuals shaking to tackle noisy problems","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Noise (video); Benchmark (surveying); Computer science; Noise measurement; Algorithm; Monte Carlo method; Test suite; Population; Artificial intelligence; Machine learning; Mathematics; Noise reduction; Statistics; Test case","score_opus":0.014128498407306123,"score_gpt":0.2667874087732113,"score_spread":0.25265891036590515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989361868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026007975,0.0000061233886,0.9595665,0.001957008,0.000089975125,0.00060836,0.0000034690631,0.00028912447,0.011471488],"genre_scores_gemma":[0.17660794,0.0000012684535,0.81983566,0.00061659067,0.000081334976,0.000096523654,0.0000034026505,0.000029894529,0.002727412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733824,0.00007877067,0.00027979395,0.0006534863,0.00094144314,0.00070825475],"domain_scores_gemma":[0.99795574,0.00019620454,0.00011311781,0.00087975926,0.00039918537,0.00045596497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010142075,0.00022534157,0.00022791392,0.00029495877,0.0002548443,0.0007639508,0.0013750325,0.00007789927,0.00039302648],"category_scores_gemma":[0.0002620975,0.0001540493,0.000026591646,0.0013325085,0.00006786491,0.00058045756,0.0004518062,0.00044677133,0.00018696398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020664433,0.0018983519,0.22161347,0.0006240917,0.0006352622,0.00069802534,0.024292484,0.3041563,0.054541815,0.17266756,0.019494124,0.19917186],"study_design_scores_gemma":[0.0018167753,0.00075414253,0.017820237,0.00017664129,0.000024956316,0.00029535653,0.00010143337,0.9387587,0.0152958045,0.00042714336,0.023382785,0.0011460818],"about_ca_topic_score_codex":0.00009738069,"about_ca_topic_score_gemma":0.00014796885,"teacher_disagreement_score":0.6346023,"about_ca_system_score_codex":0.00003996724,"about_ca_system_score_gemma":0.00036593174,"threshold_uncertainty_score":0.7366795},"labels":[],"label_agreement":null},{"id":"W1989986531","doi":"10.1109/smc.2014.6974290","title":"On VEPSO and VEDE for solving a treaty optimization problem","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Optimization problem; Vector optimization; Differential evolution; Particle swarm optimization; Computer science; Multi-swarm optimization; Mathematics","score_opus":0.016756325054187903,"score_gpt":0.27057511004080237,"score_spread":0.2538187849866145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989986531","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006905866,0.000007726028,0.98865706,0.00092557864,0.00005725927,0.0003264947,8.3516977e-7,0.000108306325,0.00984771],"genre_scores_gemma":[0.015222756,0.000010587422,0.98226094,0.00024214674,0.00003348605,0.00005303883,0.0000041794947,0.000009442662,0.0021633916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991125,0.000045274373,0.00014023225,0.00030920017,0.00019838782,0.00019444375],"domain_scores_gemma":[0.99908257,0.00038319765,0.000039760074,0.00027697795,0.00012544236,0.000092061804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047374907,0.00008345629,0.0001045835,0.000109146604,0.00013162199,0.00024286725,0.00025305385,0.000035164652,0.000040585033],"category_scores_gemma":[0.000372909,0.000067887086,0.000021043601,0.00018064704,0.000022176593,0.00020940174,0.00009347039,0.000045999623,0.00001148772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001877754,0.0001033625,0.000049141196,0.000051248477,0.000016524937,8.083682e-7,0.00024194892,0.37467456,0.00007216735,0.4725987,0.003774904,0.14839788],"study_design_scores_gemma":[0.00051289407,0.00016683615,0.000028300887,0.000007860158,0.0000021482506,0.0000020737807,0.0000028329835,0.9938238,0.0002615759,0.0041869483,0.00092150207,0.00008319899],"about_ca_topic_score_codex":0.000007907285,"about_ca_topic_score_gemma":0.0000016169266,"teacher_disagreement_score":0.61914927,"about_ca_system_score_codex":0.000019208124,"about_ca_system_score_gemma":0.000029093797,"threshold_uncertainty_score":0.2768355},"labels":[],"label_agreement":null},{"id":"W1991528932","doi":"10.1108/17563780810857121","title":"Adaptive learning by a target‐tracking system","year":2008,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Ethogram; Implementation; Space (punctuation); Quality (philosophy)","score_opus":0.024619582025407268,"score_gpt":0.2819123869717912,"score_spread":0.25729280494638396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991528932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01538551,0.0008971238,0.9815265,0.0002477087,0.00088793307,0.00004967951,0.0000013242155,0.00004237338,0.0009618622],"genre_scores_gemma":[0.8450951,0.0003480895,0.15399438,0.0000554873,0.00022125697,3.1242712e-7,0.000001414953,0.000010187421,0.00027379658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978694,0.00016212351,0.000624715,0.00019710162,0.0009458549,0.00020081428],"domain_scores_gemma":[0.99753183,0.00033206516,0.00047994815,0.000109197856,0.0013920483,0.00015493586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000689647,0.00013205856,0.00021425173,0.00024073645,0.00014129395,0.00020343724,0.00092028914,0.000050630886,0.00001291708],"category_scores_gemma":[0.00032936907,0.00011985629,0.00008257575,0.000176853,0.00007976205,0.00019932081,0.00028546373,0.00041963064,0.000013531793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016603478,0.0006623547,0.016232764,0.00006675742,0.0013274661,0.0023953593,0.013597783,0.37367177,0.00088830804,0.06480806,0.009108491,0.5170748],"study_design_scores_gemma":[0.0002994814,0.00019885291,0.00032458923,0.0001481154,0.0000064381475,0.0020255607,0.0003300801,0.9897107,0.0023529888,0.00017528464,0.004281934,0.00014595749],"about_ca_topic_score_codex":0.000013119124,"about_ca_topic_score_gemma":1.2234815e-7,"teacher_disagreement_score":0.8297096,"about_ca_system_score_codex":0.00012055639,"about_ca_system_score_gemma":0.00010260551,"threshold_uncertainty_score":0.48875976},"labels":[],"label_agreement":null},{"id":"W1993259679","doi":"10.1145/1557626.1557669","title":"Option pricing using Particle Swarm Optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Department of Science and Technology, Ministry of Science and Technology, India; University of Manitoba","keywords":"Particle swarm optimization; Swarm intelligence; Computer science; Mathematical optimization; Valuation of options; Black–Scholes model; Simple (philosophy); Multi-swarm optimization; Metaheuristic; Computational intelligence; Economics; Artificial intelligence; Econometrics; Machine learning; Mathematics","score_opus":0.04394890247619784,"score_gpt":0.31781241357080725,"score_spread":0.2738635110946094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993259679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024360102,0.00002465348,0.9941411,0.0008872854,0.00010459725,0.00015463687,1.5104614e-7,0.0002329604,0.0020186068],"genre_scores_gemma":[0.23098546,0.000011921718,0.76831156,0.0002807103,0.000036906655,0.0000016303243,0.000001324185,0.000004350754,0.00036612092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876213,0.00007779151,0.00022550055,0.00028307165,0.00038344422,0.00026809104],"domain_scores_gemma":[0.99923784,0.0000388421,0.000058050042,0.00037152338,0.00017969325,0.00011408321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038334564,0.00008706285,0.000098115095,0.000104284656,0.00014448608,0.00027106484,0.0003792021,0.00003721413,0.000065631706],"category_scores_gemma":[0.00012732488,0.00008051277,0.00002869428,0.0007927769,0.000014955657,0.0007572986,0.00007693364,0.000073843636,0.000043424494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015767666,0.00004502692,0.00002570623,0.0000015274937,0.0000024528665,0.00000348498,0.000075047516,0.93746936,0.00076254836,0.04457071,0.000029123481,0.017013418],"study_design_scores_gemma":[0.00019287509,0.00004527168,0.00017830278,0.0000048690877,0.0000024575309,0.000010164297,0.000008700113,0.99292016,0.0054801265,0.0010070403,0.000049729286,0.00010028856],"about_ca_topic_score_codex":0.000009575776,"about_ca_topic_score_gemma":2.0916862e-7,"teacher_disagreement_score":0.22854945,"about_ca_system_score_codex":0.00006522385,"about_ca_system_score_gemma":0.00005795872,"threshold_uncertainty_score":0.32832155},"labels":[],"label_agreement":null},{"id":"W1993433133","doi":"10.1142/s0129065707001202","title":"NON-LINEAR GLOBAL OPTIMIZATION VIA PARAMETERIZATION AND INVERSE FUNCTION APPROXIMATION: AN ARTIFICIAL NEURAL NETWORKS APPROACH","year":2007,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Metaheuristic Optimization Algorithms Research","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 Regina","funders":"","keywords":"Maxima and minima; Global optimization; Artificial neural network; Mathematical optimization; Simulated annealing; Computer science; Inverse; Algorithm; Function (biology); Inflection point; MATLAB; Mathematics; Artificial intelligence","score_opus":0.036571343788874354,"score_gpt":0.30295177695533027,"score_spread":0.26638043316645593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993433133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025639435,0.00006404794,0.9699376,0.00019093734,0.0037168637,0.00027450087,0.0000035651783,0.000045785688,0.00012726263],"genre_scores_gemma":[0.8967982,0.000016226728,0.10169543,0.00012544544,0.001281405,0.0000051821403,0.000045024277,0.000014192533,0.0000188874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702454,0.00021117205,0.0010055549,0.0003247859,0.001166575,0.00026738242],"domain_scores_gemma":[0.9973067,0.00008480348,0.0007582705,0.00023048426,0.0013591837,0.00026056383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014055017,0.0001905899,0.00026122216,0.00032854234,0.00012132953,0.0006134412,0.00077127566,0.0001246788,0.000008691995],"category_scores_gemma":[0.0001308874,0.00017243238,0.00007826084,0.00056250487,0.000068040776,0.0019832118,0.00013698933,0.0002490161,0.0000023199846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014207784,0.00011227885,0.00071975356,0.000011268203,0.0000548525,0.0000394091,0.00009466516,0.9791585,0.00016054521,0.0013009056,0.000041638214,0.018164057],"study_design_scores_gemma":[0.000530153,0.00025684657,0.0011002343,0.000015822445,0.000015848878,0.00081977353,0.00007727718,0.9968592,0.000027830529,0.0001091239,0.00003886124,0.00014904694],"about_ca_topic_score_codex":0.00003266591,"about_ca_topic_score_gemma":0.0000029024268,"teacher_disagreement_score":0.8711588,"about_ca_system_score_codex":0.00016865316,"about_ca_system_score_gemma":0.000051354466,"threshold_uncertainty_score":0.7031588},"labels":[],"label_agreement":null},{"id":"W1994895518","doi":"10.1007/s10898-007-9234-1","title":"Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search","year":2007,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":185,"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; Group for Research in Decision Analysis","funders":"Air Force Office of Scientific Research","keywords":"Mathematics; Variable neighborhood search; Mathematical optimization; Metaheuristic; Convergence (economics); Local search (optimization); Variable (mathematics); Guided Local Search; Algorithm","score_opus":0.021690053764088987,"score_gpt":0.29792047765924756,"score_spread":0.27623042389515856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994895518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012433433,0.00042367203,0.9842018,0.00059501914,0.00042883345,0.00033509792,0.000012338836,0.000062158215,0.013816747],"genre_scores_gemma":[0.05963765,0.0006130883,0.93920547,0.00017927765,0.0001937348,0.0000020227328,0.0000082247225,0.000022747608,0.00013779706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600273,0.00040295676,0.0009508406,0.0004527295,0.0015637592,0.0006269968],"domain_scores_gemma":[0.99622357,0.00033550576,0.00037014438,0.00042165723,0.0022602566,0.00038887197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032864604,0.00026631894,0.0004570952,0.0002856858,0.00028332198,0.000488432,0.00086276984,0.00018689712,0.00013959367],"category_scores_gemma":[0.00056510535,0.00024177576,0.000096293,0.0025361574,0.00013299494,0.0022297131,0.00035393497,0.00039840007,0.000007668465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011846253,0.00015232661,0.0004820714,0.000015401703,0.000079076206,0.000048502417,0.00021219488,0.9687564,0.000009066946,0.02451492,0.00026320145,0.0053484277],"study_design_scores_gemma":[0.0011586917,0.00045569733,0.00048606243,0.00005698383,0.000031999243,0.00023296941,0.00012150031,0.9965358,0.00013570546,0.0004399988,0.000116213676,0.00022841622],"about_ca_topic_score_codex":0.0000564047,"about_ca_topic_score_gemma":0.0000028773181,"teacher_disagreement_score":0.05951332,"about_ca_system_score_codex":0.00045743425,"about_ca_system_score_gemma":0.0006523264,"threshold_uncertainty_score":0.985933},"labels":[],"label_agreement":null},{"id":"W1996527665","doi":"10.1145/1569901.1570046","title":"Evolving an edge selection formula for ant colony optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Ant colony optimization algorithms; Selection (genetic algorithm); ANT; Computer science; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","score_opus":0.027682765713338834,"score_gpt":0.31985136021596156,"score_spread":0.2921685945026227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996527665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000860342,0.000017459235,0.9951175,0.000715963,0.00015301228,0.00053013506,0.0000012643584,0.0002889874,0.0030896603],"genre_scores_gemma":[0.04668606,0.000010532881,0.95119846,0.00034853152,0.000101711186,0.00002897072,0.000017345083,0.000008562576,0.0015998348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872506,0.00006573094,0.00023132205,0.0003682285,0.0003003963,0.00030926053],"domain_scores_gemma":[0.9988637,0.00009159431,0.00006616559,0.0002879613,0.00055083534,0.0001397751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005323772,0.00010626468,0.00012409144,0.00019960797,0.00024308507,0.00037121118,0.00047509436,0.00006399362,0.00012585598],"category_scores_gemma":[0.00030019696,0.00009866207,0.00003932987,0.00063580286,0.000012170861,0.001230958,0.000044192708,0.00007294687,0.000011634429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026418193,0.00035220516,0.00007279442,0.000016670598,0.0000151521335,0.0000022949755,0.00032330333,0.71832526,0.001037578,0.14306343,0.008340566,0.12842432],"study_design_scores_gemma":[0.00036891422,0.0004085127,0.00026495385,0.0000033910414,0.0000031041704,0.000007660685,0.0000067523383,0.995541,0.0012019126,0.0013144637,0.0007508245,0.00012855013],"about_ca_topic_score_codex":0.000008427148,"about_ca_topic_score_gemma":0.0000038578128,"teacher_disagreement_score":0.2772157,"about_ca_system_score_codex":0.00008637383,"about_ca_system_score_gemma":0.00011604457,"threshold_uncertainty_score":0.40233225},"labels":[],"label_agreement":null},{"id":"W1997744027","doi":"10.1109/cec.2012.6256132","title":"Particle Swarm Optimization with Adaptive Bounds","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Maxima and minima; Estimation of distribution algorithm; Benchmark (surveying); Mathematical optimization; Premature convergence; Metaheuristic; Multi-swarm optimization; Computer science; Convergence (economics); Evolutionary algorithm; Probabilistic logic; Local search (optimization); Algorithm; Mathematics; Artificial intelligence","score_opus":0.031256977085311194,"score_gpt":0.26984096596596324,"score_spread":0.23858398888065205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997744027","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042930033,0.000068364185,0.9832352,0.00047934928,0.00010916108,0.00014939945,5.0301094e-7,0.00018469624,0.015344044],"genre_scores_gemma":[0.3173992,0.000008171048,0.68027,0.00016484744,0.000050102302,0.000019643778,0.0000015432211,0.000008368803,0.0020780817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871355,0.000085813896,0.00014207287,0.00020657937,0.00044480516,0.00040720013],"domain_scores_gemma":[0.9990395,0.0000778095,0.00004422123,0.00040616648,0.00019353279,0.00023882839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039729773,0.0000956734,0.000096255055,0.00006214701,0.00011929979,0.0001718231,0.0003808919,0.000029919185,0.00032875608],"category_scores_gemma":[0.000059809212,0.0000705558,0.00001847094,0.0006711313,0.00005208931,0.0011292461,0.00014165381,0.00007884389,0.00019084186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037564903,0.0004870084,0.0042285924,0.000008949506,0.00006525872,0.000009235097,0.0013449886,0.5511007,0.000034299803,0.42046902,0.0017547578,0.02045968],"study_design_scores_gemma":[0.0002738198,0.000089306646,0.00043179642,0.0000025851195,0.0000036128674,0.000012960544,0.00003934576,0.9963958,0.0017357264,0.000066767105,0.0008278244,0.00012042763],"about_ca_topic_score_codex":0.000014648707,"about_ca_topic_score_gemma":0.000001350447,"teacher_disagreement_score":0.44529516,"about_ca_system_score_codex":0.000040524712,"about_ca_system_score_gemma":0.00006746826,"threshold_uncertainty_score":0.35996488},"labels":[],"label_agreement":null},{"id":"W1998625786","doi":"10.1155/2013/172193","title":"Complexity Reduction in the Use of Evolutionary Algorithms to Function Optimization: A Variable Reduction Strategy","year":2013,"lang":"en","type":"article","venue":"The Scientific World JOURNAL","topic":"Metaheuristic Optimization Algorithms Research","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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Mathematical optimization; Reduction (mathematics); Benchmark (surveying); Optimization problem; Computer science; Particle swarm optimization; Variable (mathematics); Function (biology); Algorithm; Continuous optimization; Evolutionary algorithm; Derivative-free optimization; Multi-swarm optimization; Mathematics","score_opus":0.11378218412887775,"score_gpt":0.29512209943853585,"score_spread":0.1813399153096581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998625786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017649813,0.00007392797,0.9873048,0.0068578552,0.0027303665,0.0007209243,0.000004346185,0.00003444439,0.0005083473],"genre_scores_gemma":[0.08074299,0.000024569608,0.90727013,0.00015087733,0.0005542877,0.00008290536,0.000021187085,0.000017413398,0.011135644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99649006,0.0009096378,0.000618017,0.0003840805,0.0012185381,0.00037966185],"domain_scores_gemma":[0.99755055,0.00014432397,0.00028143267,0.0008096364,0.0010885894,0.00012545772],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0032275592,0.00013725912,0.00016142974,0.0007706564,0.0009829614,0.0019549632,0.0012343704,0.00003682023,0.00075441255],"category_scores_gemma":[0.00022051143,0.00008704122,0.00006494523,0.005303374,0.00035389105,0.0019530238,0.00018686234,0.0004693791,0.0000767437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014692063,0.00013305056,0.000009800431,0.0000048394654,0.000014217588,0.0000012270345,0.00056958606,0.92584026,0.00058893795,0.010262234,0.051839117,0.01072202],"study_design_scores_gemma":[0.00022232019,0.00007272259,0.0014616605,0.000029052568,0.000009942864,0.00032520154,0.00028449734,0.9832202,0.00006509144,0.010212068,0.0039858506,0.00011135952],"about_ca_topic_score_codex":0.00015903857,"about_ca_topic_score_gemma":0.000007685721,"teacher_disagreement_score":0.08003469,"about_ca_system_score_codex":0.00014689793,"about_ca_system_score_gemma":0.000305848,"threshold_uncertainty_score":0.9990811},"labels":[],"label_agreement":null},{"id":"W2002865108","doi":"10.1109/sde.2014.7031533","title":"MDE: Differential evolution with merit-based mutation strategy","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Differential evolution; Computer science; Mutation; Evolutionary computation; Convergence (economics); Mathematical optimization; Computation; Evolution strategy; Evolutionary algorithm; Population; Algorithm; Artificial intelligence; Mathematics","score_opus":0.012621272463644025,"score_gpt":0.25082818856107414,"score_spread":0.2382069160974301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002865108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021404368,0.000004453178,0.9922492,0.0003832209,0.000099627745,0.00014552797,7.1391133e-7,0.00018027841,0.004796558],"genre_scores_gemma":[0.7597704,4.465866e-7,0.2394582,0.00005312218,0.000043960838,0.000015111095,0.000010476584,0.000007005066,0.00064125564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860185,0.00016749505,0.00016093116,0.0003092678,0.00053942145,0.00022102556],"domain_scores_gemma":[0.99911994,0.00009759027,0.000057908743,0.0003904152,0.00022513745,0.000108995366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023324862,0.00010193198,0.0001075893,0.00014087555,0.000102715974,0.00022692753,0.00039529972,0.00003837646,0.00027304687],"category_scores_gemma":[0.00007209962,0.000076653065,0.000023275157,0.00042914748,0.00004828486,0.00028654782,0.000044401047,0.0000882132,0.0000977683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005512001,0.00035281078,0.0005874735,0.00005603439,0.00004346977,0.000018163151,0.00009681444,0.17214796,0.001347276,0.65502024,0.0011433099,0.16913131],"study_design_scores_gemma":[0.00054749916,0.00020193183,0.0031946995,0.0000045944357,0.0000036503711,0.0000044404937,0.0000054900906,0.99365675,0.0011947842,0.00092053844,0.00015371095,0.00011188314],"about_ca_topic_score_codex":0.00005504221,"about_ca_topic_score_gemma":0.000014718532,"teacher_disagreement_score":0.8215088,"about_ca_system_score_codex":0.00004190157,"about_ca_system_score_gemma":0.00012628207,"threshold_uncertainty_score":0.3125821},"labels":[],"label_agreement":null},{"id":"W2003200838","doi":"10.1002/cjce.21681","title":"Vortex motion‐based particle swarm optimisation for energy consumption of alumina evaporation","year":2012,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":true,"ca_institutions":"","funders":"National Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China","keywords":"Sodium aluminate; Evaporation; Particle swarm optimization; Process engineering; Process (computing); Benchmark (surveying); Energy consumption; Vortex; Particle (ecology); Regenerative heat exchanger; Mathematical optimization; Steam reforming; Computer science; Materials science; Engineering; Algorithm; Heat exchanger; Mechanical engineering; Chemistry; Mechanics; Mathematics; Thermodynamics; Aluminium; Physics; Composite material","score_opus":0.032530352793241894,"score_gpt":0.2516932989016014,"score_spread":0.2191629461083595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003200838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052967355,0.0001571786,0.94602984,0.00055464485,0.00020290421,0.00006663411,0.0000024216113,0.0000073414576,0.0000116722995],"genre_scores_gemma":[0.9415344,0.0000013617296,0.05829783,0.00003252546,0.0001085091,0.000005155283,0.0000028370066,0.000007011033,0.000010357861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999176,0.000028764252,0.0002952721,0.000058174963,0.00022528094,0.00021648516],"domain_scores_gemma":[0.9989439,0.00016719295,0.00014665676,0.00015504524,0.0002960768,0.00029112038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007326575,0.0000637017,0.00011031383,0.00011375317,0.000044111537,0.000046017813,0.00029586244,0.000040604507,0.000021814376],"category_scores_gemma":[0.00048893294,0.0000534052,0.000054662356,0.00019604476,0.00003689986,0.00028586274,0.0000102471895,0.000086830594,0.000001010629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019351686,0.000059680242,0.0005468636,0.00009332985,0.00006700822,0.0000028278916,0.0006770894,0.8439537,0.090694234,0.050515413,0.00033155276,0.013038937],"study_design_scores_gemma":[0.00021060978,0.000016644417,0.00014030722,0.000013642905,0.0000100704,0.000011217713,0.000001769938,0.78911746,0.2101323,0.0001117559,0.00018701777,0.000047203568],"about_ca_topic_score_codex":0.00009147112,"about_ca_topic_score_gemma":0.0000086426335,"teacher_disagreement_score":0.88856703,"about_ca_system_score_codex":0.00012937743,"about_ca_system_score_gemma":0.00023166605,"threshold_uncertainty_score":0.21778008},"labels":[],"label_agreement":null},{"id":"W2004624951","doi":"10.2495/data070071","title":"Genetic Algorithms in a dynamically changing environment","year":2007,"lang":"en","type":"article","venue":"WIT transactions on information and communication technologies","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Computer science; Transformation (genetics); Genetic algorithm; Selection (genetic algorithm); Operator (biology); Generator (circuit theory); Adaptation (eye); Population; Mathematical optimization; Quality control and genetic algorithms; Mutation; Algorithm; Linear map; Genetic operator; Meta-optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.010721761327469695,"score_gpt":0.2390097840484396,"score_spread":0.2282880227209699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004624951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001021096,0.00012525482,0.99504155,0.0021305338,0.000026805405,0.00025992765,0.0000024968456,0.00044462684,0.00094772887],"genre_scores_gemma":[0.5891202,0.0016058149,0.4090298,0.00011012363,0.0000010559742,0.00007013944,0.0000049765604,0.0000039561864,0.000053940235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989183,0.000040669664,0.00041125398,0.00013303988,0.00026217522,0.00023455621],"domain_scores_gemma":[0.9989356,0.00012582366,0.00009726666,0.00075177953,0.000052684853,0.00003685394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060776586,0.00010683684,0.00010462482,0.001077273,0.00022678844,0.00014017726,0.00071158865,0.000109467015,0.000019240673],"category_scores_gemma":[0.00003923136,0.00010569316,0.000019792042,0.00077689445,0.00012414467,0.0008160981,0.00006621172,0.0003048705,0.000052347004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053731205,0.00005042073,0.000031939446,0.000008240403,0.0000058918854,6.7572324e-7,0.0006699424,0.004276459,0.00001207426,0.014512374,0.0000046412583,0.98042196],"study_design_scores_gemma":[0.00068853045,0.00011699135,0.003285808,0.0000389852,0.0000038021597,0.000024379291,0.0029330251,0.98258305,0.0010321346,0.0038018227,0.005234785,0.00025670658],"about_ca_topic_score_codex":0.000013136742,"about_ca_topic_score_gemma":0.00000890266,"teacher_disagreement_score":0.98016524,"about_ca_system_score_codex":0.00010058863,"about_ca_system_score_gemma":0.000021592372,"threshold_uncertainty_score":0.43100417},"labels":[],"label_agreement":null},{"id":"W2005381007","doi":"10.1109/iscas.2012.6272097","title":"A novel particle swarm optimization for high-level synthesis of digital filters","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Computer science; Backtracking; Scheduling (production processes); Digital filter; Algorithm; Mathematical optimization; Filter (signal processing); Mathematics; Computer vision","score_opus":0.07040461113638566,"score_gpt":0.29313235783168995,"score_spread":0.2227277466953043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005381007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008509132,0.000011952925,0.99737996,0.0005367149,0.00016119337,0.0002610061,0.000048321283,0.00007048525,0.00067945814],"genre_scores_gemma":[0.4040487,0.0000023790533,0.595373,0.000030343552,0.000028994069,0.00004402477,0.000004753281,0.0000078435405,0.00045997315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988154,0.000022712602,0.00028216353,0.00019921533,0.00033786971,0.00034263197],"domain_scores_gemma":[0.9985893,0.00054365135,0.00009036177,0.00037993636,0.0002406702,0.00015609826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039788897,0.00009562741,0.00015638935,0.000088807334,0.000058356094,0.00012952153,0.00046752018,0.00003907229,0.00011826429],"category_scores_gemma":[0.0009685001,0.00008317025,0.000052293017,0.00038999657,0.00004424763,0.0010671081,0.00016277743,0.0000343835,0.000022383498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008063284,0.0024378286,0.0020632204,0.0002022906,0.0002249181,0.0000011219615,0.0010336711,0.4277136,0.0031421457,0.3827025,0.003994364,0.1764037],"study_design_scores_gemma":[0.00032087858,0.00003669253,0.00029801636,0.0000057360953,0.0000075930575,0.0000028742113,0.00002228191,0.95661634,0.042374026,0.000106914624,0.00009729392,0.00011133172],"about_ca_topic_score_codex":0.000011597743,"about_ca_topic_score_gemma":2.9311192e-7,"teacher_disagreement_score":0.52890277,"about_ca_system_score_codex":0.000026951084,"about_ca_system_score_gemma":0.00004863441,"threshold_uncertainty_score":0.33915845},"labels":[],"label_agreement":null},{"id":"W2006129815","doi":"10.1504/ijhpsa.2008.024206","title":"Particle swarm optimisation for the design of two-connected networks with bounded rings","year":2008,"lang":"en","type":"article","venue":"International Journal of High Performance Systems Architecture","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Academy of Finland","keywords":"Computer science; Particle swarm optimization; Bounded function; Swarm behaviour; Distributed computing; Mathematical optimization; Topology (electrical circuits); Parallel computing; Algorithm; Artificial intelligence; Mathematics","score_opus":0.023441447851446053,"score_gpt":0.25994354577197315,"score_spread":0.2365020979205271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006129815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11131847,0.000254209,0.88660187,0.00066451624,0.0007526196,0.00037191427,0.0000030677195,0.000018156057,0.000015172596],"genre_scores_gemma":[0.8620312,0.0001187269,0.13732901,0.00004331415,0.00034365628,0.00002572401,0.0000018009919,0.000012043836,0.00009451891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979004,0.0001389014,0.00060021767,0.00016067798,0.0009820232,0.00021773689],"domain_scores_gemma":[0.99674916,0.0006580335,0.0006519806,0.00026203445,0.0016020233,0.000076736076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009588191,0.000135021,0.000243243,0.00018522049,0.00018128194,0.00012689414,0.0012730372,0.000041316278,0.0000077228115],"category_scores_gemma":[0.00015088062,0.00008012919,0.00006638533,0.00031114422,0.00012435796,0.00038129732,0.00008058236,0.00025754274,0.0000013470014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024958933,0.00004188046,0.00043999075,0.000014886386,0.00017865612,0.000014466095,0.00056377205,0.99263275,0.0001558577,0.001661733,0.00009868311,0.00394773],"study_design_scores_gemma":[0.0016613717,0.00040803928,0.0014799898,0.00009734556,0.00001497347,0.001142791,0.000021724543,0.9920778,0.0025922114,0.000118346536,0.00028777056,0.0000976031],"about_ca_topic_score_codex":0.000033812274,"about_ca_topic_score_gemma":0.0000014594489,"teacher_disagreement_score":0.75071275,"about_ca_system_score_codex":0.000071942304,"about_ca_system_score_gemma":0.00022363952,"threshold_uncertainty_score":0.32675734},"labels":[],"label_agreement":null},{"id":"W2007082296","doi":"10.1109/sis.2014.7011783","title":"MAX-SAT problem using evolutionary algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Evolutionary algorithm; Algorithm; Computer science; Estimation of distribution algorithm; Evolutionary computation; Domain (mathematical analysis); Similarity (geometry); Randomized algorithm; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.03233759690800255,"score_gpt":0.2895972199943828,"score_spread":0.2572596230863803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007082296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000115747105,0.000039753217,0.97851455,0.000765519,0.00023610235,0.0001832567,0.0000011232349,0.00024889526,0.01989508],"genre_scores_gemma":[0.007496505,0.0000050561152,0.9879756,0.00025535352,0.000119928234,0.000010280739,0.000004360717,0.000012468149,0.0041204426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981063,0.00018220682,0.00026114073,0.00043848317,0.0006306289,0.00038125424],"domain_scores_gemma":[0.99873894,0.00013201649,0.000060242714,0.0006315152,0.00025739675,0.00017989482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006615432,0.00012850953,0.00015050282,0.00019353812,0.00018474719,0.00019293961,0.00088390923,0.00005842918,0.00030832447],"category_scores_gemma":[0.00014002885,0.00011237034,0.000049264454,0.000655345,0.000060835853,0.0004964412,0.0004526653,0.00013424891,0.0004402272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008296449,0.0003854666,0.0012384219,0.000084939784,0.000075548116,0.000034703622,0.00035134982,0.06427794,0.0013410327,0.58347356,0.017775124,0.3309536],"study_design_scores_gemma":[0.00020498279,0.00003531154,0.00039963747,0.0000065387167,0.00000230927,0.000035586767,0.0000045602537,0.98364717,0.00015568848,0.0063196984,0.009039395,0.00014913679],"about_ca_topic_score_codex":0.0000562199,"about_ca_topic_score_gemma":9.519905e-7,"teacher_disagreement_score":0.9193692,"about_ca_system_score_codex":0.00007412255,"about_ca_system_score_gemma":0.00012155726,"threshold_uncertainty_score":0.5658377},"labels":[],"label_agreement":null},{"id":"W2008164953","doi":"10.1109/cec.2014.6900298","title":"Improved differential evolution with adaptive opposition strategy","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Differential evolution; Opposition (politics); Benchmark (surveying); Computer science; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.016420905085937684,"score_gpt":0.24460092149646467,"score_spread":0.228180016410527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008164953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088577956,0.0000038235476,0.98871386,0.0001822726,0.00008331921,0.00017796553,9.4496147e-7,0.00015161617,0.009800415],"genre_scores_gemma":[0.7901229,0.000001010187,0.20880865,0.000030715622,0.0000535864,0.000015861155,0.0000046153414,0.0000060612283,0.0009566098],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988327,0.00014150543,0.00013597142,0.0003188969,0.00034359714,0.00022730917],"domain_scores_gemma":[0.9991849,0.000058196543,0.00005224643,0.00035714923,0.0002304584,0.0001170411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001806862,0.0001015921,0.000105829684,0.000104655905,0.00010430718,0.00019555031,0.0003856209,0.00004101908,0.00013910525],"category_scores_gemma":[0.000029858891,0.00007421268,0.00002269096,0.0002915684,0.000042919724,0.0003989321,0.00010103909,0.00010149833,0.000050448427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006873482,0.00018489495,0.000058635935,0.000012165497,0.000044618675,0.0000037668128,0.00008055242,0.005667575,0.004262401,0.88485163,0.00041740478,0.10434763],"study_design_scores_gemma":[0.00044891564,0.00045232754,0.0015039805,0.0000042230868,0.000003847159,0.000006646232,0.000010543223,0.99487495,0.001074803,0.0014750178,0.000033252418,0.00011146347],"about_ca_topic_score_codex":0.00006477111,"about_ca_topic_score_gemma":0.00001321436,"teacher_disagreement_score":0.9892074,"about_ca_system_score_codex":0.00005356022,"about_ca_system_score_gemma":0.00007857149,"threshold_uncertainty_score":0.3026305},"labels":[],"label_agreement":null},{"id":"W2009993782","doi":"10.1109/tevc.2015.2395091","title":"Evolutionary Nonlinear Projection","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Evolutionary algorithm; Evolutionary computation; Cluster analysis; Artificial intelligence; Projection (relational algebra); Computer science; Population; Feature vector; Set (abstract data type); Nonlinear system; Optimization problem; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.0480719819969123,"score_gpt":0.3045901847150168,"score_spread":0.2565182027181045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009993782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006067634,0.00008215894,0.99305964,0.0012114514,0.0020327973,0.0005439172,0.000025437019,0.00069510756,0.0017427331],"genre_scores_gemma":[0.34649697,0.000027959579,0.6513276,0.00021083758,0.00020362186,0.00012977829,0.000052484764,0.000034145072,0.0015165729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968792,0.00035987707,0.000488226,0.00066541653,0.0012119386,0.0003952985],"domain_scores_gemma":[0.99793285,0.00020159247,0.00013892866,0.00046702355,0.0009340544,0.00032557722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004846262,0.00025388986,0.00021128298,0.0007434526,0.00043468107,0.00013003655,0.00050812995,0.00014164718,0.000044661912],"category_scores_gemma":[0.000052308904,0.00027210367,0.00012204213,0.00166204,0.00010649983,0.001228831,0.000009730842,0.0003842292,0.00074872846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070260605,0.00065362785,0.00001732745,0.000014049193,0.00004469826,0.000015505455,0.00028538413,0.9455002,0.00005869144,0.0013375125,0.007569773,0.04443295],"study_design_scores_gemma":[0.0008880495,0.00034963377,0.0004071668,0.000017091821,0.000012453917,0.00012307189,0.00005379387,0.9926207,0.0003389056,0.0028812157,0.002030883,0.00027704675],"about_ca_topic_score_codex":0.000055688186,"about_ca_topic_score_gemma":0.000003722795,"teacher_disagreement_score":0.3458902,"about_ca_system_score_codex":0.0006375234,"about_ca_system_score_gemma":0.0007214253,"threshold_uncertainty_score":0.9999731},"labels":[],"label_agreement":null},{"id":"W2012114154","doi":"10.1109/coginf.2011.6016145","title":"Towards agent Swarm Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Computer science; Mathematical optimization; Position (finance); Measure (data warehouse); Multi-swarm optimization; Swarm behaviour; Situated; Artificial intelligence; Algorithm; Mathematics; Data mining","score_opus":0.07652404965570282,"score_gpt":0.2875324003347756,"score_spread":0.2110083506790728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012114154","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019631805,0.000014765656,0.8350342,0.00022345824,0.00023187284,0.00011245222,4.0671185e-7,0.00020680614,0.1641564],"genre_scores_gemma":[0.009338323,0.00003573126,0.9865638,0.00026056138,0.000025527394,0.000014591361,0.0000022708896,0.000007844661,0.003751328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886054,0.00006748598,0.0001858315,0.00028126198,0.00038116318,0.0002237054],"domain_scores_gemma":[0.9990902,0.00001854087,0.00004032465,0.00051156874,0.00019603723,0.0001433482],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032571534,0.00008600357,0.00009038624,0.00013940567,0.00006928207,0.00010216999,0.0007983877,0.000037783262,0.0021187307],"category_scores_gemma":[0.00010605489,0.00007250328,0.000034792436,0.0004967348,0.000028212286,0.00036849754,0.0002851382,0.00006942773,0.000351803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001377554,0.0005438776,0.00022311903,0.000026601987,0.00006932192,0.000087352666,0.0029350438,0.14220466,0.00003007286,0.61686957,0.010649912,0.22634667],"study_design_scores_gemma":[0.00014773116,0.00003980835,0.00025833226,0.0000017145996,0.0000017675312,0.000006452151,0.000010680869,0.9953218,0.0019990837,0.0009234648,0.0011855218,0.00010361125],"about_ca_topic_score_codex":0.00005267092,"about_ca_topic_score_gemma":8.762345e-7,"teacher_disagreement_score":0.85311717,"about_ca_system_score_codex":0.000029900837,"about_ca_system_score_gemma":0.000087645145,"threshold_uncertainty_score":0.9987935},"labels":[],"label_agreement":null},{"id":"W2012131995","doi":"10.1109/nabic.2009.5393729","title":"Multi-colony parallel ant colony optimization on SMP and multi-core computers","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Chicoutimi","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Metaheuristic; Parallel computing; Node (physics); Parallel metaheuristic; Core (optical fiber); Multi-core processor; Ant colony; Distributed computing; Shared memory; Artificial intelligence; Engineering","score_opus":0.08187221506420697,"score_gpt":0.33517232368065175,"score_spread":0.2533001086164448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012131995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026594734,0.00020638066,0.9940629,0.0016122534,0.0010300715,0.0016052842,0.000022586995,0.00053076993,0.0006637762],"genre_scores_gemma":[0.002745801,0.000687894,0.9929233,0.0012030099,0.00010109511,0.0000932093,0.00010960648,0.000043325857,0.0020927633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954566,0.00037016036,0.0007752182,0.0017634128,0.0009417088,0.0006929174],"domain_scores_gemma":[0.99668044,0.0003533114,0.00039531387,0.00157891,0.0005285818,0.00046341546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008680896,0.0006358301,0.0007427766,0.0006214364,0.00027883897,0.0009283661,0.0018427118,0.00052223913,0.00008277162],"category_scores_gemma":[0.00035139814,0.00059073983,0.00014769827,0.0005107839,0.00016511264,0.0002647659,0.0023211252,0.0010062578,0.000073284995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023321287,0.00051263254,0.000074735006,0.000067410685,0.000059001275,0.000092309296,0.00028010647,0.97869045,0.000007835219,0.0016792683,0.0025717292,0.015941186],"study_design_scores_gemma":[0.0017858704,0.00023603691,0.0018525519,0.000114902265,0.000016462289,0.000022637423,0.000009215405,0.99478644,0.000022614324,0.0001941685,0.0003494436,0.0006096309],"about_ca_topic_score_codex":0.00014365473,"about_ca_topic_score_gemma":0.000021562704,"teacher_disagreement_score":0.016096001,"about_ca_system_score_codex":0.00022097581,"about_ca_system_score_gemma":0.00035345944,"threshold_uncertainty_score":0.9996544},"labels":[],"label_agreement":null},{"id":"W2012377985","doi":"10.5430/air.v1n2p99","title":"A hybrid agent based virtual organization for studying knowledge evolution in social systems","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Macro; Evolutionary algorithm; Artificial intelligence; Process (computing); Social system; Genetic algorithm; Multi-agent system; Cultural algorithm; Swarm intelligence; Machine learning; Particle swarm optimization; Meta-optimization","score_opus":0.2515909848228119,"score_gpt":0.4318724132390408,"score_spread":0.1802814284162289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012377985","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":[0.01524764,0.0001990496,0.98185796,0.0004085457,0.0007189387,0.0012229831,0.0000067892643,0.00009813571,0.00023996364],"genre_scores_gemma":[0.9916825,0.000009635488,0.007323398,0.00001119663,0.0005467931,0.00019832159,0.000018570458,0.000030282541,0.00017924944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955234,0.0011034107,0.00061894965,0.0005113239,0.001114554,0.0011283944],"domain_scores_gemma":[0.99649096,0.0010933161,0.000083282524,0.00042468111,0.0016719057,0.00023584024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007946567,0.00015925363,0.00023454231,0.0010209916,0.0007897964,0.00050781795,0.0010646665,0.0000982873,0.00006204072],"category_scores_gemma":[0.0025243245,0.00016482463,0.000053900916,0.0030926196,0.00015870508,0.0006957118,0.00039736912,0.00039321437,0.0004873977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004872882,0.001545031,0.0023291889,0.00011744779,0.000023466031,0.000008049957,0.0039096563,0.0178202,0.0019080398,0.8802815,0.0010677022,0.09094097],"study_design_scores_gemma":[0.00008878979,0.00014753126,0.0007807428,0.000028854345,0.0000029706107,0.0000031560555,0.0010159564,0.9854117,0.008744242,0.0031419138,0.00043054338,0.00020361232],"about_ca_topic_score_codex":0.00010642777,"about_ca_topic_score_gemma":0.000011917387,"teacher_disagreement_score":0.9764349,"about_ca_system_score_codex":0.00076597166,"about_ca_system_score_gemma":0.00050191703,"threshold_uncertainty_score":0.6721353},"labels":[],"label_agreement":null},{"id":"W2012552818","doi":"10.5539/cis.v1n4p139","title":"A New Approach for Data Clustering Based on PSO with Local Search","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":52,"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":"Cluster analysis; Particle swarm optimization; Computer science; Context (archaeology); Local optimum; Data mining; Cluster (spacecraft); Mathematical optimization; Swarm behaviour; Local search (optimization); Process (computing); Multi-swarm optimization; TRACE (psycholinguistics); Artificial intelligence; Machine learning; Mathematics","score_opus":0.08463517590009528,"score_gpt":0.3103042688114249,"score_spread":0.22566909291132964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012552818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003915739,0.000003052963,0.9967957,0.0002932709,0.00008469475,0.0003264529,0.000004614045,0.00006348214,0.0023895712],"genre_scores_gemma":[0.035992227,0.0000067605088,0.9631036,0.00077117945,0.00003963241,0.000008050505,0.000026974685,0.00000294124,0.00004861042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821514,0.000025078165,0.00021417388,0.00037049197,0.0008799565,0.00029516197],"domain_scores_gemma":[0.99839246,0.00011222192,0.000053876465,0.00087772554,0.00032327577,0.00024042682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010932238,0.000100148776,0.00010998055,0.00039990954,0.00047229594,0.0005897312,0.0018251205,0.000023654587,0.0000064202036],"category_scores_gemma":[0.00005717973,0.00007657643,0.000011469838,0.0010970498,0.00029027843,0.006674689,0.0007372385,0.00009812588,0.000019184887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031791045,0.000029943212,0.00007480993,0.000046036806,0.0000033302524,0.000001422404,0.0008835852,0.54593503,0.0000021108765,0.0048307707,0.0030224312,0.44513875],"study_design_scores_gemma":[0.0006157353,0.0001502626,0.0005843994,0.000009244962,7.7993855e-7,0.000039753384,0.000010951538,0.9955788,0.00004138343,0.000006992556,0.0028551843,0.00010648272],"about_ca_topic_score_codex":0.00001204459,"about_ca_topic_score_gemma":2.9914594e-7,"teacher_disagreement_score":0.44964382,"about_ca_system_score_codex":0.000035050958,"about_ca_system_score_gemma":0.00070670247,"threshold_uncertainty_score":0.56867915},"labels":[],"label_agreement":null},{"id":"W2012831795","doi":"10.1109/cec.2012.6256497","title":"Particle swarm optimization with pbest crossover","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Crossover; Particle swarm optimization; Attractor; Multi-swarm optimization; Population; Mathematical optimization; Computer science; Metaheuristic; Swarm intelligence; Local optimum; Swarm behaviour; Genetic algorithm; Artificial intelligence; Mathematics","score_opus":0.022750967383062638,"score_gpt":0.27503496686841705,"score_spread":0.2522839994853544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012831795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013121059,0.000052455933,0.9850136,0.00055571465,0.00012962958,0.00013962932,5.645202e-7,0.00017828797,0.012618022],"genre_scores_gemma":[0.30722183,0.000009755055,0.6883005,0.00027939925,0.00006705926,0.000017334612,0.0000022035233,0.000010573613,0.004091369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987342,0.000058270936,0.00014684885,0.0002029778,0.00044024026,0.00041750786],"domain_scores_gemma":[0.9990273,0.00006893158,0.000041185096,0.00045723846,0.00016837026,0.00023697042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034546593,0.000092671995,0.000090250855,0.000049533635,0.00011707682,0.00024979806,0.00039416968,0.00003095699,0.00058712665],"category_scores_gemma":[0.00008055997,0.000067629844,0.000018229426,0.0005967098,0.00004982701,0.0011985854,0.00015189893,0.00007343783,0.00035117535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003392777,0.0006499072,0.014863151,0.000018476636,0.000049223305,0.0000113890965,0.0010421015,0.75343215,0.00008059445,0.21359101,0.0030532375,0.013174834],"study_design_scores_gemma":[0.00041150005,0.000044776287,0.0009627625,0.0000025497873,0.0000030747321,0.000015597147,0.000015018001,0.992992,0.0032408182,0.0000326374,0.0021491975,0.00013006823],"about_ca_topic_score_codex":0.000013572848,"about_ca_topic_score_gemma":0.000001086853,"teacher_disagreement_score":0.30590972,"about_ca_system_score_codex":0.000030275785,"about_ca_system_score_gemma":0.000051229745,"threshold_uncertainty_score":0.6428625},"labels":[],"label_agreement":null},{"id":"W2013517059","doi":"10.1016/j.ins.2014.04.013","title":"Multi-strategy ensemble artificial bee colony algorithm","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":260,"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 Ontario Institute of Technology","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Computer science; Mathematical optimization; Artificial bee colony algorithm; Evolutionary algorithm; Set (abstract data type); Process (computing); Population; Algorithm; Local search (optimization); Search algorithm; Artificial intelligence; Mathematics","score_opus":0.06196425370856051,"score_gpt":0.3306889462073624,"score_spread":0.2687246924988019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013517059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039186512,0.000008133038,0.9864447,0.0008573444,0.00035738005,0.00017498393,0.0000036939678,0.0001577641,0.011604126],"genre_scores_gemma":[0.08057384,0.000009584158,0.91814214,0.00082479406,0.00008031207,0.0000277403,0.0000066261987,0.0000033437934,0.00033164464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978313,0.0001277905,0.0004697834,0.00022944565,0.0009697679,0.00037193205],"domain_scores_gemma":[0.99884886,0.00019369263,0.00018514274,0.00026804782,0.0003560885,0.00014815162],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021967702,0.00011495233,0.00013580198,0.000365964,0.00049381255,0.0012648741,0.0012730282,0.000058477242,0.00008009368],"category_scores_gemma":[0.00055176835,0.00009781047,0.00003906892,0.0012842369,0.00023919786,0.003947956,0.00020290053,0.0001170295,0.0011552189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001236725,0.0000338502,0.00003106518,0.0000069644125,0.0000037391233,7.2310456e-7,0.00071625755,0.0148244845,0.0001923071,0.03945737,0.0018560031,0.942876],"study_design_scores_gemma":[0.000157664,0.00009542787,0.00036638102,0.0000037255977,0.0000010300286,0.000008590322,0.00009597931,0.982078,0.0011853473,0.0018178689,0.014060763,0.000129198],"about_ca_topic_score_codex":0.000051387644,"about_ca_topic_score_gemma":0.000007478123,"teacher_disagreement_score":0.96725357,"about_ca_system_score_codex":0.000030289988,"about_ca_system_score_gemma":0.0002330927,"threshold_uncertainty_score":0.9997719},"labels":[],"label_agreement":null},{"id":"W2013520941","doi":"10.1088/1742-6596/540/1/012007","title":"High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems","year":2014,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Metaheuristic Optimization Algorithms Research","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":"Nipissing University","funders":"","keywords":"Computer science; Particle swarm optimization; Context (archaeology); Parallel computing; Parallelism (grammar); Task (project management); Supercomputer; Distributed computing; Multi-core processor; Data parallelism; Machine learning","score_opus":0.0325976283646544,"score_gpt":0.2552733162037131,"score_spread":0.22267568783905867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013520941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005098274,0.00002710587,0.993416,0.00048202794,0.0006754286,0.000098067016,0.000003221792,0.000028915043,0.00017094787],"genre_scores_gemma":[0.8785514,0.00002138647,0.12091893,0.000077598575,0.000258059,0.0000041680955,0.0000017770502,0.000010953779,0.0001556874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801004,0.00028984682,0.00044441587,0.00020513902,0.0008155596,0.00023500762],"domain_scores_gemma":[0.99765646,0.00017483793,0.00043459525,0.00032763497,0.0012382467,0.00016819235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000501985,0.00015187442,0.00030736168,0.000075445045,0.00012625824,0.00032341832,0.0005724166,0.000044883036,0.000026948981],"category_scores_gemma":[0.0001714326,0.0001246763,0.000071426846,0.0002855504,0.00008575595,0.0008221793,0.00011265988,0.0002083001,0.00003054849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003983532,0.00007411915,0.000005723822,0.000006350975,0.000029330222,0.000010328642,0.00010916761,0.78596115,0.00018819766,0.20087238,0.00007947163,0.0126239555],"study_design_scores_gemma":[0.0004035447,0.00086654886,0.000025647485,0.000053737265,0.000009641146,0.000056176672,0.000023851868,0.9560401,0.03799716,0.0042939787,0.00009371727,0.00013590329],"about_ca_topic_score_codex":0.000007805912,"about_ca_topic_score_gemma":4.812944e-7,"teacher_disagreement_score":0.87345314,"about_ca_system_score_codex":0.000049545528,"about_ca_system_score_gemma":0.00019744049,"threshold_uncertainty_score":0.50841516},"labels":[],"label_agreement":null},{"id":"W2014190703","doi":"10.1145/1830483.1830683","title":"Parallel FPGA-based implementation of scatter search","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Speedup; Field-programmable gate array; Heuristics; Parallel computing; Software; Parallelism (grammar); Population; Heuristic; Computer engineering; Computer architecture; Computer hardware; Programming language; Artificial intelligence; Operating system","score_opus":0.02962268605901686,"score_gpt":0.35463338451019805,"score_spread":0.3250106984511812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014190703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010161151,0.0000020903383,0.9847987,0.0023842528,0.00013256738,0.00018769188,0.0000022289182,0.000050781804,0.0022805138],"genre_scores_gemma":[0.3688523,0.0000010362592,0.6304302,0.00022312449,0.000021244752,0.000011862007,0.000005136798,0.0000049343507,0.00045012622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875224,0.000069828,0.00022138872,0.00022180832,0.0005228992,0.00021185666],"domain_scores_gemma":[0.9989617,0.000106492385,0.000043503715,0.0005180981,0.00027647443,0.00009377108],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005963537,0.00006643722,0.000093017356,0.00017956155,0.000048931066,0.00008324655,0.00066432304,0.000032443415,0.002585718],"category_scores_gemma":[0.000034636938,0.000056118617,0.00003559067,0.00039227193,0.000054905024,0.00020440994,0.0001377274,0.00014116989,0.00012653814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002516688,0.0005512012,0.024369847,0.00015379125,0.00007783036,0.000019989602,0.0010799345,0.011203349,0.04709149,0.25314412,0.016392536,0.6458908],"study_design_scores_gemma":[0.0009325513,0.00007853106,0.013852281,0.0000023768198,0.000002535739,0.0000030706842,0.000045010594,0.87024313,0.112963215,0.00047195112,0.0012619849,0.00014337372],"about_ca_topic_score_codex":0.00013534706,"about_ca_topic_score_gemma":0.000040353767,"teacher_disagreement_score":0.8590398,"about_ca_system_score_codex":0.000008629822,"about_ca_system_score_gemma":0.00019464435,"threshold_uncertainty_score":0.99832606},"labels":[],"label_agreement":null},{"id":"W2014633436","doi":"10.1145/1068009.1068279","title":"Isolating the benefits of respect","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Simulated annealing; Computer science; Genetic algorithm; Focus (optics); Adaptive simulated annealing; Mathematical optimization; Annealing (glass); Distributed computing; Algorithm; Artificial intelligence; Machine learning; Mathematics; Materials science; Physics","score_opus":0.03724486213064473,"score_gpt":0.29762954155802995,"score_spread":0.26038467942738525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014633436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001133839,0.000107350614,0.9509665,0.0046898546,0.000043642234,0.00008086728,4.2331078e-7,0.00004779484,0.04292974],"genre_scores_gemma":[0.2100479,0.00001895921,0.7866805,0.00025350926,0.000076813,0.000003664214,2.0155845e-7,0.0000040126815,0.0029144362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920905,0.00005830673,0.0001651587,0.00012769435,0.00031229536,0.0001274753],"domain_scores_gemma":[0.99912786,0.00023077508,0.000045697365,0.00043793945,0.00012518237,0.00003251689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005252755,0.00004119723,0.000062094776,0.000054686654,0.000068148176,0.000060714578,0.00073838973,0.000014615586,0.00022262761],"category_scores_gemma":[0.0002323841,0.000024864661,0.000023615012,0.00040406667,0.000027628063,0.0001658241,0.00021859824,0.00006737667,0.00007217922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021845995,0.000043205713,0.00020241037,0.0000046197006,0.0000122057345,5.5625685e-7,0.00062479696,0.07581164,0.00023658076,0.4420028,0.0031222252,0.47793677],"study_design_scores_gemma":[0.0000817738,0.000016799268,0.0008266785,0.0000027997087,6.895166e-7,0.0000031451852,0.000010118323,0.99249417,0.0036678861,0.0002270545,0.0026338222,0.000035046098],"about_ca_topic_score_codex":0.000011184863,"about_ca_topic_score_gemma":0.0000060198236,"teacher_disagreement_score":0.91668254,"about_ca_system_score_codex":0.000010121923,"about_ca_system_score_gemma":0.000034538174,"threshold_uncertainty_score":0.24376163},"labels":[],"label_agreement":null},{"id":"W2015047009","doi":"10.1016/j.neunet.2007.04.009","title":"Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Metaheuristic Optimization Algorithms Research","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":"National Research Council Canada","funders":"","keywords":"Artificial neural network; Computer science; Genetic programming; Artificial intelligence; Set (abstract data type); Principal component analysis; Genetic algorithm; Multi-objective optimization; Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.04843541151773481,"score_gpt":0.3534208836653585,"score_spread":0.3049854721476237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015047009","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014862429,0.000044587126,0.993724,0.00040668077,0.0010865841,0.0028892395,0.000030584997,0.0003074923,0.000024606585],"genre_scores_gemma":[0.42203254,0.0000026497835,0.5757467,0.00022327127,0.0012875762,0.00021731517,0.0004146165,0.000040092633,0.000035242796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961656,0.00016677241,0.00077409437,0.0014139017,0.0004981164,0.0009815585],"domain_scores_gemma":[0.9955994,0.0019063768,0.00036965773,0.0009807725,0.00080305646,0.00034075204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001755869,0.0003274905,0.00039089663,0.00018164719,0.0006725885,0.00030503585,0.0013791204,0.00020924308,0.0000040168616],"category_scores_gemma":[0.0012326873,0.00034493362,0.00010739535,0.0011832495,0.00011980363,0.0008808545,0.00091991894,0.00038666185,0.0000012596988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015054613,0.00009876515,0.00043300536,0.000011141207,0.000022504917,0.000001923473,0.00008859336,0.8780055,0.00002591191,0.00078885216,0.00046974735,0.1199035],"study_design_scores_gemma":[0.0009848078,0.00031698073,0.0012016608,0.0000135818655,0.000021145159,0.000020976539,0.00011209132,0.9968534,0.000025187746,0.000034466673,0.0000700543,0.00034561145],"about_ca_topic_score_codex":0.000030802592,"about_ca_topic_score_gemma":0.000024036124,"teacher_disagreement_score":0.4205463,"about_ca_system_score_codex":0.0001772172,"about_ca_system_score_gemma":0.00007716921,"threshold_uncertainty_score":0.9999003},"labels":[],"label_agreement":null},{"id":"W2015350910","doi":"10.1145/2001858.2002100","title":"Pricing transmission rights using ant colony optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Heuristic; Transmission (telecommunications); Mathematical optimization; Optimization algorithm; Optimization problem; Artificial intelligence; Algorithm; Telecommunications; Mathematics","score_opus":0.06836078390450274,"score_gpt":0.28813823565504015,"score_spread":0.21977745175053742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015350910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028836026,0.000021177591,0.9769473,0.000045045836,0.00014138017,0.00019270193,2.1009077e-7,0.0001697623,0.022194058],"genre_scores_gemma":[0.014491917,0.0000217479,0.9847384,0.000058794878,0.00002133082,0.000003610892,0.0000013818584,0.000008047453,0.00065474265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987004,0.000107425934,0.00024193709,0.00032016414,0.00037508653,0.00025503212],"domain_scores_gemma":[0.99924177,0.000060224473,0.00006174972,0.00035156676,0.00013557462,0.00014914277],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035896696,0.00010408357,0.00012046685,0.00019172637,0.00020834849,0.000098613775,0.0005283056,0.000055580822,0.00093241234],"category_scores_gemma":[0.00003969443,0.000081906335,0.000034197816,0.00061569246,0.000030600146,0.00053927273,0.00010095168,0.00008824861,0.000030028486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000554188,0.00084227097,0.0002748775,0.00008189231,0.00008544582,0.0001909654,0.0072539956,0.7572823,0.0024437301,0.112263046,0.000833707,0.11839234],"study_design_scores_gemma":[0.00019654949,0.00003955372,0.000044490927,0.000010379872,0.0000037565069,0.00001467604,0.0000054685615,0.9929157,0.0052813697,0.00062627735,0.0007453063,0.00011649721],"about_ca_topic_score_codex":0.00015329948,"about_ca_topic_score_gemma":0.0000011622025,"teacher_disagreement_score":0.23563336,"about_ca_system_score_codex":0.00004727014,"about_ca_system_score_gemma":0.00008279789,"threshold_uncertainty_score":0.99998087},"labels":[],"label_agreement":null},{"id":"W2016472674","doi":"10.1239/jap/1261670682","title":"Geometric Convergence of Genetic Algorithms Under Tempered Random Restart","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Mathematics; Algorithm; Convergence (economics); Population; Genetic algorithm; Rate of convergence; Mathematical optimization; Mutation; Class (philosophy); Computer science; Key (lock); Artificial intelligence","score_opus":0.02888184872187093,"score_gpt":0.27749243825187647,"score_spread":0.24861058953000553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016472674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05367514,0.00023824484,0.94380265,0.0006160874,0.00024646602,0.00045828838,0.0000024717842,0.000024786368,0.0009358648],"genre_scores_gemma":[0.539588,0.000095255404,0.46013087,0.00008956841,0.000058412763,0.000003397989,4.1376597e-7,0.000005416682,0.000028651624],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966648,0.00018501212,0.001254981,0.00034145435,0.0012269744,0.00032673727],"domain_scores_gemma":[0.9969588,0.00040683436,0.0007188958,0.00073840824,0.00096814503,0.00020895063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027995163,0.00017181791,0.0005503677,0.00046954895,0.00007814661,0.000081677,0.0013121231,0.00009404038,0.00012118653],"category_scores_gemma":[0.00053186395,0.0001390053,0.0001615135,0.0018722046,0.00015334642,0.00024727205,0.00012989523,0.00034772066,0.0000126037985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023730246,0.004734866,0.0043024207,0.00052478205,0.00051057554,0.00014146457,0.0020192408,0.2983475,0.013065525,0.06950227,0.0044740993,0.60000426],"study_design_scores_gemma":[0.0124555705,0.0022987844,0.11938039,0.00009787108,0.000113625036,0.00025382984,0.00012385169,0.35383856,0.030793132,0.47841558,0.0012654711,0.00096333266],"about_ca_topic_score_codex":0.0000037152636,"about_ca_topic_score_gemma":3.9448506e-7,"teacher_disagreement_score":0.59904087,"about_ca_system_score_codex":0.00011818648,"about_ca_system_score_gemma":0.00044556468,"threshold_uncertainty_score":0.56684715},"labels":[],"label_agreement":null},{"id":"W2016870475","doi":"10.1109/cec.2009.4983113","title":"The effect of preadaptation epoch length on performance in an exaptive genetic algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Epoch (astronomy); Genetic algorithm; Computer science; Fitness function; Algorithm; Mathematics; Mathematical optimization; Computer vision","score_opus":0.012996172075999271,"score_gpt":0.2831351118732784,"score_spread":0.2701389397972791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016870475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1217519,0.000046129735,0.8742506,0.0002029438,0.00009303793,0.00047657877,5.213163e-7,0.000046432535,0.0031318525],"genre_scores_gemma":[0.78402257,0.00007494688,0.21538915,0.000039756564,0.000025024707,0.000022949689,0.0000017074192,0.0000054419406,0.00041845455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984224,0.00034009016,0.00025139324,0.00026890746,0.000489613,0.00022761748],"domain_scores_gemma":[0.9988419,0.0004085382,0.00007085497,0.00050318294,0.00011414032,0.00006140997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008265803,0.00010448873,0.00013310589,0.00014104329,0.00009249227,0.00009074655,0.00065943017,0.000036408714,0.000009254239],"category_scores_gemma":[0.00012630496,0.00006543734,0.00002361432,0.0005164097,0.00004203394,0.00029119334,0.000045767632,0.00013052743,0.000021060105],"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":[0.000020919944,0.00004468876,0.00040065366,0.000004002633,0.0000033616982,0.000002979813,0.00019278612,0.018517433,0.000042576397,0.0028543577,0.000014692794,0.9779015],"study_design_scores_gemma":[0.0003521476,0.0021250702,0.06016814,0.000009695962,0.0000013655651,0.000002325855,0.000008690208,0.9313504,0.0056680343,0.00021658685,0.000029126017,0.00006842872],"about_ca_topic_score_codex":0.000010417206,"about_ca_topic_score_gemma":0.0000033132633,"teacher_disagreement_score":0.9778331,"about_ca_system_score_codex":0.000038734077,"about_ca_system_score_gemma":0.00004369326,"threshold_uncertainty_score":0.26684573},"labels":[],"label_agreement":null},{"id":"W2017351056","doi":"10.1142/s1469026815500029","title":"Collaborative Parallel Hybrid Metaheuristics on Graphics Processing Unit","year":2015,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Royal Military College of Canada","funders":"","keywords":"Computer science; Parallel computing; Metaheuristic; Speedup; Graphics processing unit; CUDA; Parallel metaheuristic; Asynchronous communication; Simulated annealing; Instruction set; Multi-core processor; Algorithm","score_opus":0.06856806425648525,"score_gpt":0.3758262417399429,"score_spread":0.30725817748345763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017351056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023237636,0.0003827498,0.9948708,0.0029579476,0.00020811641,0.00018191617,0.00001919379,0.000026484913,0.0011204534],"genre_scores_gemma":[0.5268899,0.00034373195,0.471307,0.0008750308,0.00036475374,0.000039491268,0.000028340204,0.000014662791,0.00013706447],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976573,0.000101322585,0.0006297067,0.00023166323,0.0012365682,0.00014342518],"domain_scores_gemma":[0.99286616,0.00044701938,0.00047591873,0.0001562706,0.0058021476,0.00025250463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677082,0.00014216428,0.00018988254,0.0004839206,0.00011431213,0.00043117083,0.0011061502,0.00003614532,0.000011667683],"category_scores_gemma":[0.00035845183,0.0001275848,0.0000561303,0.0006270946,0.0001519021,0.0005800615,0.00013965904,0.00025825127,0.000034165354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003502887,0.000208121,0.000076878794,0.0000048979264,0.00008631688,0.000028945067,0.00026876858,0.40406165,0.000002017166,0.4944473,0.0008258965,0.09995418],"study_design_scores_gemma":[0.00033585928,0.00018628135,0.00022912968,0.000039075574,0.000015681422,0.00027226927,0.00019189295,0.63186616,0.00021173623,0.34113508,0.025339872,0.0001769278],"about_ca_topic_score_codex":0.0000028854631,"about_ca_topic_score_gemma":4.9057587e-7,"teacher_disagreement_score":0.5266575,"about_ca_system_score_codex":0.000059673814,"about_ca_system_score_gemma":0.00061885,"threshold_uncertainty_score":0.5202757},"labels":[],"label_agreement":null},{"id":"W2017871062","doi":"10.1145/1389095.1389313","title":"Comparing genetic algorithm and guided local search methods by symmetric TSP instances","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Travelling salesman problem; Genetic algorithm; Computer science; Local search (optimization); Mathematical optimization; Heuristic; Algorithm; Reciprocal; Tournament; Local optimum; Mathematics; Artificial intelligence; Combinatorics","score_opus":0.08553397280728181,"score_gpt":0.3644186847723492,"score_spread":0.27888471196506737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017871062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092665746,0.0015558428,0.9892083,0.00030487188,0.00014067051,0.00022093378,0.000001680608,0.0001598802,0.007481162],"genre_scores_gemma":[0.023989158,0.00067401526,0.9730054,0.00014177493,0.0000321363,0.000014402504,0.0000027008698,0.000014489581,0.0021259254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99695855,0.0006605386,0.00041533765,0.0006355623,0.0008132082,0.0005168291],"domain_scores_gemma":[0.99831396,0.0004538045,0.000055695895,0.0005560813,0.00028393217,0.00033653394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001371902,0.00018584292,0.0003269321,0.00045888178,0.00031251906,0.00021116651,0.00096415344,0.00007174552,0.00008879759],"category_scores_gemma":[0.00017474146,0.0001640537,0.000040334275,0.0020294639,0.00029936573,0.00034398402,0.00067628763,0.00024420946,0.000052166404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016300446,0.00008621671,0.0012420948,0.000019898373,0.0000355606,0.00005504015,0.00024775474,0.0037271474,0.00006936365,0.0032014987,0.006715665,0.98459816],"study_design_scores_gemma":[0.00043250437,0.00005852069,0.0026535976,0.0000044778817,0.000003008993,0.00016811716,0.000030026466,0.9914579,0.0018699181,0.00022189348,0.0028999795,0.00020005906],"about_ca_topic_score_codex":0.00022967615,"about_ca_topic_score_gemma":0.0000014868644,"teacher_disagreement_score":0.98773074,"about_ca_system_score_codex":0.00007300256,"about_ca_system_score_gemma":0.00015448517,"threshold_uncertainty_score":0.6689915},"labels":[],"label_agreement":null},{"id":"W2017926151","doi":"10.1115/detc2013-12665","title":"Improved Trust Region Based MPS Method for High-Dimensional Expensive Black-Box Problems","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Black box; Computer science; Curse of dimensionality; Suite; Mathematical optimization; Sampling (signal processing); Trust region; High dimensional; Algorithm; Mathematics; Artificial intelligence","score_opus":0.031288293869971694,"score_gpt":0.2941774906111114,"score_spread":0.26288919674113975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017926151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017082378,0.000013642283,0.9897424,0.006980118,0.00028343892,0.0018969916,0.0000032351675,0.000212133,0.0006972101],"genre_scores_gemma":[0.00808834,0.0000025135446,0.98134685,0.0015036064,0.000081978185,0.00048525384,0.000021273629,0.0000265536,0.008443659],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975488,0.00023355742,0.00042264527,0.00074192876,0.000529606,0.00052346196],"domain_scores_gemma":[0.9966942,0.0007659117,0.0001524015,0.0008459194,0.0012736105,0.00026796653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006593318,0.000221732,0.00029273992,0.00023777162,0.00016934444,0.0003284725,0.0008379818,0.00011223906,0.0005857575],"category_scores_gemma":[0.00052343536,0.00017717414,0.00010031506,0.0004814152,0.000080079815,0.00055296253,0.00026040972,0.00015379066,0.00019500569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013094145,0.0011465161,0.00007373226,0.00038422798,0.0002723911,0.000039407027,0.000989267,0.09736351,0.036646817,0.2411307,0.33057573,0.29124677],"study_design_scores_gemma":[0.0011693756,0.00019047153,0.00006863259,0.000010509908,0.000005651747,0.0000070926053,0.000016753493,0.98092693,0.011989198,0.0044124387,0.00096748915,0.00023543052],"about_ca_topic_score_codex":0.00030648304,"about_ca_topic_score_gemma":0.000004880977,"teacher_disagreement_score":0.88356346,"about_ca_system_score_codex":0.00006796087,"about_ca_system_score_gemma":0.00023951629,"threshold_uncertainty_score":0.72249514},"labels":[],"label_agreement":null},{"id":"W2018626130","doi":"10.1007/s00500-013-1195-3","title":"Enhancing differential evolution with role assignment scheme","year":2013,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":27,"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":"National Natural Science Foundation of China","keywords":"Benchmark (surveying); Differential evolution; A priori and a posteriori; Scheme (mathematics); Evolutionary algorithm; Computer science; Mutation; Evolutionary computation; Range (aeronautics); Process (computing); Population; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.008135173854654523,"score_gpt":0.23140603069803328,"score_spread":0.22327085684337877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018626130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032367975,0.000039538732,0.96572846,0.00021440706,0.00019177973,0.00027773363,2.5852432e-7,0.00024859115,0.000931272],"genre_scores_gemma":[0.6522681,4.886008e-7,0.347387,0.000035683977,0.00010187829,0.000010078272,0.0000018113655,0.000010125787,0.00018483467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980595,0.0001140162,0.00027343974,0.00043926208,0.00064689526,0.00046687375],"domain_scores_gemma":[0.99888116,0.000155855,0.000119122626,0.00043599552,0.00024979876,0.00015808012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028757757,0.00015339331,0.0001677775,0.00013174147,0.00027322726,0.00042897894,0.0006199283,0.000046414745,0.00016333745],"category_scores_gemma":[0.00010622945,0.0001285008,0.000036758836,0.0004321443,0.000042971446,0.00039544737,0.00046002556,0.00020630637,0.0002810523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003620218,0.0011818842,0.034966845,0.00030399446,0.0004698465,0.00006785159,0.0055672857,0.09919035,0.14088392,0.1291154,0.0022621092,0.5859543],"study_design_scores_gemma":[0.00030174715,0.00006042019,0.0056427317,0.00003760651,0.000002685691,0.000010000334,0.000053845957,0.99066913,0.0023369736,0.0006436457,0.00006894899,0.00017229098],"about_ca_topic_score_codex":0.00007429717,"about_ca_topic_score_gemma":0.0000020634805,"teacher_disagreement_score":0.8914788,"about_ca_system_score_codex":0.0001274964,"about_ca_system_score_gemma":0.00010977018,"threshold_uncertainty_score":0.524011},"labels":[],"label_agreement":null},{"id":"W201929260","doi":"10.5220/0001703002880294","title":"AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 British Columbia","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence","score_opus":0.055886619505874306,"score_gpt":0.3439156715610338,"score_spread":0.2880290520551595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W201929260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002297648,0.000014584924,0.9967229,0.00032184253,0.00017315465,0.00047487364,0.0000022109782,0.00021721095,0.0018434674],"genre_scores_gemma":[0.01310267,0.0000054134553,0.9850631,0.00015931281,0.000045473313,0.00007109357,0.000003507929,0.000011009132,0.0015384177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986165,0.00010399297,0.000211735,0.00040496408,0.00035293793,0.00030987908],"domain_scores_gemma":[0.9988414,0.000168924,0.00004149829,0.00056731823,0.00021652039,0.0001643194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080503547,0.000093764465,0.00013060597,0.00013169885,0.0002114785,0.00011255743,0.0007886314,0.00002079998,0.000060952472],"category_scores_gemma":[0.000112246475,0.000079509766,0.000044044355,0.0002732874,0.000026426475,0.00018443933,0.0001740867,0.000065071734,0.000035600115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053525837,0.00018332804,0.000019363642,0.00003110674,0.000012364402,0.000015248581,0.00045694047,0.93558085,0.0005503267,0.010642616,0.0014595661,0.05104292],"study_design_scores_gemma":[0.00029227298,0.0000961442,0.00004341848,0.000002582691,0.000001142041,0.000073449635,0.0000046083123,0.9947294,0.002168598,0.00020280163,0.0022761412,0.00010946138],"about_ca_topic_score_codex":0.000020585725,"about_ca_topic_score_gemma":0.0000015543533,"teacher_disagreement_score":0.059148513,"about_ca_system_score_codex":0.000028996574,"about_ca_system_score_gemma":0.000083337436,"threshold_uncertainty_score":0.32423142},"labels":[],"label_agreement":null},{"id":"W2022184750","doi":"10.1007/s11047-006-9025-5","title":"Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge","year":2006,"lang":"en","type":"article","venue":"Natural Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":27,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Theory of computation; Adaptation (eye); Ridge; Computer science; Mathematics; Statistical physics; Algorithm; Physics; Geology; Paleontology","score_opus":0.02355873132136673,"score_gpt":0.27680781757727196,"score_spread":0.25324908625590525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022184750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05377399,0.00014961364,0.9416684,0.00070545473,0.0002374414,0.00032313782,0.0000018062715,0.00018889137,0.0029512998],"genre_scores_gemma":[0.8978916,0.0000012733981,0.10165988,0.00008370234,0.00018378271,0.0000048669976,0.0000102789145,0.000010168419,0.00015446999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793875,0.00030040773,0.00025480072,0.00038780528,0.0007690572,0.0003492023],"domain_scores_gemma":[0.99841017,0.0006452051,0.00017463522,0.000362897,0.00036616335,0.0000409416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054749654,0.00017530417,0.00014785663,0.0001391422,0.00046011177,0.0004541008,0.00059003656,0.00004920827,0.000006302325],"category_scores_gemma":[0.00011487561,0.00010959593,0.00003998476,0.0008576484,0.00008010437,0.00047321426,0.00012828218,0.00044262942,0.00003879786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017990486,0.000040361265,0.00013527808,0.0000073023916,0.000015529498,0.000007973301,0.00038785915,0.51394343,0.00005546758,0.47253188,0.0003795679,0.012477368],"study_design_scores_gemma":[0.00028818767,0.000081814935,0.014443968,0.000029121968,0.0000036672911,0.000008674025,0.0001958212,0.9827935,0.00010758205,0.0017286652,0.00017050997,0.00014847345],"about_ca_topic_score_codex":0.00025352804,"about_ca_topic_score_gemma":0.000037190806,"teacher_disagreement_score":0.8441176,"about_ca_system_score_codex":0.00012136357,"about_ca_system_score_gemma":0.00014673387,"threshold_uncertainty_score":0.44691923},"labels":[],"label_agreement":null},{"id":"W2023026651","doi":"10.1145/2576768.2598280","title":"Identifying and exploiting the scale of a search space in particle swarm optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Attraction; Particle swarm optimization; Convergence (economics); Local optimum; Context (archaeology); Cluster analysis; Mathematical optimization; Computer science; Swarm behaviour; Range (aeronautics); Modal; Scale (ratio); Task (project management); Metaheuristic; Local search (optimization); Exploit; Multi-swarm optimization; Local convergence; Mathematics; Artificial intelligence; Geography; Engineering; Iterative method","score_opus":0.037008208440620964,"score_gpt":0.2969969364756348,"score_spread":0.2599887280350138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023026651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052634582,0.000028072418,0.9444352,0.0012065998,0.000029490826,0.00013108685,1.3808882e-7,0.000027807771,0.0015070448],"genre_scores_gemma":[0.6686705,0.000020046042,0.33104455,0.00003660894,0.000010610731,0.0000072026482,2.5938007e-7,0.0000041149824,0.0002060884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987995,0.00024565507,0.00020509912,0.0002061618,0.0003451047,0.0001984576],"domain_scores_gemma":[0.99917245,0.0003087179,0.0000414257,0.00030764588,0.000112783346,0.00005695528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00157658,0.000054656713,0.0000974438,0.00008859501,0.00008028804,0.00015532188,0.00036204816,0.000020644928,0.000028799197],"category_scores_gemma":[0.000270618,0.000040097515,0.000014033267,0.00063328655,0.00006819262,0.00031476124,0.00031483773,0.0000906446,0.0000065791164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009314775,0.00012191538,0.013088976,0.0000863297,0.000011513542,0.0000030548933,0.007145051,0.8017786,0.0017829204,0.12386953,0.00006419628,0.052038588],"study_design_scores_gemma":[0.00021720551,0.000017757735,0.0017542496,0.000012979597,9.4382847e-7,0.0000024699225,0.00028055414,0.98784935,0.009380703,0.00042795425,0.000010520355,0.000045315646],"about_ca_topic_score_codex":0.00009940381,"about_ca_topic_score_gemma":0.000015073604,"teacher_disagreement_score":0.61603594,"about_ca_system_score_codex":0.000012781893,"about_ca_system_score_gemma":0.000024884905,"threshold_uncertainty_score":0.16351292},"labels":[],"label_agreement":null},{"id":"W2023272781","doi":"10.1016/j.asoc.2014.11.059","title":"Efficient detection of faulty nodes with cuckoo search in t-diagnosable systems","year":2014,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Larus Technologies (Canada); University of Ottawa","funders":"","keywords":"Cuckoo search; Computer science; Cuckoo; Algorithm; Biology; Particle swarm optimization","score_opus":0.012887946652731748,"score_gpt":0.2466964863436767,"score_spread":0.23380853969094495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023272781","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":[0.16134533,0.000031952342,0.8365815,0.000024230641,0.000100563746,0.00033796162,4.5692738e-7,0.00010013401,0.0014778181],"genre_scores_gemma":[0.89427704,0.0000011799211,0.10561802,0.000014702451,0.00003835488,0.00001527783,0.0000013322456,0.000014088085,0.000020018695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779695,0.00018167133,0.0004067601,0.0004751116,0.00070568227,0.00043383724],"domain_scores_gemma":[0.9982651,0.0007774913,0.00014039935,0.00050848257,0.00021068152,0.0000978303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018010145,0.00014658755,0.00029315983,0.00030360086,0.00013936871,0.00014531941,0.00062346645,0.00006316265,0.0000026378561],"category_scores_gemma":[0.00013568049,0.00013028651,0.000025072273,0.001160282,0.00007557819,0.000048489856,0.00030873987,0.00025079102,0.000028108389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008843978,0.00007633367,0.0006764909,0.00008243402,0.000007997485,0.0000022698907,0.0003750847,0.95966035,0.0009987998,0.0086030485,0.000006085333,0.029502254],"study_design_scores_gemma":[0.0005716461,0.00006992003,0.0014108331,0.000055858312,0.0000021325704,0.0000070167785,0.00009026365,0.9940842,0.0034756914,0.000045769517,0.000047219717,0.00013949416],"about_ca_topic_score_codex":0.00016475854,"about_ca_topic_score_gemma":0.000004746103,"teacher_disagreement_score":0.7329317,"about_ca_system_score_codex":0.00007513137,"about_ca_system_score_gemma":0.00007423913,"threshold_uncertainty_score":0.531293},"labels":[],"label_agreement":null},{"id":"W2023409871","doi":"10.1115/1.2803251","title":"Mode Pursuing Sampling Method for Discrete Variable Optimization on Expensive Black-Box Functions","year":2008,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":53,"is_retracted":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":"Mathematical optimization; Continuous variable; Sampling (signal processing); Discrete variable; Variable (mathematics); Black box; Mode (computer interface); Discrete space; Mathematics; Computer science; Algorithm; Control variable; Control (management); Control theory (sociology); Artificial intelligence; Statistics","score_opus":0.11295482548136972,"score_gpt":0.3639966378930159,"score_spread":0.2510418124116462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023409871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000081283715,0.000028932116,0.99789846,0.00090971886,0.0005834714,0.000396389,0.0000049229875,0.000039832932,0.00013011933],"genre_scores_gemma":[0.0014080504,0.00007074812,0.99759036,0.0002640661,0.00024576634,0.000018662895,0.000002049135,0.000025346866,0.000374941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972133,0.0005420216,0.00071286876,0.000332521,0.00084003294,0.00035924723],"domain_scores_gemma":[0.9954268,0.0023358916,0.0004523844,0.00038121926,0.0011265388,0.00027720397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002392434,0.00017376198,0.00039200936,0.0003257686,0.0003204837,0.00015528584,0.00076647237,0.00011943142,0.00007374654],"category_scores_gemma":[0.003075809,0.00014549571,0.00017059565,0.0005734971,0.000030105397,0.00057792844,0.00010852883,0.00036484946,0.000012424465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011912536,0.0000809032,2.3734036e-7,0.0000070283218,0.00005157105,0.000017061444,0.0001050125,0.9775256,0.0015273603,0.016675768,0.0018529996,0.0020373496],"study_design_scores_gemma":[0.00084879366,0.0007200049,0.0000012609194,0.000047199523,0.000027775259,0.00017207995,0.000032550834,0.98608166,0.0043081967,0.0072853244,0.00032501767,0.0001501434],"about_ca_topic_score_codex":0.0000033139047,"about_ca_topic_score_gemma":8.5378446e-8,"teacher_disagreement_score":0.009390444,"about_ca_system_score_codex":0.00013397637,"about_ca_system_score_gemma":0.00033865322,"threshold_uncertainty_score":0.5933143},"labels":[],"label_agreement":null},{"id":"W2023791340","doi":"10.1155/2014/318063","title":"Heterogeneous Differential Evolution for Numerical Optimization","year":2014,"lang":"en","type":"article","venue":"The Scientific World JOURNAL","topic":"Metaheuristic Optimization Algorithms Research","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":"Science Foundation of Ministry of Education of China; State Key Laboratory of Software Engineering; Ministry of Education of the People's Republic of China; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Differential evolution; Computer science; Set (abstract data type); Mathematical optimization; Scheme (mathematics); Population; Optimization problem; Scale (ratio); Algorithm; Mathematics","score_opus":0.019937958922853834,"score_gpt":0.27062590338786224,"score_spread":0.2506879444650084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023791340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027692455,0.000048460108,0.9931977,0.0024151662,0.0034398069,0.00024246018,0.0000023433033,0.00006111325,0.00031602918],"genre_scores_gemma":[0.5560005,0.000005378663,0.4323682,0.00013389437,0.0008825123,0.000029869789,0.000009982953,0.000022744869,0.010546966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976289,0.00038890867,0.0003641492,0.00035403238,0.00082844775,0.0004355108],"domain_scores_gemma":[0.9982718,0.00026169806,0.00019803506,0.0006016638,0.00048662562,0.00018015351],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0024091182,0.00012351018,0.00014758555,0.0003628458,0.0014983135,0.0020452763,0.001490681,0.000028008377,0.00022651404],"category_scores_gemma":[0.0004354203,0.00008236917,0.00013159332,0.001056775,0.00016281028,0.0003537918,0.00021211019,0.00023575453,0.000056810655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023820208,0.00011079015,0.000029758252,0.000008201091,0.000031650245,0.0000028545903,0.00018951387,0.92508066,0.0003898005,0.033520836,0.011448646,0.02916348],"study_design_scores_gemma":[0.00036047446,0.000042145784,0.00006252087,0.0000075045164,0.000009794529,0.00010944761,0.000003460908,0.98786986,0.00028105584,0.0051893047,0.005961514,0.00010290427],"about_ca_topic_score_codex":0.0000012621659,"about_ca_topic_score_gemma":0.0000025571662,"teacher_disagreement_score":0.5608295,"about_ca_system_score_codex":0.000100394456,"about_ca_system_score_gemma":0.00012416304,"threshold_uncertainty_score":0.9998016},"labels":[],"label_agreement":null},{"id":"W2026243033","doi":"10.1260/1748-3018.9.2.143","title":"Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies","year":2015,"lang":"en","type":"article","venue":"Journal of Algorithms & Computational Technology","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Benchmark (surveying); Multi-swarm optimization; Position (finance); Attractor; Metaheuristic; Mathematical optimization; Swarm behaviour; Computer science; Swarm intelligence; Global optimization; Mathematics; Algorithm","score_opus":0.05256136336976503,"score_gpt":0.3081965978010415,"score_spread":0.2556352344312765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026243033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003873432,0.00018384615,0.99200356,0.0031720733,0.00028315262,0.00018905799,0.000006071109,0.00014902322,0.00013980462],"genre_scores_gemma":[0.21818626,0.000012299819,0.7815942,0.00006240642,0.00007795383,0.000008403319,0.0000039554607,0.000017683426,0.000036834623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972178,0.00010479395,0.0007410463,0.00033636976,0.0012271496,0.00037284385],"domain_scores_gemma":[0.995145,0.00021183783,0.0006621588,0.0003184076,0.003397944,0.0002646072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008444218,0.00022026122,0.00039701787,0.00074449147,0.00012208302,0.00025335496,0.001081596,0.00014627626,0.000015339101],"category_scores_gemma":[0.00028330702,0.00018042701,0.00006716231,0.0018486717,0.00026885487,0.0012398433,0.00021846678,0.0004610626,0.000014041131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005421677,0.0002463129,0.0000655823,0.000003259273,0.000078777404,0.00009260469,0.00020426913,0.9055199,0.000030023215,0.088356085,0.00014933872,0.0051996503],"study_design_scores_gemma":[0.00204981,0.0013308601,0.00014752748,0.000027622562,0.000020068914,0.0010125262,0.00073994725,0.97271985,0.00036687136,0.021203225,0.00018144437,0.00020024391],"about_ca_topic_score_codex":0.0000074943096,"about_ca_topic_score_gemma":0.0000011561455,"teacher_disagreement_score":0.21431282,"about_ca_system_score_codex":0.00017826092,"about_ca_system_score_gemma":0.0013966745,"threshold_uncertainty_score":0.73576003},"labels":[],"label_agreement":null},{"id":"W2026267944","doi":"10.5267/j.ijiec.2012.03.007","title":"An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":504,"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":"Benchmark (surveying); Computer science; Population; Algorithm; Mathematical optimization; Optimization algorithm; Population-based incremental learning; Range (aeronautics); Optimization problem; Process (computing); Artificial intelligence; Machine learning; Mathematics; Genetic algorithm; Engineering","score_opus":0.04507322606150652,"score_gpt":0.31449062254271937,"score_spread":0.2694173964812128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026267944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007667584,0.000030807187,0.99619246,0.0009056603,0.0022045034,0.00036352014,0.000033106797,0.00014779698,0.000045498884],"genre_scores_gemma":[0.120334275,0.0000057516004,0.8779535,0.000079691126,0.0013498167,0.000020876436,0.00020772335,0.00003389295,0.0000144542255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974361,0.00017157543,0.00091909943,0.00022552763,0.00087042054,0.0003772458],"domain_scores_gemma":[0.9963553,0.00071751326,0.00061896787,0.0001808814,0.0018041158,0.0003232328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648801,0.00021981401,0.00029074488,0.0008225393,0.00021854397,0.0005707341,0.0010316483,0.00013814529,0.000065286215],"category_scores_gemma":[0.0015934017,0.0002345563,0.00014564971,0.0003709825,0.00004453252,0.0015158178,0.00007255416,0.0005608593,0.0000023273194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009588666,0.00021338972,0.000052492738,0.000005585669,0.00010039111,0.0000037970206,0.00017269686,0.96738034,0.00006277186,0.0020644502,0.00022000335,0.029714514],"study_design_scores_gemma":[0.0020291996,0.00023156899,0.000037123606,0.00008329688,0.00002342051,0.00008611727,0.000027066726,0.9956849,0.000071431285,0.00002902793,0.0014755785,0.00022124377],"about_ca_topic_score_codex":0.000011077676,"about_ca_topic_score_gemma":1.7100808e-7,"teacher_disagreement_score":0.1202576,"about_ca_system_score_codex":0.0002596777,"about_ca_system_score_gemma":0.0003557541,"threshold_uncertainty_score":0.9564928},"labels":[],"label_agreement":null},{"id":"W2026308324","doi":"10.1145/1143997.1144123","title":"Multiobjective evolutionary optimization for visual data mining with virtual reality spaces","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Machine learning; Knowledge extraction; Data mining; Pattern recognition (psychology)","score_opus":0.04712464955400414,"score_gpt":0.33500727094467003,"score_spread":0.2878826213906659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026308324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002441218,0.000019381052,0.99611956,0.0006911715,0.00010304052,0.0004917602,0.000038517555,0.00019666129,0.0020958006],"genre_scores_gemma":[0.045680434,0.0000045326206,0.95197797,0.000049379392,0.000133554,0.000046916917,0.0004205517,0.000014391214,0.0016722992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811345,0.00012312667,0.0002536469,0.0006815931,0.0005297034,0.00029847678],"domain_scores_gemma":[0.9981514,0.00040839316,0.000110937704,0.0007543198,0.00049670285,0.00007823864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005766025,0.00014032077,0.00015700431,0.00016149275,0.0002471935,0.00026146573,0.00089608756,0.000055521792,0.00006401793],"category_scores_gemma":[0.00028558067,0.00011568566,0.000021751011,0.0005812168,0.00008916392,0.0011792098,0.00045018768,0.00007027319,0.000007450496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055157972,0.00020655668,0.00037641454,0.00001153791,0.000028335022,0.0000043545188,0.00008137808,0.9638905,0.000013280168,0.016491296,0.009889392,0.008951793],"study_design_scores_gemma":[0.0006701613,0.00023580129,0.0008134747,0.0000075465705,0.000007400488,0.000008402985,0.00008980019,0.99735034,0.0000837123,0.00011903958,0.00044885123,0.00016547549],"about_ca_topic_score_codex":0.00023321038,"about_ca_topic_score_gemma":0.00005035462,"teacher_disagreement_score":0.04543631,"about_ca_system_score_codex":0.00007279084,"about_ca_system_score_gemma":0.00024627772,"threshold_uncertainty_score":0.47175243},"labels":[],"label_agreement":null},{"id":"W2027681005","doi":"10.5220/0005068801840191","title":"Dynamic Heterogeneous Multi-Population Cultural Algorithm for Large Scale Global Optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Computer science; Scale (ratio); Population; Mathematical optimization; Mathematics; Geography; Medicine","score_opus":0.017301278083421905,"score_gpt":0.31532460182361655,"score_spread":0.29802332374019463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027681005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000113011214,0.000024948786,0.99806315,0.00024892756,0.00041488928,0.00054748467,0.000036736863,0.00031144638,0.00023939753],"genre_scores_gemma":[0.01915915,0.000011088436,0.9794981,0.00018274333,0.00004739982,0.00006171224,0.00019583087,0.000014397647,0.00082961406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981028,0.00014007563,0.0003434984,0.0005444821,0.00041186306,0.0004573175],"domain_scores_gemma":[0.9987596,0.00006988049,0.00010575992,0.00048479458,0.00040826673,0.0001716683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049419637,0.00018035677,0.00020383358,0.00008328593,0.00025791558,0.00034619274,0.00063389,0.000100472585,0.00006687267],"category_scores_gemma":[0.00016958077,0.00015824144,0.00009689126,0.00046350586,0.000022691169,0.0005450837,0.00020484495,0.00006632902,0.000044921322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049978325,0.0002030374,0.0002490973,0.000019579418,0.000024659737,0.0000013501063,0.00008554916,0.782085,0.000007827799,0.0041856505,0.00028399314,0.21284926],"study_design_scores_gemma":[0.0009960469,0.00006768487,0.00040434927,0.000004504816,0.000006254334,0.000015803273,0.000009260218,0.99749243,0.000039444083,0.00028033418,0.00048259107,0.00020129478],"about_ca_topic_score_codex":0.000030962226,"about_ca_topic_score_gemma":0.000041259904,"teacher_disagreement_score":0.21540743,"about_ca_system_score_codex":0.00014992245,"about_ca_system_score_gemma":0.000029177196,"threshold_uncertainty_score":0.64528984},"labels":[],"label_agreement":null},{"id":"W2027723026","doi":"10.1117/12.542156","title":"Self-adaptive parameters in genetic algorithms","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Trois-Rivières; Defence Research and Development Canada","funders":"","keywords":"Computer science; Algorithm; Genetic algorithm; Quality control and genetic algorithms; Context (archaeology); Range (aeronautics); Set (abstract data type); Chromosome; Resolution (logic); Artificial intelligence; Meta-optimization; Machine learning","score_opus":0.016124700050813512,"score_gpt":0.24519389518269608,"score_spread":0.22906919513188256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027723026","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":[0.9761244,0.00010797969,0.018677026,0.0025617555,0.00027985312,0.00081481866,0.000015808535,0.00014681577,0.0012715282],"genre_scores_gemma":[0.047979537,0.00013091584,0.9513829,0.00009618444,0.00012984521,0.00017128013,0.000002080389,0.00004165879,0.00006560114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968341,3.1864353e-8,0.00082942756,0.00060381455,0.0011634517,0.0005691862],"domain_scores_gemma":[0.9974963,0.00019199072,0.0003391068,0.000115120754,0.0016714899,0.0001859744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086082105,0.00032934532,0.00044058013,0.00025948053,0.00007619068,0.00022549424,0.0022724436,0.00018277104,0.0000046375962],"category_scores_gemma":[0.00062947604,0.00028919848,0.00044216184,0.00097157457,0.00019455653,0.00080616964,0.00041191475,0.00041778997,0.000004566769],"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":[0.00004535868,0.00039296472,0.00017928844,0.00031360603,0.00040287574,0.0000011671732,0.00075041555,0.008972732,0.023744144,0.96303576,0.00043074996,0.0017309494],"study_design_scores_gemma":[0.0026761736,0.0006061368,0.0015240244,0.0002737141,0.00006890281,0.000044523058,0.0006394919,0.9225247,0.058930285,0.011321686,0.00078766554,0.00060269766],"about_ca_topic_score_codex":0.000024945499,"about_ca_topic_score_gemma":2.2867039e-7,"teacher_disagreement_score":0.95171404,"about_ca_system_score_codex":0.00037827683,"about_ca_system_score_gemma":0.00011822122,"threshold_uncertainty_score":0.999956},"labels":[],"label_agreement":null},{"id":"W2028983148","doi":"10.3166/ria.16.367-382","title":"A Distributed Guided Genetic Algorithm for Max-CSPs","year":2002,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Genetic algorithm; Algorithm; Machine learning","score_opus":0.12672744075911443,"score_gpt":0.3253241602708092,"score_spread":0.19859671951169475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028983148","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034690267,0.010781328,0.9699141,0.011504392,0.003276115,0.0014977108,0.00036223893,0.00018359844,0.0024458477],"genre_scores_gemma":[0.0021635848,0.002944924,0.84491014,0.00040775107,0.0008606219,0.0002916761,0.00007061002,0.00009245398,0.14825821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945307,0.00035775782,0.0014574953,0.0014090809,0.00071031443,0.0015346528],"domain_scores_gemma":[0.99514127,0.00087761413,0.00034449858,0.0017620807,0.0012613183,0.0006132141],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010778458,0.00051866716,0.0006203268,0.00030885634,0.0005674195,0.0008287252,0.0020159357,0.00032568641,0.0059922985],"category_scores_gemma":[0.0013062097,0.000596003,0.00037858152,0.0021947152,0.000474841,0.00053359894,0.00050582236,0.00046353432,0.0052604303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037990278,0.00054565264,0.000010430324,0.00017763786,0.000060588787,0.00011506167,0.0009124795,0.117013365,0.000053855107,0.011328011,0.0503043,0.8194748],"study_design_scores_gemma":[0.00014197161,0.000242152,0.0000098929695,0.00013320269,0.00004032674,0.0002183011,0.000101175654,0.77184004,0.0034653803,0.0027273695,0.2205907,0.0004895018],"about_ca_topic_score_codex":0.0000920025,"about_ca_topic_score_gemma":0.000004589752,"teacher_disagreement_score":0.8189853,"about_ca_system_score_codex":0.00025214988,"about_ca_system_score_gemma":0.00014084151,"threshold_uncertainty_score":0.9996491},"labels":[],"label_agreement":null},{"id":"W2032301555","doi":"10.1080/0305215x.2013.800057","title":"Modification of DIRECT for high-dimensional design problems","year":2013,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Maxima and minima; Initialization; Subroutine; Curse of dimensionality; Mathematical optimization; Convergence (economics); Computer science; Premature convergence; Algorithm; Mathematics; Genetic algorithm; Artificial intelligence","score_opus":0.027829762872468914,"score_gpt":0.23952712347066998,"score_spread":0.21169736059820107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032301555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005221736,0.000044278204,0.9981934,0.00048472255,0.00018150827,0.00084203616,0.0000027853891,0.00015526048,0.000043817443],"genre_scores_gemma":[0.04276992,0.0000122434085,0.95665675,0.000029359679,0.000020464815,0.00028825816,0.000032257834,0.000018148025,0.00017261796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989841,0.00003741303,0.0002794711,0.00024733183,0.00027101516,0.00018065187],"domain_scores_gemma":[0.9988396,0.00022414904,0.00009472893,0.00030889933,0.00046848753,0.00006412228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036371883,0.00010392373,0.00014016093,0.0002049656,0.000045844416,0.000078906276,0.00030533574,0.000045943074,0.00004707247],"category_scores_gemma":[0.00036782457,0.000103248676,0.00002918214,0.0004452954,0.000012472729,0.00045266005,0.000049191945,0.000049585262,0.000011715516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001266112,0.000029088375,0.0000016354458,0.000023785286,0.0000105712825,6.13057e-8,0.000032275315,0.9923949,0.0011168934,0.0037183054,0.0009553589,0.0017158649],"study_design_scores_gemma":[0.00024112075,0.000052239902,0.00005969124,0.00001533962,0.0000032010519,8.5560464e-7,5.65483e-7,0.994805,0.0044973274,0.00015053726,0.00007043881,0.00010372119],"about_ca_topic_score_codex":0.0000137965835,"about_ca_topic_score_gemma":2.3809505e-8,"teacher_disagreement_score":0.042717703,"about_ca_system_score_codex":0.000035968344,"about_ca_system_score_gemma":0.000044828306,"threshold_uncertainty_score":0.4210359},"labels":[],"label_agreement":null},{"id":"W2032385958","doi":"10.1109/icma.2014.6885702","title":"Comparison between differential evolution and particle swarm optimization algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Particle swarm optimization; Differential evolution; Benchmark (surveying); Robustness (evolution); Multi-swarm optimization; Mathematical optimization; Algorithm; Computer science; Swarm behaviour; Mathematics","score_opus":0.035081598719082036,"score_gpt":0.31398949959045425,"score_spread":0.2789079008713722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032385958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052619227,0.000024223378,0.9926707,0.00062490045,0.00019348864,0.00016906376,0.0000013051409,0.0002158073,0.00083859405],"genre_scores_gemma":[0.627492,0.0000046752357,0.37204155,0.000028069728,0.0001071903,0.000008689792,0.0000079883075,0.000007634523,0.00030220652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982803,0.00022243672,0.00032124345,0.00040681576,0.00046180896,0.0003073971],"domain_scores_gemma":[0.9989508,0.0002016628,0.00008108142,0.00039806557,0.00015652104,0.00021186243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045063,0.00012888145,0.00021848157,0.00010192031,0.0001834222,0.00031186792,0.0004053261,0.00006611053,0.00009666485],"category_scores_gemma":[0.0001920949,0.000115524475,0.0000295462,0.00040588254,0.000055488144,0.0004245284,0.00029233974,0.00012032756,0.000052349704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001709466,0.00045237358,0.063039266,0.000066080815,0.000106126616,0.0000027061042,0.0007399792,0.34293762,0.0004868666,0.24226645,0.0018146044,0.34807083],"study_design_scores_gemma":[0.000430401,0.000083446095,0.012491417,0.0000038573303,0.000008548367,0.0000013253651,0.000009883553,0.9848803,0.001050148,0.0006534928,0.0002459576,0.00014119691],"about_ca_topic_score_codex":0.000035853776,"about_ca_topic_score_gemma":0.0000016227337,"teacher_disagreement_score":0.6419427,"about_ca_system_score_codex":0.0000409206,"about_ca_system_score_gemma":0.00002778152,"threshold_uncertainty_score":0.47109511},"labels":[],"label_agreement":null},{"id":"W2032658116","doi":"10.1155/2011/938240","title":"Evolutionary Computation and Its Applications in Neural and Fuzzy Systems","year":2011,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Evolutionary programming; Evolutionary computation; Evolutionary algorithm; Metaheuristic; Mathematical optimization; Optimization problem; Genetic programming; Artificial neural network; Meta-optimization; Artificial intelligence; Interactive evolutionary computation; Simulated annealing; Machine learning; Algorithm; Mathematics","score_opus":0.05946061781252375,"score_gpt":0.295526399703519,"score_spread":0.23606578189099525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032658116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01821922,0.0014173863,0.97870135,0.00007567542,0.000074887386,0.00055450876,0.0000029077885,0.00010749199,0.00084655377],"genre_scores_gemma":[0.8573087,0.000045340475,0.14246503,0.00007444266,0.00003392019,0.00003535529,0.000015466372,0.000010533462,0.000011206552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815524,0.00009208853,0.0005044978,0.0006163101,0.00033602043,0.00029584317],"domain_scores_gemma":[0.99852395,0.0008247893,0.00014494448,0.00013284695,0.00021111243,0.00016236544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045748366,0.00019975932,0.00023973285,0.00031927708,0.00031656874,0.00020408454,0.0002881413,0.00007522827,0.0000029680991],"category_scores_gemma":[0.000049215192,0.00020840483,0.000017954784,0.00058744947,0.00013050935,0.00029960374,0.00040378293,0.00022045527,0.000009986738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007932188,0.00007644393,0.0017815789,0.000079291436,0.000017581777,0.0000063280454,0.0020125683,0.3853777,0.000008509318,0.514493,0.000009950949,0.09612911],"study_design_scores_gemma":[0.00013833266,0.000033672288,0.011530686,0.000020761287,0.0000042060765,0.00005475874,0.0003211107,0.9533176,0.000012623034,0.03434745,0.000021418804,0.00019741485],"about_ca_topic_score_codex":0.000042078766,"about_ca_topic_score_gemma":0.0000012704814,"teacher_disagreement_score":0.8390895,"about_ca_system_score_codex":0.000032811397,"about_ca_system_score_gemma":0.00005946336,"threshold_uncertainty_score":0.84985024},"labels":[],"label_agreement":null},{"id":"W2033093367","doi":"10.2478/mper-2014-0011","title":"Ant Colony Optimization for Data Acquisition Mission Planning","year":2014,"lang":"en","type":"article","venue":"Management and Production Engineering Review","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ant colony optimization algorithms; Computer science; Grid; Data acquisition; Data mining; Probabilistic logic; Graph; Trajectory; Mathematical optimization; Real-time computing; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.04840702640377507,"score_gpt":0.31040760650009597,"score_spread":0.2620005800963209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033093367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005697885,0.008599403,0.9875891,0.0024468647,0.00039180653,0.000701042,0.0000010287805,0.00012817862,0.0001368431],"genre_scores_gemma":[0.00045071496,0.020474795,0.977714,0.0002112096,0.00019447402,0.000059326718,0.00014843134,0.000014881905,0.0007321449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902546,0.00003932755,0.00019925564,0.00041711217,0.00018034669,0.00013850247],"domain_scores_gemma":[0.9991799,0.000037360034,0.000061635925,0.0006014809,0.000069407244,0.000050245042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012430564,0.00009431523,0.0001369596,0.0001099693,0.00009367886,0.00009487702,0.0003534308,0.000018447323,0.000014802057],"category_scores_gemma":[0.00025617096,0.000088002285,0.000014594373,0.00031190534,0.000006864105,0.00039402407,0.00024393908,0.000044593005,0.0000036552374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045112924,0.00003217542,0.000016163094,0.0074182935,0.000039075167,0.0000012765573,0.000028867544,0.61026895,0.000020137748,0.0072228503,0.025648192,0.34929955],"study_design_scores_gemma":[0.000093894356,0.0000197872,0.000038898514,0.00061526534,0.00002047527,0.0000036480762,7.045907e-7,0.8321754,0.000014147213,0.000040512947,0.16689196,0.00008531312],"about_ca_topic_score_codex":3.7746352e-7,"about_ca_topic_score_gemma":9.624638e-9,"teacher_disagreement_score":0.34921423,"about_ca_system_score_codex":0.000015286016,"about_ca_system_score_gemma":0.000004443092,"threshold_uncertainty_score":0.3588629},"labels":[],"label_agreement":null},{"id":"W2033338535","doi":"10.1007/s12293-013-0129-z","title":"A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem","year":2014,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Brandon University","funders":"Missouri University of Science and Technology; University of Missouri; Intelligent Systems Center; National Science Foundation","keywords":"Ant colony optimization algorithms; Ant colony; Computer science; Algorithm; State (computer science); Complex system; ANT; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.029943360986871843,"score_gpt":0.2619239946884852,"score_spread":0.23198063370161337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033338535","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028514215,0.000025296664,0.98972934,0.0019982802,0.0005064386,0.0006144099,0.0000023622479,0.0002682971,0.0040041786],"genre_scores_gemma":[0.29554915,0.00001509921,0.70351005,0.00041736139,0.00022951672,0.000026615697,0.000009711995,0.00003234633,0.0002101148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99669164,0.000537212,0.00045752645,0.000676404,0.0010120126,0.0006252137],"domain_scores_gemma":[0.9976512,0.00096895103,0.00017780779,0.0006966199,0.00035387097,0.0001515405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024717224,0.00028875127,0.00032532465,0.00024193316,0.0008080111,0.00069607905,0.0012992925,0.00007402534,0.00018744932],"category_scores_gemma":[0.00048207268,0.00020976183,0.00009308145,0.00087198825,0.00013129176,0.00023486662,0.0008166111,0.00038698633,0.00007450139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000857243,0.00014132519,0.00022130189,0.000054287095,0.000058328802,0.000013897121,0.0012343467,0.79484886,0.00010590677,0.033302456,0.00024687906,0.16976385],"study_design_scores_gemma":[0.0004512727,0.00016314234,0.00036038496,0.00009656049,0.000009330065,0.000027583104,0.00007160979,0.9959341,0.00087865227,0.0003019517,0.0014426138,0.00026277965],"about_ca_topic_score_codex":0.00004398868,"about_ca_topic_score_gemma":0.0000025358693,"teacher_disagreement_score":0.29269776,"about_ca_system_score_codex":0.000110263536,"about_ca_system_score_gemma":0.000114977665,"threshold_uncertainty_score":0.85538393},"labels":[],"label_agreement":null},{"id":"W2034854664","doi":"10.1145/1570256.1570317","title":"Black-box optimization benchmarking for noiseless function testbed using PSO_bounds","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Testbed; Initialization; Benchmarking; Particle swarm optimization; Computer science; Mathematical optimization; Black box; Simple (philosophy); Function (biology); Algorithm; Mathematics; Artificial intelligence","score_opus":0.03716575315474716,"score_gpt":0.30567287310543245,"score_spread":0.26850711995068527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034854664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027476338,0.000019910682,0.99515235,0.0005240122,0.0003619771,0.00047813062,0.000001737207,0.00023214855,0.0029549678],"genre_scores_gemma":[0.046484556,0.000011405622,0.9520467,0.00047034895,0.00022072806,0.000012246793,0.00002336354,0.000013608952,0.00071703905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820817,0.00007584849,0.0003516098,0.000498455,0.0004705548,0.000395348],"domain_scores_gemma":[0.9985623,0.00015788611,0.00012287319,0.00047754648,0.0005435353,0.00013590939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062656356,0.00016433658,0.00018611258,0.0002754742,0.00030068003,0.0005547022,0.0005226641,0.0000879945,0.000132609],"category_scores_gemma":[0.0002041497,0.00015840222,0.000071644026,0.00092961546,0.000039563784,0.0008072246,0.00008195351,0.00010042723,0.000011913815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015965006,0.00008252219,0.000031205258,0.00001204142,0.000012704332,0.0000015463942,0.00008141398,0.9158637,0.0003712619,0.024067696,0.0007778298,0.05868213],"study_design_scores_gemma":[0.0004912722,0.00018536042,0.00013629829,0.000011099813,0.000011532259,0.0000052262008,0.000012136487,0.99641824,0.0003471945,0.0016308037,0.00055753527,0.00019327724],"about_ca_topic_score_codex":0.000009294583,"about_ca_topic_score_gemma":0.0000012741149,"teacher_disagreement_score":0.08055458,"about_ca_system_score_codex":0.00010627291,"about_ca_system_score_gemma":0.00015512851,"threshold_uncertainty_score":0.64594555},"labels":[],"label_agreement":null},{"id":"W2036225606","doi":"10.1145/1570256.1570316","title":"Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Testbed; Benchmarking; Black box; Particle swarm optimization; Computer science; Multi-swarm optimization; Function (biology); Mathematical optimization; Benchmark (surveying); Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.039974930040846506,"score_gpt":0.3022172182142686,"score_spread":0.2622422881734221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036225606","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005337021,0.00002739157,0.9963447,0.0007196939,0.0003380105,0.0007689331,0.0000025877514,0.0003406954,0.00092432485],"genre_scores_gemma":[0.077421814,0.000023325629,0.92149365,0.00045424228,0.00019714417,0.00002424405,0.000040437855,0.000020896821,0.0003242511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765384,0.00012635134,0.0005110953,0.00063263474,0.0005562551,0.0005198082],"domain_scores_gemma":[0.9981373,0.00018748286,0.00019071394,0.00055688375,0.00073171855,0.00019588073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075094943,0.00021847452,0.00023521802,0.00023090439,0.00035638685,0.00054261246,0.0005243339,0.00011217195,0.0001683274],"category_scores_gemma":[0.00031360338,0.00021699505,0.0000849583,0.0012666684,0.00004587117,0.0012226186,0.00009612179,0.000112195965,0.000012718923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022718503,0.000109078675,0.00003300345,0.000010292262,0.000011893778,0.0000012213974,0.000090473564,0.9774936,0.00026508243,0.008722332,0.00021598158,0.013024324],"study_design_scores_gemma":[0.0008009864,0.00023765903,0.00006261649,0.000015392554,0.00002238517,0.0000050343456,0.000025567651,0.9960565,0.0019355464,0.0004765849,0.00009918275,0.00026257275],"about_ca_topic_score_codex":0.000010045257,"about_ca_topic_score_gemma":0.0000010917869,"teacher_disagreement_score":0.076888114,"about_ca_system_score_codex":0.00013493316,"about_ca_system_score_gemma":0.00014713166,"threshold_uncertainty_score":0.88488007},"labels":[],"label_agreement":null},{"id":"W2036829849","doi":"10.1080/0305215x.2013.776552","title":"Convergence analysis of the tabu-based real-coded small-world optimization algorithm","year":2013,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"National Natural Science Foundation of China; University of Otago; Ryerson University","keywords":"Tabu search; Mathematical optimization; Mathematics; Crossover; Convergence (economics); Metaheuristic; Algorithm; Benchmark (surveying); Bounded function; Computer science","score_opus":0.011920856076713844,"score_gpt":0.22510689703870265,"score_spread":0.21318604096198882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036829849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001167761,0.000029430637,0.9980104,0.000412108,0.00041119294,0.0005182497,0.000009779289,0.00022796159,0.00026408574],"genre_scores_gemma":[0.01459969,0.000045298002,0.98454887,0.00008063103,0.00003035932,0.0000913706,0.0000693567,0.000029808838,0.00050459546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788207,0.00013249509,0.000578132,0.00044931672,0.0006131562,0.0003448493],"domain_scores_gemma":[0.99769694,0.00021804773,0.00026615275,0.0009943546,0.0006900965,0.00013441876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040814743,0.00022853326,0.00034095134,0.00097513973,0.00012287678,0.00019720652,0.0010680355,0.00008735289,0.0005132073],"category_scores_gemma":[0.00031666586,0.00019689387,0.00017401749,0.0070617567,0.000051866573,0.00046868573,0.00019624927,0.00017197384,0.000011433296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.9324914e-7,0.000039737002,0.00020942761,0.000015555512,0.0001231421,5.244146e-7,0.000054952772,0.9970146,0.00005820592,0.00088601396,0.00009225709,0.0015046801],"study_design_scores_gemma":[0.00024249012,0.000015619167,0.0012809185,0.000018384555,0.000102415965,5.5644006e-7,0.0000036080937,0.99702656,0.0010518001,0.000006458954,0.000049288483,0.00020190957],"about_ca_topic_score_codex":0.0001703202,"about_ca_topic_score_gemma":0.000005095791,"teacher_disagreement_score":0.0144829145,"about_ca_system_score_codex":0.00010748117,"about_ca_system_score_gemma":0.00012503396,"threshold_uncertainty_score":0.8029099},"labels":[],"label_agreement":null},{"id":"W2037628519","doi":"10.1155/2014/713490","title":"A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy","year":2014,"lang":"en","type":"article","venue":"The Scientific World JOURNAL","topic":"Metaheuristic Optimization Algorithms Research","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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Variable (mathematics); Curse of dimensionality; Computer science; Mathematical optimization; Local optimum; Relation (database); Multi-swarm optimization; Optimization problem; Artificial intelligence; Machine learning; Algorithm; Mathematics; Data mining","score_opus":0.022236354857923052,"score_gpt":0.2619238714423197,"score_spread":0.23968751658439666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037628519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018482876,0.000032858617,0.99278283,0.0012417387,0.00056891807,0.0001324233,4.9162674e-7,0.000087990455,0.0033044352],"genre_scores_gemma":[0.40429875,0.000008594074,0.56798404,0.00018179642,0.000338803,0.000012446849,0.0000060795905,0.00002847984,0.027140971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967802,0.0009241922,0.00034127844,0.00041337303,0.0010177905,0.00052317296],"domain_scores_gemma":[0.9980152,0.00017804481,0.00021928023,0.0007172067,0.0005855893,0.00028465086],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006758332,0.0001388905,0.00014964755,0.00025464213,0.0017285048,0.004301395,0.0014208012,0.000025096599,0.00045666684],"category_scores_gemma":[0.0002755243,0.00008377725,0.000032025728,0.0025624272,0.00022033401,0.0011961365,0.0001838138,0.0005534154,0.00007332928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012543562,0.00006512806,0.000038866947,0.0000024023614,0.000013380225,0.00001033551,0.00032672423,0.9597081,0.000112857015,0.030502511,0.00038907886,0.008818057],"study_design_scores_gemma":[0.0004124144,0.00015199553,0.000064474276,0.000016820462,0.000010693001,0.00021220185,0.00005746951,0.99157816,0.00025238132,0.002323453,0.004787031,0.00013288851],"about_ca_topic_score_codex":0.000011358669,"about_ca_topic_score_gemma":0.0000128011425,"teacher_disagreement_score":0.4247988,"about_ca_system_score_codex":0.000049384413,"about_ca_system_score_gemma":0.00030901772,"threshold_uncertainty_score":0.9995711},"labels":[],"label_agreement":null},{"id":"W2038624805","doi":"10.1145/2576768.2598212","title":"An improved multi-start particle swarm-based algorithm for protein structure comparison","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Queen's University","funders":"","keywords":"Particle swarm optimization; Swarm behaviour; Computer science; Heuristic; Multi-swarm optimization; Heuristics; Local search (optimization); Agile software development; Metaheuristic; Local optimum; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.0372871004132425,"score_gpt":0.3288397382505595,"score_spread":0.291552637837317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038624805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015819032,0.0000114161885,0.99606746,0.0005635598,0.00015949599,0.0012150395,0.000020241003,0.0003341463,0.000046730747],"genre_scores_gemma":[0.17114271,1.8297372e-7,0.82785374,0.0002805433,0.00007294755,0.00012287378,0.000023968314,0.000020487432,0.00048255615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977517,0.0002451353,0.0003967518,0.0006394496,0.00042438137,0.0005425587],"domain_scores_gemma":[0.99800456,0.00012830665,0.00011428081,0.0009863155,0.0004250016,0.00034154786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007111174,0.00020344746,0.00027775095,0.00009689816,0.00021750283,0.0004073306,0.0010895766,0.000095125004,0.000102698206],"category_scores_gemma":[0.00021899407,0.00017162433,0.00006707026,0.00039405422,0.00006580664,0.0004245612,0.00010497224,0.0001652259,0.000026404292],"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":[0.000025585849,0.0006862871,0.00019072538,0.00005299861,0.00002915518,0.0000016274795,0.00023173932,0.015422682,0.030124042,0.006803162,0.0007785037,0.9456535],"study_design_scores_gemma":[0.0015389828,0.00037689533,0.00009418118,0.0000040576224,0.0000040800332,6.705999e-7,0.000013499066,0.88743204,0.10798306,0.0003122111,0.0020252848,0.00021501331],"about_ca_topic_score_codex":0.000045074183,"about_ca_topic_score_gemma":0.000029826644,"teacher_disagreement_score":0.9454385,"about_ca_system_score_codex":0.000041362106,"about_ca_system_score_gemma":0.00013281578,"threshold_uncertainty_score":0.69986373},"labels":[],"label_agreement":null},{"id":"W2038636021","doi":"10.1016/j.ins.2011.03.016","title":"Enhancing particle swarm optimization using generalized opposition-based learning","year":2011,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":473,"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 Ontario Institute of Technology","funders":"National Natural Science Foundation of China","keywords":"Premature convergence; Initialization; Mathematical optimization; Particle swarm optimization; Local optimum; Differential evolution; Population; Benchmark (surveying); Computer science; Mathematics; Ode; Applied mathematics","score_opus":0.09140839569055396,"score_gpt":0.3139838274931784,"score_spread":0.2225754318026244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038636021","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011795866,0.0000114378645,0.98496675,0.0001229782,0.0001986828,0.00016124077,8.113616e-7,0.00018066964,0.0025615839],"genre_scores_gemma":[0.31520993,0.000004360618,0.68445617,0.00027448317,0.000014655904,0.000011241717,0.000004449647,0.0000027373492,0.000021997275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818426,0.00014556327,0.0004511293,0.00019137471,0.0007186899,0.00030899205],"domain_scores_gemma":[0.9989846,0.000080817874,0.0002353957,0.00021271427,0.00036534862,0.00012117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014190401,0.000103599305,0.00010908474,0.00029852032,0.00064082805,0.0006046342,0.0006595627,0.000041628762,0.00024176546],"category_scores_gemma":[0.00033867083,0.00009428874,0.00003782104,0.0014162612,0.00012824392,0.004662014,0.00011288623,0.00009537584,0.00010107082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026973185,0.000015219032,0.00034738274,0.00000668522,0.000002709153,6.754019e-7,0.0014069258,0.9905499,0.00025887237,0.0045681256,0.000010444914,0.0028303552],"study_design_scores_gemma":[0.00023959363,0.000054871165,0.00009953142,0.000011672792,0.0000026602659,0.0000046824225,0.00012666406,0.97181934,0.027260719,0.00012862106,0.00012797472,0.00012365203],"about_ca_topic_score_codex":0.00010720542,"about_ca_topic_score_gemma":0.000001741456,"teacher_disagreement_score":0.30341405,"about_ca_system_score_codex":0.000054765012,"about_ca_system_score_gemma":0.00029711492,"threshold_uncertainty_score":0.58305013},"labels":[],"label_agreement":null},{"id":"W2039139716","doi":"10.1145/1830483.1830527","title":"A hierarchical cooperative evolutionary algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Evolutionary algorithm; Computer science; Selection (genetic algorithm); Simple (philosophy); Evolutionary computation; String (physics); Interference (communication); Algorithm; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.01415007931005871,"score_gpt":0.2829451829074897,"score_spread":0.268795103597431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039139716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013065695,0.000012235246,0.97178984,0.0025142885,0.00048798168,0.00015497702,0.000003836135,0.00021809853,0.024688076],"genre_scores_gemma":[0.006799348,0.000006130226,0.9854642,0.00036441794,0.00013089372,0.000026698819,0.0000057100106,0.0000074190916,0.00719516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854773,0.00009942893,0.00018570996,0.00037670284,0.00050053734,0.0002898833],"domain_scores_gemma":[0.9986658,0.00019796008,0.000024998613,0.00056155963,0.00032206066,0.00022762638],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034265372,0.00010481078,0.00011560528,0.00014723455,0.00017422758,0.00017549747,0.00089628185,0.000070163245,0.0013805649],"category_scores_gemma":[0.0003206169,0.000085165004,0.000039418406,0.0006012334,0.00013599797,0.0003934822,0.00038111472,0.00046958297,0.00062401866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026633604,0.00018425309,0.00007232765,0.000002720397,0.000020214698,0.00004565434,0.0001607259,0.0001895697,0.0009570597,0.68898386,0.014602088,0.29477888],"study_design_scores_gemma":[0.0002201428,0.000041391355,0.00070765667,0.0000010061351,8.949385e-7,0.00005597782,0.0000046345203,0.97650117,0.0004647116,0.003656897,0.01822351,0.00012201758],"about_ca_topic_score_codex":0.000014151845,"about_ca_topic_score_gemma":0.0000035820578,"teacher_disagreement_score":0.97631156,"about_ca_system_score_codex":0.000016546852,"about_ca_system_score_gemma":0.0002407,"threshold_uncertainty_score":0.9995323},"labels":[],"label_agreement":null},{"id":"W2040376187","doi":"10.1109/foci.2014.7007807","title":"A discrete representation for real optimization with unique search properties","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Representation (politics); Theoretical computer science","score_opus":0.04425098409286923,"score_gpt":0.30601281697414573,"score_spread":0.2617618328812765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040376187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025035237,0.0000033478314,0.9886504,0.0016633782,0.00004380414,0.0006987313,0.0000011015255,0.00019409118,0.008494805],"genre_scores_gemma":[0.055360097,0.000023330513,0.9398289,0.00008760615,0.000052247146,0.00015413287,0.000018624774,0.000016858181,0.004458219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984702,0.0002131438,0.00019519866,0.00040434048,0.0004556433,0.00026151375],"domain_scores_gemma":[0.99861383,0.00015933129,0.00004947687,0.0005266603,0.00054432306,0.00010635245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006806,0.000103204555,0.00013130736,0.00014815836,0.00016226301,0.0003188511,0.00048153647,0.00004014011,0.000034494427],"category_scores_gemma":[0.00022881849,0.00007067735,0.00002731898,0.0005019448,0.000063414154,0.00056879147,0.00012780912,0.00007264187,0.000010095213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006822718,0.00004169778,0.00024087874,0.00004448265,0.000021258156,0.0000010550561,0.00048759594,0.8860083,0.00021997692,0.098373726,0.000438944,0.014053812],"study_design_scores_gemma":[0.0004646481,0.00021574511,0.0000941004,0.0000111748,0.0000032393177,0.000004417044,0.000036255446,0.9943833,0.0041921586,0.00025746442,0.00022192237,0.00011557257],"about_ca_topic_score_codex":0.00011579356,"about_ca_topic_score_gemma":0.000010876111,"teacher_disagreement_score":0.10837496,"about_ca_system_score_codex":0.000033738666,"about_ca_system_score_gemma":0.00011444158,"threshold_uncertainty_score":0.30746883},"labels":[],"label_agreement":null},{"id":"W2040441251","doi":"10.1109/acc.2010.5530897","title":"Spiral Bacterial Foraging Optimization method","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Convergence (economics); Gradient descent; Computer science; Jump; Swarm behaviour; Algorithm; Line search; Mathematics; Artificial neural network; Artificial intelligence; Path (computing)","score_opus":0.021410444842588076,"score_gpt":0.32123055069924444,"score_spread":0.29982010585665636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040441251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020501623,0.0000014391644,0.9844349,0.0008314624,0.0011336495,0.00014476123,8.572528e-7,0.00023213563,0.01301577],"genre_scores_gemma":[0.0036311888,0.0000016980508,0.9946439,0.00019213108,0.00018619465,0.000011560045,0.0000066922685,0.000010210003,0.0013163962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874437,0.000095131625,0.00021048209,0.00033457804,0.00034925484,0.0002662036],"domain_scores_gemma":[0.9989502,0.000116465264,0.00005383452,0.0005461172,0.00019528632,0.0001381398],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00079365954,0.000098965174,0.00011600763,0.00016162213,0.00011575397,0.00042242752,0.0007778506,0.00006268733,0.0020065752],"category_scores_gemma":[0.0003131544,0.00008684369,0.000038912734,0.00045655257,0.000025173109,0.0006316297,0.00029090268,0.00020495165,0.00009689486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016620923,0.0001915547,0.0002867155,0.000023105082,0.0000412107,0.000030187433,0.00042889436,0.22795075,0.017251985,0.35986206,0.004700748,0.38921615],"study_design_scores_gemma":[0.00025150517,0.000017855271,0.00005869836,9.913023e-7,0.0000016649884,0.000015573418,0.0000030784024,0.99143463,0.0034624492,0.00051530317,0.004127426,0.000110823676],"about_ca_topic_score_codex":0.00001771794,"about_ca_topic_score_gemma":0.0000037159627,"teacher_disagreement_score":0.7634839,"about_ca_system_score_codex":0.000011795839,"about_ca_system_score_gemma":0.00009088912,"threshold_uncertainty_score":0.9989057},"labels":[],"label_agreement":null},{"id":"W2040927350","doi":"10.1162/evco_a_00023","title":"On the Behaviour of Evolution Strategies Optimising Cigar Functions","year":2010,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Eigenvalues and eigenvectors; Quadratic equation; Mathematics; Regular polygon; Applied mathematics; Mathematical optimization; Adaptation (eye); Multiplicity (mathematics); Convex function; Computer science; Mathematical analysis; Geometry; Physics","score_opus":0.017658227654193327,"score_gpt":0.2767643156877009,"score_spread":0.25910608803350754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040927350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033350863,0.000026684584,0.9607675,0.0012287694,0.0008250314,0.00027082677,0.000010357032,0.00013352261,0.0033864542],"genre_scores_gemma":[0.86368495,0.0000017185649,0.13596159,0.000037742622,0.0000737549,0.000018281136,0.000028542907,0.000009086814,0.00018433838],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982811,0.00018781536,0.00033616915,0.0003159392,0.0006673454,0.00021161504],"domain_scores_gemma":[0.9982231,0.00054400717,0.00017760786,0.00044917944,0.0005361029,0.00006996507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005575513,0.00013048066,0.00012064014,0.0002619702,0.00040337146,0.00013635986,0.0005240969,0.00007710917,0.00013453086],"category_scores_gemma":[0.00027919313,0.00010603183,0.00007161181,0.0007928252,0.00015718737,0.0006191915,0.00012378008,0.00036643303,0.000091291775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010142732,0.00017288163,0.00021907566,0.0000062925287,0.000017418104,0.000002133721,0.00015160946,0.3309491,0.0014316052,0.6604006,0.004418184,0.002220898],"study_design_scores_gemma":[0.00018435685,0.000097085096,0.029895002,0.00001134282,0.000007143865,0.000018762938,0.00009682087,0.9304712,0.00013201518,0.038901,0.000072178285,0.00011312146],"about_ca_topic_score_codex":0.00004364574,"about_ca_topic_score_gemma":0.000005495025,"teacher_disagreement_score":0.83033407,"about_ca_system_score_codex":0.00007603343,"about_ca_system_score_gemma":0.00034091243,"threshold_uncertainty_score":0.43238527},"labels":[],"label_agreement":null},{"id":"W2043879431","doi":"10.1109/cec.2014.6900579","title":"Identifying and exploiting the scale of a search space in differential evolution","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Computer science; Scale (ratio); Scale space; Space (punctuation); Artificial intelligence; Physics","score_opus":0.02722809374943415,"score_gpt":0.2845009929673625,"score_spread":0.25727289921792834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043879431","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":[0.115989216,0.000019081132,0.88179606,0.0004684455,0.000049249975,0.0000969289,1.6062022e-7,0.000018430463,0.001562396],"genre_scores_gemma":[0.9258302,0.0000072512544,0.07379956,0.000009167703,0.000020914556,0.000005205647,2.5563713e-7,0.0000032018183,0.00032427805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988648,0.00024407299,0.0001628454,0.00018808254,0.0003667791,0.0001734079],"domain_scores_gemma":[0.99934727,0.00022986537,0.00003340054,0.0002655038,0.00008164242,0.000042337317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090122473,0.000049934417,0.00009215834,0.00014141353,0.000073404044,0.000115724324,0.00036893174,0.000021363192,0.000029857387],"category_scores_gemma":[0.00016565635,0.00003494913,0.00001601665,0.00039546174,0.00006750269,0.00020052139,0.00038227724,0.00010812837,0.000006498646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000176023,0.00019353772,0.03564314,0.00019757135,0.00002267561,0.0000034987852,0.007248184,0.0033726671,0.017281339,0.8026744,0.00017735883,0.13316803],"study_design_scores_gemma":[0.00020788786,0.000015982481,0.030987985,0.000015719568,9.174124e-7,0.0000025143888,0.00017713045,0.96432567,0.002186431,0.0020283288,0.000009467752,0.00004195808],"about_ca_topic_score_codex":0.0001919415,"about_ca_topic_score_gemma":0.000032618656,"teacher_disagreement_score":0.960953,"about_ca_system_score_codex":0.000019267929,"about_ca_system_score_gemma":0.000023936853,"threshold_uncertainty_score":0.14251842},"labels":[],"label_agreement":null},{"id":"W2044079590","doi":"10.1109/ipdpsw.2014.137","title":"DisSLib: CC: A Library for Distributed Search with a Central Common Search State","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; State (computer science); Distributed computing; Interdependence; Search algorithm; Theoretical computer science; Programming language","score_opus":0.020136917053035587,"score_gpt":0.2747589418842488,"score_spread":0.2546220248312132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044079590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038441778,0.00001175681,0.9891571,0.0037252815,0.00007002945,0.00074223505,0.00009186747,0.00045561232,0.0019019676],"genre_scores_gemma":[0.34743035,0.000024196164,0.644014,0.00039691725,0.000108906446,0.00010993961,0.00030414484,0.0000705169,0.0075410586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700934,0.00033847027,0.00028861387,0.0005813242,0.00082310836,0.00095915643],"domain_scores_gemma":[0.99780345,0.0006197008,0.000040969466,0.0008277181,0.0002041044,0.000504069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006471173,0.0001930042,0.00026995878,0.00017689346,0.00023043693,0.0007677648,0.0014115417,0.00005011197,0.00012149034],"category_scores_gemma":[0.00008419484,0.00014252022,0.00006281161,0.00096466905,0.00013105197,0.00084046763,0.000558575,0.00025229112,0.000060104925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008699768,0.004110824,0.020102452,0.0006495455,0.00036555182,0.00012597373,0.0027034362,0.042342614,0.0001529053,0.48343518,0.06461131,0.38053024],"study_design_scores_gemma":[0.002261921,0.00037757758,0.0023645388,0.000013377924,0.0000035235842,0.000010285989,0.000024555202,0.9825694,0.0020694134,0.0011343658,0.008947634,0.0002233839],"about_ca_topic_score_codex":0.000043221666,"about_ca_topic_score_gemma":0.000009481252,"teacher_disagreement_score":0.9402268,"about_ca_system_score_codex":0.000039238195,"about_ca_system_score_gemma":0.00027998514,"threshold_uncertainty_score":0.7403574},"labels":[],"label_agreement":null},{"id":"W2044308622","doi":"10.5220/0005051801700175","title":"A Noise Resilient and Non-parametric Graph-based Classifier","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Parametric statistics; Graph; Algorithm; Theoretical computer science; Mathematics","score_opus":0.01661371420603852,"score_gpt":0.26492047686665515,"score_spread":0.24830676266061663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044308622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026396953,0.000021561933,0.98248726,0.0010779104,0.00012466437,0.00016970394,5.319158e-7,0.00009483747,0.013383824],"genre_scores_gemma":[0.43801844,0.000010837036,0.55921763,0.000637493,0.000026185038,0.000019716466,0.0000013159244,0.000008500001,0.002059867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848586,0.0001325576,0.000196077,0.00042184105,0.0004883845,0.0002752758],"domain_scores_gemma":[0.9985393,0.00038067086,0.00004593545,0.00064961525,0.00015677021,0.00022766009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006979468,0.000108512104,0.00014434975,0.00052581634,0.00010081273,0.00025132467,0.00053311355,0.000049854472,0.000091886875],"category_scores_gemma":[0.0004979977,0.00008748935,0.00003639783,0.0014081858,0.000078253666,0.00016194108,0.000185711,0.000117320604,0.00011361382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007531712,0.0011496877,0.018606579,0.00024815966,0.0000969907,0.00006059661,0.0003822283,0.0408298,0.0013226718,0.31765983,0.05884114,0.560727],"study_design_scores_gemma":[0.00045466557,0.00008802419,0.0070241042,0.0000044557382,0.0000023019672,0.0000022401175,0.0000024614512,0.9874809,0.00069013797,0.0009916039,0.0031414444,0.00011765451],"about_ca_topic_score_codex":0.000023855126,"about_ca_topic_score_gemma":0.0000025805152,"teacher_disagreement_score":0.9466511,"about_ca_system_score_codex":0.00001719235,"about_ca_system_score_gemma":0.00006655258,"threshold_uncertainty_score":0.35677123},"labels":[],"label_agreement":null},{"id":"W2045050140","doi":"10.1016/j.ins.2014.10.042","title":"Metaheuristics in large-scale global continues optimization: A survey","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":470,"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 Ontario Institute of Technology","funders":"","keywords":"Metaheuristic; Computer science; Parallel metaheuristic; Decomposition; Engineering optimization; Search-based software engineering; Scale (ratio); Field (mathematics); Mathematical optimization; Optimization problem; Global optimization; Management science; Industrial engineering; Artificial intelligence; Algorithm; Meta-optimization; Engineering; Mathematics; Software; Software development","score_opus":0.027505616035827466,"score_gpt":0.31277591197388566,"score_spread":0.2852702959380582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045050140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006783865,0.000017731552,0.97165173,0.00048159185,0.00034613503,0.0001684483,0.000021630482,0.00009706669,0.026537297],"genre_scores_gemma":[0.26451012,0.000031115902,0.7342972,0.0009069897,0.00003953809,0.00002425206,0.000043867,0.0000035169771,0.0001433909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744993,0.00036990258,0.000584883,0.00024280208,0.00095884956,0.0003936152],"domain_scores_gemma":[0.9985133,0.0003269617,0.00020798488,0.00035922558,0.00047171928,0.000120830184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005475319,0.00012296031,0.00019628245,0.00029864887,0.00023795737,0.00095192634,0.0013083952,0.000057697125,0.00008106792],"category_scores_gemma":[0.0023049654,0.00010777167,0.00003162055,0.0027952518,0.00016087473,0.0034025428,0.00028305192,0.00009614342,0.00017812697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008489389,0.00008995253,0.09182621,0.00002216194,0.000007751894,0.0000012514896,0.0016245018,0.80866915,4.6262483e-7,0.0630297,0.0024279323,0.03229241],"study_design_scores_gemma":[0.0003273621,0.000040111827,0.039818816,0.0000066793273,9.558958e-7,0.0000044147077,0.00006669688,0.95414716,0.000008766847,0.0006507827,0.004801141,0.0001271125],"about_ca_topic_score_codex":0.00011316205,"about_ca_topic_score_gemma":0.00012835977,"teacher_disagreement_score":0.26383173,"about_ca_system_score_codex":0.00005422459,"about_ca_system_score_gemma":0.00019383071,"threshold_uncertainty_score":0.9179448},"labels":[],"label_agreement":null},{"id":"W2046858085","doi":"10.1007/s12293-014-0141-y","title":"Incorporating domain-specific heuristics in a particle swarm optimization approach to the quadratic assignment problem","year":2014,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Brandon University","funders":"","keywords":"Heuristics; Particle swarm optimization; Complex system; Domain (mathematical analysis); Quadratic equation; Mathematical optimization; Computer science; Multi-swarm optimization; Metaheuristic; Quadratic assignment problem; Algorithm; Mathematics; Optimization problem; Artificial intelligence","score_opus":0.025472997722234998,"score_gpt":0.25568643560058646,"score_spread":0.23021343787835147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046858085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028031527,0.00004457054,0.99083436,0.001787414,0.00021235911,0.0008964298,5.1476246e-7,0.0001317948,0.0032894113],"genre_scores_gemma":[0.35476452,0.0000022953757,0.6448243,0.000198131,0.000100842844,0.000040908977,0.0000031085415,0.000016641477,0.00004922989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961907,0.0008361288,0.0007858461,0.0006899959,0.00089785986,0.0005994412],"domain_scores_gemma":[0.997889,0.000601085,0.00022982294,0.00087752764,0.00020463773,0.0001979464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042939004,0.00022920278,0.0003045425,0.00020313535,0.00038633813,0.00060583756,0.0012655419,0.000053089247,0.000010472081],"category_scores_gemma":[0.00037784787,0.00018327117,0.000048709,0.0020169166,0.00006310354,0.00015375832,0.0007589503,0.00030475983,0.000045469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002751042,0.00013712062,0.00022773174,0.000023848255,0.000006071296,0.0000025252916,0.001668836,0.928384,0.000039120583,0.05089838,0.00015353046,0.01845608],"study_design_scores_gemma":[0.00043450104,0.00007823852,0.00029112148,0.000042315263,0.000003275864,0.000009421089,0.00016164084,0.99638295,0.00012603053,0.0011412351,0.0011112429,0.00021801204],"about_ca_topic_score_codex":0.00001892478,"about_ca_topic_score_gemma":0.0000037448299,"teacher_disagreement_score":0.35196137,"about_ca_system_score_codex":0.00016279065,"about_ca_system_score_gemma":0.000083474704,"threshold_uncertainty_score":0.74735814},"labels":[],"label_agreement":null},{"id":"W2048558571","doi":"10.1109/cec.2010.5585962","title":"Bottom-up evolutionary subspace clustering","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Cluster analysis; Subspace topology; Computer science; Data mining; Correlation clustering; Genetic algorithm; Population; Clustering high-dimensional data; Evolutionary computation; Theoretical computer science; Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.018694831569438124,"score_gpt":0.2842777595558827,"score_spread":0.26558292798644456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048558571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008083337,0.00001576913,0.9702696,0.002258549,0.0012470214,0.000112534544,6.908424e-7,0.00026387395,0.02502361],"genre_scores_gemma":[0.08053166,0.000008594204,0.88883847,0.0002101631,0.00011724314,0.000013403449,0.0000019897045,0.000009568762,0.03026889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987688,0.000047689464,0.00015285525,0.00031214158,0.0004359896,0.00028253355],"domain_scores_gemma":[0.99884444,0.00011117624,0.000032658692,0.00066823704,0.00017761896,0.00016588278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037174148,0.000088693334,0.00008948019,0.00013639152,0.00013141139,0.00017592243,0.0008989491,0.00005990331,0.0008394736],"category_scores_gemma":[0.00023074113,0.00007942606,0.00003494332,0.00044885417,0.000052023246,0.0004245819,0.0004681523,0.00032278462,0.00056386326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013585072,0.00021710695,0.0019668066,0.000045164972,0.000044976485,0.000075649674,0.0006910211,0.0030275772,0.016267754,0.7899423,0.08061458,0.10709344],"study_design_scores_gemma":[0.0001698685,0.000013908954,0.0014893909,0.0000016524003,8.7668985e-7,0.00003746643,0.000007946136,0.9852525,0.0007144892,0.00066674955,0.0115314955,0.000113643204],"about_ca_topic_score_codex":0.000034112414,"about_ca_topic_score_gemma":0.00002342254,"teacher_disagreement_score":0.98222494,"about_ca_system_score_codex":0.000018511128,"about_ca_system_score_gemma":0.000114766895,"threshold_uncertainty_score":0.9191648},"labels":[],"label_agreement":null},{"id":"W2051542317","doi":"10.5430/air.v1n2p149","title":"Fuzzy adaptive catfish particle swarm optimization","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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":"National Science Council","keywords":"Catfish; Particle swarm optimization; Benchmark (surveying); Fuzzy logic; Inertia; Swarm intelligence; Multi-swarm optimization; Swarm behaviour; Mathematical optimization; Computer science; Artificial intelligence; Mathematics; Fish <Actinopterygii>; Biology; Physics; Fishery; Geography","score_opus":0.28251493352013757,"score_gpt":0.4290115289024241,"score_spread":0.14649659538228654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051542317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020700018,0.00028061288,0.98613596,0.0017158815,0.00046456986,0.00052684435,0.0000027132505,0.00018090288,0.008622503],"genre_scores_gemma":[0.80557126,0.00012751635,0.19308552,0.000077654884,0.00037220362,0.00011173865,0.000005214244,0.000025935487,0.0006229524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99444187,0.0009488988,0.00056388567,0.00058621756,0.0018607943,0.0015983232],"domain_scores_gemma":[0.99605674,0.0009316,0.00007854676,0.0009832084,0.0012876769,0.00066224707],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005855088,0.00018674134,0.00021360262,0.00041347812,0.00061022124,0.0005455503,0.0015573711,0.00012410995,0.00040404144],"category_scores_gemma":[0.0019039087,0.00018054555,0.00007516125,0.0032842318,0.00037255362,0.0015351452,0.00076456653,0.000626208,0.002845183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037769634,0.00058871077,0.00027300345,0.000012045108,0.000027643988,0.000014721311,0.0026318699,0.096930444,0.00043360385,0.7367186,0.0009123731,0.16141921],"study_design_scores_gemma":[0.000030503406,0.0001475468,0.00006519952,0.000011740705,0.000003117517,0.000009606389,0.0006991589,0.9244534,0.05415109,0.01961513,0.00058904546,0.00022448212],"about_ca_topic_score_codex":0.00016954982,"about_ca_topic_score_gemma":0.000013408503,"teacher_disagreement_score":0.82752293,"about_ca_system_score_codex":0.0002076332,"about_ca_system_score_gemma":0.00026492836,"threshold_uncertainty_score":0.99793124},"labels":[],"label_agreement":null},{"id":"W2051644915","doi":"10.1109/ccece.2014.6901103","title":"Simulated Raindrop algorithm for global optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Robustness (evolution); Simulated annealing; Benchmark (surveying); Computer science; Algorithm; Convergence (economics); Mathematical optimization; Mathematics; Geology","score_opus":0.017936386682110447,"score_gpt":0.2984521404160593,"score_spread":0.2805157537339488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051644915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000034910627,0.000008987665,0.98936915,0.0006693736,0.00029656925,0.00038981138,0.000005210568,0.00030763904,0.008949735],"genre_scores_gemma":[0.0019279373,0.0000040806776,0.9954784,0.00040321125,0.000081691585,0.000015849997,0.000023046689,0.000010017467,0.0020557719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985026,0.00011400628,0.0002554045,0.00041194988,0.00037973459,0.00033631708],"domain_scores_gemma":[0.99859935,0.00023178504,0.00006445257,0.0004954212,0.00044651618,0.00016246318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061424886,0.00012532972,0.00015945375,0.00007887713,0.0001410922,0.00026478554,0.00073074875,0.00007401259,0.00013227954],"category_scores_gemma":[0.00047063056,0.000112547204,0.00005935205,0.0006684518,0.00003279649,0.00032248592,0.00017099423,0.000053922544,0.000057026948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016595578,0.00003734848,0.0000096754375,0.00000431406,0.000011318,7.185245e-7,0.000012335836,0.6087668,0.0000016208044,0.042525902,0.0010487669,0.3475795],"study_design_scores_gemma":[0.0007100073,0.000083679755,0.00001831008,0.0000025348918,0.0000033427432,0.000004902054,0.000002078768,0.9909188,0.00007730539,0.0025849717,0.005453563,0.00014051014],"about_ca_topic_score_codex":0.0000090876165,"about_ca_topic_score_gemma":6.000583e-7,"teacher_disagreement_score":0.38215196,"about_ca_system_score_codex":0.000059291066,"about_ca_system_score_gemma":0.00007313657,"threshold_uncertainty_score":0.4589542},"labels":[],"label_agreement":null},{"id":"W2052526715","doi":"10.1109/ccece.2010.5575132","title":"A study of optimal topologies in swarm intelligence","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Network topology; Particle swarm optimization; Mathematical optimization; Swarm behaviour; Convergence (economics); Computer science; Multi-swarm optimization; Swarm intelligence; Metaheuristic; Torus; Mathematics","score_opus":0.04576576221345303,"score_gpt":0.35271652086210137,"score_spread":0.30695075864864835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052526715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23036556,0.000003491955,0.76578194,0.00017163031,0.0001695062,0.00020089283,1.7551855e-7,0.00004549714,0.0032613059],"genre_scores_gemma":[0.7116216,0.000002704364,0.28806233,0.00001178725,0.0000064891497,0.000010833175,9.1216876e-8,0.0000020191014,0.00028218157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897015,0.00006614966,0.00025753072,0.00023778205,0.00030008098,0.000168314],"domain_scores_gemma":[0.9990847,0.0001878798,0.000041571544,0.0005194258,0.000121160745,0.000045273016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005471351,0.000063619314,0.0001275976,0.00021619558,0.000022462817,0.000050669063,0.0010561235,0.00003248663,0.00015172752],"category_scores_gemma":[0.00045656902,0.0000514786,0.000015195952,0.0006386721,0.00006326982,0.00018144383,0.00046449108,0.0002039678,0.000026873644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034153894,0.0056119156,0.032625884,0.000036268648,0.00005044579,0.0002321349,0.017669016,0.083601214,0.0016232791,0.6166674,0.0004059712,0.24144235],"study_design_scores_gemma":[0.00038505808,0.00054777006,0.008555248,0.0000035115643,0.0000016684313,0.0000120144505,0.0029799894,0.9700721,0.014824675,0.0023502072,0.00008934939,0.00017842725],"about_ca_topic_score_codex":0.00017088804,"about_ca_topic_score_gemma":0.00015352722,"teacher_disagreement_score":0.88647085,"about_ca_system_score_codex":0.0000068582444,"about_ca_system_score_gemma":0.000056970723,"threshold_uncertainty_score":0.20992364},"labels":[],"label_agreement":null},{"id":"W2052822043","doi":"10.1109/cec.2014.6900566","title":"TraDE: Training device selection via multi-objective optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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; Sorting; Scheduling (production processes); Genetic algorithm; Training (meteorology); Mutation; Computation; Population; Selection (genetic algorithm); Mathematical optimization; Operator (biology); Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.044943394844312874,"score_gpt":0.30191977756390875,"score_spread":0.2569763827195959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052822043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000039342744,0.0000064559463,0.99061906,0.00050732185,0.00020384116,0.00021957225,3.7498023e-7,0.0003699393,0.008034109],"genre_scores_gemma":[0.109818816,0.000005065263,0.88885313,0.00033477318,0.00007463864,0.000023483419,0.0000046960035,0.0000149325515,0.00087044167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825186,0.00029102256,0.000258271,0.00045938487,0.00041696764,0.00032248662],"domain_scores_gemma":[0.9990906,0.0002012374,0.000084182044,0.00029033012,0.000187022,0.00014662876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075201003,0.00013969086,0.00016858002,0.00022640183,0.00020877585,0.0002160019,0.0003933751,0.00007735872,0.00019575312],"category_scores_gemma":[0.00040172532,0.00013176363,0.00004360879,0.0010561385,0.00003191562,0.00062474085,0.000088938585,0.00017157338,0.00006261105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004388594,0.000106103835,0.00008257755,0.0000109431085,0.000025976882,0.0000012095635,0.0014381807,0.7837569,0.00024929395,0.009583665,0.00023539925,0.20450534],"study_design_scores_gemma":[0.0004273043,0.000069846836,0.00052100763,0.000004266138,0.0000039259653,0.000023232718,0.000035621222,0.99691886,0.0009048877,0.000113453534,0.00081819127,0.00015940411],"about_ca_topic_score_codex":0.000033038184,"about_ca_topic_score_gemma":0.000011567137,"teacher_disagreement_score":0.21316195,"about_ca_system_score_codex":0.00007126936,"about_ca_system_score_gemma":0.00008623407,"threshold_uncertainty_score":0.5373165},"labels":[],"label_agreement":null},{"id":"W2053454544","doi":"10.1007/s00500-014-1549-5","title":"Gaussian bare-bones artificial bee colony algorithm","year":2014,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":83,"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":"Artificial bee colony algorithm; Benchmark (surveying); Mathematical optimization; Computer science; Evolutionary algorithm; Convergence (economics); Algorithm; Gaussian; Evolutionary computation; Inefficiency; Set (abstract data type); Artificial intelligence; Mathematics","score_opus":0.01978669454969349,"score_gpt":0.2815864520648099,"score_spread":0.2617997575151164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053454544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009772249,0.00004070707,0.99303854,0.0016814921,0.0007960575,0.00019638259,0.0000020927014,0.00045767918,0.0028098305],"genre_scores_gemma":[0.19515045,0.0000027664325,0.8031446,0.0005999882,0.00072764815,0.000006629431,0.0000070668398,0.000028705696,0.00033218236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971748,0.00033033962,0.00047904634,0.0006501312,0.00071440305,0.00065130886],"domain_scores_gemma":[0.9982045,0.0005965804,0.00010922605,0.0005727539,0.00026349025,0.0002534445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014681884,0.00021240205,0.00029764036,0.00023396875,0.0004805364,0.0005189506,0.0012283335,0.00013553,0.00006900125],"category_scores_gemma":[0.00068920356,0.00021207093,0.00008689855,0.00082494825,0.00009918514,0.00025365726,0.00070129323,0.000426992,0.00038780834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027011179,0.000085739965,0.00020436227,0.000021169244,0.00002243943,0.000022232858,0.00048569098,0.007380506,0.00026704412,0.039879214,0.002039723,0.9495892],"study_design_scores_gemma":[0.00021167973,0.000070149326,0.0006726736,0.000021158097,0.000004203979,0.000020668154,0.000018420247,0.98790014,0.0006311263,0.0033809955,0.0068233614,0.00024542428],"about_ca_topic_score_codex":0.000031513828,"about_ca_topic_score_gemma":0.0000032252751,"teacher_disagreement_score":0.98051965,"about_ca_system_score_codex":0.000053964253,"about_ca_system_score_gemma":0.00011984466,"threshold_uncertainty_score":0.86480016},"labels":[],"label_agreement":null},{"id":"W2053463274","doi":"10.3166/ria.22.63-85","title":"Résolution du problème de la patrouille multi-agent en utilisant des colonies compétitives de fourmis","year":2008,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Metaheuristic Optimization Algorithms Research","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":"Humanities; Biology; Philosophy","score_opus":0.10273056392287812,"score_gpt":0.33222610198921454,"score_spread":0.22949553806633644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053463274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024290433,0.0044579147,0.96475536,0.002875587,0.0006787855,0.000624327,0.000028597393,0.00015586121,0.0021331448],"genre_scores_gemma":[0.609468,0.01129626,0.36092082,0.00013129745,0.00026384217,0.000097853415,0.000009296969,0.0000480967,0.017764553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99461234,0.002070824,0.00079227757,0.0008138248,0.00049726444,0.0012134876],"domain_scores_gemma":[0.99626744,0.001670615,0.00023018768,0.00074621086,0.0006174111,0.00046810918],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028975445,0.00037983872,0.00043383846,0.0002507304,0.0010443185,0.0003653218,0.0011740631,0.0002952471,0.00086603867],"category_scores_gemma":[0.0025388903,0.00042138458,0.00022581399,0.0010564014,0.0018957331,0.00058381,0.00055691146,0.0006063326,0.00084236794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006725045,0.0028463933,0.0074616703,0.0005812632,0.00018447488,0.0017047743,0.08836375,0.6994049,0.0022307832,0.07385327,0.0044079428,0.11889355],"study_design_scores_gemma":[0.00014778996,0.00023884088,0.0021169554,0.00023520904,0.000027246082,0.0014526355,0.0012473156,0.95429933,0.016020736,0.0019120509,0.021900317,0.00040154753],"about_ca_topic_score_codex":0.00072508486,"about_ca_topic_score_gemma":0.000051979867,"teacher_disagreement_score":0.60383457,"about_ca_system_score_codex":0.0008997167,"about_ca_system_score_gemma":0.00093852077,"threshold_uncertainty_score":0.99993557},"labels":[],"label_agreement":null},{"id":"W2055948327","doi":"10.1155/2012/793196","title":"An Empirical Investigation on System and Statement Level Parallelism Strategies for Accelerating Scatter Search Using Handel‐C and Impulse‐C","year":2012,"lang":"en","type":"article","venue":"VLSI design","topic":"Metaheuristic Optimization Algorithms Research","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; Parallelism (grammar); Parallel computing; Field-programmable gate array; Computer engineering; Population; Impulse (physics); Software; Gate array; Algorithm; Computer hardware; Programming language","score_opus":0.39832346553150794,"score_gpt":0.4102852528555367,"score_spread":0.01196178732402875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055948327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08134893,0.00005949808,0.91754156,0.0002694024,0.00008286371,0.0006013449,0.000004737698,0.000056355148,0.000035319812],"genre_scores_gemma":[0.53328335,0.0000041128965,0.46647173,0.000106607964,0.00006922574,0.000033188655,0.000003924362,0.000010119902,0.000017699445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980997,0.00046481314,0.00028442527,0.00035624378,0.0003884788,0.0004063404],"domain_scores_gemma":[0.9989257,0.0003037123,0.00007012359,0.00026468872,0.00016175001,0.00027401902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019973253,0.00014580307,0.00016446022,0.00016699446,0.0003231737,0.0008369304,0.00022120777,0.00006191187,0.0000044632548],"category_scores_gemma":[0.000036751037,0.00012505024,0.000015389041,0.00019034655,0.00006913127,0.0011619478,0.00009567773,0.00010928257,0.0000034980267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032161354,0.00091820926,0.04001627,0.0024726174,0.0004057348,0.000046245914,0.0611776,0.39321676,0.07456576,0.18200013,0.005516213,0.23934285],"study_design_scores_gemma":[0.00041837984,0.00019201725,0.0026545941,0.00003355757,0.0000066060775,0.000014474037,0.00055792823,0.9935631,0.0020773131,0.00031098246,0.00001997421,0.00015107695],"about_ca_topic_score_codex":0.000026213746,"about_ca_topic_score_gemma":7.3708344e-7,"teacher_disagreement_score":0.6003463,"about_ca_system_score_codex":0.000078356854,"about_ca_system_score_gemma":0.00017157853,"threshold_uncertainty_score":0.8070539},"labels":[],"label_agreement":null},{"id":"W2056005830","doi":"10.1109/sis.2014.7011769","title":"A social-spider optimization approach for support vector machines parameters tuning","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Western University","funders":"","keywords":"Support vector machine; Computer science; sort; Kernel (algebra); Particle swarm optimization; Hyperparameter optimization; Task (project management); Machine learning; Grid; Artificial intelligence; Optimization algorithm; Mathematical optimization; Mathematics; Engineering; Database","score_opus":0.04564684391735774,"score_gpt":0.30988012263347364,"score_spread":0.2642332787161159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056005830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001611869,0.0000039885713,0.9865134,0.0011542459,0.00012567404,0.00042828813,0.000002106972,0.00022785312,0.011528367],"genre_scores_gemma":[0.009947668,0.0000018064161,0.9869951,0.0004856751,0.000092601906,0.000120669036,0.000047206544,0.000021525066,0.0022877771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832445,0.00016303442,0.00029010212,0.00046301517,0.00039365722,0.00036575526],"domain_scores_gemma":[0.99888206,0.00027281558,0.00009903884,0.00037115434,0.00025522304,0.000119687706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009929705,0.00014966208,0.00021694181,0.00017367543,0.00025930442,0.00033994394,0.0006906536,0.000071933544,0.00010311055],"category_scores_gemma":[0.00056008657,0.00013102085,0.000087651606,0.00045407328,0.000049248018,0.0003796727,0.00017160803,0.00009453575,0.000013028077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014163052,0.00020798483,0.00010098194,0.000082412575,0.000054450124,9.30569e-7,0.0005307832,0.79094124,0.000051511433,0.13827492,0.009648643,0.060092],"study_design_scores_gemma":[0.00040900827,0.00008350472,0.000037081954,0.0000013244651,0.00000618737,0.0000036042532,0.000010802906,0.9979426,0.000084778854,0.00049034023,0.0007637861,0.00016695107],"about_ca_topic_score_codex":0.000013782602,"about_ca_topic_score_gemma":4.858127e-7,"teacher_disagreement_score":0.20700142,"about_ca_system_score_codex":0.00003461016,"about_ca_system_score_gemma":0.000065601824,"threshold_uncertainty_score":0.5342875},"labels":[],"label_agreement":null},{"id":"W2056678525","doi":"10.1016/j.ins.2006.09.016","title":"Exchange strategies for multiple Ant Colony System","year":2006,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":140,"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":"Ant colony optimization algorithms; Travelling salesman problem; Computer science; Weighting; Ant colony; Scheme (mathematics); Local search (optimization); Metaheuristic; Mathematical optimization; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.03510963660855574,"score_gpt":0.2952731545360752,"score_spread":0.26016351792751946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056678525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009846078,0.000030491772,0.9768523,0.00036835572,0.00037587935,0.0004028135,0.000015107016,0.00018573679,0.020784738],"genre_scores_gemma":[0.6845203,0.0000042723527,0.31481302,0.00014775392,0.00008863077,0.00014670701,0.000016023603,0.0000026865087,0.0002606007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985726,0.000038932692,0.00035202573,0.00015061362,0.0006143715,0.00027149628],"domain_scores_gemma":[0.9989898,0.0002458442,0.0001639905,0.00020163633,0.0003480836,0.000050626644],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012005682,0.0000812619,0.0001022515,0.00028139664,0.00042036388,0.0013486805,0.00082574144,0.00003476409,0.00001261279],"category_scores_gemma":[0.00014040877,0.00006530309,0.00003327431,0.0007919882,0.00012628282,0.0046743695,0.00010425605,0.00003799005,0.000102068174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008179451,0.00003833132,0.00035291308,0.00024621215,0.0000063877796,0.0000015261683,0.0018948024,0.06508563,0.00008398008,0.87959975,0.017808262,0.03487401],"study_design_scores_gemma":[0.0002517947,0.000058653568,0.00042403516,0.000009040162,9.093378e-7,0.000006572578,0.0007859551,0.9729049,0.00021437957,0.0010054365,0.024247125,0.000091175934],"about_ca_topic_score_codex":0.00017318006,"about_ca_topic_score_gemma":0.000019616149,"teacher_disagreement_score":0.9078193,"about_ca_system_score_codex":0.000051651463,"about_ca_system_score_gemma":0.00026524425,"threshold_uncertainty_score":0.999688},"labels":[],"label_agreement":null},{"id":"W2057069530","doi":"10.1155/2012/186481","title":"Stabilizing of Subspaces Based on DPGA and Chaos Genetic Algorithm for Optimizing State Feedback Controller","year":2012,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Hermite polynomials; Linear subspace; Control theory (sociology); Convergence (economics); Controller (irrigation); Algorithm; State (computer science); CHAOS (operating system); Genetic algorithm; Mathematical optimization; Population; Full state feedback; Domain (mathematical analysis); Mathematics; Computer science; Local optimum; Control (management); Artificial intelligence","score_opus":0.019537700497482366,"score_gpt":0.24975011349248302,"score_spread":0.23021241299500067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057069530","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091252144,0.00020986407,0.9978166,0.00008045454,0.00006887033,0.00075811235,0.000004421167,0.00006217818,0.000086927044],"genre_scores_gemma":[0.2137622,0.000011874295,0.78602153,0.000012649595,0.00001947867,0.00012911699,6.490285e-7,0.000022985168,0.000019541432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841297,0.000045050456,0.00045595187,0.00024947582,0.0003420869,0.0004944868],"domain_scores_gemma":[0.9984643,0.00095137896,0.00008079322,0.0002696706,0.00007782659,0.00015601495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011135427,0.00017597541,0.00036015667,0.00026302505,0.000033271728,0.000071787064,0.00028319503,0.00005430542,0.000011824757],"category_scores_gemma":[0.000422914,0.0001565577,0.0000460272,0.00030488955,0.000038113227,0.0002047941,0.00010616165,0.00014867412,0.0000027832477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003489836,0.00015803523,0.000069362315,0.0007447931,0.000016161126,9.1490847e-7,0.0006700004,0.9737899,0.0004820296,0.0041970066,0.0000047111384,0.019863628],"study_design_scores_gemma":[0.0008478719,0.000074147116,0.00009025778,0.00018500026,0.000004619509,0.0000029077673,0.000015514075,0.9961349,0.0007671544,0.0016981787,0.000022090006,0.00015736729],"about_ca_topic_score_codex":0.000001947296,"about_ca_topic_score_gemma":1.2009382e-7,"teacher_disagreement_score":0.21284968,"about_ca_system_score_codex":0.000050220737,"about_ca_system_score_gemma":0.00002244569,"threshold_uncertainty_score":0.63842374},"labels":[],"label_agreement":null},{"id":"W2058294560","doi":"10.1145/568438.568443","title":"Review of <b>How to Solve It: Modern Heuristics</b>","year":2001,"lang":"en","type":"article","venue":"ACM SIGACT News","topic":"Metaheuristic Optimization Algorithms Research","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":"Heuristics; Computer science; Heuristic; Key (lock); Fuzzy logic; Presentation (obstetrics); Process (computing); Artificial intelligence; Management science; Programming language","score_opus":0.06339484273346335,"score_gpt":0.34172868629628994,"score_spread":0.2783338435628266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058294560","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043203203,0.0032086466,0.9593099,0.0320554,0.00021923464,0.0004556431,0.000005896361,0.00008059211,0.00462148],"genre_scores_gemma":[0.0038483879,0.024724793,0.95143193,0.016123518,0.00019082312,0.000057611538,0.000011587609,0.00003838111,0.0035729383],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978288,0.00011078616,0.0004394979,0.00048816454,0.0007148282,0.0004179494],"domain_scores_gemma":[0.9965389,0.00037992472,0.00016230578,0.0021230506,0.000479267,0.00031659674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006801263,0.00018240574,0.0003978837,0.00018328626,0.000068900714,0.00012196736,0.0024953051,0.000056579185,0.00026539873],"category_scores_gemma":[0.0051395493,0.00016422283,0.000107298154,0.0010872753,0.000039786344,0.00030351648,0.0009017064,0.0001768438,0.00027898376],"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":[0.000014180671,0.00021315433,0.00021295933,0.0012217655,0.000049690247,0.00009289317,0.0005465127,0.00066957454,0.0007405476,0.004283772,0.38790908,0.60404587],"study_design_scores_gemma":[0.0007383928,0.00037446863,0.0002550153,0.0025030526,0.000039398878,0.00008922661,0.000030032405,0.22912137,0.0017323559,0.005582829,0.75878304,0.00075079454],"about_ca_topic_score_codex":0.000042439828,"about_ca_topic_score_gemma":0.000009348039,"teacher_disagreement_score":0.6032951,"about_ca_system_score_codex":0.000047292284,"about_ca_system_score_gemma":0.00017469829,"threshold_uncertainty_score":0.6696813},"labels":[],"label_agreement":null},{"id":"W2058621231","doi":"10.1504/ijmmno.2014.065404","title":"Coupling a chaotically encoded firefly algorithm with ranking to a physics-based mathematical model for robust optimisation of a gas turbine energy system","year":2014,"lang":"en","type":"article","venue":"International Journal of Mathematical Modelling and Numerical Optimisation","topic":"Metaheuristic Optimization Algorithms Research","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":"Firefly algorithm; Chaotic; Computer science; Particle swarm optimization; Energy (signal processing); Chaos theory; Algorithm; Firefly protocol; Ranking (information retrieval); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.035475518875267203,"score_gpt":0.2725561610619726,"score_spread":0.2370806421867054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058621231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009969058,0.000013093701,0.99744374,0.0010543711,0.0000798117,0.00027091967,0.0000067052238,0.000045872555,0.00008859844],"genre_scores_gemma":[0.33518556,0.0000053957806,0.6645714,0.00006619035,0.00010335276,0.000025118517,0.0000050306617,0.000021909715,0.000016005395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966181,0.00008783126,0.0011297393,0.00035360077,0.0015266592,0.00028404768],"domain_scores_gemma":[0.99553084,0.0012695796,0.0006283794,0.0002646805,0.0020166207,0.00028987974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015791762,0.00024743835,0.0006124695,0.00029460227,0.000091354676,0.00021295792,0.0006654058,0.000101665995,0.0000079844],"category_scores_gemma":[0.00053374795,0.00019350597,0.00015687752,0.0002778462,0.000077572484,0.0003525044,0.00009017401,0.00020402437,0.0000022097865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017159755,0.00023883978,7.551783e-7,0.00012391152,0.00008332233,0.0000034117165,0.00045726862,0.9283851,0.000096737574,0.053816337,0.0000058767996,0.016616868],"study_design_scores_gemma":[0.0012972318,0.0004267915,4.4596007e-7,0.00053882605,0.00005716545,0.00006116582,0.000039765982,0.977097,0.0009284359,0.019354105,0.0000040994933,0.0001949932],"about_ca_topic_score_codex":0.0000043546543,"about_ca_topic_score_gemma":6.074123e-8,"teacher_disagreement_score":0.33418867,"about_ca_system_score_codex":0.00013757653,"about_ca_system_score_gemma":0.00017325027,"threshold_uncertainty_score":0.78909445},"labels":[],"label_agreement":null},{"id":"W2059176872","doi":"10.1145/1570256.1570315","title":"Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Testbed; Benchmarking; Particle swarm optimization; Computer science; Black box; Benchmark (surveying); Mathematical optimization; Estimation of distribution algorithm; Noise (video); Algorithm; Multi-swarm optimization; Function (biology); Mathematics; Artificial intelligence","score_opus":0.0382141392182351,"score_gpt":0.3030817286101948,"score_spread":0.2648675893919597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059176872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003068841,0.000024364603,0.9951791,0.00028605072,0.00019523969,0.00044183177,0.0000024664707,0.00015574439,0.00064634404],"genre_scores_gemma":[0.11030422,0.000019167204,0.8889641,0.0003230034,0.00016396391,0.000007708344,0.000027137423,0.00001178478,0.00017891305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846435,0.00008747754,0.0002775681,0.00051585987,0.0003405049,0.00031423444],"domain_scores_gemma":[0.99884874,0.00011547923,0.000096260665,0.0003881561,0.00037917946,0.00017217635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005735157,0.00014845195,0.00016770168,0.00022838601,0.00025575224,0.0005014258,0.00032791743,0.000049482405,0.00006155409],"category_scores_gemma":[0.000137021,0.00014308654,0.000032063763,0.0004329839,0.000040753384,0.0010580901,0.0000765255,0.000083613966,0.0000027171905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023466533,0.00013940473,0.00006727317,0.000018829003,0.000012276403,0.0000033211775,0.00012095204,0.8642979,0.0005665632,0.008784526,0.00034381732,0.12562165],"study_design_scores_gemma":[0.00046889164,0.0002698312,0.00020283168,0.000009567804,0.000010706638,0.000013077846,0.000017797729,0.9969967,0.00061745866,0.0010727257,0.0001416943,0.00017874406],"about_ca_topic_score_codex":0.000013082199,"about_ca_topic_score_gemma":0.0000016531197,"teacher_disagreement_score":0.13269874,"about_ca_system_score_codex":0.000048683054,"about_ca_system_score_gemma":0.00008942264,"threshold_uncertainty_score":0.58348995},"labels":[],"label_agreement":null},{"id":"W2059342249","doi":"10.1145/2598394.2609855","title":"An evaluation of particle swarm optimization techniques in segmentation of biomedical images","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Montréal","funders":"","keywords":"Particle swarm optimization; Image segmentation; Artificial intelligence; Segmentation; Otsu's method; Scale-space segmentation; Parametric statistics; Computer science; Pattern recognition (psychology); Pixel; Segmentation-based object categorization; Multi-swarm optimization; Computer vision; Mathematics; Algorithm; Statistics","score_opus":0.03538666463020891,"score_gpt":0.36727561634352845,"score_spread":0.33188895171331956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059342249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014492775,0.00000849574,0.98435336,0.0001783655,0.0000321034,0.00027337452,8.8900225e-7,0.00004642622,0.0006142197],"genre_scores_gemma":[0.50048065,0.000006715007,0.4994561,0.00001163041,0.000006980424,0.000017673006,0.000007945133,0.0000027337646,0.000009589385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979286,0.0005028086,0.00038631787,0.00019472191,0.0008742082,0.00011333078],"domain_scores_gemma":[0.9988443,0.000095354975,0.00012511355,0.00032820596,0.0005509356,0.000056087898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032806848,0.000056741814,0.00012247245,0.00022761831,0.000016982358,0.000027945496,0.00031886235,0.00004014517,0.00009645232],"category_scores_gemma":[0.00044478758,0.00005108459,0.000016266904,0.0007020875,0.00006743483,0.0004794769,0.000055127675,0.000040641782,0.0000020332607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013553096,0.00082067965,0.0029140052,0.00005075775,0.000010776881,4.1833732e-7,0.00060142437,0.45201644,0.057202872,0.013311739,0.00010377684,0.47295356],"study_design_scores_gemma":[0.00027054493,0.000113907096,0.000893628,0.0000069294906,0.0000033989284,3.971014e-7,0.000022160359,0.71854883,0.27974203,0.00035992818,0.0000025788956,0.000035678906],"about_ca_topic_score_codex":0.00004223315,"about_ca_topic_score_gemma":0.0000044551807,"teacher_disagreement_score":0.48598784,"about_ca_system_score_codex":0.000035927325,"about_ca_system_score_gemma":0.000085823354,"threshold_uncertainty_score":0.2083169},"labels":[],"label_agreement":null},{"id":"W2060355705","doi":"10.1504/ijica.2011.039593","title":"Particle swarm optimisation with simple and efficient neighbourhood search strategies","year":2011,"lang":"en","type":"article","venue":"International Journal of Innovative Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Particle swarm optimization; Neighbourhood (mathematics); Computer science; Locality; Mathematical optimization; Benchmark (surveying); Simple (philosophy); Local search (optimization); Swarm behaviour; Algorithm; Artificial intelligence; Mathematics","score_opus":0.038486688120589314,"score_gpt":0.3250332525350805,"score_spread":0.28654656441449117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060355705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.121324755,0.000057503512,0.877114,0.000431649,0.000037823396,0.00011337177,0.0000022292738,0.000018650442,0.0009000017],"genre_scores_gemma":[0.84199476,0.00001942794,0.15783276,0.00006148968,0.000071262504,0.000004366155,0.000001722341,0.000004900568,0.00000931145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886763,0.000057056415,0.0003463899,0.00017864168,0.00041772687,0.00013252713],"domain_scores_gemma":[0.9973406,0.00014807595,0.00024550754,0.00012217724,0.0020621298,0.00008147824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054853776,0.00008683933,0.00011537694,0.00019035957,0.00012326907,0.00024775064,0.00042468542,0.000022105973,0.0000128292995],"category_scores_gemma":[0.000052609754,0.000069031455,0.000014099068,0.00056782615,0.00012727575,0.00026542766,0.00017895682,0.00017058212,0.0000021533444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055435466,0.00037508894,0.006311999,0.000015619316,0.0002104463,0.000023808454,0.0049620424,0.02804546,0.0006528731,0.7207953,0.000034132514,0.2385178],"study_design_scores_gemma":[0.00150676,0.00039987502,0.04384502,0.000067792374,0.000010930306,0.0004012351,0.0020001396,0.93647057,0.00857255,0.0059355265,0.00052701356,0.0002626177],"about_ca_topic_score_codex":0.000015547157,"about_ca_topic_score_gemma":4.3683357e-7,"teacher_disagreement_score":0.9084251,"about_ca_system_score_codex":0.000029964116,"about_ca_system_score_gemma":0.0001473878,"threshold_uncertainty_score":0.2815021},"labels":[],"label_agreement":null},{"id":"W2060611557","doi":"10.1155/2013/749256","title":"Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems","year":2013,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":21,"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":"Fundamental Research Funds for the Central Universities; Hong Kong Polytechnic University; East China University of Science and Technology; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Swarm intelligence; Particle swarm optimization; Mathematical optimization; Computer science; Multi-swarm optimization; Variable (mathematics); Swarm behaviour; Metaheuristic; Variable neighborhood search; Artificial intelligence; Mathematics","score_opus":0.03208382069360572,"score_gpt":0.26648784778753665,"score_spread":0.23440402709393093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060611557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009294027,0.000027358039,0.9961023,0.00023123404,0.00007936752,0.0020976018,0.0000029401174,0.00022913105,0.0011371546],"genre_scores_gemma":[0.2875845,0.0000072029716,0.71103173,0.000040962736,0.000030243747,0.0010188895,0.000007960646,0.00004438016,0.00023413393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997706,0.00006531544,0.0006145473,0.00048408442,0.00055098033,0.000579052],"domain_scores_gemma":[0.99818856,0.00075350533,0.000061528,0.00056298403,0.00025440435,0.00017899787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011245632,0.00022889217,0.00030656817,0.00037725372,0.000054632175,0.00036724322,0.00081156986,0.00011775655,0.0001501982],"category_scores_gemma":[0.0006901482,0.00020997917,0.000058777307,0.0006382786,0.000040578063,0.00041715943,0.00013395086,0.00031877437,0.00007519022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011793533,0.00015931045,0.0000047817693,0.00065053196,0.000006263726,0.0000013130128,0.00016485753,0.9817041,0.00007099556,0.014138484,0.000042102605,0.0030560617],"study_design_scores_gemma":[0.00026660773,0.00016299267,0.000005890815,0.00023322685,0.000001924101,0.0000026252685,0.0000152452385,0.99387074,0.0005904148,0.004588824,0.000041452775,0.00022003507],"about_ca_topic_score_codex":0.0000061741985,"about_ca_topic_score_gemma":2.9233402e-7,"teacher_disagreement_score":0.28749156,"about_ca_system_score_codex":0.000112468675,"about_ca_system_score_gemma":0.00007963784,"threshold_uncertainty_score":0.85627025},"labels":[],"label_agreement":null},{"id":"W2061212936","doi":"10.1145/2725494.2725499","title":"Parallel Evolutionary Algorithms Performing Pairwise Comparisons","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"Center of Mathematical Sciences and Applications, Harvard University","keywords":"Differential evolution; Dimension (graph theory); Particle swarm optimization; Pairwise comparison; Logarithm; Population; Population size; Upper and lower bounds; Algorithm; Mathematical optimization; Evolutionary algorithm; Convergence (economics); Computer science; Evolutionary computation; Speedup; Mathematics; Simple (philosophy); Parallel computing; Combinatorics; Statistics; Mathematical analysis","score_opus":0.09041547469194562,"score_gpt":0.33254075602749167,"score_spread":0.24212528133554606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061212936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034480578,0.00096475147,0.9682376,0.0022088236,0.0015735069,0.00069538795,0.00002850171,0.00072546414,0.025531491],"genre_scores_gemma":[0.0028635506,0.00016176458,0.98591876,0.0001793446,0.00033242683,0.00016777808,0.00013622042,0.00003888174,0.010201258],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950605,0.00037717432,0.0007699594,0.0012914747,0.0017395971,0.00076126656],"domain_scores_gemma":[0.99546146,0.00021646326,0.00028222593,0.0022212022,0.0011738111,0.0006448612],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015661987,0.0004905025,0.0006601666,0.0005083192,0.00025746916,0.0005962721,0.0035873647,0.00040186362,0.00043463518],"category_scores_gemma":[0.0003986074,0.0004574308,0.00021505034,0.00058194774,0.00015113945,0.00046597776,0.0069749737,0.0013467096,0.0007995055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017355977,0.0005336904,0.0007566392,0.00019968496,0.00021883886,0.00013100935,0.00067402143,0.33777457,0.0000021653873,0.035679493,0.5786918,0.04532073],"study_design_scores_gemma":[0.00040315333,0.000046516634,0.00035818075,0.00004440514,0.000014285806,0.000033108485,0.00004338642,0.97201604,0.0000071421773,0.006589163,0.019921437,0.00052321266],"about_ca_topic_score_codex":0.00018925585,"about_ca_topic_score_gemma":0.0000057800767,"teacher_disagreement_score":0.63424146,"about_ca_system_score_codex":0.00041673545,"about_ca_system_score_gemma":0.001851942,"threshold_uncertainty_score":0.9999785},"labels":[],"label_agreement":null},{"id":"W2062355752","doi":"10.1016/j.ins.2012.10.012","title":"Diversity enhanced particle swarm optimization with neighborhood search","year":2012,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":402,"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 Ontario Institute of Technology","funders":"Nanchang Institute of Technology; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Particle swarm optimization; Premature convergence; Convergence (economics); Mathematical optimization; Computer science; Set (abstract data type); Diversity (politics); Multi-swarm optimization; Metaheuristic; Local search (optimization); Swarm behaviour; Artificial intelligence; Mathematics; Geography","score_opus":0.040763551329150466,"score_gpt":0.2876941417484873,"score_spread":0.24693059041933682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062355752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01907903,0.000015511721,0.96931833,0.0005310204,0.00017305385,0.00018773797,0.0000014373076,0.000122026475,0.010571876],"genre_scores_gemma":[0.8576575,0.000011676832,0.142005,0.00023776258,0.000028489498,0.000008775567,0.0000027355038,0.0000017309615,0.00004634294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790436,0.00008618084,0.00023112059,0.00014518881,0.0011864665,0.00044666204],"domain_scores_gemma":[0.9989711,0.000103246326,0.00010415235,0.0002665711,0.00034669612,0.00020822648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013900716,0.000092951464,0.00009283449,0.00019036642,0.0012488487,0.00037947303,0.0008789388,0.000032258562,0.00012288836],"category_scores_gemma":[0.0001479817,0.0000703617,0.000020368785,0.0019005873,0.00018421482,0.010232612,0.00080902694,0.00008805527,0.00022321254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001411308,0.00010031187,0.013019224,0.000017749682,0.000014304072,5.187209e-7,0.020922089,0.88029593,0.000025914673,0.035604723,0.00017439094,0.049810752],"study_design_scores_gemma":[0.00027687845,0.000084621664,0.0028163393,0.0000051194215,0.0000018032604,0.0000057071466,0.0006138239,0.99190426,0.0039361618,0.00006907366,0.00015994372,0.00012626125],"about_ca_topic_score_codex":0.000031850963,"about_ca_topic_score_gemma":0.0000010268058,"teacher_disagreement_score":0.83857846,"about_ca_system_score_codex":0.000046298148,"about_ca_system_score_gemma":0.00015192006,"threshold_uncertainty_score":0.9605261},"labels":[],"label_agreement":null},{"id":"W2063070522","doi":"10.1145/2001576.2001787","title":"Collaborative multi-swarm PSO for task matching using graphics processing units","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":52,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Particle swarm optimization; Speedup; Swarm behaviour; Matching (statistics); Curse of dimensionality; Task (project management); Swarm intelligence; Parallel computing; General-purpose computing on graphics processing units; CUDA; Graphics; Algorithm; Artificial intelligence; Mathematics","score_opus":0.13808964971586574,"score_gpt":0.3499413989395768,"score_spread":0.21185174922371108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063070522","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011139417,0.00007537083,0.9967231,0.00008300706,0.00016670831,0.0005296174,0.000008718028,0.0001745269,0.0011250188],"genre_scores_gemma":[0.050463546,0.000011655993,0.9487222,0.00018309454,0.000030214289,0.000033309923,0.0000038437793,0.000020299358,0.00053184165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983971,0.00011352744,0.00030746777,0.00042572335,0.00037465792,0.00038152334],"domain_scores_gemma":[0.9976523,0.00013359817,0.00013929517,0.0003518897,0.0015707095,0.00015223247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007092176,0.00015774647,0.0001835642,0.0002766454,0.0003872118,0.00029799092,0.0007479243,0.00006968151,0.000023394727],"category_scores_gemma":[0.00033260032,0.00013925685,0.000030859977,0.0021946959,0.00007569974,0.0007796866,0.00023006454,0.00014063288,0.000008950582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017104269,0.0017657598,0.0011618608,0.0009753257,0.0003999392,0.0001294537,0.095860235,0.037103567,0.0062033734,0.68418604,0.0021975758,0.16984582],"study_design_scores_gemma":[0.00044663486,0.000049957333,0.0000596619,0.000023919982,0.00000735037,0.0000048352385,0.00034030378,0.9935426,0.0028331364,0.0021773882,0.00031997796,0.00019421012],"about_ca_topic_score_codex":0.00008843109,"about_ca_topic_score_gemma":0.000018018325,"teacher_disagreement_score":0.9564391,"about_ca_system_score_codex":0.00004051136,"about_ca_system_score_gemma":0.0005176989,"threshold_uncertainty_score":0.56787294},"labels":[],"label_agreement":null},{"id":"W2063574152","doi":"10.1109/cec.2014.6900591","title":"Genetic algorithm with self-adaptive mutation controlled by chromosome similarity","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Adaptive mutation; Mutation; Computer science; Genetic algorithm; Algorithm; Mathematical optimization; Local optimum; Benchmarking; Chromosome; Mutation rate; Mathematics; Population","score_opus":0.007581648286463548,"score_gpt":0.23016002364458596,"score_spread":0.22257837535812242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063574152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025066297,0.000044709755,0.9927286,0.000546364,0.00009861011,0.00063193287,0.0000047373924,0.0003627796,0.0053315973],"genre_scores_gemma":[0.026325462,0.000013935955,0.9719634,0.00039356636,0.00006668982,0.000087809276,0.0000072822218,0.000018079629,0.0011237469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749804,0.0003820391,0.0003274427,0.0005654592,0.00083244266,0.00039458598],"domain_scores_gemma":[0.9982031,0.0003818175,0.00013281692,0.0005919481,0.00046034984,0.00022997042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006284216,0.00020680332,0.00035982372,0.00013881424,0.00016984236,0.00028841253,0.0007136559,0.000075974356,0.00016341014],"category_scores_gemma":[0.00013459774,0.00015382432,0.000051907675,0.0005709543,0.00006538072,0.0003723322,0.00014239652,0.00017400057,0.00013730054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029828766,0.0020317635,0.00030236563,0.00007705978,0.00095157465,0.00023486388,0.001674264,0.02054962,0.00045474197,0.053126708,0.01863572,0.901663],"study_design_scores_gemma":[0.0039690374,0.0004192495,0.0002790612,0.000004149418,0.000015622543,0.000030952993,0.000010450689,0.99280936,0.00048165835,0.0007727683,0.0009897973,0.0002178728],"about_ca_topic_score_codex":0.000041447856,"about_ca_topic_score_gemma":0.000003628876,"teacher_disagreement_score":0.97225976,"about_ca_system_score_codex":0.00007011138,"about_ca_system_score_gemma":0.00013605737,"threshold_uncertainty_score":0.6272774},"labels":[],"label_agreement":null},{"id":"W2064393454","doi":"10.1145/2739480.2754813","title":"Towards an Augmented Lagrangian Constraint Handling Approach for the (1+1)-ES","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Augmented Lagrangian method; Constraint (computer-aided design); Lagrangian; Ellipsoid; Mathematical optimization; Computer science; Function (biology); Mathematics; Applied mathematics; Physics","score_opus":0.10361272005759743,"score_gpt":0.3342784775280701,"score_spread":0.2306657574704727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064393454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000059487666,0.00007125586,0.9864631,0.0014121002,0.00019209953,0.0005286733,0.000004736499,0.00015249754,0.011116022],"genre_scores_gemma":[0.051906306,0.00000604398,0.94639313,0.00030046207,0.00009328585,0.00008277456,0.000013663371,0.000009468166,0.0011948473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986337,0.00010581159,0.00018878252,0.00031386217,0.00046865374,0.00028918646],"domain_scores_gemma":[0.99859995,0.0001575299,0.000043139884,0.0005592813,0.00040467427,0.00023543983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001589329,0.00010317097,0.00012249935,0.000079904275,0.00016622814,0.00041587543,0.0010566383,0.000040634382,0.000034487664],"category_scores_gemma":[0.00036622144,0.00006271398,0.000046823458,0.00033840566,0.000102046186,0.00029256148,0.0001750374,0.00009095784,0.000012662187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035102436,0.00036215616,0.000071919916,0.000034457316,0.00012507303,0.000005478901,0.0026204342,0.03575029,0.000099009965,0.54972583,0.008074277,0.40309593],"study_design_scores_gemma":[0.0005595262,0.00008537239,0.00003082014,0.0000016300277,0.000005645506,0.000009224561,0.00047311504,0.9939701,0.0003816251,0.00078467216,0.0036025813,0.00009567853],"about_ca_topic_score_codex":0.000054857486,"about_ca_topic_score_gemma":0.0000045330344,"teacher_disagreement_score":0.9582198,"about_ca_system_score_codex":0.000033491153,"about_ca_system_score_gemma":0.00025311732,"threshold_uncertainty_score":0.40102965},"labels":[],"label_agreement":null},{"id":"W2064449485","doi":"10.1007/978-3-642-13495-1_50","title":"Biogeography Migration Algorithm for Traveling Salesman Problem","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence","funders":"","keywords":"Travelling salesman problem; Computer science; Algorithm; Mathematical optimization; Optimization problem; Operator (biology); Optimization algorithm; Mathematics","score_opus":0.019123667480578303,"score_gpt":0.26789473001227876,"score_spread":0.24877106253170045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064449485","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004148241,0.00018785425,0.9949199,0.00087391015,0.0016315711,0.0012612266,0.000029474097,0.00020171702,0.00089021964],"genre_scores_gemma":[0.00039021068,0.00007572239,0.9980191,0.00043107278,0.0005683475,0.00005976504,0.000038446513,0.00004944115,0.00036787745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951067,0.000042555534,0.000730287,0.001879027,0.0013851051,0.00085631845],"domain_scores_gemma":[0.9963921,0.0006717208,0.0003567338,0.0014069357,0.000893238,0.00027932643],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018276718,0.0005624992,0.00055465504,0.0015285601,0.0004768046,0.0010617021,0.0035952502,0.0005158284,0.00002830946],"category_scores_gemma":[0.00014732516,0.00052777433,0.00023071902,0.0011317858,0.00074447814,0.0006043694,0.00072991924,0.0010879585,0.000028913517],"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":[0.000002062341,0.00003033466,0.000011984899,0.00003643427,0.000013294329,0.000014265257,0.00025369844,0.006194521,0.000208558,0.0078763515,0.000013118748,0.98534536],"study_design_scores_gemma":[0.0003021779,0.00015384375,0.000018519326,0.00011898519,0.000008419641,0.00003187568,7.510515e-8,0.873276,0.0019545418,0.11973297,0.0038708001,0.000531817],"about_ca_topic_score_codex":0.000024135095,"about_ca_topic_score_gemma":0.00009796575,"teacher_disagreement_score":0.9848136,"about_ca_system_score_codex":0.0001337419,"about_ca_system_score_gemma":0.0007248776,"threshold_uncertainty_score":0.99997526},"labels":[],"label_agreement":null},{"id":"W2064875201","doi":"10.1109/cec.2013.6557583","title":"Heterogeneous Multi-Population Cultural Algorithm","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Benchmark (surveying); Local search (optimization); Population; Heuristic; Computer science; Convergence (economics); Set (abstract data type); Architecture; Mathematical optimization; Cultural algorithm; State (computer science); Space (punctuation); Algorithm; State space; Theoretical computer science; Optimization problem; Mathematics; Artificial intelligence; Geography; Statistics; Meta-optimization","score_opus":0.031539383830975945,"score_gpt":0.3002613613897944,"score_spread":0.2687219775588185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064875201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006998554,0.000031007832,0.9970991,0.00047963543,0.00022544725,0.0002986547,8.84407e-7,0.00026796985,0.00089742034],"genre_scores_gemma":[0.047603045,0.0000117569325,0.94687486,0.00021974607,0.000048277296,0.000048076403,0.000012091745,0.00000849097,0.0051736683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987016,0.00008340301,0.00022565831,0.00032624806,0.00037530865,0.00028780635],"domain_scores_gemma":[0.9990329,0.000041250863,0.000046485875,0.0004374413,0.00027966575,0.00016226753],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013141462,0.000112677466,0.00011486882,0.00009927369,0.000110401845,0.00045546272,0.0006128785,0.0000491376,0.00092378864],"category_scores_gemma":[0.00007424393,0.000086471366,0.00004702203,0.00033887703,0.000026373216,0.00083124975,0.00022365693,0.00008869246,0.0016316562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.089149e-7,0.00013997612,0.00048564177,0.0000080086575,0.000028033715,0.000014128108,0.00019778713,0.0070599974,0.00018192866,0.0035435334,0.0040242667,0.9843162],"study_design_scores_gemma":[0.00020703724,0.000019344385,0.0031960853,0.0000015782673,9.672184e-7,0.00002245123,0.000007032011,0.9951915,0.00042924058,0.00034310544,0.00045835495,0.00012326591],"about_ca_topic_score_codex":0.00043053855,"about_ca_topic_score_gemma":0.000004153204,"teacher_disagreement_score":0.9881315,"about_ca_system_score_codex":0.000040469135,"about_ca_system_score_gemma":0.000016847074,"threshold_uncertainty_score":0.9999895},"labels":[],"label_agreement":null},{"id":"W2066173509","doi":"10.1109/tsmcb.2012.2213808","title":"Gaussian Bare-Bones Differential Evolution","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":268,"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":"Benchmark (surveying); Differential evolution; Gaussian; Task (project management); Mathematical optimization; Algorithm; Computer science; Differential (mechanical device); Gaussian process; Optimization problem; Mathematics; Engineering; Physics; Geology","score_opus":0.02244593156429011,"score_gpt":0.26915121364483263,"score_spread":0.24670528208054252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066173509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025696266,0.00005678643,0.9930427,0.0003715489,0.0019867998,0.00019766009,0.000011815494,0.00020576005,0.001557303],"genre_scores_gemma":[0.94345057,0.00004570125,0.05419144,0.00006435391,0.0001426296,0.00003266866,0.0000020737189,0.000020860609,0.0020497078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981172,0.0001555782,0.00027592265,0.00030118763,0.00063318986,0.00051690947],"domain_scores_gemma":[0.9987737,0.00012534234,0.000040380815,0.0006300189,0.00011762503,0.00031295294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020856036,0.0001831013,0.0001624295,0.00026573363,0.00022762464,0.00013981393,0.0005363713,0.00015732538,0.00047454334],"category_scores_gemma":[0.000011669439,0.00017461443,0.00009699297,0.000540684,0.00007568356,0.00040554637,0.0000063215343,0.00042250816,0.0005592357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018293034,0.008952024,0.00091366173,0.00021099515,0.00064057525,0.00004391774,0.009791532,0.1664613,0.012031267,0.23728213,0.0070542055,0.55643547],"study_design_scores_gemma":[0.0011079895,0.0002478869,0.0051252893,0.000034576053,0.000063403786,0.000057061323,0.00006823177,0.9584576,0.029763052,0.0007914736,0.0036006516,0.00068278745],"about_ca_topic_score_codex":0.00003599055,"about_ca_topic_score_gemma":0.000010449057,"teacher_disagreement_score":0.94088095,"about_ca_system_score_codex":0.00012545226,"about_ca_system_score_gemma":0.000064237945,"threshold_uncertainty_score":0.718803},"labels":[],"label_agreement":null},{"id":"W2066935518","doi":"10.1108/compel-08-2013-0285","title":"Ant colony optimization for the topological design of interior permanent magnet (IPM) machines","year":2014,"lang":"en","type":"article","venue":"COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"McGill University","funders":"","keywords":"Maximization; Computer science; Ant colony optimization algorithms; Topology optimization; Mathematical optimization; Graph; Discretization; Rotor (electric); Topology (electrical circuits); Magnet; Mathematics; Theoretical computer science; Engineering; Mechanical engineering; Finite element method","score_opus":0.022395498822614737,"score_gpt":0.293959789352444,"score_spread":0.2715642905298293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066935518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014084385,0.0004298913,0.99557495,0.0019953733,0.00020883376,0.00035679224,0.0000010908677,0.000015002152,0.000009629052],"genre_scores_gemma":[0.40005708,0.00069490453,0.5988468,0.00014804334,0.00012624286,0.00006149958,0.0000037552586,0.000012742397,0.000048902624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990601,0.000048527923,0.00033981938,0.00012408264,0.00022727696,0.0002001816],"domain_scores_gemma":[0.99738485,0.0021648332,0.00012336006,0.000071779854,0.00021623842,0.00003892992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133546,0.00009664375,0.0001539232,0.00013075444,0.00011472829,0.00017355617,0.00046024885,0.000032266733,0.0000036031424],"category_scores_gemma":[0.00061559794,0.00005863487,0.00004105716,0.00013728325,0.000034174842,0.00008463003,0.00008508565,0.00018328759,1.3060419e-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":[0.000033372908,0.00003659603,0.000005173065,0.000027999691,0.00004709359,4.76621e-7,0.00023364583,0.81639445,0.0002952085,0.15669061,0.000089864276,0.026145497],"study_design_scores_gemma":[0.0005311857,0.00023681745,0.000031359792,0.00002016022,0.000009862776,0.000116125906,0.000011806101,0.9746884,0.00007430334,0.023866097,0.00034822058,0.00006567001],"about_ca_topic_score_codex":0.0000015010828,"about_ca_topic_score_gemma":6.264862e-7,"teacher_disagreement_score":0.39864865,"about_ca_system_score_codex":0.00005991708,"about_ca_system_score_gemma":0.00004373257,"threshold_uncertainty_score":0.23910606},"labels":[],"label_agreement":null},{"id":"W2069636537","doi":"10.1145/2576768.2598387","title":"Dynamic multi-dimensional PSO with indirect encoding for proportional fair constrained resource allocation","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Particle swarm optimization; Mathematical optimization; Encoding (memory); Curse of dimensionality; Resource allocation; Proportionally fair; Orthogonal frequency-division multiple access; Quality of service; Algorithm; Orthogonal frequency-division multiplexing; Dynamic priority scheduling; Mathematics; Channel (broadcasting); Artificial intelligence; Computer network","score_opus":0.02317714564806146,"score_gpt":0.2839868515226085,"score_spread":0.26080970587454705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069636537","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095124997,0.00000645258,0.99456006,0.0016179648,0.000067050445,0.0005571632,0.000004474004,0.00019315213,0.002042402],"genre_scores_gemma":[0.2357012,5.847044e-7,0.7615902,0.0002133694,0.000021075117,0.000076931814,0.00004932581,0.000011628853,0.002335685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982526,0.000110936686,0.0002796963,0.00047513845,0.0005877967,0.0002938762],"domain_scores_gemma":[0.99862236,0.0003704701,0.00012052773,0.00034355643,0.00040672527,0.0001363577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010019855,0.00013703485,0.00016292768,0.00019303852,0.00021791895,0.00013533728,0.00043520567,0.00005496332,0.00007453409],"category_scores_gemma":[0.00043343144,0.0001057104,0.000039849856,0.00035943696,0.000112152535,0.00025207654,0.00010286154,0.0001070731,0.000025819107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027849528,0.001302544,0.001808264,0.00027640504,0.00034784383,0.000025501784,0.0011078005,0.24797219,0.0102323005,0.5067377,0.0061700353,0.2237409],"study_design_scores_gemma":[0.00092397706,0.0001352126,0.0007282734,0.000015169115,0.000004682996,0.000026305826,0.000014605708,0.995139,0.0008383421,0.0002997707,0.0017166006,0.000158078],"about_ca_topic_score_codex":0.0000054158663,"about_ca_topic_score_gemma":0.000011771417,"teacher_disagreement_score":0.7471668,"about_ca_system_score_codex":0.00006136496,"about_ca_system_score_gemma":0.00024605027,"threshold_uncertainty_score":0.4310745},"labels":[],"label_agreement":null},{"id":"W2070951675","doi":"10.1109/epec.2010.5697204","title":"A new meta-heuristic optimization technique: a sensory-deprived optimization algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Benchmark (surveying); Computer science; Optimization algorithm; Algorithm; Mathematical optimization; Population; Set (abstract data type); Heuristic; Meta heuristic; Parallel metaheuristic; Meta-optimization; Artificial intelligence; Mathematics","score_opus":0.031068278192772927,"score_gpt":0.29058848322193426,"score_spread":0.25952020502916134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070951675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010460961,0.00005376888,0.98898566,0.0012908094,0.0005719945,0.001051811,0.000009190865,0.0010182033,0.007017519],"genre_scores_gemma":[0.0003022745,0.00007738796,0.99088603,0.00026660465,0.00017688928,0.00018909926,0.000035747133,0.00005427872,0.008011688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963414,0.00027232713,0.00068502175,0.0010109443,0.0010717243,0.00061860407],"domain_scores_gemma":[0.99651784,0.00029605484,0.00024733978,0.001588588,0.00079158467,0.00055859773],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010778478,0.00041010234,0.0005291199,0.0006079451,0.0002783097,0.00069645594,0.0016199425,0.00026990016,0.0053287284],"category_scores_gemma":[0.00087750534,0.00035493597,0.00022270829,0.001659424,0.00009165339,0.0010333209,0.0005249431,0.000637833,0.00017497485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064228243,0.00013042515,0.0000053781064,0.000011630866,0.00026811863,0.0000361674,0.00009176366,0.90511656,0.00081837754,0.016261915,0.0031649284,0.07408829],"study_design_scores_gemma":[0.0005014253,0.000060580427,0.000007812831,0.0000034380366,0.00013164016,0.00010042817,0.0000051161464,0.9924239,0.0031511092,0.0009849906,0.0022267876,0.00040277667],"about_ca_topic_score_codex":0.000091141446,"about_ca_topic_score_gemma":0.000005025793,"teacher_disagreement_score":0.08730731,"about_ca_system_score_codex":0.000054374243,"about_ca_system_score_gemma":0.00044898636,"threshold_uncertainty_score":0.99989027},"labels":[],"label_agreement":null},{"id":"W2073491843","doi":"10.1007/s11590-008-0089-2","title":"Mesh adaptive direct search algorithms for mixed variable optimization","year":2008,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subsequence; Variable (mathematics); Algorithm; Iterated function; Convergence (economics); Mathematics; Categorical variable; Mathematical optimization; Class (philosophy); Computer science; Artificial intelligence","score_opus":0.04295862349070323,"score_gpt":0.27375931317915003,"score_spread":0.2308006896884468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073491843","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019650633,0.000050905448,0.9925301,0.0025345064,0.0006793573,0.0011689698,0.00004015586,0.00043306383,0.0025433057],"genre_scores_gemma":[0.0010938402,0.00016310599,0.99519813,0.0012360692,0.00020517303,0.00027203432,0.00020436979,0.0000683692,0.0015589142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962648,0.00037624384,0.00060432864,0.0009888214,0.0009927396,0.000773062],"domain_scores_gemma":[0.9971781,0.00051800814,0.0002055929,0.00087114144,0.0009300998,0.00029709912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093782344,0.0003511006,0.00041921993,0.0005550673,0.0007636373,0.0003078737,0.0011424344,0.00015330272,0.00029648817],"category_scores_gemma":[0.0004603338,0.0003688647,0.00013635895,0.0019016545,0.00017717408,0.0013129449,0.0002729751,0.00023128762,0.00004229836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027642829,0.00009446137,0.000019341585,0.000015298549,0.000058180016,0.000013094925,0.00023751537,0.98541987,0.000078761856,0.0025149558,0.010423612,0.0010972958],"study_design_scores_gemma":[0.0011623424,0.000113120004,0.000017942117,0.000016103433,0.000015925558,0.00003387282,0.000018861358,0.9962445,0.00066097645,0.000023630764,0.0012837878,0.00040895483],"about_ca_topic_score_codex":0.000030782463,"about_ca_topic_score_gemma":3.923437e-7,"teacher_disagreement_score":0.010824643,"about_ca_system_score_codex":0.00020816567,"about_ca_system_score_gemma":0.0003145038,"threshold_uncertainty_score":0.9998763},"labels":[],"label_agreement":null},{"id":"W2075441601","doi":"10.1007/s10489-008-0137-8","title":"The property analysis of evolutionary algorithms applied to spanning tree problems","year":2008,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"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":"National Research Council Canada","keywords":"Computer science; Locality; Encoding (memory); Evolutionary algorithm; Tree (set theory); Property (philosophy); Spanning tree; Minimum spanning tree; Algorithm; Set (abstract data type); Population; Enhanced Data Rates for GSM Evolution; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics; Discrete mathematics","score_opus":0.05081212352583217,"score_gpt":0.28260524929510505,"score_spread":0.23179312576927288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075441601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003508373,0.00012261429,0.9819065,0.0004039329,0.0000973095,0.0007441419,0.0000039464862,0.00011266916,0.016258098],"genre_scores_gemma":[0.51338315,0.00027329,0.48341182,0.00021295773,0.000059127327,0.00041533596,0.000012157178,0.000024459587,0.0022076978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969926,0.00006730737,0.00067609106,0.00068115443,0.0010646014,0.0005182748],"domain_scores_gemma":[0.99758893,0.0004272182,0.00018858576,0.0012081076,0.00037837957,0.0002087484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075305573,0.00021508911,0.0003835139,0.0004965172,0.0005188827,0.00008971517,0.0022267227,0.000069636,0.00005722404],"category_scores_gemma":[0.00016597082,0.00013326776,0.0001185919,0.0053116195,0.00029635636,0.00012522806,0.00062743196,0.00023093911,0.00018938107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004228525,0.0002340644,0.0003261377,0.000023999533,0.0006366982,0.00001074639,0.00335357,0.42469817,0.0014842944,0.120809905,0.0022434369,0.44613668],"study_design_scores_gemma":[0.000079994665,0.00006980729,0.0026000233,0.000008524973,0.000056799105,0.000008478382,0.00013832476,0.9817345,0.005847225,0.001473182,0.007687652,0.00029547256],"about_ca_topic_score_codex":0.00006729822,"about_ca_topic_score_gemma":0.000010355805,"teacher_disagreement_score":0.55703634,"about_ca_system_score_codex":0.000082885184,"about_ca_system_score_gemma":0.00019529312,"threshold_uncertainty_score":0.5434502},"labels":[],"label_agreement":null},{"id":"W2076520672","doi":"10.1023/b:anor.0000039518.73626.a5","title":"GENI Ants for the Traveling Salesman Problem","year":2004,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":19,"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 Montréal; Université du Québec à Montréal","funders":"","keywords":"Travelling salesman problem; Heuristic; Theory of computation; Benchmark (surveying); Mathematical optimization; Ant colony optimization algorithms; Probabilistic logic; Computer science; Ant colony; Bottleneck traveling salesman problem; Set (abstract data type); Nearest neighbour algorithm; Traveling purchaser problem; 2-opt; Mathematics; Algorithm; Artificial intelligence","score_opus":0.3811311709820421,"score_gpt":0.4971652540370582,"score_spread":0.11603408305501611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076520672","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024083953,0.0003206726,0.9697768,0.02518013,0.00006161031,0.0011710854,0.000016502368,0.0000308751,0.0010339037],"genre_scores_gemma":[0.46863568,0.00079056725,0.52742827,0.00025505244,0.00014272127,0.0004905454,0.000014663899,0.00002319459,0.002219281],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997605,0.00021360305,0.00035612777,0.00033110526,0.00099378,0.0005003716],"domain_scores_gemma":[0.9959151,0.00058527914,0.000024194745,0.00074449653,0.0026094287,0.000121503195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040605944,0.000088990164,0.00013650401,0.00026023723,0.0008548106,0.0004114754,0.0015170428,0.00005155618,0.000042170712],"category_scores_gemma":[0.0010754762,0.00006357532,0.00007263923,0.0012351258,0.00021158563,0.00041449085,0.00027441652,0.00026531715,0.00005838804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020484198,0.00033086172,0.000022079257,0.00006091582,0.000086485656,0.0000056009935,0.0021627448,0.46508673,0.002671116,0.47934797,0.0036981276,0.046506893],"study_design_scores_gemma":[0.00065424276,0.00030763145,0.00045010477,0.00003888165,0.0000031054872,0.000009271831,0.00017174929,0.9559102,0.02640718,0.010693294,0.0052057384,0.00014860497],"about_ca_topic_score_codex":0.00016983916,"about_ca_topic_score_gemma":0.000068506844,"teacher_disagreement_score":0.49082348,"about_ca_system_score_codex":0.000023462717,"about_ca_system_score_gemma":0.0006782581,"threshold_uncertainty_score":0.6574598},"labels":[],"label_agreement":null},{"id":"W2077028432","doi":"10.1115/detc2007-35506","title":"Multi Agent Normal Sampling Technique (MANST) for Global Optimization","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); MATLAB; Global optimization; Computer science; Toolbox; Sampling (signal processing); Mathematical optimization; Process (computing); Function (biology); Algorithm; Mathematics","score_opus":0.06185688797893931,"score_gpt":0.36301841971031407,"score_spread":0.3011615317313748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077028432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011414531,0.000017622127,0.9948893,0.0002005891,0.00024550315,0.0008428964,0.0000045659617,0.00030818424,0.0034799317],"genre_scores_gemma":[0.0022982052,0.0000085920665,0.99657357,0.00019615475,0.00006047186,0.00007003623,0.000013661315,0.000009627641,0.000769679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843246,0.000034284527,0.00034184474,0.00038028814,0.0003724901,0.00043863003],"domain_scores_gemma":[0.9988377,0.00014344617,0.00007273656,0.00040497148,0.00036834192,0.00017280597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014049952,0.00012562676,0.0001260478,0.00012573245,0.00016788875,0.00018289748,0.0006979029,0.00008499072,0.00007452811],"category_scores_gemma":[0.00028168634,0.00011793718,0.000063206426,0.00063557713,0.00003189872,0.0003491556,0.00022388723,0.000078582496,0.00001786802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033145483,0.0002925165,0.0005549568,0.00005367966,0.000034702578,0.000012188972,0.00011132199,0.8068256,0.00071818446,0.07716068,0.0007890263,0.11341405],"study_design_scores_gemma":[0.00044564417,0.000048355705,0.00023943486,0.00000555966,0.0000027395533,0.000014062013,0.00000984332,0.99390113,0.002106687,0.00022010192,0.002858246,0.00014819436],"about_ca_topic_score_codex":0.000021034753,"about_ca_topic_score_gemma":0.000011616617,"teacher_disagreement_score":0.18707557,"about_ca_system_score_codex":0.00014136537,"about_ca_system_score_gemma":0.00009492227,"threshold_uncertainty_score":0.48093387},"labels":[],"label_agreement":null},{"id":"W2077471743","doi":"10.1115/imece2013-63877","title":"A New Particle Swarm Optimization and Differential Evolution Technique for Constrained Optimization Problems","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Differential evolution; Mathematical optimization; Computer science; Optimization problem; Multi-swarm optimization; Metaheuristic; Benchmark (surveying); Imperialist competitive algorithm; Swarm intelligence; Evolutionary computation; Artificial intelligence; Mathematics","score_opus":0.018036547812713848,"score_gpt":0.25634121221376166,"score_spread":0.2383046644010478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077471743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000052629824,0.000025991803,0.99554247,0.0013236712,0.00009869912,0.0023216791,0.000002347699,0.00024305056,0.00038943693],"genre_scores_gemma":[0.044389207,0.000019901221,0.95351535,0.000049710237,0.0000503594,0.00052724837,0.000018888468,0.000015891028,0.0014134616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985368,0.00007997236,0.00033837848,0.00043041736,0.00028536684,0.0003290277],"domain_scores_gemma":[0.99882424,0.00011697571,0.00010053308,0.00031558063,0.00040801463,0.00023465822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024857523,0.0001521693,0.00016488704,0.00013763866,0.00016498828,0.000464819,0.0003131094,0.00009484351,0.00050131493],"category_scores_gemma":[0.00019901937,0.00013635943,0.00003724896,0.00044730416,0.00005660406,0.00088436325,0.00015065537,0.00007683469,0.000012969709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054402426,0.000067292895,0.000055272885,0.000033494714,0.00001740001,2.690036e-7,0.000094075964,0.9445372,0.002619861,0.044733785,0.0010098116,0.006826084],"study_design_scores_gemma":[0.0008086099,0.00011637573,0.000055140143,0.000011561095,0.0000073301817,0.000009372199,0.000011827598,0.9928043,0.0037602435,0.0022135733,0.000036320387,0.00016538556],"about_ca_topic_score_codex":0.00010195388,"about_ca_topic_score_gemma":0.000002106409,"teacher_disagreement_score":0.048267048,"about_ca_system_score_codex":0.000057157715,"about_ca_system_score_gemma":0.00014789258,"threshold_uncertainty_score":0.55605763},"labels":[],"label_agreement":null},{"id":"W2081465218","doi":"10.1007/s00500-014-1244-6","title":"An improved hybrid immune algorithm for mechanism kinematic chain isomorphism identification in intelligent design","year":2014,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Memorial University of Newfoundland","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Clonal selection; Crossover; Computer science; Robustness (evolution); Algorithm; Genetic algorithm; Artificial immune system; Clonal selection algorithm; Operator (biology); Identification (biology); Mechanism (biology); Algorithm design; Isomorphism (crystallography); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Biology","score_opus":0.02657711055640221,"score_gpt":0.2913296776285376,"score_spread":0.2647525670721354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081465218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045294416,0.00004369835,0.9972068,0.00025239005,0.0006176972,0.0011764606,0.0000025174036,0.00023353963,0.00001396311],"genre_scores_gemma":[0.269073,0.0000037809648,0.7305561,0.00009054086,0.000110859655,0.000057912475,0.000019183626,0.000026344536,0.00006226827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969282,0.00053876813,0.0008607933,0.0007090476,0.00041346083,0.00054971117],"domain_scores_gemma":[0.9974617,0.00083750894,0.00032928292,0.00088067196,0.0003389436,0.00015193851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051909764,0.00022356621,0.00035101105,0.0004083381,0.00024142482,0.00042070934,0.00136686,0.000065483866,0.00001111911],"category_scores_gemma":[0.00081489334,0.00023531482,0.00007843066,0.0005645627,0.000040739116,0.00035293997,0.00026529212,0.00021205224,0.00003476232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005615737,0.00021818488,0.000005818905,0.00007485106,0.000019734718,0.0000048421416,0.0008040982,0.049105812,0.00802382,0.015311726,0.000036922604,0.92638856],"study_design_scores_gemma":[0.0005143632,0.00015759602,0.00006401664,0.000042920237,0.000005112588,0.0000122668935,0.000031157077,0.96589285,0.016464958,0.016543828,0.000029946434,0.00024096362],"about_ca_topic_score_codex":0.000029086115,"about_ca_topic_score_gemma":0.0000010483683,"teacher_disagreement_score":0.92614764,"about_ca_system_score_codex":0.0001109551,"about_ca_system_score_gemma":0.00009500443,"threshold_uncertainty_score":0.95958596},"labels":[],"label_agreement":null},{"id":"W2081879218","doi":"10.1109/ccece.2014.6901057","title":"Optimizing Particle Swarm Optimization algorithm","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Ottawa","funders":"","keywords":"Particle swarm optimization; Computer science; Artificial neural network; Algorithm; Character (mathematics); Multi-swarm optimization; Swarm behaviour; Sample (material); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.019699099367677105,"score_gpt":0.27070962070841587,"score_spread":0.25101052134073876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081879218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003019529,0.000027763337,0.984628,0.001128122,0.00025382589,0.00015609605,5.804783e-7,0.00044284458,0.013332548],"genre_scores_gemma":[0.007947399,0.000022211043,0.9892313,0.00047623695,0.00009020076,0.00002188124,0.000004540763,0.000015955642,0.0021902667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808675,0.00019320987,0.00030279646,0.00045536738,0.0005413041,0.00042057084],"domain_scores_gemma":[0.99853885,0.00018439082,0.00007048285,0.000722429,0.00025006922,0.00023377534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084354274,0.00014099835,0.0001663945,0.00011643844,0.00018262469,0.00040077354,0.00085349375,0.00005679687,0.00033461966],"category_scores_gemma":[0.00029996506,0.00012794376,0.000050964245,0.000758377,0.000045777724,0.0006766818,0.00032050326,0.00012277684,0.0003020611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012774888,0.0000673184,0.000018229586,0.000004716478,0.0000109025905,0.0000033781257,0.00015377249,0.759226,0.000039763814,0.049143765,0.0006274078,0.19070342],"study_design_scores_gemma":[0.00036786744,0.000052658415,0.000018559924,0.0000037868272,0.00000288523,0.000007784003,0.000010934813,0.99328554,0.003079666,0.00046946245,0.0025336966,0.00016716322],"about_ca_topic_score_codex":0.000013754899,"about_ca_topic_score_gemma":6.165428e-7,"teacher_disagreement_score":0.23405948,"about_ca_system_score_codex":0.000037918082,"about_ca_system_score_gemma":0.0000509407,"threshold_uncertainty_score":0.5217395},"labels":[],"label_agreement":null},{"id":"W2081987765","doi":"10.1109/fskd.2010.5569440","title":"Sparse optimization using a mixed GA-PSO optimization framework","year":2010,"lang":"en","type":"article","venue":"2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Computer science; Mathematical optimization; Multi-swarm optimization; Metaheuristic; Flexibility (engineering); Optimization problem; Position (finance); Genetic algorithm; Evolutionary algorithm; Algorithm; Artificial intelligence; Mathematics","score_opus":0.05956056914101535,"score_gpt":0.3275770337436953,"score_spread":0.2680164646026799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081987765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031875472,0.0001033735,0.9733012,0.00041674994,0.009134496,0.0004676455,0.000051624196,0.00010979729,0.013227614],"genre_scores_gemma":[0.69770396,0.0002333724,0.2945738,0.00007918762,0.00079631805,0.00008525277,0.00011621714,0.00005161544,0.006360258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972957,0.00022957951,0.0005950302,0.00077156775,0.0007220629,0.0003861003],"domain_scores_gemma":[0.99762994,0.0002002566,0.0003272603,0.00066564156,0.0009290729,0.00024784062],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00074111996,0.00032780628,0.0003383021,0.00048231418,0.00027134895,0.0017764971,0.0010284056,0.00025318394,0.00021790047],"category_scores_gemma":[0.00050030946,0.00030442624,0.00008648297,0.00043812138,0.00013071425,0.0015130229,0.00040658907,0.0005684355,0.00006738093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038872073,0.00031174236,0.00035533877,0.000054310753,0.000087030785,0.000013305692,0.000383133,0.4183818,0.00029110452,0.5772637,0.0005811554,0.00223849],"study_design_scores_gemma":[0.0004565841,0.00006053547,0.0001759687,0.00019091404,0.000012039443,0.00004013695,0.00012098964,0.99688643,0.00007243886,0.0010683939,0.0005807724,0.00033477054],"about_ca_topic_score_codex":0.00008331169,"about_ca_topic_score_gemma":0.000021009088,"teacher_disagreement_score":0.6945164,"about_ca_system_score_codex":0.00009152308,"about_ca_system_score_gemma":0.00039510854,"threshold_uncertainty_score":0.9999408},"labels":[],"label_agreement":null},{"id":"W2082820221","doi":"10.1115/detc2008-49991","title":"Enhanced Multi-Agent Normal Sampling Technique for Global Optimization","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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; University of Manitoba","funders":"","keywords":"Mathematical optimization; Computer science; Sampling (signal processing); Global optimization; Standard deviation; Normal distribution; Algorithm; Mathematics; Statistics","score_opus":0.07399700246656397,"score_gpt":0.3443902893398757,"score_spread":0.2703932868733117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082820221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024983674,0.00001621441,0.9965345,0.00016803465,0.0001887811,0.00083963375,0.000005487296,0.00025831786,0.0019640217],"genre_scores_gemma":[0.009697101,0.000036212554,0.98862076,0.00014168963,0.000042364445,0.00024507107,0.000013227302,0.000009352951,0.0011942161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858034,0.000051872772,0.00028691487,0.00039507457,0.00034418682,0.00034158485],"domain_scores_gemma":[0.99889684,0.00008708219,0.00007079714,0.00040960548,0.00039795347,0.00013771254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034114032,0.00012696603,0.00014436932,0.00008596906,0.0002582193,0.00008808908,0.00065422535,0.00007258094,0.00008914281],"category_scores_gemma":[0.00028172482,0.000119320146,0.00006435696,0.0005528467,0.00004426026,0.0003769725,0.00021215214,0.00006931488,0.000025180076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014614225,0.0002124781,0.00008983691,0.00002605888,0.000021818902,0.000005543362,0.00013641188,0.96921176,0.0020985794,0.018252008,0.0005328707,0.009397995],"study_design_scores_gemma":[0.00053082086,0.00005311994,0.00012171856,0.000004629315,0.0000018433387,0.000027028704,0.000004192151,0.9838528,0.014650352,0.0000981766,0.0005041259,0.00015117222],"about_ca_topic_score_codex":0.00001131322,"about_ca_topic_score_gemma":0.0000017486366,"teacher_disagreement_score":0.018153831,"about_ca_system_score_codex":0.000103464496,"about_ca_system_score_gemma":0.00015785944,"threshold_uncertainty_score":0.48657343},"labels":[],"label_agreement":null},{"id":"W2083028490","doi":"10.1016/j.jpdc.2012.02.019","title":"Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems","year":2012,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":120,"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":"National Natural Science Foundation of China","keywords":"Computer science; Differential evolution; Speedup; Benchmark (surveying); Computational complexity theory; Graphics; Parallel computing; Optimization problem; Execution time; Mathematical optimization; Algorithm; Mathematics; Computer graphics (images)","score_opus":0.014252950860160355,"score_gpt":0.2382615037605564,"score_spread":0.22400855290039606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083028490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07909353,0.00032876185,0.9196832,0.00034946259,0.00014887289,0.00033261068,0.0000064389415,0.000055251432,0.0000018655905],"genre_scores_gemma":[0.5307455,0.000007219791,0.46909606,0.000026287193,0.000084735206,0.000005247166,0.000024359852,0.000008731256,0.0000018340396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789286,0.00024982446,0.0006196103,0.00026116296,0.00049614324,0.0004804282],"domain_scores_gemma":[0.99768376,0.00058299646,0.00073117315,0.00011668082,0.0005811511,0.00030424376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010554466,0.00021810435,0.0004054512,0.00019261896,0.00053294573,0.000323982,0.00020450643,0.00008407801,0.0000042207703],"category_scores_gemma":[0.0002101163,0.00017610715,0.000075391086,0.00024667513,0.00005541711,0.0005608697,0.00008604939,0.0002771474,2.8980332e-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":[0.00012995444,0.00011270779,0.0041302294,0.000048879298,0.00010744001,0.0000037443524,0.00012103921,0.9928495,0.00017429056,0.0014305352,0.000028275845,0.0008633879],"study_design_scores_gemma":[0.00523739,0.00035814018,0.0035601673,0.00010894685,0.00008210996,0.00011023459,0.000027434691,0.9901486,0.000023812217,0.00011786294,0.000012787967,0.0002124827],"about_ca_topic_score_codex":0.000016578786,"about_ca_topic_score_gemma":4.0388434e-7,"teacher_disagreement_score":0.451652,"about_ca_system_score_codex":0.000099827346,"about_ca_system_score_gemma":0.00013280251,"threshold_uncertainty_score":0.7181441},"labels":[],"label_agreement":null},{"id":"W2083110812","doi":"10.1016/j.amc.2010.03.123","title":"A real-coded biogeography-based optimization with mutation","year":2010,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":154,"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":"Benchmark (surveying); Mutation; Computer science; Operator (biology); Range (aeronautics); Domain (mathematical analysis); Global optimization; Mathematical optimization; Population; Optimization problem; Algorithm; Mathematics; Engineering; Biology; Genetics; Geography; Cartography","score_opus":0.012634191332126128,"score_gpt":0.2575149655768707,"score_spread":0.24488077424474458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083110812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009463784,0.0000027161793,0.9872351,0.00021315682,0.000053570886,0.00042198668,0.0000018571606,0.00019223914,0.0024155397],"genre_scores_gemma":[0.15806057,0.0000060781217,0.84175813,0.000044172597,0.000016400132,0.000056196848,0.000036426452,0.000014876393,0.000007157098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988255,0.000014847154,0.0002572958,0.0003216476,0.0004033041,0.00017738683],"domain_scores_gemma":[0.99903166,0.00018256236,0.00017440176,0.00028202825,0.00022804929,0.00010126962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003407866,0.000146449,0.00015748948,0.00025863876,0.00016756132,0.00031858348,0.00021257535,0.00006704577,0.000013559738],"category_scores_gemma":[0.000025916304,0.00012450852,0.000021764597,0.0006492012,0.00007374247,0.00016918605,0.000051402163,0.00013534674,0.00000895994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018645072,0.00031089573,0.000032317923,0.00015040365,0.00003595399,0.000009274859,0.0009014579,0.58789146,0.003001519,0.35024625,0.00005136859,0.057350468],"study_design_scores_gemma":[0.00060759496,0.000058737452,0.00010992787,0.0000093484205,0.0000112952885,0.00001262912,0.000034229597,0.98887795,0.0010234957,0.009086124,0.00001358487,0.00015508012],"about_ca_topic_score_codex":0.000010141795,"about_ca_topic_score_gemma":0.0000045187685,"teacher_disagreement_score":0.40098652,"about_ca_system_score_codex":0.000009098971,"about_ca_system_score_gemma":0.00007123439,"threshold_uncertainty_score":0.507731},"labels":[],"label_agreement":null},{"id":"W2083377062","doi":"10.1109/ccece.2012.6334942","title":"Strategic iniitialization of a hybrid particle swarm optimization-simullated annealing algorithm (HPSOSA) for PID controller design for a nonlinear system","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Toronto Metropolitan University","funders":"","keywords":"Simulated annealing; Particle swarm optimization; PID controller; Initialization; Benchmark (surveying); Hybrid algorithm (constraint satisfaction); Nonlinear system; Algorithm; Computer science; Control theory (sociology); Adaptive simulated annealing; Mathematical optimization; Mathematics; Control engineering; Engineering; Temperature control; Control (management); Artificial intelligence; Physics","score_opus":0.0775387255435752,"score_gpt":0.31751024152109675,"score_spread":0.23997151597752153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083377062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019519556,0.00018003411,0.99624693,0.00013852527,0.00034906712,0.0024492198,0.000088240806,0.0002388906,0.00011386876],"genre_scores_gemma":[0.14545682,0.000011939885,0.8537309,0.00006141019,0.00017963104,0.0003102483,0.000074393356,0.000034377303,0.00014027383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973905,0.00025777536,0.00081297604,0.00041174173,0.0004709957,0.00065597525],"domain_scores_gemma":[0.9966248,0.00096488046,0.00032335523,0.00040971133,0.0014090264,0.0002682251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019644261,0.00022285829,0.00044217616,0.00018945955,0.00021692265,0.00020127333,0.0005210235,0.000077073186,0.000027950327],"category_scores_gemma":[0.00036534548,0.0002028547,0.00012654232,0.00065245375,0.000051276522,0.0006563139,0.00008151915,0.00006767931,0.000008310177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007313104,0.00017532466,0.000011103795,0.00012920935,0.00008090495,9.0758647e-7,0.0001744654,0.97797203,0.00017394734,0.01824419,0.00024799912,0.0027168056],"study_design_scores_gemma":[0.002658405,0.0002434581,0.0000012132124,0.000029010529,0.00003693128,0.000010222633,0.00010850137,0.9760807,0.02031443,0.0001637135,0.00013652582,0.00021684365],"about_ca_topic_score_codex":0.000013610666,"about_ca_topic_score_gemma":2.2836214e-7,"teacher_disagreement_score":0.14526163,"about_ca_system_score_codex":0.00008315384,"about_ca_system_score_gemma":0.00021218992,"threshold_uncertainty_score":0.82721746},"labels":[],"label_agreement":null},{"id":"W2084993914","doi":"10.1016/j.camwa.2006.07.013","title":"A novel population initialization method for accelerating evolutionary algorithms","year":2007,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":377,"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":"Initialization; Population; Benchmark (surveying); Convergence (economics); Computer science; Mathematical optimization; Evolutionary algorithm; Algorithm; Set (abstract data type); Mathematics; Artificial intelligence","score_opus":0.0573682288893775,"score_gpt":0.35696257867430053,"score_spread":0.29959434978492305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084993914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027870303,0.000024006555,0.9965509,0.0003410873,0.0001051639,0.001980591,0.000015723432,0.0003311338,0.00062348024],"genre_scores_gemma":[0.0022133207,0.000004153945,0.9965015,0.00016692506,0.00019405906,0.00063036685,0.00015721534,0.000036756774,0.00009571484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782044,0.00003668833,0.00063462474,0.0005451667,0.00055179046,0.0004113157],"domain_scores_gemma":[0.997163,0.00086399,0.00034897015,0.00075694727,0.00068579207,0.00018128622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178903,0.00022074339,0.0002648077,0.00035070826,0.00048126583,0.00027861234,0.0008200805,0.000091173955,0.000007590259],"category_scores_gemma":[0.0001167044,0.00020736358,0.0000677185,0.0012220811,0.000044560486,0.00044754165,0.00018585556,0.00013554693,0.000014438806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009813569,0.00055414066,0.000058282654,0.00019082795,0.00007280452,0.00000168446,0.0008195598,0.023580195,0.0006605432,0.80418044,0.00047612295,0.16939557],"study_design_scores_gemma":[0.0005820245,0.00006009531,0.0006186399,0.00003509347,0.000020147158,0.00006398024,0.000046623038,0.98320913,0.00018564654,0.013466777,0.0014664683,0.00024538272],"about_ca_topic_score_codex":0.000015476193,"about_ca_topic_score_gemma":0.000005456142,"teacher_disagreement_score":0.95962894,"about_ca_system_score_codex":0.00013429984,"about_ca_system_score_gemma":0.00011204887,"threshold_uncertainty_score":0.8456042},"labels":[],"label_agreement":null},{"id":"W2085361024","doi":"10.1162/evco_a_00139","title":"Comparison of Constraint-Handling Mechanisms for the (1,λ)-ES on a Simple Constrained Problem","year":2014,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Constraint (computer-aided design); Mathematical optimization; Truncation (statistics); Computation; Computer science; Simple (philosophy); Adaptation (eye); Fitness function; Mathematics; Evolutionary computation; Function (biology); Field (mathematics); Algorithm; Genetic algorithm; Machine learning","score_opus":0.041268766819345236,"score_gpt":0.3392185741248197,"score_spread":0.2979498073054745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085361024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004323197,0.000048707632,0.99650943,0.0012477543,0.00023707536,0.0007784315,0.00001679282,0.000106470616,0.0006230091],"genre_scores_gemma":[0.58051306,0.0000015175524,0.41926885,0.00006758232,0.000040364688,0.000043890373,0.00003233071,0.0000066926127,0.000025712017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982091,0.00021386592,0.00047367768,0.00032944547,0.0005298288,0.00024406015],"domain_scores_gemma":[0.9964524,0.002437488,0.0002625352,0.00028204566,0.00049752346,0.00006801384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009325144,0.0001365751,0.00023536378,0.00016291445,0.00034102667,0.00007744529,0.00049320207,0.000056182296,0.000021356747],"category_scores_gemma":[0.00046025144,0.00010908051,0.000080996004,0.00038355484,0.0001578689,0.00016852218,0.000093674666,0.00012361954,0.000014688052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019786048,0.0001153088,0.00005233865,0.0000333336,0.00002968131,2.2115263e-7,0.00022499812,0.41655636,0.00030258868,0.53423345,0.00124227,0.047189645],"study_design_scores_gemma":[0.0005957329,0.00035691517,0.00057124306,0.000020338424,0.000009650335,0.0000053413373,0.00007948673,0.92944103,0.0005648815,0.06763884,0.0006092721,0.000107290885],"about_ca_topic_score_codex":0.0000073394544,"about_ca_topic_score_gemma":0.0000014442714,"teacher_disagreement_score":0.58008075,"about_ca_system_score_codex":0.000051100826,"about_ca_system_score_gemma":0.00015524856,"threshold_uncertainty_score":0.4448174},"labels":[],"label_agreement":null},{"id":"W2087109584","doi":"10.1007/s00500-013-1090-y","title":"A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization","year":2013,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Differential evolution; Mathematical optimization; Convergence (economics); Population; Constraint (computer-aided design); Mutation; Evolutionary algorithm; Selection (genetic algorithm); Computer science; Adaptive mutation; Algorithm; Lipschitz continuity; Optimization problem; Mathematics; Genetic algorithm; Artificial intelligence","score_opus":0.02297026447037997,"score_gpt":0.25480975874239786,"score_spread":0.2318394942720179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087109584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011576386,0.000020093543,0.99725753,0.00028817207,0.0006491626,0.0010279289,0.000019105617,0.000494915,0.0001273148],"genre_scores_gemma":[0.3008336,4.1690834e-7,0.69870967,0.000071622904,0.00022066539,0.000070323484,0.000043619828,0.00001822886,0.000031810323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976436,0.00018726815,0.00049499783,0.000612898,0.0005257182,0.0005355529],"domain_scores_gemma":[0.9978006,0.000710314,0.0002119491,0.0003478908,0.0007334107,0.00019583399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037780363,0.00021849504,0.00026315099,0.00025349835,0.00047676364,0.00038559872,0.0005537715,0.00010693302,0.0000987471],"category_scores_gemma":[0.00035784146,0.00022308162,0.00011830172,0.00066137913,0.00009983067,0.00046341715,0.00018940642,0.00020282243,0.000029282195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056644626,0.000116143616,0.00011674325,0.000019333489,0.000039515937,0.0000015135768,0.00013639501,0.81518745,0.000060209662,0.0040926407,0.0002780548,0.17994632],"study_design_scores_gemma":[0.0010083332,0.00015107983,0.0004465952,0.00003554694,0.000010465559,0.0000066195284,0.000034658904,0.9974413,0.000058623275,0.0005789132,0.000019429663,0.00020842787],"about_ca_topic_score_codex":0.000046131045,"about_ca_topic_score_gemma":1.8158408e-7,"teacher_disagreement_score":0.30071786,"about_ca_system_score_codex":0.00018656731,"about_ca_system_score_gemma":0.0002381717,"threshold_uncertainty_score":0.90970045},"labels":[],"label_agreement":null},{"id":"W2088134026","doi":"10.1007/s10957-010-9716-z","title":"Annealing a Genetic Algorithm for Constrained Optimization","year":2010,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Mathematical optimization; Simulated annealing; Penalty method; Convergence (economics); Computer science; Optimization problem; Genetic algorithm; Forcing (mathematics); Algorithm; Mathematics","score_opus":0.01071977768036943,"score_gpt":0.28486966622273735,"score_spread":0.27414988854236794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088134026","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026001104,0.00011617325,0.998427,0.00042014505,0.00017641787,0.0005293333,0.00001300303,0.00003942223,0.0002525226],"genre_scores_gemma":[0.001709827,0.00017211177,0.99748576,0.00012836602,0.00025783625,0.00008608165,0.0000126884925,0.000016011767,0.00013129442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986553,0.00012973131,0.0005639951,0.00022517979,0.0002454143,0.00018034577],"domain_scores_gemma":[0.9974011,0.0006119344,0.00046352466,0.00029717974,0.0010384549,0.00018777583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014892225,0.00012596071,0.00019968206,0.00028384503,0.00029711556,0.00024959113,0.00049370626,0.00009245191,0.000088739645],"category_scores_gemma":[0.00039174533,0.00011608747,0.00007274434,0.0004908042,0.00013363906,0.0004578076,0.00006168199,0.0002145528,0.0000015348239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009215771,0.000056299512,0.000004919409,0.000009449369,0.000024209574,9.997925e-7,0.000072093535,0.775779,0.000099940095,0.096270554,0.00004681713,0.12762648],"study_design_scores_gemma":[0.00064416684,0.000068650304,0.000010789687,0.0000073975693,0.000026831745,0.00014907641,0.000042488657,0.9879053,0.00021154089,0.009383878,0.0014297784,0.00012009423],"about_ca_topic_score_codex":3.0877945e-7,"about_ca_topic_score_gemma":8.6671285e-8,"teacher_disagreement_score":0.21212628,"about_ca_system_score_codex":0.000014063534,"about_ca_system_score_gemma":0.00016990384,"threshold_uncertainty_score":0.47339094},"labels":[],"label_agreement":null},{"id":"W2088999732","doi":"10.1080/15732470500254535","title":"A modified shuffled frog-leaping optimization algorithm: applications to project management","year":2006,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":188,"is_retracted":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":"Benchmark (surveying); Computer science; Algorithm; Particle swarm optimization; Flowchart; Range (aeronautics); Mathematical optimization; USable; Domain (mathematical analysis); Evolutionary algorithm; Mathematics; Artificial intelligence; Engineering","score_opus":0.0059884908602278585,"score_gpt":0.23296183450079436,"score_spread":0.2269733436405665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088999732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003468082,0.00013384556,0.9973068,0.00010975138,0.00021277455,0.00092178764,0.000023784145,0.00029377048,0.0006506654],"genre_scores_gemma":[0.040242653,0.000023019249,0.9590104,0.000094321134,0.00025431145,0.00012206018,0.000054982047,0.000030697312,0.0001675348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982467,0.000026926717,0.0003339351,0.0005834267,0.00039749956,0.00041147447],"domain_scores_gemma":[0.9991403,0.000036089536,0.00006730676,0.000500452,0.00012297393,0.00013284395],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010741529,0.00027998624,0.00023550577,0.0005220392,0.00017039973,0.0003562409,0.00055492506,0.000107845655,0.000029367153],"category_scores_gemma":[0.000021170163,0.00026546282,0.00003914536,0.0011670174,0.000021929834,0.000343997,0.0003132399,0.00023132311,0.0000021558192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019311144,0.000005175227,0.00001470447,0.00005033754,0.000025746349,0.0000054110847,0.000086028514,0.9426198,0.00030719946,0.017215015,0.00047550836,0.03919314],"study_design_scores_gemma":[0.00033677096,0.000021550197,0.00093927135,0.000017688568,0.000012675262,0.000027846108,0.000012398629,0.98979294,0.00034286323,0.0012850835,0.00691495,0.00029594917],"about_ca_topic_score_codex":0.00001863751,"about_ca_topic_score_gemma":7.804067e-7,"teacher_disagreement_score":0.04717315,"about_ca_system_score_codex":0.000074031785,"about_ca_system_score_gemma":0.00003644879,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W2090217814","doi":"10.1109/cec.2012.6256517","title":"Two phased cellular PSO: A new collaborative cellular algorithm for optimization in dynamic environments","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Toronto","funders":"","keywords":"Computer science; Particle swarm optimization; Mathematical optimization; Robustness (evolution); Benchmark (surveying); Optimization problem; Evolutionary algorithm; Scheduling (production processes); Algorithm; Artificial intelligence; Mathematics","score_opus":0.016705859204851993,"score_gpt":0.29199507160778876,"score_spread":0.27528921240293674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090217814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008137827,0.00040635368,0.9966414,0.00036692267,0.00036329142,0.0011747725,0.000016156479,0.00007615379,0.00087360066],"genre_scores_gemma":[0.0076485886,0.0000466716,0.9856487,0.000118999626,0.00009434675,0.00011673247,0.000080744125,0.000030799267,0.006214423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754685,0.00021331944,0.00045048285,0.0005128304,0.0005883858,0.00068814],"domain_scores_gemma":[0.9986355,0.00018715966,0.00013825632,0.0005727826,0.00009532337,0.0003710028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078443025,0.0002405933,0.00027892817,0.0003424401,0.00012956017,0.0001745571,0.0007044092,0.0000935031,0.0003531714],"category_scores_gemma":[0.00011725341,0.00023722356,0.00006126689,0.0011253475,0.000053487856,0.0010035668,0.00015437897,0.00014168858,0.00009332566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058199228,0.0023888482,0.00033124184,0.000049821654,0.00017110819,0.00003249589,0.0031119352,0.6256409,0.02134682,0.016005378,0.0030672983,0.3277959],"study_design_scores_gemma":[0.0023598126,0.00007619184,0.000014784271,0.0000066826537,0.000008527461,0.000001459861,0.000058991616,0.9761494,0.016505003,0.0002220268,0.0043365825,0.0002605637],"about_ca_topic_score_codex":0.000036743753,"about_ca_topic_score_gemma":0.0000036080974,"teacher_disagreement_score":0.35050842,"about_ca_system_score_codex":0.00023170009,"about_ca_system_score_gemma":0.00021491264,"threshold_uncertainty_score":0.96736956},"labels":[],"label_agreement":null},{"id":"W2092166743","doi":"10.1109/smc.2013.136","title":"Interactive Visualization of Dynamic and High-Dimensional Particle Swarm Behavior","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visualization; Computer science; Particle swarm optimization; Swarm behaviour; Dimension (graph theory); Multi-swarm optimization; Domain (mathematical analysis); Population; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.013715014490886232,"score_gpt":0.309112657851175,"score_spread":0.29539764336028873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092166743","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":[0.29420042,0.0000128575775,0.70504916,0.00026018984,0.0000736078,0.00021131917,9.3347717e-7,0.000040079954,0.00015142046],"genre_scores_gemma":[0.88050056,0.0000049422874,0.11875177,0.00005626845,0.0000039483116,0.00003260574,0.000003318587,0.000004522351,0.0006420789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991347,0.00007031478,0.00019043511,0.00020246276,0.00027441443,0.00012767785],"domain_scores_gemma":[0.99923784,0.000110197536,0.000059104026,0.00020415365,0.00030501097,0.00008369115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013742736,0.000062723615,0.00009742164,0.00007514712,0.000038974315,0.00007881676,0.00018183031,0.000024269237,0.00045946767],"category_scores_gemma":[0.00009037228,0.000052418025,0.0000140978245,0.00025530328,0.00005143276,0.0005166018,0.00020750925,0.000043179145,0.00007596779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003544111,0.002072115,0.010821037,0.00008712521,0.00013170308,0.000026069047,0.0022146935,0.0038577963,0.06985613,0.32718483,0.002467056,0.581246],"study_design_scores_gemma":[0.0002227462,0.00006125031,0.032205105,0.000004317735,0.0000034312907,0.000005035009,0.000015308735,0.956761,0.010044192,0.0006099495,0.000006364447,0.00006126852],"about_ca_topic_score_codex":0.00014327456,"about_ca_topic_score_gemma":0.0000033822205,"teacher_disagreement_score":0.9529032,"about_ca_system_score_codex":0.00001898507,"about_ca_system_score_gemma":0.000029606026,"threshold_uncertainty_score":0.5030849},"labels":[],"label_agreement":null},{"id":"W2093633881","doi":"10.1016/s0031-3203(01)00193-5","title":"Fuzzy J-Means: a new heuristic for fuzzy clustering","year":2002,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":94,"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 Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Fuzzy logic; Mathematics; Centroid; Heuristic; Maxima and minima; Fuzzy clustering; Cluster analysis; Metaheuristic; Fuzzy set; Mathematical optimization; Local search (optimization); Fuzzy classification; Artificial intelligence; Data mining; Algorithm; Computer science","score_opus":0.09070179718031579,"score_gpt":0.2905354395512421,"score_spread":0.19983364237092632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093633881","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023077705,0.00011018083,0.9913254,0.0015735873,0.00057671743,0.00054469134,0.000029811888,0.00022427604,0.0053845504],"genre_scores_gemma":[0.1377164,0.00033279162,0.85295284,0.0020177912,0.0012864482,0.00038221636,0.00016796243,0.00009505522,0.005048467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982093,0.00010117452,0.0003534089,0.00050990167,0.0003964131,0.0004298239],"domain_scores_gemma":[0.99873066,0.00028433988,0.00011600415,0.00042271608,0.00022453569,0.00022175626],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032865623,0.00017523154,0.00019064118,0.00022663911,0.0001529191,0.00032109488,0.0005349998,0.0000750614,0.00042365494],"category_scores_gemma":[0.00038232628,0.00018340758,0.000091444366,0.00036099902,0.000023379995,0.0004378698,0.00015270802,0.00014618086,0.0012256927],"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":[0.000004483292,0.0000667906,0.000040231276,0.000066295725,0.00001795146,0.000010831562,0.0003427086,0.00023329734,0.000035211055,0.000055920318,0.0084589645,0.99066734],"study_design_scores_gemma":[0.0010893375,0.00014331179,0.00017900972,0.000074568685,0.000015839998,0.000042768148,0.00001788375,0.9852925,0.00023116825,0.009689417,0.0029095735,0.00031461017],"about_ca_topic_score_codex":0.00003814632,"about_ca_topic_score_gemma":0.000016289909,"teacher_disagreement_score":0.9903527,"about_ca_system_score_codex":0.000059364316,"about_ca_system_score_gemma":0.000028036131,"threshold_uncertainty_score":0.99955195},"labels":[],"label_agreement":null},{"id":"W2094204336","doi":"10.1007/s10732-013-9235-9","title":"An iterated-tabu-search heuristic for a variant of the partial set covering problem","year":2013,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal","funders":"","keywords":"Tabu search; Mathematical optimization; Iterated local search; Local optimum; Mathematics; Guided Local Search; Iterated function; Set cover problem; Local search (optimization); Heuristic; Set (abstract data type); Algorithm; Computer science","score_opus":0.031429351791645806,"score_gpt":0.30792219586600056,"score_spread":0.27649284407435476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094204336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0129794,0.0000738328,0.9850534,0.00058750855,0.00059188704,0.00054488995,0.000029496701,0.000016647173,0.00012294548],"genre_scores_gemma":[0.653334,0.000038032864,0.34607035,0.00009815735,0.0002608873,0.000015890322,0.0000023732396,0.000023692359,0.00015657632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969863,0.0003620901,0.001005509,0.00023337893,0.0009969434,0.00041574633],"domain_scores_gemma":[0.99573857,0.0005559773,0.00060522195,0.00066308706,0.0021754082,0.00026172627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015885948,0.00016644839,0.0003795281,0.00020621,0.00017878631,0.00039672176,0.0017630197,0.00007620465,0.00006856092],"category_scores_gemma":[0.0011004637,0.000112674374,0.00015208632,0.00057054946,0.00011566111,0.00052826863,0.00024139018,0.00038904478,0.000011581608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006658186,0.0045806826,0.00856105,0.0025202408,0.0013376698,0.00065082224,0.016946949,0.6179126,0.026120933,0.16772915,0.052472383,0.10050168],"study_design_scores_gemma":[0.000879059,0.00061875535,0.0012647577,0.00008808417,0.000036174366,0.00021371586,0.000071157694,0.9891039,0.0026081088,0.003487951,0.0014633826,0.00016493071],"about_ca_topic_score_codex":0.000026143123,"about_ca_topic_score_gemma":8.830402e-7,"teacher_disagreement_score":0.64035463,"about_ca_system_score_codex":0.00006643833,"about_ca_system_score_gemma":0.0005399036,"threshold_uncertainty_score":0.45947278},"labels":[],"label_agreement":null},{"id":"W2094252454","doi":"10.5267/j.ijiec.2012.09.001","title":"Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":125,"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":"Optimization algorithm; Computer science; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.04034164815511341,"score_gpt":0.3085967055323746,"score_spread":0.2682550573772612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094252454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002367377,0.000033588367,0.9953801,0.0002244772,0.0015503053,0.00031712366,0.000023350422,0.00006234439,0.000041322353],"genre_scores_gemma":[0.31994724,0.0000044418366,0.6794152,0.00001479815,0.00050149416,0.000012492224,0.00007792852,0.000015257723,0.000011135184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977511,0.00013447778,0.0009086219,0.00017162299,0.0007641667,0.00026997694],"domain_scores_gemma":[0.9963758,0.0006228059,0.00075573917,0.0001399692,0.00190767,0.00019804452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014039573,0.00018109496,0.0003289684,0.00072620605,0.00013481721,0.00019991856,0.00078321894,0.00010651479,0.000024306657],"category_scores_gemma":[0.00082261395,0.00018729262,0.00011462534,0.00037553426,0.000047912992,0.0014683482,0.00006768893,0.00046035036,8.87216e-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":[0.00001846227,0.00020133278,0.00007528658,0.000010107544,0.00010791798,0.0000012254158,0.00036710425,0.9812203,0.000045931465,0.00062468956,0.000041664396,0.017285978],"study_design_scores_gemma":[0.0017152794,0.00037781274,0.00004605858,0.00014401432,0.000022501445,0.000047641744,0.00003665722,0.9965947,0.00061624654,0.000009766061,0.00022455501,0.00016472259],"about_ca_topic_score_codex":0.000008386523,"about_ca_topic_score_gemma":1.6448037e-7,"teacher_disagreement_score":0.31757987,"about_ca_system_score_codex":0.00018116846,"about_ca_system_score_gemma":0.00036291755,"threshold_uncertainty_score":0.76375717},"labels":[],"label_agreement":null},{"id":"W209715872","doi":"","title":"An Evolutionary Race: A Comparison of Genetic Algorithms and Particle Swarm Optimization for Training Neural Networks.","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Particle swarm optimization; Computer science; Artificial neural network; Train; Artificial intelligence; Genetic algorithm; Evolutionary algorithm; Evolutionary computation; Task (project management); Training (meteorology); Machine learning; Track (disk drive); Engineering","score_opus":0.05340394810975989,"score_gpt":0.33672010056819895,"score_spread":0.28331615245843905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W209715872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008706132,0.00032058562,0.9898208,0.00045588982,0.0001491559,0.00041700812,0.0000030609817,0.00009977272,0.000027595877],"genre_scores_gemma":[0.41915566,0.000016086879,0.58068985,0.00004077217,0.00004029713,0.000025946538,0.000007147075,0.000008201082,0.00001603235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848264,0.00008613407,0.00041373508,0.000378967,0.00031419672,0.00032433085],"domain_scores_gemma":[0.9989464,0.00014410213,0.00011625224,0.00034185962,0.00026277165,0.00018860612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032992844,0.00012224836,0.000231483,0.000090407935,0.0001623067,0.00011680107,0.0003893632,0.000059403235,0.000021858292],"category_scores_gemma":[0.00010570718,0.0001175229,0.00003859944,0.0004606867,0.00008160352,0.0004694771,0.00009654736,0.000084064515,8.4406656e-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":[0.000009803091,0.0001174323,0.00045117483,0.00000961928,0.000009196833,0.0000010974506,0.0006267156,0.95521677,0.000026906575,0.0030964077,0.000027163787,0.040407747],"study_design_scores_gemma":[0.0009527824,0.0003330571,0.00138995,0.000006316268,0.000008425726,0.0000090476005,0.00014347468,0.99627566,0.00027062523,0.00046339957,0.000016461854,0.0001307873],"about_ca_topic_score_codex":0.000020179426,"about_ca_topic_score_gemma":0.0000024256135,"teacher_disagreement_score":0.41044953,"about_ca_system_score_codex":0.000034436325,"about_ca_system_score_gemma":0.00009154352,"threshold_uncertainty_score":0.47924444},"labels":[],"label_agreement":null},{"id":"W2097348115","doi":"10.1109/cec.2006.1688425","title":"On The Convergence of Information Exchange Methods in Multiple Cooperating Swarms","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Benchmark (surveying); Swarm behaviour; Convergence (economics); Information exchange; Computer science; Multi-swarm optimization; Mathematical optimization; Work (physics); Metaheuristic; Swarm intelligence; Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.04014106437342423,"score_gpt":0.3296652656802198,"score_spread":0.28952420130679557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097348115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021461106,0.000012676841,0.9885284,0.00050847896,0.00009860757,0.0002150717,0.0000011152808,0.000028756489,0.008460786],"genre_scores_gemma":[0.19411978,0.000006767706,0.8051711,0.0002545614,0.000010522134,0.000027189679,0.0000029463486,0.0000025912332,0.00040459685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988836,0.0002653225,0.00031512362,0.00010423966,0.00028859038,0.00014313725],"domain_scores_gemma":[0.9985008,0.0009231753,0.000080631806,0.00030724943,0.00016623807,0.000021860078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001511186,0.000061033974,0.00009108318,0.00014402713,0.00005618313,0.00007420217,0.00048507669,0.000026427682,0.00025222544],"category_scores_gemma":[0.00096045865,0.000039094866,0.000018238463,0.0007204279,0.00003620294,0.00042257702,0.00013811969,0.000093409726,0.00004638175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012385624,0.00016339975,0.0022416282,0.00006581708,0.000009342008,0.000002633331,0.001790294,0.1249341,0.001902772,0.7417166,0.004674929,0.12248607],"study_design_scores_gemma":[0.00017445828,0.00002228211,0.0013622006,0.0000063553266,3.1405972e-7,6.23005e-7,0.00003093349,0.9846658,0.012366811,0.00090702047,0.00041520604,0.000047992908],"about_ca_topic_score_codex":0.00033592118,"about_ca_topic_score_gemma":0.000021146814,"teacher_disagreement_score":0.85973173,"about_ca_system_score_codex":0.000021166748,"about_ca_system_score_gemma":0.00004196044,"threshold_uncertainty_score":0.27616918},"labels":[],"label_agreement":null},{"id":"W2097431197","doi":"10.1109/iscas.1989.100610","title":"An efficient implementation of an edge detection algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 science; Enhanced Data Rates for GSM Evolution; A priori and a posteriori; Algorithm; Execution time; Edge detection; Theoretical computer science; Computer engineering; Artificial intelligence; Image processing; Image (mathematics)","score_opus":0.017615173525274832,"score_gpt":0.3342086159025628,"score_spread":0.316593442377288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097431197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015919654,0.000007103246,0.9825885,0.00001864781,0.00020294593,0.00021954584,0.0000023570958,0.000087056265,0.00095414463],"genre_scores_gemma":[0.37822038,0.0000020705706,0.6216516,0.000024859748,0.000015771595,0.000011851995,0.0000042229794,0.000005945703,0.00006331091],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850786,0.0002798816,0.00025938064,0.00030583813,0.00044378682,0.000203272],"domain_scores_gemma":[0.99896586,0.00003399048,0.0000767187,0.0005175653,0.00026458374,0.00014126563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007934289,0.00008140012,0.00009868725,0.00019876967,0.00008862042,0.0000982318,0.0003759887,0.00003267453,0.00033338013],"category_scores_gemma":[0.000038178398,0.000075334414,0.000025420562,0.00059246167,0.000026193939,0.0003828003,0.00003576657,0.000059713482,0.000021742595],"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":[0.0000013581841,0.00022457121,0.0000460917,0.000005892555,0.000008412042,0.0000025123225,0.00036148468,0.009168446,0.0031612131,0.016663218,0.000016317495,0.9703405],"study_design_scores_gemma":[0.0003060899,0.0002212114,0.00075533165,7.1272007e-7,0.0000021614233,0.00000779043,0.0001852696,0.85982585,0.13819294,0.00023375357,0.00018940443,0.000079505146],"about_ca_topic_score_codex":0.00009150229,"about_ca_topic_score_gemma":0.000020436379,"teacher_disagreement_score":0.970261,"about_ca_system_score_codex":0.00003869659,"about_ca_system_score_gemma":0.00009468844,"threshold_uncertainty_score":0.36502787},"labels":[],"label_agreement":null},{"id":"W2098540440","doi":"10.1109/tsmcb.2010.2056367","title":"Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":229,"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":"Differential evolution; Benchmark (surveying); Computer science; Mathematical optimization; Scalability; Convergence (economics); Evolutionary algorithm; Optimization problem; Global optimization; Evolution strategy; Adaptive strategies; Adaptation (eye); Artificial intelligence; Algorithm; Mathematics","score_opus":0.017948645447076427,"score_gpt":0.2511247999847204,"score_spread":0.233176154537644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098540440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028813407,0.000040063678,0.9929364,0.000115839,0.0014461863,0.0011317923,0.000045359084,0.0001977095,0.0012053234],"genre_scores_gemma":[0.92062837,0.00005063185,0.07679453,0.000025035166,0.00016407535,0.00034250063,0.000010473069,0.000048514412,0.0019358834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727076,0.00017039508,0.0005287774,0.00079720793,0.00070106384,0.0005318224],"domain_scores_gemma":[0.99802315,0.00026032914,0.0002093551,0.00067359436,0.00051317655,0.00032041423],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026121514,0.00037924765,0.00040513554,0.00026509742,0.00036406363,0.0007036288,0.00049870124,0.0002283495,0.00007456334],"category_scores_gemma":[0.0000140119355,0.0003373266,0.000092389346,0.00043912564,0.00022003132,0.0003633871,0.0000105141635,0.00047531287,0.000028555627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021211375,0.0005070474,0.000006977142,0.00009561106,0.0001758696,0.000006052447,0.0007876086,0.90902776,0.001467959,0.07975711,0.0002608199,0.007695089],"study_design_scores_gemma":[0.0012730006,0.00077963853,0.000058026915,0.000056848326,0.00006358339,0.00003412864,0.00021884243,0.9940773,0.0021127802,0.0003211284,0.0005727526,0.0004319554],"about_ca_topic_score_codex":0.00008964558,"about_ca_topic_score_gemma":0.00006612001,"teacher_disagreement_score":0.917747,"about_ca_system_score_codex":0.00006809299,"about_ca_system_score_gemma":0.00020309177,"threshold_uncertainty_score":0.99990785},"labels":[],"label_agreement":null},{"id":"W2099023497","doi":"10.1016/j.ahj.2013.08.020","title":"Reply to Koestenberger and Ravekes","year":2013,"lang":"en","type":"letter","venue":"American Heart Journal","topic":"Metaheuristic Optimization Algorithms Research","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":"Hospital for Sick Children","funders":"","keywords":"Hyperspectral imaging; Computer graphics; Computer science; Graphics processing unit; Graphics; Data science; Software engineering; Human–computer interaction; Computer graphics (images); Artificial intelligence; Operating system","score_opus":0.0280327858275841,"score_gpt":0.30433459850897027,"score_spread":0.27630181268138615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099023497","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000098603705,0.0001268474,0.18033332,0.8181162,0.00057033444,0.0002252474,0.0000034343996,0.000055894412,0.00047011627],"genre_scores_gemma":[0.000026529733,0.000119273864,0.3104575,0.68167734,0.002007033,0.000018062687,0.000002248072,0.0000344147,0.0056575863],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965972,0.0005066109,0.0004979379,0.00059748575,0.0010889589,0.00071183906],"domain_scores_gemma":[0.99747616,0.00044400527,0.00025496617,0.00084104895,0.0004791269,0.0005047173],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006762225,0.00028559234,0.00058325223,0.00058941584,0.00025410595,0.0012870936,0.0010808178,0.0001055618,0.00033350245],"category_scores_gemma":[0.00051814754,0.0002361645,0.00011940014,0.0007167784,0.0002233502,0.00033668356,0.00047362773,0.0020963848,0.0006475707],"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":[0.0000011591248,0.0000064129526,0.00014625162,0.000007685438,0.000030282717,0.00023897759,0.00008428481,0.000028406323,0.000009704355,0.00001657502,0.9808484,0.018581873],"study_design_scores_gemma":[0.00007809979,0.00021563312,0.0008302859,0.000035056135,0.0000076137667,0.0017015416,0.000010542334,0.0057625496,0.00001165771,0.0001260624,0.9909312,0.00028973477],"about_ca_topic_score_codex":0.00026504122,"about_ca_topic_score_gemma":4.990768e-7,"teacher_disagreement_score":0.13643885,"about_ca_system_score_codex":0.00007616586,"about_ca_system_score_gemma":0.000288623,"threshold_uncertainty_score":0.99974966},"labels":[],"label_agreement":null},{"id":"W2099840809","doi":"10.1162/evco.2008.16.2.151","title":"Step Length Adaptation on Ridge Functions","year":2008,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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 Toronto; Dalhousie University","funders":"","keywords":"Adaptation (eye); Ridge; Computer science; Scaling; Point (geometry); Local adaptation; Selection (genetic algorithm); Algorithm; Mathematics; Artificial intelligence; Biology; Geography; Cartography; Neuroscience; Population; Geometry","score_opus":0.050759569294842787,"score_gpt":0.2727213170812062,"score_spread":0.22196174778636343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099840809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002634856,0.00007884064,0.9900254,0.0010686466,0.00074198964,0.00029064587,0.000013305029,0.00039227217,0.0047540455],"genre_scores_gemma":[0.5747187,0.000036255115,0.42235067,0.00022583622,0.00020433134,0.000047900976,0.0002022566,0.000017766464,0.0021962803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978343,0.00024690313,0.00033129865,0.00047685835,0.0008387615,0.00027183068],"domain_scores_gemma":[0.9986502,0.00030519985,0.000120926314,0.0003556298,0.00043338473,0.00013467632],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024931913,0.00015794483,0.00013984866,0.00040338974,0.0006333226,0.000060110964,0.00036402568,0.000066824185,0.00007352488],"category_scores_gemma":[0.00017681826,0.00016675369,0.00006866571,0.00095920294,0.00008082597,0.00072178803,0.00010126137,0.00020187431,0.0011688075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027361988,0.00033511678,0.00025238993,0.0000091803695,0.000031728134,0.00003584151,0.0005104935,0.8718226,0.000027666,0.029077755,0.05686718,0.041002717],"study_design_scores_gemma":[0.00044788086,0.00017494163,0.023226406,0.000007778877,0.000003300383,0.000075502074,0.00002896478,0.9700398,0.00001421364,0.00095994346,0.0048560346,0.00016522697],"about_ca_topic_score_codex":0.000025941492,"about_ca_topic_score_gemma":0.0000015328358,"teacher_disagreement_score":0.57208383,"about_ca_system_score_codex":0.00020555139,"about_ca_system_score_gemma":0.00026449602,"threshold_uncertainty_score":0.9996089},"labels":[],"label_agreement":null},{"id":"W2103940338","doi":"10.1109/coginf.2010.5599684","title":"A study of particle swarm optimization for cognitive machines","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Computer science; Mathematical optimization; Convergence (economics); Optimization problem; Transient (computer programming); Process (computing); Metaheuristic; Algorithm; Mathematics","score_opus":0.03792158580919486,"score_gpt":0.34596273190116417,"score_spread":0.3080411460919693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103940338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112239785,0.0000026225623,0.8861211,0.00014240353,0.00014780404,0.0006761276,0.0000029194316,0.000059882328,0.0006073755],"genre_scores_gemma":[0.6269776,8.2323754e-7,0.37247637,0.000027916372,0.000019344647,0.000067359484,0.0000020215828,0.000005241213,0.00042327656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907434,0.000054482218,0.0002195852,0.00023397902,0.00026663014,0.00015099147],"domain_scores_gemma":[0.99873275,0.00032298052,0.00006829789,0.0002765226,0.00052780175,0.0000716336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042073295,0.0000689444,0.00011788155,0.000075500975,0.00007301688,0.00007381279,0.00036934865,0.00002601323,0.00014345409],"category_scores_gemma":[0.0006725285,0.000056813562,0.000025603435,0.00039145732,0.000030598447,0.00023062374,0.00013080826,0.00007329736,0.000007662019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028706982,0.013747184,0.025374994,0.00015132526,0.0004299636,0.000027497226,0.016219066,0.39298013,0.0039510187,0.21551988,0.0013349893,0.3299769],"study_design_scores_gemma":[0.0010707835,0.00028448857,0.0005786165,0.0000016269654,0.000007286703,0.0000018349095,0.00016480609,0.9937585,0.0039067725,0.00014706567,0.000011600089,0.000066631306],"about_ca_topic_score_codex":0.000026546859,"about_ca_topic_score_gemma":0.000027804499,"teacher_disagreement_score":0.60077834,"about_ca_system_score_codex":0.000003427572,"about_ca_system_score_gemma":0.000044154727,"threshold_uncertainty_score":0.23167899},"labels":[],"label_agreement":null},{"id":"W2106889879","doi":"10.5555/1486693.1486717","title":"Solving large scale optimization problems by opposition-based differential evolution (ODE)","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":73,"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; Ontario Tech University","funders":"","keywords":"Ode; Differential evolution; Initialization; Computer science; Mathematical optimization; Benchmark (surveying); Test suite; Suite; Applied mathematics; Algorithm; Mathematics; Test case; Machine learning","score_opus":0.01513323171256287,"score_gpt":0.23944514877755707,"score_spread":0.2243119170649942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106889879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039022963,0.000038267033,0.99609256,0.000610304,0.00021721926,0.0003685709,0.0000134793545,0.00035535957,0.0019140221],"genre_scores_gemma":[0.45426354,0.000022923354,0.5435253,0.00016337201,0.00005974761,0.000058518206,0.00013816691,0.000021101494,0.0017473189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977615,0.00017817543,0.0003406677,0.0005037498,0.00076152437,0.00045439322],"domain_scores_gemma":[0.99884266,0.000089196226,0.000099515324,0.0004946674,0.0002801792,0.00019376485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002826441,0.00016847366,0.00017253996,0.00021931027,0.00054481655,0.00019926812,0.0006124317,0.00009434634,0.0007339279],"category_scores_gemma":[0.00006880931,0.0001602025,0.000067138244,0.00066430925,0.00006637891,0.0006358207,0.0001708311,0.00015216171,0.000086116466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001021365,0.0008140788,0.0012990632,0.000042274634,0.00002555324,0.000009751058,0.00031094975,0.9664953,0.0022022945,0.010573796,0.017740104,0.0004766616],"study_design_scores_gemma":[0.00072987814,0.000052574876,0.00016517067,0.000011884918,0.000003974256,0.000009266055,0.000007721868,0.9974509,0.0010969423,0.0001452095,0.00013399172,0.0001924684],"about_ca_topic_score_codex":0.000039949875,"about_ca_topic_score_gemma":0.000004741263,"teacher_disagreement_score":0.4538733,"about_ca_system_score_codex":0.00019817153,"about_ca_system_score_gemma":0.00019047374,"threshold_uncertainty_score":0.8035996},"labels":[],"label_agreement":null},{"id":"W2107813763","doi":"10.1109/icec.1996.542676","title":"A scatter search approach for unconstrained continuous optimization","year":2002,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 de Montréal","funders":"","keywords":"Tabu search; Exploit; Computer science; Bridge (graph theory); Population; Simple (philosophy); Genetic algorithm; Guided Local Search; Evolutionary algorithm; Hill climbing; Local search (optimization); Mathematical optimization; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.049209304376192725,"score_gpt":0.2708317693192688,"score_spread":0.22162246494307605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107813763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010882725,0.000028652246,0.9574506,0.001525369,0.00008253701,0.0006881318,0.0000043733457,0.00020331123,0.04000612],"genre_scores_gemma":[0.013088023,0.000010056485,0.9735674,0.0003966384,0.00006447059,0.00010491214,0.000017332017,0.000015100577,0.012736011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983143,0.00010135494,0.00026115045,0.00047111415,0.0004414303,0.00041062542],"domain_scores_gemma":[0.9987085,0.00017239591,0.000041945706,0.0005235559,0.0003965934,0.0001570322],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055434665,0.00012843676,0.00017723581,0.0002091093,0.00014989986,0.0003521706,0.00077279983,0.00006623366,0.00097033964],"category_scores_gemma":[0.00016759949,0.00011213668,0.00006759897,0.0006039213,0.000078196885,0.00036174708,0.00015509503,0.00010466183,0.00007515302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001167016,0.0005868829,0.00016244594,0.000084526815,0.00007573204,0.000008114068,0.0007109744,0.78679055,0.000093088245,0.058402766,0.03582394,0.11724933],"study_design_scores_gemma":[0.0006576706,0.000061192,0.0000060655657,0.000002056563,0.0000029612963,0.000015120007,0.000028050146,0.9981625,0.00022534667,0.00006389472,0.00063465623,0.00014047633],"about_ca_topic_score_codex":0.000006285762,"about_ca_topic_score_gemma":1.8392464e-7,"teacher_disagreement_score":0.21137199,"about_ca_system_score_codex":0.000034906017,"about_ca_system_score_gemma":0.0000378439,"threshold_uncertainty_score":0.9999429},"labels":[],"label_agreement":null},{"id":"W2109681092","doi":"10.5772/9030","title":"A Collaborative Search Strategy to Solve Combinatorial Optimization and Scheduling Problems","year":2010,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Metaheuristic Optimization Algorithms Research","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 Ottawa","funders":"","keywords":"Computer science; Scheduling (production processes); Combinatorial optimization; Mathematical optimization; Mathematics; Algorithm","score_opus":0.027725495005364077,"score_gpt":0.2881115452434445,"score_spread":0.2603860502380804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109681092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000060399343,0.00006617455,0.80682796,0.00017288509,0.0006498272,0.0013507537,0.000026474385,0.00018321018,0.19071667],"genre_scores_gemma":[0.0018192853,0.000059994527,0.90989214,0.00012622506,0.00035903818,0.00014030578,0.00002944847,0.000117612624,0.08745595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969399,0.00005721178,0.0005543584,0.0009835047,0.0009986637,0.0004663691],"domain_scores_gemma":[0.9966019,0.00020724161,0.00019736437,0.00082836323,0.0017462098,0.00041894297],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000804926,0.00044343376,0.00050414616,0.0006553176,0.00026705588,0.0009313449,0.0011240804,0.0006019305,0.00014625682],"category_scores_gemma":[0.00019926055,0.00044771584,0.000066288965,0.00015297897,0.00025132587,0.00018386678,0.0009904164,0.0013732043,0.00009383847],"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":[0.000054449887,0.000059242007,7.1039307e-7,0.00020451914,0.00021143082,0.00010243313,0.002324336,0.01794842,0.0059421062,0.9246621,0.000118188575,0.04837207],"study_design_scores_gemma":[0.0029488867,0.002681928,0.0000011051369,0.001090618,0.00010379468,0.00011944009,0.00016174071,0.75169283,0.052788906,0.14015752,0.04534454,0.0029086855],"about_ca_topic_score_codex":0.000016795904,"about_ca_topic_score_gemma":0.000011226978,"teacher_disagreement_score":0.7845046,"about_ca_system_score_codex":0.00011843561,"about_ca_system_score_gemma":0.00091539964,"threshold_uncertainty_score":0.99979746},"labels":[],"label_agreement":null},{"id":"W2111053428","doi":"","title":"Improving the Probability of Success of Repeated Genetic Algorithm on Affine Object Location Problem","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Redundancy (engineering); Affine transformation; Probabilistic logic; Algorithm; Computer science; Operator (biology); Object (grammar); Genetic algorithm; Genetic operator; Mathematical optimization; Artificial intelligence; Theoretical computer science; Mathematics; Machine learning; Population-based incremental learning","score_opus":0.01733942542215965,"score_gpt":0.2629486897671943,"score_spread":0.24560926434503466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111053428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00418983,0.000048858008,0.9927161,0.0007625962,0.000046035293,0.0007201728,0.0000023631962,0.00006721233,0.0014468618],"genre_scores_gemma":[0.2405621,0.000009149651,0.75880307,0.000037305625,0.000033667722,0.000030792115,0.0000022435001,0.0000073595147,0.0005143145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979507,0.00023419708,0.0005658546,0.00038141428,0.0006505188,0.00021736154],"domain_scores_gemma":[0.99764305,0.00022819098,0.00025574432,0.000983655,0.0008328939,0.00005644792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011730709,0.00011668317,0.0001901251,0.00013224872,0.00006880835,0.00005665236,0.0009818332,0.000047638885,0.000094482],"category_scores_gemma":[0.00037295886,0.00007627216,0.000045771423,0.0010970435,0.00011534321,0.00020276847,0.00023657487,0.00012498068,0.000011634884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010562292,0.000318116,0.00020984196,0.000106112304,0.00002116873,7.614005e-7,0.00029881063,0.11888098,0.0009124953,0.0042242655,0.00010391193,0.874913],"study_design_scores_gemma":[0.0002662585,0.00015529101,0.002508692,0.000016391317,0.0000050177455,0.000003504753,0.000007789271,0.9631966,0.033349805,0.00035627687,0.000054657427,0.00007971648],"about_ca_topic_score_codex":0.00024403227,"about_ca_topic_score_gemma":0.000018778008,"teacher_disagreement_score":0.8748333,"about_ca_system_score_codex":0.00006135943,"about_ca_system_score_gemma":0.00022520372,"threshold_uncertainty_score":0.31102884},"labels":[],"label_agreement":null},{"id":"W2113434670","doi":"10.1109/icci-cc.2014.6921440","title":"Position update mechanisms for enhanced particle swarm classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 à Montréal","funders":"","keywords":"Particle swarm optimization; Position (finance); Centroid; Benchmark (surveying); Computer science; Multi-swarm optimization; Set (abstract data type); Swarm behaviour; Algorithm; Statistical classification; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.02863662732743854,"score_gpt":0.2978039184431385,"score_spread":0.2691672911157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113434670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006601402,0.0000028905301,0.9927839,0.003569594,0.00017700378,0.00030938425,0.0000013705671,0.00019172698,0.0023039572],"genre_scores_gemma":[0.43857327,0.000003669947,0.56034094,0.00042956937,0.000029488709,0.000080392994,0.000009975568,0.000006031261,0.0005266722],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888456,0.00009171415,0.00019757269,0.00031996163,0.00027256322,0.00023362142],"domain_scores_gemma":[0.9990098,0.00013081534,0.000061107334,0.00044593794,0.00024916363,0.000103215265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006473892,0.000075795986,0.000092276445,0.00005639401,0.00012712159,0.00018309789,0.00043576097,0.000038930586,0.00007316819],"category_scores_gemma":[0.00019356527,0.00006823547,0.0000359185,0.00026949155,0.000017285985,0.0003381063,0.00007332259,0.000043988937,0.0002565366],"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":[0.0000048573056,0.000039690185,7.176328e-7,0.0000057645602,0.000004554153,9.5441905e-8,0.00003476616,0.000471465,0.05473036,0.89230734,0.00044609496,0.051954295],"study_design_scores_gemma":[0.00025061503,0.000063650434,0.000063197505,0.0000016392727,0.0000021927094,7.9583833e-7,0.0000054375123,0.71790755,0.2365238,0.044334855,0.0007776368,0.00006862998],"about_ca_topic_score_codex":0.0000022024824,"about_ca_topic_score_gemma":0.000001059833,"teacher_disagreement_score":0.8479725,"about_ca_system_score_codex":0.000030341716,"about_ca_system_score_gemma":0.000029984601,"threshold_uncertainty_score":0.32973444},"labels":[],"label_agreement":null},{"id":"W2114499446","doi":"10.1109/sis.2005.1501613","title":"Information exchange in multiple cooperating swarms","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Information exchange; Computer science; Information sharing; Particle swarm optimization; Information quality; Best practice; Order (exchange); Distributed computing; Quality (philosophy); Information system; Algorithm; Engineering; World Wide Web","score_opus":0.027540648605150046,"score_gpt":0.27371216852430974,"score_spread":0.2461715199191597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114499446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020944092,0.000019165438,0.98257136,0.0013991209,0.00006789234,0.00016406606,6.4124055e-7,0.00010064,0.013582707],"genre_scores_gemma":[0.31968796,0.000017704753,0.6782406,0.0008352581,0.000050029743,0.000028210778,0.0000064939873,0.0000034153338,0.0011303503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991574,0.000048532464,0.00023694448,0.00009480533,0.00026886875,0.00019341666],"domain_scores_gemma":[0.99947196,0.000085834516,0.000032460503,0.00023504176,0.00011386819,0.00006085627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592026,0.000059271377,0.000071326074,0.00020715098,0.00005497368,0.00020904526,0.00037688122,0.000029243898,0.0002755836],"category_scores_gemma":[0.00035044167,0.00005171338,0.000012289935,0.00053336396,0.000011831923,0.0018584551,0.00017123244,0.000090581336,0.00060293823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030199747,0.000073196206,0.001675851,0.000019517036,0.0000036263148,0.0000025406432,0.003465696,0.058322012,0.00007027596,0.012242559,0.003172937,0.92094874],"study_design_scores_gemma":[0.00036266234,0.000010872353,0.0008461022,0.0000032245116,1.5608543e-7,0.0000023173357,0.000034015833,0.9841698,0.000590982,0.00001921094,0.01389359,0.00006708074],"about_ca_topic_score_codex":0.00005059873,"about_ca_topic_score_gemma":0.000053413773,"teacher_disagreement_score":0.92584777,"about_ca_system_score_codex":0.000048463968,"about_ca_system_score_gemma":0.000044130637,"threshold_uncertainty_score":0.7749752},"labels":[],"label_agreement":null},{"id":"W2115933156","doi":"10.1002/cpe.3344","title":"Exploration/exploitation of a hybrid‐enhanced MPSO‐GA algorithm on a fused CPU‐GPU architecture","year":2014,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Computer science; Metaheuristic; Algorithm; Central processing unit; Particle swarm optimization; Genetic algorithm; Parallel computing; Population; Local search (optimization); Artificial intelligence; Machine learning","score_opus":0.0282447798811836,"score_gpt":0.3269880132541699,"score_spread":0.29874323337298625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115933156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010892856,0.00017682521,0.98630387,0.0010017224,0.00026729726,0.00026563875,0.000003856666,0.000072785435,0.0010151451],"genre_scores_gemma":[0.79029465,0.00024665537,0.20883986,0.00042269335,0.000050001767,0.0000763909,0.00001376996,0.000008901457,0.000047084694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794227,0.0003845314,0.00041200308,0.00053333555,0.00051426055,0.00021362024],"domain_scores_gemma":[0.99764216,0.0011500597,0.00031670867,0.00027502852,0.00046886955,0.0001471797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005254256,0.00017911136,0.000226802,0.00020106349,0.00023176685,0.00023934954,0.00026996952,0.000040471856,0.000012974412],"category_scores_gemma":[0.0011274595,0.00017037232,0.000029123848,0.00045855483,0.00016781,0.0016473593,0.000116759766,0.00019352381,0.000011520783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033716056,0.00013032855,0.0000061512787,0.00003803891,0.00001447092,0.00000431422,0.0135567235,0.004704726,0.00037205016,0.014458585,0.0000921899,0.96658874],"study_design_scores_gemma":[0.00092058734,0.0005722646,0.00010380046,0.000058730348,0.000011966686,0.000039120558,0.0015608693,0.98340535,0.003584304,0.0065636863,0.0029166774,0.00026267002],"about_ca_topic_score_codex":0.0000135795735,"about_ca_topic_score_gemma":6.0385844e-7,"teacher_disagreement_score":0.9787006,"about_ca_system_score_codex":0.000013990949,"about_ca_system_score_gemma":0.00007415744,"threshold_uncertainty_score":0.6947582},"labels":[],"label_agreement":null},{"id":"W2118726465","doi":"10.1145/2330784.2331024","title":"Alpinist CellularDE","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Computer science; Local optimum; Local search (optimization); Evolutionary computation; Evolutionary algorithm; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.026442658303705947,"score_gpt":0.2810787584423271,"score_spread":0.25463610013862115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118726465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006144087,0.000079239326,0.8840324,0.0006589597,0.00030193894,0.000052991807,2.4245367e-7,0.00012362338,0.11468911],"genre_scores_gemma":[0.16998798,0.000011046753,0.81227887,0.00037966084,0.00013211113,0.000007886884,0.0000015877506,0.0000061162946,0.017194778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999136,0.000053858308,0.000102812795,0.00012272807,0.000290907,0.00029372607],"domain_scores_gemma":[0.99927634,0.000053420114,0.000018045153,0.00041202546,0.000055736444,0.00018441427],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004148461,0.00005267026,0.000060306502,0.00006181121,0.000061178755,0.00010158247,0.00051999616,0.000021956448,0.0007721291],"category_scores_gemma":[0.00008059195,0.000043418848,0.00002368575,0.00025869498,0.00002093525,0.00036726784,0.00021560081,0.00005981133,0.0016935805],"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":[0.0000010222572,0.00017554036,0.0018999806,0.000008685039,0.000014863322,0.000005839388,0.00034692188,0.00011025889,0.00033163314,0.9163314,0.028790096,0.051983774],"study_design_scores_gemma":[0.00030291214,0.000032286185,0.0026858808,0.0000023963776,0.0000026483365,0.000030479725,0.000021529308,0.62346333,0.008324286,0.0011007915,0.36375576,0.00027767627],"about_ca_topic_score_codex":0.0000059254585,"about_ca_topic_score_gemma":1.6506348e-7,"teacher_disagreement_score":0.9152306,"about_ca_system_score_codex":0.000013435526,"about_ca_system_score_gemma":0.000026479358,"threshold_uncertainty_score":0.9990837},"labels":[],"label_agreement":null},{"id":"W2122253034","doi":"10.5267/j.dsl.2015.1.003","title":"A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems","year":2015,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Genetic algorithm; Computer science; Algorithm; Mathematics","score_opus":0.04515366211216674,"score_gpt":0.3136975840915204,"score_spread":0.2685439219793536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122253034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009765647,0.00007164385,0.9871212,0.0020484822,0.00032519916,0.0005547778,0.000010653703,0.00004471636,0.000057631194],"genre_scores_gemma":[0.01754678,0.000014074052,0.98180485,0.00052555377,0.000032238597,0.000033989534,0.0000024058725,0.000009381263,0.000030736654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970465,0.00006551632,0.0004777606,0.0006546225,0.0013579463,0.00039766653],"domain_scores_gemma":[0.9977389,0.00041733787,0.000189793,0.00057085475,0.00074980315,0.00033329375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024465888,0.00014327881,0.00022844815,0.0005177018,0.00018861116,0.0005136008,0.0012233246,0.000031335258,0.000009343327],"category_scores_gemma":[0.001629925,0.00012519526,0.000040275936,0.0011551039,0.00091248925,0.0008285932,0.00035861676,0.00007927359,0.000005405764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019482626,0.00008499958,0.00017289979,0.000023022256,0.000012800705,0.000016502472,0.0008565145,0.41167185,0.0043472275,0.0012007048,0.0029656242,0.57862836],"study_design_scores_gemma":[0.00079956924,0.000112980655,0.00022409858,0.00001993926,0.000003898658,0.00004661106,0.000027652133,0.9945492,0.0013228045,0.0022834758,0.0004647956,0.00014494314],"about_ca_topic_score_codex":0.000010302893,"about_ca_topic_score_gemma":2.3257948e-7,"teacher_disagreement_score":0.5828774,"about_ca_system_score_codex":0.000062873216,"about_ca_system_score_gemma":0.00036680634,"threshold_uncertainty_score":0.5105314},"labels":[],"label_agreement":null},{"id":"W2123111112","doi":"10.1109/ccece.2005.1557397","title":"New operators for integer permutation-based particle swarm optimizer","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Flocking (texture); Permutation (music); Mathematical optimization; Particle swarm optimization; Computer science; Operator (biology); Swarm intelligence; Shuffling; Multi-swarm optimization; Theoretical computer science; Mathematics","score_opus":0.018389631657941278,"score_gpt":0.2867866970316604,"score_spread":0.2683970653737191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123111112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052694214,0.000028092687,0.9928889,0.0032865016,0.00016500572,0.00038076995,0.0000026583086,0.00019296218,0.0025281436],"genre_scores_gemma":[0.07657797,8.410472e-7,0.90991026,0.0005299562,0.00010008041,0.00006467927,0.000011958533,0.000014775311,0.0127894515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862206,0.000055039483,0.00028128177,0.0003616607,0.00035433588,0.0003256104],"domain_scores_gemma":[0.9987888,0.00028296202,0.000042133666,0.0004072418,0.00031676632,0.00016210439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033686936,0.0001195002,0.0001347087,0.00009524271,0.00012461803,0.0003695261,0.000550028,0.000043532425,0.0005459501],"category_scores_gemma":[0.00018279492,0.00010047534,0.00006889195,0.0004990391,0.000027426247,0.0003312671,0.00006908798,0.00006580576,0.00015744036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021641916,0.00016843714,0.00039957132,0.000015685227,0.000017624394,0.0000069891953,0.00011294792,0.70830476,0.00044953934,0.21283107,0.061176036,0.016495705],"study_design_scores_gemma":[0.00091726123,0.000048350208,0.00011361655,0.0000028467064,0.000003659194,0.000001204252,0.000009411626,0.97285706,0.02074067,0.0012802309,0.0038872662,0.0001384246],"about_ca_topic_score_codex":0.00016719577,"about_ca_topic_score_gemma":0.000011154946,"teacher_disagreement_score":0.2645523,"about_ca_system_score_codex":0.000047200883,"about_ca_system_score_gemma":0.00034744423,"threshold_uncertainty_score":0.59777707},"labels":[],"label_agreement":null},{"id":"W2123535420","doi":"10.1162/evco_a_00125","title":"On the Behaviour of the (1, λ)-ES for Conically Constrained Linear Problems","year":2014,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mathematical optimization; Convergence (economics); Applied mathematics; Rate of convergence; Invariant (physics); Resampling; Computer science; Algorithm; Key (lock)","score_opus":0.026917931491045745,"score_gpt":0.2819890344616228,"score_spread":0.25507110297057706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123535420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004873687,0.000016694215,0.9885793,0.0049558533,0.00028526929,0.00073075603,0.000012060236,0.000050515842,0.00049587834],"genre_scores_gemma":[0.8460296,0.0000015275109,0.1533705,0.00026000018,0.00005232307,0.000058384652,0.000013484176,0.000007422757,0.00020675521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985139,0.00031354075,0.0003062304,0.00023280783,0.00047212208,0.00016140922],"domain_scores_gemma":[0.99737823,0.001565518,0.00018024215,0.00035365668,0.0004823328,0.000040006234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080628385,0.00009208719,0.00011478204,0.00006070578,0.0002643667,0.00003979621,0.00066904427,0.000043748118,0.000017644788],"category_scores_gemma":[0.00093750056,0.000055990473,0.00008158297,0.00035128594,0.00018087472,0.000106168394,0.00012940161,0.000118541815,0.000014575508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010700551,0.00012812606,0.00022106164,0.000020350162,0.000017313969,1.7712904e-7,0.00012981516,0.32497615,0.00012047571,0.6663908,0.0036914349,0.0042936406],"study_design_scores_gemma":[0.00034029168,0.00014425449,0.0076606288,0.000020990106,0.0000051002007,0.0000050683925,0.000006522723,0.9618547,0.00009394081,0.029377764,0.0004267662,0.000063996325],"about_ca_topic_score_codex":0.0000043250016,"about_ca_topic_score_gemma":8.351123e-7,"teacher_disagreement_score":0.8411559,"about_ca_system_score_codex":0.000037014688,"about_ca_system_score_gemma":0.00016631378,"threshold_uncertainty_score":0.22832252},"labels":[],"label_agreement":null},{"id":"W2123682012","doi":"10.1109/tevc.2007.894200","title":"Opposition-Based Differential Evolution","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1602,"is_retracted":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":"Differential evolution; Ode; Initialization; Evolutionary algorithm; Curse of dimensionality; Population; Mathematical optimization; Benchmark (surveying); Evolutionary computation; Computer science; Nonlinear system; Algorithm; Mathematics; Artificial intelligence; Applied mathematics","score_opus":0.02639547269231373,"score_gpt":0.26227055416962397,"score_spread":0.23587508147731023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123682012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020229116,0.000022539687,0.99463934,0.0007735019,0.0011817436,0.0003937491,0.000026776128,0.0005516175,0.00038780228],"genre_scores_gemma":[0.8638417,0.000009936832,0.1355774,0.000121141595,0.000070038885,0.00007715366,0.000036397985,0.000019463912,0.00024677807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730843,0.0003116877,0.000429642,0.00058481446,0.0010003933,0.00036500898],"domain_scores_gemma":[0.9985645,0.00031073563,0.00012117523,0.00042763536,0.00037601736,0.00019992779],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015339175,0.00023790712,0.00019972541,0.00069637585,0.001005596,0.000075303615,0.0004577938,0.00012296085,0.00015441944],"category_scores_gemma":[0.000014318316,0.0002605883,0.00016192399,0.0010980164,0.00015609655,0.0006856547,0.0000044730373,0.00031406805,0.00036703714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038180537,0.000615373,0.000019502671,0.000012282953,0.000030654955,0.000022026532,0.00007656888,0.9900583,0.0005725158,0.0035898925,0.0012139619,0.003750723],"study_design_scores_gemma":[0.0008741396,0.00020825582,0.0024689082,0.000017295992,0.000011830708,0.00008148465,0.0000065452828,0.9927893,0.0011931187,0.0020434896,0.000047597045,0.00025802542],"about_ca_topic_score_codex":0.000032418895,"about_ca_topic_score_gemma":0.0000022612915,"teacher_disagreement_score":0.8618188,"about_ca_system_score_codex":0.00067377085,"about_ca_system_score_gemma":0.00045209253,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W2123728119","doi":"10.1109/ipdps.2009.5161150","title":"An Aggregated Ant Colony Optimization approach for pricing options","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Computer science; Profit (economics); Ant colony optimization algorithms; Mathematical optimization; Traverse; Node (physics); Shortest path problem; Optimization problem; Operations research; Microeconomics; Economics; Artificial intelligence; Mathematics; Engineering; Algorithm","score_opus":0.030328605132417825,"score_gpt":0.3141963081946118,"score_spread":0.283867703062194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123728119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000046619953,0.00003061122,0.99365413,0.00073214853,0.00007242802,0.00070134643,0.000002988544,0.00035718302,0.004402562],"genre_scores_gemma":[0.011287526,0.000030103543,0.98721206,0.00031800361,0.000053857395,0.00004629044,0.00007119655,0.0000092927885,0.0009716672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985308,0.00010333767,0.00026585965,0.00045123367,0.00032552925,0.00032323643],"domain_scores_gemma":[0.99868447,0.00007632698,0.0000821062,0.0005682709,0.00041335615,0.00017549808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005573793,0.000121334066,0.00015183468,0.00022828607,0.00024686765,0.00036739546,0.000737491,0.00006723083,0.000044965396],"category_scores_gemma":[0.00018560271,0.00010869787,0.000042777036,0.00087335415,0.000025316811,0.0007096509,0.00004772999,0.00008506447,0.000004945505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005383669,0.00021367079,0.0000050546587,0.000004696346,0.000005781793,0.0000010410689,0.000089564084,0.94642824,0.00018635117,0.03812651,0.0005263024,0.014407399],"study_design_scores_gemma":[0.00040277935,0.0002446047,0.000070941875,0.0000032014761,0.0000044024055,0.000008091282,0.000017519704,0.9982098,0.00044859987,0.00031684627,0.00013058876,0.00014266958],"about_ca_topic_score_codex":0.0000068161753,"about_ca_topic_score_gemma":4.904347e-7,"teacher_disagreement_score":0.05178151,"about_ca_system_score_codex":0.000056126428,"about_ca_system_score_gemma":0.00010563665,"threshold_uncertainty_score":0.44325703},"labels":[],"label_agreement":null},{"id":"W2124261928","doi":"10.1162/1063656053583423","title":"Space Complexity of Estimation of Distribution Algorithms","year":2005,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"EDAS; Estimation of distribution algorithm; Algorithm; Bayesian network; Mathematics; Computational complexity theory; Mathematical optimization; Computer science; Evolutionary algorithm; Distribution (mathematics); Space (punctuation); Genetic algorithm; Artificial intelligence","score_opus":0.035567672083433144,"score_gpt":0.3086057288146757,"score_spread":0.27303805673124254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124261928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004259702,0.00012788987,0.9932986,0.0013559682,0.00015247638,0.00024689725,0.000050294606,0.00009097258,0.00041719104],"genre_scores_gemma":[0.50932306,0.000007090661,0.49039653,0.000007564151,0.00003176597,0.0000047399135,0.00018502024,0.0000039605566,0.00004026925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981006,0.00018381212,0.0005448316,0.00027737973,0.0007109288,0.00018247378],"domain_scores_gemma":[0.99846256,0.00017885325,0.00034589172,0.00029472786,0.0006434317,0.000074519514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045264425,0.000117231146,0.00021379438,0.00018982704,0.000100048645,0.00002322388,0.00037511595,0.000059828868,0.000039676528],"category_scores_gemma":[0.00021288746,0.00012707154,0.00006910979,0.0008624064,0.0002141746,0.00068477704,0.0001484603,0.00009930415,0.000034528286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010926992,0.00023301145,0.00009359734,0.00004007572,0.000017567085,6.480081e-7,0.00016263909,0.82266474,0.00018182088,0.10948967,0.001633246,0.06547207],"study_design_scores_gemma":[0.00036569918,0.0000835726,0.018575683,0.000019730123,0.0000059078757,0.000010719815,0.000009668091,0.96779406,0.0017019854,0.011115679,0.00021566427,0.00010161945],"about_ca_topic_score_codex":0.00003478484,"about_ca_topic_score_gemma":0.0000011603418,"teacher_disagreement_score":0.50506335,"about_ca_system_score_codex":0.00015551399,"about_ca_system_score_gemma":0.00016165772,"threshold_uncertainty_score":0.51818275},"labels":[],"label_agreement":null},{"id":"W2124732687","doi":"10.1109/ccece.2008.4564775","title":"Searching for structure in data with fuzzy clusters of variable dimensionality of feature subspaces","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Linear subspace; Cluster analysis; Dimensionality reduction; Reduction (mathematics); Computer science; Fuzzy clustering; Feature (linguistics); Fuzzy logic; Data mining; Particle swarm optimization; Pattern recognition (psychology); Artificial intelligence; Curse of dimensionality; Mathematics; Algorithm","score_opus":0.03191149864284218,"score_gpt":0.24023972204035476,"score_spread":0.20832822339751259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124732687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07193891,0.00009492493,0.9253412,0.0015667869,0.00007942621,0.00060025835,0.000069393376,0.000047043828,0.00026205328],"genre_scores_gemma":[0.8209555,0.0000367458,0.17886715,0.000048901336,0.000024433863,0.000009426075,0.000015822392,0.000010021031,0.000032012384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983381,0.000017111466,0.00023568195,0.0005595056,0.00036754948,0.00048203112],"domain_scores_gemma":[0.9985904,0.00016425659,0.00009657324,0.00028363088,0.0005418635,0.00032326253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027399234,0.00020408674,0.00038142753,0.00048363238,0.000082384744,0.00009947923,0.0009777504,0.00010325215,0.000005330419],"category_scores_gemma":[0.00013311185,0.00017713704,0.000018171984,0.00084912527,0.000084939515,0.00037346815,0.00018429304,0.00035760237,1.5613729e-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":[0.00017980234,0.00016352668,0.009780969,0.0009896201,0.00018247991,0.00005070306,0.0025417658,0.022269849,0.0036306246,0.92172605,0.0011267661,0.03735782],"study_design_scores_gemma":[0.00042329996,0.00022483066,0.0035804072,0.00014379746,0.000004936438,0.000035803743,0.0000101337255,0.9941497,0.00029782375,0.00073512323,0.00019772322,0.00019640883],"about_ca_topic_score_codex":0.0014158633,"about_ca_topic_score_gemma":0.0005697868,"teacher_disagreement_score":0.97187984,"about_ca_system_score_codex":0.000059150727,"about_ca_system_score_gemma":0.000927152,"threshold_uncertainty_score":0.7223439},"labels":[],"label_agreement":null},{"id":"W2125142487","doi":"10.1109/cec.2015.7257040","title":"Ring optimization with extinction","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Extinction (optical mineralogy); Ring (chemistry); Computer science; Geology; Paleontology; Chemistry","score_opus":0.04647036512656202,"score_gpt":0.280914914358956,"score_spread":0.23444454923239397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125142487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006100338,0.0000105519075,0.9435826,0.00045558103,0.00011025999,0.00009431822,9.018191e-8,0.00021277582,0.055472802],"genre_scores_gemma":[0.0127812615,0.0000037050936,0.98444533,0.00008378981,0.00003604157,0.000010134704,0.0000022401116,0.0000065694617,0.0026309537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906224,0.000052620602,0.00010492087,0.00020946188,0.00042462297,0.00014614603],"domain_scores_gemma":[0.999157,0.000026887146,0.0000343613,0.0003085125,0.00032269995,0.00015051162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035457188,0.00006134934,0.00006272659,0.000109125416,0.000049456197,0.00018655283,0.00030173815,0.000022097813,0.00010796685],"category_scores_gemma":[0.000111438334,0.000046143574,0.0000088568995,0.0005335903,0.000017987146,0.00057397713,0.00010419906,0.000056742687,0.0000904328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053614117,0.000025381734,0.0001412698,0.0000020227333,0.0000051911848,0.0000060100942,0.00010673576,0.974345,0.000003568556,0.011583636,0.00062766497,0.013148159],"study_design_scores_gemma":[0.0003318837,0.00006361701,0.000048010763,0.0000022065235,0.0000011555234,0.000017682076,0.00002371446,0.9978017,0.0001824189,0.00013880187,0.0013179632,0.00007081319],"about_ca_topic_score_codex":0.000010656594,"about_ca_topic_score_gemma":0.0000017250071,"teacher_disagreement_score":0.05284185,"about_ca_system_score_codex":0.00004285564,"about_ca_system_score_gemma":0.00010438526,"threshold_uncertainty_score":0.18816803},"labels":[],"label_agreement":null},{"id":"W2127027513","doi":"10.1109/ccece.2008.4564490","title":"Efficiency competition on N-queen problem: DE vs. CMA-ES","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"CMA-ES; Differential evolution; Robustness (evolution); Evolution strategy; Mathematical optimization; Convergence (economics); Evolutionary algorithm; Computer science; Evolutionary computation; Rate of convergence; Mathematics; Covariance matrix; Algorithm; Key (lock)","score_opus":0.017917157975263862,"score_gpt":0.21179147958029457,"score_spread":0.1938743216050307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127027513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017966788,0.000025823309,0.9725852,0.0030801788,0.00014583024,0.00044136617,0.00000585026,0.00034828077,0.00540069],"genre_scores_gemma":[0.9608622,0.00013846367,0.037990466,0.00060406147,0.00013166943,0.000058176178,0.000005307089,0.000022820755,0.00018679547],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733067,0.0000275635,0.00033154688,0.00077109947,0.00053235027,0.0010067598],"domain_scores_gemma":[0.9981389,0.00010784556,0.00007730356,0.00023157978,0.00047579606,0.0009685542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027243263,0.00036096937,0.00035874514,0.0007363873,0.00033746357,0.0005575694,0.00092044985,0.0001575457,0.000050011662],"category_scores_gemma":[0.00010920618,0.00035613842,0.000053654392,0.00090282515,0.000090445115,0.0003438313,0.00011583411,0.00061914977,0.0000434741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001546782,0.000096984215,0.0007123044,0.00006173675,0.000025705245,0.00008944545,0.00073969894,0.0021708023,0.00020768923,0.9654041,0.00085166853,0.029624416],"study_design_scores_gemma":[0.00032826728,0.00061395305,0.0034020205,0.00010300861,0.000004339141,0.00011559608,0.0000051192833,0.99219286,0.00029515676,0.0010251065,0.0014897069,0.0004248398],"about_ca_topic_score_codex":0.0009687261,"about_ca_topic_score_gemma":0.00004790483,"teacher_disagreement_score":0.99002206,"about_ca_system_score_codex":0.0002684681,"about_ca_system_score_gemma":0.00080713077,"threshold_uncertainty_score":0.9998891},"labels":[],"label_agreement":null},{"id":"W2129526759","doi":"10.1007/3-540-44886-1_30","title":"An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":52,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ant colony optimization algorithms; Computer science; Protein structure prediction; Benchmark (surveying); Algorithm; Protein folding; Ant colony; Focus (optics); Folding (DSP implementation); Protein structure; Chemistry","score_opus":0.02395973256592805,"score_gpt":0.28334347114580616,"score_spread":0.2593837385798781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129526759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000025494637,0.0002794053,0.9929532,0.0013794262,0.0010672904,0.0037444038,0.000016396047,0.00016319561,0.0003941533],"genre_scores_gemma":[0.0005669024,0.000038894323,0.99704343,0.00083335704,0.00039210104,0.0002659793,0.00001276222,0.000058481444,0.00078807946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99483484,0.0001312755,0.00074756873,0.0019207371,0.0013902063,0.00097539736],"domain_scores_gemma":[0.99544066,0.00094209204,0.00046393494,0.0019505322,0.00093128084,0.00027147247],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0034017484,0.0005904577,0.0005373484,0.0007116738,0.00079356285,0.0016393737,0.00462044,0.00035116365,0.000030475658],"category_scores_gemma":[0.00030924095,0.00045062185,0.0001516404,0.000923689,0.0006458826,0.000918385,0.0007196891,0.0008325287,0.000010585019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050132658,0.00004477747,8.96961e-7,0.000035753597,0.000017639948,0.000011294671,0.00033088762,0.07917631,0.0003531589,0.007124268,0.000031586922,0.91286844],"study_design_scores_gemma":[0.00043603164,0.0004711905,0.0000033096776,0.000118773285,0.000011639989,0.000032442607,4.0631855e-7,0.9599411,0.0018032393,0.034629226,0.0020233935,0.0005292523],"about_ca_topic_score_codex":0.000030897216,"about_ca_topic_score_gemma":0.000021865739,"teacher_disagreement_score":0.91233915,"about_ca_system_score_codex":0.00045889232,"about_ca_system_score_gemma":0.0010386367,"threshold_uncertainty_score":0.99979454},"labels":[],"label_agreement":null},{"id":"W2131373329","doi":"10.1109/isda.2009.216","title":"A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Scalability; Differential evolution; Computer science; Ode; Benchmark (surveying); Metaheuristic; Population; Opposition (politics); Machine learning; Mathematical optimization; Artificial intelligence; Mathematics; Applied mathematics","score_opus":0.07222411078257278,"score_gpt":0.3580393510089142,"score_spread":0.2858152402263414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131373329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0091466885,0.000009656162,0.9884273,0.0013782753,0.000037751517,0.0003600853,0.000002145456,0.00023796738,0.0004001584],"genre_scores_gemma":[0.27454025,0.0000010371573,0.7245699,0.0005649707,0.000030837804,0.000014193517,0.000008761047,0.000005787543,0.00026422346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986396,0.00015607772,0.00023776708,0.00034966823,0.0002619207,0.000354991],"domain_scores_gemma":[0.9986258,0.00041110176,0.000059571135,0.00031595211,0.00042831348,0.00015923171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008094897,0.00010823594,0.00014823888,0.00012705855,0.00026244606,0.00035872773,0.00045100812,0.000057105837,0.00013012209],"category_scores_gemma":[0.0010597194,0.00010092693,0.000060402173,0.0006095938,0.00002647213,0.00023831792,0.00004127918,0.00011598319,0.000008216695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003878488,0.0006127436,0.0027959673,0.000036902402,0.000017801565,0.0000117369445,0.0001291373,0.8552892,0.07734704,0.011260883,0.00079439994,0.051665384],"study_design_scores_gemma":[0.00069427147,0.00013042023,0.0008654773,0.000005890238,0.0000034207528,0.0000036449794,0.0000020320315,0.9843431,0.012953841,0.00070293545,0.00017306532,0.00012187272],"about_ca_topic_score_codex":0.00003626138,"about_ca_topic_score_gemma":0.0000011322884,"teacher_disagreement_score":0.26539356,"about_ca_system_score_codex":0.00012635112,"about_ca_system_score_gemma":0.00034975662,"threshold_uncertainty_score":0.41156808},"labels":[],"label_agreement":null},{"id":"W2132055170","doi":"10.1109/foci.2007.372167","title":"Simulated Annealing with Opposite Neighbors","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Waterloo","funders":"","keywords":"Simulated annealing; Adaptive simulated annealing; Computer science; Annealing (glass); Opposition (politics); Rate of convergence; Algorithm; Mathematical optimization; Mathematics; Materials science","score_opus":0.011264609399555519,"score_gpt":0.2681736353011252,"score_spread":0.25690902590156967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132055170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027171662,0.000019090538,0.971519,0.00035581176,0.00007082414,0.00012589598,3.208458e-7,0.0002378747,0.024954004],"genre_scores_gemma":[0.35059524,0.0000039597658,0.64616287,0.00038906914,0.00003402668,8.015794e-7,0.0000023730051,0.00001059363,0.0028010753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986971,0.00003333047,0.00018424171,0.00028409812,0.0004549199,0.00034627988],"domain_scores_gemma":[0.9989142,0.00018814465,0.00004000625,0.0004295313,0.00023491193,0.00019322582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006690375,0.00009212985,0.00009911521,0.00017840612,0.000098443954,0.00017772455,0.000551501,0.000036634494,0.00014471446],"category_scores_gemma":[0.000066082524,0.000067186025,0.000018981682,0.00095176534,0.000033183227,0.0003084957,0.00013118598,0.000107494816,0.00009247282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013401922,0.00031270902,0.009463791,0.00004521562,0.00017588325,0.0009885995,0.0013540898,0.51052755,0.0014261177,0.23261836,0.002285314,0.24066839],"study_design_scores_gemma":[0.00032690782,0.000069481655,0.0012907964,0.000005315392,0.0000018500602,0.000015447327,0.000012172831,0.9940869,0.0026501822,0.00012353601,0.0012958621,0.00012152274],"about_ca_topic_score_codex":0.00003651496,"about_ca_topic_score_gemma":0.000006374114,"teacher_disagreement_score":0.4835594,"about_ca_system_score_codex":0.000026782494,"about_ca_system_score_gemma":0.000056320434,"threshold_uncertainty_score":0.27397665},"labels":[],"label_agreement":null},{"id":"W2132721978","doi":"10.1145/2330163.2330174","title":"Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Particle swarm optimization; Optimization problem; Embedding; Multi-swarm optimization; Mathematical optimization; Mixture model; Focus (optics); Digital watermarking; Representation (politics); Gaussian; Algorithm; Artificial intelligence; Image (mathematics); Mathematics","score_opus":0.041982000992780544,"score_gpt":0.3134523187551221,"score_spread":0.27147031776234154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132721978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005338168,0.00024137634,0.9972385,0.00070590957,0.00024929317,0.0005080522,0.0000039458723,0.000082799954,0.00043627925],"genre_scores_gemma":[0.3394662,0.000035872046,0.66006964,0.00003127928,0.000025160023,0.000053332606,0.000009949857,0.00000981129,0.00029872733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986715,0.000064196,0.00033390024,0.00022486398,0.00032757682,0.0003779695],"domain_scores_gemma":[0.99902326,0.00007283637,0.000087662615,0.00037806653,0.0002780064,0.0001601681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006553094,0.00010156322,0.0001476414,0.00008805885,0.00007667084,0.0000636883,0.00041695606,0.00007304058,0.000054356915],"category_scores_gemma":[0.00016220428,0.000084985346,0.000053661395,0.00042338326,0.000020952302,0.0005413308,0.00012465335,0.00009927547,0.000009274045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049281425,0.00013770878,0.000044991124,0.000048622984,0.0000106368125,6.898584e-8,0.00034681792,0.97624946,0.000098533645,0.014305495,0.00009319011,0.008659558],"study_design_scores_gemma":[0.00028840554,0.00005739002,0.000010290517,0.000014014915,0.000005919504,0.0000019009811,0.000020334366,0.99808604,0.0008970118,0.0003996759,0.00011614314,0.00010287639],"about_ca_topic_score_codex":0.000006202545,"about_ca_topic_score_gemma":0.0000016932626,"teacher_disagreement_score":0.3389324,"about_ca_system_score_codex":0.000039532773,"about_ca_system_score_gemma":0.000053547326,"threshold_uncertainty_score":0.34656018},"labels":[],"label_agreement":null},{"id":"W2133287603","doi":"10.5539/mer.v4n2p16","title":"CNC Machining Path Planning Optimization for Circular Hole Patterns via a Hybrid Ant Colony Optimization Approach","year":2014,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":23,"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":"King Saud University","keywords":"Ant colony optimization algorithms; Machining; Travelling salesman problem; Tool path; Numerical control; Ant colony; Path (computing); Engineering; Computer science; Software; Engineering drawing; Mathematical optimization; Mechanical engineering; Algorithm; Mathematics","score_opus":0.04380084229406707,"score_gpt":0.3085023961854714,"score_spread":0.2647015538914043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133287603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036232272,0.00005791987,0.9976329,0.00022029053,0.00028225264,0.0009531053,0.000012662876,0.00036702142,0.00011148272],"genre_scores_gemma":[0.18709214,0.0000182314,0.8120352,0.000043523494,0.00020445512,0.0003527244,0.00011267128,0.00007450183,0.000066530745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99548537,0.00044017035,0.0005350048,0.0009655774,0.0014874993,0.0010863532],"domain_scores_gemma":[0.99691504,0.0010177125,0.00009423882,0.0009011639,0.0006626596,0.0004091684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005430002,0.00028222328,0.0003932669,0.00066373864,0.0004039898,0.0005661532,0.0014243671,0.00016312269,0.000040009552],"category_scores_gemma":[0.0029795808,0.00029056682,0.00011445514,0.0010629768,0.000036255922,0.0004340462,0.0006278397,0.0007363449,0.0000098421915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013850029,0.000098051605,0.000008851414,0.00016726849,0.000025847972,0.00001073886,0.00008595527,0.98934263,0.00057522696,0.005648143,0.000092518385,0.0039309016],"study_design_scores_gemma":[0.000747247,0.0002558135,0.000009878165,0.00007407918,0.0000069373973,0.000022061984,0.000012799461,0.99695784,0.0011293597,0.00015660803,0.00031829585,0.0003090827],"about_ca_topic_score_codex":0.000018963605,"about_ca_topic_score_gemma":7.284154e-8,"teacher_disagreement_score":0.18672982,"about_ca_system_score_codex":0.00021209022,"about_ca_system_score_gemma":0.000115629045,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W2133583641","doi":"10.1016/j.asoc.2016.09.042","title":"Micro-differential evolution: Diversity enhancement and a comparative study","year":2016,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Waterloo; University of Ontario Institute of Technology","funders":"","keywords":"Benchmark (surveying); Mutation; Differential evolution; Premature convergence; Computer science; Population; Convergence (economics); Population size; Evolutionary algorithm; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Particle swarm optimization; Geography","score_opus":0.04291256960497788,"score_gpt":0.2857363797054781,"score_spread":0.24282381010050022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133583641","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":[0.226079,0.000018758834,0.77252126,0.00010548339,0.00012671844,0.0003906984,8.9991175e-7,0.00009819427,0.0006589596],"genre_scores_gemma":[0.92153686,0.0000017295961,0.07821015,0.000029372442,0.000052501255,0.0000077701725,6.478338e-7,0.000004117082,0.00015682934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982661,0.00011697587,0.000248664,0.00056423136,0.00047394476,0.00033007053],"domain_scores_gemma":[0.99894446,0.00032256736,0.00011014579,0.0003671584,0.00012719652,0.00012848782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004131301,0.00015813348,0.00023825227,0.00011161186,0.00063008006,0.00013103867,0.0006208649,0.000033240347,0.000045558583],"category_scores_gemma":[0.000025858497,0.00012133456,0.000026015374,0.00028289956,0.000095154195,0.00013000245,0.002731123,0.00010510201,0.00007722307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002952869,0.0048011392,0.08154467,0.00013670744,0.0011517233,0.00010147757,0.055637386,0.0016205569,0.061199967,0.31474614,0.0035781714,0.4751868],"study_design_scores_gemma":[0.014450987,0.001056503,0.16612038,0.00013302456,0.00009785151,0.000034844183,0.0026979465,0.794091,0.011376623,0.0072048795,0.0007142149,0.0020217653],"about_ca_topic_score_codex":0.000014408227,"about_ca_topic_score_gemma":0.0000020386242,"teacher_disagreement_score":0.79247046,"about_ca_system_score_codex":0.000097447024,"about_ca_system_score_gemma":0.000043735738,"threshold_uncertainty_score":0.49478796},"labels":[],"label_agreement":null},{"id":"W2134788587","doi":"10.1109/sis.2007.368044","title":"Applying Opposition-Based Ideas to the Ant Colony System","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":96,"is_retracted":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; Ant colony optimization algorithms; Synchronizing; Opposition (politics); Computer science; ANT; Ant colony; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Computer network","score_opus":0.02070064834814609,"score_gpt":0.29380474932538503,"score_spread":0.2731041009772389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134788587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016533451,0.000018089917,0.98348784,0.0024082693,0.00027958277,0.0007982892,0.0000014896627,0.00021509243,0.012625993],"genre_scores_gemma":[0.45742702,5.938449e-7,0.53869283,0.002292581,0.00011043245,0.0001771335,0.0000024403557,0.000010335207,0.0012866404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841535,0.000100290716,0.00024360884,0.00028198716,0.0006294331,0.00032935338],"domain_scores_gemma":[0.9984673,0.00043817062,0.000042665248,0.00061715615,0.0002324431,0.00020222583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019494196,0.00008765011,0.00010208352,0.00015811026,0.0002598566,0.000271951,0.0009936591,0.000031590997,0.000051696374],"category_scores_gemma":[0.000129841,0.00005805114,0.000035984176,0.00090184686,0.000021646034,0.00009961245,0.00018147095,0.00009578661,0.0004831194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043063832,0.0002021911,0.0004262766,0.00013019965,0.000056064768,0.00026907865,0.00062637794,0.11324373,0.0017585495,0.7031664,0.031111406,0.14896664],"study_design_scores_gemma":[0.00016735494,0.000042620555,0.0002694153,0.000016329239,0.0000022689376,0.000012859141,0.000076596545,0.98503244,0.002322004,0.000039190134,0.011918724,0.000100218545],"about_ca_topic_score_codex":0.000065012726,"about_ca_topic_score_gemma":0.000017320352,"teacher_disagreement_score":0.8717887,"about_ca_system_score_codex":0.000115770985,"about_ca_system_score_gemma":0.0001198679,"threshold_uncertainty_score":0.62096834},"labels":[],"label_agreement":null},{"id":"W2137849348","doi":"10.1016/s0166-218x(01)00338-9","title":"A survey of very large-scale neighborhood search techniques","year":2002,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":639,"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":"Heuristic; Mathematics; Mathematical optimization; Local search (optimization); Variable neighborhood search; Search algorithm; Computation; Algorithm; Incremental heuristic search; Scale (ratio); Computer science; Beam search; Metaheuristic","score_opus":0.041361962283819484,"score_gpt":0.29085607512228484,"score_spread":0.24949411283846534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137849348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055218145,0.00006242563,0.96172154,0.00009188481,0.000034358098,0.00044873872,0.000038529048,0.00018035118,0.036869977],"genre_scores_gemma":[0.22256938,0.00009037697,0.7766513,0.00005410606,0.000023698683,0.000060457023,0.00001899492,0.00003526104,0.0004963793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770087,0.000087518274,0.00052599807,0.0003700017,0.000868696,0.00044693815],"domain_scores_gemma":[0.9978788,0.00041757966,0.00015484357,0.0011246005,0.00027858498,0.00014557726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014195913,0.00018862226,0.00039028458,0.0002071049,0.00009578081,0.00012317508,0.0012296061,0.0000974069,0.00029684513],"category_scores_gemma":[0.00019306372,0.00016662074,0.000064070904,0.0010177428,0.00009415048,0.00016148848,0.0005638502,0.00021700899,0.000153591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027704962,0.0035379734,0.0010548523,0.0016509931,0.0003024093,0.000034248933,0.018829336,0.0006344219,0.0054855337,0.8500903,0.010676144,0.107676044],"study_design_scores_gemma":[0.00031895644,0.000056148085,0.00042351618,0.000029591913,0.000008434212,0.000004612092,0.00012863081,0.98518145,0.009655465,0.0037661376,0.00017896765,0.00024807738],"about_ca_topic_score_codex":0.000012154551,"about_ca_topic_score_gemma":0.0000045414236,"teacher_disagreement_score":0.984547,"about_ca_system_score_codex":0.000029447701,"about_ca_system_score_gemma":0.000043727607,"threshold_uncertainty_score":0.67945963},"labels":[],"label_agreement":null},{"id":"W2138588773","doi":"10.1007/s10489-014-0613-2","title":"Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution","year":2014,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":169,"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":"Curse of dimensionality; Differential evolution; Computer science; Particle swarm optimization; Divergence (linguistics); Population; Mathematical optimization; Swarm behaviour; Algorithm; Artificial intelligence; Mathematics","score_opus":0.027362055206610544,"score_gpt":0.2593924310048275,"score_spread":0.23203037579821695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138588773","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":[0.07992144,0.0000786347,0.919275,0.0001855715,0.000079408324,0.00025641406,4.789673e-7,0.000029049435,0.00017402606],"genre_scores_gemma":[0.9842278,0.000045293295,0.01560247,0.000050914725,0.000026158374,0.000023041208,7.6156124e-7,0.000005375619,0.000018224597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988088,0.00013961573,0.0001976355,0.00029501144,0.00039727567,0.00016168469],"domain_scores_gemma":[0.99888635,0.00054474815,0.0000809163,0.00029515332,0.000115559495,0.0000773045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005627984,0.00009666267,0.00012370157,0.000044584904,0.00014011128,0.00006137567,0.00027367886,0.000034954148,0.000008475542],"category_scores_gemma":[0.0002977269,0.00007011492,0.000014953336,0.00024214278,0.000088952824,0.00009940513,0.00021490554,0.00010214348,0.000010086346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002719506,0.000083478866,0.00015056117,0.00007292034,0.000017419263,4.4000484e-7,0.00033193402,0.11996106,0.0054712985,0.8159259,0.000010197282,0.057947602],"study_design_scores_gemma":[0.00009288314,0.000053826043,0.0013882859,0.000016484842,0.000005683741,0.0000014318731,0.0000065417335,0.87053597,0.124692366,0.003130831,0.000008939035,0.00006677146],"about_ca_topic_score_codex":0.0000063748926,"about_ca_topic_score_gemma":5.0761736e-7,"teacher_disagreement_score":0.90430635,"about_ca_system_score_codex":0.000017417797,"about_ca_system_score_gemma":0.000016886532,"threshold_uncertainty_score":0.28592032},"labels":[],"label_agreement":null},{"id":"W2139068682","doi":"10.1162/evco.2006.14.3.291","title":"Optimum Tracking with Evolution Strategies","year":2006,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Evolution strategy; Mathematical optimization; Simple (philosophy); Adaptation (eye); Tracking (education); Computer science; Population; Contrast (vision); Task (project management); Scaling; Evolutionary algorithm; Optimization problem; Mathematics; Artificial intelligence; Engineering","score_opus":0.012970411711664439,"score_gpt":0.25958375829585384,"score_spread":0.2466133465841894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139068682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031676607,0.00021091514,0.9887429,0.0005774709,0.00019965172,0.0002593443,0.0000030067183,0.00042041286,0.0064186417],"genre_scores_gemma":[0.60597944,0.0000018391097,0.39357635,0.00001577446,0.00010984754,0.000016657943,0.000048691112,0.00001024163,0.00024114012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980052,0.00015747518,0.00031364197,0.00045180973,0.00074389105,0.00032796848],"domain_scores_gemma":[0.9988622,0.00013233785,0.00013793535,0.00027601488,0.000521187,0.00007032094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002709088,0.00016780783,0.00014372478,0.0003097725,0.0003212798,0.0003558215,0.00039191337,0.00006163509,0.000028725446],"category_scores_gemma":[0.0000257979,0.00015730597,0.000041675048,0.0010042249,0.00009196152,0.0018719417,0.00008843902,0.00015299207,0.000097607626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001103095,0.00010010244,0.0004343008,0.000011619944,0.000010243296,0.000016729024,0.00004748335,0.804158,0.00012571093,0.19024405,0.0017940074,0.0030467208],"study_design_scores_gemma":[0.0004194214,0.00009957353,0.06499462,0.00001518179,0.0000048940556,0.000067025605,0.000046043882,0.90560687,0.00004205585,0.028240025,0.00027275272,0.00019152506],"about_ca_topic_score_codex":0.00009944594,"about_ca_topic_score_gemma":0.000008004761,"teacher_disagreement_score":0.6028118,"about_ca_system_score_codex":0.00025020508,"about_ca_system_score_gemma":0.0003791508,"threshold_uncertainty_score":0.64147514},"labels":[],"label_agreement":null},{"id":"W2139894151","doi":"10.1109/cec.2011.5949948","title":"Enhanced Differential Evolution using center-based sampling","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Ode; Differential evolution; Benchmark (surveying); Range (aeronautics); Algorithm; Computer science; Mathematical optimization; Scale (ratio); Ordinary differential equation; Mathematics; Differential equation; Applied mathematics; Mathematical analysis; Engineering","score_opus":0.1214932312729185,"score_gpt":0.31931635794331137,"score_spread":0.19782312667039287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139894151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009660673,0.000005098855,0.98606825,0.000021680868,0.00037178386,0.00012670609,0.0000012533407,0.00016137677,0.003583202],"genre_scores_gemma":[0.519426,5.323621e-7,0.48042068,0.000023275865,0.00002334561,0.0000030793224,0.0000015682872,0.000004867376,0.000096629155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987676,0.00008366274,0.0002026245,0.00031290436,0.00034734412,0.00028586012],"domain_scores_gemma":[0.99920905,0.000040108407,0.000057772257,0.0004201805,0.00016209242,0.00011081447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015721822,0.00009844099,0.000107665386,0.00017312728,0.00011767233,0.000096447846,0.0005544151,0.000041184685,0.0007255328],"category_scores_gemma":[0.00007037103,0.00008845562,0.000047410318,0.00034988794,0.000033735436,0.0002644512,0.00016938343,0.00008749004,0.00006531737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021381286,0.004054637,0.005871853,0.00022495675,0.00023266797,0.000052147396,0.0029699751,0.033583235,0.2074114,0.5838106,0.00041105394,0.16116366],"study_design_scores_gemma":[0.00035711227,0.000029559358,0.001233946,0.000008867423,0.000002431423,0.0000018834543,0.000006244335,0.9784809,0.019192636,0.0005573554,0.000018685152,0.00011039357],"about_ca_topic_score_codex":0.0000705568,"about_ca_topic_score_gemma":0.000002835598,"teacher_disagreement_score":0.94489765,"about_ca_system_score_codex":0.00007780029,"about_ca_system_score_gemma":0.000099732664,"threshold_uncertainty_score":0.7944076},"labels":[],"label_agreement":null},{"id":"W2139944186","doi":"10.4018/ijamc.2014100101","title":"Centroid Opposition-Based Differential Evolution","year":2014,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Metaheuristic Optimization Algorithms Research","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; University of Waterloo; Ontario Tech University","funders":"","keywords":"Ode; Differential evolution; Centroid; Benchmark (surveying); Population; Mathematical optimization; Algorithm; Mathematics; Opposition (politics); Nonlinear system; Evolutionary algorithm; Computer science; Applied mathematics; Artificial intelligence; Physics; Law","score_opus":0.008994044164295193,"score_gpt":0.25966775568062767,"score_spread":0.2506737115163325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139944186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025382873,0.000024920922,0.99143046,0.0008223945,0.002401358,0.00011948567,0.0000028046693,0.00006412408,0.002596157],"genre_scores_gemma":[0.7757348,0.0000028985266,0.22326593,0.0001826001,0.0007778744,0.0000015933218,0.000006694108,0.000013569362,0.000014027143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657744,0.00017860849,0.0009431468,0.00031925013,0.0016645696,0.00031695573],"domain_scores_gemma":[0.996752,0.00080165995,0.00082819106,0.0003338314,0.0010632062,0.00022110595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012836963,0.00020170189,0.0003567579,0.00062762736,0.00014092114,0.00042004368,0.0019752525,0.000063740335,0.00008773274],"category_scores_gemma":[0.00058047206,0.00018682334,0.00018787108,0.0003466569,0.00007086429,0.00020678373,0.00029531232,0.00036905787,0.000040754738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016307854,0.00051477976,0.00021315322,0.00003101693,0.00038971048,0.000075987096,0.00020570266,0.16407889,0.006294495,0.7334521,0.0011569064,0.09342418],"study_design_scores_gemma":[0.0014662007,0.00008235533,0.00085950125,0.000050704497,0.000032000964,0.00006545873,0.000012446827,0.98120314,0.0013373145,0.013647214,0.001061898,0.00018177045],"about_ca_topic_score_codex":0.0000043612513,"about_ca_topic_score_gemma":3.2108346e-7,"teacher_disagreement_score":0.81712425,"about_ca_system_score_codex":0.0003371346,"about_ca_system_score_gemma":0.0002537282,"threshold_uncertainty_score":0.7618435},"labels":[],"label_agreement":null},{"id":"W2140727865","doi":"10.1007/s11721-009-0037-5","title":"A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models","year":2009,"lang":"en","type":"article","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Swarm behaviour; Mathematical optimization; Distributed computing; Artificial intelligence; Mathematics","score_opus":0.03775588129553465,"score_gpt":0.28717538900988326,"score_spread":0.24941950771434862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140727865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016058639,0.00014514754,0.98175794,0.0006973831,0.000053844986,0.00051922206,0.000004115804,0.0001014013,0.0006623151],"genre_scores_gemma":[0.78003633,0.00006399572,0.21943244,0.00016204799,0.000016539998,0.00003231642,0.000003059867,0.000008868106,0.00024439965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787736,0.00015025517,0.00046010225,0.0005188233,0.00062352273,0.00036992645],"domain_scores_gemma":[0.9980682,0.00015541015,0.0001426179,0.00069340057,0.0007712506,0.00016908617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003775164,0.00019688992,0.00026561605,0.00010419261,0.00009943618,0.00014164999,0.0008238173,0.00005560476,0.00004286566],"category_scores_gemma":[0.00018279329,0.00015503126,0.00005234345,0.0009082515,0.00015264693,0.0005301409,0.000094729185,0.00015272497,0.00004264278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043487817,0.00020351974,0.000009488736,0.000011708239,0.000016568567,0.000017802073,0.0010907539,0.95124704,0.00026843813,0.03459501,0.000032781372,0.012463409],"study_design_scores_gemma":[0.000119886354,0.0005996932,0.000012981195,0.000027283751,0.0000075878593,0.000027602444,0.000034165947,0.87815815,0.112784095,0.008036822,0.000025521116,0.00016622434],"about_ca_topic_score_codex":0.00003724829,"about_ca_topic_score_gemma":0.000016034557,"teacher_disagreement_score":0.7639777,"about_ca_system_score_codex":0.000056927638,"about_ca_system_score_gemma":0.0001934857,"threshold_uncertainty_score":0.6321991},"labels":[],"label_agreement":null},{"id":"W2141099810","doi":"10.1109/sis.2007.368047","title":"Multiple Cooperating Swarms for Data Clustering","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Cluster analysis; Particle swarm optimization; Swarm behaviour; Computer science; Swarm intelligence; Class (philosophy); Multi-swarm optimization; Data mining; Artificial intelligence; Machine learning","score_opus":0.12319308962569467,"score_gpt":0.37335254450904226,"score_spread":0.25015945488334757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141099810","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008217321,0.000018530542,0.99600494,0.00034004703,0.00025818835,0.00029323678,0.000005244911,0.0001471487,0.0028505104],"genre_scores_gemma":[0.023932848,0.0000032147434,0.9739173,0.0002378059,0.00009304496,0.0000070452593,0.000027203172,0.000008917831,0.0017726377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987606,0.000022021482,0.0002410099,0.00039465874,0.00025711156,0.0003246115],"domain_scores_gemma":[0.99805593,0.0006091588,0.000036214107,0.0010070526,0.0001805241,0.000111121924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018869203,0.000073806594,0.00009295898,0.00008616005,0.00015457363,0.0002431142,0.0016010533,0.000029780966,0.000043723394],"category_scores_gemma":[0.0012338771,0.00006333822,0.000015693231,0.00030073238,0.000018169301,0.00050735264,0.0011017384,0.0000687823,0.000036777088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028737411,0.0001626891,0.0010873505,0.00008331853,0.0000475185,0.000028224014,0.0005297689,0.017934378,0.0026091516,0.023179172,0.013501404,0.9408083],"study_design_scores_gemma":[0.00034744025,0.000021077652,0.000067114284,0.0000033940096,9.3839213e-7,0.0000053108497,0.00001905574,0.9896097,0.001382678,0.000035599438,0.008420652,0.00008701791],"about_ca_topic_score_codex":0.000031566156,"about_ca_topic_score_gemma":0.00006398206,"teacher_disagreement_score":0.97167534,"about_ca_system_score_codex":0.000018931907,"about_ca_system_score_gemma":0.000052512685,"threshold_uncertainty_score":0.2975182},"labels":[],"label_agreement":null},{"id":"W2141258159","doi":"10.1109/cec.2006.1688618","title":"The Distribution Genetic Algorithm: Evolving a Population of Distributions","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Genetic algorithm; Test suite; Population; Computer science; Mutation; Representation (politics); Algorithm; Suite; Binary number; Artificial intelligence; Mathematics; Machine learning; Test case","score_opus":0.008355095890864766,"score_gpt":0.25147543850480114,"score_spread":0.24312034261393636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141258159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005542611,0.00016576864,0.99763304,0.00051268615,0.00014141554,0.00017296926,0.000034916367,0.000079977275,0.0007049359],"genre_scores_gemma":[0.51611096,0.000030661395,0.48256847,0.0000073049528,0.000079233665,0.000027605141,0.00021287396,0.0000066112534,0.000956312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998603,0.00011399073,0.0003653797,0.00020692968,0.0004655623,0.00024516523],"domain_scores_gemma":[0.99877745,0.00022103595,0.000112023125,0.00048211063,0.00035960274,0.00004780685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039208017,0.000081857914,0.00009673017,0.00004260927,0.00035218988,0.00019364551,0.0005487975,0.000036631445,0.00003067376],"category_scores_gemma":[0.00022032607,0.000058312384,0.000052983163,0.0007104062,0.000065988155,0.00021756519,0.000160625,0.000075803124,0.000017368056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029527862,0.0001978748,0.006581217,0.000014874524,0.000025739777,0.000005371635,0.000022680017,0.0076582516,0.00013086195,0.62151366,0.011108221,0.35273826],"study_design_scores_gemma":[0.00010912896,0.000016363174,0.13069557,0.000003371228,0.0000034560737,0.000005299258,0.0000029798446,0.85852915,0.0003224673,0.0090419175,0.0012045671,0.00006576088],"about_ca_topic_score_codex":0.0005080989,"about_ca_topic_score_gemma":0.000015066366,"teacher_disagreement_score":0.85087085,"about_ca_system_score_codex":0.00007856961,"about_ca_system_score_gemma":0.000051530005,"threshold_uncertainty_score":0.27087954},"labels":[],"label_agreement":null},{"id":"W2142265433","doi":"10.1162/evco_a_00088","title":"Resampling versus Repair in Evolution Strategies Applied to a Constrained Linear Problem","year":2012,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Resampling; Mathematical optimization; Computer science; Mathematics; Algorithm","score_opus":0.038483416312789384,"score_gpt":0.3212428018295873,"score_spread":0.2827593855167979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142265433","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004110784,0.0001285674,0.98961025,0.00050326216,0.0005297833,0.00071115914,0.0000073592414,0.00045834057,0.003940476],"genre_scores_gemma":[0.4903013,0.0000019817176,0.50940037,0.00003428325,0.00012422117,0.00006181486,0.000029718154,0.000009324272,0.000036974998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766535,0.00019812699,0.0004882932,0.00045625938,0.0006368008,0.00055516826],"domain_scores_gemma":[0.99874806,0.00034458187,0.00011037983,0.0003100076,0.00026343882,0.00022353703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095758465,0.00018095768,0.0001948237,0.0005812565,0.00018538248,0.00007017731,0.0003550177,0.00009161822,0.000023572737],"category_scores_gemma":[0.00020571635,0.00020236507,0.0000552085,0.0016088681,0.00006155727,0.001088999,0.00022428445,0.00021611864,0.00022001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010907575,0.00019879296,0.00046756663,0.000030606105,0.000020126923,0.0000037402863,0.0009536733,0.770634,0.00022512871,0.22224215,0.0013013138,0.0038138076],"study_design_scores_gemma":[0.0010207894,0.00010858825,0.021352416,0.000023524826,0.000004494363,0.00001039395,0.0002639144,0.97092664,0.000017200971,0.00538893,0.00063789275,0.00024520248],"about_ca_topic_score_codex":0.00005427649,"about_ca_topic_score_gemma":0.000008138104,"teacher_disagreement_score":0.48619053,"about_ca_system_score_codex":0.00051577645,"about_ca_system_score_gemma":0.00041202697,"threshold_uncertainty_score":0.8252208},"labels":[],"label_agreement":null},{"id":"W2143062943","doi":"10.14796/jwmm.r208-06","title":"Robustness of the Rainpak Algorithm for Storm Direction and Speed","year":2002,"lang":"en","type":"article","venue":"Journal of Water Management Modeling","topic":"Metaheuristic Optimization Algorithms Research","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":true,"ca_institutions":"Philips (Canada); University of Guelph","funders":"","keywords":"Robustness (evolution); Storm; Algorithm; Computer science; Speedup; Meteorology; Geography; Parallel computing","score_opus":0.04076184767571714,"score_gpt":0.25778022770569653,"score_spread":0.2170183800299794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143062943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003609341,0.000098608776,0.9944715,0.00088610954,0.0004946849,0.0002095412,5.600052e-7,0.000008204876,0.00022148275],"genre_scores_gemma":[0.13320658,0.0002389719,0.8644909,0.000054512984,0.00017570518,0.0000038990365,3.6092487e-7,0.000013499882,0.0018155258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881506,0.000058918766,0.0003887343,0.00012823357,0.00044311126,0.00016593805],"domain_scores_gemma":[0.99927086,0.000025965124,0.00015041322,0.00020893592,0.00029618738,0.000047647918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009279367,0.000077370874,0.00015316665,0.00020666303,0.000105289524,0.0001036307,0.00048345982,0.000022892182,0.000013697977],"category_scores_gemma":[0.000020788186,0.00004509248,0.00007748252,0.00016161668,0.000020331363,0.00034768847,0.00023473818,0.00009358978,5.4135324e-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":[0.000004320644,0.000052283965,0.0000064669434,0.000048410937,0.00006855152,0.0000030680137,0.00033439617,0.85225374,0.000065218555,0.00024141792,0.00028454067,0.1466376],"study_design_scores_gemma":[0.0005215326,0.00004018405,0.000008656506,0.00003265815,0.000024306706,0.000016320035,0.00004257908,0.99800825,0.0004633901,0.00040889072,0.00037759676,0.00005561607],"about_ca_topic_score_codex":0.0000019165886,"about_ca_topic_score_gemma":2.88743e-7,"teacher_disagreement_score":0.14658198,"about_ca_system_score_codex":0.000034761208,"about_ca_system_score_gemma":0.000004822652,"threshold_uncertainty_score":0.1838818},"labels":[],"label_agreement":null},{"id":"W2144540753","doi":"","title":"Toward effective initialization for large-scale search spaces","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Simon Fraser University; Ontario Tech University","funders":"","keywords":"Initialization; Population; Mathematical optimization; Computer science; Benchmark (surveying); Latin hypercube sampling; Differential evolution; Particle swarm optimization; A priori and a posteriori; Monte Carlo method; Algorithm; Mathematics; Statistics","score_opus":0.036817982566020466,"score_gpt":0.33767853261290937,"score_spread":0.3008605500468889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144540753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018962873,0.000028203114,0.98569655,0.0028045794,0.00014560756,0.0008208858,0.0000055354194,0.00018413729,0.010124881],"genre_scores_gemma":[0.23092538,0.000024964933,0.7655692,0.0008976282,0.00015415564,0.00008708122,0.000023996046,0.000012031173,0.002305576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985499,0.00013866297,0.00016276201,0.00036065798,0.00042710357,0.00036091756],"domain_scores_gemma":[0.9988618,0.00024308029,0.00003144822,0.00032367423,0.00041613032,0.0001239048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079123594,0.00010077574,0.00014262038,0.00018249491,0.00014184049,0.0003218648,0.0005273767,0.000055667824,0.00009793483],"category_scores_gemma":[0.00017998638,0.00008754422,0.000052098843,0.0006020258,0.000017222834,0.0004192526,0.000103900944,0.00008287851,0.00006673286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047050853,0.00043724486,0.00018899201,0.000063222564,0.00003340819,0.000012224644,0.003701186,0.0055654976,0.00042312988,0.7779725,0.008086714,0.20346881],"study_design_scores_gemma":[0.0006703453,0.000297287,0.00085109036,0.000006266718,0.000002920378,0.000004098641,0.00006709419,0.983642,0.0070654545,0.0036734736,0.0035853344,0.00013467144],"about_ca_topic_score_codex":0.000005844683,"about_ca_topic_score_gemma":0.0000021735755,"teacher_disagreement_score":0.97807646,"about_ca_system_score_codex":0.000040740193,"about_ca_system_score_gemma":0.00007531871,"threshold_uncertainty_score":0.356995},"labels":[],"label_agreement":null},{"id":"W2144573902","doi":"10.1109/sde.2011.5952059","title":"Opposition-based Differential Evolution with protective generation jumping","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Jumping; Ode; Differential evolution; Benchmark (surveying); Population; Computer science; Mathematical optimization; Mathematics; Algorithm; Applied mathematics; Biology","score_opus":0.05788424346125766,"score_gpt":0.24946776960908906,"score_spread":0.19158352614783142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144573902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012304444,0.0000029319826,0.9948571,0.00013455031,0.000104211766,0.00041515662,9.131687e-7,0.00016187652,0.0030928233],"genre_scores_gemma":[0.6418395,2.2085729e-7,0.35788205,0.0000384197,0.000042821302,0.000072434435,0.0000052433843,0.0000056421204,0.0001136494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988395,0.00013104232,0.00013816495,0.00031870094,0.00038627407,0.00018633624],"domain_scores_gemma":[0.9992673,0.000023168777,0.000056159282,0.00031797405,0.0002513974,0.00008403624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016329176,0.000094854586,0.00008296052,0.00017301292,0.0001722103,0.00012053541,0.000296392,0.00003869671,0.00029967772],"category_scores_gemma":[0.000032567314,0.00007314353,0.000023607729,0.00038876059,0.000040320538,0.0003835652,0.000054686396,0.000092204085,0.00004701101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015361742,0.0011984179,0.0012510392,0.000071098104,0.00016378118,0.000055539032,0.0020477828,0.017321168,0.030937554,0.92141294,0.0009493148,0.024437737],"study_design_scores_gemma":[0.00034602906,0.0001638421,0.001552764,0.0000070049887,0.0000041360904,0.0000037994455,0.000009118943,0.9782307,0.018651303,0.0009147689,0.000004372784,0.000112122885],"about_ca_topic_score_codex":0.00009731916,"about_ca_topic_score_gemma":0.000014884518,"teacher_disagreement_score":0.96090955,"about_ca_system_score_codex":0.0001276305,"about_ca_system_score_gemma":0.00015856826,"threshold_uncertainty_score":0.32812607},"labels":[],"label_agreement":null},{"id":"W2145224703","doi":"","title":"The AID Method for Global Optimization","year":2011,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Metaheuristic Optimization Algorithms Research","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":"Simon Fraser University","funders":"","keywords":"Mathematical optimization; Global optimization; Benchmark (surveying); Minification; Computer science; Population; Diversification (marketing strategy); Function (biology); Mathematics; Biology","score_opus":0.13021669306649986,"score_gpt":0.42655424674246667,"score_spread":0.2963375536759668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145224703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007987715,0.00018953708,0.9880477,0.0025557016,0.00041475377,0.0014857347,0.000022917866,0.00013223643,0.0071434397],"genre_scores_gemma":[0.0007065838,0.00020363962,0.9938566,0.00008991056,0.00016902402,0.00095173076,0.000022556682,0.000021214832,0.0039787344],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957196,0.0010804438,0.00047948083,0.0006720742,0.0011644431,0.00088393973],"domain_scores_gemma":[0.99496484,0.0009151943,0.000034016106,0.0012559111,0.0025783805,0.000251679],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006144621,0.00017350224,0.00017770675,0.00022922216,0.0024602,0.0010532682,0.0024370435,0.00011272298,0.00012898014],"category_scores_gemma":[0.0019431851,0.00012914305,0.00009212886,0.0022249913,0.00024365817,0.0007330831,0.0006035346,0.0003559608,0.00013914233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027521333,0.00019302091,0.000020182315,0.0000121979165,0.000076333126,0.000007696262,0.00070685,0.11717242,0.00003298577,0.6577772,0.004519446,0.21945417],"study_design_scores_gemma":[0.00046491346,0.00016488574,0.000043771634,0.0000049914124,0.000004490403,0.00002124027,0.00012177715,0.9846103,0.0004253382,0.0054474967,0.008542479,0.0001482927],"about_ca_topic_score_codex":0.0002800124,"about_ca_topic_score_gemma":0.0000824281,"teacher_disagreement_score":0.8674379,"about_ca_system_score_codex":0.00022573772,"about_ca_system_score_gemma":0.0007465078,"threshold_uncertainty_score":0.9999837},"labels":[],"label_agreement":null},{"id":"W2145283597","doi":"10.1016/j.patcog.2012.12.011","title":"Particle swarm classification: A survey and positioning","year":2013,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Montréal","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Computer science; Data mining; Domain (mathematical analysis); Machine learning; Statistical classification; Artificial intelligence; Mathematics; Geography","score_opus":0.08805631784522881,"score_gpt":0.2949895283895999,"score_spread":0.20693321054437108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145283597","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":[0.18472661,0.00003358708,0.8131979,0.0010957249,0.000088240035,0.00021954383,0.0000062588597,0.00008903571,0.00054308685],"genre_scores_gemma":[0.9704077,0.00003834406,0.02889061,0.00034497178,0.000039550425,0.00010363104,0.00006655844,0.00000802536,0.000100611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988725,0.00024081075,0.00018759236,0.00028350006,0.00022718371,0.00018841526],"domain_scores_gemma":[0.9990855,0.0001933174,0.00006181172,0.00022259908,0.00031909448,0.00011769376],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043898,0.00007644863,0.00008074582,0.00006430917,0.00012465901,0.00042961168,0.0001791388,0.000035544956,0.0003323809],"category_scores_gemma":[0.00015618336,0.000075884556,0.00001517449,0.0002760326,0.000033084154,0.0006187049,0.00008946831,0.00008633652,0.0011369002],"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":[0.0000017037535,0.000064867,0.02587774,0.000015050759,0.000012170965,0.0000025769007,0.00023493517,0.000008725093,0.0003701851,0.00013901223,0.00043188027,0.97284114],"study_design_scores_gemma":[0.00024239704,0.00003581203,0.37771836,0.000012584579,0.00000224237,0.000015470641,0.000020933823,0.6190724,0.0011514042,0.0015776646,0.00002803933,0.00012268094],"about_ca_topic_score_codex":0.00019766568,"about_ca_topic_score_gemma":0.000011752711,"teacher_disagreement_score":0.9727185,"about_ca_system_score_codex":0.000020549798,"about_ca_system_score_gemma":0.000018761852,"threshold_uncertainty_score":0.9996408},"labels":[],"label_agreement":null},{"id":"W2145321636","doi":"10.1109/nabic.2014.6921883","title":"Effect of communication topologies on hybrid evolutionary algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Network topology; Particle swarm optimization; Computer science; Evolutionary algorithm; Swarm behaviour; Population; Genetic algorithm; Algorithm; Hybrid algorithm (constraint satisfaction); Swarm intelligence; Algorithm design; Mathematical optimization; Topology (electrical circuits); Mathematics; Artificial intelligence; Machine learning; Computer network","score_opus":0.014674970879015823,"score_gpt":0.3017582855312839,"score_spread":0.2870833146522681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145321636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021762315,0.00006034285,0.97411895,0.0009430493,0.00012553242,0.00018144939,0.0000014722742,0.00014894866,0.022244008],"genre_scores_gemma":[0.6287239,0.000042534695,0.37001067,0.0001117765,0.000024982159,0.000025143501,0.000009235437,0.0000068611785,0.0010448789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982878,0.0007243376,0.00020841877,0.00021730052,0.0004000251,0.00016213405],"domain_scores_gemma":[0.99720347,0.0015001895,0.00007674988,0.001022511,0.00014254863,0.000054531283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012645607,0.000092426955,0.00016818798,0.00014635865,0.000093600196,0.000039645238,0.0010825025,0.00003016035,0.0000929101],"category_scores_gemma":[0.00089543994,0.00007018512,0.000046742585,0.0002549523,0.000115451396,0.00017672284,0.00035319096,0.00011341565,0.000111578665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029533354,0.00013149311,0.00028332087,0.00004166665,0.00002692743,0.0000019995393,0.000061280065,0.005956351,0.00012845211,0.19619246,0.0089832675,0.78816324],"study_design_scores_gemma":[0.00033996397,0.00072018075,0.00072783884,0.000011856003,0.0000027032388,0.000005934495,0.0000027936044,0.9739899,0.01991076,0.00257522,0.0016247123,0.00008816203],"about_ca_topic_score_codex":0.000034438403,"about_ca_topic_score_gemma":3.2443984e-7,"teacher_disagreement_score":0.96803355,"about_ca_system_score_codex":0.000029483379,"about_ca_system_score_gemma":0.000025995138,"threshold_uncertainty_score":0.28620657},"labels":[],"label_agreement":null},{"id":"W2148600622","doi":"10.1109/cec.2006.1688534","title":"Opposition-Based Differential Evolution for Optimization of Noisy Problems","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Waterloo","funders":"Russian Science Foundation","keywords":"Initialization; Differential evolution; Computer science; Benchmark (surveying); Mathematical optimization; Population; Optimization problem; Convergence (economics); Optimization algorithm; Algorithm; Artificial intelligence; Mathematics","score_opus":0.014550683731895941,"score_gpt":0.2494523002541933,"score_spread":0.23490161652229735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148600622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009081008,0.000010911684,0.99747723,0.00041275885,0.00014078064,0.0005441651,0.0000104528135,0.000102109116,0.0012107864],"genre_scores_gemma":[0.32740888,7.511683e-7,0.6719715,0.000016503325,0.00004561359,0.00006834088,0.000070243674,0.00000774865,0.0004104108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988118,0.000059968548,0.00031549358,0.00025326386,0.0003687746,0.00019073734],"domain_scores_gemma":[0.9990026,0.00013544035,0.000114872906,0.0002937481,0.00040751026,0.00004587854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020701166,0.0000893147,0.00012725045,0.00020372681,0.00009114113,0.00008863597,0.0003537899,0.000052751773,0.0001224528],"category_scores_gemma":[0.00005655306,0.00008168054,0.000062764884,0.0004016859,0.0000393888,0.00022447691,0.000048081514,0.0000387031,0.000004728118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005034768,0.00013399459,0.00007310171,0.00003405125,0.00000432926,1.9001625e-7,0.0000050132144,0.8787881,0.0010044064,0.11914193,0.0005782188,0.00023160249],"study_design_scores_gemma":[0.0006453817,0.00008272696,0.0002359407,0.000009436025,0.0000054445527,7.888861e-7,0.0000013814525,0.9919851,0.0034434772,0.0034702006,0.00003059755,0.000089529],"about_ca_topic_score_codex":0.000062059946,"about_ca_topic_score_gemma":0.00000698375,"teacher_disagreement_score":0.32731807,"about_ca_system_score_codex":0.0000815444,"about_ca_system_score_gemma":0.00012115355,"threshold_uncertainty_score":0.3330836},"labels":[],"label_agreement":null},{"id":"W2153260109","doi":"10.1109/tcyb.2013.2279211","title":"Differential Evolution With Two-Level Parameter Adaptation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":226,"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":"Beijing Information Science and Technology University; Huazhong University of Science and Technology; Shenyang Institute of Automation; National Natural Science Foundation of China; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Population; Convergence (economics); Computer science; Adaptive control; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence; Control (management)","score_opus":0.034222114150810334,"score_gpt":0.26010961615886075,"score_spread":0.2258875020080504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153260109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002534359,0.0000034972745,0.9955922,0.00023506765,0.0005002602,0.00022667175,0.00000933295,0.0001599939,0.000738659],"genre_scores_gemma":[0.7237379,0.0000063803905,0.2750639,0.000056199486,0.000040418094,0.000032527987,0.0000024127635,0.000016755785,0.001043542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813896,0.0001982028,0.00024960862,0.00041966338,0.0006863571,0.0003072067],"domain_scores_gemma":[0.9986348,0.0003006483,0.00007822849,0.00060013065,0.00023123836,0.00015490921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016679772,0.00018175795,0.00016382022,0.00023631431,0.00018396576,0.00018858185,0.0004477528,0.000071245835,0.00012904312],"category_scores_gemma":[0.000024032877,0.00015747674,0.0000604793,0.00048527354,0.00008480435,0.00024251454,0.0000035048122,0.00027180795,0.00016385656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060295497,0.000420141,0.00001704668,0.000016146254,0.00006925615,0.0000042079328,0.00047181704,0.7455037,0.00048018957,0.015736023,0.00008708268,0.23713407],"study_design_scores_gemma":[0.0008775725,0.000312057,0.00036895287,0.000015774074,0.000023308416,0.00001257548,0.000011385557,0.99247026,0.0045300866,0.0010084747,0.00016881328,0.00020072889],"about_ca_topic_score_codex":0.00006479174,"about_ca_topic_score_gemma":0.00007046354,"teacher_disagreement_score":0.7212035,"about_ca_system_score_codex":0.000090270376,"about_ca_system_score_gemma":0.000078801924,"threshold_uncertainty_score":0.6421715},"labels":[],"label_agreement":null},{"id":"W2153654605","doi":"10.1016/j.dam.2005.05.020","title":"First vs. best improvement: An empirical study","year":2005,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Constructive; Travelling salesman problem; Mathematics; Enhanced Data Rates for GSM Evolution; Heuristic; Mathematical optimization; Greedy algorithm; Algorithm; Combinatorics; Computer science; Artificial intelligence; Process (computing)","score_opus":0.03618088057861051,"score_gpt":0.3343979954373477,"score_spread":0.2982171148587372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153654605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013010465,0.000010016452,0.97444224,0.0008484209,0.00006647698,0.0011011241,0.000005423323,0.0002357707,0.010280045],"genre_scores_gemma":[0.22978704,0.0000096722315,0.76893836,0.00027703302,0.00014614704,0.00020119664,0.000008458524,0.000038207043,0.00059388933],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974986,0.000030621344,0.0005434904,0.00056330714,0.00090678566,0.00045720473],"domain_scores_gemma":[0.9979301,0.00014853665,0.00014529645,0.0013884542,0.00011074528,0.0002768342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068139855,0.00024611803,0.00032467715,0.00014947735,0.00024892672,0.00041286534,0.001465636,0.00006793712,0.0001800786],"category_scores_gemma":[0.000076558295,0.00020890027,0.00005263067,0.0005102863,0.000061433144,0.00036241623,0.00057106366,0.00021438238,0.00064524333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010240784,0.031857993,0.0012084622,0.0009188799,0.00065958843,0.00013368687,0.09435702,0.030140914,0.0015321319,0.5644032,0.009560479,0.26512527],"study_design_scores_gemma":[0.0009570917,0.0004543014,0.00013684669,0.000010226412,0.000027023518,0.0000059215486,0.0010234759,0.9891595,0.0005341466,0.0041857525,0.0031257633,0.00037993037],"about_ca_topic_score_codex":0.0000053438102,"about_ca_topic_score_gemma":0.000018629684,"teacher_disagreement_score":0.9590186,"about_ca_system_score_codex":0.00006974202,"about_ca_system_score_gemma":0.000063844214,"threshold_uncertainty_score":0.85187054},"labels":[],"label_agreement":null},{"id":"W2154369526","doi":"10.1109/cec.2006.1688554","title":"Opposition-Based Differential Evolution Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":184,"is_retracted":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":"Differential evolution; Initialization; Evolutionary algorithm; Computer science; Benchmark (surveying); Population; Evolutionary computation; Swarm intelligence; Mathematical optimization; Computational intelligence; Algorithm; Artificial intelligence; Cultural algorithm; Machine learning; Optimization problem; Particle swarm optimization; Meta-optimization; Mathematics","score_opus":0.011364183312258833,"score_gpt":0.24798302129868888,"score_spread":0.23661883798643005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154369526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017211305,0.000014399955,0.98853606,0.0009693196,0.0002965727,0.00015384755,0.000003356643,0.000300793,0.009553544],"genre_scores_gemma":[0.5212448,5.027218e-7,0.47629106,0.000092364404,0.0001580181,0.000024564199,0.00002972393,0.00000896819,0.0021499675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984653,0.00010244296,0.00022603203,0.00033721232,0.00058357633,0.0002854237],"domain_scores_gemma":[0.9991675,0.000095510164,0.000047574293,0.00043691357,0.00016134014,0.00009114857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017397468,0.00011026254,0.00010781975,0.00020637471,0.00015505942,0.00026270997,0.0005533917,0.000053246895,0.00051830086],"category_scores_gemma":[0.00002856104,0.00009856068,0.000058014768,0.0005060736,0.000045273482,0.00026555473,0.00010211711,0.00009297468,0.00022750217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053667304,0.00041903878,0.0003508314,0.000014285601,0.000012978576,0.000023419065,0.000010467796,0.020110354,0.0027766838,0.9561235,0.011373786,0.008779321],"study_design_scores_gemma":[0.0003844915,0.000031643438,0.0024303067,0.000003148815,0.000002620023,0.0000035610021,0.0000014926381,0.9869175,0.001872412,0.0079303635,0.00029645427,0.00012598786],"about_ca_topic_score_codex":0.0002396642,"about_ca_topic_score_gemma":0.0000078017965,"teacher_disagreement_score":0.9668072,"about_ca_system_score_codex":0.00014501806,"about_ca_system_score_gemma":0.00012401423,"threshold_uncertainty_score":0.5675031},"labels":[],"label_agreement":null},{"id":"W2155370091","doi":"10.1007/s00500-010-0642-7","title":"Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems","year":2010,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":220,"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":"Differential evolution; Initialization; Ode; Opposition (politics); Mathematical optimization; Computer science; Population; Mathematics; Evolutionary algorithm; Algorithm; Applied mathematics","score_opus":0.0106242622329757,"score_gpt":0.2517901850349561,"score_spread":0.2411659228019804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155370091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010812785,0.000009975844,0.98595744,0.0004002738,0.0016428439,0.00073320576,0.000007755814,0.0003736646,0.000062033294],"genre_scores_gemma":[0.5531131,1.5518289e-7,0.44650847,0.000054469125,0.00019439775,0.000025390613,0.00005447866,0.000016179456,0.00003334867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776065,0.0000867013,0.00046565212,0.0006194061,0.00056011905,0.0005074631],"domain_scores_gemma":[0.9978333,0.0006341396,0.00025600672,0.00044023644,0.00067724264,0.0001590609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062940025,0.00021004972,0.00025474583,0.00024200974,0.00061475084,0.00038966388,0.0006218525,0.00013064328,0.00007702191],"category_scores_gemma":[0.0005322509,0.00021647043,0.00009764831,0.00042737814,0.000075691634,0.0003065992,0.00022308454,0.00031136317,0.000014547421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105985755,0.000118201904,0.000049868497,0.00004024757,0.000017400485,0.0000016179138,0.00007075597,0.9469119,0.029144727,0.017948335,0.00011609068,0.0055703144],"study_design_scores_gemma":[0.0010622783,0.00008557779,0.00020400221,0.000043567958,0.000009635037,0.000005769177,0.0000035812013,0.9918062,0.005139809,0.0013867258,0.00001007547,0.00024274124],"about_ca_topic_score_codex":0.000021867487,"about_ca_topic_score_gemma":0.0000057113143,"teacher_disagreement_score":0.54230034,"about_ca_system_score_codex":0.0001062022,"about_ca_system_score_gemma":0.00026067326,"threshold_uncertainty_score":0.88274086},"labels":[],"label_agreement":null},{"id":"W2155437809","doi":"10.5539/cis.v7n4p30","title":"Research on the Optimization of Boiler Efficiency based on Artificial Bee Colony Algorithm","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":7,"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; Natural Science Foundation of Guangdong Province","keywords":"Boiler (water heating); Computer science; Process engineering; Combustion; Algorithm; Waste management; Engineering; Chemistry","score_opus":0.043424435851101,"score_gpt":0.32452368198077197,"score_spread":0.28109924612967097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155437809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055546704,0.0000019374545,0.98942196,0.0018869644,0.00027128347,0.00030904048,0.0000029824764,0.000043762837,0.0075065726],"genre_scores_gemma":[0.45732316,0.000015746586,0.53961855,0.0028293198,0.00012734457,0.00003901853,0.0000067108645,0.000007160805,0.000032951866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668616,0.00028594842,0.00042009412,0.00029786397,0.0019494739,0.00036047542],"domain_scores_gemma":[0.99694526,0.0009985799,0.00014659727,0.0005767439,0.0011953396,0.00013748389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006604267,0.00011167616,0.00013323517,0.0009599809,0.00073665637,0.0007574033,0.001410835,0.000045300174,0.00003239547],"category_scores_gemma":[0.0006428312,0.00007688495,0.00002836811,0.0029796194,0.00071761396,0.0020345745,0.00035571554,0.00023920648,0.00007367525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068144127,0.000057328398,0.000005275806,0.000009286913,0.0000011794965,1.6904809e-7,0.00035822377,0.6714163,0.000014973138,0.15348892,0.0008330462,0.17380847],"study_design_scores_gemma":[0.0001715712,0.00039752,0.00046073995,0.000022788214,6.796976e-7,0.000001361145,0.000021083824,0.99607426,0.0009750638,0.0002949426,0.0014942179,0.00008579326],"about_ca_topic_score_codex":0.0000052145633,"about_ca_topic_score_gemma":8.973491e-8,"teacher_disagreement_score":0.4567677,"about_ca_system_score_codex":0.00004984942,"about_ca_system_score_gemma":0.00026365425,"threshold_uncertainty_score":0.7303658},"labels":[],"label_agreement":null},{"id":"W2156166258","doi":"10.1007/978-3-540-24855-2_28","title":"The Shifting Balance Genetic Algorithm as More than Just Another Island Model GA","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Genetic algorithm; Balance (ability); Computer science; Population; Mechanism (biology); Extension (predicate logic); Diversity (politics); Algorithm; Machine learning; Biology; Sociology; Demography; Physics","score_opus":0.02227296970483767,"score_gpt":0.2779428918105858,"score_spread":0.2556699221057481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156166258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010231644,0.0008325017,0.991889,0.002227071,0.0010453468,0.0006155863,0.000012701508,0.00015456536,0.0032129737],"genre_scores_gemma":[0.0047579887,0.00031351057,0.9903913,0.0014112432,0.00050251215,0.00002500605,0.0000045359425,0.000077399294,0.0025165088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938497,0.000074809555,0.0007114494,0.0018876826,0.0023063153,0.0011700725],"domain_scores_gemma":[0.9959327,0.0006843455,0.0003645555,0.002265455,0.00050596864,0.00024698896],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0013286301,0.00066522526,0.0005392681,0.0005749704,0.0008377183,0.0016416999,0.0061642583,0.00033256487,0.0000477843],"category_scores_gemma":[0.00026860563,0.0004996156,0.00015480843,0.0009357115,0.0012846715,0.00049667765,0.0019450339,0.0011757914,0.00010661988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002927641,0.000019652267,0.000016261156,0.000018942419,0.000013567513,0.00009622592,0.0006674468,0.60434175,0.000007943517,0.0068294792,0.000037917198,0.38794792],"study_design_scores_gemma":[0.00031321915,0.00007259091,0.000047358044,0.00017861654,0.0000064416545,0.00008065045,3.709984e-7,0.90028155,0.00013335118,0.097973466,0.00038954514,0.0005228214],"about_ca_topic_score_codex":0.000043006934,"about_ca_topic_score_gemma":0.000040579114,"teacher_disagreement_score":0.3874251,"about_ca_system_score_codex":0.00042567114,"about_ca_system_score_gemma":0.0017402046,"threshold_uncertainty_score":0.99974555},"labels":[],"label_agreement":null},{"id":"W2157833270","doi":"10.1109/cec.2007.4424748","title":"Quasi-oppositional Differential Evolution","year":2007,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":447,"is_retracted":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":"Ode; Initialization; Differential evolution; Benchmark (surveying); Applied mathematics; Computer science; Test suite; Suite; Population; Algorithm; Mathematical optimization; Mathematics; Test case; Machine learning","score_opus":0.016240567198564735,"score_gpt":0.28615696652471695,"score_spread":0.2699163993261522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157833270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084619585,0.000012068324,0.98112977,0.0004540632,0.0003134852,0.00008141424,8.7491594e-7,0.0001496313,0.017012471],"genre_scores_gemma":[0.61467844,0.0000018121805,0.382074,0.00009878047,0.00012738907,0.0000032526377,0.000006232122,0.0000045279535,0.0030055642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987559,0.00004236963,0.00017889425,0.0002266369,0.0005400182,0.00025623845],"domain_scores_gemma":[0.99929374,0.0001092304,0.000029735467,0.00027544415,0.00015949692,0.00013233106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004351965,0.00006697008,0.0000685152,0.0001696101,0.00010493957,0.000107736596,0.0004428569,0.00003903708,0.0007285219],"category_scores_gemma":[0.000067629146,0.000058959886,0.00003600291,0.0003668006,0.000032209042,0.00025755275,0.00015232823,0.00008703107,0.00032573944],"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":[0.000006079612,0.00022647549,0.00022676613,0.0000032902676,0.000009393323,0.000011644981,0.000036103876,0.00009220767,0.00063861714,0.9779631,0.0016634265,0.019122912],"study_design_scores_gemma":[0.00042598188,0.000117587784,0.021057075,0.0000035704222,0.000002509511,0.000029638913,0.0000112882,0.9667965,0.0017963063,0.008378687,0.0012069802,0.00017386365],"about_ca_topic_score_codex":0.000016112139,"about_ca_topic_score_gemma":0.0000042299316,"teacher_disagreement_score":0.9695844,"about_ca_system_score_codex":0.00007193014,"about_ca_system_score_gemma":0.000060554103,"threshold_uncertainty_score":0.79768044},"labels":[],"label_agreement":null},{"id":"W2159117717","doi":"10.1109/sis.2011.5952559","title":"A Particle Swarm Optimization approach to mixed attribute data-set classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Montréal","funders":"","keywords":"Particle swarm optimization; Centroid; Categorical variable; Set (abstract data type); Rounding; Computer science; Data set; Measure (data warehouse); Artificial intelligence; Pattern recognition (psychology); Fitness function; Multi-swarm optimization; Data mining; Mathematics; Algorithm; Machine learning; Genetic algorithm","score_opus":0.3497252586867309,"score_gpt":0.34054428968997214,"score_spread":0.009180968996758743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159117717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020155017,0.000013604404,0.9865983,0.00058477797,0.00016683918,0.00045213292,0.000018342324,0.00028432874,0.011680098],"genre_scores_gemma":[0.0641095,0.00001257905,0.9344255,0.00024408668,0.000035535,0.000068084264,0.00015481011,0.000014008389,0.00093590457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774855,0.0001952655,0.00036171635,0.0007683887,0.00054585,0.00038019943],"domain_scores_gemma":[0.9971611,0.00006409925,0.00008179681,0.0020672092,0.00031912842,0.00030666785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010624136,0.00013816178,0.00015877897,0.00014117113,0.00013562785,0.00023499665,0.0022750895,0.000061000977,0.00017513693],"category_scores_gemma":[0.00039874832,0.0001238748,0.000024845456,0.0012984795,0.00003869888,0.0009689197,0.0009453651,0.000103750055,0.0003317839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007996146,0.0025495226,0.0019730683,0.000078552584,0.00017454974,0.0000176516,0.0054830075,0.4478613,0.00052732404,0.41413012,0.05615562,0.070969306],"study_design_scores_gemma":[0.0002351042,0.00004080309,0.0013780334,0.000002103289,0.0000060174593,0.0000054115853,0.00007562896,0.9960291,0.0011503124,0.00007502114,0.00084068143,0.00016177823],"about_ca_topic_score_codex":0.00004963328,"about_ca_topic_score_gemma":0.0000022770441,"teacher_disagreement_score":0.54816777,"about_ca_system_score_codex":0.00004511382,"about_ca_system_score_gemma":0.00010010799,"threshold_uncertainty_score":0.50514674},"labels":[],"label_agreement":null},{"id":"W2159490979","doi":"10.1109/ccece.2006.277284","title":"A Hybrid Evolutionary Approach for Combinatorial Problems in Dynamic Environments","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Heuristics; Benchmark (surveying); Premature convergence; Mathematical optimization; Computer science; Convergence (economics); Evolutionary algorithm; Population; Genetic algorithm; Local optimum; Optimization problem; Local search (optimization); Artificial intelligence; Mathematics","score_opus":0.011762356098054778,"score_gpt":0.2395804344214939,"score_spread":0.22781807832343912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159490979","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025405435,0.000057699574,0.99410343,0.00015798093,0.00019838668,0.0007483227,0.0000059405716,0.000054626413,0.00441953],"genre_scores_gemma":[0.17512056,0.000008928187,0.81989783,0.000028349617,0.000046902147,0.0002947212,0.00008299137,0.000013003265,0.0045067454],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985399,0.00007123968,0.00026913616,0.00040430584,0.0004003813,0.00031501666],"domain_scores_gemma":[0.9994522,0.00007892978,0.000047105248,0.0003396712,0.00002937052,0.00005276635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003776589,0.0001037288,0.00013122182,0.00013270017,0.00006931722,0.000072912146,0.0005720359,0.00003694705,0.000023981234],"category_scores_gemma":[0.00003994283,0.00009906984,0.00003942974,0.00023600832,0.000042907657,0.00025739695,0.00018108785,0.00008975352,0.000023321803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003362626,0.0037781997,0.002622637,0.00013926734,0.00003723947,0.000018148516,0.000092848604,0.46869165,0.0007181591,0.49505916,0.016692428,0.012116619],"study_design_scores_gemma":[0.0010294298,0.00004312169,0.001614992,0.00000218178,0.0000011019506,0.0000055567325,0.0000018922814,0.9717212,0.00005552566,0.023052026,0.0023589812,0.000114017035],"about_ca_topic_score_codex":0.000047018395,"about_ca_topic_score_gemma":0.0000010113687,"teacher_disagreement_score":0.5030295,"about_ca_system_score_codex":0.00015193364,"about_ca_system_score_gemma":0.00006049528,"threshold_uncertainty_score":0.4039951},"labels":[],"label_agreement":null},{"id":"W2162419369","doi":"10.1109/ipdpsw.2010.5470706","title":"A parallel Particle swarm optimization algorithm for option pricing","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Science and Technology, Ministry of Science and Technology, India; University of Manitoba","keywords":"Particle swarm optimization; Computer science; Mathematical optimization; Valuation of options; Parallel algorithm; Algorithm; Finance; Mathematics; Economics","score_opus":0.024213644342041197,"score_gpt":0.298744247885616,"score_spread":0.2745306035435748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162419369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023066798,0.000008586757,0.9967776,0.0009015419,0.00047560944,0.0005790466,0.000002319011,0.00028828517,0.000736338],"genre_scores_gemma":[0.0052559655,0.000012260108,0.9925211,0.00018200188,0.00012464564,0.00013726047,0.000011451191,0.000015744332,0.0017395498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848354,0.000047957517,0.00029324915,0.00042122675,0.00037526683,0.00037878245],"domain_scores_gemma":[0.9986379,0.00018718405,0.00008178865,0.000526747,0.00039002943,0.00017630936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070748717,0.00012504209,0.0001394396,0.00010818798,0.00020582619,0.00033789885,0.00060343987,0.000079800106,0.00013067859],"category_scores_gemma":[0.00029631823,0.00011307618,0.000058974987,0.00051213696,0.00003954015,0.00065252645,0.00015521472,0.00016034298,0.00005867744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010071175,0.00021005674,0.000055089367,0.00001554436,0.000024386267,0.000003852608,0.00023616345,0.4347907,0.0011382687,0.1369368,0.0007957329,0.42578334],"study_design_scores_gemma":[0.00061470026,0.000059618465,0.00006742286,0.0000023016235,0.000004199202,0.000009066159,0.000010932505,0.993901,0.0031089264,0.0008866507,0.0011862625,0.00014888146],"about_ca_topic_score_codex":0.000013855042,"about_ca_topic_score_gemma":0.0000036978595,"teacher_disagreement_score":0.55911034,"about_ca_system_score_codex":0.000024085184,"about_ca_system_score_gemma":0.00009091566,"threshold_uncertainty_score":0.4611113},"labels":[],"label_agreement":null},{"id":"W2165619664","doi":"10.1109/cec.2005.1554767","title":"BMPGA: A Bi-Objective Multi-population Genetic Algorithm for Multi-modal Function Optimization","year":2005,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Fitness function; Modal; Mathematical optimization; Genetic algorithm; Population; Similarity (geometry); Fitness approximation; Computer science; Algorithm; Function (biology); Measure (data warehouse); Mathematics; Data mining; Artificial intelligence; Image (mathematics)","score_opus":0.040648948215666114,"score_gpt":0.31668574368854585,"score_spread":0.27603679547287974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165619664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000053887947,0.00008559016,0.99727714,0.00028158168,0.00047942487,0.0012629296,0.000013377558,0.00039153473,0.00015450854],"genre_scores_gemma":[0.0045899744,0.000028737086,0.9917779,0.00021799159,0.00026702398,0.0002502007,0.00007711599,0.00003599426,0.0027550922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975627,0.0001592036,0.00051251915,0.00077045296,0.0005226916,0.0004724184],"domain_scores_gemma":[0.99827224,0.0001368921,0.00017262233,0.00054680917,0.00068096106,0.00019047385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048566397,0.0002481495,0.00024384202,0.00042335203,0.00031642383,0.000302739,0.0005194316,0.00014705611,0.00015881092],"category_scores_gemma":[0.00025253367,0.00023873089,0.00011286817,0.00079848245,0.00003863051,0.0009706229,0.0001719327,0.00014846642,0.000094776806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008953658,0.0002114749,0.0001050366,0.0000078500725,0.0000281555,8.6199844e-7,0.00010338447,0.5787285,0.000021723297,0.0007048993,0.00019108066,0.4198881],"study_design_scores_gemma":[0.001919484,0.0001389791,0.005083194,0.000006282904,0.000017428067,0.0000088110655,0.000020226487,0.99172217,0.00021715835,0.000070562324,0.00051419536,0.00028153224],"about_ca_topic_score_codex":0.00009430771,"about_ca_topic_score_gemma":0.00003102049,"teacher_disagreement_score":0.41960657,"about_ca_system_score_codex":0.00022307632,"about_ca_system_score_gemma":0.00010754649,"threshold_uncertainty_score":0.9735163},"labels":[],"label_agreement":null},{"id":"W2165687335","doi":"10.1109/tevc.2009.2017517","title":"Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":114,"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; Cluster analysis; Local optimum; Mathematical optimization; Genetic algorithm; Population; Artificial intelligence; Algorithm; Mathematics; Machine learning","score_opus":0.01815384982913662,"score_gpt":0.2780752823084465,"score_spread":0.2599214324793099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165687335","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015629946,0.000048272297,0.9957219,0.00048345843,0.0013262338,0.0015722507,0.000050729595,0.000553821,0.00008706431],"genre_scores_gemma":[0.24819435,0.000020317115,0.75099623,0.00018727228,0.00012950186,0.00017119994,0.000100416124,0.000023917859,0.00017678598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971387,0.00024753905,0.0006133942,0.0008247101,0.0007453168,0.00043033238],"domain_scores_gemma":[0.997973,0.00032501077,0.00022779807,0.00039964917,0.000897592,0.00017694163],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030076355,0.00031732666,0.00026015774,0.0008012868,0.0007529976,0.00017393945,0.00035898373,0.00018998006,0.00003671249],"category_scores_gemma":[0.000034695357,0.0003565601,0.00018624518,0.0012014513,0.00005487545,0.0011172924,0.0000036167567,0.0002541374,0.000055990113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044652486,0.00027841635,0.0000016342613,0.0000054064717,0.000023903283,9.969774e-7,0.00007381372,0.6607782,0.0000779425,0.00016973571,0.00013039443,0.33841485],"study_design_scores_gemma":[0.0016125315,0.00082396396,0.0036417102,0.000020797946,0.000034159926,0.000017917206,0.000019001935,0.99013114,0.00039387794,0.002899856,0.000056406934,0.00034865222],"about_ca_topic_score_codex":0.000022940003,"about_ca_topic_score_gemma":0.0000017615896,"teacher_disagreement_score":0.3380662,"about_ca_system_score_codex":0.00042052884,"about_ca_system_score_gemma":0.00018467652,"threshold_uncertainty_score":0.99988866},"labels":[],"label_agreement":null},{"id":"W2166980895","doi":"10.1109/iccas.2008.4694190","title":"Data clustering using multi-objective hybrid evolutionary algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Cluster analysis; Correlation clustering; Computer science; CURE data clustering algorithm; Centroid; k-medians clustering; Data mining; Cluster (spacecraft); Single-linkage clustering; Determining the number of clusters in a data set; Set (abstract data type); Population; Algorithm; Canopy clustering algorithm; Fuzzy clustering; Artificial intelligence","score_opus":0.14694405825942372,"score_gpt":0.34469260005736907,"score_spread":0.19774854179794535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166980895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016736351,0.00012830074,0.9976883,0.000116411225,0.00037448914,0.00022000901,0.000053479405,0.00023605088,0.0010155787],"genre_scores_gemma":[0.0044319197,0.00005334672,0.9936635,0.00011633375,0.00010592317,0.0000055336864,0.000039014194,0.000015708209,0.0015687245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997802,0.0001502803,0.00029209236,0.00073269644,0.00062499865,0.00039794194],"domain_scores_gemma":[0.9977783,0.00012862564,0.000071285576,0.0015696171,0.00027797476,0.0001741787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040935833,0.00015437853,0.000179113,0.00022444043,0.0004055802,0.00009681105,0.002088175,0.00003681673,0.00013624893],"category_scores_gemma":[0.00024376581,0.00014813743,0.00003525675,0.00057735614,0.000114196926,0.0013413524,0.0027107499,0.00018717645,0.00014146387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004829788,0.0026945404,0.0037113188,0.00012927876,0.00067906,0.0046273298,0.0024347256,0.22189166,0.0014027322,0.004047317,0.044821467,0.7135123],"study_design_scores_gemma":[0.0003724842,0.000017616938,0.00097533094,0.000006139435,0.000002898649,0.0006297874,0.0000151444,0.9964708,0.00014730831,0.000048755053,0.0011309194,0.00018279481],"about_ca_topic_score_codex":0.00016634913,"about_ca_topic_score_gemma":0.0000041150397,"teacher_disagreement_score":0.77457917,"about_ca_system_score_codex":0.00012132081,"about_ca_system_score_gemma":0.00032865713,"threshold_uncertainty_score":0.60408694},"labels":[],"label_agreement":null},{"id":"W2169859438","doi":"10.1109/iciinfs.2009.5429852","title":"A new biologically inspired optimization algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":80,"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":"Maxima and minima; Algorithm; Computer science; Optimization algorithm; Function (biology); Hybrid algorithm (constraint satisfaction); Algorithm design; Field (mathematics); Mathematical optimization; Mathematics","score_opus":0.021761481337537303,"score_gpt":0.27960280231395934,"score_spread":0.25784132097642204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169859438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000027110698,0.00004384585,0.97825176,0.0038425135,0.00012537734,0.00018001407,6.554012e-7,0.00043760453,0.017115539],"genre_scores_gemma":[0.00049709284,0.000047308,0.99339986,0.0012782707,0.000077752615,0.0000032257735,0.000007593573,0.000004352198,0.0046845204],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985683,0.00008565786,0.00025886373,0.00040400572,0.0003825996,0.000300594],"domain_scores_gemma":[0.99896234,0.000057810325,0.000057138957,0.0004951419,0.0001783345,0.00024925824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002821654,0.00012567584,0.00014959522,0.00016883666,0.00009099176,0.00027100852,0.00093123893,0.00007871185,0.00073101086],"category_scores_gemma":[0.0001894129,0.00009892573,0.000046890065,0.0009173059,0.000017400313,0.00041923628,0.00013022777,0.000105735504,0.00016755809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018454859,0.000048537167,0.0000055246987,5.3973906e-7,0.0000049256014,0.000010547375,0.000030072055,0.036405623,0.00004543705,0.032497544,0.0039465628,0.92700285],"study_design_scores_gemma":[0.00032286698,0.00017335101,0.00024812078,0.000002426071,0.0000013484763,0.000010715402,0.000002004773,0.9933582,0.00021724787,0.0033198325,0.0022073826,0.00013652367],"about_ca_topic_score_codex":0.00001355737,"about_ca_topic_score_gemma":2.7737127e-7,"teacher_disagreement_score":0.9569526,"about_ca_system_score_codex":0.000032174776,"about_ca_system_score_gemma":0.00015329555,"threshold_uncertainty_score":0.8004056},"labels":[],"label_agreement":null},{"id":"W2170004572","doi":"10.1109/cec.2006.1688373","title":"A Genetic Binary Particle Swarm Optimization Model","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":51,"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":"Particle swarm optimization; Multi-swarm optimization; Swarm behaviour; Computer science; Binary number; Population; Mathematical optimization; Genetic algorithm; Metaheuristic; Algorithm; Artificial intelligence; Mathematics; Machine learning","score_opus":0.021080757186811277,"score_gpt":0.2615487551243775,"score_spread":0.2404679979375662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170004572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017046607,0.00005486199,0.9900733,0.00084699225,0.000060657774,0.00015575212,8.612747e-7,0.0002564936,0.006846369],"genre_scores_gemma":[0.10621109,0.000013697093,0.8863643,0.00015096097,0.00003658281,0.000021750522,0.0000028001298,0.000010191282,0.007188656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986754,0.00005990327,0.00024257167,0.00032579975,0.00040386748,0.0002924522],"domain_scores_gemma":[0.9991686,0.000042351134,0.000041591647,0.00049254403,0.00016010567,0.00009482734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018597142,0.00009660793,0.00009562669,0.00009083158,0.0001055493,0.00019990644,0.0005296855,0.000038492533,0.00014260485],"category_scores_gemma":[0.00003870239,0.0000878911,0.000033555625,0.00060114096,0.000033643573,0.0003381523,0.00020322569,0.00006244854,0.00014132519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012333279,0.00007077891,0.00009860951,0.0000026316043,0.000002162258,0.0000071146633,0.00001714648,0.97511905,0.00011400669,0.021915061,0.0015857532,0.0010664675],"study_design_scores_gemma":[0.00023128398,0.000024374425,0.00027298363,0.0000015353393,0.000002185173,0.000005762468,0.000002067133,0.9967987,0.00067439204,0.0017908323,0.00008265946,0.00011321203],"about_ca_topic_score_codex":0.000045775614,"about_ca_topic_score_gemma":0.0000022438833,"teacher_disagreement_score":0.104506426,"about_ca_system_score_codex":0.000032426535,"about_ca_system_score_gemma":0.00008681285,"threshold_uncertainty_score":0.35840952},"labels":[],"label_agreement":null},{"id":"W2170337736","doi":"10.1007/978-3-540-87700-4_1","title":"On the Behaviour of the (1+1)-ES for a Simple Constrained Problem","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Constraint (computer-aided design); Computer science; Markov chain; Simple (philosophy); Mathematical optimization; Limit (mathematics); Applied mathematics; Algorithm; Mathematics; Geometry; Mathematical analysis","score_opus":0.03279748623791293,"score_gpt":0.27825102728650414,"score_spread":0.2454535410485912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170337736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056746605,0.000066750494,0.9940183,0.0023958082,0.00050922413,0.0013747092,0.000026702806,0.000043753116,0.0015079795],"genre_scores_gemma":[0.13490988,0.00004665394,0.86156034,0.0020558496,0.00023105508,0.0000924956,0.000005781337,0.00005074673,0.0010471956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966427,0.00008023734,0.00052111177,0.0009010913,0.0013571433,0.0004977231],"domain_scores_gemma":[0.99508053,0.0022745023,0.00036662002,0.0016354887,0.0005513377,0.000091505375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012189703,0.00033791596,0.0003891614,0.0003537742,0.0004407649,0.00023651152,0.004924715,0.0001714899,0.000034816923],"category_scores_gemma":[0.00060372223,0.00019222019,0.00018665273,0.0006909792,0.0015796961,0.00014727663,0.0010996206,0.00060773845,0.000007153283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002004091,0.00014334077,0.00008068296,0.00009870913,0.000042212778,0.000028231952,0.00181505,0.14923653,0.00009771805,0.44986016,0.001469349,0.397108],"study_design_scores_gemma":[0.00032215804,0.0002398617,0.000083459614,0.00018913232,0.0000077780705,0.00004629628,2.4568587e-7,0.8573212,0.0015971513,0.13875744,0.0011093066,0.00032598936],"about_ca_topic_score_codex":0.000011263764,"about_ca_topic_score_gemma":0.000015150201,"teacher_disagreement_score":0.70808464,"about_ca_system_score_codex":0.00011564504,"about_ca_system_score_gemma":0.0009868114,"threshold_uncertainty_score":0.9151427},"labels":[],"label_agreement":null},{"id":"W2172684101","doi":"10.2316/journal.201.2014.3.201-2583","title":"EFFICIENT, SWARM-BASED PATH FINDING IN UNKNOWN GRAPHS USING REINFORCEMENT LEARNING","year":2014,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Metaheuristic Optimization Algorithms Research","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; Computer science; Swarm behaviour; Task (project management); Path (computing); Artificial intelligence; Soundness; Node (physics); Metaheuristic; Routing (electronic design automation); Machine learning; Ant colony optimization algorithms; Engineering; Computer network","score_opus":0.0283763983215228,"score_gpt":0.27598496384125815,"score_spread":0.24760856551973534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172684101","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":[0.011947225,0.00044063403,0.9858976,0.000050966944,0.0004647379,0.00045937556,4.5320508e-7,0.00006384122,0.00067513704],"genre_scores_gemma":[0.99633974,0.000014255842,0.0032715295,0.00007874866,0.000047210884,0.000029030174,0.000001956597,0.000011329127,0.00020621289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977794,0.00044341266,0.0005226307,0.00040098414,0.00044696342,0.00040657388],"domain_scores_gemma":[0.99889046,0.00033852764,0.00017022676,0.0003133564,0.00014410792,0.00014331409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018298638,0.00016887797,0.00031401162,0.00041289534,0.00017480676,0.0003083346,0.0003674611,0.00006445518,0.000008359817],"category_scores_gemma":[0.00036581373,0.00014654371,0.000054635955,0.00046826105,0.00004587128,0.00007954341,0.0000872101,0.0002089165,0.000021382479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008403192,0.00002388208,0.0014095923,0.000053961918,0.000010303882,0.0000043014024,0.00015243539,0.9792608,0.00019124619,0.01582103,0.000008533664,0.0030554992],"study_design_scores_gemma":[0.0007607976,0.00009960277,0.00009851258,0.0001373695,0.000005242211,0.0000046618125,0.00003764206,0.9965788,0.000125919,0.00003471811,0.0019564857,0.00016022759],"about_ca_topic_score_codex":0.00012773255,"about_ca_topic_score_gemma":0.0000013541681,"teacher_disagreement_score":0.9843925,"about_ca_system_score_codex":0.00008509571,"about_ca_system_score_gemma":0.000059886566,"threshold_uncertainty_score":0.5975879},"labels":[],"label_agreement":null},{"id":"W2219941422","doi":"10.1142/s0218126616500237","title":"Engineering a Memetic Algorithm from Discrete Cuckoo Search and Tabu Search for Cell Assignment of Hybrid Nanoscale CMOL Circuits","year":2015,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Metaheuristic Optimization Algorithms Research","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":"Tabu search; Cuckoo search; Metaheuristic; Simulated annealing; Memetic algorithm; Local search (optimization); Guided Local Search; Mathematical optimization; Particle swarm optimization; Algorithm; Heuristics; Computer science; Search algorithm; Combinatorial optimization; Mathematics","score_opus":0.030509443732595135,"score_gpt":0.2551045024162703,"score_spread":0.22459505868367519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219941422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029560138,0.0020227993,0.9667603,0.000114183975,0.0009534148,0.00047806962,0.000035964033,0.000017214456,0.000057920188],"genre_scores_gemma":[0.92722934,0.00013479531,0.07213972,0.000022374867,0.00031382238,0.000012428389,0.0000039914685,0.000029507373,0.000114017465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968718,0.00025619203,0.00083941,0.0003774025,0.0012363623,0.00041879894],"domain_scores_gemma":[0.99741864,0.00052108645,0.00029574404,0.00034582987,0.00081482664,0.00060386845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020941587,0.00021790023,0.0006358866,0.0004200644,0.00008286855,0.00040399758,0.0007141374,0.000059951388,0.000002439711],"category_scores_gemma":[0.00006617788,0.00018997966,0.00010148104,0.00030952937,0.00007049572,0.00042717732,0.00026646996,0.00026665078,0.0000016698327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007691032,0.0010300487,0.002310511,0.0030448497,0.0015665523,0.0008256046,0.014205838,0.4010935,0.030209571,0.006316165,0.005227631,0.5340928],"study_design_scores_gemma":[0.0016539585,0.0005817669,0.00026400504,0.00025589095,0.000028402908,0.00025855764,0.0002351048,0.9910109,0.0049296506,0.0000604267,0.00051436335,0.00020694292],"about_ca_topic_score_codex":0.00009282329,"about_ca_topic_score_gemma":1.8907355e-7,"teacher_disagreement_score":0.8976692,"about_ca_system_score_codex":0.00010656224,"about_ca_system_score_gemma":0.0002913686,"threshold_uncertainty_score":0.7747146},"labels":[],"label_agreement":null},{"id":"W2223637016","doi":"10.5539/mas.v10n2p67","title":"A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Applied Science Private University","keywords":"Crossover; Penalty method; Benchmark (surveying); Mathematical optimization; Genetic algorithm; Algorithm; Computer science; Fitness function; Black box; Global optimization; Function (biology); Operator (biology); Meta-optimization; Mathematics; Artificial intelligence","score_opus":0.01452656236483714,"score_gpt":0.28222983393434126,"score_spread":0.26770327156950413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2223637016","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000083078055,0.000017464981,0.99532515,0.00062747026,0.00013036837,0.00032785194,0.000008841974,0.00016840461,0.0033113735],"genre_scores_gemma":[0.08726219,0.000010139688,0.91216946,0.00040842293,0.000031640007,0.000041676078,7.0621167e-7,0.000010616573,0.00006513535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965109,0.00009142222,0.0002717865,0.0011344146,0.0013963457,0.00059508864],"domain_scores_gemma":[0.99830693,0.00020881755,0.00010604282,0.0008536711,0.0002042087,0.0003203409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014728551,0.00020253255,0.00019084701,0.00027036338,0.00041294526,0.0005174212,0.0014658183,0.000041091615,0.000034323846],"category_scores_gemma":[0.00018439301,0.00015025752,0.000028845463,0.0010446117,0.00037364877,0.0003890575,0.0005221034,0.00009177231,0.00003474915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057975003,0.000039141993,0.000019471747,0.0000040709356,0.0000023980224,0.000009102867,0.000047069698,0.24754395,0.0017065518,0.0058521335,0.000020036548,0.74475026],"study_design_scores_gemma":[0.00054533547,0.00004718983,0.00025402056,0.000015323329,0.000002984322,0.00001741358,0.000001972163,0.9888307,0.0025785477,0.0074320235,0.00006498114,0.00020952184],"about_ca_topic_score_codex":0.000006149806,"about_ca_topic_score_gemma":2.1376461e-7,"teacher_disagreement_score":0.74454075,"about_ca_system_score_codex":0.00020302099,"about_ca_system_score_gemma":0.00036471724,"threshold_uncertainty_score":0.61273235},"labels":[],"label_agreement":null},{"id":"W2242413628","doi":"10.1109/ssci.2015.44","title":"The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Mathematical optimization; Probability distribution; Quantum; RADIUS; Computer science; Dynamism; Mathematics; Statistical physics; Algorithm; Statistics; Physics; Quantum mechanics","score_opus":0.032795895382092266,"score_gpt":0.287970921871704,"score_spread":0.2551750264896117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2242413628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028599832,0.00003369017,0.968198,0.0012982036,0.00013166813,0.001575326,0.000010321957,0.000053006534,0.000099965204],"genre_scores_gemma":[0.8604321,0.00003165363,0.13911314,0.000009101596,0.0000097511465,0.0002752293,0.000017732124,0.000009950701,0.00010134268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981807,0.00029172585,0.00047155007,0.00026109544,0.0005208094,0.00027413902],"domain_scores_gemma":[0.99676365,0.0014481181,0.00024509113,0.000713484,0.0007501121,0.00007954262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033623327,0.00012419907,0.00018065504,0.000040832707,0.00031211314,0.00010935257,0.00075501576,0.000044272147,0.000010718532],"category_scores_gemma":[0.0023511592,0.000065878055,0.000066155466,0.00069097336,0.00018733426,0.0002914259,0.0001630982,0.00009663085,0.0000026067098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039908668,0.00005444303,0.00024968013,0.000053174557,0.000013023247,2.8634703e-8,0.00008788713,0.9752834,0.000031300337,0.022880863,0.000081214275,0.0012251182],"study_design_scores_gemma":[0.0004565829,0.00085335766,0.00006209604,0.000023465875,0.000009906516,8.1620874e-7,0.000015307136,0.9882163,0.009911268,0.00035978254,0.000019689054,0.00007147347],"about_ca_topic_score_codex":0.000010426278,"about_ca_topic_score_gemma":0.0000022809043,"teacher_disagreement_score":0.8318323,"about_ca_system_score_codex":0.00009078177,"about_ca_system_score_gemma":0.0001413351,"threshold_uncertainty_score":0.28147268},"labels":[],"label_agreement":null},{"id":"W2269275143","doi":"10.1007/s00500-016-2060-y","title":"Multilevel framework for large-scale global optimization","year":2016,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":41,"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 Ontario Institute of Technology","funders":"","keywords":"Benchmark (surveying); Computer science; Mathematical optimization; Global optimization; Scale (ratio); Artificial intelligence; Algorithm; Mathematics","score_opus":0.027759238281192516,"score_gpt":0.32675317089325784,"score_spread":0.2989939326120653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269275143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012525637,0.000035755842,0.9969348,0.001214668,0.00067996304,0.0003398991,0.000026144182,0.00034713183,0.00029638407],"genre_scores_gemma":[0.05442591,0.000004505696,0.94487745,0.00024274293,0.00022605812,0.0000124795215,0.0000044450276,0.000015016841,0.00019139363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980791,0.00009337731,0.000328734,0.0005386813,0.0003967416,0.00056332385],"domain_scores_gemma":[0.9978797,0.00090950995,0.00013932057,0.0005274384,0.0003854549,0.00015861115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072966143,0.00015278807,0.00018720166,0.000072426024,0.00029590065,0.00019708158,0.000877874,0.00010671542,0.0000578891],"category_scores_gemma":[0.0014770711,0.00011865999,0.000082312035,0.00044290122,0.000032781623,0.00029074211,0.0004710898,0.00008600081,0.00005898664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018713916,0.0001936473,0.002357581,0.000051030016,0.000044999942,0.0000060678412,0.0006170537,0.22147885,0.00003251723,0.27758786,0.0017416502,0.49587002],"study_design_scores_gemma":[0.0006210972,0.00003325201,0.00033722384,0.00006366112,0.0000031589273,0.000005183032,0.000012620874,0.9867714,0.0000801868,0.010846523,0.0010533796,0.0001722852],"about_ca_topic_score_codex":0.0000020557359,"about_ca_topic_score_gemma":6.243592e-7,"teacher_disagreement_score":0.7652926,"about_ca_system_score_codex":0.00010877146,"about_ca_system_score_gemma":0.000108064996,"threshold_uncertainty_score":0.4838814},"labels":[],"label_agreement":null},{"id":"W2270857366","doi":"10.5555/2772879.2772915","title":"Particle Field Optimization: A New Paradigm for Swarm Intelligence","year":2015,"lang":"en","type":"article","venue":"BIBSYS Brage (BIBSYS (Norway))","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Swarm intelligence; Computer science; Metaheuristic; Swarm behaviour; Perspective (graphical); Field (mathematics); Abstraction; Heuristic; Set (abstract data type); Swarm robotics; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.09194608042434352,"score_gpt":0.3293864798193408,"score_spread":0.2374403993949973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270857366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012280543,0.00062641024,0.9859145,0.006645509,0.001012456,0.0010764265,0.000017845732,0.00029924823,0.0042847707],"genre_scores_gemma":[0.0601811,0.00021499589,0.91858715,0.0021270884,0.0008469773,0.00029378993,0.00003999467,0.00008762333,0.017621282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610245,0.0002262965,0.00078357646,0.000980967,0.0009967192,0.0009100063],"domain_scores_gemma":[0.9959667,0.0007863543,0.0002324498,0.0014469072,0.00051997317,0.0010476138],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001257821,0.0003712974,0.00044857487,0.00037860541,0.0002577616,0.00078375556,0.0020828652,0.00019366866,0.0005602362],"category_scores_gemma":[0.0019488735,0.00036184257,0.00016540241,0.0022996604,0.00008258336,0.0010681461,0.0005523923,0.00027885239,0.00083827035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001652308,0.00048933056,0.000218636,0.000122305,0.0001267041,0.00010416379,0.0025281168,0.5973663,0.00006640921,0.13546307,0.107028246,0.15632151],"study_design_scores_gemma":[0.0009851493,0.00048740816,0.00002578566,0.000031726144,0.000018633715,0.000043136977,0.00007155232,0.96345663,0.0036653078,0.0072071496,0.023532424,0.00047508327],"about_ca_topic_score_codex":0.00010323118,"about_ca_topic_score_gemma":0.000016323138,"teacher_disagreement_score":0.36609036,"about_ca_system_score_codex":0.00013311337,"about_ca_system_score_gemma":0.0006569908,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W2274259390","doi":"","title":"Center-based initialization for large-scale black-box problems","year":2009,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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; Ontario Tech University","funders":"","keywords":"Initialization; Population; Computer science; Mathematical optimization; Benchmark (surveying); Latin hypercube sampling; Differential evolution; Particle swarm optimization; Algorithm; Monte Carlo method; Mathematics; Statistics","score_opus":0.13442718921251331,"score_gpt":0.38022876577346093,"score_spread":0.24580157656094762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274259390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022938395,0.0000046705763,0.9790244,0.009318966,0.0008388709,0.00060244085,0.00006723712,0.00018002854,0.00973398],"genre_scores_gemma":[0.8838123,0.000024431536,0.11368883,0.0015969841,0.00021798626,0.00007525568,0.00013603688,0.000015446234,0.00043274305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973545,0.000105510335,0.0006185113,0.0006616646,0.00084256515,0.00041723004],"domain_scores_gemma":[0.99771845,0.00016702851,0.00019676615,0.00048475093,0.001269374,0.00016362644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006085897,0.00022504044,0.00020173004,0.00038012787,0.00017524343,0.00067343755,0.001553237,0.00010060954,0.00043975093],"category_scores_gemma":[0.00045844528,0.00022625332,0.00011108938,0.0004544793,0.000076432756,0.00044422058,0.00008707345,0.00019766518,0.000317511],"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":[0.000061728075,0.0006105954,0.00001773965,0.000009482887,0.000012186954,0.0000047224394,0.0004088564,0.020616265,0.00071938586,0.90109974,0.0002702839,0.07616899],"study_design_scores_gemma":[0.00012746107,0.00030353965,0.000029710827,0.00005821129,0.0000026325065,0.0000018815267,0.000049898208,0.8652788,0.015027209,0.11686165,0.0020493767,0.00020964295],"about_ca_topic_score_codex":0.0000060845414,"about_ca_topic_score_gemma":0.000021940214,"teacher_disagreement_score":0.8835829,"about_ca_system_score_codex":0.00010931997,"about_ca_system_score_gemma":0.00021070444,"threshold_uncertainty_score":0.9226343},"labels":[],"label_agreement":null},{"id":"W2283584817","doi":"","title":"Use of line search in the Luus-Jaakola optimization procedure","year":2007,"lang":"en","type":"article","venue":"Computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Line search; Convergence (economics); Line (geometry); Point (geometry); Mathematical optimization; Set (abstract data type); Mathematics; Local search (optimization); Simple (philosophy); Rate of convergence; Algorithm; Variable (mathematics); Computer science; Telecommunications","score_opus":0.11854189777934346,"score_gpt":0.36838683067648526,"score_spread":0.2498449328971418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283584817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015692364,0.00007022234,0.99690855,0.0006596221,0.00009144006,0.00034738742,0.0000032803941,0.00003326639,0.0003169871],"genre_scores_gemma":[0.40409344,0.00003445451,0.5955049,0.00024341808,0.000031148727,0.0000071559925,0.000017245686,0.0000066784196,0.0000615445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769753,0.00018464238,0.00055005366,0.00032627984,0.00096228975,0.0002792299],"domain_scores_gemma":[0.99725765,0.0014489051,0.0000800086,0.00034925516,0.00079474115,0.00006942603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019490515,0.00011622471,0.00013789407,0.0003846046,0.00008518037,0.00016527441,0.0010966394,0.00005800874,0.000045674427],"category_scores_gemma":[0.0007598399,0.00009243,0.000041723524,0.00202347,0.00012919531,0.00048645327,0.00017985259,0.00024833155,0.00002518904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009619754,0.00011079537,0.00036351773,0.000017238826,0.0000048639426,0.000011735989,0.0007396237,0.9263908,0.0000074028926,0.06216871,0.000075966585,0.010099687],"study_design_scores_gemma":[0.000055212036,0.000059501763,0.0025067101,0.000022030179,0.0000014525474,0.000022973154,0.000080055615,0.9931454,0.00080087647,0.0031422835,0.000072089715,0.00009140359],"about_ca_topic_score_codex":0.000029485818,"about_ca_topic_score_gemma":0.000006987616,"teacher_disagreement_score":0.4025242,"about_ca_system_score_codex":0.000046936177,"about_ca_system_score_gemma":0.00021080872,"threshold_uncertainty_score":0.37691858},"labels":[],"label_agreement":null},{"id":"W2293252417","doi":"10.1109/cjece.2015.2496338","title":"Improving Tabu Search Performance by Means of Automatic Parameter Tuning","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":10,"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":"Tabu search; Heuristic; Mathematical optimization; A priori and a posteriori; Convergence (economics); Set (abstract data type); Computer science; Value (mathematics); Algorithm; Reliability (semiconductor); Genetic algorithm; Mathematics; Machine learning","score_opus":0.00812752825112632,"score_gpt":0.19180998557235354,"score_spread":0.18368245732122723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293252417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07699443,0.00033716756,0.9223088,0.00017815607,0.000116331736,0.00004257024,0.000001120504,0.000011238094,0.0000102010845],"genre_scores_gemma":[0.83710057,0.00003674137,0.16272825,0.000028577331,0.000062041196,8.872644e-7,1.4965806e-7,0.000008953492,0.000033815402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988861,0.000038254533,0.00033239552,0.00013219063,0.0002486039,0.00036243227],"domain_scores_gemma":[0.99876976,0.0003309608,0.00008250712,0.00014167059,0.00018633419,0.00048876257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003884171,0.000098553486,0.00020792942,0.00040484927,0.000050477076,0.00009275018,0.00047108633,0.0000397271,0.000013575862],"category_scores_gemma":[0.00017029309,0.00007082289,0.000041325842,0.0004215958,0.00003100566,0.00031666917,0.000045175257,0.00018513839,0.0000011297225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002309493,0.000013821501,0.0015421071,0.00007616107,0.000053718006,0.000049833194,0.00018797406,0.0075834785,0.0034769883,0.0009673926,0.0003823055,0.9856639],"study_design_scores_gemma":[0.00023409096,0.00020070565,0.0012569203,0.00007975321,0.0000038317507,0.00012062726,6.4989166e-7,0.99641466,0.0012932739,0.000014538514,0.00028854358,0.000092398404],"about_ca_topic_score_codex":0.00007577355,"about_ca_topic_score_gemma":0.000004417624,"teacher_disagreement_score":0.98883116,"about_ca_system_score_codex":0.00007833351,"about_ca_system_score_gemma":0.0003156746,"threshold_uncertainty_score":0.28880736},"labels":[],"label_agreement":null},{"id":"W2293644487","doi":"10.1109/icmla.2015.102","title":"Population Migration Using Dominance in Multi-population Cultural Algorithms","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Dominance (genetics); Population; Computer science; Benchmark (surveying); Evolutionary algorithm; Artificial intelligence; Algorithm; Mathematical optimization; Machine learning; Mathematics; Demography; Biology; Geography","score_opus":0.15176759125991227,"score_gpt":0.3840975725180516,"score_spread":0.23232998125813933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293644487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1015168,0.000043117598,0.8973714,0.00023832294,0.00031931186,0.00027109883,9.4210174e-7,0.000096291405,0.00014271434],"genre_scores_gemma":[0.41812357,0.000004797771,0.5814384,0.000030386029,0.000034758767,0.000008983779,0.000029357985,0.0000062192244,0.00032352892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824125,0.00019070305,0.0003991378,0.00038011692,0.00052774523,0.00026106657],"domain_scores_gemma":[0.99910414,0.000036676283,0.00011668817,0.000330571,0.00028397728,0.00012793549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007012998,0.00012919224,0.00017356213,0.0002629481,0.000071280694,0.0002309171,0.00034777762,0.00007758521,0.000012207634],"category_scores_gemma":[0.00036789107,0.00011481502,0.000030891257,0.00093928433,0.000016744521,0.001586744,0.00012011804,0.00011557918,0.000032102118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037069458,0.00045337965,0.2816778,0.000036308007,0.000017858765,0.000041060004,0.0031228017,0.5880197,0.0008529388,0.017013354,0.000627091,0.10810063],"study_design_scores_gemma":[0.00063947873,0.00001762213,0.09125608,0.000010560902,0.0000014575993,0.000008846851,0.000045595018,0.9073116,0.000108075714,0.00041046715,0.000053105916,0.00013709549],"about_ca_topic_score_codex":0.0057144943,"about_ca_topic_score_gemma":0.000495342,"teacher_disagreement_score":0.31929192,"about_ca_system_score_codex":0.00028398307,"about_ca_system_score_gemma":0.000054894033,"threshold_uncertainty_score":0.8638644},"labels":[],"label_agreement":null},{"id":"W2293808320","doi":"10.1609/socs.v2i1.18213","title":"The Compressed Differential Heuristic","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Uncompressed video; Heuristic; Differential (mechanical device); Computer science; Compression (physics); State (computer science); Algorithm; State space; Space (punctuation); Theoretical computer science; Mathematics; Artificial intelligence; Statistics; Engineering","score_opus":0.015766394318122462,"score_gpt":0.26618553507636256,"score_spread":0.2504191407582401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293808320","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":[0.38964295,0.00042999568,0.045876645,0.27234113,0.08671361,0.0035175676,0.000086685235,0.00069840485,0.200693],"genre_scores_gemma":[0.9954756,0.000115253795,0.0011537668,0.00014604631,0.0004933119,0.00003427801,0.0000031767904,0.00001910969,0.0025594374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99650264,0.00006762598,0.0003970138,0.00041499722,0.002286334,0.00033142086],"domain_scores_gemma":[0.99655217,0.0006355685,0.00016463912,0.00037655354,0.0021631352,0.0001079175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006718384,0.00015238421,0.00017601163,0.00006301754,0.00045974125,0.0008332106,0.004071773,0.0000648326,0.00006358861],"category_scores_gemma":[0.00079433864,0.000098188735,0.0001454676,0.0005749444,0.0001746349,0.0002094122,0.0016422389,0.00041579088,0.000030250152],"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":[0.00008365243,0.00032475,0.00040575842,0.000022157734,0.00010479882,0.0000027122157,0.00015641797,0.00020749004,0.034502335,0.9575335,0.0052641258,0.0013923312],"study_design_scores_gemma":[0.0034131377,0.00029602714,0.0034487208,0.00016886582,0.000028112834,0.000048762995,0.00011726165,0.34600738,0.5425082,0.055573657,0.04790223,0.00048766282],"about_ca_topic_score_codex":0.000026403297,"about_ca_topic_score_gemma":8.309304e-7,"teacher_disagreement_score":0.90195984,"about_ca_system_score_codex":0.00013931554,"about_ca_system_score_gemma":0.00017048392,"threshold_uncertainty_score":0.80346686},"labels":[],"label_agreement":null},{"id":"W2296772665","doi":"10.1609/socs.v1i1.18153","title":"Single-Frontier Bidirectional Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Frontier; Computer science; Node (physics); Work (physics); Search algorithm; Algorithm; Engineering; Geography","score_opus":0.01840111607900786,"score_gpt":0.2670439970903512,"score_spread":0.24864288101134335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296772665","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":[0.6295579,0.000022008731,0.0101687675,0.05414808,0.04869964,0.001721582,0.00004510864,0.0004524848,0.25518444],"genre_scores_gemma":[0.98520416,0.000011965401,0.010414206,0.00012476621,0.00083442434,0.000032901804,0.0000028402142,0.00002846868,0.0033462706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99573165,0.000042390864,0.0004010211,0.0005331306,0.0028970486,0.00039478135],"domain_scores_gemma":[0.99698514,0.00028666572,0.00013770766,0.00034095644,0.0020761278,0.00017343336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017885331,0.00018513466,0.00019640083,0.000273051,0.00025368293,0.00050737197,0.004107234,0.00013153619,0.00019787715],"category_scores_gemma":[0.0007129803,0.00014609896,0.00015370703,0.000826598,0.00027731818,0.0005146113,0.001273367,0.0008635992,0.00007229651],"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":[0.00008639098,0.000622103,0.0023798193,0.00001957999,0.00007475848,0.0000012035101,0.00018368248,0.00013038864,0.25620696,0.7322488,0.0037995484,0.0042467522],"study_design_scores_gemma":[0.0017947983,0.0003330533,0.0025268057,0.000062584535,0.000010480602,0.000049135448,0.00004063727,0.08703454,0.84878737,0.018381858,0.040560387,0.00041835156],"about_ca_topic_score_codex":0.000064695705,"about_ca_topic_score_gemma":0.0000013357989,"teacher_disagreement_score":0.71386695,"about_ca_system_score_codex":0.00017759216,"about_ca_system_score_gemma":0.00017108691,"threshold_uncertainty_score":0.7632331},"labels":[],"label_agreement":null},{"id":"W2304131403","doi":"10.1007/s00500-016-2116-z","title":"Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism","year":2016,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":97,"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":"Science Foundation of Ministry of Education of China; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Firefly algorithm; Benchmark (surveying); Swarm intelligence; Convergence (economics); Premature convergence; Computer science; Mathematical optimization; Set (abstract data type); Mechanism (biology); Optimization problem; Algorithm; Particle swarm optimization; Mathematics","score_opus":0.013661660258551011,"score_gpt":0.261132166591145,"score_spread":0.24747050633259401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2304131403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007844065,0.00014560697,0.9897575,0.001148697,0.0002226935,0.00047085763,0.000006352987,0.0002825002,0.00012172889],"genre_scores_gemma":[0.4648448,0.000043484102,0.53443325,0.0001576374,0.00006944889,0.000012785701,0.000003444746,0.000029561732,0.0004055677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970574,0.0002844636,0.00037362028,0.0007850912,0.00081087806,0.0006885416],"domain_scores_gemma":[0.997532,0.0011477813,0.000106345185,0.0006165153,0.00031421162,0.00028312998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009064523,0.00026591925,0.00034235892,0.00022100766,0.0002662818,0.00027414892,0.0007042571,0.00008628694,0.00004395437],"category_scores_gemma":[0.00021207637,0.0001706667,0.00005265009,0.00052623026,0.000117216034,0.00038667783,0.0005965613,0.00023870137,0.000057458055],"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":[0.000026618274,0.00008282075,0.00016826073,0.000023543802,0.00008116886,0.000053957578,0.0003682094,0.00036059483,0.00043982553,0.0034797613,0.000030551357,0.99488467],"study_design_scores_gemma":[0.0029590684,0.00022850718,0.0023654802,0.00009657008,0.0000117723985,0.000096325966,0.000021303948,0.9918769,0.0004574069,0.0015431491,0.000056947953,0.00028658516],"about_ca_topic_score_codex":0.000019727033,"about_ca_topic_score_gemma":0.0000020494547,"teacher_disagreement_score":0.9945981,"about_ca_system_score_codex":0.000099545316,"about_ca_system_score_gemma":0.00015888644,"threshold_uncertainty_score":0.6959586},"labels":[],"label_agreement":null},{"id":"W2312556230","doi":"10.1007/s12597-016-0256-7","title":"Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems","year":2016,"lang":"en","type":"article","venue":"OPSEARCH","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Mathematical optimization; Particle swarm optimization; Benchmark (surveying); Multi-swarm optimization; Arithmetic function; Computer science; Premature convergence; Simplex algorithm; Algorithm; Metaheuristic; Convergence (economics); Population; Mathematics; Linear programming; Artificial intelligence","score_opus":0.03600050729763214,"score_gpt":0.31658430017333494,"score_spread":0.2805837928757028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312556230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043113923,0.00003089578,0.99408305,0.0033766674,0.000104628896,0.0009797643,0.00003102071,0.00019839013,0.0007644512],"genre_scores_gemma":[0.08046833,0.00003832744,0.9178268,0.00011578675,0.000097357384,0.00023415395,0.00001617651,0.000033832315,0.0011692542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687356,0.00012948946,0.0003894743,0.0007500116,0.0010012854,0.0008562017],"domain_scores_gemma":[0.9974037,0.00045976814,0.00010088282,0.0006889815,0.0010117228,0.00033496553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009997449,0.00021090421,0.0002337464,0.00009835481,0.00034912743,0.0006680064,0.0008527702,0.00010315187,0.00028920246],"category_scores_gemma":[0.00078981085,0.00014344738,0.0000628361,0.0010744601,0.00021444872,0.0009852407,0.00034632056,0.000105312145,0.000052224266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008555852,0.00015815937,0.00060661393,0.00003564851,0.000033757635,0.0000067557453,0.00007922144,0.9473294,0.000107061525,0.0270811,0.00043449373,0.024042223],"study_design_scores_gemma":[0.0019682113,0.00027695563,0.00015947374,0.000038618422,0.00000859341,0.000017582537,0.000009101714,0.9945036,0.0012288501,0.00080081425,0.00074653677,0.00024164976],"about_ca_topic_score_codex":0.000018076362,"about_ca_topic_score_gemma":0.0000054245097,"teacher_disagreement_score":0.08003719,"about_ca_system_score_codex":0.00022720783,"about_ca_system_score_gemma":0.00036254665,"threshold_uncertainty_score":0.64416015},"labels":[],"label_agreement":null},{"id":"W2327567064","doi":"10.1109/tevc.2015.2451701","title":"Simple Probabilistic Population-Based Optimization","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Universität Leipzig; European Social Fund; Ryerson University","keywords":"Metaheuristic; Mathematical optimization; Quadratic assignment problem; Scheme (mathematics); Probabilistic logic; Population; Ant colony optimization algorithms; Optimization problem; Computer science; Simple (philosophy); Combinatorial optimization; Extremal optimization; Multi-swarm optimization; Meta-optimization; Mathematics; Artificial intelligence","score_opus":0.037638567515969966,"score_gpt":0.2932689703359937,"score_spread":0.25563040282002375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327567064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044104626,0.000020550273,0.99660033,0.0006414111,0.0007974821,0.0005685852,0.00002245582,0.00055198104,0.00035614808],"genre_scores_gemma":[0.6232246,0.0000020728064,0.37624547,0.00015282979,0.00004277712,0.00007697699,0.00011083498,0.000020197354,0.00012421844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727994,0.0003457336,0.00048139933,0.0005676419,0.0010045469,0.0003207136],"domain_scores_gemma":[0.9979895,0.00033416072,0.0001497151,0.00045226244,0.0007836983,0.0002906827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043328,0.00022375953,0.00019951804,0.000567053,0.00035350828,0.00016741683,0.00042670945,0.00010984462,0.0000721993],"category_scores_gemma":[0.00009695374,0.00024250879,0.000091851354,0.00137868,0.000052003437,0.0007931745,0.000004777443,0.00022264852,0.00015733164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002610986,0.0002724719,0.000044031935,0.000011822179,0.0000127421845,0.000003566138,0.00005163545,0.99214244,0.0000035099158,0.0008021176,0.0006321601,0.0059973695],"study_design_scores_gemma":[0.00084707653,0.0001984818,0.0007529778,0.000012930666,0.000012663958,0.000011259615,0.00001187122,0.99392873,0.00006720772,0.0038250533,0.00008769003,0.00024404179],"about_ca_topic_score_codex":0.000091054484,"about_ca_topic_score_gemma":0.000006731029,"teacher_disagreement_score":0.62278354,"about_ca_system_score_codex":0.00057373435,"about_ca_system_score_gemma":0.0005193843,"threshold_uncertainty_score":0.9889222},"labels":[],"label_agreement":null},{"id":"W2327951994","doi":"10.1007/s13675-016-0075-x","title":"Variable neighborhood search: basics and variants","year":2016,"lang":"en","type":"article","venue":"EURO Journal on Computational Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"HEC Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Variable neighborhood search; Heuristics; Metaheuristic; Implementation; Mathematical optimization; Variable (mathematics); Computer science; Local search (optimization); Mathematics; Perturbation (astronomy); Theoretical computer science; Algorithm","score_opus":0.02770844702848207,"score_gpt":0.2690038019831024,"score_spread":0.24129535495462034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327951994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000628156,0.00003619576,0.9904571,0.0062488005,0.00043730266,0.00014873703,0.000009217891,0.000080559534,0.002519243],"genre_scores_gemma":[0.032430228,0.00013381267,0.9657378,0.0008257723,0.00020095384,0.000003796779,0.00000832082,0.00002753081,0.00063175696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973584,0.00042072948,0.0004494701,0.0004077649,0.0010285657,0.00033510718],"domain_scores_gemma":[0.9974454,0.0008860011,0.00024836758,0.0002548326,0.0008353905,0.00033003016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010332945,0.00018193286,0.00018452754,0.00040682548,0.00037221733,0.0005823226,0.00053699873,0.000062674386,0.00028752716],"category_scores_gemma":[0.0008361213,0.00013281556,0.000042163192,0.00070475624,0.000063145424,0.0010646253,0.00018657654,0.00024250727,0.000086250766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001607059,0.0000686209,0.000106451465,0.0000033447275,0.000022335587,0.000026864738,0.000040547526,0.7909808,0.00001180614,0.18775295,0.00041229653,0.020557871],"study_design_scores_gemma":[0.0011436451,0.00020047963,0.0014902054,0.000047975675,0.000005363493,0.00028585098,0.0000030812894,0.97885436,0.000015442387,0.01687005,0.00091031817,0.00017325673],"about_ca_topic_score_codex":5.3480613e-7,"about_ca_topic_score_gemma":3.0237974e-8,"teacher_disagreement_score":0.1878735,"about_ca_system_score_codex":0.00010131983,"about_ca_system_score_gemma":0.00033106646,"threshold_uncertainty_score":0.561535},"labels":[],"label_agreement":null},{"id":"W2330450097","doi":"10.1142/9789812777171_0047","title":"AN ALGORITHM TO COMPUTE THE MINIMAL TELESCOPERS FOR RATIONAL FUNCTIONS (DIFFERENTIAL – INTEGRAL CASE)","year":2002,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Differential (mechanical device); Computer science; Algorithm; Mathematics; Applied mathematics; Physics","score_opus":0.04591984734920518,"score_gpt":0.3045564444783878,"score_spread":0.25863659712918263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330450097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044558768,0.000011413918,0.99290085,0.003925998,0.0006542331,0.0006530533,0.00003865911,0.00014456359,0.0012256257],"genre_scores_gemma":[0.048700184,0.0000017411696,0.94317067,0.0007370564,0.0004051693,0.00015630946,0.000026136404,0.00001608408,0.0067866286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828804,0.00014126705,0.00027991427,0.00047534608,0.00044912015,0.00036628894],"domain_scores_gemma":[0.9984775,0.00024705508,0.00003999794,0.000590555,0.00038207177,0.0002628044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031662983,0.0001570288,0.00014701755,0.00017841406,0.0005446451,0.0005829463,0.0008899195,0.00004905522,0.0012530541],"category_scores_gemma":[0.0000975416,0.000106732354,0.00008538302,0.0005405829,0.000070780006,0.00038017365,0.00017865232,0.00014876679,0.000220677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017024926,0.00055464316,0.000018273127,0.000008922439,0.000095043084,0.00009302553,0.0013484637,0.012268699,0.0002949129,0.050270386,0.12968664,0.805344],"study_design_scores_gemma":[0.00036672523,0.00025138044,0.000060697646,0.0000019846339,0.0000068417307,0.0002975517,0.00013082162,0.9889101,0.00010322221,0.00008145843,0.009641612,0.00014761867],"about_ca_topic_score_codex":0.00005030237,"about_ca_topic_score_gemma":0.000048293405,"teacher_disagreement_score":0.9766414,"about_ca_system_score_codex":0.000050279938,"about_ca_system_score_gemma":0.000060915583,"threshold_uncertainty_score":0.99965996},"labels":[],"label_agreement":null},{"id":"W2336667609","doi":"10.1186/s40064-016-2064-1","title":"A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems","year":2016,"lang":"en","type":"article","venue":"SpringerPlus","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cuckoo search; Mathematical optimization; Algorithm; Computer science; Metaheuristic; Cuckoo; Integer programming; Local search (optimization); Minimax; Integer (computer science); Mathematics; Particle swarm optimization","score_opus":0.024177316203590905,"score_gpt":0.30438415835012406,"score_spread":0.28020684214653313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336667609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056047233,0.00009125662,0.9956236,0.0017840529,0.00028093465,0.001022776,0.000026160393,0.00030516685,0.0008100083],"genre_scores_gemma":[0.0017573797,0.000043984346,0.996606,0.00010984424,0.00013902348,0.0001888141,0.0000058095998,0.000037551436,0.0011115951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967914,0.00016455814,0.00038945777,0.0009084232,0.00091828697,0.0008278684],"domain_scores_gemma":[0.99757594,0.00036381691,0.00013265187,0.0008607318,0.00077104423,0.00029581593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001454521,0.0002723881,0.00032033064,0.00019507538,0.00026810775,0.00039174032,0.0011454753,0.00007583483,0.000092973976],"category_scores_gemma":[0.0003068711,0.00018658034,0.00009158181,0.0006818178,0.00008713464,0.00073703175,0.00047020867,0.0001326734,0.000052712487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003529238,0.00009249356,0.00021688928,0.00007174081,0.00010490625,0.000029793582,0.00014915437,0.15010187,0.00021498038,0.0106268255,0.000511847,0.8378442],"study_design_scores_gemma":[0.0013916254,0.0001971357,0.000086351814,0.00008228237,0.000013386577,0.00006480136,0.000006382472,0.99351794,0.0020021626,0.001027084,0.0012988751,0.00031194792],"about_ca_topic_score_codex":0.00006440568,"about_ca_topic_score_gemma":0.0000045803945,"teacher_disagreement_score":0.8434161,"about_ca_system_score_codex":0.00028646179,"about_ca_system_score_gemma":0.0004114402,"threshold_uncertainty_score":0.7608525},"labels":[],"label_agreement":null},{"id":"W2336871472","doi":"10.5267/j.dsl.2016.2.004","title":"Improved symbiotic organisms search algorithm for solving unconstrained function optimization","year":2016,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":49,"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":"Function optimization; Algorithm; Mathematical optimization; Optimization algorithm; Search algorithm; Global optimization; Parametric statistics; Local search (optimization); Computer science; Function (biology); Optimization problem; Mathematics; Genetic algorithm","score_opus":0.021710752453510593,"score_gpt":0.28740480125346857,"score_spread":0.26569404879995795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336871472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014367263,0.000008605528,0.99146014,0.004928196,0.0012531643,0.0006548077,0.000009626809,0.00019681272,0.00005190603],"genre_scores_gemma":[0.045236498,0.000009553585,0.95321375,0.0011958159,0.00011702421,0.000035168312,0.0000030930232,0.00002215985,0.00016694677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99574465,0.00007428181,0.0005151899,0.0011545683,0.0016552744,0.00085601275],"domain_scores_gemma":[0.99645555,0.0011760747,0.00014540715,0.0009809426,0.0008860231,0.000355977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035774948,0.00021936755,0.00023132407,0.00087450637,0.00072582165,0.000941357,0.0021115346,0.00007785264,0.00015224336],"category_scores_gemma":[0.0018382631,0.00015415056,0.00009070016,0.0026825652,0.00059207756,0.0021751805,0.00050718937,0.0001266119,0.00008332709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013719151,0.00003592713,0.000024395602,0.0000040462046,0.000008377636,0.000004835403,0.000083488245,0.008005157,0.18310794,0.0015392773,0.0006003578,0.8065725],"study_design_scores_gemma":[0.0010120374,0.00013561135,0.00011434497,0.000032823227,0.000005223084,0.000017075618,0.000024181369,0.9775751,0.020214858,0.00049696385,0.00013124914,0.00024053031],"about_ca_topic_score_codex":0.0000053885265,"about_ca_topic_score_gemma":5.1096214e-7,"teacher_disagreement_score":0.9695699,"about_ca_system_score_codex":0.0002912457,"about_ca_system_score_gemma":0.00043091385,"threshold_uncertainty_score":0.9077527},"labels":[],"label_agreement":null},{"id":"W2345571104","doi":"10.1007/978-3-540-34690-6_10","title":"Cooperative Particle Swarm Optimizers: A Powerful and Promising Approach","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Particle swarm optimization; Computer science; Swarm behaviour; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.11493309945976436,"score_gpt":0.3668129265798155,"score_spread":0.25187982712005114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345571104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009650771,0.0050410316,0.91729087,0.00035533297,0.00021359492,0.00055532483,0.000009054324,0.0000682777,0.0764569],"genre_scores_gemma":[0.009854323,0.0014229376,0.9186619,0.00021118828,0.00012553393,0.00006187447,0.000032922668,0.000050519266,0.069578804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972145,0.00009366551,0.0007258425,0.000895039,0.0007311316,0.00033983926],"domain_scores_gemma":[0.9975477,0.00096752006,0.00021627411,0.00032951916,0.0008446903,0.00009432088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065060257,0.00037106313,0.0005591363,0.0002675628,0.00017978385,0.00021699523,0.0006083814,0.0001239752,0.000026087533],"category_scores_gemma":[0.00031424695,0.00035452185,0.00006458283,0.00025589374,0.0006876272,0.0002744281,0.0007989156,0.000447085,0.000056881767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009449198,0.00005384514,0.000007198124,0.00007883946,0.0001042716,0.00004915228,0.0011721976,0.6897819,2.3358805e-7,0.30010962,0.00088962616,0.0077436487],"study_design_scores_gemma":[0.00014539364,0.00007769149,0.000009466521,0.00020109516,0.000010273269,0.00003493042,0.000119310935,0.9268042,0.000027293818,0.07157201,0.00064741244,0.00035095672],"about_ca_topic_score_codex":0.000011574444,"about_ca_topic_score_gemma":0.0000027833278,"teacher_disagreement_score":0.23702224,"about_ca_system_score_codex":0.00020598898,"about_ca_system_score_gemma":0.00020244166,"threshold_uncertainty_score":0.9998907},"labels":[],"label_agreement":null},{"id":"W2359483975","doi":"","title":"The Performance Analysis of Particle Swarm Optimization for Solving Continuous Optimization Problem","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Multi-swarm optimization; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Computer science; Metaheuristic; Meta-optimization; Derivative-free optimization; Imperialist competitive algorithm; Swarm behaviour; Optimization problem; Population; Function (biology); Algorithm; Mathematics","score_opus":0.01764427329822725,"score_gpt":0.2615115871013189,"score_spread":0.24386731380309162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359483975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001254192,0.000115368784,0.9966396,0.00043717268,0.000019091216,0.0013085735,0.000011067916,0.00011873304,0.00009619978],"genre_scores_gemma":[0.025826503,0.00023819912,0.9725605,0.00006764751,0.00004346194,0.0008593592,0.00005999524,0.000015131567,0.00032916834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983425,0.000052266747,0.00056611974,0.00041917828,0.00029372965,0.00032621121],"domain_scores_gemma":[0.9978209,0.0003730598,0.00027941697,0.0006436396,0.00079601037,0.000086988104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048128018,0.0001403233,0.00025408767,0.00021835069,0.00082472543,0.00015540492,0.0009960626,0.00005348309,0.000011553538],"category_scores_gemma":[0.00000897428,0.0001182751,0.00013263179,0.0023346015,0.00012872479,0.00031832742,0.00020578204,0.000082671664,0.000006339234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038849976,0.000086102256,0.0003278013,0.00001482426,0.00014429857,1.4286674e-7,0.00025440857,0.9727205,0.00013981902,0.003734072,0.00025575384,0.022318369],"study_design_scores_gemma":[0.00028444195,0.000037923506,0.0003040652,0.0000042388824,0.0000752072,0.000005028263,0.000008785334,0.9917173,0.0026714262,0.000026126661,0.004739008,0.00012649626],"about_ca_topic_score_codex":0.0000071057925,"about_ca_topic_score_gemma":0.0000015897845,"teacher_disagreement_score":0.024572311,"about_ca_system_score_codex":0.00004814863,"about_ca_system_score_gemma":0.00010913775,"threshold_uncertainty_score":0.63432044},"labels":[],"label_agreement":null},{"id":"W2361773057","doi":"","title":"A Hybrid Global Optimization Algorithm for Non-differential Function Based on Elitist Maintained Genetic and Alopex Algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Algorithm; Meta-optimization; Genetic algorithm; Population-based incremental learning; Global optimization; Convergence (economics); Computer science; Mathematical optimization; Differential evolution; Cultural algorithm; Optimization algorithm; Hybrid algorithm (constraint satisfaction); Feature (linguistics); Function (biology); Optimization problem; Mathematics","score_opus":0.01633046758345071,"score_gpt":0.25120888118034834,"score_spread":0.23487841359689762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2361773057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000055487402,0.000038853614,0.99578935,0.00053913705,0.00062821334,0.00091818714,0.000069048496,0.00020346972,0.0017582672],"genre_scores_gemma":[0.011241977,0.00003695535,0.98675555,0.000517816,0.00026697034,0.00016087915,0.00007157076,0.000027316964,0.0009209543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974801,0.00013685737,0.0004138819,0.0007469742,0.0006010028,0.0006211617],"domain_scores_gemma":[0.99846256,0.00018446876,0.00011054763,0.0005704947,0.00031921317,0.00035270472],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003029057,0.00029557268,0.00027825483,0.0002184121,0.0002914961,0.00047847364,0.0004672704,0.000090229456,0.0006921746],"category_scores_gemma":[0.00014301484,0.00028024954,0.000092956274,0.00047464127,0.000076755445,0.0003528747,0.00015594909,0.00011850039,0.000048119648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004280777,0.0005365131,0.000068727626,0.000044160097,0.00006496363,0.000019145245,0.000034945188,0.09004618,0.000015163879,0.0031274601,0.010400748,0.8955992],"study_design_scores_gemma":[0.0020243977,0.0004884208,0.00053675467,0.000013143483,0.000020293572,0.000027439986,0.000006996833,0.99562705,0.00005855073,0.00030145163,0.00058843486,0.00030704043],"about_ca_topic_score_codex":0.000024667903,"about_ca_topic_score_gemma":8.6276737e-7,"teacher_disagreement_score":0.9055809,"about_ca_system_score_codex":0.00012889924,"about_ca_system_score_gemma":0.000055806442,"threshold_uncertainty_score":0.99996495},"labels":[],"label_agreement":null},{"id":"W2366166569","doi":"","title":"Projective Genetic Algorithm","year":2000,"lang":"en","type":"article","venue":"Huadong Li-Gong Daxue xuebao","topic":"Metaheuristic Optimization Algorithms Research","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":"Projective test; Genetic algorithm; Computer science; Genetic representation; Meta-optimization; Algorithm; Mathematical optimization; Mathematics; Pure mathematics","score_opus":0.012398324068519073,"score_gpt":0.2545149076565105,"score_spread":0.24211658358799146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366166569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024831346,0.0003148068,0.96564186,0.000611649,0.00049832463,0.00073108217,0.000018403163,0.00054195017,0.029158786],"genre_scores_gemma":[0.008381967,0.00018575172,0.959646,0.00041404951,0.00043620847,0.00015233953,0.000020696572,0.000061099774,0.030701894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996374,0.00031923878,0.00050174707,0.0009896238,0.0009815408,0.00083383033],"domain_scores_gemma":[0.9976826,0.00018270404,0.00010275262,0.0013814727,0.00031346508,0.00033702512],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005591494,0.00032975976,0.00035208132,0.00034516276,0.0003456557,0.0006136345,0.0017923095,0.00014646267,0.0017537294],"category_scores_gemma":[0.00014861756,0.00031815135,0.00012878906,0.0015445877,0.00015360246,0.0007408701,0.00031984266,0.00042318326,0.002322561],"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":[0.000010035381,0.00020209898,0.00017515029,0.000021075577,0.000058561658,0.00020195055,0.0006886113,0.004885105,0.000041367664,0.0015711585,0.004335905,0.987809],"study_design_scores_gemma":[0.00078843563,0.00016406336,0.005466522,0.00003424388,0.00001577824,0.00014263415,0.0000319071,0.9541731,0.0007104587,0.0012133985,0.036715932,0.00054354325],"about_ca_topic_score_codex":0.00014492199,"about_ca_topic_score_gemma":0.0000052505748,"teacher_disagreement_score":0.9872654,"about_ca_system_score_codex":0.00012977183,"about_ca_system_score_gemma":0.00031704534,"threshold_uncertainty_score":0.99992704},"labels":[],"label_agreement":null},{"id":"W2371524223","doi":"","title":"Order Preserving of Gene Section for Solving Traveling Salesman Problems Using Genetic Algorithms","year":2000,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"CAE (Canada)","funders":"","keywords":"Travelling salesman problem; Section (typography); 2-opt; Genetic algorithm; Computer science; Algorithm; Bottleneck traveling salesman problem; Mathematical optimization; Mathematics","score_opus":0.04986389685302601,"score_gpt":0.298596668648474,"score_spread":0.248732771795448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371524223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009244825,0.00012516227,0.98854476,0.0000743447,0.00020164416,0.0006433913,0.0000034416928,0.0001097638,0.0010526673],"genre_scores_gemma":[0.011318084,0.000043225333,0.9866708,0.0000329318,0.00016467868,0.000035945137,0.000005317753,0.000026722935,0.0017023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980374,0.000071850154,0.00051490054,0.00048310866,0.0004705397,0.0004221991],"domain_scores_gemma":[0.99860674,0.00014525637,0.00011239729,0.00051739806,0.0005066498,0.00011156391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006236805,0.0001565287,0.00022834096,0.00016891837,0.00021125037,0.00016914881,0.00071353925,0.00008417577,0.00052511034],"category_scores_gemma":[0.00012037961,0.00015159666,0.000071344286,0.0008056461,0.000039971124,0.00040223295,0.000105187595,0.000113281625,0.0000069648727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007745921,0.000098993114,0.00009825613,0.00014324546,0.00004748813,0.0000026108034,0.000629689,0.86569726,0.0136540625,0.00031107027,0.00009931028,0.11921025],"study_design_scores_gemma":[0.00042728364,0.0000692024,0.00023858645,0.00002981384,0.000010206565,0.000024190571,0.000015687536,0.99161655,0.0065730275,0.00045031297,0.0003766,0.00016854808],"about_ca_topic_score_codex":0.0002047908,"about_ca_topic_score_gemma":0.0000110600095,"teacher_disagreement_score":0.12591927,"about_ca_system_score_codex":0.000052276377,"about_ca_system_score_gemma":0.00015748125,"threshold_uncertainty_score":0.6181932},"labels":[],"label_agreement":null},{"id":"W2373190274","doi":"","title":"A New Particle Swarm Optimization Based on Three Evolution Model","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Computer science; Mathematical optimization; Multi-swarm optimization; Process (computing); Population; Meta-optimization; Algorithm; Mathematics","score_opus":0.039366016759225846,"score_gpt":0.2581069767003025,"score_spread":0.21874095994107667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2373190274","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001491134,0.000023399647,0.9949811,0.0005416307,0.000024059906,0.00081084896,0.000004854707,0.00037893222,0.00322023],"genre_scores_gemma":[0.015091714,0.0000027465435,0.98371196,0.00042369886,0.00007362564,0.0003042665,0.000014565991,0.000021602533,0.00035581406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983078,0.0000511853,0.00032555545,0.00060768233,0.00035719623,0.00035054496],"domain_scores_gemma":[0.99843514,0.00006020975,0.000103610604,0.00092227146,0.00022536004,0.0002534378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022740226,0.0001813635,0.00014690794,0.00019798795,0.00022609207,0.00015684955,0.0011135858,0.00007686418,0.000090065325],"category_scores_gemma":[0.000003610718,0.00018515512,0.00007272841,0.0009401002,0.000037342543,0.00032304972,0.00020700105,0.00015111714,0.00033073066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006047292,0.0001953571,0.000059425318,0.0000053977415,0.000007728391,7.617546e-7,0.00012155005,0.8934765,0.000088951325,0.06464067,0.0012201859,0.040177424],"study_design_scores_gemma":[0.00043525384,0.000047539528,0.00021029293,0.000006872377,0.000007211232,0.0000028894983,0.0000015347597,0.99012476,0.001310486,0.0060503595,0.0016119595,0.00019085848],"about_ca_topic_score_codex":0.00003895562,"about_ca_topic_score_gemma":0.0000043322093,"teacher_disagreement_score":0.09664824,"about_ca_system_score_codex":0.00011757321,"about_ca_system_score_gemma":0.00028306493,"threshold_uncertainty_score":0.75504065},"labels":[],"label_agreement":null},{"id":"W2394358333","doi":"","title":"A Technique of Implementing Genetic Algorithm by Bit-Operation","year":2002,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Computer science; Decimal; Binary number; Genetic algorithm; Integer (computer science); Algorithm; Encoding (memory); Binary code; Mutation; Integer programming; Transformation (genetics); Code (set theory); Arithmetic; Parallel computing; Set (abstract data type); Mathematics; Programming language; Artificial intelligence","score_opus":0.018962249031497697,"score_gpt":0.26734365600290005,"score_spread":0.24838140697140235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394358333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000041364572,0.00010596495,0.9935757,0.0002989422,0.000033456836,0.0003019698,0.000004567191,0.00009310637,0.0055449475],"genre_scores_gemma":[0.006272309,0.000060304814,0.99073946,0.00007825978,0.000018697187,0.000050947245,0.0000035071735,0.00000787629,0.0027686397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986602,0.000089840825,0.00032795043,0.00026149282,0.00040580516,0.00025469862],"domain_scores_gemma":[0.99924403,0.00005114401,0.00007488522,0.0004002567,0.00015937747,0.00007033208],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039277898,0.00008462956,0.00011686176,0.00014649783,0.000080688485,0.00010061403,0.0005292443,0.000037318998,0.0015027197],"category_scores_gemma":[0.000052219795,0.00007683027,0.00003115068,0.0004753201,0.00002793484,0.00022004165,0.00022589543,0.00007306105,0.00006567303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.1583658e-7,0.00018655445,0.00013118851,0.000023538334,0.000023512574,0.000007382202,0.0002295032,0.0004127486,0.019963417,0.00519821,0.032349534,0.941474],"study_design_scores_gemma":[0.00014280035,0.000048201102,0.000024464453,0.0000030444933,0.0000015915482,0.000009428779,0.00000594169,0.94839984,0.047272414,0.0001379341,0.00387033,0.00008403195],"about_ca_topic_score_codex":0.00003829602,"about_ca_topic_score_gemma":7.905886e-7,"teacher_disagreement_score":0.9479871,"about_ca_system_score_codex":0.000020157266,"about_ca_system_score_gemma":0.000019039051,"threshold_uncertainty_score":0.99941003},"labels":[],"label_agreement":null},{"id":"W2395810761","doi":"","title":"Effect of parallelism on the efficiency of binary tree search.","year":2011,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematics; Parallelism (grammar); Tree (set theory); Binary tree; Binary number; Binary search tree; Parallel computing; Arithmetic; Computer science; Algorithm; Combinatorics","score_opus":0.029611683932986674,"score_gpt":0.27663260161618075,"score_spread":0.24702091768319406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395810761","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":[0.9693274,0.00011689742,0.010294196,0.00030926501,0.001033588,0.0007084433,0.0000029843436,0.00007519321,0.018132055],"genre_scores_gemma":[0.9967087,0.000020548381,0.0030474244,0.000022157592,0.000002823342,0.000018089291,7.539977e-7,0.000009999523,0.00016949506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976878,0.00072763424,0.0003064292,0.00027245312,0.0007372465,0.0002684223],"domain_scores_gemma":[0.99746954,0.0011229877,0.000121788435,0.0010014503,0.0001989625,0.00008529065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008864,0.00013188676,0.0002529931,0.00017391842,0.00008214094,0.000020260244,0.0016580629,0.000055002904,0.00010255872],"category_scores_gemma":[0.000489661,0.000084026186,0.000091762224,0.00080256374,0.00020037229,0.00011021698,0.00036127644,0.0001980779,0.000060801627],"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":[0.000065127744,0.0003709431,0.0005282041,0.00005676052,0.000033520515,0.000015538837,0.0010989201,0.0001160271,0.0007465876,0.9895561,0.0007401887,0.0066720955],"study_design_scores_gemma":[0.004170222,0.012401345,0.01228467,0.00017924154,0.000041053987,0.000012858215,0.00007128902,0.40630856,0.4738046,0.089948036,0.000204448,0.00057367753],"about_ca_topic_score_codex":0.000043353677,"about_ca_topic_score_gemma":2.0936172e-7,"teacher_disagreement_score":0.8996081,"about_ca_system_score_codex":0.000019787933,"about_ca_system_score_gemma":0.00007962626,"threshold_uncertainty_score":0.34264883},"labels":[],"label_agreement":null},{"id":"W2399372960","doi":"10.1007/s11227-016-1739-2","title":"Finding approximate solutions of NP-hard optimization and TSP problems using elephant search algorithm","year":2016,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Metaheuristic Optimization Algorithms Research","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":"Lakehead University","funders":"Universidade de Macau","keywords":"Travelling salesman problem; Computer science; Metaheuristic; Mathematical optimization; Firefly algorithm; Search algorithm; Beam search; Algorithm; Combinatorial optimization; Guided Local Search; Local search (optimization); Best-first search; Optimization problem; Benchmark (surveying); Mathematics; Particle swarm optimization","score_opus":0.08004275425044745,"score_gpt":0.2965008824944893,"score_spread":0.21645812824404187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399372960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026654005,0.00032470239,0.9719294,0.00070491235,0.0001650254,0.00017153482,0.000003326062,0.00001989053,0.000027259164],"genre_scores_gemma":[0.20173833,0.00022282854,0.7978581,0.000019103267,0.00012055221,5.1086215e-7,2.9337124e-7,0.000015032657,0.000025221345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746895,0.0004874206,0.00072183454,0.00017901197,0.0007253661,0.00041742017],"domain_scores_gemma":[0.99781156,0.0007315922,0.0003274412,0.0002961444,0.00070165016,0.0001316378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048892065,0.00013624495,0.00028549085,0.00035845442,0.0003601066,0.00011973822,0.0008060564,0.00005147639,0.000021337326],"category_scores_gemma":[0.0003095896,0.00007913387,0.00006685445,0.0006109239,0.00016588265,0.00072140136,0.00054269575,0.00023412672,0.0000016068449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013330469,0.000119208155,0.0005073774,0.000089150235,0.000107170796,0.00001359583,0.003272945,0.8408728,0.032526977,0.0011446116,0.00006621051,0.12126657],"study_design_scores_gemma":[0.00052832614,0.00009407658,0.00010119687,0.00019174274,0.00002057265,0.00048593266,0.00012737491,0.99594736,0.002087598,0.00030629302,0.000009741715,0.00009976368],"about_ca_topic_score_codex":0.000022920209,"about_ca_topic_score_gemma":2.68282e-7,"teacher_disagreement_score":0.17508432,"about_ca_system_score_codex":0.00008625007,"about_ca_system_score_gemma":0.00018338514,"threshold_uncertainty_score":0.32269853},"labels":[],"label_agreement":null},{"id":"W2399994135","doi":"10.1080/03155986.2006.11732744","title":"Gender-Specific Genetic Algorithms<sup>*</sup>","year":2006,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":25,"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":"Algorithm; Computer science","score_opus":0.06962427366799345,"score_gpt":0.326702144233142,"score_spread":0.2570778705651485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399994135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024822217,0.00068351714,0.95815,0.0005651266,0.0002322013,0.0010964994,0.00004892141,0.0001249847,0.0366165],"genre_scores_gemma":[0.73870224,0.00067744195,0.25000173,0.00045082573,0.0012907484,0.00086341443,0.0005300017,0.000041224353,0.0074423617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99524933,0.0002774863,0.0009626686,0.0003052769,0.0026121582,0.0005930834],"domain_scores_gemma":[0.9964243,0.00037692967,0.00010622549,0.00054432714,0.0023153906,0.00023282797],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002787193,0.00018765754,0.00021676582,0.00092394033,0.00080115837,0.0034045887,0.0007696863,0.0001389515,0.00010555962],"category_scores_gemma":[0.00022838834,0.00016598504,0.000045972087,0.001152744,0.0001594548,0.0036848937,0.0003817707,0.0003731311,0.0009086112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013377248,0.000048374197,0.0005808307,0.000119802105,0.000024567622,0.0000101916075,0.0012940045,0.18503621,0.000018724173,0.74050903,0.036202576,0.036142293],"study_design_scores_gemma":[0.0004297936,0.00004414826,0.0025201978,0.000012325168,7.378585e-7,0.00006761242,0.00023781227,0.7393154,0.000025046564,0.0004402637,0.2567541,0.00015254416],"about_ca_topic_score_codex":0.00033234974,"about_ca_topic_score_gemma":0.0000014702185,"teacher_disagreement_score":0.7400688,"about_ca_system_score_codex":0.00015546722,"about_ca_system_score_gemma":0.0004984645,"threshold_uncertainty_score":0.9998693},"labels":[],"label_agreement":null},{"id":"W2401722941","doi":"10.1609/aaai.v25i1.7815","title":"Euclidean Heuristic Optimization","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":37,"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":"Heuristics; Heuristic; Mathematical optimization; Optimization problem; Consistency (knowledge bases); Computer science; Reduction (mathematics); Euclidean geometry; Dimensionality reduction; Mathematics; Artificial intelligence","score_opus":0.1529758557561552,"score_gpt":0.30765553793865236,"score_spread":0.15467968218249717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401722941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016476236,0.000011255723,0.93631244,0.0010414602,0.00045973604,0.00044070967,0.000003259435,0.00013486722,0.059948646],"genre_scores_gemma":[0.8268186,0.000045542223,0.17233098,0.0001370033,0.000043138243,0.000025711106,5.1263794e-7,0.000016257358,0.0005822679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997757,0.00003709632,0.00061144267,0.0005136392,0.0007129181,0.00036789547],"domain_scores_gemma":[0.99773675,0.000090976435,0.0003743731,0.00044832932,0.0012122148,0.00013734886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000702048,0.00020710172,0.00023715959,0.0002149229,0.00020218712,0.00022427272,0.002748182,0.00008249821,0.00056491507],"category_scores_gemma":[0.0011721766,0.00015760941,0.00009771439,0.0010975214,0.00028341016,0.000469428,0.0005195897,0.00028193483,0.00017908019],"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":[0.00003085946,0.00018143698,0.0000915838,0.000025127407,0.00001329502,9.35389e-7,0.0010920776,0.0016080294,0.00081050367,0.9676749,0.00015518264,0.028316053],"study_design_scores_gemma":[0.000025767898,0.00015656896,0.00011219274,0.00007376375,0.000009086257,0.000005354715,0.00015073891,0.79046035,0.117667295,0.09110788,0.000036037392,0.00019495716],"about_ca_topic_score_codex":0.0000341452,"about_ca_topic_score_gemma":0.0000015702461,"teacher_disagreement_score":0.876567,"about_ca_system_score_codex":0.000039354167,"about_ca_system_score_gemma":0.00011975809,"threshold_uncertainty_score":0.6427125},"labels":[],"label_agreement":null},{"id":"W2402720751","doi":"10.2166/hydro.2013.118","title":"Evaluation and application of Fuzzy Differential Evolution approach for benchmark optimization and reservoir operation problems","year":2013,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Metaheuristic Optimization Algorithms Research","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":"Western University","funders":"","keywords":"Initialization; Benchmark (surveying); Differential evolution; Mathematical optimization; Fuzzy logic; Algorithm; Computer science; Interval (graph theory); Convergence (economics); Mathematics; Artificial intelligence","score_opus":0.018680762783150073,"score_gpt":0.27227927390834344,"score_spread":0.2535985111251934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402720751","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016188389,0.00013762448,0.98199886,0.00016388857,0.00005994437,0.0011447067,0.0000022674803,0.000007963106,0.000296345],"genre_scores_gemma":[0.44851932,0.00006767712,0.5512897,0.0000076628485,0.000036026962,0.000045674667,0.000019110317,0.0000049275864,0.000009899129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803084,0.000108810615,0.00081166293,0.000105906496,0.0008111653,0.00013163239],"domain_scores_gemma":[0.997504,0.000103235565,0.00072779984,0.00020022626,0.0013722712,0.00009250066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019023508,0.00009862094,0.00020751788,0.00029072075,0.00011042233,0.00020726972,0.00025050386,0.00006798153,0.000008383214],"category_scores_gemma":[0.00039883645,0.00008140547,0.000042455853,0.00023186672,0.00005154866,0.0019450817,0.000088975234,0.00010429669,6.411168e-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":[0.000007569072,0.00007147773,0.00010340238,0.00020613447,0.00003840306,2.495312e-8,0.0006670038,0.9802473,0.00043452482,0.003210545,0.00015847533,0.014855106],"study_design_scores_gemma":[0.0008486375,0.00021042259,0.00060726947,0.000020546708,0.000045155244,0.000028271696,0.00008629127,0.9948115,0.00011684698,0.003135023,0.000012849457,0.000077184624],"about_ca_topic_score_codex":0.000008550284,"about_ca_topic_score_gemma":5.4854746e-7,"teacher_disagreement_score":0.43233094,"about_ca_system_score_codex":0.000079629186,"about_ca_system_score_gemma":0.00013219453,"threshold_uncertainty_score":0.33196187},"labels":[],"label_agreement":null},{"id":"W2406981943","doi":"10.4230/dagsemproc.09171.1","title":"09171 Abstracts Collection – Adaptive, Output Sensitive, Online and Parameterized Algorithms","year":2009,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Metaheuristic Optimization Algorithms Research","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":"Parameterized complexity; Computer science; Informatics; Research center; Center (category theory); Work (physics); Algorithm; Theoretical computer science; Engineering; Electrical engineering","score_opus":0.03175318269460307,"score_gpt":0.28721453522514434,"score_spread":0.25546135253054125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406981943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037202988,0.000036710404,0.9564362,0.0010435507,0.0006567393,0.0015601572,0.00035297283,0.00034130114,0.0023693363],"genre_scores_gemma":[0.23804481,0.00015748259,0.75839984,0.0015161443,0.0001579753,0.00005383523,0.00037494433,0.000038814804,0.0012561376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640834,0.00010959245,0.0012641139,0.00049765856,0.00085383235,0.00086646277],"domain_scores_gemma":[0.99722046,0.00032425235,0.0005062722,0.0007626164,0.00075474044,0.00043168358],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008935406,0.00046085418,0.00060216786,0.00056745776,0.00047198674,0.0007944836,0.0007788192,0.00025219517,0.000013561179],"category_scores_gemma":[0.00040607463,0.0004308099,0.00016957,0.0007908716,0.00014939246,0.001881675,0.0003193273,0.000528542,0.00006338693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013787451,0.0048512253,0.00083547086,0.0007442669,0.0010091986,0.00020498889,0.020880874,0.01016079,0.00055825425,0.018691719,0.020716216,0.91996825],"study_design_scores_gemma":[0.0035768861,0.00079687126,0.0053935223,0.00007526035,0.000027649583,0.00018075408,0.00034526474,0.97863525,0.0007418882,0.0007312308,0.008934634,0.00056079624],"about_ca_topic_score_codex":0.00002168777,"about_ca_topic_score_gemma":0.000006810679,"teacher_disagreement_score":0.96847445,"about_ca_system_score_codex":0.00015308084,"about_ca_system_score_gemma":0.00016040637,"threshold_uncertainty_score":0.9998144},"labels":[],"label_agreement":null},{"id":"W2409271889","doi":"10.1162/evco_a_00187","title":"Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems","year":2016,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Computer science; Combinatorial optimization; Local search (optimization); Memetic algorithm; Evolutionary algorithm; Population; Optimization problem; Context (archaeology); Greedy randomized adaptive search procedure; Greedy algorithm; Mathematics","score_opus":0.016413554861362017,"score_gpt":0.2668385528779645,"score_spread":0.25042499801660245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409271889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018634816,0.0002536406,0.9947437,0.0009983516,0.0013354828,0.001297152,0.00009216671,0.00060804246,0.0004851024],"genre_scores_gemma":[0.422995,0.000029320145,0.5762033,0.00003376998,0.00023783738,0.00016921287,0.00019098406,0.00003017192,0.000110437664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969262,0.00032943056,0.00062327966,0.00077895017,0.0008479852,0.0004941935],"domain_scores_gemma":[0.9973058,0.000484902,0.0003516746,0.00037998773,0.001308879,0.00016872412],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048262972,0.0002998919,0.00027145434,0.0002552904,0.00046094685,0.00027528167,0.00065565394,0.00012254913,0.000040409162],"category_scores_gemma":[0.00024481423,0.00025242806,0.0001035176,0.00053377374,0.00009768206,0.0021787365,0.00017335745,0.00011512849,0.000027674316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046666933,0.00020988354,0.000039227223,0.000034135894,0.00007714238,0.0000017750842,0.0001120353,0.5753066,0.0003597231,0.39119443,0.027453749,0.0051646135],"study_design_scores_gemma":[0.0017269896,0.0002789752,0.00032243817,0.0000207498,0.000013561964,0.000018376943,0.000016436348,0.7756553,0.00013428395,0.2211131,0.0004281846,0.0002715517],"about_ca_topic_score_codex":0.000017473474,"about_ca_topic_score_gemma":3.43676e-7,"teacher_disagreement_score":0.42280865,"about_ca_system_score_codex":0.00067593186,"about_ca_system_score_gemma":0.00063785625,"threshold_uncertainty_score":0.9999928},"labels":[],"label_agreement":null},{"id":"W2409344786","doi":"","title":"MO-LOST: Adaptive ant trail untangling in multi-objective multi-colony robot foraging (Extended Abstract)","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Foraging; Robot; Computer science; Context (archaeology); Ant colony; Population; Set (abstract data type); Artificial intelligence; Ant colony optimization algorithms; Interference (communication); Geography; Ecology; Biology; Computer network","score_opus":0.08171710937599094,"score_gpt":0.33434531644790777,"score_spread":0.2526282070719168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409344786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037150825,0.0002336514,0.9921975,0.00017982938,0.00036832018,0.00066746684,0.0000061098654,0.00017588673,0.0024561374],"genre_scores_gemma":[0.508761,0.000014510044,0.49020484,0.000101288635,0.000046484325,0.000037488535,0.0000022120382,0.000019131054,0.0008130563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99695665,0.00019980015,0.00054428354,0.0006474825,0.00063075114,0.0010210488],"domain_scores_gemma":[0.9982726,0.000428171,0.0001570352,0.00048564395,0.00030098725,0.00035558216],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014477341,0.00029174561,0.0003623631,0.00050011574,0.00016234761,0.00019188209,0.000886856,0.00012964434,0.00015743524],"category_scores_gemma":[0.00050075026,0.00026599746,0.00009364317,0.0010414378,0.00008782349,0.0014734477,0.00040298508,0.0004995365,0.0002805359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036949,0.014333513,0.03834429,0.0002549374,0.00068322074,0.00086597435,0.081420496,0.25761187,0.009974631,0.08418921,0.0008960265,0.51105636],"study_design_scores_gemma":[0.0014633732,0.0000534988,0.06603556,0.000029006515,0.000005272989,0.000018365907,0.000639787,0.9284895,0.0027670693,0.00012522333,0.00004715128,0.00032618557],"about_ca_topic_score_codex":0.00036004814,"about_ca_topic_score_gemma":0.00013878815,"teacher_disagreement_score":0.67087764,"about_ca_system_score_codex":0.0003265689,"about_ca_system_score_gemma":0.00017099643,"threshold_uncertainty_score":0.9999792},"labels":[],"label_agreement":null},{"id":"W242727464","doi":"10.1016/j.jpdc.2012.01.003","title":"Parallel Ant Colony Optimization on Graphics Processing Units","year":2012,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Chicoutimi","funders":"Agence Nationale de la Recherche","keywords":"Ant colony optimization algorithms; Computer science; Graphics; Graphics processing unit; ANT; Computer graphics (images); Parallel metaheuristic; Parallel computing; Metaheuristic; Artificial intelligence; Operating system; Meta-optimization","score_opus":0.041069458023383416,"score_gpt":0.2981955138576753,"score_spread":0.2571260558342919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W242727464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005648012,0.00077292154,0.9923618,0.00062994735,0.00026533645,0.00010912624,0.000004944571,0.000040519615,0.0001674376],"genre_scores_gemma":[0.6469975,0.00013250993,0.35243723,0.00016789489,0.00022377727,9.63972e-7,0.000012607973,0.000009792268,0.000017712506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979373,0.00020810342,0.00061212876,0.00018476637,0.0006122412,0.00044544775],"domain_scores_gemma":[0.99781203,0.00025931478,0.0005976562,0.0001857815,0.0007884862,0.00035671677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246106,0.00017494852,0.00030365196,0.00025618717,0.00032165903,0.00029895076,0.00048379044,0.00008493361,0.000007727928],"category_scores_gemma":[0.0005135697,0.0001443318,0.000054851775,0.0010517471,0.00006444302,0.0006531487,0.00018568756,0.00037597227,0.0000029671435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004566716,0.00020706789,0.003018905,0.00003683046,0.000040187857,0.000030293202,0.00043030188,0.97344923,0.0000082155,0.0112820305,0.0010051026,0.010446147],"study_design_scores_gemma":[0.00086612144,0.00017830404,0.0043746484,0.00008765088,0.00001546543,0.00020588512,0.000071945295,0.99299455,0.000010560575,0.00023818463,0.0007903611,0.00016633817],"about_ca_topic_score_codex":0.0000030375825,"about_ca_topic_score_gemma":1.4066072e-7,"teacher_disagreement_score":0.6413495,"about_ca_system_score_codex":0.000055049928,"about_ca_system_score_gemma":0.0001679038,"threshold_uncertainty_score":0.58856803},"labels":[],"label_agreement":null},{"id":"W2473387183","doi":"10.1504/ijcat.2016.077799","title":"Cellular implementation of the great salmon run algorithm for designing a black-box identifier applied to engine coldstart modelling","year":2016,"lang":"en","type":"article","venue":"International Journal of Computer Applications in Technology","topic":"Metaheuristic Optimization Algorithms Research","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":"Identification (biology); Identifier; Black box; Metaheuristic; Computer science; Artificial neural network; Automotive industry; Automotive engine; Algorithm; Machine learning; Artificial intelligence; Engineering; Automotive engineering","score_opus":0.017446040162054508,"score_gpt":0.3054915472670831,"score_spread":0.2880455071050286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473387183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041022063,0.000023816045,0.991872,0.0029032698,0.00030212168,0.00073609623,0.000014709396,0.000029894156,0.000015860287],"genre_scores_gemma":[0.2413492,0.000018205303,0.7583009,0.000055690787,0.00011030906,0.00012442145,0.0000021360686,0.000011493561,0.000027616868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981721,0.000041835778,0.0007878382,0.00026272706,0.00053257943,0.0002028877],"domain_scores_gemma":[0.99774766,0.0002489678,0.0004543684,0.0004107193,0.0010852779,0.000053016647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006164346,0.0001230281,0.00022406904,0.0010418586,0.000053901982,0.000062120285,0.00223686,0.00008290936,0.0000119938295],"category_scores_gemma":[0.000026898508,0.000087642446,0.00008945725,0.00081672514,0.00009049712,0.00019674427,0.00040380706,0.00013913731,0.0000049035043],"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":[0.0000141694045,0.00013310924,0.00041222316,0.000010104736,0.00012574033,0.000004122285,0.00032112192,0.05162329,0.013184675,0.057396114,0.00040948854,0.87636584],"study_design_scores_gemma":[0.001958979,0.00015087289,0.00024208757,0.000102088015,0.00002009354,0.00004019784,0.000101949845,0.70029896,0.2531007,0.038717438,0.005047094,0.0002195458],"about_ca_topic_score_codex":0.0000051213083,"about_ca_topic_score_gemma":0.000002243257,"teacher_disagreement_score":0.8761463,"about_ca_system_score_codex":0.000182119,"about_ca_system_score_gemma":0.0001396595,"threshold_uncertainty_score":0.41566798},"labels":[],"label_agreement":null},{"id":"W2474290527","doi":"10.1007/s12351-016-0251-z","title":"A new monarch butterfly optimization with an improved crossover operator","year":2016,"lang":"en","type":"article","venue":"Operational Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Crossover; Operator (biology); Benchmark (surveying); Butterfly; Computer science; Swarm intelligence; Mathematical optimization; Metaheuristic; Artificial intelligence; Particle swarm optimization; Mathematics; Algorithm; Ecology","score_opus":0.05427543962770417,"score_gpt":0.36476054377367145,"score_spread":0.3104851041459673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474290527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015793645,0.00003345874,0.9853568,0.010064213,0.00008636494,0.0006409304,0.000016444035,0.00009810857,0.0021243016],"genre_scores_gemma":[0.065894686,0.00005408517,0.9022592,0.0003263495,0.00037408154,0.00017826425,0.000031407373,0.000047535297,0.030834405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949691,0.0005916848,0.0003484118,0.00093702006,0.0023767708,0.0007770035],"domain_scores_gemma":[0.99521637,0.0005048003,0.000042761178,0.0010386829,0.0025467153,0.00065064844],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020589137,0.00020410797,0.00019083284,0.0004471616,0.00065151515,0.0015556286,0.0016435245,0.0001075526,0.0020404719],"category_scores_gemma":[0.0007893534,0.00012734743,0.000034200617,0.0013463147,0.00020478426,0.0026167887,0.0004968002,0.00032653217,0.0003740407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001902934,0.0014226888,0.0029389248,0.000063339874,0.00027475992,0.00024700255,0.0018807054,0.17540868,0.063383475,0.40375057,0.04381086,0.30491605],"study_design_scores_gemma":[0.0021724778,0.00086601084,0.0006433123,0.000031768916,0.0000020145637,0.000035073303,0.00001925134,0.98446405,0.005284364,0.0007093667,0.0054605496,0.00031174906],"about_ca_topic_score_codex":0.00019246471,"about_ca_topic_score_gemma":0.00002992476,"teacher_disagreement_score":0.8090554,"about_ca_system_score_codex":0.00023374309,"about_ca_system_score_gemma":0.0025324107,"threshold_uncertainty_score":0.99948084},"labels":[],"label_agreement":null},{"id":"W2478279759","doi":"","title":"A hybrid genetic algorithm for the Generalized Traveling Salesman Problem","year":2001,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"","keywords":"Travelling salesman problem; Heuristics; Vertex (graph theory); Bottleneck traveling salesman problem; Algorithm; Computer science; Benchmark (surveying); Genetic algorithm; 2-opt; Lin–Kernighan heuristic; Mathematical optimization; Selection (genetic algorithm); Christofides algorithm; Mathematics; Graph; Combinatorics; Theoretical computer science; Artificial intelligence","score_opus":0.03526207476254649,"score_gpt":0.2775623342991926,"score_spread":0.24230025953664608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478279759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002045734,0.001349637,0.9935745,0.0016551358,0.00024368364,0.0008191219,0.000014706693,0.00010908137,0.00018838435],"genre_scores_gemma":[0.097051375,0.00084791414,0.90099275,0.000215534,0.00014910333,0.00017139401,0.000026864112,0.000014739901,0.00053034973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980558,0.00015535437,0.0004173032,0.00055287854,0.00042901465,0.0003896181],"domain_scores_gemma":[0.99844015,0.00039424893,0.00012767034,0.00031602485,0.00056710636,0.00015477998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030144985,0.00019745508,0.00019157422,0.0001094068,0.00058117014,0.00028687672,0.00058481196,0.00004932818,0.000048422455],"category_scores_gemma":[0.000049426722,0.00016390368,0.000060978917,0.00031237202,0.0001551123,0.00016678937,0.000186139,0.00012017529,0.000023722592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009198517,0.00004592238,0.00017472354,0.000017233267,0.000039673818,0.0000119073975,0.00020174938,0.110658884,0.000028123874,0.005364712,0.0013296637,0.8821182],"study_design_scores_gemma":[0.00069868576,0.0000886776,0.01317017,0.000012369178,0.000017638566,0.00023984183,0.000022066895,0.9558002,0.000011230373,0.026541952,0.0031973731,0.00019983767],"about_ca_topic_score_codex":0.000033981803,"about_ca_topic_score_gemma":0.0000020522866,"teacher_disagreement_score":0.8819184,"about_ca_system_score_codex":0.000040381077,"about_ca_system_score_gemma":0.00028852987,"threshold_uncertainty_score":0.6683798},"labels":[],"label_agreement":null},{"id":"W2478686883","doi":"","title":"Plenary lecture 8: towards opposition and center-based sampling for high-dimensional search spaces","year":2009,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Opposition (politics); Computer science; Population; Computational intelligence; Particle swarm optimization; Differential evolution; Artificial intelligence; Evolutionary computation; Algorithm; Machine learning; Sociology; Political science; Law","score_opus":0.16433286751321077,"score_gpt":0.3920872015279786,"score_spread":0.22775433401476783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478686883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005164538,0.000020273965,0.97094744,0.022015784,0.00055641955,0.0003695682,0.00005016095,0.00009648011,0.0007793117],"genre_scores_gemma":[0.83085847,0.000022439986,0.16790357,0.000892152,0.00014639644,0.000024490257,0.00007573344,0.000008535213,0.00006818705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771464,0.00009920887,0.00038977442,0.00063196535,0.00082548463,0.00033891408],"domain_scores_gemma":[0.99833137,0.00024889596,0.00009629682,0.00029284784,0.0008612314,0.00016935766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005058412,0.00020580602,0.00018703248,0.0003441211,0.00021885308,0.0006874585,0.00083179923,0.000093058254,0.00022648514],"category_scores_gemma":[0.0002970785,0.00019452993,0.00006229133,0.00025556685,0.00010637281,0.0003552733,0.00011246443,0.00024939838,0.000043163855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014092222,0.0002150159,0.0000077898285,0.000007855455,0.000015704843,0.00000839204,0.000111025794,0.01884008,0.0038899044,0.5830807,0.00006114141,0.39362147],"study_design_scores_gemma":[0.00010363434,0.0004248149,0.00016606555,0.00006718887,0.0000029219705,0.000008681429,0.000021154901,0.87017816,0.03968703,0.08902707,0.0001225053,0.00019078293],"about_ca_topic_score_codex":0.000031820327,"about_ca_topic_score_gemma":0.000008777807,"teacher_disagreement_score":0.8513381,"about_ca_system_score_codex":0.0000937826,"about_ca_system_score_gemma":0.0002441261,"threshold_uncertainty_score":0.79327005},"labels":[],"label_agreement":null},{"id":"W2483094115","doi":"10.5539/mas.v10n10p118","title":"Three-Step Parameters Tuning Model for Time-Constrained Genetic Algorithms","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Applied Science Private University","keywords":"Sizing; Fitness function; Genetic algorithm; Computer science; Constraint (computer-aided design); Mathematical optimization; Algorithm; Limit (mathematics); Range (aeronautics); Function (biology); Power (physics); Mathematics","score_opus":0.03909815834210596,"score_gpt":0.2760703041704464,"score_spread":0.23697214582834047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483094115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042707927,0.000016582422,0.996255,0.00056056574,0.00012318189,0.00079582236,0.000017045184,0.0002419597,0.0015627944],"genre_scores_gemma":[0.17227413,0.0000035013868,0.8267422,0.00016756392,0.000027566888,0.00018210431,0.0000010520333,0.000020828773,0.00058107515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611217,0.00002222276,0.00040286116,0.0012454478,0.0012214652,0.0009958094],"domain_scores_gemma":[0.9976134,0.00034828746,0.00014124854,0.0011668312,0.00032726693,0.00040298287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015027805,0.0002519283,0.00027270228,0.0003761139,0.0005151142,0.0004351949,0.0029295923,0.00007437896,0.000025688647],"category_scores_gemma":[0.000271636,0.00018695166,0.000075445605,0.00095902017,0.00089707883,0.00053538237,0.0006046003,0.00010987535,0.00014871436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001530182,0.000059343893,0.000012925499,0.000011196073,0.000013084139,0.0000037411428,0.0003256426,0.038772542,0.17538662,0.020246316,0.0002197465,0.7649335],"study_design_scores_gemma":[0.00065995567,0.000037041187,0.000037576225,0.000011071649,0.0000047425415,0.0000074872214,0.0000031868315,0.9588755,0.002728638,0.037316915,0.000032191823,0.00028571504],"about_ca_topic_score_codex":0.0000052782893,"about_ca_topic_score_gemma":0.0000023639016,"teacher_disagreement_score":0.92010295,"about_ca_system_score_codex":0.00015350626,"about_ca_system_score_gemma":0.00066001486,"threshold_uncertainty_score":0.7623667},"labels":[],"label_agreement":null},{"id":"W2486008835","doi":"10.4018/978-1-59140-303-6.ch007","title":"Genetic Algorithm and Other Meta-Heuristics","year":2005,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Metaheuristic Optimization Algorithms Research","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; Group for Research in Decision Analysis","funders":"","keywords":"Crossover; Heuristics; Computer science; Mathematical optimization; Markov chain; Genetic algorithm; Schema (genetic algorithms); Heuristic; Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.04210831364145856,"score_gpt":0.2825978863054838,"score_spread":0.24048957266402526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486008835","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":[1.2540117e-7,0.0016100411,0.4757732,0.0000779225,0.00020857304,0.00027357083,0.00014562954,0.0001326197,0.52177835],"genre_scores_gemma":[0.000048287748,0.000058951384,0.7690775,0.0010241424,0.00044872987,0.000027478522,0.0000016060119,0.00006873518,0.22924456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99673134,0.000071508635,0.00060942257,0.0010104703,0.0010475997,0.0005296626],"domain_scores_gemma":[0.99765193,0.000109172164,0.000269433,0.0012573483,0.00029204265,0.0004200432],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029166232,0.00060458644,0.0008106707,0.00016629315,0.00014057421,0.00045975842,0.0012775804,0.00039158177,0.00031355847],"category_scores_gemma":[0.000055545723,0.00054871134,0.00025811783,0.000052021707,0.00022283166,0.00007008371,0.0008101986,0.00041121666,0.00043430543],"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":[0.0000018764418,0.00000972599,0.0000010494535,0.00001744482,0.0005200083,0.00010890512,0.00002106766,0.000077692275,2.5024266e-7,0.8466959,0.002326067,0.15022004],"study_design_scores_gemma":[0.0005355205,0.00013707973,0.000017571865,0.000041615294,0.0007667165,0.00037214745,0.0000013555687,0.12656166,0.000009560062,0.31849602,0.5520058,0.0010549093],"about_ca_topic_score_codex":0.00003545975,"about_ca_topic_score_gemma":0.000008032244,"teacher_disagreement_score":0.54967976,"about_ca_system_score_codex":0.00014684863,"about_ca_system_score_gemma":0.00028518666,"threshold_uncertainty_score":0.99969643},"labels":[],"label_agreement":null},{"id":"W2489851903","doi":"10.1007/978-3-319-19833-0","title":"Artificial Intelligence Applications in Information and Communication Technologies","year":2015,"lang":"en","type":"book","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Representation (politics); Machine learning; Data science","score_opus":0.14100114230022337,"score_gpt":0.40326447669611154,"score_spread":0.26226333439588817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489851903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003195206,0.0073014516,0.9776415,0.0011797223,0.00015703424,0.00095452776,0.000020907431,0.00018859764,0.012553052],"genre_scores_gemma":[0.017958673,0.0173507,0.9561097,0.00022157955,0.000086275955,0.0012568537,0.0005330956,0.0000508989,0.006432244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99697345,0.000150763,0.0012634686,0.000516817,0.00078654796,0.00030894065],"domain_scores_gemma":[0.9959614,0.0017184941,0.00039265602,0.0006984837,0.0011694094,0.00005952972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014377577,0.00032439493,0.00048342755,0.0012865914,0.00015596786,0.00020874073,0.0016108542,0.00024763425,0.0000066258262],"category_scores_gemma":[0.0012785019,0.0003330264,0.000034233868,0.0012005486,0.0008307195,0.0008662033,0.0016299292,0.0007615331,0.000102910504],"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":[0.0000045637144,0.00004125331,0.000019680643,0.00010700354,0.00002466207,0.000003900997,0.0018716187,0.122812666,3.6020914e-8,0.63665867,0.0008998038,0.23755611],"study_design_scores_gemma":[0.000019658071,0.000033616852,0.000012817225,0.00015890891,0.0000035144487,0.0000069189086,0.0011644468,0.3181513,0.000008570219,0.6764623,0.0037532845,0.00022466881],"about_ca_topic_score_codex":0.000018091132,"about_ca_topic_score_gemma":0.000066145105,"teacher_disagreement_score":0.23733145,"about_ca_system_score_codex":0.0006930128,"about_ca_system_score_gemma":0.00065918564,"threshold_uncertainty_score":0.9999122},"labels":[],"label_agreement":null},{"id":"W2494254613","doi":"10.4018/978-1-4666-5784-7.ch011","title":"Designing Parallel Meta-Heuristic Methods","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in systems analysis, software engineering, and high performance computing book series","topic":"Metaheuristic Optimization Algorithms Research","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 du Québec à Montréal","funders":"","keywords":"Heuristics; Computer science; Heuristic; Variety (cybernetics); Synchronization (alternating current); Meta heuristic; Focus (optics); Distributed computing; Theoretical computer science; Artificial intelligence; Algorithm","score_opus":0.01738171284749794,"score_gpt":0.2726138149475595,"score_spread":0.25523210210006153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2494254613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000070110796,0.062346727,0.93457264,0.000015696989,0.00070728024,0.00036696484,0.000008828752,0.0004436353,0.0015312273],"genre_scores_gemma":[0.003553437,0.017894803,0.9361512,0.00004062722,0.0002898964,0.000070037015,0.000053092142,0.00010823348,0.04183867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99572396,0.00018080152,0.001384818,0.0012285841,0.00076691015,0.00071491965],"domain_scores_gemma":[0.99652016,0.0010866039,0.0007105561,0.0011050358,0.00034607603,0.00023158896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020173597,0.00086485065,0.002368835,0.0014706993,0.0003207027,0.00052421435,0.0011143626,0.00031607013,0.000044187796],"category_scores_gemma":[0.00032227335,0.00081144396,0.00033518614,0.0006003806,0.00017237126,0.0012425316,0.0005502735,0.00076013687,0.00001549749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005848037,0.000006841628,0.00019268892,0.0008465276,0.0017196441,0.000022170463,0.00015583524,0.91605306,9.3863747e-7,0.07303472,0.00003843626,0.007923313],"study_design_scores_gemma":[0.00022065274,0.00010756573,0.00013419052,0.00032214925,0.0011689193,0.000041477066,0.0000058275,0.84905106,0.000010479052,0.00019184293,0.14786504,0.0008807692],"about_ca_topic_score_codex":0.000030716164,"about_ca_topic_score_gemma":0.0000060762673,"teacher_disagreement_score":0.14782661,"about_ca_system_score_codex":0.00014963032,"about_ca_system_score_gemma":0.0001019219,"threshold_uncertainty_score":0.99943364},"labels":[],"label_agreement":null},{"id":"W2499749362","doi":"10.4208/eajam.030915.210416a","title":"Direct Gravitational Search Algorithm for Global Optimisation Problems","year":2016,"lang":"en","type":"article","venue":"East Asian Journal on Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Convergence (economics); Algorithm; Mathematical optimization; Computer science; Heuristic; Gravitational search algorithm; Mathematics","score_opus":0.03520732284600403,"score_gpt":0.29498889026498853,"score_spread":0.2597815674189845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499749362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017269316,0.000009914921,0.9803049,0.0037710553,0.00026181273,0.0006685945,0.00003695722,0.00009148281,0.01483804],"genre_scores_gemma":[0.0041956697,0.000008785698,0.99429977,0.00008768838,0.0002555372,0.00009111627,0.0000061135283,0.000028043845,0.0010272478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972248,0.000070159644,0.0005950371,0.00037523048,0.0012263318,0.00050843705],"domain_scores_gemma":[0.9982318,0.0002163297,0.00028231755,0.00042982935,0.0004777393,0.00036195206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015336976,0.00023203087,0.00029847637,0.00020614263,0.00030376893,0.00045023786,0.00092434854,0.00008599124,0.00010015189],"category_scores_gemma":[0.00021712022,0.00015851512,0.00012584103,0.00047857035,0.00007985062,0.00028976306,0.00011023451,0.00017617716,0.00022257757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011249491,0.00029654044,0.0000029133773,0.00004288193,0.000067393325,0.000008280965,0.0006495886,0.0014585799,0.00024185608,0.33722308,0.0011946224,0.658803],"study_design_scores_gemma":[0.004681249,0.00059740804,0.000110509165,0.0003304526,0.000036727426,0.00047493947,0.00045681067,0.7244276,0.001447633,0.26088652,0.0057294257,0.00082077127],"about_ca_topic_score_codex":7.738942e-8,"about_ca_topic_score_gemma":1.7068771e-7,"teacher_disagreement_score":0.722969,"about_ca_system_score_codex":0.00026379275,"about_ca_system_score_gemma":0.0002775369,"threshold_uncertainty_score":0.6464059},"labels":[],"label_agreement":null},{"id":"W2504529984","doi":"10.1007/978-3-319-41000-5_16","title":"A Discrete Monarch Butterfly Optimization for Chinese TSP Problem","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"","keywords":"Computer science; Metaheuristic; Travelling salesman problem; Mathematical optimization; Swarm intelligence; Heuristic; Particle swarm optimization; Discretization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01733436334669416,"score_gpt":0.2874959649051844,"score_spread":0.2701616015584903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504529984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000025615577,0.00018000118,0.9905146,0.003031199,0.0010333278,0.00153201,0.00003492507,0.00019962821,0.0034717557],"genre_scores_gemma":[0.003000385,0.0000730855,0.9929213,0.00053468806,0.0005968683,0.000101580474,0.000018592978,0.00006982127,0.0026836863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944193,0.000071703194,0.0007776977,0.0021039275,0.0016294961,0.000997915],"domain_scores_gemma":[0.99557906,0.0010966951,0.00037967807,0.0017329454,0.00090201636,0.0003095914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016205814,0.00065561326,0.0006609159,0.0013221011,0.0003861934,0.00090777484,0.004444833,0.00033916964,0.00008701889],"category_scores_gemma":[0.00041863477,0.00048325557,0.00018691507,0.0009376173,0.00067344954,0.0009312779,0.0017550362,0.00056592165,0.000049091897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017344926,0.000029482459,0.000036086556,0.00009361086,0.000019031238,0.00002767506,0.0003020427,0.48152658,0.00003502914,0.01966391,0.000039197654,0.49821],"study_design_scores_gemma":[0.000495596,0.00018397768,0.000014704987,0.00023270435,0.0000052237815,0.00003128149,3.431073e-8,0.8790665,0.000096494594,0.11862367,0.0007023461,0.00054742384],"about_ca_topic_score_codex":0.00000596506,"about_ca_topic_score_gemma":0.000008549459,"teacher_disagreement_score":0.49766257,"about_ca_system_score_codex":0.00036207616,"about_ca_system_score_gemma":0.00087505527,"threshold_uncertainty_score":0.99976194},"labels":[],"label_agreement":null},{"id":"W2506699195","doi":"10.4018/978-1-61520-809-8.ch012","title":"Verification of Attributes in Linked Lists Using Ant Colony Metaphor","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Metaheuristic Optimization Algorithms Research","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":"Tree traversal; Computer science; Ant colony optimization algorithms; Artificial intelligence; Ant colony; Machine learning; Algorithm","score_opus":0.053485515308848784,"score_gpt":0.3070188871783098,"score_spread":0.253533371869461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506699195","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":[0.0007029921,0.0005518204,0.41077194,0.000179807,0.0013598937,0.0014208625,0.0002853673,0.00017806662,0.58454925],"genre_scores_gemma":[0.10075189,0.00006125491,0.85093874,0.00024242287,0.000400892,0.00005279615,0.00004466685,0.00011374915,0.047393613],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740475,0.00007558235,0.0007354954,0.0006418973,0.0007957901,0.00034645622],"domain_scores_gemma":[0.9976033,0.00012611915,0.00047191622,0.0010799017,0.0005585237,0.00016026066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006066785,0.0003134973,0.00060208514,0.000276529,0.000068103946,0.00013884471,0.0012578132,0.00049909373,0.000041472504],"category_scores_gemma":[0.00023793499,0.00032265158,0.00014379452,0.00012881357,0.00018327143,0.000106390995,0.00043158856,0.00052112184,0.00004459085],"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":[0.000016493725,0.000029874593,0.000026272599,0.000055595505,0.000051017265,0.00004909849,0.000081177284,0.00017211985,0.001233276,0.9930831,0.000093119765,0.005108857],"study_design_scores_gemma":[0.0030312145,0.0004830201,0.0014616093,0.0009547931,0.00022411445,0.00020385094,0.000013116498,0.49743932,0.008367419,0.43476343,0.050480533,0.0025775628],"about_ca_topic_score_codex":0.00016188342,"about_ca_topic_score_gemma":0.00006409102,"teacher_disagreement_score":0.5583197,"about_ca_system_score_codex":0.0002608775,"about_ca_system_score_gemma":0.0006614401,"threshold_uncertainty_score":0.9999226},"labels":[],"label_agreement":null},{"id":"W2510587864","doi":"10.1007/978-3-319-45823-6_18","title":"An Active-Set Evolution Strategy for Optimization with Known Constraints","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Set (abstract data type); Mathematical optimization; Artificial intelligence; Theoretical computer science; Programming language; Mathematics","score_opus":0.0314904697842053,"score_gpt":0.29939555789141503,"score_spread":0.26790508810720975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510587864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000307367,0.000051472452,0.9952835,0.0003646869,0.0005567752,0.001016788,0.000057788784,0.00015877104,0.0025071844],"genre_scores_gemma":[0.024055472,0.000020468744,0.97471845,0.00018691746,0.00036973626,0.00004197029,0.00003089979,0.000047704056,0.000528367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99585927,0.00007639584,0.00046469204,0.0016927739,0.001180355,0.0007265117],"domain_scores_gemma":[0.99637794,0.0005545714,0.00034029642,0.0013175085,0.0011181671,0.0002915319],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095863297,0.0004826107,0.0004589194,0.0009604038,0.00032813437,0.00068597164,0.0026025567,0.00030590987,0.000110723166],"category_scores_gemma":[0.00016118592,0.0003673347,0.000072193216,0.00060520304,0.0012720982,0.0013316092,0.00034757098,0.00042077707,0.000019622068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017461101,0.000021574228,0.0000033085623,0.000016635924,0.000009667709,0.000011002073,0.00008201661,0.5400565,0.000025852645,0.026896494,0.0000054497214,0.43285403],"study_design_scores_gemma":[0.00060231047,0.0005863249,0.000019507457,0.00018694774,0.000007793895,0.000047119724,2.7203822e-7,0.96402246,0.00042578287,0.033456113,0.00014286139,0.0005024914],"about_ca_topic_score_codex":0.0000050823783,"about_ca_topic_score_gemma":0.000017198785,"teacher_disagreement_score":0.43235153,"about_ca_system_score_codex":0.0005139817,"about_ca_system_score_gemma":0.0017346661,"threshold_uncertainty_score":0.99987787},"labels":[],"label_agreement":null},{"id":"W2511231963","doi":"10.5539/mas.v10n11p131","title":"A Survey on Evolutionary Computation: Methods and Their Applications in Engineering","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":21,"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; Cloud computing; Evolutionary computation; Smart grid; Swarm intelligence; Evolutionary algorithm; Scheduling (production processes); Distributed computing; Evolutionary programming; Evolution strategy; Artificial intelligence; Machine learning; Mathematical optimization; Particle swarm optimization","score_opus":0.04076719215808219,"score_gpt":0.33433162384453674,"score_spread":0.2935644316864546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511231963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064097485,0.000044835597,0.9979085,0.00023894406,0.00004429745,0.00029196977,0.000005751796,0.0000726885,0.0007520415],"genre_scores_gemma":[0.62469506,0.0000059683334,0.3751408,0.000034943158,0.000008753505,0.00006886113,0.000001155367,0.000004894363,0.000039567505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984412,0.000083336985,0.0001940062,0.00060576777,0.0003684122,0.0003072369],"domain_scores_gemma":[0.998334,0.0009462576,0.000044741977,0.000415285,0.000119195676,0.00014050602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002208043,0.0001166834,0.00013225127,0.0003922013,0.000170326,0.0001249266,0.00080277916,0.00003093639,0.000004207779],"category_scores_gemma":[0.00022126862,0.00007933604,0.0000118298685,0.0015019261,0.00022897917,0.00026087326,0.00034034,0.00009209414,0.000018428442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000864217,0.00013209203,0.0009563509,0.000014761587,0.0000059261874,0.0000013159718,0.00079894025,0.021241382,0.07030069,0.09683538,0.000061871615,0.8096427],"study_design_scores_gemma":[0.00016975675,0.0000115153325,0.032739103,0.000007354566,2.4871224e-7,0.0000014368462,0.0000043480395,0.9590706,0.0013025816,0.006496642,0.000086782224,0.00010962459],"about_ca_topic_score_codex":0.000009256077,"about_ca_topic_score_gemma":0.000002329101,"teacher_disagreement_score":0.9378292,"about_ca_system_score_codex":0.000111301764,"about_ca_system_score_gemma":0.00015520967,"threshold_uncertainty_score":0.32352298},"labels":[],"label_agreement":null},{"id":"W2523130346","doi":"10.1007/978-3-319-44254-9_6","title":"Cohort Intelligence for Solving Travelling Salesman Problems","year":2016,"lang":"en","type":"book-chapter","venue":"Intelligent systems reference library","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Knapsack problem; Travelling salesman problem; Cohort; Mathematical optimization; Computer science; Combinatorial optimization; Mach number; Mathematics; Artificial intelligence; Statistics; Engineering; Aerospace engineering","score_opus":0.07795382506558972,"score_gpt":0.27691218402052686,"score_spread":0.19895835895493713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523130346","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":[6.0348754e-7,0.0020528843,0.6863643,0.00022837437,0.0014623441,0.002516206,0.00015341188,0.00054533506,0.30667657],"genre_scores_gemma":[0.0018283527,0.0063527944,0.047459796,0.000120278426,0.00092777645,0.00062716106,0.00024260119,0.00031844052,0.9421228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933753,0.00011891415,0.0020955107,0.0019921788,0.0013713158,0.0010467842],"domain_scores_gemma":[0.9945566,0.0012290328,0.0009763846,0.0021480555,0.00053465203,0.00055526476],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010169126,0.00097157055,0.0012332627,0.00091888284,0.00030796288,0.0013473413,0.0042504203,0.0008290178,0.0007838096],"category_scores_gemma":[0.00014689831,0.00078922865,0.0003658696,0.00021876163,0.00023285189,0.0015492374,0.00092445087,0.0008305722,0.0016006038],"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":[0.000015273195,0.000032137083,0.000031537467,0.0007242977,0.00023594139,0.00003244001,0.00015268047,0.0010358386,0.00002071394,0.9440208,0.0032519405,0.05044642],"study_design_scores_gemma":[0.00019675816,0.0003236518,0.000005171348,0.0045850524,0.00006938471,0.000093946444,0.000023001976,0.20788935,0.0013912331,0.11205144,0.67143834,0.0019326787],"about_ca_topic_score_codex":0.000009069443,"about_ca_topic_score_gemma":0.0000012816907,"teacher_disagreement_score":0.8319693,"about_ca_system_score_codex":0.00018938816,"about_ca_system_score_gemma":0.00068867195,"threshold_uncertainty_score":0.99968934},"labels":[],"label_agreement":null},{"id":"W2525485218","doi":"10.1109/tcbb.2015.2446484","title":"An Effective Application of Bacteria Quorum Sensing and Circular Elimination in MOPSO","year":2015,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Metaheuristic Optimization Algorithms Research","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":"Cascades (Canada)","funders":"Chongqing Three Gorges University","keywords":"Benchmark (surveying); Convergence (economics); Mathematical optimization; Particle swarm optimization; Quorum sensing; Computer science; Swarm behaviour; Pareto principle; Multi-swarm optimization; Set (abstract data type); Mathematics; Bacteria","score_opus":0.019480827765210314,"score_gpt":0.3061639911421978,"score_spread":0.28668316337698746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525485218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032623414,0.000018211678,0.9665275,0.00033343837,0.0001102163,0.0003096485,0.000018432807,0.000027775977,0.00003135544],"genre_scores_gemma":[0.6455378,0.0000125292445,0.354343,0.00005686924,0.000007327068,0.000008243301,0.00002922641,0.0000028822153,0.0000021181531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901026,0.00015731015,0.000342397,0.00018900586,0.00016672783,0.00013426805],"domain_scores_gemma":[0.9989574,0.00034489462,0.00011790435,0.00021347201,0.0002673921,0.00009892341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055623066,0.00010776082,0.00016500878,0.00035017118,0.000087552056,0.00005766822,0.0001720744,0.000103630155,0.0000012594098],"category_scores_gemma":[0.00006109201,0.00010182037,0.000018068144,0.00036269912,0.00012637168,0.00051130104,0.00001138934,0.00012939288,0.0000038778685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058765545,0.00020286345,0.0005210712,0.000092697774,0.0000418786,0.0000015775385,0.0023954473,0.18100892,0.0012064814,0.005294584,0.000005612682,0.8091701],"study_design_scores_gemma":[0.0005775033,0.0002635347,0.0061759786,0.000011927195,0.0000059797267,0.00002670961,0.000121400146,0.98322934,0.00097355864,0.008495586,0.000016416769,0.00010205437],"about_ca_topic_score_codex":0.000029027038,"about_ca_topic_score_gemma":0.0000073110673,"teacher_disagreement_score":0.809068,"about_ca_system_score_codex":0.000040302246,"about_ca_system_score_gemma":0.000065543805,"threshold_uncertainty_score":0.4152114},"labels":[],"label_agreement":null},{"id":"W2528426493","doi":"","title":"Learning a multi-criteria classification method using machine learning & metaheuristics techniques","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Machine learning; Metaheuristic; Artificial intelligence; Computer science; Multiple-criteria decision analysis; Context (archaeology); Data mining; Mathematics; Mathematical optimization","score_opus":0.0817681822815171,"score_gpt":0.4011949090831579,"score_spread":0.3194267268016408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528426493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006396641,0.00002862773,0.99573946,0.000300562,0.0003085235,0.00028983553,0.000001601306,0.0008867916,0.0018049423],"genre_scores_gemma":[0.03178848,0.000034508546,0.96518284,0.00007247362,0.00009883766,0.000024970117,0.0000147450455,0.00003683104,0.002746292],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970305,0.0007623927,0.00050153944,0.0006684366,0.0005874185,0.00044971844],"domain_scores_gemma":[0.9979203,0.00041492147,0.00023376962,0.0006705478,0.0005461818,0.00021432387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027698525,0.0002423769,0.00030436052,0.0004324842,0.00045360846,0.0006013789,0.0010126852,0.00016211155,0.0003803481],"category_scores_gemma":[0.0024394558,0.00022206173,0.00008694689,0.0008968086,0.000078027966,0.0005418277,0.00049953535,0.0012917478,0.000062318955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013814216,0.00032515166,0.002366532,0.000063730826,0.000083696505,0.000038797323,0.00079613517,0.0047910605,0.6242477,0.043606516,0.000116451956,0.32355037],"study_design_scores_gemma":[0.00020174914,0.00005571583,0.00022351924,0.0000064935057,0.000013830883,0.000053118816,0.00002858897,0.9693883,0.019826131,0.00021127459,0.009740026,0.00025123122],"about_ca_topic_score_codex":0.0001627825,"about_ca_topic_score_gemma":0.000015154004,"teacher_disagreement_score":0.9645973,"about_ca_system_score_codex":0.000046068442,"about_ca_system_score_gemma":0.00012873148,"threshold_uncertainty_score":0.9055414},"labels":[],"label_agreement":null},{"id":"W2531264864","doi":"","title":"What can we learn from No Free Lunch? a first attempt to characterize the concept of a searchable function","year":2001,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Computer science; Field (mathematics); Function (biology); Scalar field; Theoretical computer science; Algorithm; Mathematical optimization; Mathematics","score_opus":0.038457576213986024,"score_gpt":0.2670666784141812,"score_spread":0.22860910220019517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531264864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066333725,0.00016973903,0.950013,0.0389545,0.0011130237,0.0006943667,0.000034922396,0.00013538531,0.00225172],"genre_scores_gemma":[0.31666467,0.0039267903,0.3684385,0.015473748,0.0022795545,0.00064169953,0.00023612868,0.00018757134,0.29215133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776214,0.00015883564,0.00032581,0.00047861447,0.00086925214,0.0004053735],"domain_scores_gemma":[0.9972596,0.00044146267,0.00009133071,0.0013903985,0.00059787784,0.00021932204],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005453466,0.00015215976,0.00022745178,0.00013722466,0.00020259758,0.0004685802,0.0017039605,0.000060293303,0.0030378532],"category_scores_gemma":[0.00048364114,0.00010690124,0.000058345915,0.001072124,0.000102279075,0.00068111933,0.0008628168,0.00020701633,0.00050851214],"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":[0.0006998642,0.0013272645,0.004357292,0.00014927759,0.0007594038,0.00016349195,0.039197907,0.016902512,0.009030518,0.046375122,0.37741512,0.50362223],"study_design_scores_gemma":[0.0015683234,0.00065024465,0.0065672593,0.00014996623,0.000018959385,0.0000150435935,0.0008937434,0.66023636,0.0022632787,0.0018914657,0.32525438,0.0004910188],"about_ca_topic_score_codex":0.0015649119,"about_ca_topic_score_gemma":0.0003808962,"teacher_disagreement_score":0.6433338,"about_ca_system_score_codex":0.000060462608,"about_ca_system_score_gemma":0.0001570525,"threshold_uncertainty_score":0.9978735},"labels":[],"label_agreement":null},{"id":"W2536934950","doi":"","title":"IPO: An Inclined Planes System Optimization Algorithm","year":2016,"lang":"en","type":"article","venue":"Computing and Informatics / Computers and Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Ottawa","funders":"","keywords":"Benchmark (surveying); Heuristic; Plane (geometry); Algorithm; Inclined plane; Motion (physics); Computer science; Space (punctuation); Optimization algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Geometry; Mechanical engineering","score_opus":0.03183234500161008,"score_gpt":0.28805459476112216,"score_spread":0.2562222497595121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536934950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020661575,0.000052052434,0.9963664,0.00029738824,0.0005511429,0.00019731304,0.0000059758245,0.00027759298,0.0001860155],"genre_scores_gemma":[0.18988998,0.00009023452,0.80969584,0.0001347331,0.00015159075,0.0000027449985,0.000007665547,0.000010583273,0.00001664785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980372,0.000115486226,0.00081034546,0.0003190718,0.0003493785,0.00036848654],"domain_scores_gemma":[0.9984546,0.0004002919,0.00023968464,0.00039362538,0.00023332007,0.00027848603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008357308,0.00021820319,0.00027476606,0.00024299955,0.00043482467,0.00075144885,0.0005854532,0.00008516565,0.00000529442],"category_scores_gemma":[0.00008728653,0.00016084773,0.000030607156,0.00034532594,0.00016874271,0.0008324638,0.00055489957,0.00012238484,0.000018382276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003874185,0.00002696998,0.000017791235,0.000049920094,0.000011316458,0.0000045314305,0.001260517,0.06905172,0.000007555405,0.03418592,0.000038763992,0.8953411],"study_design_scores_gemma":[0.00007876111,0.00015865863,0.000019512188,0.00014422863,0.000004496461,0.000051626885,0.0003461635,0.99773926,0.0002991644,0.00071874785,0.00019941921,0.00023996527],"about_ca_topic_score_codex":0.00001477298,"about_ca_topic_score_gemma":6.9691436e-7,"teacher_disagreement_score":0.9286875,"about_ca_system_score_codex":0.000034705627,"about_ca_system_score_gemma":0.00004870531,"threshold_uncertainty_score":0.72462386},"labels":[],"label_agreement":null},{"id":"W2548142931","doi":"10.1504/ijcse.2016.080208","title":"Constraint handling in probability collectives using a modified feasibility-based rule","year":2016,"lang":"en","type":"article","venue":"International Journal of Computational Science and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Variety (cybernetics); Heuristic; Computer science; Constraint (computer-aided design); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.04244383824893212,"score_gpt":0.31466687842316077,"score_spread":0.27222304017422866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548142931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2565167,0.00001715636,0.74236834,0.00074826187,0.00023646372,0.000063371415,0.0000018060706,0.000009022778,0.00003890456],"genre_scores_gemma":[0.72017527,0.0000024061737,0.27976125,0.000026178723,0.000029719276,0.0000011236568,1.1040201e-7,0.0000021486303,0.0000017840866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981192,0.00003663893,0.00037703634,0.00020015008,0.001097788,0.00016921102],"domain_scores_gemma":[0.9977331,0.00043112808,0.00013040823,0.00007775308,0.0015133808,0.00011419204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019198955,0.00007727493,0.00012652234,0.00065597333,0.00006033003,0.00020395515,0.0006340577,0.000020996753,0.000007486681],"category_scores_gemma":[0.0015295267,0.000057690377,0.000031350948,0.0006028409,0.00019558873,0.0008430012,0.00010989607,0.0000919586,4.4224248e-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":[0.00001628547,0.000052817697,0.0017817896,0.000005360488,0.000010050432,0.000018921533,0.000108088054,0.97755826,0.005247602,0.0078553315,0.0000022058698,0.0073432866],"study_design_scores_gemma":[0.0007382616,0.000030036214,0.012056275,0.0000908579,0.0000010848404,0.00007216294,0.000007795558,0.97634846,0.00063025585,0.00994156,0.000010722129,0.00007255362],"about_ca_topic_score_codex":0.0000052994283,"about_ca_topic_score_gemma":5.520622e-7,"teacher_disagreement_score":0.46365857,"about_ca_system_score_codex":0.00036329098,"about_ca_system_score_gemma":0.0009653164,"threshold_uncertainty_score":0.23525453},"labels":[],"label_agreement":null},{"id":"W2548265241","doi":"10.1007/s11721-016-0128-z","title":"Inertia weight control strategies for particle swarm optimization","year":2016,"lang":"en","type":"article","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertia; Benchmark (surveying); Particle swarm optimization; Computer science; Control (management); Mathematical optimization; Selection (genetic algorithm); Convergence (economics); Population; Control theory (sociology); Mathematics; Artificial intelligence; Economics","score_opus":0.02980599871837959,"score_gpt":0.29919085737402534,"score_spread":0.26938485865564576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548265241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021781141,0.00013881858,0.9941186,0.0037162346,0.00043428657,0.0005350597,0.0000138661335,0.00022407479,0.00060127606],"genre_scores_gemma":[0.6445909,0.00014263928,0.35342318,0.0002983624,0.00013045284,0.00018723273,0.000002812926,0.00002237073,0.0012020153],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979151,0.00012024918,0.00045347228,0.0005465637,0.00043998056,0.0005246469],"domain_scores_gemma":[0.99777466,0.00065757777,0.00012616547,0.0006635418,0.00058169337,0.0001963516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006384197,0.0001871246,0.00020990381,0.000114315866,0.00015769375,0.00030398046,0.0011141042,0.00007557598,0.0001973723],"category_scores_gemma":[0.00060867774,0.00012968648,0.00008159115,0.0005175586,0.0001346508,0.0009966244,0.00013527312,0.00008040188,0.00021638823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007303786,0.0001749022,0.00014213103,0.000037021855,0.000060049177,0.0000103330285,0.00042374182,0.41742954,0.0017210045,0.43996295,0.0012952117,0.13867009],"study_design_scores_gemma":[0.00039220086,0.00014244358,0.00001909617,0.000023221439,0.000007888436,0.000005421958,0.000039221537,0.94027424,0.045046017,0.011622877,0.0022115002,0.00021586109],"about_ca_topic_score_codex":0.0000115678795,"about_ca_topic_score_gemma":0.0000031533555,"teacher_disagreement_score":0.6443731,"about_ca_system_score_codex":0.00006667697,"about_ca_system_score_gemma":0.00023974094,"threshold_uncertainty_score":0.52884614},"labels":[],"label_agreement":null},{"id":"W2548462403","doi":"10.1109/ccece.2016.7726711","title":"Analyzing surveillance videos using automatically generated processing sequences with knowledge-augmented genetic algorithms","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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; Western University","funders":"","keywords":"Computer science; Sharpening; Image processing; Genetic algorithm; Noise (video); Domain (mathematical analysis); Algorithm; Enhanced Data Rates for GSM Evolution; Domain knowledge; Artificial intelligence; Image (mathematics); Object (grammar); Data mining; Machine learning","score_opus":0.0340904532320344,"score_gpt":0.30765059559695823,"score_spread":0.2735601423649238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548462403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013225447,0.00028180404,0.9843763,0.00049961713,0.000104265804,0.0003003562,0.0000028835598,0.000460467,0.0007488282],"genre_scores_gemma":[0.18891872,0.000028728311,0.80994487,0.000044971424,0.00007228042,0.000021166681,0.0000015203751,0.000026402002,0.00094132853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699074,0.00035783244,0.0005406666,0.00079355977,0.0006620242,0.00065515365],"domain_scores_gemma":[0.997682,0.00024984512,0.00018830269,0.0006351197,0.00096144766,0.00028333132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000757796,0.00028214746,0.00034362538,0.00033192104,0.00029984058,0.00054770074,0.0010263474,0.00007585397,0.00017505644],"category_scores_gemma":[0.00023768841,0.00016448807,0.000044643402,0.0019057944,0.00020135402,0.0007193039,0.0002767487,0.000111151785,0.00007572292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025335088,0.00030441605,0.01135568,0.00016562355,0.00018243364,0.00019304086,0.0004116714,0.01733079,0.039185897,0.001565881,0.00026542717,0.9290138],"study_design_scores_gemma":[0.00056703214,0.0000819623,0.002079783,0.000118137825,0.000008092316,0.00007549813,0.000010186148,0.993119,0.0033948165,0.00013137069,0.000085070984,0.00032903053],"about_ca_topic_score_codex":0.00004279847,"about_ca_topic_score_gemma":0.000024753908,"teacher_disagreement_score":0.97578824,"about_ca_system_score_codex":0.00016937684,"about_ca_system_score_gemma":0.0007346271,"threshold_uncertainty_score":0.6707629},"labels":[],"label_agreement":null},{"id":"W2550177187","doi":"10.1080/18756891.2016.1256577","title":"A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems","year":2016,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Hebei GEO University; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Harmony search; Cuckoo search; Knapsack problem; Computer science; Continuous knapsack problem; Algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.05379947559308494,"score_gpt":0.3403491801626603,"score_spread":0.28654970456957535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550177187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037364653,0.00020036487,0.99236584,0.0040861713,0.0018203596,0.00063294766,0.00026448825,0.00005014172,0.00020603875],"genre_scores_gemma":[0.23136595,0.000071863054,0.76703334,0.00014458578,0.00081834977,0.000045864523,0.000021243883,0.000030957246,0.0004678781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99405634,0.00021050032,0.0012901317,0.00051731843,0.0034158144,0.0005098835],"domain_scores_gemma":[0.9881957,0.0012671159,0.000581139,0.00031274758,0.009282572,0.00036070705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002108107,0.00028767943,0.00042643753,0.0005580511,0.00013478941,0.00075105793,0.0029037488,0.000072496674,0.00005204295],"category_scores_gemma":[0.00026475452,0.00019208826,0.00020293826,0.00058029883,0.00021958121,0.0012213698,0.00028487894,0.00024306761,0.0000802034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001450197,0.00034325704,0.00023834819,0.0000431791,0.0005510333,0.00010968711,0.00019780622,0.6681398,0.00017661555,0.060024336,0.0010659792,0.26896495],"study_design_scores_gemma":[0.0012293318,0.0005707444,0.00017731909,0.00040884627,0.000012764942,0.002968714,0.00007948704,0.9848297,0.0015986282,0.0053769415,0.0024581964,0.00028929452],"about_ca_topic_score_codex":0.00006525715,"about_ca_topic_score_gemma":0.0000018274725,"teacher_disagreement_score":0.31668994,"about_ca_system_score_codex":0.00063700526,"about_ca_system_score_gemma":0.0012428937,"threshold_uncertainty_score":0.7833132},"labels":[],"label_agreement":null},{"id":"W2553081833","doi":"10.1007/s10458-016-9350-8","title":"A novel abstraction for swarm intelligence: particle field optimization","year":2016,"lang":"en","type":"article","venue":"Autonomous Agents and Multi-Agent Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Carleton University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Abstraction; Swarm intelligence; Swarm behaviour; Computer science; Perspective (graphical); Metaheuristic; Field (mathematics); Set (abstract data type); Heuristic; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.07563508047408636,"score_gpt":0.3298468779985365,"score_spread":0.25421179752445017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553081833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013264943,0.00009646617,0.9956675,0.0008272551,0.000976307,0.0008763601,0.00001608585,0.00012159326,0.000091935304],"genre_scores_gemma":[0.7109598,0.00023127891,0.28291705,0.00018538193,0.00013524418,0.00031115898,0.000008025463,0.000026352925,0.005225733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843615,0.00005266346,0.0004411816,0.00047352994,0.00025556912,0.00034090562],"domain_scores_gemma":[0.9988038,0.00025396346,0.00017080556,0.00037955883,0.0002071703,0.00018473578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053952873,0.0001542735,0.00018636792,0.00009214629,0.00019660481,0.0002767459,0.00033961798,0.000086181535,0.000041705058],"category_scores_gemma":[0.00023442585,0.00011117444,0.000052818512,0.0001563365,0.000030143661,0.0004303228,0.000118832126,0.000054355183,0.000044434782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073222865,0.0013325829,0.0013962672,0.0003573272,0.00029080792,0.00002265098,0.0020758405,0.4944169,0.0070138713,0.030146083,0.0034428944,0.45943156],"study_design_scores_gemma":[0.00067642477,0.00009926391,0.00034318352,0.000038188882,0.000009003967,0.000015485717,0.0000491666,0.99069345,0.002261379,0.000014799955,0.005632829,0.00016684741],"about_ca_topic_score_codex":0.00011503907,"about_ca_topic_score_gemma":0.0000035414068,"teacher_disagreement_score":0.71275043,"about_ca_system_score_codex":0.00008798652,"about_ca_system_score_gemma":0.00006257659,"threshold_uncertainty_score":0.45335624},"labels":[],"label_agreement":null},{"id":"W2555627513","doi":"10.3390/computers6010005","title":"Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm","year":2017,"lang":"en","type":"article","venue":"Computers","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"University of Manitoba","keywords":"Bees algorithm; Algorithm; Honey Bees; Computer science; Benchmark (surveying); Nectar; Search algorithm; Population; Set (abstract data type); Implementation; Metaheuristic; Pollen; Ecology; Biology","score_opus":0.019505825423100354,"score_gpt":0.27521930378174836,"score_spread":0.255713478358648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555627513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014146741,0.00009114945,0.99295384,0.0017002636,0.0024965734,0.00039660468,0.00001968141,0.00012219233,0.0008050112],"genre_scores_gemma":[0.04458288,0.00007536539,0.95394164,0.0002685093,0.00027826987,0.00001987618,0.000009903061,0.00003080819,0.00079272024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971282,0.00025700187,0.00044235095,0.00062998995,0.0010552325,0.00048723136],"domain_scores_gemma":[0.9962491,0.00026448967,0.00042934003,0.0025041301,0.0003523181,0.00020063792],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0007367091,0.00025715883,0.0003755937,0.00018752833,0.0009078801,0.0006084587,0.005629243,0.00010958912,0.000038467235],"category_scores_gemma":[0.00025265338,0.00019924184,0.00021668807,0.00038871105,0.00042047136,0.0007726923,0.0029440157,0.0002940749,0.00006999481],"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":[0.0000069280586,0.00018836385,0.0005140663,0.000038487786,0.00009970067,0.000035440138,0.0006841596,0.000736855,0.0001886127,0.0033311262,0.007337725,0.9868385],"study_design_scores_gemma":[0.00090969366,0.00009796544,0.014054326,0.0000656943,0.000014251782,0.000019611578,0.00002249485,0.97868174,0.0011709778,0.0008894262,0.003829375,0.0002444492],"about_ca_topic_score_codex":0.00013792363,"about_ca_topic_score_gemma":0.0000032352918,"teacher_disagreement_score":0.9865941,"about_ca_system_score_codex":0.00007428735,"about_ca_system_score_gemma":0.00014057645,"threshold_uncertainty_score":0.9997508},"labels":[],"label_agreement":null},{"id":"W2558365135","doi":"10.4018/ijcini.2016100102","title":"Adaptive Multiobjective Memetic Optimization","year":2016,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Research Manitoba; University of Manitoba","funders":"","keywords":"Computer science; Memetic algorithm; Mathematical optimization; Benchmark (surveying); Robustness (evolution); Multi-objective optimization; Cluster analysis; Optimization problem; Artificial intelligence; Evolutionary algorithm; Machine learning; Algorithm; Mathematics","score_opus":0.021546082505127184,"score_gpt":0.30913127475491275,"score_spread":0.28758519224978557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558365135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001184445,0.00021355353,0.99645597,0.0005412274,0.0008076514,0.000116443654,0.000010681825,0.000014562342,0.0006554575],"genre_scores_gemma":[0.74167925,0.0009385921,0.25697282,0.00020953301,0.00008504759,0.0000028308687,0.0000013947144,0.000004957941,0.000105587154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981412,0.00007389528,0.0007068092,0.00010233941,0.0008163128,0.00015944314],"domain_scores_gemma":[0.99399054,0.0010670249,0.0005778947,0.00008697179,0.0041627367,0.000114824325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005481706,0.0001289979,0.00017267227,0.00046229525,0.000055235156,0.00019965213,0.00074672245,0.00004629133,0.00006546092],"category_scores_gemma":[0.0018717393,0.000080697406,0.00007186006,0.00024792118,0.00014437946,0.0019076831,0.00023482811,0.00019619522,0.000021456153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018682174,0.00007986255,0.000110466994,0.000006651659,0.00030526926,0.000043277832,0.002040365,0.0065789414,0.000060821425,0.017168032,0.00006386388,0.97335565],"study_design_scores_gemma":[0.00069424097,0.0003019835,0.0003537769,0.00038100354,0.000017200933,0.00035860625,0.0006639631,0.986941,0.006289813,0.003675221,0.00012816222,0.00019501975],"about_ca_topic_score_codex":0.0000024946232,"about_ca_topic_score_gemma":5.4731186e-7,"teacher_disagreement_score":0.98036206,"about_ca_system_score_codex":0.00008340296,"about_ca_system_score_gemma":0.00011808689,"threshold_uncertainty_score":0.32907447},"labels":[],"label_agreement":null},{"id":"W2558678654","doi":"10.1109/cec.2016.7744130","title":"An superior tracking artificial bee colony for global optimization problems","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"National Natural Science Foundation of China","keywords":"Artificial bee colony algorithm; Benchmark (surveying); Bees algorithm; Dimension (graph theory); Convergence (economics); Computer science; Global optimization; Mathematical optimization; Tracking (education); Population; Optimization problem; Artificial intelligence; Metaheuristic; Algorithm; Mathematics","score_opus":0.048139316156334454,"score_gpt":0.3261855864511146,"score_spread":0.2780462702947802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558678654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042690185,0.000009134692,0.9952855,0.0024867197,0.0002243475,0.0006341028,0.000016528993,0.0002517947,0.00066495186],"genre_scores_gemma":[0.072009265,0.000009625646,0.92694116,0.00016242109,0.00013462131,0.00011319203,0.0000075533553,0.000013876242,0.0006082588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983484,0.000095223135,0.00031475068,0.00047897242,0.00038292434,0.00037971645],"domain_scores_gemma":[0.9987944,0.00013614351,0.00006315511,0.00037041903,0.00045807209,0.00017782427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005702642,0.00012209118,0.00014766083,0.000074993484,0.00017631463,0.00041381776,0.00075033394,0.00007393076,0.00022239905],"category_scores_gemma":[0.0003155083,0.00008554891,0.00004891261,0.00042379217,0.00005687679,0.0010171516,0.00008992411,0.000034293917,0.00003583332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044571832,0.00038899324,0.000505353,0.000032973992,0.000032131215,0.000006067511,0.00023131707,0.20683883,0.006717337,0.28285885,0.0014730945,0.50087047],"study_design_scores_gemma":[0.00041388973,0.00021180866,0.0001200086,0.000009641714,0.0000036121348,0.0000060724274,0.000008042695,0.99288094,0.0019204807,0.0031958844,0.0010724516,0.00015715395],"about_ca_topic_score_codex":0.000010538543,"about_ca_topic_score_gemma":0.000018217263,"teacher_disagreement_score":0.78604215,"about_ca_system_score_codex":0.00009831723,"about_ca_system_score_gemma":0.00016240831,"threshold_uncertainty_score":0.3990454},"labels":[],"label_agreement":null},{"id":"W2559580056","doi":"10.1109/cec.2016.7743845","title":"A radius-free quantum particle swarm optimization technique for dynamic optimization problems","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Multi-swarm optimization; Particle swarm optimization; Metaheuristic; Mathematical optimization; Computer science; Derivative-free optimization; Quantum; Continuous optimization; RADIUS; Optimization problem; Physics; Mathematics; Quantum mechanics","score_opus":0.022964089647289004,"score_gpt":0.2825203099912922,"score_spread":0.25955622034400316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559580056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001619348,0.00004173154,0.99125326,0.005591583,0.00019874501,0.0019100036,0.000015626714,0.00050772325,0.0004651466],"genre_scores_gemma":[0.011121825,0.0001274633,0.9847233,0.00012926503,0.000034673416,0.0009474089,0.000014591271,0.000040336396,0.0028611294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997587,0.00015268556,0.0005184616,0.0006735159,0.00051554694,0.00055278395],"domain_scores_gemma":[0.99747133,0.00035825922,0.00016756228,0.0012043226,0.00058923435,0.00020927674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010073102,0.0002165102,0.00023134738,0.0002242151,0.00020510469,0.00024858638,0.0013877194,0.00012163057,0.00022737443],"category_scores_gemma":[0.0010217852,0.00015396743,0.00008629188,0.00084560143,0.00008530372,0.0010273267,0.0003700906,0.00007695344,0.000034310768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016617038,0.00013351474,0.000017704273,0.000033317963,0.000020190299,0.0000018665651,0.000056147834,0.94708997,0.0021077744,0.04226985,0.0012642556,0.0069888034],"study_design_scores_gemma":[0.0013039804,0.00017790573,0.000008657036,0.000034786513,0.0000073463298,0.000012835512,0.0000052624796,0.9856077,0.00786671,0.004280703,0.00043666057,0.0002574703],"about_ca_topic_score_codex":0.000008183843,"about_ca_topic_score_gemma":0.0000033625793,"teacher_disagreement_score":0.038517725,"about_ca_system_score_codex":0.0001576621,"about_ca_system_score_gemma":0.00016784524,"threshold_uncertainty_score":0.6278609},"labels":[],"label_agreement":null},{"id":"W2559710837","doi":"10.1109/cec.2016.7744349","title":"Heritage-dynamic cultural algorithm for multi-population solutions","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Minnesota Pollution Control Agency","keywords":"Population; Set (abstract data type); Computer science; Limit (mathematics); Genetic algorithm; Cultural algorithm; Algorithm; Cultural heritage; Space (punctuation); Artificial intelligence; Mathematical optimization; Machine learning; Population-based incremental learning; Mathematics; Sociology; Geography; Archaeology","score_opus":0.07195878933908625,"score_gpt":0.34952968208199,"score_spread":0.2775708927429037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559710837","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000603767,0.000035247973,0.9970749,0.0015790757,0.0003280376,0.00041396628,0.000021854226,0.00027268895,0.00021386727],"genre_scores_gemma":[0.010879353,0.00002855193,0.9760899,0.00006804328,0.000042403433,0.0000963457,0.00001530939,0.000011448975,0.012768654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986163,0.00007110315,0.0002464983,0.00038190442,0.00028506242,0.0003991703],"domain_scores_gemma":[0.9989203,0.0001524605,0.00005599845,0.00041035755,0.00033233428,0.0001285509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000362409,0.00011583322,0.0001264038,0.000115790644,0.00024047763,0.0001557412,0.0005585546,0.000058198555,0.0001127461],"category_scores_gemma":[0.0002523647,0.00007263833,0.000076269745,0.00029692092,0.0000452097,0.00077885203,0.00018922416,0.00004728332,0.0001383163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014580551,0.000081046564,0.000053967462,0.00000584669,0.000018871271,0.0000018650011,0.000058902966,0.00010693135,0.0005717233,0.024548888,0.001450973,0.9730995],"study_design_scores_gemma":[0.0007019587,0.000034731722,0.0013018455,0.0000072511134,0.000002800925,0.000007677841,0.00001256646,0.9954912,0.00010823519,0.0008033867,0.001387441,0.00014092123],"about_ca_topic_score_codex":0.0000262005,"about_ca_topic_score_gemma":0.000022526356,"teacher_disagreement_score":0.9953843,"about_ca_system_score_codex":0.00010637289,"about_ca_system_score_gemma":0.00004381703,"threshold_uncertainty_score":0.29621053},"labels":[],"label_agreement":null},{"id":"W2561899334","doi":"10.1007/s00500-016-2466-6","title":"Incremental cooperative coevolution for large-scale global optimization","year":2016,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":19,"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":"Sensitivity (control systems); Coevolution; Global optimization; Computer science; Mathematical optimization; Convergence (economics); Algorithm; Artificial intelligence; Mathematics","score_opus":0.019270535777701034,"score_gpt":0.30132901702112963,"score_spread":0.2820584812434286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561899334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079575094,0.000037949318,0.99668753,0.0007993911,0.0004253883,0.00044023606,0.000037097725,0.00023797214,0.00053870014],"genre_scores_gemma":[0.28116468,0.000004897292,0.7183202,0.00016026551,0.00014640727,0.00001480122,0.000014146988,0.0000103896045,0.00016421668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982697,0.00013889032,0.00030859796,0.0004634238,0.0003730247,0.00044639173],"domain_scores_gemma":[0.99869275,0.00028012466,0.0001200706,0.00030397007,0.00048447758,0.00011862901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076202245,0.00013918274,0.00016293525,0.000068329995,0.00038095523,0.00016509296,0.000536478,0.00006045463,0.00004889763],"category_scores_gemma":[0.00044995316,0.00010919298,0.00005685439,0.0004909531,0.000041734755,0.00043144665,0.00039669021,0.000054185937,0.000043049316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008565832,0.000537795,0.0070913364,0.00007742421,0.00013768555,0.000009858921,0.001408397,0.5984008,0.0011413675,0.14686221,0.010784528,0.23346299],"study_design_scores_gemma":[0.001114381,0.00008121182,0.0003615777,0.00003120076,0.0000038656867,0.0000071573663,0.000034166806,0.996789,0.00032817785,0.0003866513,0.00070612505,0.0001565112],"about_ca_topic_score_codex":0.000005751514,"about_ca_topic_score_gemma":0.000003837425,"teacher_disagreement_score":0.39838824,"about_ca_system_score_codex":0.00024604826,"about_ca_system_score_gemma":0.00013730816,"threshold_uncertainty_score":0.44527605},"labels":[],"label_agreement":null},{"id":"W2573363997","doi":"10.3166/ria.30.393-418","title":"Un nouvel algorithme de propagation de labels avec barrages","year":2016,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Metaheuristic Optimization Algorithms Research","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","score_opus":0.04858913367081801,"score_gpt":0.2999341714413143,"score_spread":0.25134503777049627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573363997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012228844,0.0019824228,0.9602775,0.03139911,0.0014132775,0.0005825188,0.00003061224,0.00017748507,0.0029141924],"genre_scores_gemma":[0.18078408,0.0049729357,0.53905654,0.0007062939,0.0012525256,0.00018867136,0.000010597215,0.00012358266,0.2729048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952257,0.0007158497,0.00093897927,0.0010195067,0.00065187446,0.0014480482],"domain_scores_gemma":[0.996054,0.00094035926,0.00028476,0.0012862044,0.00081572693,0.00061894854],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002925704,0.0003917161,0.00039746708,0.00029367645,0.00040388756,0.0004966971,0.0016035914,0.0003052473,0.0041018813],"category_scores_gemma":[0.0023141922,0.0003448383,0.00017565455,0.0013871723,0.00049975555,0.0009014211,0.00046870284,0.00041570896,0.0033175906],"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":[0.000014720363,0.0004044238,0.00021276131,0.00014454708,0.00005451699,0.00018029055,0.0047720373,0.023389703,0.0075819604,0.06332019,0.002631716,0.89729315],"study_design_scores_gemma":[0.000110437635,0.00016853957,0.000099035824,0.00038330094,0.000022087414,0.00024901069,0.00013722848,0.80682826,0.15801287,0.008320907,0.025270151,0.0003981727],"about_ca_topic_score_codex":0.00011719489,"about_ca_topic_score_gemma":0.000009051218,"teacher_disagreement_score":0.89689493,"about_ca_system_score_codex":0.0005576586,"about_ca_system_score_gemma":0.0008621102,"threshold_uncertainty_score":0.99990034},"labels":[],"label_agreement":null},{"id":"W2576466201","doi":"10.1609/socs.v7i1.18396","title":"Weighted Lateral Learning in Real-Time Heuristic Search","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Null-move heuristic; Incremental heuristic search; Heuristic; Robustness (evolution); Artificial intelligence; Property (philosophy); Consistent heuristic; Machine learning; Weighting; Search algorithm; Beam search; Algorithm; Mathematical optimization; Mathematics","score_opus":0.015487406199933024,"score_gpt":0.2745171946361062,"score_spread":0.2590297884361732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576466201","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":[0.85805213,0.000029825384,0.001210803,0.023534166,0.0046304436,0.0008753819,0.000014442154,0.00027213705,0.111380644],"genre_scores_gemma":[0.98890895,0.00011815134,0.003901183,0.00007088397,0.00027391742,0.000027495045,0.00000927925,0.00003260696,0.0066575203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587774,0.0001676183,0.0005244936,0.0006147547,0.0023438763,0.00047150347],"domain_scores_gemma":[0.99712735,0.0004825736,0.0001331831,0.0002709916,0.0018448058,0.0001410979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018736204,0.00019174274,0.00028592604,0.00028318117,0.00017585297,0.0004677577,0.002578656,0.00011269673,0.00018823301],"category_scores_gemma":[0.000787807,0.00016475658,0.00012313365,0.0013634893,0.00013420187,0.0003958507,0.0015753331,0.0007574653,0.00009951197],"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":[0.00028238425,0.0011659954,0.01761399,0.00012361098,0.00015414198,0.00006111228,0.0011983672,0.0031588352,0.18080989,0.79123133,0.0013290055,0.0028713183],"study_design_scores_gemma":[0.0034285693,0.00033597488,0.0077760103,0.00033785397,0.000011333111,0.0000585175,0.00007844111,0.67594594,0.2930279,0.015518129,0.002962313,0.00051898754],"about_ca_topic_score_codex":0.00013547849,"about_ca_topic_score_gemma":9.792869e-7,"teacher_disagreement_score":0.7757132,"about_ca_system_score_codex":0.00036525767,"about_ca_system_score_gemma":0.00031493112,"threshold_uncertainty_score":0.67185783},"labels":[],"label_agreement":null},{"id":"W2577609954","doi":"","title":"Explorations of Quantum-Classical Approaches to Scheduling a Mars Lander Activity Problem","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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; Mars landing; Mathematical optimization; Scheduling (production processes); Quantum; Quantum computer; Mars Exploration Program; Algorithm; Theoretical computer science; Mathematics; Exploration of Mars","score_opus":0.49408787309912444,"score_gpt":0.3803586932931276,"score_spread":0.11372917980599684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577609954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018899718,0.0000028777154,0.976422,0.017129038,0.000118242606,0.00033558934,0.000019764915,0.00006736878,0.0040151607],"genre_scores_gemma":[0.869601,0.000008051005,0.12992525,0.00007981045,0.000054514123,0.000086274275,0.0000023522891,0.000008188813,0.00023455716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973372,0.00019432846,0.0004390192,0.00053908123,0.0012056107,0.0002847108],"domain_scores_gemma":[0.9977773,0.00062519824,0.00014631635,0.00034729336,0.0009197162,0.00018421892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090367405,0.00015755066,0.00020924199,0.0003692025,0.00013352429,0.00016101386,0.0008098346,0.0000810833,0.00021128966],"category_scores_gemma":[0.001397243,0.0001176866,0.00006007122,0.0008316388,0.00014460122,0.0004915978,0.00019525451,0.00016177745,0.00043032033],"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":[0.000030364617,0.00018741078,0.000011089044,0.000005750896,0.000009785523,9.197909e-7,0.00015560183,0.00557941,0.0032196224,0.83633757,0.00005807723,0.15440443],"study_design_scores_gemma":[0.00003946278,0.00013955926,0.00015873008,0.00005452146,0.0000019758156,0.0000015211917,0.00004130751,0.767632,0.047633495,0.18402457,0.00010657139,0.00016626513],"about_ca_topic_score_codex":0.000008803863,"about_ca_topic_score_gemma":0.000015310798,"teacher_disagreement_score":0.867711,"about_ca_system_score_codex":0.00010477692,"about_ca_system_score_gemma":0.0005660072,"threshold_uncertainty_score":0.55310404},"labels":[],"label_agreement":null},{"id":"W2586424584","doi":"10.1109/intech.2016.7845071","title":"Solving the MAX-SAT problem by binary enhanced fireworks algorithm","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Algorithm; Binary number; Binary search algorithm; Computer science; Genetic algorithm; Differential evolution; Mathematical optimization; Mathematics; Search algorithm","score_opus":0.011133409711474886,"score_gpt":0.25055273836376957,"score_spread":0.23941932865229468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586424584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000650166,0.00020516172,0.9812035,0.010062953,0.0002445881,0.0003380576,0.0000049984214,0.0002520691,0.007623635],"genre_scores_gemma":[0.014933853,0.00031054954,0.926473,0.0007615569,0.00011496366,0.00010733942,0.000004123257,0.00002678213,0.05726783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977835,0.0001948211,0.00030493474,0.0005233717,0.0006646035,0.00052878284],"domain_scores_gemma":[0.99805105,0.000572456,0.00008627073,0.0009267959,0.00020541542,0.0001580381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007791606,0.00017112917,0.00015962917,0.00008447206,0.00027421428,0.00029876598,0.0017230909,0.00008089162,0.0007925045],"category_scores_gemma":[0.00016565609,0.00008356529,0.000061528015,0.000603432,0.00012524363,0.0005631355,0.00069627067,0.00017224057,0.00065802754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022291028,0.00005771453,0.000011814611,0.000005219014,0.00002094505,0.0000072633657,0.00016736706,0.000059959675,0.0043652803,0.0037553892,0.049182795,0.94236404],"study_design_scores_gemma":[0.00073875644,0.00013490318,0.000107663545,0.000077221295,0.0000056476215,0.000018503204,0.000033328975,0.9507125,0.018209063,0.0040684436,0.025480246,0.00041371112],"about_ca_topic_score_codex":0.00001680899,"about_ca_topic_score_gemma":0.0000013543257,"teacher_disagreement_score":0.95065254,"about_ca_system_score_codex":0.00006285708,"about_ca_system_score_gemma":0.00010319289,"threshold_uncertainty_score":0.8677369},"labels":[],"label_agreement":null},{"id":"W2587275747","doi":"10.29173/cais144","title":"An Evaluation of Genetic Algorithm Solutions in Optimization and Machine Learning","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Metaheuristic Optimization Algorithms Research","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":"Genetic algorithm; Selection (genetic algorithm); Set (abstract data type); Computer science; Population-based incremental learning; Population; Mathematical optimization; Algorithm; Cultural algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.04358473973625091,"score_gpt":0.27932477459704547,"score_spread":0.23574003486079456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587275747","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":[0.60867786,0.0004342092,0.38645807,0.0013252817,0.000109446875,0.0012597572,0.000039732284,0.00005516389,0.0016405096],"genre_scores_gemma":[0.8587712,0.00013108617,0.14096414,0.00001700956,0.000015344727,0.00004255116,0.0000033325962,0.00001006456,0.00004527632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977964,0.00013177401,0.00052280125,0.00030680475,0.0009621085,0.00028008435],"domain_scores_gemma":[0.9466746,0.000108821565,0.0005195446,0.00020184969,0.052391678,0.0001035387],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001969545,0.0001508477,0.00029261588,0.00032145367,0.000098726756,0.0011390779,0.0012117116,0.00009124347,0.00005078391],"category_scores_gemma":[0.0122776255,0.00012763558,0.00004494514,0.0007244725,0.00023587508,0.006983185,0.0004847986,0.0002105468,9.882397e-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":[0.000040099832,0.00087409746,0.16841567,0.0005024484,0.00014704581,9.847053e-7,0.044780448,0.21746501,0.014825367,0.012723055,0.0003349763,0.53989077],"study_design_scores_gemma":[0.00044978995,0.00016043955,0.05413001,0.00006323851,0.00002328434,0.000008333278,0.00030724425,0.94070435,0.002201646,0.0017655637,0.000072756266,0.00011334804],"about_ca_topic_score_codex":0.00041750347,"about_ca_topic_score_gemma":0.000006641438,"teacher_disagreement_score":0.72323936,"about_ca_system_score_codex":0.000058378162,"about_ca_system_score_gemma":0.00030487208,"threshold_uncertainty_score":0.99989784},"labels":[],"label_agreement":null},{"id":"W2588455578","doi":"10.1109/ssci.2016.7850283","title":"A dynamic cooperative hybrid MPSO+GA on hybrid CPU+GPU fused multicore","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Computer science; SIMD; Multi-core processor; Parallel computing; Central processing unit; Particle swarm optimization; Heuristic; Metaheuristic; Hybrid algorithm (constraint satisfaction); Algorithm; Computer hardware; Artificial intelligence; Constraint satisfaction","score_opus":0.020754922683939192,"score_gpt":0.2861552619509846,"score_spread":0.2654003392670454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588455578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065100486,0.000017461622,0.981764,0.0035004355,0.00038874667,0.00048767542,0.000030428162,0.00032842482,0.0069727334],"genre_scores_gemma":[0.7814428,0.00006484743,0.18952397,0.0009801697,0.00005228632,0.00007801487,0.000010589182,0.0000374739,0.027809845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717844,0.00025531658,0.00037508502,0.00081161165,0.000810191,0.00056937913],"domain_scores_gemma":[0.99741393,0.00061543746,0.00008952402,0.0011237276,0.00045303407,0.00030436864],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00046591603,0.00026311577,0.0002847257,0.00027398893,0.00020289664,0.00026774872,0.0013181252,0.000037655114,0.0011835627],"category_scores_gemma":[0.0006165193,0.00016348528,0.00008219957,0.00041398662,0.00014901279,0.00047789002,0.00041495066,0.00017926192,0.0024433748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001640506,0.0012900864,0.00032865576,0.000046990473,0.00023303865,0.0008524308,0.0005383229,0.0037095838,0.007804563,0.06977377,0.03851846,0.87674004],"study_design_scores_gemma":[0.0016190654,0.00027863355,0.00040203615,0.00003678716,0.0000042592255,0.000041423715,0.00001119366,0.9798245,0.013002327,0.00060195697,0.0038218468,0.00035600027],"about_ca_topic_score_codex":0.000016788854,"about_ca_topic_score_gemma":0.0000073664182,"teacher_disagreement_score":0.97611487,"about_ca_system_score_codex":0.00016558406,"about_ca_system_score_gemma":0.00021475625,"threshold_uncertainty_score":0.9997295},"labels":[],"label_agreement":null},{"id":"W2588763176","doi":"10.1109/ssci.2016.7850253","title":"Effects of centralized population initialization in differential evolution","year":2016,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Initialization; Benchmark (surveying); Population; Differential evolution; Population size; Computer science; Mathematical optimization; Convergence (economics); Centroid; Algorithm; Mathematics; Artificial intelligence; Demography","score_opus":0.008625469344585879,"score_gpt":0.2578360088285876,"score_spread":0.2492105394840017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588763176","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":[0.04017587,0.0000101301275,0.9589202,0.00014754779,0.00020766855,0.000227773,6.4811627e-7,0.000049342812,0.00026081988],"genre_scores_gemma":[0.9747503,0.000018757584,0.024973938,0.000008064463,0.00001828711,0.000010679856,0.0000033739539,0.0000043062278,0.00021231869],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99885833,0.00020345328,0.00026201716,0.0001876221,0.00032746958,0.00016108529],"domain_scores_gemma":[0.9994478,0.00014631437,0.000075599026,0.0001879687,0.0000970364,0.000045244797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000151066,0.00006412426,0.00012014399,0.0002514986,0.000019549723,0.000023678596,0.00023923213,0.000044338198,0.000097759745],"category_scores_gemma":[0.0003782921,0.000043971417,0.000025426787,0.0004544606,0.00001922176,0.00038078483,0.00008123819,0.000026331922,0.000013124029],"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":[0.00004763552,0.00038230798,0.029506588,0.000137339,0.000018077078,0.000007917934,0.00018357232,0.0005146625,0.06927229,0.8135752,0.00016085127,0.08619353],"study_design_scores_gemma":[0.0024958085,0.000060136193,0.5910971,0.000080398495,0.0000042218408,0.0000013725718,0.0000017305381,0.37427428,0.022631539,0.009188654,0.000024141806,0.00014057806],"about_ca_topic_score_codex":0.00011213862,"about_ca_topic_score_gemma":0.000010062966,"teacher_disagreement_score":0.9345744,"about_ca_system_score_codex":0.0000837944,"about_ca_system_score_gemma":0.000029998771,"threshold_uncertainty_score":0.17931023},"labels":[],"label_agreement":null},{"id":"W2590432748","doi":"10.1007/s00521-017-2903-1","title":"Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem","year":2017,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":88,"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":"Knapsack problem; Computer science; Algorithm; Mathematical optimization; Continuous knapsack problem; Combinatorial optimization; Mathematics","score_opus":0.05245572999880233,"score_gpt":0.35898073008463927,"score_spread":0.30652500008583694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590432748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020212919,0.0000538927,0.9967317,0.0013216997,0.00006800552,0.0010970583,0.000029854926,0.00016483953,0.00033082766],"genre_scores_gemma":[0.082425825,0.000027176928,0.9162087,0.00008443867,0.0001694255,0.00023393478,0.000049050104,0.000019021962,0.0007824336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984383,0.00005491336,0.00031116023,0.00060746336,0.00023965037,0.00034850757],"domain_scores_gemma":[0.99828464,0.00017244638,0.000232506,0.00081329606,0.0003459186,0.00015120403],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00035239983,0.00017031623,0.00018560998,0.00009496635,0.0016663974,0.0011332507,0.0010840592,0.00006487819,0.0000042402908],"category_scores_gemma":[0.00005996313,0.00016149043,0.000049371145,0.00017641053,0.00014300612,0.00038088908,0.00041664232,0.00017140179,0.000007533322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029904152,0.00014795245,0.00026740367,0.000047630077,0.000021893284,0.0000017175213,0.00010796095,0.1675252,0.00020555737,0.007101892,0.00024652848,0.8243233],"study_design_scores_gemma":[0.0005504216,0.00004877868,0.0011818454,0.00001107391,0.000007130957,0.000012025768,0.000012823268,0.99640906,0.00009202836,0.0003858352,0.0011159498,0.00017300266],"about_ca_topic_score_codex":0.000027644566,"about_ca_topic_score_gemma":0.000001971016,"teacher_disagreement_score":0.8288839,"about_ca_system_score_codex":0.000022628525,"about_ca_system_score_gemma":0.00005566519,"threshold_uncertainty_score":0.9999037},"labels":[],"label_agreement":null},{"id":"W2595987953","doi":"","title":"Globalization Strategies for Mesh Adaptive Direct Search","year":2008,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Class (philosophy); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.030999096613937804,"score_gpt":0.2786702842659811,"score_spread":0.24767118765204332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595987953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011143818,0.00041266892,0.9908676,0.0015137132,0.000138944,0.0012635026,0.000047619134,0.0009360753,0.0037055044],"genre_scores_gemma":[0.29823154,0.00028734756,0.69852835,0.0005727143,0.00010879597,0.0005912388,0.000028924249,0.00004354289,0.00160752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968167,0.0002901449,0.00047056214,0.00070520584,0.00083862955,0.0008787451],"domain_scores_gemma":[0.9974408,0.00031408746,0.00014664621,0.0010526603,0.0006853238,0.00036043773],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010398885,0.00030552488,0.00037027756,0.00051248074,0.0005746111,0.00044286664,0.0014016966,0.00020451235,0.000030330038],"category_scores_gemma":[0.00037322158,0.00030741308,0.00016083782,0.0013899727,0.00015921092,0.0011394128,0.0003911505,0.00026530464,0.000027669543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013821063,0.00042702505,0.002847861,0.00006182605,0.00012408433,0.00013269516,0.0013079958,0.09165344,0.0009317576,0.8603465,0.014186296,0.027842332],"study_design_scores_gemma":[0.00048994576,0.00023548226,0.0028735886,0.000016092878,0.000008364855,0.0000982406,0.00009172255,0.98594004,0.0030466565,0.0048210006,0.0020425913,0.00033626857],"about_ca_topic_score_codex":0.0018660651,"about_ca_topic_score_gemma":0.00024652536,"teacher_disagreement_score":0.89428663,"about_ca_system_score_codex":0.0004028852,"about_ca_system_score_gemma":0.0010901234,"threshold_uncertainty_score":0.9999378},"labels":[],"label_agreement":null},{"id":"W2596534719","doi":"","title":"Nonsmooth Optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search","year":2006,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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; Group for Research in Decision Analysis","funders":"","keywords":"Variable neighborhood search; Mathematical optimization; Metaheuristic; Convergence (economics); Variable (mathematics); Guided Local Search; Local search (optimization); Mathematics; Computer science; Algorithm","score_opus":0.014882220476374133,"score_gpt":0.24637892971795808,"score_spread":0.23149670924158394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2596534719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039081028,0.00083789224,0.98242635,0.0021570548,0.000102342616,0.0010524777,0.000042460037,0.0008185778,0.012172024],"genre_scores_gemma":[0.13843161,0.0003589487,0.85792506,0.0004992933,0.0001416171,0.00024479153,0.000039517083,0.0000697747,0.0022894072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533665,0.0006124896,0.0006105789,0.0010513149,0.001177069,0.0012118688],"domain_scores_gemma":[0.99718285,0.00042138842,0.00012678876,0.0013123616,0.0005892461,0.00036739578],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016968163,0.0004268617,0.00049320015,0.00061669445,0.0005694803,0.0009307277,0.0012877743,0.0002984183,0.00011460578],"category_scores_gemma":[0.00024506496,0.00042847454,0.000099755984,0.0022681395,0.00020526658,0.0014229078,0.00095520966,0.00063371414,0.000026453932],"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":[0.000047332418,0.0003219007,0.0022579166,0.00003496751,0.000053539196,0.000062465995,0.00025997267,0.64660776,0.00034615726,0.33923858,0.0013393407,0.0094300555],"study_design_scores_gemma":[0.0006195456,0.00018597784,0.0024089613,0.000033109474,0.00001586304,0.00007503641,0.000041874988,0.98959315,0.002604276,0.0035040549,0.0004823468,0.00043578027],"about_ca_topic_score_codex":0.0140200555,"about_ca_topic_score_gemma":0.00024326514,"teacher_disagreement_score":0.34298542,"about_ca_system_score_codex":0.00042505527,"about_ca_system_score_gemma":0.00067011407,"threshold_uncertainty_score":0.9998167},"labels":[],"label_agreement":null},{"id":"W2599286397","doi":"10.1007/s10710-017-9298-8","title":"A univariate marginal distribution algorithm based on extreme elitism and its application to the robotic inverse displacement problem","year":2017,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Metaheuristic Optimization Algorithms Research","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 British Columbia","funders":"British Columbia Knowledge Development Fund; Canada Foundation for Innovation","keywords":"Particle swarm optimization; Computer science; Algorithm; Univariate; Differential evolution; Heuristic; Displacement (psychology); Domain (mathematical analysis); Mathematical optimization; Mathematics; Artificial intelligence; Machine learning","score_opus":0.019666172899434866,"score_gpt":0.2691151408858998,"score_spread":0.2494489679864649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599286397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009139125,0.00020353458,0.9928351,0.0047927396,0.000105832994,0.0009864095,0.000014242818,0.00006477863,0.00008345787],"genre_scores_gemma":[0.21666703,0.000094226496,0.7816121,0.0001821317,0.000120874276,0.0004886928,0.00005096203,0.000020539997,0.00076344766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983812,0.0001180329,0.00020809777,0.00052479724,0.00039421322,0.00037368474],"domain_scores_gemma":[0.998743,0.00006463281,0.00012369415,0.00072216353,0.00013854788,0.00020794898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006799458,0.00018194426,0.00015451913,0.00007340563,0.00097588485,0.00086955767,0.000632382,0.00004586219,0.000006787003],"category_scores_gemma":[0.00012693249,0.00013471475,0.000024803476,0.00018423548,0.00006901926,0.00015103359,0.00038535477,0.00012918955,0.000019936824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013940066,0.0000764173,0.0007645769,0.000055574183,0.000013959043,0.0000062666054,0.00010182451,0.01923524,0.00004045973,0.0023792018,0.00015783866,0.9771547],"study_design_scores_gemma":[0.00043149182,0.00015621292,0.010305703,0.00003645206,0.000015902751,0.000008720885,0.0000063204807,0.98183185,0.000027249764,0.00052189385,0.0065001776,0.00015802585],"about_ca_topic_score_codex":0.00021566241,"about_ca_topic_score_gemma":0.000024131825,"teacher_disagreement_score":0.97699666,"about_ca_system_score_codex":0.000043150667,"about_ca_system_score_gemma":0.00005869553,"threshold_uncertainty_score":0.8385165},"labels":[],"label_agreement":null},{"id":"W2602337361","doi":"10.1109/tpds.2017.2687461","title":"Adaptive Particle Swarm Optimization with Heterogeneous Multicore Parallelism and GPU Acceleration","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Parallel computing; Multi-core processor; Data parallelism; CUDA; Xeon; Xeon Phi; Particle swarm optimization; Vectorization (mathematics); Graphics processing unit; Parallel processing; Parallelism (grammar); Algorithm","score_opus":0.041198773977055084,"score_gpt":0.27206910627444064,"score_spread":0.23087033229738557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602337361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005808596,0.00013101131,0.992575,0.00043376628,0.00022575643,0.0005671148,0.000083555475,0.000114828115,0.000060404993],"genre_scores_gemma":[0.96898687,0.00019402761,0.03037811,0.000026536507,0.000027780432,0.00014924021,0.000016213624,0.000014696743,0.00020652615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983453,0.00013642067,0.00030390322,0.0005249115,0.00037950353,0.00030995288],"domain_scores_gemma":[0.9985701,0.000094066825,0.0001836443,0.00066374615,0.0002315904,0.0002568494],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00021327549,0.00022087253,0.00026188974,0.000073188254,0.0010708283,0.0011346469,0.00035763966,0.00010230083,0.000010051146],"category_scores_gemma":[0.000018852892,0.00018185056,0.000034627155,0.00012970169,0.00015707855,0.00072464655,0.000010987484,0.00016696536,0.000010639445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000840443,0.0000994851,0.000060188348,0.000018614171,0.00005311857,0.000022828446,0.00014566563,0.99631625,0.000021424283,0.00052014523,0.00002552564,0.0026327276],"study_design_scores_gemma":[0.0013671053,0.00025426046,0.00038960885,0.000035308713,0.000023444649,0.00008476938,0.0000685232,0.99725646,0.00021795534,0.000025608655,0.000054639975,0.00022231681],"about_ca_topic_score_codex":0.00016152121,"about_ca_topic_score_gemma":0.000025247038,"teacher_disagreement_score":0.9631783,"about_ca_system_score_codex":0.00004311927,"about_ca_system_score_gemma":0.0000520227,"threshold_uncertainty_score":0.99990225},"labels":[],"label_agreement":null},{"id":"W2604181050","doi":"10.1609/aaai.v31i1.10658","title":"Problem Difficulty and the Phase Transition in Heuristic Search","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Heuristic; Benchmark (surveying); Mathematical optimization; Computer science; Incremental heuristic search; Phase transition; Beam search; Greedy algorithm; Work (physics); Search algorithm; Mathematics; Physics; Thermodynamics","score_opus":0.09672332879444981,"score_gpt":0.35426040957173177,"score_spread":0.257537080777282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604181050","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":[0.3323093,0.000088341236,0.56075805,0.07851659,0.0005860975,0.0037861052,0.000023502771,0.00013583382,0.023796191],"genre_scores_gemma":[0.99417514,0.00010208346,0.005384892,0.000078397825,0.000033193348,0.00004583065,3.5178775e-7,0.0000076292936,0.00017246934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980693,0.000065549524,0.00046785478,0.00043914828,0.0006398093,0.00031833092],"domain_scores_gemma":[0.9983843,0.00019616746,0.0002681482,0.000489649,0.0005776251,0.00008413809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001627176,0.00016229878,0.00025779475,0.00011958607,0.0005549934,0.0009025902,0.0025750813,0.00006136632,0.000032318872],"category_scores_gemma":[0.0010639278,0.000096306016,0.00006341063,0.00032169328,0.001025835,0.00046176597,0.0004931852,0.00039784002,0.000022215298],"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":[0.0001861459,0.00022112766,0.00005297657,0.00005228181,0.000008637615,0.0000013858831,0.003131335,0.00021049798,0.002605572,0.87081313,0.000032995595,0.122683935],"study_design_scores_gemma":[0.00034491814,0.00012465441,0.00046103267,0.00015422751,0.0000073694823,0.0000063516673,0.00031071357,0.8539197,0.034317788,0.11019044,0.000021231352,0.00014159988],"about_ca_topic_score_codex":0.00013759195,"about_ca_topic_score_gemma":0.000016162367,"teacher_disagreement_score":0.85370916,"about_ca_system_score_codex":0.000029699437,"about_ca_system_score_gemma":0.00009067506,"threshold_uncertainty_score":0.8703698},"labels":[],"label_agreement":null},{"id":"W2604948093","doi":"10.24963/ijcai.2017/69","title":"Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Regina; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Heuristics; Consistent heuristic; Heuristic; Bidirectional search; Incremental heuristic search; Mathematical optimization; Search algorithm; Beam search; Best-first search; Mathematics; Node (physics); Null-move heuristic; Computer science; Algorithm; Combinatorics; Engineering","score_opus":0.03792417353133272,"score_gpt":0.31625790404090603,"score_spread":0.27833373050957333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604948093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005745936,0.000013119311,0.96465117,0.0043366337,0.00031251015,0.00030461024,0.000010394156,0.00019954555,0.024426082],"genre_scores_gemma":[0.13445568,0.000008359763,0.843845,0.00027706832,0.00010996594,0.00004647907,0.000004783547,0.000023850842,0.021228798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717015,0.00011793697,0.0002619541,0.00069796643,0.0011727131,0.0005792616],"domain_scores_gemma":[0.99694735,0.00020439271,0.00007665821,0.0017580864,0.00049363216,0.0005199097],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006671128,0.00019259017,0.0002247032,0.00019472466,0.0015191562,0.001779217,0.0020015442,0.00006119496,0.0016373015],"category_scores_gemma":[0.00056029,0.0001511889,0.000051947893,0.0002597725,0.00024822843,0.0006302467,0.00091490435,0.000287096,0.0020654807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004145094,0.0017135418,0.021034926,0.00012674814,0.00052231055,0.0011376401,0.0041766493,0.5709071,0.0011748519,0.045703076,0.108730294,0.24435832],"study_design_scores_gemma":[0.00056100235,0.00017836895,0.03317167,0.00002196553,0.0000064161245,0.00006017149,0.000028005072,0.9564435,0.0006206767,0.000073311,0.00852769,0.00030722847],"about_ca_topic_score_codex":0.000646721,"about_ca_topic_score_gemma":0.0000672354,"teacher_disagreement_score":0.38553637,"about_ca_system_score_codex":0.000069621776,"about_ca_system_score_gemma":0.0004975469,"threshold_uncertainty_score":0.9997807},"labels":[],"label_agreement":null},{"id":"W2610402045","doi":"10.1007/s10489-017-0926-z","title":"Cooperative co-evolution with sensitivity analysis-based budget assignment strategy for large-scale global optimization","year":2017,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":24,"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":"Computer science; Benchmark (surveying); Sensitivity (control systems); Mathematical optimization; Global optimization; Function (biology); Scale (ratio); Representation (politics); Algorithm; Mathematics; Biology","score_opus":0.032784693096042504,"score_gpt":0.33636197840259846,"score_spread":0.303577285306556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610402045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012209633,0.0000098446435,0.9945088,0.00024372249,0.00006488677,0.0008705401,0.00009725361,0.00009624108,0.003986624],"genre_scores_gemma":[0.64477396,0.000004445159,0.3548662,0.000064183514,0.00002688933,0.00011368015,0.00006796503,0.00000806406,0.000074638396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997671,0.00012197445,0.00031971565,0.00077872694,0.0006386156,0.00046993254],"domain_scores_gemma":[0.997534,0.00019280241,0.000303975,0.001182839,0.0006067611,0.00017964042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010713385,0.00023507721,0.0003337252,0.00013121685,0.0009831805,0.0007610968,0.0008409584,0.00009643059,0.00006514518],"category_scores_gemma":[0.00013882438,0.00020565419,0.00008092864,0.00069459376,0.00017052147,0.00040513463,0.00014540444,0.00013289516,0.00003486922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057523852,0.00013968645,0.0004757679,0.000009637111,0.00010839312,0.000004596873,0.000050429975,0.93531704,0.000032953663,0.060073357,0.000070773094,0.003659847],"study_design_scores_gemma":[0.0003257595,0.00015417358,0.0010725723,0.0000068434865,0.00006689136,0.0000018589391,0.00007904982,0.9923383,0.0052237534,0.00039509128,0.000085646025,0.000250041],"about_ca_topic_score_codex":0.000051971474,"about_ca_topic_score_gemma":0.00014797508,"teacher_disagreement_score":0.64465183,"about_ca_system_score_codex":0.00025234005,"about_ca_system_score_gemma":0.00029785463,"threshold_uncertainty_score":0.8386334},"labels":[],"label_agreement":null},{"id":"W2612133367","doi":"10.1007/s10846-017-0564-z","title":"An Improved Genetic Algorithm Approach on Mechanism Kinematic Structure Enumeration with Intelligent Manufacturing","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Crossover; Genetic algorithm; Isomorphism (crystallography); Convergence (economics); Identification (biology); Algorithm; Mathematical optimization; Kinematics; Computer science; Enumeration; Rate of convergence; Kinematic chain; Process (computing); Mechanism (biology); Simple (philosophy); Mathematics; Key (lock); Artificial intelligence; Combinatorics","score_opus":0.02560892340122091,"score_gpt":0.28213616677069153,"score_spread":0.25652724336947064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612133367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002714068,0.00016085147,0.99429923,0.00012562453,0.0017774636,0.00076657184,0.000002998448,0.000038617552,0.000114553135],"genre_scores_gemma":[0.5112864,0.00005103799,0.4879354,0.00003423274,0.0004665565,0.000012692705,0.0000033854192,0.000036000005,0.00017433678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587065,0.00038750932,0.0012487195,0.000529918,0.0014810276,0.0004821473],"domain_scores_gemma":[0.99522245,0.00013342424,0.0017545678,0.001691531,0.00076404895,0.0004339827],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011731001,0.0003950829,0.00070326735,0.0005031053,0.0004780584,0.0021070838,0.002710128,0.00014897209,0.00003757072],"category_scores_gemma":[0.00018854518,0.00027347408,0.0001416536,0.00015245804,0.000077171855,0.0008429932,0.00016917566,0.0005680819,0.000021221736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037297723,0.0003013162,0.000027304337,0.00019202834,0.0002493729,0.000118584765,0.00074991153,0.9304937,0.0006032725,0.00285753,0.00003872719,0.06433095],"study_design_scores_gemma":[0.00038490572,0.0011124652,0.00022292041,0.00025883407,0.000049223272,0.0011292718,0.00025591545,0.98127735,0.0144303115,0.000547423,0.000034125336,0.000297287],"about_ca_topic_score_codex":0.00006174648,"about_ca_topic_score_gemma":0.0000026762582,"teacher_disagreement_score":0.5085723,"about_ca_system_score_codex":0.00025440953,"about_ca_system_score_gemma":0.00021694564,"threshold_uncertainty_score":0.99997175},"labels":[],"label_agreement":null},{"id":"W2612309007","doi":"","title":"The multi-modal traveling salesman problem","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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 Laval","funders":"","keywords":"Travelling salesman problem; Computer science; Modal; 2-opt; Mathematical optimization; Mathematics; Algorithm; Materials science","score_opus":0.023466016471735254,"score_gpt":0.26414754496934023,"score_spread":0.240681528497605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612309007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043998595,0.0009844475,0.9530116,0.021044966,0.00027640956,0.00076114485,0.000014695596,0.00040777726,0.023059027],"genre_scores_gemma":[0.034203816,0.0011779326,0.94936365,0.00012534694,0.000039302442,0.00011841873,0.000117876254,0.000046779805,0.014806903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884861,0.007801751,0.0008099246,0.0011860062,0.0010446828,0.0006715098],"domain_scores_gemma":[0.98947924,0.002244413,0.00057628064,0.0041021598,0.0032873566,0.00031057396],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.01274411,0.00041064195,0.0003907549,0.00019413453,0.0010550424,0.0023121329,0.0058058277,0.0002996516,0.00003420015],"category_scores_gemma":[0.0024434617,0.0003540976,0.00023905639,0.00070780964,0.00029758635,0.00023289354,0.0032368442,0.0012598851,0.00009471154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007866907,0.0007862673,0.00019550252,0.0001381196,0.00012326644,0.000019379095,0.007784555,0.0038291162,0.00040262757,0.5004426,0.0018746878,0.48439598],"study_design_scores_gemma":[0.0004983215,4.6206566e-7,0.00090332056,0.0005841562,0.000016410646,0.000014236068,0.000029567733,0.9662915,0.0043968665,0.0136955455,0.013106959,0.00046264406],"about_ca_topic_score_codex":0.00025143242,"about_ca_topic_score_gemma":0.000445428,"teacher_disagreement_score":0.96246237,"about_ca_system_score_codex":0.00016177694,"about_ca_system_score_gemma":0.0007230127,"threshold_uncertainty_score":0.9998911},"labels":[],"label_agreement":null},{"id":"W2618467698","doi":"10.1007/s12293-017-0234-5","title":"A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function","year":2017,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Premature convergence; Crossover; Maxima and minima; Algorithm; Population; Genetic algorithm; Population-based incremental learning; Mathematical optimization; Computer science; Benchmark (surveying); Curse of dimensionality; Operator (biology); Convergence (economics); Mathematics; Artificial intelligence","score_opus":0.0210543000855937,"score_gpt":0.2711286795990178,"score_spread":0.2500743795134241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618467698","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026168318,0.0002459193,0.99484164,0.0004705119,0.001142759,0.00028275774,0.0000040391737,0.000121957,0.0002735932],"genre_scores_gemma":[0.14939936,0.00002771054,0.84961146,0.00012791106,0.000380402,0.000021471242,0.000005080642,0.00002591278,0.00040071984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771553,0.00011374195,0.0004172804,0.0007415206,0.00046237433,0.00054956943],"domain_scores_gemma":[0.99797755,0.00022830657,0.0003004275,0.0009317542,0.00035632835,0.0002056007],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006524606,0.00022607725,0.00031067932,0.00021037144,0.0012476592,0.0013955985,0.0010489288,0.00005860794,0.000026167434],"category_scores_gemma":[0.0004045486,0.00023039275,0.00009835983,0.00010838161,0.00012037037,0.0003265413,0.0010092908,0.00013374336,0.000006932017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007681153,0.000036343008,0.000045680605,0.000021802814,0.000047321773,0.0000366393,0.00005898195,0.007186391,0.00010966437,0.00094838603,0.0003333183,0.9911678],"study_design_scores_gemma":[0.0010604528,0.00011069618,0.0018041461,0.000028155202,0.000029657593,0.00009779578,0.000009280301,0.9937288,0.00039698282,0.0013557812,0.0011270548,0.00025120671],"about_ca_topic_score_codex":0.000058572936,"about_ca_topic_score_gemma":9.64243e-7,"teacher_disagreement_score":0.9909166,"about_ca_system_score_codex":0.000037382124,"about_ca_system_score_gemma":0.00008416894,"threshold_uncertainty_score":0.99964106},"labels":[],"label_agreement":null},{"id":"W2626465488","doi":"10.1007/s11750-017-0452-5","title":"Rejoinder on: On learning and branching: a survey","year":2017,"lang":"en","type":"article","venue":"Top","topic":"Metaheuristic Optimization Algorithms Research","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":"Branching (polymer chemistry); Computer science; Mathematical economics; Mathematics; Chemistry","score_opus":0.062619957460186,"score_gpt":0.3566546196720717,"score_spread":0.2940346622118857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626465488","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":[0.060562346,0.00007180245,0.8148466,0.005681566,0.000775029,0.00032391227,0.0000033967358,0.00022456267,0.11751083],"genre_scores_gemma":[0.9659223,0.000030295258,0.020584868,0.00026816287,0.000076078344,0.000004739658,0.0000017628286,0.000010589724,0.013101235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990241,0.0001778887,0.00008367503,0.00026775367,0.00028396858,0.00016261285],"domain_scores_gemma":[0.9989799,0.0002523822,0.000061031904,0.0005604035,0.00005980515,0.000086475215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009093171,0.00006728055,0.000093381976,0.00006474716,0.00043233376,0.00053733593,0.00052001764,0.00003069065,0.00006216135],"category_scores_gemma":[0.0016524555,0.000058134345,0.000013472166,0.000059796497,0.000044145694,0.00015245803,0.00023368734,0.00020920449,0.00014851465],"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":[0.000085145744,0.00022108764,0.07950943,0.00003850107,0.00005723605,0.00014560926,0.0013967489,0.003672882,0.00009939378,0.08836782,0.007598888,0.81880724],"study_design_scores_gemma":[0.00083842047,0.00030868864,0.434366,0.00003591177,0.0000016840731,0.000010165357,0.0000064430706,0.54546344,0.00042593386,0.0010133659,0.017269127,0.00026080452],"about_ca_topic_score_codex":0.000051330517,"about_ca_topic_score_gemma":0.0000052983482,"teacher_disagreement_score":0.9053599,"about_ca_system_score_codex":0.000011020577,"about_ca_system_score_gemma":0.00003022079,"threshold_uncertainty_score":0.51815426},"labels":[],"label_agreement":null},{"id":"W2716434414","doi":"10.1109/ccece.2017.7946758","title":"Comparative strategies for knowledge migration in Multi Objective Optimization Problems","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Computer science; Benchmark (surveying); Premature convergence; Population; Convergence (economics); Context (archaeology); Diversity (politics); Negotiation; Local optimum; Management science; Artificial intelligence; Mathematical optimization; Operations research; Machine learning; Particle swarm optimization; Geography; Economics; Mathematics; Sociology; Economic growth","score_opus":0.13882465547835723,"score_gpt":0.3944310061601637,"score_spread":0.2556063506818065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2716434414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003074021,0.00004058992,0.98811597,0.00036880706,0.00017408543,0.0010202416,0.0000036649092,0.000072860894,0.009896358],"genre_scores_gemma":[0.29444775,0.000017607374,0.7036806,0.000013344829,0.000022851716,0.0002326659,0.000011273949,0.000006908984,0.0015670278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988154,0.00010453445,0.00026276894,0.00039804864,0.00017621835,0.00024303459],"domain_scores_gemma":[0.9984873,0.00018084407,0.00016065275,0.00055767846,0.00054962933,0.00006389289],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004940733,0.00012857647,0.00020748148,0.00019734568,0.00036715282,0.0010616558,0.00085687416,0.000061173494,0.00002393543],"category_scores_gemma":[0.0002715529,0.0001144265,0.00003724861,0.00019893948,0.000083056046,0.0017870014,0.00017933223,0.0000958263,0.000022834558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012367751,0.00028323344,0.00030447132,0.000036343266,0.000019554294,0.0000011151201,0.0070999283,0.9276478,0.00010493003,0.06120407,0.00045945292,0.0028267116],"study_design_scores_gemma":[0.0008568063,0.00007285564,0.0015970811,0.000016557366,0.0000018885063,8.1066923e-7,0.00033424326,0.9949723,0.00046853404,0.0013566276,0.00018505241,0.00013726838],"about_ca_topic_score_codex":0.00015227232,"about_ca_topic_score_gemma":0.0013255416,"teacher_disagreement_score":0.29414034,"about_ca_system_score_codex":0.00007375544,"about_ca_system_score_gemma":0.00027320557,"threshold_uncertainty_score":0.9999753},"labels":[],"label_agreement":null},{"id":"W2724857582","doi":"10.1109/cec.2017.7969333","title":"Optimal parameter regions for particle swarm optimization algorithms","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation","keywords":"Benchmark (surveying); Particle swarm optimization; Mathematical optimization; Computer science; Parameter space; Network topology; Neighbourhood (mathematics); Set (abstract data type); Algorithm; Multi-swarm optimization; Mathematics; Statistics","score_opus":0.07439562715932886,"score_gpt":0.34799736460180053,"score_spread":0.2736017374424717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724857582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016548342,0.000018667546,0.9915231,0.0051417444,0.0003728354,0.0005278385,0.000005871504,0.00019554756,0.0020489371],"genre_scores_gemma":[0.01732838,0.00002306909,0.9759542,0.0002083867,0.000105318264,0.00015630388,0.000007822067,0.000018824068,0.006197701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982616,0.000059609083,0.00029381923,0.0005264019,0.00039012186,0.00046844743],"domain_scores_gemma":[0.9972727,0.00028786037,0.00016775464,0.0016482656,0.00039599222,0.0002274329],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005892501,0.00015088767,0.00018850013,0.00007772974,0.00089559035,0.0013198745,0.001743013,0.0000750807,0.000109726876],"category_scores_gemma":[0.0013422708,0.00013390253,0.00009936669,0.00014622304,0.00012868366,0.0011243368,0.00044746994,0.000099245335,0.0000720356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025702579,0.00026390923,0.0001257669,0.000022600132,0.000072190334,0.000016825785,0.0002626156,0.7072596,0.00005775784,0.24253297,0.009112357,0.040247723],"study_design_scores_gemma":[0.00067627075,0.00008962724,0.00012372901,0.000005014817,0.000007547321,0.000008017585,0.000011403075,0.9928157,0.0022070706,0.0015187296,0.0023544675,0.0001824219],"about_ca_topic_score_codex":0.000031187432,"about_ca_topic_score_gemma":0.0000022899212,"teacher_disagreement_score":0.2855561,"about_ca_system_score_codex":0.000035733392,"about_ca_system_score_gemma":0.0000959207,"threshold_uncertainty_score":0.9997169},"labels":[],"label_agreement":null},{"id":"W2726889624","doi":"10.1109/cec.2017.7969373","title":"Schematic study on interaction and imbalance effects of variables for Large-Scale Optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Computer science; Feature (linguistics); Set (abstract data type); Variable (mathematics); Scale (ratio); Range (aeronautics); Class (philosophy); Artificial intelligence; Mathematical optimization; Machine learning; Mathematics; Engineering","score_opus":0.02319838527638896,"score_gpt":0.3350724411407125,"score_spread":0.31187405586432354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726889624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004359495,0.000008129276,0.9932309,0.00015469735,0.0002135268,0.0008240879,0.0000012803525,0.00003269468,0.0011752362],"genre_scores_gemma":[0.45624396,0.00000893301,0.5431755,0.000021908543,0.000019069526,0.00007065952,0.0000013875401,0.0000063554544,0.0004522148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991293,0.000064946464,0.00018325765,0.00026044995,0.00022553095,0.00013651293],"domain_scores_gemma":[0.99854094,0.0004139355,0.00017637109,0.0006378362,0.00018130375,0.00004963256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052467175,0.00008034562,0.00016743077,0.0000896349,0.00022807755,0.00025151667,0.0004390292,0.00002847626,0.000012028617],"category_scores_gemma":[0.000994559,0.00006689663,0.00002012394,0.00007363446,0.00002026772,0.0004503884,0.00019270348,0.00005295552,0.000002995294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064306555,0.019783996,0.049245212,0.0068172202,0.0011290404,0.000048199076,0.018608555,0.40652192,0.00546916,0.39290452,0.006109935,0.092719205],"study_design_scores_gemma":[0.0010157864,0.00032478702,0.0026530705,0.000044200096,0.000008042023,9.920171e-7,0.000072290415,0.99292046,0.0025950612,0.00027522788,0.000022457894,0.00006764419],"about_ca_topic_score_codex":0.000007211795,"about_ca_topic_score_gemma":0.0000019513943,"teacher_disagreement_score":0.58639854,"about_ca_system_score_codex":0.000014026668,"about_ca_system_score_gemma":0.000021086375,"threshold_uncertainty_score":0.27279654},"labels":[],"label_agreement":null},{"id":"W2727779323","doi":"10.1145/3071178.3071294","title":"Reconsidering constraint release for active-set evolution strategies","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Computer science; Constraint (computer-aided design); Mathematics; Programming language","score_opus":0.07974443942710047,"score_gpt":0.2980548241210974,"score_spread":0.21831038469399694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727779323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075270556,0.00008302859,0.92116946,0.0017594522,0.0002040522,0.00044137862,0.000015752235,0.000038023176,0.0010183069],"genre_scores_gemma":[0.798574,0.000028573639,0.20122513,0.000016107384,0.000032021017,0.000028920791,0.0000013075522,0.0000048795814,0.00008909701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892324,0.000016101589,0.0002713311,0.000327894,0.00026994979,0.00019148459],"domain_scores_gemma":[0.9982265,0.000111117646,0.00039102056,0.00018936092,0.0010052047,0.00007677817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023036226,0.000118758,0.00015325319,0.00008051844,0.00072075194,0.00039674636,0.0007172408,0.000053100146,0.0000076098204],"category_scores_gemma":[0.00048708625,0.000101950165,0.00004875423,0.00009068891,0.00038766413,0.00068865204,0.00036703466,0.00009583017,0.0000018089515],"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":[0.00013700608,0.00015149965,0.0089696115,0.0006197072,0.00014577419,0.0000013940488,0.0027238769,0.021978019,0.005908249,0.72150797,0.002575766,0.23528114],"study_design_scores_gemma":[0.00037542457,0.00006035014,0.08797358,0.000057127472,0.000010592847,0.000027961718,0.00037514247,0.77575684,0.00053970097,0.1346235,0.00008084217,0.0001189782],"about_ca_topic_score_codex":0.00001804114,"about_ca_topic_score_gemma":0.0000015896427,"teacher_disagreement_score":0.7537788,"about_ca_system_score_codex":0.00004338986,"about_ca_system_score_gemma":0.00032880774,"threshold_uncertainty_score":0.5543514},"labels":[],"label_agreement":null},{"id":"W2729624476","doi":"10.1109/cec.2017.7969582","title":"Analyzing effects of ordering vectors in mutation schemes on performance of Differential Evolution","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Mutation; Benchmark (surveying); Differential evolution; Evolutionary algorithm; Computer science; Algorithm; Monte Carlo method; Mathematical optimization; Population; Mathematics; Closeness; Scheme (mathematics); Statistics","score_opus":0.013935486590853162,"score_gpt":0.2830008049415279,"score_spread":0.26906531835067476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729624476","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":[0.5447374,0.0000069102257,0.4548368,0.000015019751,0.00008983645,0.000070997696,1.0435181e-7,0.000008581659,0.00023439391],"genre_scores_gemma":[0.97375697,0.0000114674085,0.026154056,0.0000010152814,0.000010487813,0.0000047136923,6.2168044e-7,0.0000032249343,0.000057426663],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991973,0.00004665018,0.00020175487,0.00016022491,0.00027269823,0.00012138967],"domain_scores_gemma":[0.9991834,0.00010467042,0.00016561618,0.00041337957,0.000106223786,0.00002671689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019630512,0.00006141978,0.00013738134,0.00025023674,0.00007613832,0.00004738486,0.0004650012,0.000028341918,0.000012016992],"category_scores_gemma":[0.00048354326,0.000054268352,0.000024554312,0.00018448304,0.000044646127,0.00034624062,0.00014389721,0.00006974557,0.0000031580016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015825027,0.0009476414,0.37754983,0.0022050967,0.00013245418,0.000025527997,0.0012806131,0.071342014,0.079515524,0.11303647,0.00002839099,0.35377818],"study_design_scores_gemma":[0.0002861692,0.00008594686,0.210164,0.00006428517,0.0000015459601,2.7433438e-7,0.000001968596,0.718955,0.07035876,0.000041873875,7.3192956e-7,0.000039466806],"about_ca_topic_score_codex":0.00007240944,"about_ca_topic_score_gemma":0.0000059676813,"teacher_disagreement_score":0.647613,"about_ca_system_score_codex":0.000035198,"about_ca_system_score_gemma":0.00003858627,"threshold_uncertainty_score":0.22129992},"labels":[],"label_agreement":null},{"id":"W2733295096","doi":"","title":"Topological Order Based Planner for Solving POMDPs","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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":"Domain (mathematical analysis); Computer science; Exploit; Topology (electrical circuits); Planner; Partially observable Markov decision process; Space (punctuation); Focus (optics); Point (geometry); Order (exchange); Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Markov chain; Machine learning; Geometry","score_opus":0.02806009940555429,"score_gpt":0.2753866726496715,"score_spread":0.2473265732441172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733295096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043332615,0.0003708756,0.95812213,0.023696793,0.00031518418,0.0008397501,0.000040106304,0.00056150847,0.015620329],"genre_scores_gemma":[0.026544236,0.00013625334,0.9676345,0.00046914766,0.000046189827,0.00017456913,0.000313643,0.000036029305,0.00464546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99322325,0.0035146468,0.00064134825,0.0012449649,0.00075974874,0.00061601854],"domain_scores_gemma":[0.9887963,0.0031955878,0.00043174555,0.0029765447,0.0042994292,0.0003004064],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007832753,0.0003889982,0.0004754226,0.0003384632,0.0004719322,0.0011389432,0.003627642,0.0004103413,0.00018717977],"category_scores_gemma":[0.005372497,0.00038404687,0.00024210461,0.00064016576,0.00015554533,0.00019826327,0.0019061412,0.00074838207,0.00004328531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031534208,0.0022269243,0.00030703095,0.00042301076,0.00016177064,0.00003096806,0.004165266,0.009122043,0.00041806695,0.8022915,0.011481012,0.16934088],"study_design_scores_gemma":[0.0006195559,0.0000011814863,0.0003572123,0.000364001,0.000018822915,0.0000056543754,0.000010570455,0.9656519,0.004319317,0.014748074,0.013459829,0.00044383953],"about_ca_topic_score_codex":0.00012546146,"about_ca_topic_score_gemma":0.0000711396,"teacher_disagreement_score":0.9565299,"about_ca_system_score_codex":0.00015204122,"about_ca_system_score_gemma":0.0008093477,"threshold_uncertainty_score":0.99989796},"labels":[],"label_agreement":null},{"id":"W2734286001","doi":"10.1609/socs.v8i1.18432","title":"Cost-Based Heuristics and Node Re-Expansions across the Phase Transition","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maxima and minima; Heuristics; Heuristic; Mathematical optimization; Node (physics); Operator (biology); Phase transition; Computer science; Incremental heuristic search; Function (biology); Mathematics; Search algorithm; Beam search; Physics; Chemistry","score_opus":0.036317253744548195,"score_gpt":0.33844281529169334,"score_spread":0.30212556154714515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734286001","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":[0.4704867,0.00014968307,0.16615137,0.3295205,0.015114574,0.0029608402,0.00038802173,0.00031860903,0.014909707],"genre_scores_gemma":[0.99421024,0.000060083064,0.004425111,0.00069348805,0.0002613472,0.000051438146,0.00001046128,0.000018941868,0.00026887562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717546,0.000070121,0.000356664,0.00042901278,0.0016632656,0.00030546612],"domain_scores_gemma":[0.99704,0.00049981097,0.00013247116,0.0003010396,0.0019063845,0.00012032228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011511871,0.0001524814,0.0001746396,0.00006222188,0.000442807,0.00054897537,0.0017231015,0.00007829647,0.00003759131],"category_scores_gemma":[0.00076666044,0.000106385356,0.00010125549,0.0006318579,0.0002203613,0.0002438146,0.00062166376,0.00042403163,0.000008218127],"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":[0.0008166078,0.0038882925,0.0010107477,0.00023884983,0.0003250977,0.00003271324,0.006961789,0.0088597415,0.1162601,0.8405146,0.0097261835,0.011365272],"study_design_scores_gemma":[0.0050215973,0.00030293452,0.00039503834,0.00015497943,0.00002291901,0.000031251777,0.00040207553,0.69266057,0.28508395,0.00764525,0.007996113,0.00028330405],"about_ca_topic_score_codex":0.000034531913,"about_ca_topic_score_gemma":0.0000015645472,"teacher_disagreement_score":0.83286935,"about_ca_system_score_codex":0.0001274807,"about_ca_system_score_gemma":0.00019093846,"threshold_uncertainty_score":0.52937824},"labels":[],"label_agreement":null},{"id":"W2737764623","doi":"10.5539/mas.v11n8p98","title":"Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Maxima and minima; Particle swarm optimization; Algorithm; Computer science; Node (physics); Benchmark (surveying); Cloud computing; Mathematical optimization; Set (abstract data type); Metaheuristic; Swarm behaviour; Mathematics","score_opus":0.06585642420768888,"score_gpt":0.327663178659391,"score_spread":0.2618067544517021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737764623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015177252,0.000030476722,0.9962103,0.0012190061,0.00033602718,0.00086816406,0.000031364358,0.00015959557,0.0009933034],"genre_scores_gemma":[0.12092988,0.00001823397,0.8784396,0.00010146392,0.000137536,0.00009602288,0.000029366645,0.00001661862,0.00023128791],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660707,0.000025129055,0.00035711797,0.0012307733,0.0010921105,0.0006877829],"domain_scores_gemma":[0.99557704,0.00009990025,0.0002970307,0.003340197,0.0004295092,0.00025632745],"candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0029820062,0.00017947952,0.00019257834,0.0001380562,0.0024676027,0.00288571,0.006823908,0.000060774553,0.000010311049],"category_scores_gemma":[0.0004866857,0.00017311875,0.00002960953,0.0004662952,0.00046677567,0.0028582483,0.0021399665,0.00013864176,0.000030650106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001618656,0.00026803117,0.000108656306,0.000042496493,0.000022205868,0.0000049480213,0.0014651197,0.20032378,0.033867955,0.040258598,0.001089388,0.72253263],"study_design_scores_gemma":[0.00043265696,0.000027659195,0.000068479014,0.000007867278,0.000006170293,0.000003198918,0.000014201438,0.987193,0.0076963496,0.0039102565,0.00042700593,0.00021317431],"about_ca_topic_score_codex":0.000048632388,"about_ca_topic_score_gemma":0.000013210111,"teacher_disagreement_score":0.7868692,"about_ca_system_score_codex":0.00009538834,"about_ca_system_score_gemma":0.0004542425,"threshold_uncertainty_score":0.99883103},"labels":[],"label_agreement":null},{"id":"W2738900493","doi":"10.1016/j.advengsoft.2017.07.002","title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","year":2017,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4981,"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":"Mathematical optimization; Convergence (economics); Computer science; Swarm behaviour; Pareto principle; Multi-objective optimization; Algorithm; Mathematics","score_opus":0.02226714207504294,"score_gpt":0.28126251541246283,"score_spread":0.2589953733374199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738900493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022785322,0.0015663903,0.99580276,0.0001323384,0.0010321925,0.00082337507,0.000013981442,0.0005849512,0.000021241001],"genre_scores_gemma":[0.0028115136,0.00042504337,0.99589527,0.000020275103,0.00012503813,0.0005169166,0.000007860231,0.00006367259,0.00013440226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776757,0.000024723184,0.00042210094,0.00065323367,0.00040555588,0.0007268123],"domain_scores_gemma":[0.99774224,0.0005504815,0.00015068288,0.0011887423,0.0001886248,0.00017922954],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006817822,0.00032154872,0.00037838652,0.00035131516,0.00022315336,0.0004468677,0.0019866726,0.00011480848,0.0000063107323],"category_scores_gemma":[0.002425936,0.0003373233,0.000091290414,0.000336281,0.00004254769,0.0017718776,0.0003597974,0.00026571302,0.000012838456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035783678,0.000030747637,0.000081521706,0.0001517995,0.000015167557,0.000017578996,0.00008784579,0.90588903,0.000034060376,0.000670499,0.000034558845,0.09298362],"study_design_scores_gemma":[0.0008497847,0.00007460366,0.00017722057,0.00016605026,0.0000050598856,0.000010040134,0.0000023670336,0.9875738,0.0008737554,0.00033433922,0.009536867,0.0003961197],"about_ca_topic_score_codex":0.0000073711485,"about_ca_topic_score_gemma":0.0000010951704,"teacher_disagreement_score":0.0925875,"about_ca_system_score_codex":0.00013043497,"about_ca_system_score_gemma":0.000081343445,"threshold_uncertainty_score":0.99990785},"labels":[],"label_agreement":null},{"id":"W2745034076","doi":"10.23977/jaip.2017.21001","title":"Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem","year":2017,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Metaheuristic Optimization Algorithms Research","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":"Ant colony optimization algorithms; Particle swarm optimization; Metaheuristic; Swarm intelligence; Artificial bee colony algorithm; Meta-optimization; Computer science; Genetic algorithm; Multi-swarm optimization; Parallel metaheuristic; Mathematical optimization; Evolutionary computation; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Machine learning","score_opus":0.18043772774683559,"score_gpt":0.4225814047868751,"score_spread":0.24214367704003953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745034076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008634175,0.00022310035,0.9811569,0.0076566855,0.00086396484,0.0002853347,0.000005745063,0.000017741639,0.0011563447],"genre_scores_gemma":[0.545685,0.00049114047,0.45327887,0.0001394751,0.00033961135,0.000006800643,0.0000019549425,0.000016014972,0.000041114003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968818,0.00030971773,0.0011627404,0.00031284898,0.001057627,0.00027528894],"domain_scores_gemma":[0.9934896,0.0012354717,0.0026086282,0.0005892504,0.0018762997,0.00020072746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002663777,0.00017299177,0.0004090189,0.00035897293,0.00044577624,0.0005311326,0.0011904307,0.00010329579,0.000048285983],"category_scores_gemma":[0.006074046,0.00015418211,0.00009856254,0.0002500683,0.0002697656,0.0031176799,0.0002503195,0.00066319684,0.00007562501],"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":[0.00037432162,0.00092347676,0.00044242042,0.000042446856,0.00011205719,0.0000631898,0.0010275387,0.0017940473,0.0010139171,0.007224898,0.00023209667,0.9867496],"study_design_scores_gemma":[0.00023750261,0.002185554,0.0050434126,0.00037455923,0.000122062585,0.00041666633,0.0010255538,0.90961444,0.02565648,0.053302474,0.0015968385,0.00042444855],"about_ca_topic_score_codex":0.00006393766,"about_ca_topic_score_gemma":0.000009455164,"teacher_disagreement_score":0.98632514,"about_ca_system_score_codex":0.000078205485,"about_ca_system_score_gemma":0.00024655307,"threshold_uncertainty_score":0.72716385},"labels":[],"label_agreement":null},{"id":"W2745529579","doi":"10.5539/cis.v10n3p60","title":"The Application of Possibility Distribution for Solving Standard Quadratic Optimization Problems","year":2017,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Conestoga College","funders":"","keywords":"Simplex; Computer science; Mathematical optimization; Simplex algorithm; Distribution (mathematics); Applied mathematics; Function (biology); Quadratic equation; Quadratic function; Quadratic form (statistics); Quadratic programming; Set (abstract data type); Optimization problem; Mathematics; Algorithm; Linear programming; Mathematical analysis; Combinatorics","score_opus":0.019187210960543048,"score_gpt":0.30762612544560974,"score_spread":0.2884389144850667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745529579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006039692,0.000014060215,0.997676,0.00064109813,0.00021713316,0.0005981333,0.000014418224,0.000029992736,0.00020518691],"genre_scores_gemma":[0.5361587,0.00006742856,0.46359217,0.000049007962,0.000033141634,0.0000659308,0.000022095579,0.00000228691,0.00000924191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869114,0.000019908237,0.00039693963,0.000187602,0.00051113876,0.00019328902],"domain_scores_gemma":[0.9973494,0.0001595047,0.00042939815,0.00075342174,0.0012321963,0.000076126984],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025745928,0.00007187833,0.00010139615,0.000075637654,0.0020161394,0.0019263348,0.0012462193,0.000025474625,6.9709984e-7],"category_scores_gemma":[0.00076348725,0.0000515699,0.000023907822,0.0002840638,0.0005293353,0.00767983,0.00040323334,0.00005111807,0.0000016669626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015947322,0.000018814444,0.00060966704,0.000103229046,0.0000056390445,2.714728e-8,0.00087627437,0.09547822,0.000051306382,0.25154662,0.00017623298,0.65111804],"study_design_scores_gemma":[0.00023481196,0.000065215216,0.005013082,0.000010043269,0.0000015320144,0.0000015611714,0.000011025307,0.9914036,0.0003566228,0.0013252908,0.0015163599,0.000060872273],"about_ca_topic_score_codex":0.000009918721,"about_ca_topic_score_gemma":0.00000112696,"teacher_disagreement_score":0.89592534,"about_ca_system_score_codex":0.000051544957,"about_ca_system_score_gemma":0.00020606579,"threshold_uncertainty_score":0.9992831},"labels":[],"label_agreement":null},{"id":"W2746560485","doi":"10.1155/2017/4851493","title":"Self-Adaptive Artificial Bee Colony for Function Optimization","year":2017,"lang":"en","type":"article","venue":"Journal of Control Science and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Natural Science Foundation of Hunan Province; China Scholarship Council; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Position (finance); Local search (optimization); Mathematical optimization; Population; Global optimization; Artificial bee colony algorithm; Set (abstract data type); Computer science; Function optimization; Artificial intelligence; Mathematics; Genetic algorithm","score_opus":0.017110231790516607,"score_gpt":0.25928566604580516,"score_spread":0.24217543425528856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746560485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008037383,0.00005421529,0.99750334,0.00087169325,0.0005299369,0.00013785914,7.275509e-7,0.000016964246,0.00008153988],"genre_scores_gemma":[0.5813189,0.000023402023,0.418421,0.000038100356,0.00018050963,0.0000046981386,4.1087755e-8,0.0000037957805,0.000009531574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989461,0.000010891744,0.00022615393,0.0001328876,0.00048432828,0.00019969462],"domain_scores_gemma":[0.99841195,0.00010712027,0.00023773858,0.00015209078,0.0009563976,0.0001347004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019478536,0.00006523775,0.00014080013,0.00022155116,0.00044631737,0.0007251621,0.0006239451,0.000027298602,0.0000018969862],"category_scores_gemma":[0.0012584667,0.00005556886,0.0000324542,0.0001677844,0.00006682022,0.0016430035,0.00006112056,0.00009108876,5.1956346e-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":[0.00007755709,0.000045597466,0.000041494317,0.000021047317,0.000044906592,0.000008187107,0.00018594593,0.9313313,0.007587616,0.025830928,0.0001300827,0.03469538],"study_design_scores_gemma":[0.0005040635,0.00022844989,0.0007924128,0.000010356855,0.000012181087,0.000015800872,0.000006954839,0.9973635,0.0001921324,0.00023572815,0.0005781941,0.000060213228],"about_ca_topic_score_codex":0.0000012082187,"about_ca_topic_score_gemma":2.383802e-7,"teacher_disagreement_score":0.5805152,"about_ca_system_score_codex":0.000059000657,"about_ca_system_score_gemma":0.00022533823,"threshold_uncertainty_score":0.6992755},"labels":[],"label_agreement":null},{"id":"W2747118187","doi":"10.1016/j.artint.2017.05.004","title":"MM: A bidirectional search algorithm that is guaranteed to meet in the middle","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":41,"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","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Incremental heuristic search; Bidirectional search; Beam search; Heuristic; Best-first search; Search algorithm; Algorithm; Computer science; Disjoint sets; Node (physics); Search problem; Depth-first search; Iterative deepening depth-first search; Null-move heuristic; Mathematics; Artificial intelligence","score_opus":0.2313199868634884,"score_gpt":0.3798248892331862,"score_spread":0.1485049023696978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747118187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028793472,0.00004150385,0.9801928,0.011234068,0.0005919303,0.00047138354,0.0000123763,0.000054551234,0.004522062],"genre_scores_gemma":[0.7651695,0.000045742632,0.23052064,0.0027410265,0.00032525868,0.00013360787,0.000002260894,0.000024108402,0.0010378689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718755,0.00024808798,0.0003607073,0.0005975965,0.0010794101,0.00052667194],"domain_scores_gemma":[0.99769634,0.0003532955,0.000085734566,0.0014843517,0.00024073171,0.00013954566],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020835518,0.00016397657,0.0001837835,0.00028123605,0.00073575333,0.0013399255,0.0036729092,0.00006810561,0.00022285848],"category_scores_gemma":[0.0006161369,0.00012877851,0.000074634845,0.00070706115,0.0001931245,0.0004613886,0.00057727884,0.00023625926,0.00079993095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029208506,0.000363583,0.0005586339,0.000013627133,0.00003090124,0.0001337419,0.013545721,0.0024305822,0.00033280737,0.17858632,0.0029703367,0.8010045],"study_design_scores_gemma":[0.00004133646,0.000096055286,0.0009173176,0.000032314983,0.0000035595915,0.000027786622,0.0006478056,0.94020253,0.026848778,0.027980044,0.0029549813,0.0002474858],"about_ca_topic_score_codex":0.000801578,"about_ca_topic_score_gemma":0.00026891995,"teacher_disagreement_score":0.937772,"about_ca_system_score_codex":0.00005589284,"about_ca_system_score_gemma":0.00014639323,"threshold_uncertainty_score":0.99997807},"labels":[],"label_agreement":null},{"id":"W275567961","doi":"","title":"Knowledge Sharing Through Agent Migration with Multi-Population Cultural Algorithm.","year":2013,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Computer science; Knowledge transfer; Population; Algorithm; Multi-agent system; Knowledge sharing; Space (punctuation); Machine learning; Artificial intelligence; Knowledge management","score_opus":0.12282401886980042,"score_gpt":0.401062454957069,"score_spread":0.27823843608726856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W275567961","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009675255,0.00028192237,0.9806393,0.0066553745,0.00032412328,0.0014727704,0.0000044363996,0.00023646513,0.000710404],"genre_scores_gemma":[0.14652263,0.00072171557,0.8364882,0.0004798005,0.0010017935,0.0010922349,0.000094777424,0.000073670446,0.013525192],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957557,0.0005322282,0.00036507344,0.0007338466,0.0016909357,0.00092226075],"domain_scores_gemma":[0.9963148,0.0003643235,0.00008560659,0.001170735,0.0018496336,0.00021488224],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025033804,0.00023762016,0.00022320155,0.00008404785,0.0012806676,0.0015012346,0.0020385943,0.000116014104,0.00016498523],"category_scores_gemma":[0.0002365663,0.00014301586,0.00014481196,0.001992086,0.00032340974,0.0021254818,0.00105047,0.0009855196,0.0006739408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052440795,0.0015470647,0.008634838,0.0004760472,0.0010019991,0.000039136412,0.13468356,0.021774925,0.0027322574,0.052658875,0.4084569,0.36794198],"study_design_scores_gemma":[0.0005265212,0.00008934057,0.0057806307,0.000030636882,0.000005258429,0.000009863476,0.00072843087,0.9864683,0.00045306052,0.0013995321,0.0043013724,0.00020700949],"about_ca_topic_score_codex":0.0020207923,"about_ca_topic_score_gemma":0.000069053734,"teacher_disagreement_score":0.9646934,"about_ca_system_score_codex":0.00036634976,"about_ca_system_score_gemma":0.00018103294,"threshold_uncertainty_score":0.9995353},"labels":[],"label_agreement":null},{"id":"W2755736673","doi":"10.1007/s00500-017-2833-y","title":"Self-feedback differential evolution adapting to fitness landscape characteristics","year":2017,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Calgary","funders":"Science and Technology Planning Project of Guangdong Province; National Natural Science Foundation of China","keywords":"Fitness landscape; Differential evolution; Mathematical optimization; Benchmark (surveying); Local optimum; Evolutionary algorithm; Computer science; Population; Unimodality; Evolutionary computation; Fitness approximation; Convergence (economics); Suite; Fitness function; Mathematics; Genetic algorithm","score_opus":0.022423525962077775,"score_gpt":0.2862679210519532,"score_spread":0.26384439508987545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755736673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042493,0.000014398757,0.95355874,0.0005670116,0.0012934301,0.00024091049,0.000003867969,0.00035983897,0.001468797],"genre_scores_gemma":[0.7651992,0.0000018820916,0.23402216,0.00004957537,0.00050140894,0.000004067792,0.00000485575,0.000017166725,0.000199698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774384,0.000120807505,0.00039215447,0.00057791936,0.00059126335,0.0005740144],"domain_scores_gemma":[0.9977063,0.00020538733,0.00031482923,0.001160892,0.0003565308,0.0002560842],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00065349456,0.00020128801,0.00028165418,0.00016778985,0.0012812872,0.0014146479,0.00204961,0.000079260615,0.00004686756],"category_scores_gemma":[0.0010441593,0.00020388917,0.00006860516,0.00022635823,0.000039595107,0.00039453176,0.0017722525,0.0002564496,0.0002263362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006983015,0.0005643179,0.11000464,0.0005499225,0.0002988846,0.00021513512,0.006069361,0.011874282,0.0017206575,0.07110577,0.0041160416,0.79341114],"study_design_scores_gemma":[0.0003294422,0.000037086684,0.061509255,0.000050875387,0.000006544365,0.000011511405,0.000019446245,0.93705016,0.00007265971,0.00014431658,0.0005342866,0.0002343938],"about_ca_topic_score_codex":0.00002030673,"about_ca_topic_score_gemma":0.0000019779325,"teacher_disagreement_score":0.9251759,"about_ca_system_score_codex":0.00006894961,"about_ca_system_score_gemma":0.00012222207,"threshold_uncertainty_score":0.999622},"labels":[],"label_agreement":null},{"id":"W2760225670","doi":"10.1016/j.swevo.2017.09.010","title":"Opposition based learning: A literature review","year":2017,"lang":"en","type":"review","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":465,"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 Ontario Institute of Technology","funders":"","keywords":"Computer science; Opposition (politics); Reinforcement learning; Variety (cybernetics); Artificial intelligence; Artificial neural network; Machine learning; Management science; Operations research; Law; Mathematics; Political science","score_opus":0.0812765835477228,"score_gpt":0.39083411779178967,"score_spread":0.30955753424406685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760225670","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":[6.7152053e-9,0.6198131,0.3786579,0.00026907047,0.00020951417,0.00056301797,0.000012117577,0.00011082218,0.00036445342],"genre_scores_gemma":[8.6656246e-7,0.9176486,0.080298856,0.00010693587,0.00014060044,0.000108004766,0.0011096724,0.000024713596,0.0005617604],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970588,0.0007312881,0.0005876732,0.0007426183,0.0005890639,0.00029057037],"domain_scores_gemma":[0.9979688,0.00026781906,0.0005905407,0.0005427913,0.00044631457,0.00018375102],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007536682,0.0003746389,0.0009644448,0.00043625836,0.0006088771,0.00054538506,0.00072535843,0.00024085169,0.00001559627],"category_scores_gemma":[0.0003496065,0.00033034096,0.00025781442,0.00063161575,0.000080653874,0.00061258243,0.00028657328,0.0006092802,0.00010661296],"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":[8.786578e-7,0.000033461452,3.1883818e-7,0.027080357,0.000030053652,0.0000391751,0.000013676575,0.0005568798,1.8165036e-8,0.00055753725,0.005370482,0.9663172],"study_design_scores_gemma":[0.00013207154,0.00007578944,0.000004518535,0.043452747,0.000108987646,0.00018009156,2.9681334e-7,0.18614578,3.265941e-8,0.00040805407,0.7692063,0.00028532252],"about_ca_topic_score_codex":0.0000021455878,"about_ca_topic_score_gemma":1.1085819e-7,"teacher_disagreement_score":0.96603185,"about_ca_system_score_codex":0.00013220128,"about_ca_system_score_gemma":0.00058529596,"threshold_uncertainty_score":0.9999149},"labels":[],"label_agreement":null},{"id":"W2761792028","doi":"10.3390/e19100533","title":"Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm","year":2017,"lang":"en","type":"article","venue":"Entropy","topic":"Metaheuristic Optimization Algorithms Research","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":"Lakehead University","funders":"Universidade de Macau","keywords":"Algorithm; Group (periodic table); Mathematics; Optimization algorithm; Entropy (arrow of time); Computer science; Mathematical optimization; Physics; Thermodynamics","score_opus":0.016868376212537336,"score_gpt":0.34071398765853517,"score_spread":0.3238456114459978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761792028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000117677206,0.000046021752,0.99770933,0.0006142002,0.00062437606,0.00037324216,0.000027333374,0.00012414569,0.00036366525],"genre_scores_gemma":[0.026913835,0.000047691134,0.972231,0.00006610552,0.0000697227,0.000028153449,0.00006281059,0.000027117665,0.0005535762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972975,0.00033123395,0.0005097609,0.00059932366,0.0008535873,0.0004085733],"domain_scores_gemma":[0.99682015,0.00017484053,0.00059155555,0.0016937758,0.0005489258,0.0001707375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010151458,0.0002097219,0.00031668795,0.0002539438,0.00051371055,0.0007796119,0.0019004508,0.0000934281,0.00037870937],"category_scores_gemma":[0.0009038157,0.00020712393,0.00011745707,0.0003149958,0.00019939618,0.00095888483,0.00039514387,0.00016918602,0.000040271734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042922944,0.0004149914,0.0009367306,0.00007662416,0.00008308488,0.00004463886,0.00013869001,0.7148475,0.0016642781,0.044975992,0.00027062718,0.23650391],"study_design_scores_gemma":[0.0012256067,0.00008404043,0.0020574697,0.00001916102,0.000011173553,0.0000055556566,0.0000029253144,0.9909214,0.0044239825,0.0007163934,0.00034017552,0.00019212367],"about_ca_topic_score_codex":0.000051227034,"about_ca_topic_score_gemma":0.000001227404,"teacher_disagreement_score":0.2760739,"about_ca_system_score_codex":0.00011162034,"about_ca_system_score_gemma":0.00015153478,"threshold_uncertainty_score":0.8446269},"labels":[],"label_agreement":null},{"id":"W2765104507","doi":"10.3390/a10040120","title":"A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design","year":2017,"lang":"en","type":"article","venue":"Algorithms","topic":"Metaheuristic Optimization Algorithms Research","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 Victoria","funders":"","keywords":"Mathematical optimization; Firefly algorithm; Robustness (evolution); Metaheuristic; Computer science; Multi-objective optimization; Global optimization; Optimization problem; Benchmark (surveying); Cuckoo search; Engineering optimization; Derivative-free optimization; Imperialist competitive algorithm; Mathematics; Meta-optimization; Particle swarm optimization","score_opus":0.14554111394063857,"score_gpt":0.4533124861904382,"score_spread":0.3077713722497996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765104507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016441169,0.00002723659,0.9946009,0.000901401,0.0008890267,0.001877932,0.000012443915,0.00023351771,0.0012931464],"genre_scores_gemma":[0.17504096,0.000002241939,0.8245354,0.00010918649,0.00010134825,0.00014316618,0.000013931901,0.000017876962,0.000035870213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934534,0.0028972824,0.0006877619,0.0012175037,0.0011288136,0.00061527296],"domain_scores_gemma":[0.9958332,0.0006548004,0.00042392442,0.0022981083,0.00052474195,0.0002651967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038832778,0.00037878106,0.00077618955,0.0002799481,0.0006159879,0.00095326925,0.0028647052,0.00022314377,0.000041697982],"category_scores_gemma":[0.0010423916,0.000347959,0.00009406863,0.00084618217,0.00010345811,0.00046747894,0.0005326932,0.0006193593,0.000068705674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033085904,0.0028096982,0.00034317252,0.000047545953,0.00022611572,0.00043731823,0.0006163204,0.9313543,0.00024529907,0.021039607,0.0011130393,0.041436713],"study_design_scores_gemma":[0.0024616132,0.00057913014,0.0035787022,0.00003727442,0.000016272652,0.000013183456,0.00017608987,0.9915504,0.0010198605,0.0001749041,0.00006265778,0.00032991526],"about_ca_topic_score_codex":0.000094408475,"about_ca_topic_score_gemma":0.0000103273605,"teacher_disagreement_score":0.17487654,"about_ca_system_score_codex":0.00058518903,"about_ca_system_score_gemma":0.00028420548,"threshold_uncertainty_score":0.99989724},"labels":[],"label_agreement":null},{"id":"W2765572224","doi":"10.3390/a10040123","title":"A Selection Process for Genetic Algorithm Using Clustering Analysis","year":2017,"lang":"en","type":"article","venue":"Algorithms","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Rimouski; Université du Québec à Chicoutimi","funders":"","keywords":"Cluster analysis; Selection (genetic algorithm); Benchmark (surveying); Fitness function; Population; Genetic algorithm; Computer science; Fitness proportionate selection; Mathematical optimization; Mathematics; Data mining; Artificial intelligence","score_opus":0.06824753057029719,"score_gpt":0.37790900271863154,"score_spread":0.30966147214833434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765572224","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008686606,0.00004671291,0.99761546,0.00018685259,0.00042599897,0.00052498234,0.0000150174865,0.00015024697,0.00016607335],"genre_scores_gemma":[0.016427968,0.000014091442,0.9825414,0.000050229108,0.00029699685,0.00010769837,0.000008006661,0.000028576855,0.0005250337],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997585,0.00007277285,0.00039416214,0.00077078614,0.00059671607,0.0005805512],"domain_scores_gemma":[0.9975907,0.00009562291,0.00033709288,0.0011202444,0.00064898573,0.00020733058],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00061270315,0.00022702992,0.0003862681,0.0005048891,0.001273235,0.0011759823,0.0017923148,0.000107587206,0.000036691108],"category_scores_gemma":[0.00030534103,0.00023010006,0.00020094517,0.000908514,0.00008735942,0.0008532624,0.00036200904,0.00015498442,0.00001508755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000108954755,0.00015473687,0.0015105302,0.000079633,0.00093739684,0.000042102234,0.0004442908,0.17613472,0.00021179815,0.00027219602,0.00006837369,0.8201333],"study_design_scores_gemma":[0.00049082347,0.00006424819,0.0026410704,0.000009243105,0.00015787182,0.00003965264,0.000013799344,0.9948293,0.0005924633,0.00066181883,0.00022463419,0.00027507567],"about_ca_topic_score_codex":0.00016905802,"about_ca_topic_score_gemma":0.000017744209,"teacher_disagreement_score":0.81985825,"about_ca_system_score_codex":0.00010383744,"about_ca_system_score_gemma":0.0001825688,"threshold_uncertainty_score":0.9998609},"labels":[],"label_agreement":null},{"id":"W2770783293","doi":"10.1007/s11721-017-0150-9","title":"Self-adaptive particle swarm optimization: a review and analysis of convergence","year":2017,"lang":"en","type":"review","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation","keywords":"Particle swarm optimization; Computer science; Flocking (texture); A priori and a posteriori; Mathematical optimization; Convergence (economics); Population; Swarm behaviour; Multi-swarm optimization; Algorithm; Mathematics","score_opus":0.15445702536842004,"score_gpt":0.41579937633606756,"score_spread":0.2613423509676475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770783293","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":[6.7070407e-9,0.51512295,0.4838364,0.000041729476,0.00009493272,0.0005527697,0.000023796314,0.000047860547,0.00027951522],"genre_scores_gemma":[0.0000016870501,0.7903688,0.20905611,0.000048827245,0.000018429939,0.00010523286,0.000020356003,0.00001950338,0.00036107897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99593806,0.00050576625,0.0013496985,0.0010452921,0.0007257497,0.00043541266],"domain_scores_gemma":[0.99490273,0.0005662243,0.0012628064,0.0021808841,0.00079854357,0.00028879408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001464337,0.0004598319,0.0026912338,0.00049842946,0.0001975627,0.0002111521,0.0029241242,0.00018376119,0.00025280874],"category_scores_gemma":[0.0008931764,0.0003906155,0.0005114379,0.0032558176,0.00026917018,0.0004298028,0.0009827929,0.00036728854,0.00009127768],"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":[0.0000012789012,0.00010167387,0.0000028172506,0.017895427,0.001861065,0.000022932005,0.00014979087,0.0037967204,6.09063e-9,0.008076043,0.0001084863,0.9679838],"study_design_scores_gemma":[0.0000321453,0.00008145911,9.2787144e-7,0.0051503386,0.006099728,0.00002262007,0.0000055806563,0.78674716,0.000013142681,0.00007979673,0.20126787,0.0004992271],"about_ca_topic_score_codex":0.000020937252,"about_ca_topic_score_gemma":0.0000021015765,"teacher_disagreement_score":0.96748453,"about_ca_system_score_codex":0.00008841361,"about_ca_system_score_gemma":0.0005963962,"threshold_uncertainty_score":0.99985456},"labels":[],"label_agreement":null},{"id":"W2772539238","doi":"10.1109/smc.2017.8122931","title":"Improving firefly algorithm performance using fuzzy logic","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Firefly algorithm; Benchmark (surveying); Fuzzy logic; Firefly protocol; Mathematical optimization; Computer science; Set (abstract data type); Algorithm; Mathematics; Artificial intelligence; Particle swarm optimization","score_opus":0.06505188777408158,"score_gpt":0.3275452574985629,"score_spread":0.26249336972448134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772539238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017718866,0.000038276317,0.9845232,0.00034604553,0.00047045498,0.00014872124,0.0000011788528,0.00014665819,0.012553625],"genre_scores_gemma":[0.057956345,0.000021244814,0.9386023,0.00011907177,0.00011988063,0.000006307814,8.674068e-7,0.000011217304,0.0031627093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835575,0.000044498855,0.00023080577,0.00044124093,0.00049941766,0.00042830294],"domain_scores_gemma":[0.9978836,0.00004191346,0.00018010142,0.0015131164,0.00022661204,0.00015466733],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00062635733,0.00014062334,0.0001620676,0.000114579794,0.0010115252,0.0011642991,0.0022326708,0.000060792412,0.00011089035],"category_scores_gemma":[0.00026850947,0.00011840552,0.000046273963,0.00014509395,0.000102632046,0.0014165503,0.0010050078,0.00017014473,0.0001802662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013823123,0.000039188213,0.00042423923,0.00002139439,0.000011165542,0.000028134657,0.00006215999,0.0010877149,0.00032378186,0.005374914,0.0001344634,0.9924915],"study_design_scores_gemma":[0.00022453233,0.0000387953,0.0015942062,0.000007438097,0.0000025459078,0.00002480791,0.0000050628305,0.9961522,0.0010230971,0.00049105356,0.00027355307,0.00016269683],"about_ca_topic_score_codex":0.00016990269,"about_ca_topic_score_gemma":0.0000013898065,"teacher_disagreement_score":0.9950645,"about_ca_system_score_codex":0.000058912316,"about_ca_system_score_gemma":0.00014717312,"threshold_uncertainty_score":0.99987257},"labels":[],"label_agreement":null},{"id":"W2775992423","doi":"10.5539/mas.v12n1p32","title":"Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Metaheuristic; Particle swarm optimization; Mathematical optimization; Multi-swarm optimization; Derivative-free optimization; Benchmark (surveying); Maxima and minima; Optimization problem; Heuristic; Meta-optimization; Convergence (economics); Swarm intelligence; Algorithm; Artificial intelligence; Mathematics","score_opus":0.07607040322973167,"score_gpt":0.316116814479459,"score_spread":0.2400464112497273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775992423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006839091,0.00009959246,0.9732263,0.0011250869,0.0005998013,0.00044992255,0.000008066981,0.00023491621,0.023572389],"genre_scores_gemma":[0.633323,0.000007790693,0.3654182,0.00022074961,0.0000447367,0.000073822244,0.0000013864176,0.000014983219,0.0008953247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949775,0.000032189437,0.00043327702,0.0014442111,0.0021326132,0.0009802419],"domain_scores_gemma":[0.9951899,0.00013754002,0.00027608516,0.0034987286,0.00047631765,0.00042144072],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0021866309,0.00033215765,0.00050029194,0.00038797138,0.0023469857,0.003153938,0.009287629,0.00007240032,0.00007764866],"category_scores_gemma":[0.0010060967,0.00027217402,0.00013940182,0.0007843261,0.0015026134,0.0016399034,0.0026780216,0.00040440084,0.00027746105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065804394,0.000711215,0.00011161403,0.00006628328,0.00043838695,0.00010000966,0.0021030114,0.020675188,0.3832336,0.49438116,0.0008331491,0.097280584],"study_design_scores_gemma":[0.00062452466,0.000019035875,0.0008457094,0.000003679947,0.000041458054,0.000020231271,0.0000058413666,0.9892901,0.0042467783,0.0041504996,0.00041106928,0.00034103013],"about_ca_topic_score_codex":0.000052966097,"about_ca_topic_score_gemma":0.0000049247906,"teacher_disagreement_score":0.96861494,"about_ca_system_score_codex":0.00012909021,"about_ca_system_score_gemma":0.0005991569,"threshold_uncertainty_score":0.99997306},"labels":[],"label_agreement":null},{"id":"W2779495064","doi":"10.5539/mas.v12n1p148","title":"Movement Particle Swarm Optimization Algorithm","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Metaheuristic; Maxima and minima; Benchmark (surveying); Mathematical optimization; Derivative-free optimization; Algorithm; Trigonometric functions; Computer science; Firefly algorithm; Heuristic; Sine; Optimization problem; Meta-optimization; Meta heuristic; Mathematics","score_opus":0.029166827466259485,"score_gpt":0.29766299813020297,"score_spread":0.2684961706639435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779495064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002828527,0.00001516959,0.9842221,0.000913163,0.00028170948,0.00033070153,0.000002196378,0.00015955445,0.013792537],"genre_scores_gemma":[0.32366216,0.000013828258,0.6753241,0.00029868362,0.000045187735,0.000056313533,9.984774e-7,0.000010088007,0.0005886548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674326,0.000024364372,0.00027269387,0.000851723,0.0014570032,0.0006509366],"domain_scores_gemma":[0.9970042,0.000036594538,0.00019586182,0.0021529284,0.0002855129,0.00032489342],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015467742,0.0001593851,0.00016225467,0.00013723168,0.001930785,0.002262403,0.004304301,0.000041087183,0.0000515847],"category_scores_gemma":[0.00021209841,0.00014970818,0.000032752978,0.0004920791,0.0006511115,0.0013229564,0.001484704,0.00014088048,0.00014683745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058563237,0.00021080274,0.0000953309,0.000008745912,0.000011076773,0.000015743664,0.00080601295,0.30213115,0.022796325,0.08995192,0.00016727457,0.5837998],"study_design_scores_gemma":[0.0003379118,0.000019228606,0.00046624316,0.0000033238812,0.0000020599534,0.0000017353277,0.000009638679,0.96995616,0.022469493,0.0064531467,0.00010182715,0.00017925214],"about_ca_topic_score_codex":0.000021211987,"about_ca_topic_score_gemma":0.0000012334466,"teacher_disagreement_score":0.667825,"about_ca_system_score_codex":0.000122003854,"about_ca_system_score_gemma":0.0002952116,"threshold_uncertainty_score":0.99936855},"labels":[],"label_agreement":null},{"id":"W2782154123","doi":"10.5539/cis.v11n1p26","title":"Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner","year":2018,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":8,"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 Regina","funders":"","keywords":"Firefly algorithm; Metaheuristic; Computer science; Algorithm; Benchmark (surveying); Firefly protocol; Mathematical optimization; Fuzzy logic; Artificial intelligence; Mathematics","score_opus":0.028264744141957353,"score_gpt":0.3069744063316145,"score_spread":0.27870966218965715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782154123","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044431454,0.000009044149,0.99126345,0.00014290331,0.00077922957,0.00015747457,0.0000020187006,0.000100624675,0.003102117],"genre_scores_gemma":[0.10902154,0.000015664265,0.8896043,0.0011701849,0.00014152461,0.0000047980748,0.0000018376978,0.0000030371614,0.000037153986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981193,0.000039743412,0.00036383976,0.00030970055,0.0007791056,0.00038831873],"domain_scores_gemma":[0.9982121,0.000080589634,0.00012460146,0.00049035484,0.0008718369,0.0002205271],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00096025784,0.0001275339,0.00013412694,0.00048072202,0.000539033,0.0013160232,0.0010286758,0.000039691186,0.00002672858],"category_scores_gemma":[0.00013453257,0.00010788017,0.000025158693,0.001646447,0.0006420373,0.011546657,0.0007438275,0.00010100536,0.00016362235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001854734,0.000013959494,0.00001760693,0.000008586715,0.000004191449,7.1462574e-7,0.0019621013,0.0003565003,0.0002710426,0.009807444,0.00032120544,0.9872348],"study_design_scores_gemma":[0.00021073683,0.000086686305,0.0010031159,0.0000104053415,0.0000012734421,0.000025332969,0.000012542517,0.9918573,0.0030347775,0.0004815899,0.0031283705,0.00014787541],"about_ca_topic_score_codex":0.0000114682725,"about_ca_topic_score_gemma":1.3426674e-7,"teacher_disagreement_score":0.9915008,"about_ca_system_score_codex":0.000049898772,"about_ca_system_score_gemma":0.00022254449,"threshold_uncertainty_score":0.9997207},"labels":[],"label_agreement":null},{"id":"W2782645119","doi":"10.1016/j.swevo.2018.01.006","title":"Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm","year":2018,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation, United Arab Emirates; National Research Foundation","keywords":"Benchmark (surveying); Particle swarm optimization; Acceleration; Computer science; Parameter space; Mathematical optimization; Set (abstract data type); Swarm behaviour; Algorithm; Mathematics; Statistics","score_opus":0.01850253828105613,"score_gpt":0.2757660492307485,"score_spread":0.25726351094969235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782645119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025804618,0.00061761437,0.99188703,0.0037572177,0.00018367094,0.00086605584,0.000016388256,0.00005708999,0.00003447081],"genre_scores_gemma":[0.36504576,0.00013297438,0.6341393,0.00025195658,0.00012689336,0.00010623882,0.000011762922,0.00001178254,0.00017334743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982917,0.00032330502,0.00037040332,0.000367543,0.00039237033,0.0002547108],"domain_scores_gemma":[0.99562407,0.0032061436,0.00018509706,0.00030295824,0.00060201396,0.000079738486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010585163,0.00014401502,0.00020968556,0.000062369225,0.0006018355,0.000163543,0.00034450076,0.000060542337,0.000011011073],"category_scores_gemma":[0.00056204,0.0000918423,0.000064580156,0.00034934626,0.0008889138,0.00042269615,0.00015507857,0.000099560355,0.0000082555425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024007312,0.000103480204,0.00010849809,0.000024441002,0.00014087783,0.0000011435641,0.0012888286,0.9453217,0.000022372187,0.01008198,0.0019709554,0.04069569],"study_design_scores_gemma":[0.0018028324,0.0002119139,0.0007897868,0.0000116760275,0.00004913948,0.000028512846,0.000055724522,0.9852654,0.00011187506,0.011462913,0.00009924735,0.000110995104],"about_ca_topic_score_codex":0.000027020395,"about_ca_topic_score_gemma":5.7963524e-7,"teacher_disagreement_score":0.3624653,"about_ca_system_score_codex":0.000023556771,"about_ca_system_score_gemma":0.00007922319,"threshold_uncertainty_score":0.46288928},"labels":[],"label_agreement":null},{"id":"W2785532129","doi":"10.1109/ssci.2017.8285170","title":"Differential evolution with self-adaptive mutation scaling factor","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Benchmark (surveying); Differential evolution; Mutation; Adaptive mutation; Evolutionary algorithm; Mathematical optimization; Computer science; Scaling; Evolutionary computation; Factor (programming language); Algorithm; Mathematics; Genetic algorithm; Artificial intelligence","score_opus":0.026464571282159826,"score_gpt":0.28347648962304134,"score_spread":0.2570119183408815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785532129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012819169,0.000003634282,0.9827885,0.00024414263,0.00018384357,0.00018389945,0.0000017686682,0.00018687893,0.003588164],"genre_scores_gemma":[0.666589,0.0000014764436,0.3328626,0.000008388629,0.00004344205,0.000007978776,0.0000012633765,0.000005341532,0.00048050165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987631,0.00006082013,0.00013149933,0.00031735093,0.0005112964,0.00021594229],"domain_scores_gemma":[0.99879116,0.00005134072,0.00012556309,0.0006590975,0.00026862603,0.00010418533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000988843,0.00010038634,0.000106422995,0.00009643352,0.0005216798,0.00070690905,0.0007862149,0.000040157636,0.00014445219],"category_scores_gemma":[0.00009581998,0.00007442779,0.000025467201,0.00009739583,0.000050838753,0.00087442715,0.00021926008,0.00010076098,0.00008536085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016841828,0.00087345147,0.006985285,0.00008148007,0.00044013772,0.00020174812,0.0037186488,0.0062633334,0.001555083,0.6774788,0.00067101436,0.3015626],"study_design_scores_gemma":[0.0004494782,0.00007828263,0.034194913,0.000007488849,0.0000046032533,0.000008629387,0.000016992462,0.96305907,0.0014225347,0.0006097127,0.000027639098,0.000120631186],"about_ca_topic_score_codex":0.00007984086,"about_ca_topic_score_gemma":0.000010790085,"teacher_disagreement_score":0.95679575,"about_ca_system_score_codex":0.00008173631,"about_ca_system_score_gemma":0.00010559951,"threshold_uncertainty_score":0.681674},"labels":[],"label_agreement":null},{"id":"W2785593625","doi":"10.1109/ssci.2017.8285208","title":"Particle swarm optimization for large-scale clustering on apache spark","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Queen's University","funders":"","keywords":"SPARK (programming language); Cluster analysis; Computer science; Particle swarm optimization; Big data; Data mining; Canopy clustering algorithm; CURE data clustering algorithm; Correlation clustering; Algorithm; Machine learning","score_opus":0.05665801471600643,"score_gpt":0.33550272724398117,"score_spread":0.2788447125279747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785593625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035770514,0.0000081900125,0.9878991,0.0025500802,0.00033390496,0.00038200605,0.0000047453423,0.00013662106,0.008327607],"genre_scores_gemma":[0.12067559,0.000014489146,0.87388366,0.00033365915,0.000110930756,0.00007471931,0.0000051290735,0.000017269389,0.0048845294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986179,0.00004659309,0.00020352172,0.00039867207,0.00034019887,0.0003931004],"domain_scores_gemma":[0.99826014,0.00012515063,0.000109898894,0.0011683732,0.00018446274,0.00015198192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007154464,0.00011135221,0.00013745218,0.000056467114,0.00068526214,0.00082261977,0.001128332,0.00005089742,0.00014843607],"category_scores_gemma":[0.00052660157,0.000098182805,0.000054039552,0.000099966375,0.00003077599,0.0005573224,0.00039593378,0.000079773716,0.00009023414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046781628,0.0003262562,0.00051405234,0.000043789027,0.000030850446,0.000006648058,0.00049131573,0.90835667,0.00007971368,0.05071148,0.0032563217,0.036136124],"study_design_scores_gemma":[0.0007139072,0.00008215578,0.00025357134,0.000008186168,0.0000029936816,0.0000015616034,0.000016886248,0.9936182,0.0029657765,0.00023763184,0.0019743966,0.00012474977],"about_ca_topic_score_codex":0.0000096004815,"about_ca_topic_score_gemma":0.000013574255,"teacher_disagreement_score":0.120317884,"about_ca_system_score_codex":0.00003542179,"about_ca_system_score_gemma":0.00004130805,"threshold_uncertainty_score":0.79325414},"labels":[],"label_agreement":null},{"id":"W2786860463","doi":"10.1109/ssci.2017.8280865","title":"Enhancing discrete differential evolution by conducting election","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Population; Evolutionary algorithm; Benchmark (surveying); Computer science; Voting; Algorithm; Mathematical optimization; Differential evolution; Optimization problem; Artificial intelligence; Mathematics; Law","score_opus":0.03544235075367868,"score_gpt":0.3211389056219243,"score_spread":0.28569655486824563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786860463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00561516,0.000014392642,0.98645073,0.00063078554,0.0005975436,0.00011128188,9.730732e-7,0.00012889996,0.006450211],"genre_scores_gemma":[0.91126746,0.0000045522147,0.07744875,0.000019150655,0.00011811644,0.000010009166,0.0000039284123,0.000007596507,0.011120407],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987711,0.00007596652,0.00018216169,0.0003329486,0.00036840275,0.00026946358],"domain_scores_gemma":[0.9988944,0.000047586676,0.0001368743,0.0006934647,0.00013410416,0.000093557064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031760556,0.0000906518,0.00010625638,0.00006996044,0.0008618419,0.00088915264,0.0008246627,0.00004488255,0.00028810857],"category_scores_gemma":[0.0005107783,0.0000792286,0.00003505219,0.00009192971,0.000050641687,0.0009963126,0.00033563664,0.0001373685,0.00011221586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024087767,0.00022034513,0.0023271816,0.00006923749,0.00012463363,0.000014179125,0.00056551595,0.00033611074,0.679342,0.17629234,0.020695098,0.11998931],"study_design_scores_gemma":[0.00035631505,0.000045849065,0.0013018001,0.00001024169,0.000004346841,0.00000847561,0.000020847816,0.8749608,0.12152459,0.0010763675,0.00050137914,0.00018902405],"about_ca_topic_score_codex":0.00022483256,"about_ca_topic_score_gemma":0.000020437905,"teacher_disagreement_score":0.909002,"about_ca_system_score_codex":0.000072828865,"about_ca_system_score_gemma":0.000051124476,"threshold_uncertainty_score":0.8574119},"labels":[],"label_agreement":null},{"id":"W2786922041","doi":"10.1109/ssci.2017.8280938","title":"Differential evolution with center-based mutation for large-scale optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Differential evolution; Mutation; Crossover; Mathematical optimization; Convergence (economics); Dimension (graph theory); Scheme (mathematics); Computer science; Optimization problem; Algorithm; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.020182096716315913,"score_gpt":0.2914879912554281,"score_spread":0.2713058945391122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786922041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004163468,0.0000022408678,0.9965563,0.0009302829,0.00024102598,0.00052114885,0.000016650636,0.0001279341,0.0011880789],"genre_scores_gemma":[0.29844257,0.0000010013323,0.7006261,0.00003968498,0.00005238229,0.0000684276,0.000060581995,0.000011225162,0.00069806323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987171,0.000049893497,0.00017606503,0.00037067535,0.00039990077,0.00028636173],"domain_scores_gemma":[0.99848056,0.000058465448,0.000167607,0.0007642707,0.00042946142,0.000099607256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002069224,0.00011428091,0.00012329502,0.00012502441,0.00066789275,0.0006882876,0.00073214463,0.0000515302,0.00014028374],"category_scores_gemma":[0.00015028422,0.00009220138,0.000043897497,0.00010470338,0.000040122275,0.0007012192,0.00010898338,0.00005963696,0.000012986117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026141023,0.0012169925,0.0053897635,0.00013061489,0.00007490725,0.000013145179,0.0002945264,0.8888703,0.00017185106,0.083931066,0.0018431207,0.017802328],"study_design_scores_gemma":[0.0021199328,0.00013683672,0.0018051751,0.000011236624,0.0000064062506,0.000002718757,0.000009637398,0.99475384,0.0006717638,0.00018412425,0.00017368815,0.00012465032],"about_ca_topic_score_codex":0.00001512423,"about_ca_topic_score_gemma":0.00002579123,"teacher_disagreement_score":0.29802623,"about_ca_system_score_codex":0.00006474317,"about_ca_system_score_gemma":0.000114605085,"threshold_uncertainty_score":0.6637173},"labels":[],"label_agreement":null},{"id":"W2787282612","doi":"10.1109/ssci.2017.8280800","title":"A sugeno-based search width decay schedule in the ACO&lt;inf&gt;R&lt;/inf&gt; algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brandon University","funders":"","keywords":"Parameterized complexity; Ant colony optimization algorithms; Algorithm; Computer science; Artificial intelligence; Schedule; Mathematics","score_opus":0.0404433890114737,"score_gpt":0.3231557445284848,"score_spread":0.28271235551701107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787282612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001660058,0.00013856278,0.9644323,0.009135328,0.00040239998,0.0009551458,0.000019338506,0.00018128323,0.023075594],"genre_scores_gemma":[0.12604663,0.00010255509,0.8659129,0.001318006,0.00028079693,0.00018738637,0.00002818865,0.00005908001,0.006064431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939309,0.00079011224,0.00064468005,0.0010718369,0.0023265155,0.0012359907],"domain_scores_gemma":[0.9939261,0.0008045121,0.00021238932,0.004002637,0.00064227637,0.00041204953],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0049321093,0.0003971977,0.00045482107,0.0005348054,0.0011109976,0.0025685003,0.007389896,0.0001976275,0.0006871411],"category_scores_gemma":[0.0009144802,0.00028743665,0.00016916776,0.0010869176,0.00042709016,0.001022153,0.0013022454,0.0006792905,0.0008329488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007571814,0.0017041708,0.0034721564,0.0001611886,0.00016352622,0.00095298875,0.0022648186,0.015521347,0.0013581681,0.14786683,0.017307634,0.8091515],"study_design_scores_gemma":[0.0014876504,0.00015397745,0.0053496994,0.000029218145,0.000007862176,0.000022142796,0.000032576765,0.9771426,0.0025098645,0.00081108033,0.012052915,0.00040041632],"about_ca_topic_score_codex":0.00023902158,"about_ca_topic_score_gemma":0.00015805433,"teacher_disagreement_score":0.9616212,"about_ca_system_score_codex":0.00014471156,"about_ca_system_score_gemma":0.0009987283,"threshold_uncertainty_score":0.9999578},"labels":[],"label_agreement":null},{"id":"W2789395900","doi":"10.5267/j.dsl.2018.2.001","title":"A new MCDM-based approach using BWM and SAW for optimal search model","year":2018,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":40,"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":"Multiple-criteria decision analysis; Mathematical optimization; Computer science; Operations research; Mathematics; Management science; Engineering","score_opus":0.09725112470109384,"score_gpt":0.375205683065182,"score_spread":0.2779545583640881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789395900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030108346,0.000011535431,0.96742487,0.0017029257,0.00018770417,0.00041097868,0.0000033773094,0.00005914847,0.00009113196],"genre_scores_gemma":[0.06684596,0.0000012129989,0.93097275,0.0019999626,0.00009579302,0.000009417993,8.159955e-7,0.000011960376,0.00006209535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612606,0.000066888664,0.00030570597,0.0010311217,0.0017742099,0.0006960125],"domain_scores_gemma":[0.997703,0.00045826266,0.000070033064,0.00085051346,0.00045670537,0.00046149653],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0035307992,0.00016399944,0.00019488487,0.00074203225,0.0006028312,0.0011274302,0.00209037,0.000049737886,0.000015771226],"category_scores_gemma":[0.00077240664,0.00014093738,0.000053553962,0.0021741576,0.0008066626,0.0010666598,0.0006306207,0.00014130857,0.000019464946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008927983,0.00006570901,0.00009349671,0.000015389922,0.000006811859,0.0000067620563,0.0010441806,0.76713276,0.047909405,0.0052572466,0.0051202634,0.17325868],"study_design_scores_gemma":[0.0005513061,0.00006169713,0.000069846654,0.000012134462,0.0000026090709,0.000013125885,0.000013544105,0.9952853,0.0032629126,0.0003160855,0.00024114222,0.00017032336],"about_ca_topic_score_codex":0.000019623052,"about_ca_topic_score_gemma":3.7632032e-7,"teacher_disagreement_score":0.2281525,"about_ca_system_score_codex":0.00010449685,"about_ca_system_score_gemma":0.0007461611,"threshold_uncertainty_score":0.9999095},"labels":[],"label_agreement":null},{"id":"W2790288482","doi":"10.1093/imaman/dpy002","title":"The alpha male genetic algorithm","year":2018,"lang":"en","type":"article","venue":"IMA Journal of Management Mathematics","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Alpha (finance); Computer science; Algorithm; Mathematics; Statistics","score_opus":0.01650732607309121,"score_gpt":0.2827583594331851,"score_spread":0.2662510333600939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790288482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015327359,0.00023926087,0.9873584,0.0010386561,0.0005374089,0.00017281706,4.9352246e-7,0.000018203042,0.010481477],"genre_scores_gemma":[0.0008768142,0.0004544394,0.9929717,0.00010885769,0.00022432818,0.0000040533623,9.879675e-8,0.0000126488085,0.005347014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787986,0.000090806185,0.00062781,0.00013376793,0.00097455556,0.0002931927],"domain_scores_gemma":[0.9980488,0.00023272862,0.00043314655,0.00063369295,0.0005382931,0.000113341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015632556,0.000117678246,0.00017861211,0.00018133619,0.00024811,0.00046061614,0.0019443623,0.000024414272,0.00012932428],"category_scores_gemma":[0.000143869,0.00007541856,0.000086149834,0.00043960378,0.00014345598,0.00024350973,0.00046125677,0.00013686503,0.00023835491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005641161,0.00026223273,0.000017293214,0.00008481218,0.00030566397,0.00034189102,0.0006764136,0.00024312694,0.000012909158,0.06235579,0.036065806,0.8996284],"study_design_scores_gemma":[0.0006402656,0.0002712239,0.00028767376,0.00007045729,0.000050106424,0.0007099775,0.00021274512,0.90990245,0.0002619637,0.032453507,0.054965593,0.00017402269],"about_ca_topic_score_codex":3.706808e-7,"about_ca_topic_score_gemma":1.984614e-7,"teacher_disagreement_score":0.9096593,"about_ca_system_score_codex":0.000041769366,"about_ca_system_score_gemma":0.000040556042,"threshold_uncertainty_score":0.44417322},"labels":[],"label_agreement":null},{"id":"W2791361378","doi":"10.1142/s1469026818500025","title":"Focus Group: An Optimization Algorithm Inspired by Human Behavior","year":2018,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Metaheuristic Optimization Algorithms Research","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 Regina","funders":"Shahid Rajaee Teacher Training University","keywords":"Computer science; Benchmark (surveying); Evolutionary algorithm; Genetic algorithm; Focus (optics); Mathematical optimization; Algorithm; Optimization problem; Optimization algorithm; Meta-optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.03366772749157315,"score_gpt":0.3608316276317359,"score_spread":0.3271639001401627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791361378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040299175,0.00012210068,0.9978684,0.00074422685,0.0002327199,0.00024149299,0.000032288215,0.000044228404,0.00031156148],"genre_scores_gemma":[0.2451136,0.00010231126,0.7535673,0.00028466733,0.00065415294,0.00006692931,0.00011365341,0.000017115475,0.00008029005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977195,0.0000946395,0.0007449507,0.00032068853,0.0009438733,0.0001763082],"domain_scores_gemma":[0.99626964,0.00015267324,0.00045577277,0.00022668768,0.0026696548,0.00022554626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048578143,0.00015485346,0.0001762852,0.00040350188,0.00025895808,0.0004548098,0.0014244854,0.00006951829,0.00015151208],"category_scores_gemma":[0.000049900664,0.00015246039,0.000064790336,0.00044008487,0.00024984367,0.0011476581,0.00017650115,0.00018838194,0.000024679961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012662276,0.00077253295,0.0001322849,0.0000031138356,0.000100023266,0.000014114351,0.0002885039,0.071022965,0.00024037683,0.112478726,0.00076283264,0.81417185],"study_design_scores_gemma":[0.00033195267,0.0004376253,0.00039967953,0.000019057587,0.000020598214,0.00026507687,0.00007176557,0.9452462,0.0011784087,0.045512956,0.0062802825,0.00023638963],"about_ca_topic_score_codex":0.000021860658,"about_ca_topic_score_gemma":0.0000027564045,"teacher_disagreement_score":0.87422323,"about_ca_system_score_codex":0.00007687769,"about_ca_system_score_gemma":0.00011206957,"threshold_uncertainty_score":0.6217154},"labels":[],"label_agreement":null},{"id":"W2793816938","doi":"10.1007/s10462-018-9616-4","title":"A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":61,"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":"Chaotic; Robustness (evolution); Computer science; Scalability; Evolutionary algorithm; Algorithm; Mathematical optimization; Convergence (economics); Heuristic; Mathematics; Machine learning; Artificial intelligence","score_opus":0.05226224469424341,"score_gpt":0.31553211472671294,"score_spread":0.26326987003246954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793816938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028992869,0.010202413,0.95754945,0.0018054463,0.00023808386,0.0010121603,0.0000037144657,0.000056931847,0.00013895056],"genre_scores_gemma":[0.75907737,0.034724873,0.20519826,0.00067834434,0.00012261477,0.0001287608,0.000010686189,0.000017555401,0.000041545554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974871,0.0003582314,0.00071014435,0.0005341565,0.00061484403,0.00029549462],"domain_scores_gemma":[0.9970481,0.0003172682,0.0002824339,0.0007113337,0.0014960621,0.00014482037],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010753758,0.00020065243,0.00037496525,0.00008727092,0.00040058364,0.0000847022,0.0007506917,0.000053754913,0.00018600607],"category_scores_gemma":[0.00034808659,0.00013619974,0.000047352256,0.00138952,0.0038862512,0.00053203455,0.0002762767,0.00020683915,0.000063112406],"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":[0.00002170759,0.00008082291,0.00036448592,0.0015916084,0.000046052188,0.0000027676372,0.0015311864,0.0006583838,0.0004669335,0.014316217,0.00012426973,0.98079556],"study_design_scores_gemma":[0.00005835778,0.00047909183,0.0015406123,0.0017903185,0.00003853389,0.000050060993,0.00024024463,0.9688447,0.020966956,0.0045246305,0.0011256137,0.0003408737],"about_ca_topic_score_codex":0.000056584817,"about_ca_topic_score_gemma":0.000014726929,"teacher_disagreement_score":0.9804547,"about_ca_system_score_codex":0.00004793389,"about_ca_system_score_gemma":0.0004422847,"threshold_uncertainty_score":0.9988246},"labels":[],"label_agreement":null},{"id":"W2795749052","doi":"10.1007/s40747-018-0071-2","title":"A quarter century of particle swarm optimization","year":2018,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":70,"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":"Fundamental Research Funds for the Central Universities; Shenzhen Municipal Science and Technology Innovation Council; National Natural Science Foundation of China","keywords":"Quarter (Canadian coin); Computational intelligence; Particle swarm optimization; Particle (ecology); Swarm intelligence; Computer science; Artificial intelligence; Biology; Geography; Algorithm","score_opus":0.06197484055818507,"score_gpt":0.3127475510839839,"score_spread":0.2507727105257988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795749052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012051598,0.00023716423,0.9940117,0.00018641734,0.0010919446,0.0004469062,0.0000056061144,0.00013067428,0.0026844318],"genre_scores_gemma":[0.91050845,0.00005250825,0.08837448,0.000068908994,0.00025742504,0.000035770478,0.000011661777,0.000018518756,0.00067226647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976583,0.0002727967,0.0006717455,0.00040429423,0.0006236017,0.00036922167],"domain_scores_gemma":[0.99781686,0.00012815326,0.00022353642,0.0008688115,0.0007875138,0.00017514902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062263146,0.00015590686,0.00028379975,0.00016127218,0.00012274524,0.00019600532,0.0009760821,0.000057933787,0.0003635752],"category_scores_gemma":[0.00013960205,0.00014116937,0.000075471835,0.00075580133,0.00015734114,0.00025343223,0.00022888194,0.00008525391,0.00043165815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012063338,0.0015018019,0.002533301,0.00060191925,0.0004183191,0.000036809502,0.010802561,0.531874,0.006380065,0.37264732,0.047131646,0.025951607],"study_design_scores_gemma":[0.00016496998,0.00018223819,0.000116717194,0.000035890313,0.00000536889,0.00001342096,0.00015323884,0.9861528,0.005157682,0.000063652944,0.007811869,0.00014213119],"about_ca_topic_score_codex":0.00006117872,"about_ca_topic_score_gemma":0.000002857851,"teacher_disagreement_score":0.9093033,"about_ca_system_score_codex":0.00006420322,"about_ca_system_score_gemma":0.0000694508,"threshold_uncertainty_score":0.575672},"labels":[],"label_agreement":null},{"id":"W2799537251","doi":"10.1139/tcsme-2002-0012","title":"EXHAUSTIVE SEARCH APPROXIMATIONS IN DESIGN OPTIMIZATION: AN ALGORITHMIC IMPLEMENTATION","year":2002,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Convergence (economics); Extension (predicate logic); Computer science; Sensitivity (control systems); Variance (accounting); Optimization problem; Algorithm; Mathematics; Engineering","score_opus":0.047804559404910366,"score_gpt":0.275645289553464,"score_spread":0.22784073014855366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799537251","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004176755,0.000020318808,0.99793285,0.0009802785,0.00013786407,0.0007932531,0.00003585802,0.00004398114,0.000013806568],"genre_scores_gemma":[0.1157625,0.000017351014,0.883901,0.00004904472,0.000019297477,0.00015736636,0.000006623774,0.000017449165,0.0000693978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880916,0.00004394338,0.00028367364,0.00023877791,0.0002812679,0.00034318975],"domain_scores_gemma":[0.9991147,0.00011307304,0.000037081536,0.00036291056,0.00016323796,0.00020899373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005425209,0.000108656015,0.00013289081,0.00015921897,0.00025461018,0.00007660027,0.00063021114,0.000089383124,0.00016550499],"category_scores_gemma":[0.000032198994,0.000107492124,0.00016272182,0.0010229967,0.00002728015,0.00040275682,0.000013478732,0.0002109776,0.0000012207012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.861605e-7,0.00002518236,5.421235e-7,0.000016359594,0.000022863846,2.0387823e-7,0.00056258164,0.9905411,0.0000952418,0.0040670275,0.000041441624,0.0046268445],"study_design_scores_gemma":[0.00038801506,0.000047521815,0.0000102743425,0.000009856196,0.000011158696,0.0000034631125,0.00013817905,0.9976495,0.0014563244,0.00011348552,0.00006684895,0.000105365005],"about_ca_topic_score_codex":0.0023594922,"about_ca_topic_score_gemma":0.001881686,"teacher_disagreement_score":0.115720734,"about_ca_system_score_codex":0.00039601678,"about_ca_system_score_gemma":0.000207358,"threshold_uncertainty_score":0.43834016},"labels":[],"label_agreement":null},{"id":"W2800623967","doi":"10.1108/ijicc-05-2017-0050","title":"Synchronous self-learning Pareto strategy","year":2018,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","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; Evolutionary algorithm; Mathematical optimization; Pareto principle; Artificial intelligence; Robustness (evolution); Multi-objective optimization; Benchmark (surveying); Machine learning; Mathematics","score_opus":0.016422321517909722,"score_gpt":0.30956461577052047,"score_spread":0.29314229425261074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800623967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03935095,0.00031945077,0.9564628,0.0003976776,0.0012276794,0.000047380705,5.165558e-7,0.000048507667,0.0021450536],"genre_scores_gemma":[0.8670905,0.00035306002,0.13160722,0.00010545622,0.00068187143,2.183627e-7,6.965565e-7,0.000009219536,0.00015173946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812067,0.00013666501,0.0005736978,0.00019536712,0.0007636173,0.00021000447],"domain_scores_gemma":[0.99719137,0.00026375474,0.00038520645,0.0001400549,0.0018598619,0.0001597809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078075496,0.00012449166,0.00017280267,0.00025532127,0.00010077686,0.00044756386,0.0010670971,0.000051112216,0.00004981945],"category_scores_gemma":[0.0003365422,0.00011078004,0.00006417366,0.00018378989,0.000092566326,0.00016641918,0.0003496654,0.00032480605,0.00003735211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040928782,0.00033231374,0.004423932,0.000018481369,0.0005496929,0.00028228902,0.004424448,0.032994006,0.00016909983,0.03821129,0.0011298397,0.91742367],"study_design_scores_gemma":[0.00030783674,0.0006182008,0.001308956,0.0000908823,0.00001227982,0.00069717853,0.00017197298,0.9835835,0.001474233,0.001525523,0.010046094,0.00016336165],"about_ca_topic_score_codex":0.000010292382,"about_ca_topic_score_gemma":0.0000011951865,"teacher_disagreement_score":0.9505895,"about_ca_system_score_codex":0.000082165425,"about_ca_system_score_gemma":0.00014709786,"threshold_uncertainty_score":0.4517479},"labels":[],"label_agreement":null},{"id":"W2802761309","doi":"10.1016/j.aci.2018.04.001","title":"Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems","year":2018,"lang":"en","type":"article","venue":"Applied Computing and Informatics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"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; Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Feature (linguistics); Algorithm; Feature selection; Binary number; Set (abstract data type); Binary search algorithm; Metaheuristic; Swarm behaviour; Hybrid algorithm (constraint satisfaction); Bat algorithm; Mathematical optimization; Meta-optimization; Selection (genetic algorithm); Feature vector; Search algorithm; Artificial intelligence; Mathematics; Constraint satisfaction","score_opus":0.012673804922401007,"score_gpt":0.25712613895544956,"score_spread":0.24445233403304856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802761309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049356245,0.000021089707,0.9928997,0.00011448702,0.00021130154,0.0005932339,0.0000025529316,0.0002753441,0.0009466706],"genre_scores_gemma":[0.141343,0.000020870946,0.85809034,0.00022115071,0.00017200112,0.000028940474,0.000020080282,0.000014249765,0.00008935548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855524,0.000021750404,0.00043822275,0.00023990068,0.00031240427,0.00043247594],"domain_scores_gemma":[0.9988234,0.00016585863,0.00022941253,0.00026052495,0.00039067346,0.00013009894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066141074,0.00017596183,0.00020058357,0.00012236225,0.0006076604,0.00045731908,0.00035376984,0.00007075724,0.0000059413564],"category_scores_gemma":[0.000072325834,0.00016818305,0.000031785763,0.00047522562,0.000081151215,0.00046855517,0.00027148204,0.00016290865,0.000017010547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007991357,0.000052873,0.0000061271903,0.00011103647,0.000033428514,2.4615815e-7,0.0029253776,0.2547369,0.0003615187,0.003012696,0.0014530116,0.7372988],"study_design_scores_gemma":[0.0006031686,0.00018428748,0.000010274913,0.000026929487,0.0000080948375,0.000013413976,0.00009211701,0.9826541,0.0151384855,0.0004422048,0.00062592194,0.00020102096],"about_ca_topic_score_codex":0.000002184688,"about_ca_topic_score_gemma":2.2085038e-7,"teacher_disagreement_score":0.73709774,"about_ca_system_score_codex":0.000045971265,"about_ca_system_score_gemma":0.000072420225,"threshold_uncertainty_score":0.6858306},"labels":[],"label_agreement":null},{"id":"W2809652621","doi":"10.5539/mas.v12n7p73","title":"Collaborative Strategy for Grey Wolf Optimization Algorithm","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Particle swarm optimization; Algorithm; Computer science; Set (abstract data type); Mathematical optimization; Metaheuristic; Feature (linguistics); Heuristics; Search algorithm; Mathematics","score_opus":0.027697085923670064,"score_gpt":0.31303267740908164,"score_spread":0.2853355914854116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809652621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000312516,0.000021432501,0.98633057,0.00025271124,0.00030187308,0.0008942697,0.00001714069,0.00019644656,0.011954332],"genre_scores_gemma":[0.07493291,0.000008517229,0.92389613,0.00021663298,0.0001500453,0.00018109684,0.0000068438294,0.000016742351,0.00059104955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682564,0.000040949544,0.00032217882,0.0010267395,0.0010903626,0.0006941106],"domain_scores_gemma":[0.9967766,0.0001237534,0.00014743478,0.00081283384,0.0018573792,0.00028200244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001503462,0.0001981897,0.00020725491,0.00036677948,0.000914761,0.0010024998,0.0021271927,0.00007015024,0.000059830723],"category_scores_gemma":[0.00022874416,0.00018607503,0.00003121151,0.0031509227,0.0009664283,0.00096930936,0.00041208204,0.00011632971,0.00008057374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034149736,0.00018952796,0.0000056099257,0.000017222386,0.00002062089,0.0000037357818,0.0025021033,0.23428217,0.014335825,0.12584685,0.0012966874,0.6214655],"study_design_scores_gemma":[0.00042650744,0.00012898442,0.000022331536,0.0000035950386,0.0000029642767,0.0000029424232,0.00005072695,0.98178697,0.0081729265,0.008668626,0.0005114743,0.00022196969],"about_ca_topic_score_codex":0.000004308522,"about_ca_topic_score_gemma":0.0000022088718,"teacher_disagreement_score":0.7475048,"about_ca_system_score_codex":0.00013528783,"about_ca_system_score_gemma":0.0010192061,"threshold_uncertainty_score":0.96671295},"labels":[],"label_agreement":null},{"id":"W2882970722","doi":"10.1007/s40092-018-0282-6","title":"An improved particle swarm optimization with a new swap operator for team formation problem","year":2018,"lang":"en","type":"article","venue":"Journal of industrial engineering international","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Swap (finance); Mathematical optimization; Computer science; Multi-swarm optimization; Operator (biology); Benchmark (surveying); Swarm intelligence; Metaheuristic; Algorithm; Mathematics","score_opus":0.02977743464231963,"score_gpt":0.2796475117979031,"score_spread":0.24987007715558346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2882970722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037116138,0.0000063056345,0.99415857,0.0008548604,0.00088540337,0.00027010316,0.000004294132,0.000042064636,0.00006680454],"genre_scores_gemma":[0.15736231,0.000004677681,0.8401557,0.000056221023,0.0022398927,0.000014127597,0.000008634571,0.000019408593,0.0001389962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998657,0.00002455129,0.0004993454,0.0001480289,0.0004791942,0.0001919181],"domain_scores_gemma":[0.99804634,0.00007228152,0.00028488727,0.00016332044,0.0012270286,0.00020612877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006464006,0.00011416886,0.0001506121,0.00022827825,0.00006180198,0.0003812036,0.00069323694,0.000075801814,0.000042639975],"category_scores_gemma":[0.00047614943,0.00009316286,0.00004661147,0.00031461727,0.000018372042,0.0018022263,0.000044700468,0.0001889255,0.0000028706932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016083056,0.000084679865,0.000053800253,0.0000044565613,0.00007145831,0.0000018884591,0.0002653011,0.98478574,0.0021767546,0.0017320339,0.0014345902,0.009228462],"study_design_scores_gemma":[0.0022805734,0.00096650765,0.000013449572,0.000045851953,0.000009189454,0.000068976035,0.000015773036,0.98023075,0.014086102,0.000029772746,0.0021451956,0.00010788488],"about_ca_topic_score_codex":0.000006726244,"about_ca_topic_score_gemma":0.0000012078041,"teacher_disagreement_score":0.15400283,"about_ca_system_score_codex":0.00014746493,"about_ca_system_score_gemma":0.00037510315,"threshold_uncertainty_score":0.37990713},"labels":[],"label_agreement":null},{"id":"W2883655384","doi":"10.5539/mas.v12n8p142","title":"Travelling Salesman Problem Solution Based-on Grey Wolf Algorithm over Hypercube Interconnection Network","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Hypercube; Travelling salesman problem; Computer science; Benchmark (surveying); Speedup; Algorithm; Interconnection; Mathematical optimization; Path (computing); Parallel computing; Mathematics","score_opus":0.022408285008274048,"score_gpt":0.2692940664572157,"score_spread":0.24688578144894166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883655384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069930573,0.000012837412,0.9835976,0.0002900428,0.00062575156,0.00049909175,0.0000021940027,0.0003047457,0.0139684435],"genre_scores_gemma":[0.4851643,0.000005123234,0.51375115,0.0004829195,0.00034918613,0.00005042736,0.0000031248899,0.000018608589,0.00017514196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99574,0.00008873224,0.00039857833,0.0012766022,0.0015337542,0.00096231094],"domain_scores_gemma":[0.9979881,0.00013525631,0.00014807937,0.001004817,0.00041986746,0.0003038897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026037996,0.00026299278,0.00022900531,0.00045133973,0.0010977896,0.00073109264,0.001851263,0.00010223325,0.00008034919],"category_scores_gemma":[0.000083059495,0.0002486197,0.000057188943,0.002634818,0.0007564721,0.00071806484,0.00040376905,0.00033317105,0.0002715135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043710632,0.0002529385,0.00005557853,0.00002290586,0.000012728299,0.000009381618,0.0013652929,0.09779345,0.026066337,0.031604856,0.0009738853,0.84179896],"study_design_scores_gemma":[0.00034120833,0.0001490915,0.00030944377,0.000028621207,0.000003870381,0.000007420698,0.000008048209,0.985163,0.0038048127,0.009339512,0.0005615825,0.00028340623],"about_ca_topic_score_codex":0.000023285862,"about_ca_topic_score_gemma":0.000011805543,"teacher_disagreement_score":0.8873695,"about_ca_system_score_codex":0.0002789443,"about_ca_system_score_gemma":0.00036120333,"threshold_uncertainty_score":0.9999966},"labels":[],"label_agreement":null},{"id":"W2885803011","doi":"10.1007/s00500-018-3448-7","title":"A self-feedback strategy differential evolution with fitness landscape analysis","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"National Natural Science Foundation of China","keywords":"Fitness landscape; Mathematical optimization; Differential evolution; Computer science; Evolutionary algorithm; Fitness approximation; Convergence (economics); Global optimization; Range (aeronautics); Fitness function; Genetic algorithm; Mathematics; Engineering","score_opus":0.013702865392779696,"score_gpt":0.26663239255909643,"score_spread":0.2529295271663167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885803011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045128353,0.000028366121,0.95239097,0.000082776256,0.00017366237,0.00014319993,0.0000016804595,0.00037180728,0.0016791564],"genre_scores_gemma":[0.8213296,0.0000010986001,0.17822778,0.000020397329,0.0002567624,0.0000025325917,0.000008399663,0.000010643876,0.00014276584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787885,0.00017526594,0.00028609097,0.00057027885,0.0006237113,0.0004657809],"domain_scores_gemma":[0.99842995,0.00015522903,0.00014938504,0.00058487273,0.0005273381,0.00015320502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038629957,0.00017750659,0.00027399525,0.0004004796,0.0003596322,0.00043330085,0.00080702617,0.00006342177,0.00014386926],"category_scores_gemma":[0.000058691858,0.00014670489,0.000079905265,0.002730875,0.00008689559,0.00025333773,0.00035619517,0.00017325823,0.00009690333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021845836,0.0018261947,0.22108647,0.0004228033,0.0066755945,0.00020834446,0.008913548,0.43854862,0.00041622898,0.10065169,0.0025583722,0.21847369],"study_design_scores_gemma":[0.00041942354,0.00013709407,0.02401403,0.000010656666,0.00007610276,0.000010202299,0.000034916666,0.9748153,0.000056329296,0.00016864852,0.000068041794,0.00018926013],"about_ca_topic_score_codex":0.000029408578,"about_ca_topic_score_gemma":0.000018943821,"teacher_disagreement_score":0.77620125,"about_ca_system_score_codex":0.00005949856,"about_ca_system_score_gemma":0.00016398408,"threshold_uncertainty_score":0.59824514},"labels":[],"label_agreement":null},{"id":"W2886040690","doi":"10.1016/j.amc.2018.07.037","title":"Estimation distribution algorithms on constrained optimization problems","year":2018,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":32,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"EDAS; Estimation of distribution algorithm; Mathematical optimization; Mathematics; Selection (genetic algorithm); Benchmark (surveying); Evolutionary algorithm; Optimization problem; Algorithm; Gaussian; Computer science; Artificial intelligence","score_opus":0.025235277532960235,"score_gpt":0.2853783659093228,"score_spread":0.2601430883763626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886040690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005119845,0.0000063599555,0.9948341,0.00029190732,0.00010483254,0.00052267895,0.0000068709487,0.00019697881,0.0035242657],"genre_scores_gemma":[0.1841349,0.000010661644,0.8155286,0.000073050425,0.000051394585,0.00004717516,0.00011642314,0.000012204002,0.000025635976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859434,0.000024034201,0.0003543499,0.0003623414,0.00043000627,0.00023493815],"domain_scores_gemma":[0.9990533,0.00017764134,0.00018760048,0.00024534026,0.00023858428,0.00009754312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005076731,0.0001602002,0.00017115746,0.000119116085,0.000290527,0.00031950933,0.00020829118,0.00007135017,0.00001844934],"category_scores_gemma":[0.000088278815,0.00015050062,0.00002149624,0.00046497627,0.00012378828,0.00019737486,0.00010649643,0.000091979215,0.0000641757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047548797,0.00015609903,6.2173314e-7,0.000062248655,0.000016179752,8.972008e-7,0.0006274814,0.33533484,0.00010257097,0.39307794,0.00023699756,0.27037936],"study_design_scores_gemma":[0.00042254414,0.000141703,0.000018798743,0.000026841926,0.0000074672853,0.00001008216,0.000032007836,0.9691494,0.00053552666,0.029449085,0.000054518685,0.00015202067],"about_ca_topic_score_codex":0.0000019858196,"about_ca_topic_score_gemma":2.839668e-7,"teacher_disagreement_score":0.6338146,"about_ca_system_score_codex":0.00004515463,"about_ca_system_score_gemma":0.000045526438,"threshold_uncertainty_score":0.61372375},"labels":[],"label_agreement":null},{"id":"W2889022283","doi":"10.1109/ccece.2018.8447811","title":"IIR Filter Design Using Constrained Multiobjective Cuckoo Search Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Passband; Infinite impulse response; Stopband; Cuckoo search; Cuckoo; Computer science; Finite impulse response; Minification; Algorithm; Digital filter; Filter (signal processing); Control theory (sociology); Mathematical optimization; Mathematics; Band-pass filter; Electronic engineering; Engineering; Artificial intelligence; Particle swarm optimization","score_opus":0.09338677641159308,"score_gpt":0.3447761642855397,"score_spread":0.2513893878739466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889022283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017513715,0.00001290154,0.9928591,0.00024098478,0.00032613036,0.00048992137,0.0000060893517,0.00023259822,0.0056570987],"genre_scores_gemma":[0.012912429,0.0000045692577,0.9842148,0.0002745312,0.00022025954,0.000013428579,0.0000022681718,0.000018994484,0.0023387482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971875,0.00047203348,0.00031288055,0.0006425495,0.0007691561,0.00061586493],"domain_scores_gemma":[0.9975822,0.0003948484,0.000057871457,0.0006977113,0.0010077,0.00025969977],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001204785,0.00019474005,0.00021790544,0.00033706936,0.0003343036,0.00038022932,0.000984224,0.00009101577,0.0012968784],"category_scores_gemma":[0.0002725288,0.00017053226,0.00006056771,0.0010423409,0.00040724472,0.0005748398,0.00048368511,0.00021555861,0.0005665025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006049879,0.00062374794,0.00015969001,0.000033525474,0.00030776014,0.0002249413,0.0058162124,0.01376608,0.008766196,0.020410232,0.0061405273,0.9436906],"study_design_scores_gemma":[0.00056437374,0.00016149013,0.00007311881,0.000008523589,0.0000040529135,0.00005159989,0.000051476327,0.9780591,0.02002692,0.00048074254,0.00030877104,0.0002098465],"about_ca_topic_score_codex":0.000108892105,"about_ca_topic_score_gemma":0.0000018490471,"teacher_disagreement_score":0.964293,"about_ca_system_score_codex":0.000112612455,"about_ca_system_score_gemma":0.00044968256,"threshold_uncertainty_score":0.9996161},"labels":[],"label_agreement":null},{"id":"W2889120335","doi":"10.1109/access.2018.2868236","title":"Gradient Population Optimization: A Tensorflow-Based Heterogeneous Non-Von-Neumann Paradigm for Large-Scale Search","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Metaheuristic Optimization Algorithms Research","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":"Royal Military College of Canada","funders":"","keywords":"Computer science; Scale (ratio); Von Neumann architecture; Artificial intelligence; Cartography; Geography","score_opus":0.04404424430391283,"score_gpt":0.34496184066283403,"score_spread":0.3009175963589212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889120335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005259285,0.000023851806,0.99113715,0.00079665025,0.0012082633,0.0010754702,0.000037346203,0.00019825388,0.00026372456],"genre_scores_gemma":[0.62382007,0.000014341641,0.37366346,0.00076200394,0.000674493,0.00031128468,0.00011740715,0.00006221161,0.0005747097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698496,0.00016055765,0.00048174307,0.0008305427,0.000791407,0.00075077335],"domain_scores_gemma":[0.9975945,0.0001985273,0.00014509918,0.0010575413,0.0007208244,0.00028351968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007471159,0.00024717,0.0003003958,0.00040299617,0.0005680085,0.00090058387,0.0018921813,0.00012639802,0.00016543934],"category_scores_gemma":[0.00008418294,0.00023874988,0.00012646744,0.0010630199,0.000081597456,0.0008413514,0.00023889042,0.00014928174,0.000094028874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057606896,0.00025931004,0.0009922219,0.00006911982,0.00003400741,0.000014046531,0.00029505574,0.99301183,0.00003475771,0.0006008186,0.0021830606,0.002448191],"study_design_scores_gemma":[0.0009898533,0.00021883729,0.0005567539,0.000022343682,0.000010566913,0.000010534359,0.0000026903213,0.991513,0.0053447546,0.0002163313,0.00083861366,0.00027570804],"about_ca_topic_score_codex":0.00006222779,"about_ca_topic_score_gemma":0.00002802652,"teacher_disagreement_score":0.6185608,"about_ca_system_score_codex":0.00011960319,"about_ca_system_score_gemma":0.00016239607,"threshold_uncertainty_score":0.9735938},"labels":[],"label_agreement":null},{"id":"W2889458022","doi":"10.1109/ccece.2018.8447876","title":"Length Scale-Based Differential Evolution","year":2018,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Metric (unit); Scale (ratio); Similarity (geometry); Mathematical optimization; Algorithm; Computer science; Mathematics; Differential evolution; Population; Function (biology); Artificial intelligence; Engineering","score_opus":0.01655239095856732,"score_gpt":0.2738917590475778,"score_spread":0.2573393680890105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889458022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063602213,0.0000038474645,0.9799181,0.0008405395,0.0004359937,0.000108139546,8.76015e-7,0.00021975733,0.017836716],"genre_scores_gemma":[0.6968549,6.2209847e-7,0.30118096,0.00012485469,0.00017015645,0.0000074699296,0.0000020752761,0.0000055265805,0.0016534562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875456,0.00009574224,0.00014808969,0.0002961122,0.00045875492,0.00024675252],"domain_scores_gemma":[0.99906576,0.00005962056,0.00003175596,0.0005088866,0.00021641595,0.00011758118],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020239087,0.00008006867,0.0000847679,0.00015098928,0.0001476384,0.00016444083,0.00064074795,0.000040995135,0.0011200155],"category_scores_gemma":[0.00007901704,0.00006641256,0.00003499372,0.0004503548,0.000110491965,0.00021032317,0.00024856246,0.00007346857,0.00074309425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036430574,0.00053427374,0.0015382862,0.000025951087,0.000036245787,0.000009107621,0.00020543177,0.0006035909,0.0023705806,0.88091755,0.025143022,0.08857955],"study_design_scores_gemma":[0.0003093809,0.00008358659,0.001803005,0.000002828491,0.0000016701434,0.000001566738,0.000002668737,0.99199355,0.002530745,0.0024683608,0.0007123143,0.000090304085],"about_ca_topic_score_codex":0.000022190088,"about_ca_topic_score_gemma":0.000009139692,"teacher_disagreement_score":0.99139,"about_ca_system_score_codex":0.00007882429,"about_ca_system_score_gemma":0.0001152517,"threshold_uncertainty_score":0.9997931},"labels":[],"label_agreement":null},{"id":"W2892078476","doi":"10.1007/s00500-018-3530-1","title":"Majority voting for discrete population-based optimization algorithms","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":79,"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":"Voting; Computer science; Algorithm; Population; Political science","score_opus":0.02588868168157974,"score_gpt":0.3187401715354348,"score_spread":0.29285148985385506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892078476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000469788,0.000019251693,0.99686307,0.0006163475,0.0008272028,0.0004962788,0.0000071924323,0.00044228742,0.0002586126],"genre_scores_gemma":[0.28323424,3.0034323e-7,0.7158931,0.00019008618,0.0005166367,0.000010390159,0.000046382334,0.000021797814,0.000087078835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997654,0.00015103002,0.00048360377,0.00064497686,0.0005211764,0.0005451921],"domain_scores_gemma":[0.9976815,0.00070017285,0.00024133548,0.0004897121,0.00073050463,0.00015680076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012156831,0.00019792693,0.0002350114,0.00022686306,0.00078285736,0.0004435177,0.0008310103,0.00008721649,0.00003926004],"category_scores_gemma":[0.0011453002,0.00020329442,0.0000962034,0.0008327154,0.00006585044,0.00036289473,0.00028138852,0.00014826855,0.000025100988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009788386,0.000049896684,0.005080122,0.000053907374,0.000022124175,0.000002218914,0.00019720792,0.9175349,0.00001435728,0.009062452,0.00028518293,0.06768784],"study_design_scores_gemma":[0.0005252714,0.000086429114,0.0014183886,0.00003457724,0.0000064569626,0.0000029127195,0.0000078205485,0.99687004,0.00017171254,0.00042753137,0.00021290577,0.00023594183],"about_ca_topic_score_codex":0.00005674447,"about_ca_topic_score_gemma":0.0000037451534,"teacher_disagreement_score":0.28276446,"about_ca_system_score_codex":0.00010229887,"about_ca_system_score_gemma":0.00014042908,"threshold_uncertainty_score":0.82901055},"labels":[],"label_agreement":null},{"id":"W2893066337","doi":"10.1007/s00500-018-3536-8","title":"Phasor particle swarm optimization: a simple and efficient variant of PSO","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":262,"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":"Particle swarm optimization; Phasor; Multi-swarm optimization; Benchmark (surveying); Mathematical optimization; Mathematics; Derivative-free optimization; Algorithm; Meta-optimization; Computer science; Electric power system; Power (physics)","score_opus":0.021212477101301974,"score_gpt":0.29485239913755534,"score_spread":0.27363992203625337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893066337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027727922,0.00007038524,0.9710366,0.00025643263,0.00017448758,0.00015731856,0.0000014794589,0.000106788626,0.0004685814],"genre_scores_gemma":[0.617903,0.0000022236231,0.38190964,0.00006703238,0.0000796675,0.0000013176494,9.778398e-7,0.0000068050945,0.000029332872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998509,0.00011300773,0.00033588044,0.00036140665,0.00036895738,0.0003117432],"domain_scores_gemma":[0.99867964,0.00028042958,0.0001324447,0.00039922664,0.00037728145,0.00013097266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007621873,0.000104862724,0.0001752926,0.00008429066,0.00022589906,0.00014049478,0.00043775674,0.000034808556,0.00005765384],"category_scores_gemma":[0.00040993822,0.00009953604,0.00002858715,0.0007004975,0.00015999968,0.00010298049,0.0005199716,0.00007920048,0.000018222067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010966242,0.00018035492,0.0005413805,0.00004154054,0.000031798907,0.000015998146,0.0020633556,0.92805845,0.0002591631,0.015755365,0.00022989135,0.05281173],"study_design_scores_gemma":[0.00042621797,0.000101518934,0.0002602759,0.000015393458,0.0000044964218,0.000022431343,0.000025733318,0.99654365,0.0021431171,0.00018464665,0.0001688199,0.00010372259],"about_ca_topic_score_codex":0.000014939046,"about_ca_topic_score_gemma":5.2053207e-7,"teacher_disagreement_score":0.5901751,"about_ca_system_score_codex":0.000018815426,"about_ca_system_score_gemma":0.000082060156,"threshold_uncertainty_score":0.4058962},"labels":[],"label_agreement":null},{"id":"W2896746300","doi":"10.1016/j.eswa.2018.10.034","title":"Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":28,"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":"Computer science; Machine learning; Artificial intelligence; Sorting; Heuristic; Cluster analysis; Genetic algorithm; Homogeneity (statistics); Algorithm","score_opus":0.03849707131950852,"score_gpt":0.3231924971725831,"score_spread":0.28469542585307456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896746300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033818583,0.00055773294,0.99247515,0.0000887381,0.00012646409,0.0029117484,0.000024684163,0.0002788509,0.00015475873],"genre_scores_gemma":[0.6878329,0.000040566498,0.3080634,0.0000363257,0.0002925199,0.0035305943,0.000055157707,0.00003650555,0.00011207375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689525,0.00036973853,0.0006215316,0.00096233544,0.00051709,0.0006340467],"domain_scores_gemma":[0.99744576,0.00040004947,0.00024241122,0.000807015,0.00076155463,0.00034322898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011211687,0.0003266128,0.00039756103,0.00028057475,0.0010774491,0.00069297315,0.00078079564,0.00013043627,0.000010429347],"category_scores_gemma":[0.000106128165,0.0002904623,0.00006599164,0.00078798406,0.0002798014,0.0007789172,0.00032274966,0.00030272987,0.00004273612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00145692,0.006836444,0.0059388066,0.004868689,0.00261463,0.00009160989,0.07452126,0.105037615,0.10217654,0.2558011,0.002996321,0.43766004],"study_design_scores_gemma":[0.0010874823,0.00082833116,0.00033634843,0.00007882053,0.000012783465,0.00013597019,0.001352191,0.98984873,0.002186551,0.000022174998,0.0037093337,0.00040127197],"about_ca_topic_score_codex":0.00023842584,"about_ca_topic_score_gemma":0.00026045844,"teacher_disagreement_score":0.8848111,"about_ca_system_score_codex":0.0002289369,"about_ca_system_score_gemma":0.00010533784,"threshold_uncertainty_score":0.99995476},"labels":[],"label_agreement":null},{"id":"W2898330572","doi":"10.1007/s10732-018-9397-6","title":"Swarm hyperheuristic framework","year":2018,"lang":"fr","type":"article","venue":"Journal of Heuristics","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"","keywords":"Metaheuristic; Computer science; Swarm behaviour; Swarm intelligence; Benchmark (surveying); Parallel metaheuristic; Heuristics; Novelty; Artificial intelligence; Mathematical optimization; Algorithm; Machine learning; Mathematics; Particle swarm optimization; Meta-optimization","score_opus":0.0637507977471381,"score_gpt":0.35187492677389764,"score_spread":0.28812412902675955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898330572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008572863,0.009033521,0.9568239,0.0074874223,0.020526346,0.00016699983,0.000030034667,0.000034947076,0.005039534],"genre_scores_gemma":[0.06298277,0.004103811,0.9061635,0.0010033165,0.010277902,0.000001700498,0.0000015613992,0.00008006355,0.015385377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99405545,0.0007009617,0.0018335041,0.00045513047,0.002001928,0.0009530076],"domain_scores_gemma":[0.989193,0.0018635294,0.0013267598,0.0010479407,0.0056864717,0.0008822773],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0027460386,0.0004128216,0.0008140492,0.0005581978,0.00035858195,0.0007833523,0.0023189918,0.00039408746,0.0014976554],"category_scores_gemma":[0.014260817,0.0003964724,0.00031353623,0.0015683597,0.001110115,0.00069031207,0.0005604713,0.0016468148,0.0014480407],"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":[0.0002835632,0.0022796686,0.0013495098,0.0006305129,0.0008210951,0.005899269,0.005588914,0.004201246,0.00008625781,0.47897008,0.2699659,0.22992402],"study_design_scores_gemma":[0.0010061094,0.0025695264,0.0011388963,0.00093472196,0.00026510603,0.0033096813,0.00011971302,0.33863664,0.00065872667,0.05590141,0.59480834,0.00065112015],"about_ca_topic_score_codex":0.00002211752,"about_ca_topic_score_gemma":0.0000017691049,"teacher_disagreement_score":0.42306867,"about_ca_system_score_codex":0.000245792,"about_ca_system_score_gemma":0.0014542849,"threshold_uncertainty_score":0.9998487},"labels":[],"label_agreement":null},{"id":"W2898430231","doi":"10.1016/j.jcde.2018.10.006","title":"A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metaheuristic; Nonlinear system; Mathematical optimization; Differential evolution; Maxima and minima; Heuristic; Optimization problem; Computer science; Mathematics; Algorithm","score_opus":0.02470235642766716,"score_gpt":0.2602231180731059,"score_spread":0.23552076164543873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898430231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073718103,0.00018673508,0.9920171,0.000051121155,0.00017891303,0.00017945126,0.0000042341794,0.000008478257,0.0000021605865],"genre_scores_gemma":[0.48509538,0.000014768842,0.51481605,0.0000013264928,0.00006141402,0.0000019051018,0.0000022609408,0.0000043360533,0.0000025803972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989612,0.000059511694,0.00047517996,0.000096119795,0.00031572217,0.00009227876],"domain_scores_gemma":[0.99766403,0.0007677467,0.00028709118,0.000059420607,0.0011635425,0.000058153797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068736967,0.00007217472,0.00018152822,0.0003613327,0.000054900873,0.000059863414,0.00012547115,0.0000363684,0.0000021350172],"category_scores_gemma":[0.00044221716,0.000069711794,0.0000291418,0.00025698447,0.000037696158,0.0003161472,0.000036850906,0.000061784194,8.003013e-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":[0.000019172589,0.000023678213,0.000026589276,0.0000874273,0.00002979061,3.3179543e-7,0.00014086653,0.9899129,0.0017474564,0.0067891716,0.000010899538,0.0012116837],"study_design_scores_gemma":[0.0004855921,0.00024363541,0.00048223726,0.00009077573,0.0000144399655,0.0000267734,0.000010523951,0.99752265,0.00042141508,0.00064178614,0.000003964407,0.000056205943],"about_ca_topic_score_codex":0.0000022198096,"about_ca_topic_score_gemma":3.184555e-8,"teacher_disagreement_score":0.47772357,"about_ca_system_score_codex":0.000030295276,"about_ca_system_score_gemma":0.00011028749,"threshold_uncertainty_score":0.28427646},"labels":[],"label_agreement":null},{"id":"W2898701277","doi":"10.1007/s10489-018-1313-0","title":"A new method for feature selection based on intelligent water drops","year":2018,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Computer science; Feature selection; Artificial intelligence; Classifier (UML); Generalization; Data mining; Pattern recognition (psychology); Support vector machine; Fitness function; Visualization; Feature (linguistics); Machine learning; Genetic algorithm; Mathematics","score_opus":0.029857906943767703,"score_gpt":0.3428462592031508,"score_spread":0.3129883522593831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898701277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000073851434,0.0000080789505,0.9919898,0.0014475635,0.0004260504,0.0008092748,0.0000029068592,0.00021134633,0.0050975746],"genre_scores_gemma":[0.01986317,0.0000059061094,0.97576886,0.0013048585,0.00037154616,0.00012784833,0.000014234579,0.000028993903,0.0025145593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976669,0.00008466811,0.00031628617,0.00080116384,0.00056096044,0.00057001325],"domain_scores_gemma":[0.99821645,0.00037561212,0.00007405815,0.0006924704,0.0003994851,0.00024191912],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095556094,0.00024305377,0.00023098938,0.00029284472,0.00025414012,0.00026571538,0.0011801767,0.00013724435,0.00048751093],"category_scores_gemma":[0.0001634029,0.00018394935,0.0000872219,0.00077242113,0.00006200564,0.00012751753,0.00016818331,0.00026056185,0.0008846152],"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":[0.00014215177,0.0001212392,0.0000058780506,0.00002638558,0.000033933713,0.0000018872636,0.0006755178,0.018414155,0.005408229,0.16580474,0.014113551,0.7952523],"study_design_scores_gemma":[0.00007743418,0.00023882656,0.0000043145396,0.000006388665,0.000004464673,0.000002538505,0.000007921796,0.5799588,0.39238936,0.008124051,0.019035578,0.00015033319],"about_ca_topic_score_codex":0.000030501758,"about_ca_topic_score_gemma":0.000006871513,"teacher_disagreement_score":0.795102,"about_ca_system_score_codex":0.00010945212,"about_ca_system_score_gemma":0.00017844958,"threshold_uncertainty_score":0.9998933},"labels":[],"label_agreement":null},{"id":"W2898863354","doi":"10.1007/978-3-319-99719-3_4","title":"A General Method for Selection Function Optimization in Genetic Algorithms","year":2018,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Metaheuristic Optimization Algorithms Research","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":"Selection (genetic algorithm); Mathematical optimization; Population; Computer science; Fitness function; Set (abstract data type); Genetic algorithm; Mutation; Optimization problem; Algorithm; Meta-optimization; Artificial intelligence; Mathematics","score_opus":0.030879790552336295,"score_gpt":0.308801472387631,"score_spread":0.2779216818352947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898863354","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000032795688,0.000072560346,0.98506576,0.00004518068,0.00044039602,0.0016780877,0.00006067068,0.00013654308,0.012497525],"genre_scores_gemma":[0.000002698265,0.00020856509,0.94776636,0.000047152287,0.0003350172,0.00023715984,0.000041890296,0.00014211546,0.05121901],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99607086,0.000024966004,0.0013203676,0.0010382287,0.00091735314,0.00062821864],"domain_scores_gemma":[0.9970611,0.00036362294,0.000732023,0.00035316398,0.0013428752,0.00014725336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016411721,0.0005550132,0.0007381849,0.0012155884,0.00012817947,0.00045223182,0.0008291927,0.00046524437,0.00024660034],"category_scores_gemma":[0.00075225753,0.0006171968,0.000090066125,0.00038255847,0.00008755876,0.00030823806,0.00031699188,0.0005748738,0.000048722963],"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":[0.00004106318,0.00024161584,0.000029835115,0.0018693233,0.0001231886,0.000019824207,0.0019161105,0.0398443,0.0000487047,0.901203,0.003744157,0.050918896],"study_design_scores_gemma":[0.0004842968,0.00018205114,0.000019900968,0.00019456517,0.000043486623,0.000016654069,0.00001284148,0.75866246,0.000032315984,0.2361418,0.0037715933,0.00043801984],"about_ca_topic_score_codex":0.000014883933,"about_ca_topic_score_gemma":0.000023246332,"teacher_disagreement_score":0.7188182,"about_ca_system_score_codex":0.00048492945,"about_ca_system_score_gemma":0.00026565793,"threshold_uncertainty_score":0.99962795},"labels":[],"label_agreement":null},{"id":"W2898968595","doi":"10.1109/dasc/picom/datacom/cyberscitec.2018.00053","title":"Hunting Algorithm Visualization and Performance Evaluation Through BDI Agent Simulation","year":2018,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Athabasca University","funders":"","keywords":"NetLogo; Computer science; Flexibility (engineering); Convergence (economics); Perception; Visualization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.06939797069354116,"score_gpt":0.38293928044087483,"score_spread":0.3135413097473337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898968595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010146886,0.000035996374,0.9840874,0.000120646655,0.00027115908,0.0003540341,4.958441e-7,0.00014066172,0.004842701],"genre_scores_gemma":[0.56556076,0.00003731937,0.43353447,0.0001783432,0.0001698821,0.000017450773,0.000011472409,0.000009892602,0.00048041673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981049,0.00017042023,0.00027719454,0.0003946728,0.0008229359,0.000229897],"domain_scores_gemma":[0.9984804,0.00010781876,0.00010284644,0.00033319517,0.0009122225,0.00006353411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001258872,0.00011052097,0.000103781946,0.00012484594,0.00030224913,0.0002916564,0.00025715813,0.000053109376,0.00028552947],"category_scores_gemma":[0.0003008563,0.000102300306,0.0000166077,0.00065698614,0.00007099018,0.0013026118,0.00020704062,0.00005995964,0.0001137442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034023487,0.000039626015,0.00066901935,0.0000129603795,0.000012186322,4.6608258e-7,0.0011976286,0.014942014,0.000039653714,0.008722884,0.00015253184,0.97420764],"study_design_scores_gemma":[0.0003231324,0.00011287277,0.0038379938,0.000010509513,0.000006385714,0.0000026661678,0.000022988304,0.9928659,0.001128508,0.0004969912,0.0010681483,0.00012388207],"about_ca_topic_score_codex":0.000020681759,"about_ca_topic_score_gemma":0.0000022447712,"teacher_disagreement_score":0.9779239,"about_ca_system_score_codex":0.000062503655,"about_ca_system_score_gemma":0.00007624902,"threshold_uncertainty_score":0.41716856},"labels":[],"label_agreement":null},{"id":"W2899710160","doi":"10.1007/s13369-018-3617-0","title":"Discrete Sine-Cosine Algorithm (DSCA) with Local Search for Solving Traveling Salesman Problem","year":2018,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Travelling salesman problem; Trigonometric functions; Mathematical optimization; Algorithm; Crossover; Computer science; Heuristic; Optimization problem; Mathematics; Artificial intelligence","score_opus":0.018594481368583046,"score_gpt":0.2773544058793667,"score_spread":0.25875992451078367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899710160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033367982,0.00005829684,0.9951242,0.00072624773,0.0002730488,0.00035736183,0.0000042263046,0.000055589462,0.000064181455],"genre_scores_gemma":[0.23280358,0.00001755438,0.76674217,0.00004041804,0.00026559795,0.000020734435,9.682467e-7,0.000019259112,0.00008969467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997721,0.000011681697,0.00028560017,0.00044658995,0.00073840586,0.0007967343],"domain_scores_gemma":[0.9979955,0.00014037165,0.00006408832,0.00024078116,0.0011313901,0.0004278997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026930051,0.00016914138,0.00019016117,0.00035234113,0.0010136077,0.0009931144,0.00086511445,0.000037017548,0.000003262332],"category_scores_gemma":[0.00015344507,0.00013375755,0.000043249478,0.0010155233,0.000380357,0.0012866857,0.00013587819,0.00021833758,0.0000019128813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007186724,0.000076737044,0.0001575245,0.0002743488,0.000094267576,0.00004580949,0.0038944178,0.19345103,0.018625941,0.01827452,0.0003746871,0.76465887],"study_design_scores_gemma":[0.00056354626,0.0005251761,0.00012751375,0.00011850045,0.000006353308,0.00031955924,0.00008270931,0.9932667,0.0037366739,0.00025028578,0.00079862995,0.0002043294],"about_ca_topic_score_codex":0.0000032697146,"about_ca_topic_score_gemma":0.0000015830534,"teacher_disagreement_score":0.7998157,"about_ca_system_score_codex":0.00009418062,"about_ca_system_score_gemma":0.00037712968,"threshold_uncertainty_score":0.9576626},"labels":[],"label_agreement":null},{"id":"W2901417794","doi":"10.1016/j.eswa.2018.11.031","title":"Using semi-independent variables to enhance optimization search","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"National Science Foundation","keywords":"Mathematical optimization; Simulated annealing; Variable neighborhood search; Computer science; Particle swarm optimization; Variable (mathematics); Convergence (economics); Population; Optimization problem; Algorithm; Metaheuristic; Mathematics","score_opus":0.03783625714219308,"score_gpt":0.35223674048402737,"score_spread":0.3144004833418343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901417794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000061091356,0.0001359977,0.99402046,0.0004333635,0.00017412045,0.0016219295,0.000006688884,0.00022851526,0.0033178446],"genre_scores_gemma":[0.07029994,0.00001713263,0.92622024,0.00018671367,0.00050607347,0.0012347678,0.000010867161,0.000034853438,0.0014893966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742436,0.00016446612,0.00037317892,0.0007448961,0.0008474485,0.0004456166],"domain_scores_gemma":[0.9971717,0.000104932624,0.00010461309,0.0012794128,0.0010110165,0.0003283162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006005082,0.00018527928,0.00021409578,0.00028923104,0.00051864184,0.00048338255,0.0011080599,0.00007824092,0.00008123504],"category_scores_gemma":[0.000046473,0.00016184292,0.000024000317,0.0017894817,0.00008549258,0.00037933074,0.00028380437,0.00012046596,0.00029183167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015353164,0.00017123908,0.00006867187,0.000037138812,0.000054354834,0.0000042315914,0.0016993408,0.9521577,0.003998869,0.035977058,0.0020804645,0.0037355972],"study_design_scores_gemma":[0.000121666875,0.00007345957,0.000008946177,0.000042392083,0.0000031162892,0.000038158556,0.00012273683,0.98153764,0.0037734357,0.00002104222,0.014039121,0.0002182972],"about_ca_topic_score_codex":0.00029857896,"about_ca_topic_score_gemma":0.0000062120207,"teacher_disagreement_score":0.07023884,"about_ca_system_score_codex":0.00019128232,"about_ca_system_score_gemma":0.00029843408,"threshold_uncertainty_score":0.6599763},"labels":[],"label_agreement":null},{"id":"W2902191103","doi":"10.1109/access.2018.2884130","title":"A Double Evolutionary Learning Moth-Flame Optimization for Real-Parameter Global Optimization Problems","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":50,"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":"China Scholarship Council; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Convergence (economics); Scalability; Differential evolution; Evolutionary algorithm; Computer science; Test suite; Mathematical optimization; Global optimization; Optimization problem; Algorithm; Artificial intelligence; Machine learning; Test case; Mathematics","score_opus":0.05894618279539715,"score_gpt":0.3579400372393824,"score_spread":0.29899385444398524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902191103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056807237,0.00004012215,0.99301994,0.0005151735,0.0009708609,0.0011239273,0.0000119110155,0.00042269405,0.003327288],"genre_scores_gemma":[0.037745815,0.00011501174,0.9598816,0.00016346261,0.0004975004,0.00033714197,0.0000843367,0.000040025818,0.0011351272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728316,0.00016586362,0.0004975601,0.00079584395,0.0006727142,0.0005848669],"domain_scores_gemma":[0.9973688,0.00019487886,0.00027653962,0.00064605125,0.0013203744,0.00019335006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006146891,0.00024418157,0.00026150447,0.00023296557,0.0005090208,0.0009963448,0.0016342192,0.00016094108,0.00021184902],"category_scores_gemma":[0.00032411196,0.00024525035,0.000089312394,0.0015147963,0.00015205088,0.0022850088,0.0003547832,0.00016020024,0.00005113976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005045436,0.0000759334,0.00057431567,0.000033661574,0.000028537426,0.0000016665862,0.00010511071,0.9915237,0.000013166648,0.002051239,0.0020725026,0.003469723],"study_design_scores_gemma":[0.001199284,0.00020797201,0.00015025078,0.000028587998,0.000014747336,0.000011823048,0.0000056920912,0.99600786,0.00031816846,0.0009982183,0.00078168337,0.00027572352],"about_ca_topic_score_codex":0.00012743802,"about_ca_topic_score_gemma":0.000008454975,"teacher_disagreement_score":0.03717774,"about_ca_system_score_codex":0.00024678587,"about_ca_system_score_gemma":0.00025999176,"threshold_uncertainty_score":1},"labels":[],"label_agreement":null},{"id":"W2902772664","doi":"10.5539/mas.v13n1p10","title":"Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Heuristic; Link (geometry); Meta heuristic; Computer science; Connection (principal bundle); Point (geometry); Algorithm; Section (typography); Interval (graph theory); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.024354603280323378,"score_gpt":0.28074872257577305,"score_spread":0.25639411929544964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902772664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004628225,0.000006884565,0.9932937,0.00070437917,0.00015857766,0.0018084733,0.000010016845,0.0002401284,0.0033150397],"genre_scores_gemma":[0.6338201,0.0000064755773,0.36517838,0.00016126619,0.00024472727,0.00038652951,0.000014001172,0.00001688668,0.00017163844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970757,0.000048106773,0.0002800914,0.0010533303,0.0010767736,0.00046603737],"domain_scores_gemma":[0.9982474,0.00010578726,0.000091705224,0.00059022545,0.0007535887,0.0002113224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001945981,0.00016580877,0.00014155815,0.0004890032,0.00078492105,0.0004167277,0.0008524491,0.0000954058,0.000010349531],"category_scores_gemma":[0.000097226286,0.00016106247,0.000023205821,0.0015120618,0.0002979411,0.0005986084,0.00020249057,0.00015958768,0.000026139109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005555412,0.00008819187,0.000022148266,0.000047144073,0.0000043164328,2.0954991e-7,0.0005290783,0.83560157,0.054418776,0.017185338,0.000051441904,0.09199625],"study_design_scores_gemma":[0.0005067565,0.00022052611,0.000111098525,0.00001054132,0.0000037315367,0.0000019429417,0.0000059520685,0.98245186,0.014980912,0.0014285439,0.00013144256,0.00014669381],"about_ca_topic_score_codex":0.000005601868,"about_ca_topic_score_gemma":0.0000016590558,"teacher_disagreement_score":0.6333573,"about_ca_system_score_codex":0.00017943361,"about_ca_system_score_gemma":0.00034038135,"threshold_uncertainty_score":0.6567937},"labels":[],"label_agreement":null},{"id":"W2903829737","doi":"","title":"A unified modular analysis of online and stochastic optimization: adaptivity, optimism, non-convexity","year":2016,"lang":"en","type":"article","venue":"Spiral (Imperial College London)","topic":"Metaheuristic Optimization Algorithms Research","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convexity; Modular design; Computer science; Optimism; Mathematical optimization; Mathematics; Economics; Psychology; Programming language; Financial economics","score_opus":0.021208500633754263,"score_gpt":0.26850188312055157,"score_spread":0.2472933824867973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903829737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04589385,0.000063677515,0.95160353,0.00089847273,0.00033741753,0.000533695,0.00030734704,0.00009165113,0.0002703328],"genre_scores_gemma":[0.739551,0.000051986575,0.25942492,0.00008863054,0.00012468788,0.000028245822,0.000027113583,0.000023719342,0.0006796632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700254,0.00025527255,0.000677713,0.00078248564,0.0008014146,0.00048056524],"domain_scores_gemma":[0.9975278,0.00032926124,0.0002753389,0.000969629,0.000578433,0.00031953477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067073735,0.0003050608,0.0007913554,0.0008101356,0.0001693724,0.000114477305,0.00094757095,0.00015669948,0.00035763616],"category_scores_gemma":[0.0005380692,0.00023596351,0.00018972464,0.0029132527,0.00030538172,0.000616872,0.0007186495,0.00014119573,0.000009763727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005428121,0.0008015059,0.00042931034,0.00008027242,0.0016143839,0.00010359205,0.0004211727,0.9233167,0.0062779714,0.05501817,0.00027283197,0.01112127],"study_design_scores_gemma":[0.002076459,0.00031253556,0.0010406474,0.00002669976,0.0001853621,0.0000074712398,0.00001759461,0.9950481,0.0005390246,0.0003808782,0.000057635993,0.0003075897],"about_ca_topic_score_codex":0.000071223316,"about_ca_topic_score_gemma":0.000037603768,"teacher_disagreement_score":0.69365716,"about_ca_system_score_codex":0.000082564104,"about_ca_system_score_gemma":0.0003225843,"threshold_uncertainty_score":0.9622313},"labels":[],"label_agreement":null},{"id":"W2903934645","doi":"","title":"Online variance-reducing optimization","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Metaheuristic Optimization Algorithms Research","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 British Columbia","funders":"","keywords":"Computer science; Variance (accounting); Business","score_opus":0.074319189957712,"score_gpt":0.38425176134099914,"score_spread":0.3099325713832871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903934645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005992728,0.0000037223424,0.8889251,0.006139473,0.0008956659,0.00014621596,0.000008571163,0.0002471805,0.10303483],"genre_scores_gemma":[0.6099212,0.00006274024,0.3756945,0.00030978787,0.0005659872,0.000040114202,0.00015432427,0.000021412718,0.0132299205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978309,0.00021106274,0.00035056996,0.0005787894,0.00077498105,0.00025364984],"domain_scores_gemma":[0.9976098,0.00021857799,0.00018577867,0.00048044385,0.0013792375,0.00012617264],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031720885,0.00014687153,0.00012948507,0.00040619203,0.00035129575,0.00055876566,0.0011117567,0.000060923718,0.0023867348],"category_scores_gemma":[0.0014204388,0.00015213722,0.000053712454,0.0006189344,0.00013379693,0.00066375406,0.00025646895,0.0003389431,0.0004288156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002094455,0.00031207933,0.00058424973,0.0000037618554,0.0000773064,0.00001598907,0.0015003408,0.556681,0.0004885334,0.40862614,0.0012236971,0.03046597],"study_design_scores_gemma":[0.0003118248,0.00011596462,0.0011938488,0.00003141371,0.00000373141,0.000013050146,0.00012468,0.9942599,0.00021310999,0.00203694,0.0015451271,0.00015042226],"about_ca_topic_score_codex":0.00006859145,"about_ca_topic_score_gemma":0.0000051099805,"teacher_disagreement_score":0.6093219,"about_ca_system_score_codex":0.000086487584,"about_ca_system_score_gemma":0.00019275722,"threshold_uncertainty_score":0.9985252},"labels":[],"label_agreement":null},{"id":"W2904097672","doi":"10.22111/ieco.2018.26308.1072","title":"OSA: Orientation Search Algorithm","year":2019,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Mathematical optimization; Algorithm; Particle swarm optimization; Set (abstract data type); Computer science; Series (stratigraphy); Orientation (vector space); Position (finance); Meta-optimization; Optimization problem; Optimization algorithm; Genetic algorithm; Metaheuristic; Mathematics","score_opus":0.24301169876003859,"score_gpt":0.5708841893507449,"score_spread":0.32787249059070633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904097672","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04429477,0.0029649646,0.93666524,0.0005252323,0.0020353934,0.0013283954,0.00003416301,0.00012588667,0.012025943],"genre_scores_gemma":[0.46364263,0.013911403,0.5038061,0.0013041374,0.00084931264,0.00019993787,0.00009620167,0.0002417664,0.015948515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99467003,0.0006585268,0.0009442325,0.0008305621,0.0022552903,0.0006413697],"domain_scores_gemma":[0.9959899,0.0005921986,0.00053842465,0.0012433489,0.0011866359,0.00044948293],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031996078,0.00030638036,0.0006680475,0.0014120326,0.00026592347,0.0036468052,0.007618324,0.000118108226,0.015113466],"category_scores_gemma":[0.0004052013,0.00028533465,0.00016300115,0.003198301,0.00011610212,0.005546172,0.0029783216,0.00058457605,0.00048592035],"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":[0.000112569316,0.0008771861,0.16640681,0.00023210951,0.0004180678,0.00016781453,0.0010025793,0.008214739,0.026803864,0.0063337125,0.04921547,0.74021506],"study_design_scores_gemma":[0.0027866152,0.00008297569,0.37119383,0.0003930147,0.0000541201,0.0000865559,0.00017936568,0.53544617,0.054222412,0.013284402,0.020803748,0.0014668105],"about_ca_topic_score_codex":0.00039558514,"about_ca_topic_score_gemma":0.0000036127308,"teacher_disagreement_score":0.73874825,"about_ca_system_score_codex":0.0001631059,"about_ca_system_score_gemma":0.00046199746,"threshold_uncertainty_score":0.9999599},"labels":[],"label_agreement":null},{"id":"W2904101610","doi":"10.1142/s1469026818500220","title":"Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems","year":2018,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Minimax; Heuristics; Algorithm; Integer programming; Integer (computer science); Local search (optimization); Convergence (economics); Search algorithm; Mathematics","score_opus":0.035359440985362985,"score_gpt":0.348958549445557,"score_spread":0.313599108460194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904101610","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000032418407,0.00018676551,0.9966131,0.0015818635,0.00065710925,0.00043328924,0.000055376422,0.000047542668,0.0003925452],"genre_scores_gemma":[0.0232558,0.00016640394,0.975031,0.00030307533,0.0009824737,0.00010021997,0.000041762927,0.000012937127,0.00010632749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770695,0.000047202935,0.0008085579,0.00035250277,0.0008636136,0.00022119649],"domain_scores_gemma":[0.9927086,0.000463635,0.0005189104,0.00016823043,0.005961633,0.00017894946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006516859,0.00017314598,0.0002094386,0.0003128351,0.00027098734,0.00045433655,0.0010812962,0.00007596822,0.00008126048],"category_scores_gemma":[0.00021950074,0.00016780214,0.0001206548,0.0005230321,0.00021985552,0.0008732214,0.00018486929,0.00014310451,0.00002082251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000138815785,0.00013866692,0.000050666036,0.0000050844224,0.00011154623,0.0000018071311,0.000094404924,0.63715214,0.0000066414573,0.04286236,0.00035644768,0.31920636],"study_design_scores_gemma":[0.00034275436,0.0001350696,0.00012426267,0.000032574913,0.000013738204,0.00023291842,0.000037933747,0.94282,0.00019973543,0.050624415,0.0052823946,0.00015416628],"about_ca_topic_score_codex":0.000008366419,"about_ca_topic_score_gemma":0.0000016499152,"teacher_disagreement_score":0.3190522,"about_ca_system_score_codex":0.00014924863,"about_ca_system_score_gemma":0.00032171584,"threshold_uncertainty_score":0.68427724},"labels":[],"label_agreement":null},{"id":"W2906805910","doi":"10.5430/air.v7n2p74","title":"Expansion of Particle Multi-Swarm Optimization","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Swarm behaviour; Benchmark (surveying); Computer science; Metaheuristic; Mathematical optimization; Key (lock); Swarm intelligence; Algorithm; Artificial intelligence; Mathematics","score_opus":0.31086710282853114,"score_gpt":0.4640725909067391,"score_spread":0.15320548807820794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906805910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062617734,0.000057730118,0.99149495,0.0007443945,0.00024321197,0.000371907,0.000002013646,0.0000857113,0.00073829904],"genre_scores_gemma":[0.6278527,0.00007187097,0.37151718,0.000022605518,0.0001172106,0.000027226755,0.0000020960704,0.000013374027,0.0003757568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996106,0.0006233502,0.0006056476,0.0005543419,0.0014216631,0.0006889408],"domain_scores_gemma":[0.99556315,0.00055156613,0.00008982059,0.0009429914,0.0026161775,0.00023631095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038295484,0.000121719444,0.000188031,0.00040816359,0.00037567757,0.00022602026,0.0014075802,0.00009189433,0.0005174757],"category_scores_gemma":[0.0023422984,0.00011381826,0.000055828943,0.0029462557,0.00075725565,0.0005175195,0.0006218624,0.00029532603,0.0008551908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009567796,0.0008011222,0.00017161322,0.000047686724,0.000033363656,0.000022897804,0.0037469002,0.110424794,0.028740596,0.3650181,0.0003960087,0.49050125],"study_design_scores_gemma":[0.000024479428,0.00019193625,0.000016184436,0.000012783687,9.3675095e-7,0.0000017199546,0.00017940534,0.6335368,0.36337742,0.0025281797,0.00005880166,0.00007134098],"about_ca_topic_score_codex":0.0001519411,"about_ca_topic_score_gemma":0.00002213472,"teacher_disagreement_score":0.6215909,"about_ca_system_score_codex":0.000068000736,"about_ca_system_score_gemma":0.00030433747,"threshold_uncertainty_score":0.99992275},"labels":[],"label_agreement":null},{"id":"W2909544216","doi":"10.1109/smc.2018.00055","title":"A New Optimization Algorithm Based on the Behavior of BrunsVigia Flower","year":2018,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Computer science; Algorithm; Curse of dimensionality; Optimization algorithm; Property (philosophy); Optimization problem; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02280422490466756,"score_gpt":0.2831225132978113,"score_spread":0.26031828839314375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909544216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020274445,0.00000460442,0.9882327,0.0012607491,0.0002823004,0.00041887243,0.0000025167492,0.000088479,0.009689513],"genre_scores_gemma":[0.003624132,0.0000026644204,0.9924789,0.00055191026,0.000103136495,0.000033536136,0.0000033295187,0.000011816367,0.0031905973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983523,0.00012979726,0.00026170662,0.00030935006,0.0007229556,0.00022391889],"domain_scores_gemma":[0.9982084,0.00022876989,0.00008381208,0.00089846004,0.000455519,0.0001250574],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005173399,0.00011768009,0.00012829469,0.00016695552,0.000117136944,0.00013802364,0.001021764,0.000053048963,0.003565729],"category_scores_gemma":[0.0002207341,0.00007657739,0.00005576902,0.00088416395,0.00009402905,0.00017877943,0.00014944382,0.000102982354,0.00015253806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034856428,0.0007296693,0.00020703796,0.000012965973,0.00004554559,0.000019428806,0.00042789584,0.16926302,0.00022461299,0.0597062,0.063404836,0.7059239],"study_design_scores_gemma":[0.0002733233,0.0001542153,0.00011986895,0.000007636582,0.0000052839046,0.0000017871484,0.0000050245967,0.9944251,0.0038383044,0.000068527705,0.0010112316,0.0000897003],"about_ca_topic_score_codex":0.00006258768,"about_ca_topic_score_gemma":0.0000035039361,"teacher_disagreement_score":0.82516205,"about_ca_system_score_codex":0.00002664612,"about_ca_system_score_gemma":0.00028963888,"threshold_uncertainty_score":0.99734515},"labels":[],"label_agreement":null},{"id":"W2911022377","doi":"10.47749/t/unicamp.2017.986608","title":"Meta-heurísticas GRASP e BRKGA aplicadas ao problema da diversidade máxima","year":2017,"lang":"gl","type":"dissertation","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Discovery Air (Canada); Adidas (Canada)","funders":"","keywords":"GRASP; Metaheuristic; Mathematical optimization; Path (computing); Sorting; Heuristics; Mathematics; Benchmark (surveying); Local search (optimization); Set (abstract data type); Function (biology); Fitness landscape; Greedy randomized adaptive search procedure; Computer science; Algorithm; Greedy algorithm; Population","score_opus":0.17508494056828378,"score_gpt":0.37026980972015305,"score_spread":0.19518486915186928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911022377","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032268648,0.0021064365,0.8015379,0.004093619,0.0041568,0.004375287,0.00023951098,0.0006738076,0.18249398],"genre_scores_gemma":[0.01978141,0.001516029,0.25052553,0.00050861045,0.00066033367,0.0007155522,0.0015483279,0.00028699278,0.7244572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9879367,0.0008482375,0.0018294788,0.0033623697,0.004078074,0.001945168],"domain_scores_gemma":[0.988304,0.001022617,0.0017138349,0.005216069,0.0025023676,0.0012410922],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0024060882,0.0015599991,0.002536734,0.0011136997,0.0022390892,0.005769686,0.008767113,0.0009997713,0.012312379],"category_scores_gemma":[0.002215976,0.0013584762,0.0012139993,0.0012270354,0.00054940634,0.0017407612,0.0015666082,0.0017814855,0.005325022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086640555,0.00623497,0.0012190155,0.007394325,0.052525755,0.0037377824,0.021607956,0.00725632,0.0018119115,0.3734477,0.15785831,0.36603954],"study_design_scores_gemma":[0.0030162777,0.00081963826,0.003064484,0.00037529258,0.009015216,0.0001584729,0.00068220356,0.8998199,0.003577769,0.0065664393,0.06863547,0.004268794],"about_ca_topic_score_codex":0.0010975976,"about_ca_topic_score_gemma":0.0001267717,"teacher_disagreement_score":0.89256364,"about_ca_system_score_codex":0.0002481648,"about_ca_system_score_gemma":0.0015293532,"threshold_uncertainty_score":0.99971485},"labels":[],"label_agreement":null},{"id":"W2912697490","doi":"10.3311/ppci.12813","title":"Parallel Ant Colony Algorithm for Shortest Path Problem","year":2019,"lang":"en","type":"article","venue":"Periodica Polytechnica Civil Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Transport Canada","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Shortest path problem; Ant colony; Novelty; Cloud computing; Path (computing); Routing (electronic design automation); Algorithm; State (computer science); Swarm intelligence; Mathematical optimization; Artificial intelligence; Theoretical computer science; Particle swarm optimization; Mathematics; Computer network","score_opus":0.009268536292438226,"score_gpt":0.2346189388027018,"score_spread":0.22535040251026356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912697490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010079287,0.0002885838,0.9954466,0.0005583941,0.00033681266,0.001260953,0.000028532777,0.0008539401,0.0011253596],"genre_scores_gemma":[0.074723065,0.00008013568,0.9235098,0.00009427631,0.00010901465,0.000499049,0.000019154932,0.00006099704,0.00090453203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973287,0.000029847912,0.00048401108,0.00073998916,0.0006178926,0.0007995574],"domain_scores_gemma":[0.99813735,0.00025071017,0.00010263332,0.0010442071,0.0001758554,0.00028924888],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006404488,0.00031136055,0.0004190296,0.00030320487,0.00013978426,0.0003448848,0.0014629279,0.00015871128,0.00014862746],"category_scores_gemma":[0.00017585454,0.00031208518,0.00015296225,0.00072304945,0.000049595492,0.00036794355,0.00045091784,0.00033676057,0.000094079245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056546876,0.0012888302,0.00072260626,0.0008574116,0.00053654134,0.00017845842,0.0016797693,0.49411124,0.036889657,0.09023022,0.020707002,0.3527417],"study_design_scores_gemma":[0.0005456857,0.00018322647,0.00023505006,0.000048556827,0.000007724123,0.0000344823,0.0000057259717,0.94642556,0.00023951291,0.000079961006,0.051820874,0.0003736652],"about_ca_topic_score_codex":0.000012097267,"about_ca_topic_score_gemma":0.0000029579107,"teacher_disagreement_score":0.4523143,"about_ca_system_score_codex":0.00012436455,"about_ca_system_score_gemma":0.00022448518,"threshold_uncertainty_score":0.9999331},"labels":[],"label_agreement":null},{"id":"W2917147437","doi":"10.4018/ijcini.2019010104","title":"Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape","year":2019,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Differential evolution; Evolvability; Computer science; Fitness landscape; Benchmark (surveying); Evolutionary algorithm; Fitness approximation; Evolutionary computation; Rendering (computer graphics); Algorithm; Differential (mechanical device); Fitness function; Mathematical optimization; Artificial intelligence; Genetic algorithm; Machine learning; Mathematics","score_opus":0.018489961153369715,"score_gpt":0.33052501484002056,"score_spread":0.31203505368665085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917147437","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":[0.1611696,0.00009172409,0.8374167,0.000053737374,0.0007722026,0.00009610507,0.000014365742,0.0000063981693,0.00037913793],"genre_scores_gemma":[0.964365,0.0002364736,0.035222657,0.0000684519,0.00003450117,0.0000012128262,0.00001101926,0.0000040743494,0.000056599554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798375,0.000051066865,0.0007136489,0.00009313818,0.0010217996,0.00013660075],"domain_scores_gemma":[0.99648595,0.00052283023,0.0006296206,0.00011279601,0.0021792306,0.00006956699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003511537,0.00012743377,0.00022864834,0.0006234035,0.000034366945,0.000119490076,0.00071218866,0.000047206086,0.000091956106],"category_scores_gemma":[0.00027619954,0.00009544742,0.00009202439,0.00029400905,0.00008187543,0.0007772703,0.0001294916,0.00028789806,0.00001648525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004378276,0.0003133543,0.0046327366,0.00012370636,0.00040829025,0.000020662914,0.000873357,0.05373956,0.00042907678,0.0020852345,0.000030375128,0.9369058],"study_design_scores_gemma":[0.00042233628,0.00033240113,0.0066803265,0.0003128212,0.000013903342,0.00004566362,0.00014712082,0.98796815,0.003782884,0.00018319607,0.000008074705,0.000103151826],"about_ca_topic_score_codex":0.0000030669144,"about_ca_topic_score_gemma":2.7296025e-7,"teacher_disagreement_score":0.9368027,"about_ca_system_score_codex":0.00005050382,"about_ca_system_score_gemma":0.00012136966,"threshold_uncertainty_score":0.3892233},"labels":[],"label_agreement":null},{"id":"W2920794693","doi":"10.4018/ijsir.2019040101","title":"Modified Firefly Algorithm With Chaos Theory for Feature Selection","year":2019,"lang":"en","type":"article","venue":"International Journal of Swarm Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Manitoba","funders":"","keywords":"Firefly algorithm; Computer science; Particle swarm optimization; Feature selection; Maximization; Maxima and minima; Mathematical optimization; Algorithm; Hyperparameter optimization; Mutual information; Metaheuristic; Artificial intelligence; Support vector machine; Mathematics","score_opus":0.058083235880678985,"score_gpt":0.3974881111283455,"score_spread":0.3394048752476665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920794693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018122729,0.00018041297,0.99284846,0.0025922314,0.0009074653,0.00053800695,0.000012839252,0.000028408735,0.0010799109],"genre_scores_gemma":[0.36350662,0.00045842153,0.6280272,0.00019037603,0.00095911085,0.000056438384,0.000012452159,0.000048465692,0.0067409026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995064,0.0003917412,0.0005321422,0.00040576508,0.0030867246,0.0005196122],"domain_scores_gemma":[0.9894136,0.0014865381,0.00030582037,0.00033906286,0.008228596,0.00022635139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055661816,0.00017189248,0.00027750898,0.0011536285,0.00013741176,0.0005017728,0.0030577786,0.00011750416,0.00015576753],"category_scores_gemma":[0.0008896214,0.0001291217,0.00013995075,0.0009675178,0.00017622916,0.0009189031,0.00028585293,0.00093585165,0.00010782933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010244382,0.0005216597,0.00026609568,0.00005493509,0.0006793303,0.00015061817,0.0016508134,0.06456314,0.0012233893,0.1179792,0.002335807,0.8095506],"study_design_scores_gemma":[0.000852078,0.0015958513,0.00014710602,0.0001635212,0.000009057554,0.00057992083,0.0004708197,0.92374176,0.033171937,0.032629654,0.0063727195,0.0002655993],"about_ca_topic_score_codex":0.000016792734,"about_ca_topic_score_gemma":0.0000018311405,"teacher_disagreement_score":0.8591786,"about_ca_system_score_codex":0.00034459017,"about_ca_system_score_gemma":0.0007749599,"threshold_uncertainty_score":0.56821644},"labels":[],"label_agreement":null},{"id":"W2928383288","doi":"10.1007/s11590-019-01452-7","title":"Dynamic improvements of static surrogates in direct search optimization","year":2019,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Multiplicative function; Mathematical optimization; Context (archaeology); Computer science; Variable (mathematics); Quadratic equation; Surrogate model; Algorithm; Mathematics","score_opus":0.008472196214212488,"score_gpt":0.25822590222485003,"score_spread":0.24975370601063754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928383288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00862617,0.000021337448,0.9877182,0.0014042882,0.0002566509,0.0007302719,0.000009070504,0.000081509825,0.0011524967],"genre_scores_gemma":[0.17579153,0.00009546656,0.8231054,0.00038743505,0.0000074500135,0.000032894633,0.00009100007,0.000034145978,0.00045469363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973929,0.0002944661,0.00059447734,0.0005441935,0.0007587419,0.00041523486],"domain_scores_gemma":[0.9985515,0.00022784105,0.00019910048,0.00065957423,0.0002661913,0.00009580684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007910994,0.00019093123,0.00031480513,0.00071825966,0.00004970764,0.00014617872,0.00076551887,0.00006751992,0.00045587213],"category_scores_gemma":[0.0001563157,0.00019945689,0.00005654451,0.0016507823,0.000061014936,0.00084504706,0.00022665583,0.00016416186,0.000048658094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010140028,0.000083109655,0.0019965086,0.00005756009,0.000017407408,0.0000029858088,0.00023829324,0.9955286,0.0008330709,0.00019821776,0.000051052022,0.0009831048],"study_design_scores_gemma":[0.0009282784,0.00006084873,0.00050142314,0.00003884873,0.000003803279,0.0000012995926,0.000031424614,0.99758077,0.00064712815,0.000008643676,0.000014036061,0.00018351954],"about_ca_topic_score_codex":0.00005662105,"about_ca_topic_score_gemma":0.0000032199252,"teacher_disagreement_score":0.16716535,"about_ca_system_score_codex":0.00016065342,"about_ca_system_score_gemma":0.000113625545,"threshold_uncertainty_score":0.8133616},"labels":[],"label_agreement":null},{"id":"W2932517410","doi":"10.1007/978-981-13-6569-0_10","title":"Expectation Algorithm (ExA): A Socio-inspired Optimization Methodology","year":2019,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"CMA-ES; Backtracking; Algorithm; Mathematical optimization; Computer science; Wilcoxon signed-rank test; Particle swarm optimization; Optimization problem; Mathematics; Artificial intelligence; Evolution strategy; Evolutionary algorithm; Statistics","score_opus":0.2318761286488891,"score_gpt":0.41986661053555224,"score_spread":0.18799048188666315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932517410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.7648087e-7,0.0025782946,0.9716374,0.0007863521,0.0018208515,0.00081715646,0.000029033228,0.00017149922,0.022158999],"genre_scores_gemma":[0.000120153854,0.0020311794,0.9537257,0.0002858638,0.00018984014,0.00007099867,0.00014351554,0.00006323397,0.043369543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952456,0.00037750255,0.0012826178,0.0013192869,0.0012842937,0.0004907205],"domain_scores_gemma":[0.9929923,0.0038248918,0.00063120684,0.00068437774,0.0017578955,0.00010936054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013850463,0.0005760153,0.0009511991,0.0010239249,0.00022910205,0.00013639958,0.0014085998,0.00037801437,0.00031679083],"category_scores_gemma":[0.0009637937,0.0006104733,0.00019825427,0.000456423,0.0005403131,0.00043710097,0.0009324601,0.0007005694,0.000491308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006473162,0.000025876085,0.0000034833902,0.000062295076,0.0001520408,0.000038601404,0.0010708426,0.67852974,2.1081532e-7,0.2560194,0.00047362148,0.06361736],"study_design_scores_gemma":[0.00016236094,0.00011329864,0.0000069831613,0.00017179287,0.00001768993,0.000028562732,0.00017103221,0.8324446,0.0000131560155,0.16539182,0.0010074511,0.0004712358],"about_ca_topic_score_codex":0.000011281151,"about_ca_topic_score_gemma":0.000005358173,"teacher_disagreement_score":0.15391485,"about_ca_system_score_codex":0.000644718,"about_ca_system_score_gemma":0.00063742604,"threshold_uncertainty_score":0.9996347},"labels":[],"label_agreement":null},{"id":"W2941876110","doi":"","title":"A Novel Orthogonal Direction Mesh Adaptive Direct Search Approach for SVM Hyperparameter Tuning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Metaheuristic Optimization Algorithms Research","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":"École de Technologie Supérieure","funders":"","keywords":"Hyperparameter; Computer science; Support vector machine; Maxima and minima; Benchmark (surveying); Mathematical optimization; Hyperparameter optimization; Algorithm; Gaussian; Artificial intelligence; Mathematics; Machine learning","score_opus":0.15542877817283618,"score_gpt":0.23399246412884456,"score_spread":0.07856368595600838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941876110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025961527,0.000042107167,0.9816372,0.00004193851,0.00062812195,0.001445256,0.00009312607,0.00027599247,0.01324011],"genre_scores_gemma":[0.65881675,0.00007877011,0.33169934,0.000049144004,0.00012967917,0.000018293144,0.00010234829,0.000051392253,0.009054283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961764,0.000357652,0.0003221318,0.0020563458,0.00038458145,0.000702866],"domain_scores_gemma":[0.9965054,0.0006392948,0.00025251336,0.0014835106,0.0008343979,0.00028487478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011746677,0.0004485959,0.00061587844,0.000730842,0.00028209155,0.00032891688,0.0021612262,0.00040230123,0.000038486494],"category_scores_gemma":[0.00022738775,0.000501854,0.00038132322,0.0011447406,0.00015268147,0.00050614314,0.0025192192,0.0009413415,0.00006004914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011208342,0.00025345196,0.00026934512,0.00013815361,0.00025646185,0.000021570893,0.00019399417,0.9577131,0.00007593804,0.038803797,0.00021945487,0.0019426075],"study_design_scores_gemma":[0.0008326143,0.00012075603,0.00023211433,0.00004851956,0.00006403816,0.000009302151,0.000060856295,0.9970297,0.00011104284,0.0006183533,0.00034467253,0.00052800594],"about_ca_topic_score_codex":0.00012459376,"about_ca_topic_score_gemma":0.0000048910656,"teacher_disagreement_score":0.6562206,"about_ca_system_score_codex":0.00031359665,"about_ca_system_score_gemma":0.00065779866,"threshold_uncertainty_score":0.9997433},"labels":[],"label_agreement":null},{"id":"W2943484134","doi":"10.13164/mendel.2018.1.001","title":"Increasing Population (μ + λ)-CMA-ES with Centre and Elitism (IPOP!+)","year":2018,"lang":"en","type":"article","venue":"MENDEL","topic":"Metaheuristic Optimization Algorithms Research","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":"Elitism; Population; Operator (biology); Selection (genetic algorithm); Mathematics; Computer science; Artificial intelligence; Sociology; Biology; Political science; Genetics; Demography","score_opus":0.014122309049467126,"score_gpt":0.25796643456351354,"score_spread":0.2438441255140464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943484134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08253107,0.00008256798,0.9118764,0.0012598342,0.00013414542,0.00017743591,0.0000029786338,0.00013809442,0.003797477],"genre_scores_gemma":[0.63962066,0.0000148029585,0.35954025,0.00010378261,0.00008869199,0.0000026569055,0.000005812148,0.000008269877,0.0006150608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988914,0.00012710594,0.00013157072,0.00029146543,0.00033734724,0.00022111299],"domain_scores_gemma":[0.99932337,0.000073544295,0.00004903208,0.00029298328,0.00014871117,0.00011235531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003873438,0.00008769862,0.00009876814,0.00010074399,0.00016629956,0.00019982444,0.00022437582,0.00003178332,0.00006248586],"category_scores_gemma":[0.00011309206,0.000072003946,0.000009663023,0.00027189814,0.000061142586,0.00034269298,0.00014949143,0.00006797526,0.00003788363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000117525684,0.00022437793,0.044158366,0.00009982725,0.00011832815,0.00009689447,0.004902517,0.0006198056,0.0009850934,0.23806073,0.0036589096,0.70695764],"study_design_scores_gemma":[0.0007190458,0.00013893512,0.047154468,0.000055190227,0.000008859307,0.00008557836,0.00006537872,0.9443039,0.00041479064,0.0030266508,0.0037677893,0.00025944834],"about_ca_topic_score_codex":0.00021370643,"about_ca_topic_score_gemma":0.00002552572,"teacher_disagreement_score":0.94368404,"about_ca_system_score_codex":0.000028467812,"about_ca_system_score_gemma":0.00003958481,"threshold_uncertainty_score":0.29362357},"labels":[],"label_agreement":null},{"id":"W2946653631","doi":"10.3233/jifs-179042","title":"Cellular Estimation Gaussian Algorithm for Continuous Domain","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Estimation of distribution algorithm; EDAS; Benchmark (surveying); Computer science; Gaussian; Algorithm; Domain (mathematical analysis); Fitness function; Independence (probability theory); Genetic algorithm; Artificial intelligence; Mathematical optimization; Pattern recognition (psychology); Face (sociological concept); Mathematics; Machine learning; Statistics","score_opus":0.016922968185648004,"score_gpt":0.2768238549748743,"score_spread":0.2599008867892263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946653631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011058801,0.00089720613,0.991956,0.000315167,0.0036284921,0.0008784421,0.0000071202153,0.00003320278,0.001178522],"genre_scores_gemma":[0.08288638,0.00008363827,0.9128587,0.00006924968,0.0006234951,0.000029520686,0.000008150463,0.000039225302,0.0034015947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684846,0.00026122734,0.0011993236,0.00028337544,0.0010256943,0.0003818892],"domain_scores_gemma":[0.9968702,0.0003589618,0.0008973983,0.00051517604,0.0011049861,0.00025326613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002703836,0.00019458993,0.0005450961,0.00046276717,0.00008522092,0.00049232953,0.0011500808,0.0001061345,0.00004334361],"category_scores_gemma":[0.00019568695,0.00015596006,0.0002326064,0.00043435523,0.00003094644,0.00053746335,0.00010128495,0.00025132246,0.00020949316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013368536,0.0009012341,0.0005576851,0.0008850863,0.0008432311,0.0003393968,0.0031217972,0.15021482,0.0040677493,0.11927819,0.01838127,0.7012759],"study_design_scores_gemma":[0.00061583513,0.0005619771,0.000017120838,0.00017376484,0.00001472064,0.00023102207,0.00031463944,0.9781566,0.0022856982,0.0016180231,0.015817862,0.00019272631],"about_ca_topic_score_codex":0.000014495926,"about_ca_topic_score_gemma":2.4962068e-7,"teacher_disagreement_score":0.8279418,"about_ca_system_score_codex":0.00018346711,"about_ca_system_score_gemma":0.00022589123,"threshold_uncertainty_score":0.6359867},"labels":[],"label_agreement":null},{"id":"W2947748448","doi":"10.3390/info10060184","title":"FPGA Implementation of Crossover Module of Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"Information","topic":"Metaheuristic Optimization Algorithms Research","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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Field-programmable gate array; Computer science; Travelling salesman problem; Genetic algorithm; Parallel computing; Software; Realization (probability); Computer architecture; Field (mathematics); Algorithm; Embedded system; Operating system; Mathematics; Artificial intelligence","score_opus":0.009378328181832317,"score_gpt":0.28732289737290556,"score_spread":0.27794456919107324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947748448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02516229,0.000009075615,0.97289455,0.000039154453,0.00016097502,0.0002716053,0.000016594642,0.00001972832,0.001426011],"genre_scores_gemma":[0.40087214,0.000014628615,0.5988935,0.00005500026,0.000011871137,0.000010185276,0.000030675485,0.0000036189756,0.00010837357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988601,0.000026280828,0.00045973904,0.00007186228,0.00046640213,0.00011556864],"domain_scores_gemma":[0.9989839,0.00003332976,0.0002653131,0.00031293242,0.0003740044,0.000030509205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023940488,0.00005500977,0.0001057059,0.00018700268,0.000019113306,0.00005149604,0.00032072104,0.000030014586,0.000262774],"category_scores_gemma":[0.00002614989,0.000053386953,0.000031010048,0.0003522859,0.000019508145,0.0014397267,0.00010535775,0.000039861672,0.00014455465],"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":[0.0000037499917,0.000019946163,0.0014364248,0.00008438277,0.000015606856,1.4451052e-7,0.0013473874,0.015872333,0.00016657203,0.0034102534,0.00029223072,0.97735095],"study_design_scores_gemma":[0.00071222987,0.000088077475,0.037169322,0.0000072480784,0.0000024146625,0.0000023927587,0.00010126273,0.93732524,0.023095036,0.00046539897,0.000958717,0.00007264884],"about_ca_topic_score_codex":0.000075516626,"about_ca_topic_score_gemma":5.5899824e-7,"teacher_disagreement_score":0.9772783,"about_ca_system_score_codex":0.0000241612,"about_ca_system_score_gemma":0.00008613472,"threshold_uncertainty_score":0.2877191},"labels":[],"label_agreement":null},{"id":"W2950684230","doi":"10.1007/s12065-019-00255-0","title":"A hybridization of cuckoo search and particle swarm optimization for solving nonlinear systems","year":2019,"lang":"en","type":"article","venue":"Evolutionary Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cuckoo search; Computer science; Benchmark (surveying); Particle swarm optimization; Mathematical optimization; Nonlinear system; Algorithm; Jacobian matrix and determinant; Maxima and minima; Mathematics; Applied mathematics","score_opus":0.027030708892089632,"score_gpt":0.28929755897404397,"score_spread":0.26226685008195433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950684230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010804297,0.00056059426,0.98713505,0.0002480867,0.0003985445,0.00070075155,0.000018773442,0.000046023404,0.00008788458],"genre_scores_gemma":[0.5608473,0.00018215107,0.43828553,0.000028574046,0.000057029985,0.000039445044,0.000024287503,0.000013492754,0.0005221972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983628,0.00008118449,0.00040299495,0.00037577888,0.00051391905,0.0002633454],"domain_scores_gemma":[0.9983027,0.00039687642,0.00009576507,0.00037555574,0.00073495484,0.0000941379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005491617,0.00010737649,0.0001736845,0.00009958602,0.00010456735,0.00007674817,0.0004349578,0.000043149237,0.000046480465],"category_scores_gemma":[0.00034531395,0.000109215114,0.00003066549,0.0005315119,0.000049688682,0.00046210308,0.00019583387,0.00008354083,0.00001834759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013146786,0.000046553025,0.0008864892,0.00011472951,0.000011816689,8.021362e-7,0.0001378507,0.97534734,0.00020012514,0.021132255,0.00010742183,0.0020014658],"study_design_scores_gemma":[0.0001181443,0.00009919338,0.00019189475,0.000041708066,0.0000033798667,0.000009411799,0.00010969983,0.99448377,0.004495446,0.00012693896,0.00020806064,0.00011235271],"about_ca_topic_score_codex":0.00116182,"about_ca_topic_score_gemma":0.000029623192,"teacher_disagreement_score":0.550043,"about_ca_system_score_codex":0.00008946116,"about_ca_system_score_gemma":0.00038683417,"threshold_uncertainty_score":0.4453663},"labels":[],"label_agreement":null},{"id":"W2952134149","doi":"","title":"Micro-Differential Evolution: Diversity Enhancement and Comparative Study","year":2015,"lang":"en","type":"preprint","venue":"e-scholar@UOIT (University of Ontario Institute of Technology)","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Waterloo","keywords":"Benchmark (surveying); Differential evolution; Mutation; Premature convergence; Convergence (economics); Population; Population size; Computer science; Mathematical optimization; Algorithm; Local optimum; Evolutionary algorithm; Mathematics; Artificial intelligence; Geography; Particle swarm optimization; Biology","score_opus":0.0564197442637231,"score_gpt":0.26809129027884737,"score_spread":0.21167154601512428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952134149","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":[0.8777279,0.00018757048,0.118937016,0.00021764114,0.0006920489,0.0009456457,0.00004037962,0.00011403075,0.001137758],"genre_scores_gemma":[0.33552635,0.000036960755,0.66298425,0.000003979175,0.000013538911,0.0000020880161,0.00002856908,0.0000056411677,0.0013986496],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973389,0.00004440804,0.00038069667,0.0010154896,0.0008726767,0.00034784465],"domain_scores_gemma":[0.9967202,0.0000038534554,0.0006034406,0.0013635751,0.0011207361,0.0001881756],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007330122,0.00036659045,0.00094207464,0.0009885175,0.000747873,0.00007748221,0.0033483559,0.00049330347,0.00017022122],"category_scores_gemma":[0.0000719708,0.00045754478,0.00013724627,0.0007184962,0.00060358905,0.00089361233,0.017654315,0.0018987086,0.000025527144],"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":[0.003698553,0.039145097,0.10049204,0.003377298,0.022226166,0.0038626024,0.3165056,0.016758824,0.009192016,0.12237242,0.026537076,0.33583227],"study_design_scores_gemma":[0.02794053,0.0066395435,0.18330173,0.0013126286,0.0022150823,0.0001296342,0.015808105,0.04414086,0.0053877383,0.02515363,0.6825604,0.005410155],"about_ca_topic_score_codex":0.012836259,"about_ca_topic_score_gemma":0.00832976,"teacher_disagreement_score":0.65602326,"about_ca_system_score_codex":0.0013299381,"about_ca_system_score_gemma":0.0016909795,"threshold_uncertainty_score":0.9997876},"labels":[],"label_agreement":null},{"id":"W2954659334","doi":"10.1016/j.ins.2019.07.016","title":"A parameter-free particle swarm optimization algorithm using performance classifiers","year":2019,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Multi-swarm optimization; Computer science; Term (time); Mathematical optimization; Basis (linear algebra); Set (abstract data type); Algorithm; Swarm behaviour; Metaheuristic; Mathematics; Artificial intelligence","score_opus":0.04348540951782416,"score_gpt":0.29345685362402735,"score_spread":0.2499714441062032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954659334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031368725,0.000013908593,0.9631543,0.0003576621,0.00068261614,0.000325615,0.0000040996947,0.00014465999,0.0039483807],"genre_scores_gemma":[0.11286194,0.000025930201,0.8864683,0.00041966542,0.000031823787,0.00001557349,0.000005174601,0.000004648045,0.0001669296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975599,0.000086341024,0.00049437693,0.00027156965,0.0011574142,0.00043040275],"domain_scores_gemma":[0.9984977,0.00016043369,0.00024467803,0.0006306179,0.00032656052,0.00013998058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013636522,0.00013729764,0.00015418475,0.0003222133,0.00036059803,0.0010214493,0.0016205916,0.00005987571,0.00017415559],"category_scores_gemma":[0.0003559349,0.0001191319,0.000043766817,0.0019101127,0.00017967299,0.008747533,0.0003841705,0.00012427795,0.00040748835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002553371,0.000017087217,0.0008101746,0.000015489804,0.000005527894,3.9415758e-7,0.000597382,0.92443323,0.000018823275,0.0021256725,0.00018918184,0.07178449],"study_design_scores_gemma":[0.0003749822,0.000101469814,0.00027062814,0.000013922612,0.0000022208505,0.000014412087,0.00017563022,0.9967878,0.0009980862,0.00018065849,0.0009125307,0.00016768268],"about_ca_topic_score_codex":0.000023556353,"about_ca_topic_score_gemma":3.0228264e-7,"teacher_disagreement_score":0.081493214,"about_ca_system_score_codex":0.00008031828,"about_ca_system_score_gemma":0.00029083036,"threshold_uncertainty_score":0.98498595},"labels":[],"label_agreement":null},{"id":"W2954693883","doi":"10.1007/s12530-019-09291-8","title":"A hybridization of grey wolf optimizer and differential evolution for solving nonlinear systems","year":2019,"lang":"en","type":"article","venue":"Evolving Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Benchmark (surveying); Nonlinear system; Differential evolution; Computer science; Parametric statistics; Mathematical optimization; Wilcoxon signed-rank test; Dimension (graph theory); Optimization problem; Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.013778275741461258,"score_gpt":0.25152922724335797,"score_spread":0.2377509515018967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954693883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01886163,0.0011183451,0.97590834,0.0000366858,0.002219527,0.0014774466,0.000021457166,0.000116516065,0.00024007139],"genre_scores_gemma":[0.92933756,0.000022278846,0.06867438,0.0000036883905,0.00023645953,0.00009134651,0.00002068405,0.00003071589,0.0015828973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771076,0.0001658371,0.0006281654,0.0005249871,0.00061166345,0.00035856542],"domain_scores_gemma":[0.99769807,0.00036889027,0.00036000568,0.0006093739,0.0008460689,0.00011759343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091784605,0.00018676766,0.00043945454,0.00031057344,0.00013195358,0.00048689256,0.0005093003,0.000102429265,0.000016081107],"category_scores_gemma":[0.00045430873,0.0001753236,0.000069717724,0.00038548285,0.00003913681,0.00064480345,0.0002153277,0.00010923398,0.000020396843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022206077,0.00064124545,0.02729961,0.010349323,0.0006801616,0.000019381325,0.0031245411,0.72439945,0.03479035,0.18853043,0.0067978688,0.003145559],"study_design_scores_gemma":[0.0008140226,0.000118059455,0.0006363138,0.00026122207,0.00001265212,0.000023642973,0.00008159224,0.9975617,0.0001262336,0.000033045108,0.00015172143,0.00017979555],"about_ca_topic_score_codex":0.0001435738,"about_ca_topic_score_gemma":0.000001134118,"teacher_disagreement_score":0.9104759,"about_ca_system_score_codex":0.00012139383,"about_ca_system_score_gemma":0.00013834695,"threshold_uncertainty_score":0.7149489},"labels":[],"label_agreement":null},{"id":"W2955270668","doi":"10.1145/3321707.3321724","title":"Large-scale noise-resilient evolution-strategies","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Fondation Pour La Conservation Du Saumon Atlantique","keywords":"Computer science; Noise (video); Ranking (information retrieval); Weighting; Mathematical optimization; Stochastic gradient descent; Algorithm; Bounded function; Curse of dimensionality; Reinforcement learning; Mathematics; Artificial intelligence; Artificial neural network","score_opus":0.012223312574504394,"score_gpt":0.24314924502124322,"score_spread":0.2309259324467388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955270668","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":[0.24853057,0.00025881545,0.74575776,0.0008000016,0.00026481398,0.00038578565,0.0000059401455,0.000058576148,0.0039377334],"genre_scores_gemma":[0.88890064,0.000032881937,0.11039058,0.0000370499,0.000024566389,0.000013358939,0.0000015302918,0.000006145557,0.0005932338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984578,0.000026594742,0.00032014228,0.00039068103,0.0005527182,0.00025208876],"domain_scores_gemma":[0.9986177,0.00006592166,0.0001961135,0.00016412875,0.0008714832,0.00008465894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023578611,0.00013600414,0.00016954605,0.00012347741,0.00018691267,0.00016070547,0.0006920471,0.00005707209,0.000050112452],"category_scores_gemma":[0.00005195647,0.00010999449,0.00005071983,0.0005069225,0.00013326474,0.00045580935,0.00049171405,0.00012622961,0.000037442936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005317509,0.00038465674,0.12409947,0.0005323487,0.000085477055,5.772113e-7,0.0043466086,0.105612926,0.007891649,0.7450872,0.0048152357,0.007090699],"study_design_scores_gemma":[0.00029250295,0.000056737204,0.19106162,0.00003631447,0.000006011918,0.000015922093,0.00052858953,0.7731385,0.00013378356,0.03443533,0.00017905203,0.00011562582],"about_ca_topic_score_codex":0.0000108887025,"about_ca_topic_score_gemma":7.86822e-7,"teacher_disagreement_score":0.7106519,"about_ca_system_score_codex":0.00004693399,"about_ca_system_score_gemma":0.0002586648,"threshold_uncertainty_score":0.4485445},"labels":[],"label_agreement":null},{"id":"W2957818796","doi":"10.1007/s12065-019-00246-1","title":"Chemical reaction optimization: survey on variants","year":2019,"lang":"en","type":"article","venue":"Evolutionary Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Saskatchewan","funders":"","keywords":"Metaheuristic; Tabu search; Ant colony optimization algorithms; Computer science; Simulated annealing; Meta-optimization; Parallel metaheuristic; Mathematical optimization; Robustness (evolution); Particle swarm optimization; Genetic algorithm; Optimization problem; Multi-swarm optimization; Algorithm; Machine learning; Mathematics","score_opus":0.03461061903126983,"score_gpt":0.293270391387825,"score_spread":0.2586597723565552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957818796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069816783,0.00004739274,0.9901116,0.0004885399,0.00092657894,0.00028557927,0.000013055526,0.00016731342,0.0072617605],"genre_scores_gemma":[0.47075576,0.00015869972,0.5214938,0.0004615943,0.00018793359,0.000037438585,0.00017590892,0.00003346532,0.006695396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976949,0.00025383345,0.00033996106,0.0006215424,0.00076686277,0.00032290266],"domain_scores_gemma":[0.99797934,0.0005112477,0.00009990373,0.0008376597,0.0004265788,0.00014527536],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006853762,0.00016164289,0.00016532298,0.00020158787,0.00010096835,0.0000950961,0.0010188156,0.00010217259,0.00096687244],"category_scores_gemma":[0.00066987256,0.00016145887,0.00005080583,0.0010644901,0.000057577498,0.000548421,0.00023012,0.00026406458,0.0033109721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009534996,0.00048261226,0.0024766529,0.00002020873,0.000045027828,0.000020799513,0.00014311181,0.87646794,0.00074106664,0.093809985,0.010119837,0.015577424],"study_design_scores_gemma":[0.000083617444,0.00007674772,0.010701568,0.000017702923,0.0000013709742,0.000021848573,0.0000059373297,0.9866772,0.00081935135,0.00078400015,0.0006162572,0.00019436683],"about_ca_topic_score_codex":0.00005622702,"about_ca_topic_score_gemma":8.772564e-7,"teacher_disagreement_score":0.4700576,"about_ca_system_score_codex":0.0001791148,"about_ca_system_score_gemma":0.00018345709,"threshold_uncertainty_score":0.99994636},"labels":[],"label_agreement":null},{"id":"W2961079568","doi":"10.5267/j.ijiec.2019.6.002","title":"Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems","year":2019,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":327,"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":"Benchmark (surveying); Algorithm; Simple (philosophy); Dimension (graph theory); Mathematical optimization; Computer science; Optimization problem; Optimization algorithm; Continuous optimization; Process (computing); Mathematics; Multi-swarm optimization","score_opus":0.06204859873689692,"score_gpt":0.3061649612971565,"score_spread":0.24411636256025956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961079568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011773179,0.0000950155,0.9922504,0.001253837,0.0044537177,0.0005889991,0.000033893546,0.00009175763,0.000055099066],"genre_scores_gemma":[0.055511273,0.000034708817,0.942699,0.00008959315,0.0014070233,0.000038332775,0.000069899834,0.000053440806,0.00009673591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970833,0.000050262344,0.0010430315,0.00032850908,0.0011491424,0.0003457554],"domain_scores_gemma":[0.9957309,0.0008254338,0.00059100374,0.00027769295,0.0023811744,0.00019381914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011566376,0.00023596948,0.00039574495,0.00092134025,0.00009448045,0.0005909222,0.0016583508,0.00014794017,0.00006421501],"category_scores_gemma":[0.0009965538,0.00023488763,0.00023829321,0.0006794871,0.000025688398,0.0011172182,0.00022546775,0.00045155088,0.000013240688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013977408,0.000075851014,0.000097817676,0.0000103047205,0.00027480655,0.000009834542,0.00011038955,0.9702231,0.00011184957,0.0033124166,0.000537629,0.025222005],"study_design_scores_gemma":[0.0022460092,0.0001678234,0.000058975333,0.000103196035,0.000025192769,0.000091272486,0.000025829804,0.9934305,0.0002753388,0.0006954426,0.0026528237,0.00022757398],"about_ca_topic_score_codex":0.000024321942,"about_ca_topic_score_gemma":0.0000012367225,"teacher_disagreement_score":0.054333955,"about_ca_system_score_codex":0.000256272,"about_ca_system_score_gemma":0.00043991243,"threshold_uncertainty_score":0.95784396},"labels":[],"label_agreement":null},{"id":"W2963239290","doi":"10.1007/s10479-019-03343-7","title":"Biologically Inspired Parent Selection in Genetic Algorithms","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":50,"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":"Quality control and genetic algorithms; Genetic algorithm; Selection (genetic algorithm); Computer science; Genetic representation; Theory of computation; Algorithm; Mathematical optimization; Simple (philosophy); Artificial intelligence; Meta-optimization; Mathematics; Machine learning","score_opus":0.23378032836205606,"score_gpt":0.45118896427990646,"score_spread":0.2174086359178504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963239290","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":[0.55125904,0.00046708382,0.43243974,0.008042311,0.00019356282,0.0018884448,0.000015521277,0.000099666606,0.0055946563],"genre_scores_gemma":[0.86287194,0.00062739116,0.13469894,0.00011568159,0.000044150416,0.00012537633,0.0000103649645,0.000011226562,0.0014949157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968411,0.0007483513,0.00047937216,0.00047733274,0.000952702,0.000501175],"domain_scores_gemma":[0.9975671,0.00025291013,0.00002749397,0.00046275306,0.0015634974,0.0001262545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024369275,0.00010222442,0.00020330513,0.0008853577,0.00015679767,0.00021169096,0.0009899966,0.000092747185,0.00033556085],"category_scores_gemma":[0.0007738998,0.00008931255,0.0000469511,0.0025205598,0.00011376106,0.00035916307,0.0003595421,0.00036334083,0.0003228685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079456826,0.002085334,0.042335384,0.00013224997,0.000100337755,0.00004591493,0.0014066059,0.6540633,0.022748774,0.090537034,0.0037739947,0.18269165],"study_design_scores_gemma":[0.0002727835,0.0003687812,0.059369057,0.00001955564,3.8634403e-7,0.0000044781973,0.000032051787,0.934696,0.0033679265,0.00050296145,0.0012565835,0.00010941175],"about_ca_topic_score_codex":0.00049163454,"about_ca_topic_score_gemma":0.00007148031,"teacher_disagreement_score":0.31161293,"about_ca_system_score_codex":0.000040894083,"about_ca_system_score_gemma":0.00038586403,"threshold_uncertainty_score":0.4149929},"labels":[],"label_agreement":null},{"id":"W2964295186","doi":"","title":"An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Metaheuristic Optimization Algorithms Research","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":"Travelling salesman problem; Mathematical optimization; Computer science; Ant colony; Metaheuristic; Ant colony optimization algorithms; Node (physics); Set (abstract data type); Mathematics; Engineering","score_opus":0.07879904856835628,"score_gpt":0.21999015396789937,"score_spread":0.1411911053995431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964295186","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":[0.06420201,0.000057739908,0.93403375,0.00007560742,0.00035603647,0.0006575537,0.000009541053,0.00016856221,0.00043916676],"genre_scores_gemma":[0.9569337,0.000016200902,0.04236051,0.000040973024,0.000110426015,0.000005655136,0.000005767502,0.0000145107615,0.0005122805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846786,0.00023128813,0.00018194994,0.00044465603,0.00015267615,0.000521589],"domain_scores_gemma":[0.99835426,0.0002810365,0.000113434944,0.00076221075,0.00023082271,0.00025822874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001104107,0.00014865398,0.00017230387,0.000111484944,0.0004640976,0.00013389633,0.0012584729,0.000043418444,0.000013912868],"category_scores_gemma":[0.00004227766,0.00012165521,0.000090284244,0.00058095023,0.00007867042,0.00032916613,0.00017991889,0.0001178798,0.000047030484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028016451,0.00013515151,0.00032783923,0.00003963349,0.000040212595,0.000021062042,0.00027295385,0.6421705,0.00016311996,0.35580793,0.00015474122,0.0008388524],"study_design_scores_gemma":[0.0006702692,0.00005843047,0.00025639945,0.000011168806,0.00003019462,0.000015112694,0.00012004347,0.9969163,0.00063067797,0.000119539145,0.0010114562,0.00016040538],"about_ca_topic_score_codex":0.000035868885,"about_ca_topic_score_gemma":0.0000027322255,"teacher_disagreement_score":0.89273167,"about_ca_system_score_codex":0.00012404326,"about_ca_system_score_gemma":0.00009660501,"threshold_uncertainty_score":0.49609557},"labels":[],"label_agreement":null},{"id":"W2965549869","doi":"10.24963/ijcai.2019/867","title":"Optimally Efficient Bidirectional Search","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Israel Science Foundation; Hebrew University of Jerusalem; National Science Foundation","keywords":"Computer science; Heuristic; Bidirectional search; Efficient algorithm; Algorithm; Mathematical optimization; Node (physics); Search algorithm; Best-first search; Mathematics; Beam search","score_opus":0.017984793049340395,"score_gpt":0.27779904959208485,"score_spread":0.2598142565427444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965549869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00672417,0.000018204424,0.89827496,0.0008331318,0.00036718574,0.00019491502,7.333516e-7,0.00017771553,0.09340897],"genre_scores_gemma":[0.15914528,0.000007911982,0.7955125,0.00023733493,0.000053311524,0.000011324287,0.0000028281263,0.000011482445,0.045018047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983793,0.000080666345,0.00014777975,0.0003579417,0.00075441966,0.00027992844],"domain_scores_gemma":[0.99898434,0.00013633043,0.00001814392,0.0004982618,0.0002379221,0.00012500164],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000589066,0.00007675683,0.000095133044,0.00019369062,0.00006478547,0.00018683214,0.00072506524,0.00003249391,0.0027344166],"category_scores_gemma":[0.000052686853,0.000064687854,0.000041036597,0.00066211633,0.000024761537,0.0001335877,0.00035223772,0.00012597615,0.005086945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010637081,0.00026472245,0.0015193028,0.000023711209,0.00003610336,0.000016981337,0.00019878012,0.55416685,0.001346685,0.4068735,0.003662101,0.031880666],"study_design_scores_gemma":[0.00019812617,0.00003969717,0.001296964,0.000002353592,4.5229163e-7,0.000011962174,0.0000069921794,0.9942133,0.0011661039,0.00006962983,0.0029070103,0.00008743908],"about_ca_topic_score_codex":0.0000150799315,"about_ca_topic_score_gemma":2.921489e-7,"teacher_disagreement_score":0.44004643,"about_ca_system_score_codex":0.00004483789,"about_ca_system_score_gemma":0.00016535941,"threshold_uncertainty_score":0.99817723},"labels":[],"label_agreement":null},{"id":"W2965568940","doi":"10.4018/ijamc.2019070102","title":"Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems","year":2019,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Asynchronous communication; Parallel metaheuristic; Particle swarm optimization; Computer science; Multi-swarm optimization; Mathematical optimization; Swarm behaviour; Optimization problem; Distributed computing; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01689514643104261,"score_gpt":0.2886573688923704,"score_spread":0.2717622224613278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965568940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009381302,0.00011213019,0.98691785,0.0005398705,0.0016909556,0.00062677806,0.000011857744,0.000051194627,0.00066805683],"genre_scores_gemma":[0.54467124,0.000012759777,0.45469478,0.00022838284,0.00026725105,0.000009698345,0.000014750839,0.000019123348,0.00008199765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674755,0.00009566136,0.0010663948,0.0004224221,0.001239742,0.00042822963],"domain_scores_gemma":[0.9956049,0.00089845306,0.0008158236,0.00030516568,0.002187004,0.00018867635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018335684,0.00023030318,0.00044992304,0.0002800259,0.00014588426,0.00037667097,0.0014242567,0.00007133668,0.00017541737],"category_scores_gemma":[0.00046466847,0.00020564909,0.00017130892,0.00036177956,0.000036999067,0.00038369847,0.00041631493,0.00029717098,0.000058297483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013251138,0.00024480684,0.00007917467,0.000020519645,0.00025044865,0.000015706702,0.0003780861,0.92803466,0.00032003093,0.06448744,0.00026685404,0.005769738],"study_design_scores_gemma":[0.0028872914,0.00019036153,0.00012367567,0.000053992146,0.000027030765,0.00007372731,0.00004734465,0.99277616,0.00078827166,0.0013571791,0.0014617147,0.00021323403],"about_ca_topic_score_codex":0.0000021546946,"about_ca_topic_score_gemma":4.8276644e-7,"teacher_disagreement_score":0.53528994,"about_ca_system_score_codex":0.00016749492,"about_ca_system_score_gemma":0.00034492929,"threshold_uncertainty_score":0.8386126},"labels":[],"label_agreement":null},{"id":"W2965831557","doi":"10.4018/ijamc.2019100102","title":"Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization","year":2019,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Big data; Data processing; Data mining; Artificial intelligence; Database","score_opus":0.08629247167844621,"score_gpt":0.3261662510747974,"score_spread":0.23987377939635118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965831557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010296378,0.00017430974,0.9844577,0.000090089125,0.0039103213,0.00033796957,0.000009636056,0.000060907274,0.00066269544],"genre_scores_gemma":[0.4369728,0.000020997035,0.56240654,0.00007489624,0.00046239176,8.7235424e-7,0.000012676403,0.000025227479,0.000023587832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958254,0.00015742556,0.0013040145,0.0006703593,0.0016151113,0.00042766382],"domain_scores_gemma":[0.9953031,0.00068603235,0.0014271602,0.0008373196,0.0015555721,0.00019080596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002263952,0.00028699142,0.0005230745,0.0007859493,0.0001466424,0.00066014845,0.0040913057,0.00008888636,0.00006186297],"category_scores_gemma":[0.0007195374,0.00027719862,0.00012467899,0.00060157396,0.00005907406,0.00059363176,0.0019959183,0.00042774033,0.000023453416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030810137,0.00012601116,0.00023653336,0.000021948632,0.0003352026,0.000071595336,0.00057171594,0.9490556,0.0008589809,0.00037965638,0.000024113631,0.048287794],"study_design_scores_gemma":[0.0013137382,0.00005456649,0.00009074294,0.00013412561,0.000043019372,0.0004159712,0.0000951495,0.99680847,0.00040787616,0.00014350696,0.0002341809,0.00025864018],"about_ca_topic_score_codex":0.000014680836,"about_ca_topic_score_gemma":0.0000012009057,"teacher_disagreement_score":0.42667642,"about_ca_system_score_codex":0.00024776917,"about_ca_system_score_gemma":0.0004434214,"threshold_uncertainty_score":0.999968},"labels":[],"label_agreement":null},{"id":"W2966254368","doi":"10.1109/iraniancee.2019.8786601","title":"DTO: Donkey Theorem Optimization","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Donkey; Metaheuristic; Computer science; Optimization problem; Mathematical optimization; Optimization algorithm; Benchmark (surveying); Algorithm; Artificial intelligence; Mathematics; Geography","score_opus":0.01106807928409314,"score_gpt":0.2543633859597186,"score_spread":0.24329530667562543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966254368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011735243,0.000013769812,0.87315637,0.0007325349,0.00029370183,0.00019949557,4.499145e-7,0.0001950676,0.12529127],"genre_scores_gemma":[0.028591422,0.000023611283,0.9492113,0.00042575737,0.00003364816,0.0000085801485,0.0000049601404,0.0000109185,0.021689774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987656,0.00008894142,0.00016880268,0.0003218727,0.00042870108,0.00022609558],"domain_scores_gemma":[0.9988923,0.000112298854,0.000041931668,0.0006770311,0.00018083419,0.00009559076],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039720302,0.0000864045,0.00010660057,0.00012903166,0.00005023739,0.00022794084,0.00081282906,0.00004217217,0.0034327158],"category_scores_gemma":[0.0000987224,0.00007005687,0.000034031782,0.0005622931,0.000021023172,0.00047067265,0.00025706578,0.00008644784,0.001806103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032120297,0.00004740642,0.00038483035,0.000009248026,0.000011769098,0.000003684537,0.00010002765,0.41594407,0.00006151441,0.56651837,0.002098044,0.014817807],"study_design_scores_gemma":[0.00022333408,0.000029966735,0.00009239694,0.0000027965862,8.2537457e-7,0.000004764966,0.0000082246,0.99568236,0.000358846,0.0010996264,0.0023985112,0.00009837462],"about_ca_topic_score_codex":0.000008687576,"about_ca_topic_score_gemma":3.4369657e-7,"teacher_disagreement_score":0.57973826,"about_ca_system_score_codex":0.000025999361,"about_ca_system_score_gemma":0.000067221,"threshold_uncertainty_score":0.9989711},"labels":[],"label_agreement":null},{"id":"W2966288920","doi":"10.1016/j.ins.2019.08.016","title":"Variable neighborhood algebraic Differential Evolution: An application to the Linear Ordering Problem with Cumulative Costs","year":2019,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":41,"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 per Stranieri di Perugia; Ryerson University","keywords":"Permutation (music); Benchmark (surveying); Differential evolution; Differential (mechanical device); Suite; Variable (mathematics); Computer science; Algebraic number; Mathematics; Algorithm; Mathematical optimization; Theoretical computer science","score_opus":0.013136658311947564,"score_gpt":0.2766363749169204,"score_spread":0.26349971660497284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966288920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004145653,0.0000038867242,0.98542273,0.0010489778,0.00014865804,0.0008999354,0.000003705944,0.000107611195,0.008218843],"genre_scores_gemma":[0.7491703,9.812851e-7,0.2502186,0.00033070153,0.000047769452,0.0000980556,0.000011812302,0.0000036696301,0.000118091484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998004,0.00008769482,0.00031565363,0.00027094668,0.0010339394,0.00028775085],"domain_scores_gemma":[0.99863255,0.00010573114,0.00017144189,0.0004928093,0.00047544282,0.000122001155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094296056,0.00011602042,0.00011023392,0.00023595881,0.00044581015,0.00075618946,0.0013185551,0.000034810822,0.00008861099],"category_scores_gemma":[0.00008337774,0.000071818075,0.000015411655,0.0022626035,0.0000844975,0.0046362295,0.00023909158,0.0001141218,0.00057105324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014503529,0.00004294317,0.0012306056,0.000017725753,0.000011388734,1.1852704e-7,0.003053978,0.48299778,0.0000897162,0.46671543,0.00012650208,0.045699306],"study_design_scores_gemma":[0.00021296754,0.00019983854,0.0020701347,0.0000123885275,0.0000019002877,0.0000054947327,0.0002499426,0.99336135,0.00007389277,0.0012129839,0.0024767139,0.00012237496],"about_ca_topic_score_codex":0.00012157743,"about_ca_topic_score_gemma":0.000013715709,"teacher_disagreement_score":0.7450247,"about_ca_system_score_codex":0.000088647554,"about_ca_system_score_gemma":0.00030214663,"threshold_uncertainty_score":0.73399246},"labels":[],"label_agreement":null},{"id":"W2966481281","doi":"10.24963/ijcai.2019/170","title":"Conditions for Avoiding Node Re-expansions in Bounded Suboptimal Search","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"","keywords":"Bounded function; Node (physics); Mathematical optimization; Constant (computer programming); Quality (philosophy); Regular polygon; Computer science; Upper and lower bounds; Mathematics; Engineering","score_opus":0.04593565137038163,"score_gpt":0.3442280730466101,"score_spread":0.29829242167622844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966481281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023464652,0.000011477567,0.96059173,0.0023510603,0.00023621645,0.000771492,0.000013105402,0.00012388642,0.012436384],"genre_scores_gemma":[0.51627505,0.000010100165,0.4755578,0.00029830978,0.000030267644,0.00008822755,0.000025912168,0.000015788735,0.007698532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981552,0.00016400698,0.00029045573,0.00046208553,0.00043953888,0.0004887107],"domain_scores_gemma":[0.9981096,0.000849905,0.00003299476,0.0006183211,0.0002461729,0.00014304374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011433489,0.00011059986,0.00018289419,0.0003963176,0.00020712582,0.00026501197,0.0007636223,0.00006496971,0.00086347276],"category_scores_gemma":[0.00034392,0.000105257895,0.00006286804,0.0008113014,0.000044929402,0.00048816437,0.0002743055,0.0002556285,0.0004963843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037962072,0.0004962452,0.0058638616,0.000122026955,0.00004949426,0.000026144691,0.0022021797,0.07715659,0.007960909,0.8932392,0.01023191,0.0026135053],"study_design_scores_gemma":[0.00078148535,0.00006156559,0.0011309361,0.000014118487,0.0000014268817,0.0000039998913,0.00020752312,0.99354756,0.0013061635,0.0018939715,0.0009061697,0.00014505361],"about_ca_topic_score_codex":0.000072689254,"about_ca_topic_score_gemma":0.000026636491,"teacher_disagreement_score":0.916391,"about_ca_system_score_codex":0.000091071524,"about_ca_system_score_gemma":0.0002581637,"threshold_uncertainty_score":0.94544214},"labels":[],"label_agreement":null},{"id":"W2967371416","doi":"10.1049/el.2019.1965","title":"Optimal search algorithm in a big database using interpolation–extrapolation method","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"Universiti Malaysia Pahang","keywords":"Extrapolation; Interpolation (computer graphics); Binary search algorithm; Computer science; Search algorithm; Algorithm; Convergence (economics); Data mining; Mathematics; Artificial intelligence; Statistics","score_opus":0.028123793298025394,"score_gpt":0.3221547195347365,"score_spread":0.2940309262367111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967371416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026011053,0.00015498945,0.97151625,0.0014076717,0.00026824552,0.0004398062,0.0000055965993,0.000077249635,0.00011914595],"genre_scores_gemma":[0.021981034,0.000025911891,0.9771042,0.00061400566,0.00008646916,0.00001221415,0.000034963297,0.00002786767,0.00011337561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696374,0.00048697565,0.00039588584,0.0006615878,0.0007254501,0.0007663346],"domain_scores_gemma":[0.99867266,0.00025852598,0.00009329224,0.00074554613,0.000113737326,0.000116258336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017229643,0.0001931042,0.00023447443,0.00073970685,0.00008481501,0.00026253876,0.0008874768,0.00007078222,0.00010379816],"category_scores_gemma":[0.00006800113,0.00020883922,0.0000639741,0.0015153554,0.000030229052,0.0007472035,0.00032109822,0.0006105291,0.00009870994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053131254,0.00024670895,0.0019105093,0.00007222905,0.00011120389,0.00008179805,0.0015968449,0.4722837,0.27828148,0.01495924,0.00044102332,0.22996214],"study_design_scores_gemma":[0.00059678603,0.000053898515,0.00016830873,0.00001938433,0.0000036855347,0.000028416895,0.00001762975,0.9952688,0.0029414918,0.00006867335,0.0006152384,0.00021765694],"about_ca_topic_score_codex":0.00011379935,"about_ca_topic_score_gemma":0.000009121403,"teacher_disagreement_score":0.52298516,"about_ca_system_score_codex":0.00037915015,"about_ca_system_score_gemma":0.00036763097,"threshold_uncertainty_score":0.8516216},"labels":[],"label_agreement":null},{"id":"W2968560091","doi":"10.22098/joape.2019.5522.1414","title":"FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems","year":2020,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Metaheuristic Optimization Algorithms Research","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":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Optimization algorithm; Mathematical optimization; Computer science; Engineering optimization; Power optimization; Power (physics); Optimization problem; Algorithm; Mathematics; Power consumption; Physics","score_opus":0.1893431910617587,"score_gpt":0.4907672931665305,"score_spread":0.3014241021047718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968560091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001721513,0.002646457,0.9935973,0.0006272545,0.0008740225,0.0013775715,0.000027137692,0.00019571919,0.00048232972],"genre_scores_gemma":[0.018205892,0.001565338,0.9791159,0.0004075365,0.00022014858,0.00018136011,0.00004822814,0.00011468229,0.00014091357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99594295,0.00016041614,0.0011691725,0.00085787557,0.0012350905,0.0006344891],"domain_scores_gemma":[0.9967316,0.0004826863,0.00074067136,0.0006091495,0.0008950209,0.00054088456],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016475947,0.0004109123,0.00078061176,0.0008449531,0.0003571233,0.0036490243,0.0047202427,0.00015207875,0.0017285408],"category_scores_gemma":[0.0016519956,0.00042094203,0.00028778455,0.0026540838,0.000046076104,0.005587969,0.0016790897,0.00038651368,0.00000920343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013770367,0.00009990414,0.00056923216,0.00006937253,0.00014983895,0.000015638198,0.00027433754,0.9804124,0.0016119805,0.00013976527,0.00406936,0.012574362],"study_design_scores_gemma":[0.0007797272,0.00002972041,0.00041697474,0.00016155139,0.000037633654,0.000006653481,0.000017538216,0.9944966,0.0020889086,0.00018972534,0.001312531,0.00046243914],"about_ca_topic_score_codex":0.000032997872,"about_ca_topic_score_gemma":4.3458002e-7,"teacher_disagreement_score":0.018033741,"about_ca_system_score_codex":0.00013199309,"about_ca_system_score_gemma":0.00026833813,"threshold_uncertainty_score":0.9998242},"labels":[],"label_agreement":null},{"id":"W2969626082","doi":"10.35378/gujs.484643","title":"DGO: Dice Game Optimizer","year":2019,"lang":"en","type":"article","venue":"GAZI UNIVERSITY JOURNAL OF SCIENCE","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Calgary","funders":"","keywords":"Dice; Computer science; Metaheuristic; Benchmark (surveying); Set (abstract data type); Mathematical optimization; Particle swarm optimization; Artificial intelligence; Ant colony optimization algorithms; Algorithm; Mathematics","score_opus":0.007401533093730508,"score_gpt":0.21909546990426787,"score_spread":0.21169393681053736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969626082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054149013,0.000037476715,0.9279534,0.0010354588,0.0007994165,0.00009613007,0.0000010336557,0.000023837767,0.015904196],"genre_scores_gemma":[0.55975646,0.00011753056,0.4322362,0.00015346018,0.00004827951,3.177886e-8,1.2118996e-7,0.0000051003153,0.0076828087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979157,0.00008134785,0.00019042236,0.0002520999,0.0012467512,0.0003136846],"domain_scores_gemma":[0.997941,0.00012033844,0.00024759053,0.00040853937,0.000979857,0.00030266878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016479761,0.00007993377,0.00016751539,0.0006900573,0.00017606372,0.00019112787,0.0032557868,0.000029229586,0.00022941931],"category_scores_gemma":[0.00021673043,0.00007314222,0.00007123238,0.0020656025,0.0005144872,0.002501397,0.0005675249,0.00024282945,0.00019445206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048390336,0.0014015934,0.014935563,0.00014845813,0.00026789928,0.00341873,0.016812092,0.36297372,0.056373794,0.398224,0.017790597,0.12716965],"study_design_scores_gemma":[0.0029965383,0.00063522725,0.010857939,0.000097935634,0.000023291197,0.00073540217,0.0013356511,0.9315765,0.0029384692,0.0009511205,0.04733307,0.00051887374],"about_ca_topic_score_codex":0.0000059833283,"about_ca_topic_score_gemma":2.478751e-7,"teacher_disagreement_score":0.5686028,"about_ca_system_score_codex":0.0001749533,"about_ca_system_score_gemma":0.0007711156,"threshold_uncertainty_score":0.60501164},"labels":[],"label_agreement":null},{"id":"W2969862035","doi":"10.1007/s13042-019-00996-5","title":"Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm","year":2019,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Computer science; Feature (linguistics); Heuristic; Algorithm; Artificial intelligence; Computational intelligence; Selection (genetic algorithm); Rough set; Optimization problem; Set (abstract data type); Wilcoxon signed-rank test; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.009192440010017096,"score_gpt":0.25996537604980174,"score_spread":0.2507729360397846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969862035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030155687,0.00034415402,0.99276936,0.0014650939,0.00031269787,0.00012729639,0.0000051382663,0.00003404072,0.0019266366],"genre_scores_gemma":[0.15499845,0.00048336206,0.84174794,0.0002493671,0.00021082711,0.0000032151593,0.000045642475,0.000025050627,0.0022361528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981276,0.00026286038,0.00026981597,0.00029439962,0.00087640923,0.0001689063],"domain_scores_gemma":[0.99876946,0.00016640566,0.00031139175,0.00011569571,0.00051287294,0.00012419603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006780263,0.00016795546,0.00021235282,0.00039736426,0.00008808465,0.0003602243,0.00042762974,0.00010227469,0.00003752808],"category_scores_gemma":[0.00015070505,0.00014308044,0.000058536218,0.00025861154,0.00004466198,0.00032018792,0.0001366533,0.00068949006,0.0000042085703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051041006,0.00013299088,0.0036595315,0.000015837262,0.00006641273,0.000011517567,0.0002250368,0.9590326,0.000027061009,0.000571029,0.000271642,0.03593532],"study_design_scores_gemma":[0.0010820867,0.00040044205,0.0007730709,0.00003818613,0.000012047046,0.00028447446,0.000040656512,0.9935056,0.00003076108,0.000041724295,0.0036502122,0.00014074036],"about_ca_topic_score_codex":0.00000934738,"about_ca_topic_score_gemma":1.3232986e-7,"teacher_disagreement_score":0.15198289,"about_ca_system_score_codex":0.000055732587,"about_ca_system_score_gemma":0.0000737831,"threshold_uncertainty_score":0.5834651},"labels":[],"label_agreement":null},{"id":"W2973195070","doi":"10.1007/s11063-019-10112-x","title":"Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training","year":2019,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Curse of dimensionality; Artificial neural network; Computer science; Particle swarm optimization; Decomposition; Computational intelligence; Factorization; Swarm intelligence; Mathematical optimization; Swarm behaviour; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.02485344995936932,"score_gpt":0.26620710131832226,"score_spread":0.24135365135895293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973195070","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":[0.13668196,0.00007604906,0.85930264,0.0031248573,0.00024466877,0.0003951892,0.0000010898538,0.00012060883,0.000052937405],"genre_scores_gemma":[0.9218772,0.000004416253,0.07568353,0.0022569553,0.00008217129,0.00002321695,0.00001562278,0.00002164284,0.000035222733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978152,0.0003026826,0.00036561815,0.00055589614,0.00041073374,0.00054986594],"domain_scores_gemma":[0.99918735,0.00020221114,0.00014150323,0.0002336669,0.000119781194,0.00011548516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006042385,0.00019071017,0.00027650225,0.00012660972,0.00023257949,0.00057102105,0.0003132127,0.000060925962,0.000022641534],"category_scores_gemma":[0.00015585303,0.00018026079,0.000030488804,0.0010256745,0.000053594427,0.0012237622,0.00010277618,0.00027176316,0.0000045085453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003133363,0.000018687197,0.0039218017,0.00003183056,0.0000027848507,0.0000073903866,0.0018315468,0.9883127,0.0007808449,0.000032398828,0.0000272602,0.005001384],"study_design_scores_gemma":[0.0020585086,0.000041122654,0.0011511173,0.00006143937,0.0000039492465,0.000005341354,0.000113187205,0.9960675,0.0002638488,0.000010453903,0.00002509188,0.00019843654],"about_ca_topic_score_codex":0.0000063108882,"about_ca_topic_score_gemma":0.000004494463,"teacher_disagreement_score":0.7851953,"about_ca_system_score_codex":0.00005500821,"about_ca_system_score_gemma":0.00008067243,"threshold_uncertainty_score":0.73508215},"labels":[],"label_agreement":null},{"id":"W2975160087","doi":"10.1109/iccse.2019.8845360","title":"Tackling Deceptive Optimization Problems Using Opposition-based DE with Center-based Latin Hypercube Initialization","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Initialization; Population; Computer science; Latin hypercube sampling; Benchmark (surveying); Mathematical optimization; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Monte Carlo method; Statistics","score_opus":0.028997078482653354,"score_gpt":0.2737080330282571,"score_spread":0.24471095454560374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975160087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006061247,0.000005984025,0.9907458,0.00023820724,0.00010182061,0.0007413675,0.000006657493,0.00022942225,0.0018695382],"genre_scores_gemma":[0.2646041,0.0000020033829,0.734692,0.00046200797,0.000023954128,0.0000227932,0.00008548526,0.00002664773,0.000081012055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978586,0.0002699947,0.0003287901,0.0005075759,0.00063612254,0.00039891712],"domain_scores_gemma":[0.998384,0.00018762078,0.000163312,0.00047323396,0.00063087465,0.00016098504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045664105,0.00019948193,0.000198846,0.00035178266,0.0001736905,0.00040990222,0.00045515312,0.00008945521,0.0004891109],"category_scores_gemma":[0.00008401727,0.00017799242,0.00004413806,0.001067424,0.00004863309,0.00062097743,0.000070807146,0.00013960016,0.000042922864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016020422,0.00012224303,0.0035276061,0.00003869692,0.000011456286,0.0000044090175,0.00010544897,0.99392307,0.000713116,0.0010839431,0.000013275856,0.00044069614],"study_design_scores_gemma":[0.0013505826,0.00016597068,0.00007814743,0.00010831677,0.00000884283,0.0000070424085,0.000027377911,0.9943802,0.0035632427,0.00003986707,0.00003165824,0.00023874991],"about_ca_topic_score_codex":0.000052634157,"about_ca_topic_score_gemma":0.0000053660447,"teacher_disagreement_score":0.25854287,"about_ca_system_score_codex":0.00022371975,"about_ca_system_score_gemma":0.00064677675,"threshold_uncertainty_score":0.72583205},"labels":[],"label_agreement":null},{"id":"W2976813994","doi":"10.1109/iccse.2019.8845065","title":"Differential Evolution Algorithm Based on a Competition Scheme","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Crossover; Differential evolution; Competition (biology); Mathematical optimization; Mutation; Algorithm; Computer science; Population; Scheme (mathematics); Mathematics; Artificial intelligence","score_opus":0.010302146531306967,"score_gpt":0.2475222060849511,"score_spread":0.23722005955364414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976813994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008688766,0.0000029651553,0.9837061,0.00057885674,0.0004727524,0.00028350358,0.0000033636445,0.00021605125,0.013867519],"genre_scores_gemma":[0.26322022,0.0000015691455,0.7327703,0.00028476058,0.00007507171,0.0000148968065,0.000024795612,0.000011656359,0.0035967065],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998285,0.000117665164,0.00018226978,0.00040676436,0.0007467008,0.00026161966],"domain_scores_gemma":[0.99892557,0.00011322334,0.00004956744,0.000631259,0.00017036842,0.00010999619],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019201222,0.00011913551,0.00013476846,0.00024725703,0.00007328094,0.00016653401,0.00053644105,0.00005724461,0.0028935564],"category_scores_gemma":[0.00004788147,0.000103933606,0.00005764246,0.0004620626,0.000027910959,0.000245164,0.00012524007,0.000147853,0.0020475767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003311596,0.00083827035,0.0009382543,0.000052331514,0.0000323038,0.000014978736,0.00006188922,0.013664929,0.0025988712,0.89804065,0.0022157512,0.08150866],"study_design_scores_gemma":[0.0006597011,0.00013290384,0.0016643893,0.000012274549,0.0000011895646,0.0000016995979,0.000003901591,0.9956654,0.0006509098,0.00056265254,0.0005167436,0.00012827873],"about_ca_topic_score_codex":0.000015120733,"about_ca_topic_score_gemma":5.075938e-7,"teacher_disagreement_score":0.9820004,"about_ca_system_score_codex":0.00010383259,"about_ca_system_score_gemma":0.00009818187,"threshold_uncertainty_score":0.99872947},"labels":[],"label_agreement":null},{"id":"W2977652100","doi":"10.5220/0008169001960204","title":"ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Local optimum; CMA-ES; Computer science; Simulated annealing; Multi-swarm optimization; Global optimization; Scalability; Optimization problem; Algorithm; Evolution strategy; Mathematics; Evolutionary computation","score_opus":0.08821480649629737,"score_gpt":0.3281255347339308,"score_spread":0.23991072823763343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977652100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017885103,0.00016045183,0.9962866,0.0004152255,0.0004850695,0.0016365607,0.000013104072,0.00031536442,0.00050881633],"genre_scores_gemma":[0.012441285,0.000022887143,0.9698387,0.0002719478,0.000029736142,0.0001847229,0.000036297875,0.000040600215,0.017133843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706286,0.00019866643,0.00051564496,0.00093551027,0.0006963175,0.0005910182],"domain_scores_gemma":[0.99739546,0.00029474555,0.00015420828,0.0011211253,0.0007870799,0.00024736513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010660003,0.00029477064,0.0005237644,0.00031887164,0.00019433555,0.00033166597,0.0010027509,0.00013345048,0.00090699265],"category_scores_gemma":[0.0005796332,0.00023989745,0.00023414468,0.00071691565,0.000044808035,0.0007500489,0.0004201948,0.0001622918,0.00045662333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015237315,0.00029747878,0.000047966838,0.00005633796,0.0002415524,0.0000042282913,0.000090396585,0.98655874,0.0006095213,0.010211918,0.0006483547,0.0012182803],"study_design_scores_gemma":[0.0018803456,0.00011155431,0.00005930051,0.0000068132,0.00009522606,0.0000070298297,0.000012206109,0.9951538,0.001549332,0.000118888194,0.00070927595,0.0002962658],"about_ca_topic_score_codex":0.000049277332,"about_ca_topic_score_gemma":0.0000029174244,"teacher_disagreement_score":0.026447877,"about_ca_system_score_codex":0.00007852202,"about_ca_system_score_gemma":0.00014347525,"threshold_uncertainty_score":0.9930934},"labels":[],"label_agreement":null},{"id":"W2980792681","doi":"10.1007/s00521-019-04530-0","title":"Sine–cosine crow search algorithm: theory and applications","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":85,"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":"Sine; Algorithm; Trigonometric functions; Benchmark (surveying); Discrete cosine transform; Computer science; Search algorithm; Mathematics; Artificial intelligence","score_opus":0.015251669369342387,"score_gpt":0.3009023027928943,"score_spread":0.2856506334235519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980792681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032082535,0.0003897287,0.9924531,0.00074751524,0.000035689573,0.0009375849,0.000009641697,0.00022093987,0.0019975218],"genre_scores_gemma":[0.5885196,0.00017641131,0.40624395,0.00086692354,0.0005105389,0.00032511845,0.000053598702,0.00004313119,0.003260739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983158,0.00017020598,0.00025849458,0.0006313666,0.00030158428,0.00032255487],"domain_scores_gemma":[0.9982448,0.0006350728,0.000071201495,0.00064794155,0.00020421026,0.00019674814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068075856,0.00015741821,0.00018288095,0.00015420676,0.00044299936,0.000354278,0.00055873196,0.000058308397,0.000024134455],"category_scores_gemma":[0.000021590844,0.00014940751,0.000031027685,0.00072795886,0.00013462995,0.00018023877,0.00052902894,0.0002704126,0.00009936543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022227907,0.00006279541,0.00041705612,0.00003899093,0.000012261666,7.8685144e-7,0.00011408675,0.0013172621,0.0003178218,0.2089527,0.000080864396,0.7886832],"study_design_scores_gemma":[0.0003478714,0.000046160887,0.0028429143,0.000011323391,0.0000071419,0.000052131563,0.000049209004,0.97725123,0.00016217952,0.004203656,0.014810474,0.00021568999],"about_ca_topic_score_codex":0.000010495174,"about_ca_topic_score_gemma":3.6694277e-7,"teacher_disagreement_score":0.97593397,"about_ca_system_score_codex":0.000014585986,"about_ca_system_score_gemma":0.00004656208,"threshold_uncertainty_score":0.60926616},"labels":[],"label_agreement":null},{"id":"W2987410708","doi":"10.1007/s11590-019-01497-8","title":"Monotonic grey box direct search optimization","year":2019,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Rio Tinto (Canada); Giro (Canada); Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Monotonic function; Mathematical optimization; Computer science; Optimization problem; Computational intelligence; Function (biology); Algorithm; Mathematics; Artificial intelligence","score_opus":0.012358154721532576,"score_gpt":0.24862522730838083,"score_spread":0.23626707258684826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987410708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042045844,0.00004832531,0.98114437,0.0050523053,0.00064928527,0.0008143509,0.000005658975,0.00042854206,0.011436715],"genre_scores_gemma":[0.0156625,0.00016108384,0.97874355,0.0023366946,0.00010064858,0.000059649377,0.00008933888,0.0000659609,0.0027805676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965092,0.00037339103,0.00050565426,0.0009075853,0.0010545392,0.0006496135],"domain_scores_gemma":[0.9977248,0.00020834676,0.0001658258,0.0012224831,0.0004370586,0.00024154021],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007864032,0.0002948583,0.00033131702,0.00057876157,0.00021750799,0.0007178367,0.001296319,0.0001225393,0.0015890014],"category_scores_gemma":[0.00018641326,0.00030433803,0.00011254269,0.0017398471,0.000072063485,0.0015453628,0.00037773923,0.00028676697,0.00067983306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009046346,0.000051773903,0.00031400216,0.000019546582,0.000028105685,0.000008128468,0.00015001233,0.995142,0.00020258864,0.0011239968,0.0017047365,0.0012460889],"study_design_scores_gemma":[0.0006782865,0.000052663083,0.0000789452,0.00001872004,0.000007304215,0.000009903092,0.000013204658,0.9971598,0.0006137961,0.0000069206817,0.0010188146,0.00034166686],"about_ca_topic_score_codex":0.000023469378,"about_ca_topic_score_gemma":4.5687304e-7,"teacher_disagreement_score":0.015242043,"about_ca_system_score_codex":0.00019399174,"about_ca_system_score_gemma":0.00016428065,"threshold_uncertainty_score":0.9999409},"labels":[],"label_agreement":null},{"id":"W2988771853","doi":"10.48550/arxiv.1911.01012","title":"StoMADS: Stochastic blackbox optimization using probabilistic estimates","year":2019,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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; Polytechnique Montréal","funders":"","keywords":"Mathematical optimization; Probabilistic logic; Martingale (probability theory); Convergence (economics); Computer science; Mathematics; Probability distribution; Stochastic optimization; Set (abstract data type); Algorithm; Convergence of random variables; Random variable; Applied mathematics; Artificial intelligence","score_opus":0.02513923476694892,"score_gpt":0.28021499125755706,"score_spread":0.25507575649060815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988771853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013914309,0.0010036959,0.98987937,0.0011850281,0.0008969743,0.0035040728,0.000097468575,0.0018185851,0.00022336267],"genre_scores_gemma":[0.07331989,0.00011036106,0.9243753,0.00042927236,0.000201456,0.00070226844,0.00015606062,0.00018332308,0.0005220852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99327755,0.0005029225,0.001273354,0.0019415295,0.0015531125,0.0014515235],"domain_scores_gemma":[0.99296767,0.00061009685,0.00093319366,0.0038670027,0.0009956567,0.00062641036],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017166097,0.0009979355,0.0011401074,0.0015184975,0.00037474555,0.0018565,0.0037991167,0.00096261734,0.000105897256],"category_scores_gemma":[0.0022347523,0.0010653768,0.00034838743,0.0013869059,0.00022028247,0.00076526625,0.0045170984,0.0017373061,0.00007859127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022557699,0.00022188305,0.00039239836,0.0002877588,0.0000972584,0.00004603228,0.00019183925,0.98648965,0.00007504647,0.009490701,0.00038821786,0.002296671],"study_design_scores_gemma":[0.0004397446,0.00010204738,0.0003754749,0.00033061227,0.00009224657,0.0001201737,0.00001639799,0.9909684,0.00020125459,0.0063368985,0.000033007836,0.0009837429],"about_ca_topic_score_codex":0.002008205,"about_ca_topic_score_gemma":0.00006499073,"teacher_disagreement_score":0.071928464,"about_ca_system_score_codex":0.0017088254,"about_ca_system_score_gemma":0.002428369,"threshold_uncertainty_score":0.99917966},"labels":[],"label_agreement":null},{"id":"W2991539177","doi":"10.1109/smc42975.2020.9283242","title":"Swarm Based Algorithms for Neural Network Training","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Overfitting; Firefly algorithm; Computer science; Artificial neural network; Ant colony optimization algorithms; Algorithm; Particle swarm optimization; Swarm behaviour; Artificial intelligence; Machine learning; Optimization algorithm; Swarm intelligence; Mathematical optimization; Mathematics","score_opus":0.15166154031169068,"score_gpt":0.3290099753626263,"score_spread":0.1773484350509356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991539177","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028623026,0.000026188705,0.9828334,0.0142596,0.00028316403,0.00035674314,0.0000035544142,0.00031768664,0.0018910281],"genre_scores_gemma":[0.017263038,0.0000018261755,0.9753413,0.00651756,0.0004496398,0.000051573148,0.000008911347,0.000016214535,0.0003498978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984055,0.000076718694,0.00024388364,0.0004399266,0.00037343445,0.0004605345],"domain_scores_gemma":[0.9988011,0.0003767545,0.00005104153,0.00029683896,0.0001606281,0.00031364238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004249905,0.00012417269,0.0001844317,0.00004525418,0.00014609049,0.00023703274,0.0008888004,0.00004238892,0.0001424122],"category_scores_gemma":[0.00034332502,0.00011120174,0.00008483685,0.0006898515,0.000029643728,0.00022556917,0.0001498833,0.00012297032,0.00004441719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016381733,0.000028397571,0.000035893267,0.000026552461,0.00002058331,0.000016123513,0.0005015449,0.7609835,0.000022020404,0.01933121,0.02293061,0.19608717],"study_design_scores_gemma":[0.00052744517,0.00012648017,0.00003045966,0.0000023650784,0.000002800247,0.0000016412729,0.000017077555,0.98360986,0.00016923308,0.00036622555,0.015011583,0.0001348491],"about_ca_topic_score_codex":0.0000035046353,"about_ca_topic_score_gemma":5.730375e-7,"teacher_disagreement_score":0.22262631,"about_ca_system_score_codex":0.000014233534,"about_ca_system_score_gemma":0.00014704402,"threshold_uncertainty_score":0.45346755},"labels":[],"label_agreement":null},{"id":"W2994068620","doi":"10.1016/j.micpro.2019.102949","title":"An analytical framework for high-speed hardware particle swarm optimization","year":2019,"lang":"en","type":"article","venue":"Microprocessors and Microsystems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":19,"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; Implementation; Particle swarm optimization; Benchmark (surveying); Speedup; Key (lock); Computer engineering; Software; Embedded system; Parallel computing; Computer architecture; Distributed computing; Algorithm; Operating system","score_opus":0.020090199278023475,"score_gpt":0.2989993548084793,"score_spread":0.2789091555304558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994068620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11165449,0.00030489152,0.8863176,0.00035000782,0.00033016046,0.0008519143,0.000029021474,0.00012390109,0.000038026203],"genre_scores_gemma":[0.6087623,0.000045555993,0.38931188,0.00019815064,0.00012647675,0.000053360352,0.000038806822,0.00003665558,0.0014267706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980894,0.00007993906,0.00039341673,0.00070182444,0.00027571278,0.0004597235],"domain_scores_gemma":[0.99845433,0.00014008602,0.00013361615,0.00060619216,0.00043454923,0.00023124057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005578666,0.00019893912,0.00033392807,0.00010753895,0.00019275538,0.00073299656,0.0006457157,0.00015055752,0.000074388],"category_scores_gemma":[0.00012009765,0.00017846521,0.000048797083,0.0005036417,0.00005251314,0.0005115391,0.00012340907,0.00014350406,0.000058989604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043062062,0.0015686848,0.012029347,0.0035448468,0.0004139241,0.000042367472,0.007847438,0.40328774,0.13146032,0.39331424,0.002260263,0.04380021],"study_design_scores_gemma":[0.0007894323,0.00019618761,0.00009460499,0.00006162025,0.0000127606845,0.000018988183,0.00010663126,0.97822464,0.017578386,0.00046644866,0.0021762277,0.00027406932],"about_ca_topic_score_codex":0.000038595877,"about_ca_topic_score_gemma":0.0000018650992,"teacher_disagreement_score":0.5749369,"about_ca_system_score_codex":0.00004213875,"about_ca_system_score_gemma":0.00008619484,"threshold_uncertainty_score":0.72775996},"labels":[],"label_agreement":null},{"id":"W2999073106","doi":"10.5539/cis.v13n1p41","title":"Artificial God Optimization - A Creation","year":2020,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Computer science; Field (mathematics); Multi-swarm optimization; Benchmark (surveying); Swarm intelligence; Metaheuristic; Optimization problem; Genetic algorithm; Mathematical optimization; Simple (philosophy); Artificial intelligence; Derivative-free optimization; Focus (optics); Algorithm; Machine learning; Mathematics; Physics; Epistemology","score_opus":0.028139097350418158,"score_gpt":0.277713342972343,"score_spread":0.24957424562192485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999073106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026262828,0.0000070480246,0.99214244,0.003813207,0.00018642936,0.00016442245,0.0000015796606,0.0001429558,0.0032792783],"genre_scores_gemma":[0.11699849,0.00003899923,0.8792148,0.0035979012,0.0001190542,0.000008632709,0.000010667016,0.000002957327,0.000008468114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985964,0.00003102004,0.000318082,0.00023050519,0.00062382995,0.00020018626],"domain_scores_gemma":[0.99897254,0.000041518888,0.000100314646,0.00021007739,0.00043115995,0.00024441455],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00053115474,0.000084821535,0.000093310926,0.00025526757,0.000313815,0.0012624295,0.0006617501,0.0000268624,0.00004061943],"category_scores_gemma":[0.00023563183,0.00007816372,0.000016629061,0.0017142909,0.00017860736,0.011802275,0.0003925282,0.00007960547,0.000103190534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007672623,0.000018195278,0.00007684484,0.000025501673,0.0000037138002,0.0000010411599,0.00411675,0.35261476,0.00008587069,0.16831195,0.00085265865,0.47388506],"study_design_scores_gemma":[0.00010382458,0.00006450851,0.0004634624,0.0000033281513,9.739015e-7,0.0000058478468,0.000014961116,0.9952471,0.00040749248,0.00013220118,0.0034634671,0.00009283625],"about_ca_topic_score_codex":0.000001907705,"about_ca_topic_score_gemma":3.043881e-8,"teacher_disagreement_score":0.64263237,"about_ca_system_score_codex":0.00002498255,"about_ca_system_score_gemma":0.00016716801,"threshold_uncertainty_score":0.99977434},"labels":[],"label_agreement":null},{"id":"W3005942432","doi":"10.23977/cpcs.2020.41001","title":"A hybrid algorithm based on cuckoo search and differential evolution for numerical optimization","year":2020,"lang":"en","type":"article","venue":"Computing Performance and Communication systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Cuckoo search; Differential evolution; Benchmark (surveying); Mathematical optimization; Evolutionary algorithm; Local search (optimization); Algorithm; Metaheuristic; Meta-optimization; Computer science; Optimization problem; Global optimization; Evolutionary computation; Population; Cuckoo; Mathematics; Particle swarm optimization","score_opus":0.033952841115957054,"score_gpt":0.27565463037756505,"score_spread":0.24170178926160799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005942432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069558504,0.00023539671,0.99089223,0.0010073441,0.00010560236,0.00056732737,0.0000061100754,0.00014112476,0.00008904061],"genre_scores_gemma":[0.7650932,0.00007921677,0.23451586,0.00014116993,0.00007379244,0.000031383428,0.000035329755,0.000012192077,0.000017839813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983636,0.0002820016,0.00035222378,0.00038844917,0.00036782093,0.00024591846],"domain_scores_gemma":[0.9986044,0.00030354992,0.00011251083,0.00053488143,0.0002727121,0.00017195372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055078865,0.00015126147,0.00023015837,0.00013079903,0.0005420172,0.00041010207,0.00060577766,0.000050359788,0.0000032853827],"category_scores_gemma":[0.000084425,0.00014595561,0.000030802585,0.00031305721,0.00006908436,0.00027074534,0.00034731912,0.00020165018,0.0000060336474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030930405,0.000059754315,0.0005910928,0.00015334942,0.000014914314,5.6224496e-7,0.00037499095,0.9336907,0.000011517365,0.0033757645,0.0002060785,0.061490327],"study_design_scores_gemma":[0.0007302768,0.00023111684,0.00067677564,0.00007416289,0.000005478107,0.000009348459,0.000030708146,0.99773955,0.000036896723,0.000007358982,0.00030514848,0.00015320076],"about_ca_topic_score_codex":0.000010670689,"about_ca_topic_score_gemma":4.0448633e-8,"teacher_disagreement_score":0.75813735,"about_ca_system_score_codex":0.000048595317,"about_ca_system_score_gemma":0.00007241791,"threshold_uncertainty_score":0.5951897},"labels":[],"label_agreement":null},{"id":"W3008204235","doi":"10.5539/mas.v14n3p30","title":"Parallel Whale Optimization Algorithm for Maximum Flow Problem","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Maximum flow problem; Computer science; Algorithm; Speedup; Optimization algorithm; Whale; Flow (mathematics); Graph; Parallel computing; Mathematical optimization; Theoretical computer science; Mathematics","score_opus":0.03306968845705108,"score_gpt":0.26837070637336846,"score_spread":0.23530101791631738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008204235","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020589857,0.00003062097,0.9921538,0.0024611524,0.0001216326,0.0011141713,0.000012196638,0.00030593784,0.0037983858],"genre_scores_gemma":[0.0039353464,0.000014054559,0.99454254,0.00095527107,0.000093351126,0.00026331493,0.000011561415,0.000021758697,0.00016283015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965427,0.000037458594,0.00036092938,0.0011577663,0.0011915287,0.0007096187],"domain_scores_gemma":[0.9982533,0.00010623179,0.00012444305,0.0006211118,0.00041457525,0.00048032292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011910052,0.00021094563,0.00023995609,0.00018281961,0.0005430034,0.0007761326,0.0025255673,0.00006542662,0.000045161418],"category_scores_gemma":[0.00016208335,0.00020318083,0.000055882083,0.0018228046,0.000295594,0.00082881417,0.00063539145,0.0001662608,0.000081561266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000661944,0.00004244022,9.4126483e-7,0.000016638609,0.000004618164,0.0000020150394,0.00067940383,0.64550567,0.0018236302,0.0057515935,0.00041237165,0.34575406],"study_design_scores_gemma":[0.00058541924,0.000059557024,0.0000028040365,0.0000036160382,0.0000035548558,0.0000031933923,0.000016515427,0.9910706,0.001166054,0.0060723983,0.0007715771,0.00024470655],"about_ca_topic_score_codex":0.0000021810697,"about_ca_topic_score_gemma":2.5445271e-7,"teacher_disagreement_score":0.34556493,"about_ca_system_score_codex":0.00008275164,"about_ca_system_score_gemma":0.00046158736,"threshold_uncertainty_score":0.82854736},"labels":[],"label_agreement":null},{"id":"W3009600022","doi":"10.1007/s12652-020-01829-y","title":"Machine learning based metaheuristic hybrids for S-box optimization","year":2020,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"A Thinking Ape (Canada); University of Prince Edward Island","funders":"","keywords":"Computer science; Metaheuristic; Hill climbing; Heuristic; Artificial intelligence; Machine learning; Task (project management); Domain (mathematical analysis); GRASP; Hyper-heuristic; Mathematical optimization; Mathematics; Engineering","score_opus":0.06293116070639058,"score_gpt":0.30634718657521226,"score_spread":0.24341602586882166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009600022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047571765,0.0005168724,0.99742895,0.0010096297,0.00023593732,0.00021579268,0.0000015194101,0.000051694573,0.000063874926],"genre_scores_gemma":[0.3389634,0.00011046652,0.66013163,0.00055144046,0.0001912529,0.0000015170702,0.000003982752,0.0000166088,0.000029727415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787784,0.00018034085,0.00082645006,0.00030817726,0.0005219246,0.00028524298],"domain_scores_gemma":[0.99751306,0.00062609784,0.0006206208,0.00014907142,0.0008370418,0.00025410805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012592793,0.00017345262,0.0003916934,0.00025144813,0.00032752546,0.00041566044,0.00071707787,0.000040902858,0.00006154781],"category_scores_gemma":[0.0014053792,0.00015570273,0.0001377973,0.00044657078,0.00005490503,0.00033028913,0.00019790076,0.0003715389,0.000004581484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052239815,0.000053842097,0.00008595244,0.00006780324,0.00004371376,0.000021355425,0.0006274349,0.9677604,0.00008835849,0.004491931,0.00007575188,0.026631245],"study_design_scores_gemma":[0.0004757098,0.0006869221,0.000012475458,0.000053145875,0.000030448131,0.00003396236,0.0000613865,0.99513304,0.0016443911,0.00037995167,0.0013346763,0.00015386853],"about_ca_topic_score_codex":0.0000030829476,"about_ca_topic_score_gemma":1.4527788e-7,"teacher_disagreement_score":0.33848765,"about_ca_system_score_codex":0.00003725785,"about_ca_system_score_gemma":0.00012062716,"threshold_uncertainty_score":0.63493735},"labels":[],"label_agreement":null},{"id":"W3011183614","doi":"10.1016/j.aci.2020.03.002","title":"Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions","year":2020,"lang":"en","type":"article","venue":"Applied Computing and Informatics","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Ant colony optimization algorithms; Swarm intelligence; Computer science; Particle swarm optimization; Cover (algebra); Algorithm; Swarm behaviour; Product (mathematics); Variable (mathematics); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.060022377711279815,"score_gpt":0.2991870005784786,"score_spread":0.23916462286719875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011183614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012360701,0.00003117974,0.99025375,0.00010284723,0.00012809456,0.00020552106,0.000004221931,0.00009495762,0.007943383],"genre_scores_gemma":[0.43361327,0.0000578565,0.5660889,0.00018912615,0.00002276176,0.000007426761,0.0000030168137,0.0000065708027,0.000011038498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985372,0.00004672519,0.0006350437,0.00018657192,0.00033677227,0.0002577053],"domain_scores_gemma":[0.9987634,0.0005321491,0.00020047408,0.00026908232,0.00010055245,0.00013435274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051734125,0.000144039,0.00029129453,0.00014935156,0.00009704362,0.000106493855,0.0005105731,0.000062565225,0.000010306341],"category_scores_gemma":[0.0004806403,0.00013487737,0.00003482685,0.00084415614,0.0000872481,0.00018259864,0.00044642185,0.00020666415,0.00006466135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065095686,0.0001669212,0.00012754615,0.00031967164,0.000082676255,0.000004625516,0.010249853,0.5343507,0.000022708722,0.040123563,0.00034553875,0.4141411],"study_design_scores_gemma":[0.000396272,0.00006231816,0.000080660655,0.000019009136,0.000006906939,0.0000014618441,0.0004890375,0.99751276,0.0010054063,0.0000660309,0.00022186058,0.00013826031],"about_ca_topic_score_codex":0.0000133567955,"about_ca_topic_score_gemma":4.078889e-7,"teacher_disagreement_score":0.46316206,"about_ca_system_score_codex":0.000027149877,"about_ca_system_score_gemma":0.000066861176,"threshold_uncertainty_score":0.55001396},"labels":[],"label_agreement":null},{"id":"W3013330614","doi":"10.4018/ijssci.2020040105","title":"Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization","year":2020,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Simulated annealing; Mathematical optimization; Computer science; Particle swarm optimization; Convergence (economics); Combinatorial optimization; Population; Swarm behaviour; Algorithm; Mathematics","score_opus":0.035049917283533495,"score_gpt":0.324975396373623,"score_spread":0.2899254790900895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013330614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027416113,0.000060378617,0.991769,0.004141199,0.0010290545,0.00019712752,0.000011944913,0.000028262213,0.000021391459],"genre_scores_gemma":[0.52377313,0.000010845294,0.47542092,0.0006335649,0.0001382278,0.0000048859542,0.000010376805,0.0000058154938,0.0000022050494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684083,0.00006406188,0.00070855004,0.00036184012,0.0018038613,0.00022087804],"domain_scores_gemma":[0.9948645,0.0006439361,0.00038682582,0.00010385844,0.003779266,0.00022159569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001368845,0.00012896804,0.0002056263,0.00048474246,0.00009823266,0.00049789617,0.0015734235,0.000034458317,0.000024489413],"category_scores_gemma":[0.0039471965,0.00012637138,0.00006259271,0.0009117925,0.00016470489,0.0016352788,0.00021015092,0.00017043523,0.0000033612343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067039284,0.00006088231,0.0010330845,0.000010437484,0.000009597268,0.000016287702,0.00013617643,0.9610798,0.000029087838,0.012358419,0.00006598124,0.02513326],"study_design_scores_gemma":[0.0004666369,0.00021801268,0.0011131252,0.00003851831,0.000003068251,0.000036282814,0.000020219743,0.97924584,0.00053809624,0.018108273,0.00008838196,0.00012352249],"about_ca_topic_score_codex":0.000010712546,"about_ca_topic_score_gemma":3.5533049e-7,"teacher_disagreement_score":0.52103156,"about_ca_system_score_codex":0.00019038041,"about_ca_system_score_gemma":0.00091682345,"threshold_uncertainty_score":0.5153275},"labels":[],"label_agreement":null},{"id":"W3020948674","doi":"","title":"A progressive barrier derivative-free trust-region algorithm for constrained optimization","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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":"Trust region; Mathematical optimization; Constrained optimization; Domain (mathematical analysis); Computer science; Quadratic equation; Constrained optimization problem; Quadratic programming; Derivative-free optimization; Software; Optimization problem; Derivative (finance); Algorithm; Linear programming; Mathematics; Multi-swarm optimization","score_opus":0.015797965763422062,"score_gpt":0.2590720878640632,"score_spread":0.24327412210064114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020948674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015151118,0.00026776353,0.98717344,0.008692618,0.00019050283,0.0021953268,0.00011894878,0.00095092383,0.00025897604],"genre_scores_gemma":[0.009159634,0.00009017457,0.98572487,0.0007453748,0.00016883641,0.0020735785,0.000023895995,0.00007299047,0.0019406257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99641734,0.00030419772,0.0006606382,0.0009056932,0.000700068,0.0010120434],"domain_scores_gemma":[0.995878,0.00061392866,0.00039481683,0.0017096782,0.0008792416,0.00052436546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096751674,0.00042018,0.00044263888,0.000514385,0.00039432626,0.0004366634,0.0020173131,0.0002961443,0.00009156959],"category_scores_gemma":[0.0023371787,0.00032976366,0.00020232279,0.0009869733,0.00028677215,0.0011290415,0.0006224057,0.00021537025,0.000012248077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098119504,0.00035986007,0.0011198263,0.00004618597,0.00015557915,0.00018330572,0.00031312645,0.010642794,0.0013380316,0.09876786,0.0069187363,0.88005656],"study_design_scores_gemma":[0.0021404843,0.00026660296,0.00032962704,0.00007054473,0.000017163826,0.00018750671,0.000023483264,0.98252714,0.0069845524,0.0056343134,0.0013587436,0.00045983176],"about_ca_topic_score_codex":0.00011346655,"about_ca_topic_score_gemma":0.000014901543,"teacher_disagreement_score":0.97188437,"about_ca_system_score_codex":0.00044243337,"about_ca_system_score_gemma":0.00065124076,"threshold_uncertainty_score":0.9999154},"labels":[],"label_agreement":null},{"id":"W3021164925","doi":"","title":"Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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; Group for Research in Decision Analysis","funders":"","keywords":"Algorithm; Noise (video); Computer science; Mathematical optimization; Resampling; Optimization problem; Function (biology); Mathematics; Artificial intelligence; Image (mathematics)","score_opus":0.03554069277027166,"score_gpt":0.2584986466939801,"score_spread":0.22295795392370843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021164925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037444363,0.00035302018,0.9941201,0.0028059531,0.00013370099,0.0012373081,0.000051892708,0.0003996882,0.00052385166],"genre_scores_gemma":[0.05013445,0.00036690972,0.9479537,0.00021703025,0.00010205892,0.00020366203,0.000005782559,0.00006371329,0.00095272047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568325,0.00071614416,0.0007327682,0.0007448315,0.0012212294,0.0009017931],"domain_scores_gemma":[0.9962205,0.00057798554,0.00036123052,0.0016402956,0.00088872045,0.0003112165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023459634,0.00035881737,0.00045968752,0.0006258897,0.0003664134,0.00028134018,0.0020330027,0.00022140851,0.00008075271],"category_scores_gemma":[0.00047451217,0.00023406162,0.0001784879,0.0018946796,0.00033436005,0.0009030742,0.0008698784,0.00035033352,0.000012975803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026343947,0.00020491371,0.00059931964,0.000024842442,0.00009336445,0.000020285586,0.00047053452,0.91436577,0.0020642404,0.012397649,0.00039063045,0.06934208],"study_design_scores_gemma":[0.00048175224,0.00015163723,0.00034592755,0.00009347388,0.000019778801,0.00005189505,0.000045531062,0.9883245,0.009654258,0.00038653376,0.00014621715,0.0002984999],"about_ca_topic_score_codex":0.0020343116,"about_ca_topic_score_gemma":0.00008245998,"teacher_disagreement_score":0.073958695,"about_ca_system_score_codex":0.00049224077,"about_ca_system_score_gemma":0.0006287418,"threshold_uncertainty_score":0.9544756},"labels":[],"label_agreement":null},{"id":"W3021543573","doi":"","title":"Optimization of Algorithms with OPAL","year":2012,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Metaheuristic Optimization Algorithms Research","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":"Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Exploit; Solver; sort; Python (programming language); Measure (data warehouse); Algorithm; Optimization problem; Variety (cybernetics); Artificial intelligence; Data mining; Programming language","score_opus":0.015520695504705147,"score_gpt":0.2533568589037115,"score_spread":0.23783616339900637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021543573","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014007733,0.00046731587,0.994945,0.00095597975,0.00012253899,0.0005266149,0.00001192633,0.0004264952,0.0011433574],"genre_scores_gemma":[0.16093141,0.000088906025,0.83789045,0.0002801834,0.00009474219,0.00015201012,0.000015360767,0.000036998004,0.000509924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973338,0.00020718595,0.00047028853,0.00037303957,0.0008065573,0.00080908794],"domain_scores_gemma":[0.9976479,0.00014729159,0.00027135812,0.0011149544,0.00036776916,0.00045073562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011159072,0.0002598587,0.00033983996,0.0005464355,0.00015148157,0.00016017037,0.001099301,0.00015656473,0.00007220796],"category_scores_gemma":[0.00023313191,0.00022751254,0.00008291991,0.0014011416,0.00011799667,0.0011474435,0.0003691993,0.00028247276,0.000015683221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009284085,0.001120089,0.03806382,0.0000939861,0.00014639522,0.000039888877,0.001080302,0.730126,0.001127208,0.14798066,0.0014765506,0.078652225],"study_design_scores_gemma":[0.00045340467,0.00015423879,0.0056175217,0.000023904351,0.000013830988,0.00010816247,0.00002774534,0.98714584,0.0054487213,0.00016001449,0.00057381287,0.00027278523],"about_ca_topic_score_codex":0.00082336203,"about_ca_topic_score_gemma":0.000028646351,"teacher_disagreement_score":0.25701982,"about_ca_system_score_codex":0.00018538345,"about_ca_system_score_gemma":0.00023673211,"threshold_uncertainty_score":0.92776924},"labels":[],"label_agreement":null},{"id":"W3026871182","doi":"10.1109/iccicc46617.2019.9146092","title":"Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Computer science; Key (lock); Cluster analysis; Dragonfly; Algorithm design; Algorithm; Artificial intelligence; Geology","score_opus":0.017103044214502358,"score_gpt":0.2685953774522413,"score_spread":0.25149233323773895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026871182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044504377,0.0000588953,0.9934653,0.0016551948,0.00022640987,0.00069331995,0.0000025276588,0.00012532157,0.0037285278],"genre_scores_gemma":[0.0009837323,0.00002843959,0.98705167,0.00040006114,0.00005424178,0.00004146558,0.0000057052343,0.000014145518,0.01142054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885154,0.00009908572,0.00018478485,0.00035065287,0.0002578412,0.000256103],"domain_scores_gemma":[0.99873406,0.00047445728,0.00006824678,0.00042049625,0.00022303707,0.00007969894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073582557,0.00011554332,0.00012525734,0.000094076066,0.00020777209,0.0006330336,0.00057117065,0.000053738593,0.000043756743],"category_scores_gemma":[0.0002149045,0.00007929559,0.000030506755,0.0003056844,0.000038509916,0.00048246983,0.00036093596,0.00012422384,0.000009673236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055839605,0.000027603328,0.000077166755,0.0000544082,0.000031372,0.0000024857736,0.0008494339,0.46456304,0.000098732715,0.026612978,0.0010372977,0.5066399],"study_design_scores_gemma":[0.00030171542,0.00004793873,0.00014574047,0.000010925195,0.000002743408,0.0000210447,0.00003582035,0.9968598,0.00021168005,0.00019915082,0.0020533698,0.00011010487],"about_ca_topic_score_codex":0.00004147857,"about_ca_topic_score_gemma":0.000005541817,"teacher_disagreement_score":0.5322967,"about_ca_system_score_codex":0.000021733666,"about_ca_system_score_gemma":0.00006227866,"threshold_uncertainty_score":0.6104357},"labels":[],"label_agreement":null},{"id":"W3027599221","doi":"10.1287/trsc.2021.1045","title":"Machine-Learning–Based Column Selection for Column Generation","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column (typography); Column generation; Selection (genetic algorithm); Computer science; Artificial intelligence; Mathematics","score_opus":0.04327470079641512,"score_gpt":0.3154515595752874,"score_spread":0.2721768587788723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027599221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055735824,0.000035120942,0.9925038,0.0007846236,0.00033304212,0.00034346088,0.000016305354,0.00016144643,0.000248625],"genre_scores_gemma":[0.5041382,0.000014853791,0.49380437,0.00031326758,0.00005966103,0.000104975756,0.00014431735,0.000011532298,0.0014088836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763656,0.00009117542,0.00033111792,0.0006612343,0.0009404719,0.00033944513],"domain_scores_gemma":[0.9976416,0.00013127166,0.00012164486,0.00028091477,0.0016605291,0.00016401247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011560551,0.00011060028,0.00013481054,0.00023335396,0.00068692624,0.0004982754,0.0005265916,0.000047856327,0.00014284738],"category_scores_gemma":[0.00045534925,0.00012525059,0.000059233826,0.002790699,0.00013406068,0.00087466574,0.000010770614,0.0001297241,0.000021025533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000363953,0.00040703244,0.011768967,0.000110518406,0.000024123661,0.000021942667,0.0014188444,0.7152207,0.13656335,0.09410295,0.0012799931,0.03904518],"study_design_scores_gemma":[0.0004865965,0.00007245419,0.00386333,0.0000045212355,0.0000061386922,0.0000027206709,0.000012117503,0.94654053,0.04398058,0.00018733181,0.004704429,0.00013922669],"about_ca_topic_score_codex":0.0000412179,"about_ca_topic_score_gemma":0.0002323175,"teacher_disagreement_score":0.49869943,"about_ca_system_score_codex":0.00009072406,"about_ca_system_score_gemma":0.0012302025,"threshold_uncertainty_score":0.52833503},"labels":[],"label_agreement":null},{"id":"W3031120581","doi":"10.35378/gujs.567472","title":"GO: Group Optimization","year":2020,"lang":"en","type":"article","venue":"GAZI UNIVERSITY JOURNAL OF SCIENCE","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Group (periodic table); Benchmark (surveying); Population; Mathematical optimization; Test functions for optimization; Computer science; Optimization problem; Multi-swarm optimization; Mathematics; Medicine; Geography","score_opus":0.023375228490334295,"score_gpt":0.23235297684360762,"score_spread":0.20897774835327332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031120581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008801652,0.000018645003,0.9918959,0.003292675,0.00019031344,0.0000440346,7.170794e-7,0.000022158403,0.0036553596],"genre_scores_gemma":[0.1416206,0.00009652288,0.85771996,0.00031060295,0.00006062565,1.9115165e-8,1.9925423e-7,0.0000030526926,0.00018839727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845654,0.000070675575,0.00016822915,0.00020116547,0.00090351445,0.00019986997],"domain_scores_gemma":[0.998457,0.000054094922,0.00020895447,0.0001686107,0.0007208015,0.00039053248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082196767,0.00006116149,0.00011628346,0.00032960568,0.00027133428,0.0001661122,0.0023287379,0.000021006455,0.00007339792],"category_scores_gemma":[0.0004207804,0.00005911535,0.00004726034,0.0025784862,0.0004288879,0.0022586791,0.00039979484,0.00016778919,0.000026667953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086680375,0.00018568849,0.00075811235,0.000027464575,0.000035673947,0.0007464101,0.004205018,0.8499665,0.00545991,0.10483123,0.0058753826,0.027821891],"study_design_scores_gemma":[0.00041971635,0.0001823221,0.00039297572,0.000008581631,0.0000049823207,0.000055328543,0.0002546365,0.9934521,0.00030047935,0.00006741538,0.0047753686,0.00008609789],"about_ca_topic_score_codex":0.0000017772805,"about_ca_topic_score_gemma":1.3733441e-7,"teacher_disagreement_score":0.14348556,"about_ca_system_score_codex":0.00009895932,"about_ca_system_score_gemma":0.0004091139,"threshold_uncertainty_score":0.43274128},"labels":[],"label_agreement":null},{"id":"W3035777076","doi":"10.1109/tfuzz.2020.3003506","title":"Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":273,"is_retracted":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; Job shop scheduling; Fuzzy logic; Selection (genetic algorithm); Particle swarm optimization; Differential evolution; Ant colony optimization algorithms; Algorithm; Metaheuristic; Scheduling (production processes); Cuckoo search; Artificial intelligence; Mathematics","score_opus":0.030944227083376012,"score_gpt":0.26883499607407885,"score_spread":0.23789076899070283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035777076","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004991199,0.000114967384,0.9958938,0.00052320125,0.0009874352,0.00097495347,0.000034508805,0.00064330106,0.00032874622],"genre_scores_gemma":[0.2841331,0.000046213114,0.71457905,0.0002291044,0.0002509165,0.00018346196,0.0000035396674,0.000079358084,0.0004952716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963502,0.0004656772,0.0006854724,0.00090789463,0.000811656,0.0007790782],"domain_scores_gemma":[0.9982379,0.00016167267,0.0002207179,0.00045206107,0.00039637217,0.00053127797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088329194,0.0003594715,0.0004298273,0.00032938842,0.0006728895,0.0007804609,0.0008125534,0.00023030645,0.000022677948],"category_scores_gemma":[0.000035769477,0.00038342,0.00015713972,0.001641116,0.00004043375,0.0007694224,0.000013032949,0.0006962448,0.00007695618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039070404,0.00029954402,0.0000056720983,0.00037124075,0.00029379432,0.000029257844,0.0018910948,0.75925946,0.18873629,0.0018204125,0.0003452294,0.04690895],"study_design_scores_gemma":[0.0006176288,0.0002059408,4.3562642e-7,0.00007710535,0.00003312141,0.00008451786,0.0001667914,0.97972417,0.018398093,0.00017532089,0.00015280208,0.00036406695],"about_ca_topic_score_codex":0.00037579922,"about_ca_topic_score_gemma":0.0000061573905,"teacher_disagreement_score":0.28363398,"about_ca_system_score_codex":0.00042728873,"about_ca_system_score_gemma":0.0004261004,"threshold_uncertainty_score":0.9998618},"labels":[],"label_agreement":null},{"id":"W3037515722","doi":"10.1007/978-981-15-5097-3_1","title":"A New Hybrid Binary Algorithm of Bat Algorithm and Differential Evolution for Feature Selection and Classification","year":2020,"lang":"en","type":"book-chapter","venue":"Springer tracts in nature-inspired computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"","keywords":"Feature selection; Algorithm; Feature (linguistics); Binary number; Differential evolution; Computer science; Hybrid algorithm (constraint satisfaction); Pattern recognition (psychology); Binary search algorithm; Feature vector; Selection (genetic algorithm); Artificial intelligence; Search algorithm; Mathematics","score_opus":0.017753577867762244,"score_gpt":0.26655588747376263,"score_spread":0.2488023096060004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037515722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018902094,0.0021295962,0.99387056,0.0005157013,0.00085095334,0.0010959718,0.00005179902,0.00018784644,0.0011085598],"genre_scores_gemma":[0.0375422,0.00023783547,0.9583959,0.00006949053,0.0007980226,0.000012387975,0.00012273798,0.00009925032,0.0027221825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965977,0.00008824706,0.00077189936,0.0013252476,0.00075715926,0.0004597559],"domain_scores_gemma":[0.9978057,0.00039229236,0.00069070753,0.00043579077,0.00038707687,0.00028839972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004978247,0.0005429305,0.00079171336,0.00080253696,0.00020641703,0.00026107402,0.000621937,0.0007748103,0.000005936048],"category_scores_gemma":[0.00022637271,0.0005913722,0.00013985018,0.00033679197,0.00008447729,0.00035608298,0.00049140095,0.0017991815,0.0000024136618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051375668,0.00007212197,0.000063569576,0.00038066524,0.0001519013,0.000042255193,0.00020391725,0.0006196996,0.00067102595,0.07371974,0.00053078006,0.92349297],"study_design_scores_gemma":[0.0012037585,0.00023015088,0.0054874644,0.00034871136,0.00005677999,0.000060841718,0.000004746946,0.9842465,0.00042521098,0.005552956,0.0018689185,0.0005139769],"about_ca_topic_score_codex":0.00001924025,"about_ca_topic_score_gemma":0.0000045898564,"teacher_disagreement_score":0.9836268,"about_ca_system_score_codex":0.00026762625,"about_ca_system_score_gemma":0.0003816057,"threshold_uncertainty_score":0.99965376},"labels":[],"label_agreement":null},{"id":"W3037776504","doi":"10.1609/aaai.v34i10.7240","title":"Improving First-Order Optimization Algorithms (Student Abstract)","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 à Montréal","funders":"","keywords":"Convergence (economics); Computer science; Simple (philosophy); Variation (astronomy); Optimization algorithm; Algorithm; Order (exchange); First order; Mathematical optimization; Optimization problem; Mathematics; Applied mathematics","score_opus":0.09028591067846972,"score_gpt":0.3156316506765263,"score_spread":0.2253457399980566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037776504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010694921,0.000019807581,0.97793627,0.013848586,0.00035406178,0.00060375896,0.0000049641167,0.00015492164,0.0060081575],"genre_scores_gemma":[0.7663791,0.00008538384,0.23237292,0.0007069408,0.00017550305,0.00004948697,0.0000014558722,0.000029194556,0.00019998889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706656,0.000019083132,0.0007261878,0.00069501175,0.0010764844,0.00041667162],"domain_scores_gemma":[0.99727994,0.00012309667,0.000482211,0.00033449417,0.0015524185,0.00022785379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005401275,0.00025838337,0.00029210802,0.0001274414,0.00028775423,0.0005923372,0.0029277524,0.00010183676,0.00034965595],"category_scores_gemma":[0.0015194351,0.00020452599,0.00010564374,0.0014165909,0.00019828079,0.0006211873,0.0008095377,0.00041155628,0.00014499624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010409842,0.0005834834,0.00024304944,0.00026556975,0.00007381561,0.0000048724687,0.006024017,0.1483153,0.0107072,0.6007214,0.0007670431,0.2321901],"study_design_scores_gemma":[0.000042842483,0.00014573227,0.00006858723,0.00005571666,0.0000084968415,0.0000019483205,0.00027902238,0.9424273,0.05417609,0.0024910069,0.00009226672,0.00021099157],"about_ca_topic_score_codex":0.00003523381,"about_ca_topic_score_gemma":0.0000020332,"teacher_disagreement_score":0.794112,"about_ca_system_score_codex":0.000056309862,"about_ca_system_score_gemma":0.0001686163,"threshold_uncertainty_score":0.8340328},"labels":[],"label_agreement":null},{"id":"W3038180300","doi":"10.1007/s10489-020-01736-x","title":"A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm","year":2020,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"Zhejiang Province Public Welfare Technology Application Research Project; National Natural Science Foundation of China","keywords":"Computer science; Support vector machine; Classifier (UML); Binary tree; Artificial intelligence; Pattern recognition (psychology); Binary number; Binary classification; Algorithm; Data mining; Mathematics","score_opus":0.0812980354975812,"score_gpt":0.3059682613424227,"score_spread":0.22467022584484153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038180300","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023864886,0.00006744464,0.99410313,0.0032102808,0.00015904647,0.0008592271,0.000015947984,0.00018238359,0.0013786986],"genre_scores_gemma":[0.17058177,0.000084923275,0.8265442,0.0025298428,0.000052067946,0.000100201265,0.000013666409,0.000019672185,0.00007365305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971657,0.00018889128,0.00047903648,0.00089795864,0.0008479543,0.0004204529],"domain_scores_gemma":[0.9979387,0.0006687352,0.00017737442,0.0007097303,0.00022228218,0.00028316144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075712206,0.00029865836,0.0003668077,0.00009120441,0.0002797936,0.00041299633,0.0012557385,0.0001143534,0.000069297006],"category_scores_gemma":[0.0006649267,0.00022407014,0.00007520453,0.0010678428,0.0004079223,0.00015881937,0.0005089176,0.00033508107,0.00014273354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037356387,0.00039187755,0.000027694361,0.0000390099,0.000052617484,0.000047891204,0.00046583984,0.78997463,0.00024488335,0.07813394,0.00018124943,0.13006681],"study_design_scores_gemma":[0.0015595144,0.00011234204,0.00007403149,0.000013945232,0.000018298842,0.000012033466,0.000087299864,0.99671084,0.0007607859,0.00034627222,0.00005886625,0.00024577658],"about_ca_topic_score_codex":0.000057780795,"about_ca_topic_score_gemma":0.000006511929,"teacher_disagreement_score":0.20673622,"about_ca_system_score_codex":0.000086740234,"about_ca_system_score_gemma":0.00018044533,"threshold_uncertainty_score":0.9137315},"labels":[],"label_agreement":null},{"id":"W3038266931","doi":"10.1007/s00521-020-05124-x","title":"Volcano eruption algorithm for solving optimization problems","year":2020,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":41,"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":"","keywords":"Computer science; Heuristic; Algorithm; Mathematical optimization; Optimization problem; Mathematics; Artificial intelligence","score_opus":0.03808148974480686,"score_gpt":0.29749284758205263,"score_spread":0.2594113578372458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038266931","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000059246933,0.00008628286,0.9953724,0.0031352013,0.000049235092,0.0008246162,0.000008090177,0.00029913682,0.00016578013],"genre_scores_gemma":[0.03743172,0.000028958955,0.96134835,0.0005721296,0.00035930972,0.00014878114,0.000039418588,0.000016551396,0.000054784028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988235,0.000034153512,0.00025330854,0.0004655064,0.00019264015,0.0002308488],"domain_scores_gemma":[0.999118,0.00017198044,0.00010483534,0.00021329071,0.00022122034,0.0001707304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019374692,0.00011579123,0.00013825264,0.00006215333,0.00042334484,0.00029487172,0.00038333324,0.000043574626,0.0000044750072],"category_scores_gemma":[0.00006959881,0.00011789301,0.000036875623,0.0005210071,0.000036952133,0.00021048888,0.00019583508,0.00012128938,0.000007392437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.3845083e-7,0.000018218032,0.000024355766,0.000037101923,0.000006871109,3.3852987e-7,0.00016320987,0.43304336,0.00016853328,0.0049295607,0.00031866317,0.56128895],"study_design_scores_gemma":[0.000252859,0.000054137538,0.00003992164,0.0000058191495,0.0000054403095,0.0000059675926,0.000012418221,0.99522126,0.00006939635,0.00026639385,0.0039441483,0.0001222497],"about_ca_topic_score_codex":0.0000032016753,"about_ca_topic_score_gemma":1.3055184e-7,"teacher_disagreement_score":0.5621779,"about_ca_system_score_codex":0.000015899226,"about_ca_system_score_gemma":0.000033358756,"threshold_uncertainty_score":0.48075375},"labels":[],"label_agreement":null},{"id":"W3041876715","doi":"","title":"A Dynamic Algorithm Framework to Automatically Design a Multi-Objective Local Search","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Algorithm design; Algorithm","score_opus":0.02373381528130716,"score_gpt":0.28847793271245953,"score_spread":0.2647441174311524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041876715","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015720272,0.00027593263,0.9852514,0.008430546,0.00046509787,0.002136129,0.00006181389,0.00066095416,0.002560869],"genre_scores_gemma":[0.023891887,0.00014491162,0.96835726,0.00024859956,0.000015698108,0.0002732431,0.00009610872,0.00008841718,0.0068838997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9809908,0.013389027,0.0009107541,0.0020134386,0.0017268772,0.00096905127],"domain_scores_gemma":[0.98195153,0.006039723,0.00037786725,0.0053096265,0.005608908,0.0007123609],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012519582,0.0006106192,0.0007739032,0.0007788085,0.00041439413,0.0016508998,0.0058554197,0.00062671007,0.00019183011],"category_scores_gemma":[0.006037652,0.0006499871,0.00028103468,0.0017163971,0.00034205875,0.0002923944,0.0075102146,0.0019826104,0.0009000521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024585639,0.0019043642,0.00008200775,0.00039454456,0.00035141062,0.00006724195,0.02669828,0.11873244,0.00021217829,0.07316343,0.0010079223,0.7773616],"study_design_scores_gemma":[0.00044488657,0.000002138707,0.0006625374,0.0013118611,0.00001982358,0.000015744961,0.00008074101,0.98947906,0.0021515617,0.004852499,0.0003682794,0.00061084674],"about_ca_topic_score_codex":0.00046774987,"about_ca_topic_score_gemma":0.0000843854,"teacher_disagreement_score":0.8707466,"about_ca_system_score_codex":0.00061180466,"about_ca_system_score_gemma":0.0018302399,"threshold_uncertainty_score":0.99987787},"labels":[],"label_agreement":null},{"id":"W3043371792","doi":"10.1007/s00366-020-01101-z","title":"Adaptive multi-tracker optimization algorithm for global optimization problems: emphasis on applications in chemical engineering","year":2020,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Particle swarm optimization; Computer science; Mathematical optimization; Engineering optimization; Meta-optimization; Algorithm; Multi-swarm optimization; Global optimization; Optimization problem; Derivative-free optimization; Metaheuristic; Genetic algorithm; Mathematics","score_opus":0.02017940809623833,"score_gpt":0.24406265440797975,"score_spread":0.22388324631174142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043371792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026871738,0.00003971552,0.99766725,0.00037570595,0.00014114473,0.0012569538,0.00001924372,0.00046078552,0.000012350415],"genre_scores_gemma":[0.00528164,0.000007487237,0.99398065,0.00011044785,0.00010170439,0.0004061322,0.00006468492,0.00004259841,0.000004680576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981085,0.00002532353,0.00036503153,0.0006848117,0.0003865643,0.00042978447],"domain_scores_gemma":[0.99892205,0.000220287,0.00008652831,0.00032052165,0.00019631001,0.0002543107],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015487247,0.00030575294,0.00029394368,0.0001828964,0.000056413475,0.00017788532,0.0006417691,0.0000981269,0.000003656977],"category_scores_gemma":[0.00010996615,0.00030829464,0.000057317615,0.001448752,0.000020806372,0.00036585348,0.0001282792,0.00020601433,0.000004190796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074074055,0.00008151821,0.000016947357,0.00004010304,0.000032250686,0.0000042939027,0.00016238296,0.9876237,0.000011143181,0.0010449911,0.000029230227,0.010946053],"study_design_scores_gemma":[0.0011779139,0.00016341108,0.00004354878,0.00007244121,0.000007901281,0.000005534065,0.000007473431,0.99780124,0.0001717514,0.000002016727,0.00021954803,0.0003271895],"about_ca_topic_score_codex":0.000003395796,"about_ca_topic_score_gemma":1.9742737e-7,"teacher_disagreement_score":0.010618863,"about_ca_system_score_codex":0.00022559986,"about_ca_system_score_gemma":0.00009035981,"threshold_uncertainty_score":0.99993694},"labels":[],"label_agreement":null},{"id":"W3045156188","doi":"10.1007/s11063-020-10290-z","title":"An Analysis of Activation Function Saturation in Particle Swarm Optimization Trained Neural Networks","year":2020,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Activation function; Artificial neural network; Particle swarm optimization; Saturation (graph theory); Computer science; Control theory (sociology); Mathematics; Artificial intelligence; Algorithm","score_opus":0.030625162446549385,"score_gpt":0.2784958512715978,"score_spread":0.24787068882504842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045156188","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":[0.18273893,0.000010950774,0.81017274,0.006776849,0.000051753028,0.00014430757,7.4113524e-7,0.00009784367,0.0000058837873],"genre_scores_gemma":[0.9696405,0.0000016263199,0.02767542,0.0025245945,0.00006923405,0.0000117367435,0.00006366482,0.000011111219,0.0000021396754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982516,0.00022623263,0.00041623777,0.00043359157,0.00042551447,0.0002468353],"domain_scores_gemma":[0.9992273,0.00006247307,0.00021950636,0.00022072525,0.00015309457,0.00011690259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002505917,0.00013120112,0.00023337646,0.00027588877,0.00009530168,0.00025123692,0.00035087872,0.00005562721,0.000017077631],"category_scores_gemma":[0.00014345034,0.00012998786,0.00005591025,0.003586197,0.00004217526,0.0017700709,0.000039656705,0.00018833738,7.0047975e-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":[0.00004848629,0.000032548498,0.001187841,0.0000139432495,0.000018118299,0.0000019231854,0.0008756046,0.9670687,0.007560322,0.000039749535,0.000015394386,0.023137389],"study_design_scores_gemma":[0.00033666694,0.000072589624,0.008763617,0.000005465843,0.000044897904,4.7976727e-7,0.000052903088,0.9895713,0.0010287534,0.0000038156513,0.0000018576862,0.00011770928],"about_ca_topic_score_codex":0.000013611606,"about_ca_topic_score_gemma":0.0000030250753,"teacher_disagreement_score":0.78690153,"about_ca_system_score_codex":0.00004479638,"about_ca_system_score_gemma":0.00003188939,"threshold_uncertainty_score":0.53007513},"labels":[],"label_agreement":null},{"id":"W3045216940","doi":"10.1109/iccicc46617.2019.9146062","title":"Cognitive Hybrid PSO/SA Combinatorial Optimization","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Particle swarm optimization; Simulated annealing; Computer science; Mathematical optimization; Population; Swarm behaviour; Artificial intelligence; Algorithm; Mathematics","score_opus":0.01563574098735643,"score_gpt":0.27639860263778804,"score_spread":0.2607628616504316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045216940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006599832,0.000014072634,0.9509651,0.0003151943,0.001387442,0.00039877626,0.0000041048797,0.0002073824,0.04604791],"genre_scores_gemma":[0.3518914,0.00003495043,0.6368961,0.00059025764,0.00018069467,0.00003563233,0.00005899684,0.000029559964,0.01028244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832004,0.00013436757,0.00022463193,0.00042809924,0.0006065433,0.0002862961],"domain_scores_gemma":[0.9986659,0.00024866598,0.00006840666,0.00042323515,0.0004594391,0.00013435252],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003984614,0.00012161723,0.00016276016,0.00015916368,0.000076894605,0.00027366524,0.0006459717,0.000041319876,0.0019787238],"category_scores_gemma":[0.00027475244,0.00011212159,0.000044935325,0.0005040175,0.000033484077,0.0005823102,0.00030784155,0.00013781033,0.0015794784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008862323,0.0007599662,0.0016069085,0.00007124864,0.0001610618,0.000078329664,0.0004830292,0.21404323,0.00010828636,0.7145003,0.014014959,0.054084055],"study_design_scores_gemma":[0.0010713197,0.00009318605,0.000072170784,0.00000852108,0.000003204261,0.000009539812,0.000014387015,0.995523,0.0011006824,0.0011697788,0.00077665236,0.00015760494],"about_ca_topic_score_codex":0.000010409943,"about_ca_topic_score_gemma":1.2558472e-7,"teacher_disagreement_score":0.7814797,"about_ca_system_score_codex":0.00003765295,"about_ca_system_score_gemma":0.00012879842,"threshold_uncertainty_score":0.9991979},"labels":[],"label_agreement":null},{"id":"W3046155783","doi":"10.1002/eng2.12212","title":"A critical analysis of the bat algorithm","year":2020,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Metaheuristic Optimization Algorithms Research","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":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Particle swarm optimization; Simulated annealing; Computer science; Algorithm; Parallel metaheuristic; Transparency (behavior); Set (abstract data type); Mathematical optimization; MATLAB; Metric (unit); Multi-swarm optimization; Swarm behaviour; Mathematics; Artificial intelligence; Engineering; Programming language","score_opus":0.013528581523987311,"score_gpt":0.254937177008857,"score_spread":0.24140859548486968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046155783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061894715,0.00005828519,0.99745965,0.0012864313,0.0002683102,0.00006345046,0.000001769892,0.000086614295,0.00015654972],"genre_scores_gemma":[0.5216977,0.0000034253897,0.4780282,0.00012401147,0.000054980246,0.000008202941,0.0000016313578,0.000009871973,0.00007197574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987842,0.000019356004,0.0003039907,0.0002506536,0.00048347388,0.00015837059],"domain_scores_gemma":[0.99897605,0.00014814545,0.00005882609,0.000547844,0.00014146004,0.000127667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026384735,0.00007496432,0.00019057153,0.00012009389,0.00002980076,0.000057373698,0.00038639482,0.000028973973,0.00005871584],"category_scores_gemma":[0.002143902,0.00005739256,0.0001287478,0.0022691982,0.000025098223,0.000109748515,0.00025656258,0.00012216078,0.000002689247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.356218e-7,0.000068729256,0.0019929316,0.00007532064,0.00065171695,0.0006064542,0.0009052401,0.9614015,0.0014453745,0.009251776,0.000997937,0.022602078],"study_design_scores_gemma":[0.000019982379,0.000008974984,0.003995153,0.0000041090448,0.00005053949,0.00001954278,0.0000018266186,0.9937393,0.0012376838,0.000030755833,0.00083207065,0.00006003251],"about_ca_topic_score_codex":0.0000049536475,"about_ca_topic_score_gemma":9.0154025e-8,"teacher_disagreement_score":0.52107877,"about_ca_system_score_codex":0.000014486287,"about_ca_system_score_gemma":0.000049737584,"threshold_uncertainty_score":0.25666058},"labels":[],"label_agreement":null},{"id":"W3080812861","doi":"10.3390/app10175791","title":"A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":63,"is_retracted":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":"Benchmark (surveying); Mathematical optimization; Optimization problem; Optimization algorithm; Computer science; Energy (signal processing); Algorithm; Medicine; Mathematics; Statistics","score_opus":0.024152006813521264,"score_gpt":0.2714993904695966,"score_spread":0.24734738365607534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080812861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065930355,0.000035349964,0.99217695,0.0045957468,0.000044343975,0.0004819581,0.0000027510791,0.00015391568,0.0024430337],"genre_scores_gemma":[0.018066458,0.000016614717,0.9791973,0.00247388,0.0000650404,0.00011645814,0.0000074915224,0.000008368413,0.000048384725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978855,0.00006115308,0.0002639238,0.0007812835,0.0007057073,0.00030238106],"domain_scores_gemma":[0.9988579,0.00005665762,0.00008945058,0.00030293583,0.00008974593,0.00060331175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026237025,0.00014640919,0.00015395107,0.00013552066,0.00030925954,0.0004900436,0.00095164427,0.000040446816,0.000043793312],"category_scores_gemma":[0.0000266454,0.00013037947,0.00001530813,0.0015581163,0.00008411582,0.00046390042,0.00040443038,0.0000659265,0.000033048058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004312924,0.000038647257,0.000011761872,0.0000042958723,0.0000047686344,9.1358703e-7,0.0014539977,0.130181,0.00045219358,0.08394158,0.0012892543,0.7826173],"study_design_scores_gemma":[0.00018482169,0.00029853953,0.000018679837,0.0000024988115,0.0000024959998,0.0000020151024,0.000101856,0.9876383,0.0013232519,0.001299406,0.0089551695,0.00017295641],"about_ca_topic_score_codex":0.00007172586,"about_ca_topic_score_gemma":0.000003996895,"teacher_disagreement_score":0.8574573,"about_ca_system_score_codex":0.00002784112,"about_ca_system_score_gemma":0.00016135762,"threshold_uncertainty_score":0.53167206},"labels":[],"label_agreement":null},{"id":"W3083093249","doi":"10.1109/cec48606.2020.9185905","title":"A Collective Intelligence Strategy for Enhancing Population-based optimization Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Swarm intelligence; Population; Benchmark (surveying); Computer science; Particle swarm optimization; Cluster analysis; Metaheuristic; Computational intelligence; Mathematical optimization; Algorithm; Variable (mathematics); Dimension (graph theory); Differential evolution; Artificial intelligence; Mathematics","score_opus":0.07282423385889047,"score_gpt":0.32890944278569056,"score_spread":0.2560852089268001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083093249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009439888,0.000018108682,0.99595195,0.001870324,0.00010710094,0.0007616329,0.000008062282,0.0002511659,0.0010222264],"genre_scores_gemma":[0.06438223,0.0000024600445,0.93407124,0.0007935953,0.00007868679,0.00010609965,0.000036028006,0.000016755364,0.00051287387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982699,0.00009865993,0.00038143754,0.00054040505,0.0004122462,0.0002973165],"domain_scores_gemma":[0.99839884,0.00047005975,0.00010695788,0.00027054694,0.0005322324,0.00022137357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032638764,0.00014936671,0.0001940829,0.00014049491,0.00020384225,0.00031826828,0.0006249325,0.00006392269,0.00025251388],"category_scores_gemma":[0.00088657276,0.00014474124,0.00006649835,0.0012966858,0.000020018977,0.00040124654,0.00009031615,0.00010395487,0.000025832524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010625558,0.000027477548,0.00003136477,0.000019716743,0.000009868404,0.0000014368696,0.00020295061,0.97995573,0.00002356786,0.006163253,0.0001352541,0.013418768],"study_design_scores_gemma":[0.00024336853,0.00019708165,0.000053000967,0.0000061808846,0.000004223494,7.5843315e-7,0.000043297205,0.9955149,0.0028994088,0.0008108774,0.00005717027,0.00016970318],"about_ca_topic_score_codex":0.000046673078,"about_ca_topic_score_gemma":0.000010653979,"teacher_disagreement_score":0.06437279,"about_ca_system_score_codex":0.00015383826,"about_ca_system_score_gemma":0.000731028,"threshold_uncertainty_score":0.5902376},"labels":[],"label_agreement":null},{"id":"W3083168801","doi":"10.1109/cec48606.2020.9185622","title":"A Novel Center-based Differential Evolution Algorithm","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Computer science; Center (category theory); Differential evolution; Algorithm","score_opus":0.036347911771434475,"score_gpt":0.2680558174597069,"score_spread":0.23170790568827243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083168801","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022590268,0.0000059298095,0.9923714,0.0052689,0.00022560485,0.00017395966,0.000012135104,0.00030521888,0.0016142883],"genre_scores_gemma":[0.0852668,0.0000011527233,0.9133894,0.0009294796,0.00013513895,0.00001361832,0.000014378937,0.000010887914,0.00023917486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984497,0.0000617103,0.00021237096,0.0004161472,0.0005773554,0.00028272823],"domain_scores_gemma":[0.99915576,0.00005753359,0.00004743049,0.0003333612,0.00012481117,0.0002810746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099774305,0.00011921929,0.00013545778,0.000093702925,0.0000889702,0.00019654224,0.0007937101,0.000044850447,0.0004611996],"category_scores_gemma":[0.000117443684,0.000105751045,0.00006454206,0.0005988786,0.000040846182,0.0002370866,0.00026339784,0.0001352665,0.00024134787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008087499,0.003232114,0.0009715294,0.00017178203,0.00019909297,0.00010937286,0.00081858353,0.01647491,0.018431623,0.5563623,0.03214985,0.370998],"study_design_scores_gemma":[0.0008925827,0.000073544295,0.000290284,0.0000029091016,0.0000020746784,0.0000032554135,0.000004864,0.996844,0.0005713001,0.0001572827,0.0010374588,0.00012046409],"about_ca_topic_score_codex":0.000018793968,"about_ca_topic_score_gemma":7.665198e-7,"teacher_disagreement_score":0.9803691,"about_ca_system_score_codex":0.000073726245,"about_ca_system_score_gemma":0.0001284511,"threshold_uncertainty_score":0.5049812},"labels":[],"label_agreement":null},{"id":"W3083305467","doi":"10.1109/cec48606.2020.9185801","title":"Particle Swarm optimization with pbest Perturbations","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Metaheuristic; Swarm behaviour; Computer science; Mathematical optimization; Premature convergence; Stall (fluid mechanics); Engineering; Mathematics; Algorithm; Aerospace engineering","score_opus":0.03605183170860734,"score_gpt":0.25568046351547946,"score_spread":0.21962863180687212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083305467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017510881,0.000019160962,0.97321963,0.018810378,0.00003081168,0.00016297051,9.650768e-7,0.00027692082,0.0073040696],"genre_scores_gemma":[0.12215871,0.000014303375,0.8745985,0.0018876471,0.000048704493,0.000021467997,0.000005654092,0.000010831533,0.0012541786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989205,0.000056809175,0.00015112002,0.000299716,0.00037613758,0.00019570616],"domain_scores_gemma":[0.9991414,0.000074385425,0.00003575913,0.00029284696,0.00021673582,0.00023888944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010277352,0.00008324809,0.00008982508,0.000032794786,0.00011565205,0.0002413728,0.0004628765,0.000022824,0.00044863176],"category_scores_gemma":[0.00019157858,0.00006341682,0.000018467765,0.00090975297,0.000035398258,0.00049094873,0.00012437068,0.000079360834,0.00021553971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000683574,0.000057998823,0.00024798972,0.0000066120365,0.000013536212,0.000010545302,0.00056217436,0.9460345,0.000045928162,0.04792464,0.0014808859,0.0036083565],"study_design_scores_gemma":[0.00027979107,0.00010831204,0.000085748914,0.0000018133547,0.0000029685018,0.000004338954,0.000030216986,0.99689156,0.0010127756,0.0000352842,0.001450542,0.00009665277],"about_ca_topic_score_codex":0.0000063652546,"about_ca_topic_score_gemma":0.0000010998365,"teacher_disagreement_score":0.1219836,"about_ca_system_score_codex":0.00001592762,"about_ca_system_score_gemma":0.000093778086,"threshold_uncertainty_score":0.49122033},"labels":[],"label_agreement":null},{"id":"W3083331092","doi":"10.1109/cec48606.2020.9185640","title":"A Dimension-Wise Particle Swarm Optimization Algorithm Optimized via Self-Tuning","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Dimension (graph theory); Convergence (economics); Mathematical optimization; Algorithm; Sinc function; Computer science; Maxima; Multi-swarm optimization; Mathematics","score_opus":0.025018188623965324,"score_gpt":0.26486012517601587,"score_spread":0.23984193655205055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083331092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006750908,0.0000695292,0.99041545,0.0062947427,0.00018997412,0.00045137227,0.0000020788332,0.0011327061,0.0013766158],"genre_scores_gemma":[0.006589244,0.000060641345,0.9908998,0.0019266674,0.00010103594,0.000040681098,0.000011280356,0.000031886313,0.00033874868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970652,0.0002734587,0.0005120528,0.0007574968,0.0008363036,0.0005555228],"domain_scores_gemma":[0.99794936,0.00022989407,0.00013156579,0.0006386645,0.00040660187,0.0006439331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005235068,0.00024587754,0.00032391513,0.00010601269,0.00025410423,0.00040326477,0.0009867192,0.0000888466,0.0005829928],"category_scores_gemma":[0.00034994385,0.00022619533,0.00009661084,0.0015139608,0.000045953435,0.00087925873,0.00068258896,0.00023220983,0.00037989803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017140968,0.00018245571,0.000023453758,0.000017544462,0.000063036205,0.000065198314,0.0011487317,0.9525541,0.00026274894,0.0022728029,0.001725488,0.04166731],"study_design_scores_gemma":[0.0012452615,0.00011853983,0.000007523701,0.000005613097,0.00001371085,0.000013706042,0.00003296858,0.9947884,0.0027105496,0.00008484086,0.00070166367,0.00027723046],"about_ca_topic_score_codex":0.000015906107,"about_ca_topic_score_gemma":1.9534687e-7,"teacher_disagreement_score":0.0422343,"about_ca_system_score_codex":0.00006184346,"about_ca_system_score_gemma":0.00014808652,"threshold_uncertainty_score":0.9223978},"labels":[],"label_agreement":null},{"id":"W3083389769","doi":"10.3390/app10186173","title":"A Spring Search Algorithm Applied to Engineering Optimization Problems","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":143,"is_retracted":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":"Computer science; Mathematical optimization; Spring (device); Algorithm; Mathematics; Engineering; Mechanical engineering","score_opus":0.0391604103543382,"score_gpt":0.25997314368535607,"score_spread":0.22081273333101786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083389769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025586912,0.000025027524,0.99098957,0.0023887656,0.00009953538,0.00066493935,0.0000015163589,0.00036589324,0.0052088597],"genre_scores_gemma":[0.096433565,0.000007819434,0.9024951,0.0008138548,0.00010918608,0.000099639634,0.000001430933,0.000015521939,0.000023873894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996984,0.00002881623,0.00031649903,0.000887351,0.0011684397,0.0006148966],"domain_scores_gemma":[0.9989009,0.00012483833,0.000053021282,0.000344799,0.00010130064,0.00047516113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009985397,0.00019046715,0.00021940867,0.00028834675,0.00031436965,0.0006847098,0.0019107693,0.000052394484,0.00005959033],"category_scores_gemma":[0.000106357016,0.00018081735,0.000032356376,0.003509925,0.000092146794,0.00031538386,0.0007637457,0.0002060323,0.00026637787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013610197,0.0000146422835,0.000008346262,0.000015056619,0.000005340483,0.0000020025882,0.0008069696,0.9442707,0.002005185,0.02023313,0.00008117649,0.03255614],"study_design_scores_gemma":[0.0001784365,0.000056533452,0.000036394955,0.0000063767816,0.0000018209618,0.0000015457796,0.0000663167,0.9954077,0.0030341514,0.00008896126,0.0008967734,0.00022499292],"about_ca_topic_score_codex":0.0000146921675,"about_ca_topic_score_gemma":4.896768e-7,"teacher_disagreement_score":0.09617769,"about_ca_system_score_codex":0.00004456973,"about_ca_system_score_gemma":0.00016843814,"threshold_uncertainty_score":0.7373518},"labels":[],"label_agreement":null},{"id":"W3086117886","doi":"10.1007/978-3-030-53552-0","title":"Learning and Intelligent Optimization","year":2020,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Management science; Artificial intelligence; Optimization problem; Operations research; Mathematical optimization; Algorithm; Mathematics; Engineering","score_opus":0.019819963772325627,"score_gpt":0.2804922444318599,"score_spread":0.26067228065953424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086117886","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.772501e-7,0.0003903068,0.99540126,0.001158879,0.00065621705,0.00035462278,0.000001162686,0.0001761727,0.0018604103],"genre_scores_gemma":[0.00085209205,0.00032893746,0.99668884,0.00061334507,0.00028884897,0.000009356999,0.000011059131,0.00002872315,0.0011788206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627125,0.00014243888,0.00044949824,0.0014515142,0.0011583566,0.0005269275],"domain_scores_gemma":[0.99800843,0.0006000561,0.00020606107,0.0005702672,0.00032220493,0.00029296355],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00097096484,0.0003556186,0.00042229771,0.00077830337,0.00025734204,0.0010397051,0.0020827833,0.0002064583,0.000041337156],"category_scores_gemma":[0.0008065885,0.00034172242,0.00005275001,0.0016211261,0.0004909366,0.00047604126,0.0021324868,0.0010785612,0.000037168367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013686449,0.000008319756,0.000014984953,0.000029243887,0.0000040160016,0.000031432184,0.00042437864,0.62505054,0.000003453171,0.0005718826,0.00003986124,0.37382054],"study_design_scores_gemma":[0.00012600739,0.00015574617,0.000006083742,0.00009852539,0.0000034511431,0.000030628587,1.9834461e-7,0.9910013,0.00015146771,0.005650709,0.0024425094,0.00033336564],"about_ca_topic_score_codex":0.0000042196343,"about_ca_topic_score_gemma":0.000002162098,"teacher_disagreement_score":0.37348717,"about_ca_system_score_codex":0.00029733608,"about_ca_system_score_gemma":0.0011276243,"threshold_uncertainty_score":0.9999973},"labels":[],"label_agreement":null},{"id":"W3095288545","doi":"10.1115/detc2020-22519","title":"Enhanced Particle Swarm Optimization via Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Reinforcement learning; Convergence (economics); Mathematical optimization; Multi-swarm optimization; Computer science; Swarm behaviour; Function (biology); Optimization problem; Rate of convergence; Mathematics; Artificial intelligence","score_opus":0.029799692061631605,"score_gpt":0.27488222554676184,"score_spread":0.24508253348513023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095288545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000118157026,0.000012064469,0.98812014,0.0031959321,0.000067089946,0.0001786019,6.2633994e-8,0.0003365151,0.007971463],"genre_scores_gemma":[0.6141335,0.000022955637,0.38348883,0.0009378055,0.00004550567,0.000016527363,0.0000042259344,0.000008835567,0.0013418348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985926,0.00009247405,0.00025819553,0.0003283726,0.00046048008,0.00026784057],"domain_scores_gemma":[0.99921846,0.00006077385,0.00006517278,0.00024296185,0.00017096518,0.00024167416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022213599,0.00009601972,0.00011483717,0.00003607205,0.00012632199,0.00018585035,0.0004934496,0.00003069422,0.0008971738],"category_scores_gemma":[0.00032248668,0.000089297595,0.000033092878,0.00067906146,0.000021393387,0.00043552305,0.00028261502,0.00013388236,0.00042703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000408229,0.000011393274,0.000008669618,0.0000054785924,0.000006867403,0.00000245288,0.000381089,0.98278517,0.0008341577,0.0047734543,0.00015056781,0.011036624],"study_design_scores_gemma":[0.00029512198,0.0001114752,0.0000064666683,0.000001917896,0.0000016670615,8.745453e-7,0.000020140984,0.9674074,0.03154356,0.00002849044,0.00047586797,0.0001070009],"about_ca_topic_score_codex":0.000008233895,"about_ca_topic_score_gemma":2.3296464e-7,"teacher_disagreement_score":0.6140153,"about_ca_system_score_codex":0.000027556507,"about_ca_system_score_gemma":0.000049080594,"threshold_uncertainty_score":0.9823424},"labels":[],"label_agreement":null},{"id":"W3095372394","doi":"10.1093/bioinformatics/btaa945","title":"ALeS: adaptive-length spaced-seed design","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Metaheuristic Optimization Algorithms Research","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Heuristic; Software; Source code; Computation; Similarity (geometry); Algorithm; Code (set theory); Sensitivity (control systems); Sequence (biology); Data mining; Theoretical computer science; Programming language; Artificial intelligence; Biology","score_opus":0.08403751598559044,"score_gpt":0.27139443497950566,"score_spread":0.1873569189939152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095372394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012041261,0.00003733157,0.99217045,0.002128303,0.00012325405,0.00033286377,0.0000051206184,0.00022570924,0.0049649454],"genre_scores_gemma":[0.005348897,0.000046840636,0.993045,0.0012748007,0.000064650136,0.000010386299,0.0000040880277,0.0000111891495,0.00019418112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837875,0.000088771296,0.00039255514,0.00018416565,0.0006174251,0.00033832502],"domain_scores_gemma":[0.99870116,0.00019334335,0.00013416281,0.0004508756,0.00018129127,0.0003391434],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00039991163,0.00015538243,0.000193901,0.00010262659,0.00011173922,0.00026333044,0.0010808242,0.0000639507,0.000068193054],"category_scores_gemma":[0.0005036046,0.00013688905,0.00005344026,0.0007205994,0.000057913476,0.00075278146,0.0003648954,0.00017405502,0.0012826355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020238376,0.00046883788,0.00040143324,0.00082664215,0.00048588068,0.00019435197,0.056502733,0.1635991,0.00064968446,0.2537267,0.19745198,0.32549027],"study_design_scores_gemma":[0.00032760383,0.00016158506,0.00008390615,0.000008147,0.000003967564,0.0000062558133,0.00022717369,0.9943033,0.00051160314,0.00014400628,0.004053848,0.00016860347],"about_ca_topic_score_codex":0.000002560119,"about_ca_topic_score_gemma":1.9854845e-7,"teacher_disagreement_score":0.8307042,"about_ca_system_score_codex":0.00003627089,"about_ca_system_score_gemma":0.00020068244,"threshold_uncertainty_score":0.99949497},"labels":[],"label_agreement":null},{"id":"W3095424117","doi":"10.3390/app10217683","title":"DM: Dehghani Method for Modifying Optimization Algorithms","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":34,"is_retracted":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":"Meta-optimization; Mathematical optimization; Computer science; Derivative-free optimization; Multi-swarm optimization; Particle swarm optimization; Optimization algorithm; Population; Metaheuristic; Test functions for optimization; Optimization problem; Imperialist competitive algorithm; Engineering optimization; Continuous optimization; Algorithm; Mathematics","score_opus":0.10231333471973202,"score_gpt":0.3566389437694204,"score_spread":0.2543256090496884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095424117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009586988,0.000044981563,0.98971313,0.003982966,0.00015919434,0.0005710524,0.000004345806,0.00022599935,0.0052887183],"genre_scores_gemma":[0.006036407,0.000013270749,0.9920713,0.0015275914,0.00012991992,0.00012322467,0.0000047683016,0.000011380586,0.00008215573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975973,0.000077884164,0.00030614357,0.0008182676,0.00074349047,0.00045690994],"domain_scores_gemma":[0.99874777,0.00043669707,0.00012299311,0.00028029724,0.00016508951,0.0002471813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014758417,0.00015588435,0.00021462035,0.0001465813,0.00054515916,0.0005537614,0.0016966003,0.000057984955,0.00006175746],"category_scores_gemma":[0.0003547076,0.00013812799,0.000055721397,0.0017512452,0.0001322317,0.00046944377,0.00033424314,0.00011110341,0.00004363106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005707647,0.000022726628,0.000004861809,0.000020146063,0.000009713927,0.0000013020378,0.00072092924,0.8472635,0.00074671285,0.08906222,0.00087386894,0.061268274],"study_design_scores_gemma":[0.00031265206,0.00008312763,0.0000027251376,0.0000025790657,0.0000046336886,0.000002334808,0.000116947194,0.9920815,0.003445909,0.0023292648,0.0014413486,0.00017693907],"about_ca_topic_score_codex":0.0000069692287,"about_ca_topic_score_gemma":3.5028594e-7,"teacher_disagreement_score":0.14481801,"about_ca_system_score_codex":0.000025401234,"about_ca_system_score_gemma":0.00020177792,"threshold_uncertainty_score":0.56326956},"labels":[],"label_agreement":null},{"id":"W3096178443","doi":"10.1002/nme.6573","title":"A<scp><i>Canis lupus</i></scp>inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem","year":2020,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Convergence (economics); Mathematical optimization; Nonlinear system; Algorithm; Mathematics; Computer science","score_opus":0.04108880310469128,"score_gpt":0.35029132472247365,"score_spread":0.3092025216177824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096178443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007963244,0.00027539278,0.99522024,0.0020081033,0.0014975396,0.0007117089,0.000044891673,0.00013934448,0.000023163262],"genre_scores_gemma":[0.0013230132,0.000099493576,0.99760723,0.00024309245,0.00041757963,0.00015520431,0.000009865102,0.00006177165,0.000082743805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974459,0.00015070631,0.00081518374,0.00051008386,0.0005208391,0.00055729505],"domain_scores_gemma":[0.995551,0.0031495385,0.00021651397,0.00016440263,0.0004521886,0.0004663491],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016785426,0.0003128887,0.0005080158,0.00034955924,0.000089396824,0.00058617385,0.0010908828,0.00011921109,0.000008085694],"category_scores_gemma":[0.0074027404,0.0003059506,0.000183347,0.00043331317,0.00003978652,0.0005127612,0.00024565437,0.0005316564,0.0000012140887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005284769,0.000055692428,0.000060066337,0.00010623248,0.00028157057,0.000065444,0.0006954299,0.9499366,0.0072363494,0.003884362,0.0007880258,0.03683736],"study_design_scores_gemma":[0.0016711426,0.00023358261,0.000013042477,0.00007448311,0.00001611716,0.0002268882,0.00003463547,0.9739206,0.0031133418,0.0004911032,0.020028628,0.00017645379],"about_ca_topic_score_codex":0.0000031478685,"about_ca_topic_score_gemma":6.309188e-8,"teacher_disagreement_score":0.03666091,"about_ca_system_score_codex":0.0001276719,"about_ca_system_score_gemma":0.00011201698,"threshold_uncertainty_score":0.99993926},"labels":[],"label_agreement":null},{"id":"W3096938982","doi":"10.5430/air.v9n1p54","title":"Investigation of differential evolution and particle swarm optimization in search performance","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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; Differential evolution; Benchmark (surveying); Computer science; Task (project management); Key (lock); Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.2163849946774048,"score_gpt":0.3695313440947222,"score_spread":0.15314634941731742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096938982","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":[0.34365407,0.000043509077,0.6539631,0.0019290935,0.000039624956,0.00027276267,0.000001039677,0.000026579257,0.00007023003],"genre_scores_gemma":[0.9724603,0.00015116301,0.027262544,0.000021429709,0.000054116765,0.00002312666,0.0000034490577,0.000008750825,0.000015111751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968711,0.0006011487,0.0005148182,0.00046914964,0.0010722796,0.00047146913],"domain_scores_gemma":[0.9984326,0.00035950582,0.000053889,0.00029302321,0.00060662685,0.0002543808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018815551,0.000100897785,0.00017317527,0.00032313316,0.00015691291,0.00017036048,0.0006084042,0.000074267075,0.00006142701],"category_scores_gemma":[0.0008566746,0.00010175067,0.00002351112,0.0026991395,0.00031748385,0.00061366346,0.00044693027,0.0004020307,0.000055201344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019526645,0.0001702196,0.017814664,0.00028088994,0.00001938232,0.000013990607,0.009347593,0.6288658,0.034096885,0.18747428,0.00004529726,0.12167578],"study_design_scores_gemma":[0.00004619126,0.00019811171,0.0015039344,0.000021252343,9.801038e-7,8.0607964e-7,0.00026906896,0.8619414,0.13390416,0.0020338513,0.0000023612515,0.00007786907],"about_ca_topic_score_codex":0.00012731717,"about_ca_topic_score_gemma":0.000010313333,"teacher_disagreement_score":0.62880623,"about_ca_system_score_codex":0.00008163668,"about_ca_system_score_gemma":0.00022953887,"threshold_uncertainty_score":0.41492718},"labels":[],"label_agreement":null},{"id":"W3097616020","doi":"10.18280/jesa.530415","title":"Design of Two-Dimensional Recursive Digital Filter Using Multi Particle Swarm Optimization Algorithm","year":2020,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Initialization; Mathematical optimization; Infinite impulse response; Algorithm; Convergence (economics); Local optimum; Computer science; Multi-swarm optimization; Filter (signal processing); Mathematics; Digital filter","score_opus":0.07068733666797483,"score_gpt":0.30333183551319576,"score_spread":0.23264449884522093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097616020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00294519,0.0002575255,0.99567974,0.00033691083,0.0002493137,0.00032202393,0.000016792474,0.00014660227,0.000045893405],"genre_scores_gemma":[0.035454642,0.000022952321,0.9640807,0.00016038251,0.0001343043,0.000004151178,0.0000043311975,0.000040145194,0.00009842686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965397,0.0006241271,0.00091023854,0.00041081817,0.001043552,0.00047153753],"domain_scores_gemma":[0.99735904,0.00031292834,0.00059504545,0.00033193477,0.00092337816,0.00047767957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068341976,0.00025293644,0.00041526725,0.00017800949,0.00029474802,0.00072794827,0.0008701393,0.00006312741,0.00015233457],"category_scores_gemma":[0.00086923473,0.00022550765,0.00012611515,0.0010095721,0.00015217796,0.0016319294,0.00038128032,0.00030976158,0.00006787921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128802885,0.000099792866,0.00006193571,0.000027357895,0.00007080915,0.00013933661,0.0006983774,0.92611086,0.0007040137,0.00012953664,0.00019270252,0.07175238],"study_design_scores_gemma":[0.0010881333,0.00023987662,0.00044844038,0.000097177224,0.000023343546,0.00052257086,0.000027438145,0.995082,0.0018549518,0.0003755802,0.00001215142,0.00022834317],"about_ca_topic_score_codex":0.00000589248,"about_ca_topic_score_gemma":6.284357e-8,"teacher_disagreement_score":0.07152403,"about_ca_system_score_codex":0.0001392401,"about_ca_system_score_gemma":0.00033762556,"threshold_uncertainty_score":0.9195935},"labels":[],"label_agreement":null},{"id":"W3104800136","doi":"","title":"ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Logarithm; Heuristic; Computer science; Algorithm; Mathematical optimization; Work (physics); Mathematics; Engineering","score_opus":0.03633757259381575,"score_gpt":0.28543745454321406,"score_spread":0.2490998819493983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104800136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027832543,0.00012133898,0.9937777,0.0009308565,0.00044600727,0.00060656655,0.00005280654,0.0005765807,0.00070488773],"genre_scores_gemma":[0.8087251,0.00001961573,0.1863538,0.0031111904,0.00050650275,0.00017557401,0.001015757,0.000034076846,0.000058382164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971442,0.00018996597,0.00096782704,0.00035557238,0.0009794052,0.00036298553],"domain_scores_gemma":[0.9978679,0.000060716768,0.0005539993,0.0003729447,0.00077168545,0.0003727486],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003349988,0.00024828207,0.00029792747,0.00019774426,0.0003073609,0.002864814,0.00088150206,0.000121170575,0.000040803723],"category_scores_gemma":[0.000209102,0.00022666898,0.00004703032,0.0008449279,0.00004905582,0.011303861,0.00014346163,0.00026885915,0.00024920588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004180873,0.00006846855,0.00006593598,0.00031599132,0.000030480505,0.0000096932135,0.01570022,0.17141527,0.0002849159,0.0017669748,0.0014102239,0.80889],"study_design_scores_gemma":[0.0005918538,0.00016518054,0.00011335359,0.00003192029,0.0000062301456,0.000013405507,0.0004715725,0.9937882,0.0006492716,0.000021926948,0.0038921633,0.00025495925],"about_ca_topic_score_codex":0.00008324276,"about_ca_topic_score_gemma":2.462112e-7,"teacher_disagreement_score":0.8223729,"about_ca_system_score_codex":0.000055238765,"about_ca_system_score_gemma":0.00020450953,"threshold_uncertainty_score":0.9981703},"labels":[],"label_agreement":null},{"id":"W3106774124","doi":"10.1007/978-3-030-63833-7_3","title":"Discrete Mother Tree Optimization for the Traveling Salesman Problem","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Regina","funders":"","keywords":"Travelling salesman problem; Swap (finance); Computer science; Swarm intelligence; Mathematical optimization; Metaheuristic; Swarm behaviour; Particle swarm optimization; Combinatorial optimization; Tree (set theory); Algorithm; Mathematics; Combinatorics","score_opus":0.02949725858594019,"score_gpt":0.27493606879401594,"score_spread":0.24543881020807576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106774124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6077426e-7,0.00026697046,0.98896,0.0058875484,0.0007300407,0.0013981849,0.000011441265,0.00014947314,0.0025961632],"genre_scores_gemma":[0.0016153314,0.000085047606,0.99574447,0.0011962806,0.0005133125,0.000056233413,0.000012307599,0.00006057658,0.00071643095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960174,0.00005651489,0.00059449987,0.0014905578,0.0012310569,0.00060996227],"domain_scores_gemma":[0.9966363,0.0012061982,0.0002991738,0.0012368013,0.00042612938,0.00019545038],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012516887,0.00046519298,0.00045468472,0.00036915214,0.0004685429,0.0010284529,0.0043103187,0.0002208847,0.000044730372],"category_scores_gemma":[0.00030174432,0.00033723866,0.0001623893,0.0008059626,0.0005644437,0.00044162397,0.0009784504,0.0006697658,0.00002303519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050224016,0.0000068540057,0.0000018505483,0.000030334071,0.000013194508,0.000006677845,0.0003201478,0.68132555,0.000006535773,0.015648339,0.000030559288,0.30260494],"study_design_scores_gemma":[0.00029110748,0.00011769309,0.0000057895145,0.00010796143,0.000012612672,0.000013339315,2.1016983e-7,0.9627813,0.000113149785,0.034681745,0.0014968988,0.0003781823],"about_ca_topic_score_codex":0.00000821759,"about_ca_topic_score_gemma":0.000021220052,"teacher_disagreement_score":0.30222675,"about_ca_system_score_codex":0.00017746942,"about_ca_system_score_gemma":0.00059036684,"threshold_uncertainty_score":0.999908},"labels":[],"label_agreement":null},{"id":"W3111443264","doi":"10.1109/smc42975.2020.9283201","title":"Discrete Coordinate Descent (DCD)","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"","keywords":"Mathematical optimization; Coordinate descent; Dimension (graph theory); Optimization problem; Metaheuristic; Algorithm; Computation; Computer science; Mathematics; Discrete optimization; Scale (ratio)","score_opus":0.0390599429223153,"score_gpt":0.2759273410338389,"score_spread":0.23686739811152363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111443264","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005966254,0.000020385523,0.9620799,0.019903501,0.00009094653,0.000118819626,0.0000010094288,0.00022339984,0.017502401],"genre_scores_gemma":[0.2605337,0.000027761049,0.732159,0.0036423923,0.00008953163,0.000011255612,0.0000029585938,0.000012832798,0.00352059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988754,0.000067983456,0.00015323205,0.0003095447,0.000364699,0.00022913278],"domain_scores_gemma":[0.9992336,0.000048459453,0.00002764268,0.0003094261,0.00009802172,0.00028283312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000159105,0.000079690195,0.00010392704,0.000039232582,0.0000643998,0.0002026369,0.0008478019,0.000022038004,0.0005094373],"category_scores_gemma":[0.00018141528,0.00006439935,0.00003535424,0.00052331464,0.000028083175,0.00025155075,0.0004356485,0.00010231585,0.0007751445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024651807,0.00014626593,0.0010361013,0.00009322372,0.000089193396,0.00024325281,0.0022202358,0.008805211,0.0010812529,0.6759245,0.11510604,0.19523011],"study_design_scores_gemma":[0.00019955133,0.000046279372,0.00023351755,0.0000018291868,0.0000012492519,0.0000025768904,0.000011511284,0.9852465,0.001353646,0.00034773248,0.0124566555,0.000098993994],"about_ca_topic_score_codex":0.0000072217263,"about_ca_topic_score_gemma":3.430273e-7,"teacher_disagreement_score":0.97644126,"about_ca_system_score_codex":0.000015086402,"about_ca_system_score_gemma":0.000052646166,"threshold_uncertainty_score":0.99631727},"labels":[],"label_agreement":null},{"id":"W3111860947","doi":"10.1109/smc42975.2020.9283154","title":"One-array Differential Evolution Algorithm with a Novel Replacement Strategy for Numerical Optimization","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Differential evolution; Algorithm; Mathematical optimization; Mathematics","score_opus":0.05184049782155163,"score_gpt":0.28797012378769027,"score_spread":0.23612962596613862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111860947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009798558,0.000008210517,0.99544924,0.0026620189,0.00009711883,0.00081862306,0.000020866997,0.00024531188,0.0006887908],"genre_scores_gemma":[0.023765188,0.000004276185,0.9752653,0.00026760492,0.000141652,0.00012131587,0.00005146497,0.00002148429,0.00036169283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782246,0.00006522334,0.00034093094,0.0006771061,0.00071710424,0.00037716795],"domain_scores_gemma":[0.9987019,0.00009141202,0.000116879404,0.0003805217,0.00040938857,0.0002999254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017918929,0.00017758437,0.000235453,0.00009421169,0.00015893701,0.00030171633,0.000573609,0.00006649295,0.00029132966],"category_scores_gemma":[0.00012477895,0.00015379052,0.000056157198,0.00067801616,0.000044187316,0.00043336902,0.00012627259,0.00013230297,0.00001658358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002265665,0.0005924163,0.000037048463,0.000059037495,0.00013608907,0.0000022864135,0.00024935667,0.91871,0.0008801174,0.04352597,0.0010404976,0.03454065],"study_design_scores_gemma":[0.0016257252,0.000871634,0.00008318807,0.000006585989,0.000014414228,0.00000406254,0.00002167356,0.9961332,0.0007094317,0.00008305859,0.00023599034,0.0002110277],"about_ca_topic_score_codex":0.000022438502,"about_ca_topic_score_gemma":9.3179966e-7,"teacher_disagreement_score":0.077423245,"about_ca_system_score_codex":0.00009699033,"about_ca_system_score_gemma":0.00022894247,"threshold_uncertainty_score":0.6271395},"labels":[],"label_agreement":null},{"id":"W3118961950","doi":"10.1109/ssci47803.2020.9308591","title":"Evolving Feedforward Neural Networks Using a Quasi-Opposition-Based Differential Evolution for Data Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Computer science; Differential evolution; Artificial intelligence; Artificial neural network; Feedforward neural network; Feed forward; Reinforcement learning; Machine learning; Evolutionary computation; Engineering","score_opus":0.15415972766611666,"score_gpt":0.34053056587657693,"score_spread":0.18637083821046027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118961950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001905955,0.00003160862,0.99532574,0.0032610411,0.00030754367,0.00056641997,0.000021408487,0.00023101816,0.00006461739],"genre_scores_gemma":[0.6411581,9.548351e-7,0.35802814,0.00028435964,0.00022946521,0.00001952701,0.00025311214,0.000013902894,0.000012458003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979174,0.00016867631,0.00037018186,0.0007117712,0.0004744669,0.00035751722],"domain_scores_gemma":[0.9981946,0.00027932192,0.00014223479,0.0008657285,0.00028963067,0.00022850641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033573818,0.00015565209,0.00018597984,0.000116556475,0.00030207966,0.0005251814,0.0014598934,0.00008135845,0.00008926741],"category_scores_gemma":[0.00041682163,0.00015020923,0.000064332795,0.00064403936,0.00004483344,0.000888605,0.00040813122,0.0001562845,0.000008887758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017337874,0.00056986633,0.0016705071,0.00023169006,0.00013322418,0.000011894315,0.0002506095,0.85481447,0.008526136,0.08940868,0.011827557,0.032381967],"study_design_scores_gemma":[0.0005380792,0.00009015385,0.00042200706,0.000007522707,0.000019179724,0.0000020900197,0.000015620497,0.99849886,0.000048031598,0.00015538334,0.000035235105,0.0001678313],"about_ca_topic_score_codex":0.000029462351,"about_ca_topic_score_gemma":0.0000035080343,"teacher_disagreement_score":0.6409675,"about_ca_system_score_codex":0.00015493782,"about_ca_system_score_gemma":0.00017674983,"threshold_uncertainty_score":0.6125355},"labels":[],"label_agreement":null},{"id":"W3119201936","doi":"10.1007/s00521-020-05637-5","title":"Application of ameliorated Harris Hawks optimizer for designing of low-power signed floating-point MAC architecture","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Particle swarm optimization; Algorithm; Computer science; Floating point; Mathematical optimization; Mathematics","score_opus":0.013986101614174552,"score_gpt":0.2867019832602045,"score_spread":0.27271588164602995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119201936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004542007,0.00013535257,0.99381393,0.0005074719,0.000034485936,0.0007145737,0.000018768967,0.0000865917,0.00014679236],"genre_scores_gemma":[0.5004711,0.000004355697,0.49931222,0.000059199054,0.00002984071,0.000057172914,0.000024341596,0.000011147054,0.00003062823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842227,0.000119458215,0.0004982128,0.00048031102,0.00025306302,0.00022668949],"domain_scores_gemma":[0.9976768,0.000613961,0.00030659034,0.00053354667,0.000769003,0.00010013501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038050552,0.00014163992,0.00027921467,0.00012953914,0.00021313877,0.000081008584,0.0004112509,0.00006643683,0.000007787862],"category_scores_gemma":[0.00019105629,0.00014091982,0.00007367463,0.00090138474,0.00009499236,0.0000785366,0.00022767,0.00015146771,0.0000016978881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036178557,0.0003930407,0.00032279507,0.0005059151,0.00008778076,0.0000023628309,0.001283748,0.45781425,0.21729062,0.02794245,0.0003735616,0.2939473],"study_design_scores_gemma":[0.00041412035,0.00005520054,0.00015655618,0.000028786893,0.000011706858,0.000009830596,0.000055508262,0.94150066,0.056106716,0.0012891063,0.00024073046,0.00013107918],"about_ca_topic_score_codex":0.000010153769,"about_ca_topic_score_gemma":6.2368485e-7,"teacher_disagreement_score":0.4959291,"about_ca_system_score_codex":0.000014241877,"about_ca_system_score_gemma":0.000091828675,"threshold_uncertainty_score":0.57465434},"labels":[],"label_agreement":null},{"id":"W3119250667","doi":"10.1109/iscmi51676.2020.9311551","title":"NichePSO and the Merging Subswarm Problem","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Local optimum; Computer science; Particle swarm optimization; Mathematical optimization; Multi-swarm optimization; Algorithm design; Metaheuristic; Optimization problem; Local search (optimization); Algorithm; Mathematics","score_opus":0.02376218483954947,"score_gpt":0.25307507488677844,"score_spread":0.22931289004722896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119250667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008532253,0.00011934978,0.9284861,0.054389276,0.000028238159,0.00017483077,1.7070656e-7,0.00010150913,0.016615195],"genre_scores_gemma":[0.13390565,0.00014500492,0.8579101,0.0056529613,0.00008267231,0.000027991395,6.442769e-7,0.000010559224,0.0022644084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913824,0.00010884413,0.0001237512,0.0002195378,0.00026517655,0.00014448354],"domain_scores_gemma":[0.999399,0.00018707193,0.000025699028,0.00021338309,0.00006314346,0.00011168762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044498776,0.00005699654,0.000090816546,0.00002223854,0.000098501696,0.00022757385,0.00056202005,0.000014404956,0.000087221],"category_scores_gemma":[0.0002031815,0.000032826996,0.000019257126,0.0003856746,0.00008116901,0.00016055877,0.00040086283,0.000101777085,0.00006289302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017888282,0.000017004724,0.00019402242,0.000030661828,0.000031871143,0.000012762817,0.0035310672,0.0015167603,0.00007971931,0.9192654,0.0075332257,0.0677696],"study_design_scores_gemma":[0.0005195216,0.000012795242,0.000044652614,0.0000012333854,0.0000017856269,0.000004077258,0.000026673473,0.9920457,0.00023366285,0.0015602068,0.0054990035,0.000050681967],"about_ca_topic_score_codex":0.000013955113,"about_ca_topic_score_gemma":7.379289e-7,"teacher_disagreement_score":0.99052894,"about_ca_system_score_codex":0.000004232245,"about_ca_system_score_gemma":0.000034991263,"threshold_uncertainty_score":0.21944998},"labels":[],"label_agreement":null},{"id":"W3119959628","doi":"10.1007/s00521-020-05475-5","title":"A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Engineering optimization; Multidisciplinary design optimization; Benchmark (surveying); Engineering design process; Optimization problem; Continuous optimization; Heuristics; Algorithm; Mathematics; Multi-swarm optimization; Multidisciplinary approach; Engineering; Geography","score_opus":0.08416650638952775,"score_gpt":0.3329561978124539,"score_spread":0.24878969142292615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119959628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010397183,0.00007653409,0.9971923,0.000615659,0.00003418395,0.00080006465,0.000018879924,0.00013028974,0.00009236784],"genre_scores_gemma":[0.14541323,0.000010998135,0.85421705,0.0001378453,0.00006176421,0.00007062799,0.000034106724,0.000020818385,0.00003357862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773663,0.00023888207,0.00036872033,0.00086811295,0.0003937783,0.0003938917],"domain_scores_gemma":[0.9984247,0.00039516183,0.00007055108,0.00043256738,0.00031916043,0.00035782254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043017417,0.00019552634,0.00025296054,0.00014051916,0.0006066667,0.00052324607,0.00034604428,0.000072822506,0.0000068198037],"category_scores_gemma":[0.000099885656,0.00019538924,0.000026691985,0.000905617,0.00009278818,0.00017610723,0.00087294716,0.00026887044,0.0000041439544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003144736,0.00018009619,0.00014355128,0.000038910606,0.000011526182,0.0000032629903,0.0003682707,0.9619553,0.0005503401,0.001061264,0.000024368317,0.035659976],"study_design_scores_gemma":[0.00040244253,0.00004321811,0.00067897234,0.000016756157,0.000010892306,0.00013808753,0.00005256742,0.99824756,0.00011056575,0.000051860305,0.000036094665,0.00021097969],"about_ca_topic_score_codex":0.000040877545,"about_ca_topic_score_gemma":2.793131e-7,"teacher_disagreement_score":0.1443735,"about_ca_system_score_codex":0.000041032497,"about_ca_system_score_gemma":0.000121618716,"threshold_uncertainty_score":0.7967742},"labels":[],"label_agreement":null},{"id":"W3120100312","doi":"10.1109/ssci47803.2020.9308550","title":"Enhancing SHADE and L-SHADE Algorithms Using Ordered Mutation","year":2020,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Differential evolution; Algorithm; Computer science; Population; Mathematical optimization; Reduction (mathematics); Algorithm design; Mathematics","score_opus":0.059521488459502,"score_gpt":0.30737175239499187,"score_spread":0.24785026393548987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120100312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017586733,0.00008112401,0.9935835,0.003469509,0.00008670813,0.00018183293,0.0000014297564,0.00018040209,0.0006568457],"genre_scores_gemma":[0.049388614,0.000018080742,0.9489576,0.0013317482,0.000091415386,0.0000051424054,0.0000034865336,0.000013149971,0.0001907558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985241,0.000094368195,0.00025970014,0.00044570985,0.00041317745,0.0002629027],"domain_scores_gemma":[0.9992069,0.000115370116,0.000063532534,0.00021591967,0.00014759427,0.00025070042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002631949,0.00011864344,0.00015503958,0.00009772781,0.00013978618,0.00032851045,0.00037957472,0.000049155173,0.00013571746],"category_scores_gemma":[0.00038270652,0.00011214166,0.000024282403,0.0006828599,0.000037811038,0.00053188606,0.0003127062,0.00013326826,0.000048665224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005633519,0.00032328934,0.0006748082,0.0005759393,0.00029647417,0.00074777147,0.022240391,0.07062345,0.11848866,0.07284739,0.0033109174,0.7098146],"study_design_scores_gemma":[0.0002939481,0.000042731222,0.00008422749,0.000006567537,0.000003861153,0.000025734906,0.00008229896,0.9895997,0.009046966,0.00029735622,0.00038217072,0.0001344364],"about_ca_topic_score_codex":0.000046442845,"about_ca_topic_score_gemma":0.0000024580559,"teacher_disagreement_score":0.91897625,"about_ca_system_score_codex":0.000026932745,"about_ca_system_score_gemma":0.00010480023,"threshold_uncertainty_score":0.4573004},"labels":[],"label_agreement":null},{"id":"W3124562095","doi":"10.20944/preprints201611.0070.v1","title":"Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm","year":2016,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"University of Manitoba","keywords":"Bees algorithm; Algorithm; Benchmark (surveying); Honey Bees; Computer science; Set (abstract data type); Implementation; Mathematics; Ecology; Biology","score_opus":0.07965608506299912,"score_gpt":0.33767349345051634,"score_spread":0.2580174083875172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124562095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013531417,0.0002576477,0.9732436,0.0021910248,0.0034550764,0.0018554123,0.0002061424,0.0004419116,0.0048177643],"genre_scores_gemma":[0.35419527,0.0014362796,0.626955,0.00047116273,0.001349446,0.0008152995,0.00012907563,0.0002822097,0.014366309],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992105,0.0011017771,0.0012980127,0.0021630034,0.002422714,0.00090946216],"domain_scores_gemma":[0.99177414,0.0005388486,0.0009985137,0.0052527604,0.0010645213,0.000371237],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0025687995,0.00071886985,0.0009820675,0.00051933376,0.00035317257,0.00015605811,0.007862726,0.0006116994,0.00092935696],"category_scores_gemma":[0.0011118936,0.00052701356,0.00062818005,0.00093029084,0.00050269655,0.00049143814,0.019415352,0.0014094795,0.0011576286],"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":[0.00017643014,0.0033822095,0.10106781,0.0018068973,0.0027049996,0.0002448018,0.00896792,0.004252454,0.015529406,0.01640236,0.0041025095,0.8413622],"study_design_scores_gemma":[0.0035142265,0.0001576555,0.16981983,0.0014524237,0.00023354239,0.000076245975,0.00014203951,0.6883583,0.08607642,0.035423372,0.012291004,0.002454933],"about_ca_topic_score_codex":0.00024125702,"about_ca_topic_score_gemma":0.0000043218884,"teacher_disagreement_score":0.83890724,"about_ca_system_score_codex":0.000328886,"about_ca_system_score_gemma":0.0006955569,"threshold_uncertainty_score":0.9999839},"labels":[],"label_agreement":null},{"id":"W3127404465","doi":"10.3390/app11031286","title":"Binary Spring Search Algorithm for Solving Various Optimization Problems","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Binary number; Meta-optimization; Algorithm; Computer science; Population; Multi-swarm optimization; Optimization problem; Genetic algorithm; Mathematical optimization; Metaheuristic; Mathematics","score_opus":0.04237186069110017,"score_gpt":0.2957470829865803,"score_spread":0.25337522229548015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127404465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017244299,0.0001662368,0.9936163,0.00075082626,0.0002826842,0.00048259372,0.0000030038466,0.00016674511,0.0043591866],"genre_scores_gemma":[0.012575976,0.00005964417,0.9865112,0.00018726599,0.00008543026,0.000117712356,0.0000065492727,0.00001332891,0.00044289825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997235,0.00006125291,0.00031308492,0.00086024,0.00091317564,0.00061725144],"domain_scores_gemma":[0.9985521,0.0003732015,0.00008057804,0.0004582732,0.00037812028,0.00015773185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018464813,0.00015204794,0.00019489179,0.00024807887,0.000836158,0.00090415176,0.0012455502,0.000065512206,0.000068694484],"category_scores_gemma":[0.00014208173,0.0001448758,0.000054243537,0.0020584806,0.0002099627,0.00051962,0.0006144889,0.00015105616,0.000032044503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001094394,0.00008932638,0.000028297114,0.00003162309,0.000013995383,0.000011909461,0.00045029973,0.832548,0.0020156458,0.044444334,0.0001666709,0.12019881],"study_design_scores_gemma":[0.00027653636,0.000052188887,0.000030329207,0.000012572156,0.000003482043,0.000011189076,0.00010733156,0.9927242,0.0048748413,0.0011410667,0.0005800955,0.00018618077],"about_ca_topic_score_codex":0.000019463456,"about_ca_topic_score_gemma":0.0000028883985,"teacher_disagreement_score":0.16017619,"about_ca_system_score_codex":0.000057806174,"about_ca_system_score_gemma":0.0006206872,"threshold_uncertainty_score":0.8718756},"labels":[],"label_agreement":null},{"id":"W3134001436","doi":"","title":"On the difficulty of generalizing deep reinforcement learning framework for combinatorial optimization","year":2021,"lang":"en","type":"preprint","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Metaheuristic Optimization Algorithms Research","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":"Reinforcement learning; Computer science; Heuristics; Quadratic assignment problem; Combinatorial optimization; Graph; Artificial intelligence; Travelling salesman problem; Mathematical optimization; Optimization problem; Theoretical computer science; Machine learning; Mathematics; Algorithm","score_opus":0.03848310886771322,"score_gpt":0.28865100042795283,"score_spread":0.2501678915602396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134001436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018841242,0.0003189075,0.97762173,0.00037829383,0.0011177765,0.00075640687,0.0000015950349,0.000064833075,0.0008992149],"genre_scores_gemma":[0.78070676,0.00088187633,0.2155637,0.000009402292,0.0004230298,0.00001557005,0.00009632893,0.000042110496,0.0022612298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949586,0.0017373111,0.0003782189,0.00077593385,0.001663372,0.00048656124],"domain_scores_gemma":[0.99299675,0.003194688,0.0005280671,0.00079290045,0.0022780467,0.00020957454],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0025730168,0.00023939437,0.00049566344,0.00031302997,0.0015773657,0.00042436775,0.0013965386,0.00040247038,0.00007703615],"category_scores_gemma":[0.00310043,0.00024014738,0.00018416837,0.00067751267,0.00035190256,0.00020574951,0.0020595356,0.002552307,0.0000018175144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014102297,0.00012407219,0.00020264623,0.00034225784,0.00018033793,0.000021056301,0.0043770676,0.9519241,0.0003459773,0.04145229,0.00017991997,0.0007092861],"study_design_scores_gemma":[0.0006234566,0.00070471247,0.00017278966,0.00041448066,0.000024231927,0.0000029402981,0.0020064786,0.99261904,0.00046138462,0.0018677597,0.000874366,0.00022835546],"about_ca_topic_score_codex":0.00021865472,"about_ca_topic_score_gemma":0.0000024491032,"teacher_disagreement_score":0.762058,"about_ca_system_score_codex":0.00021449281,"about_ca_system_score_gemma":0.00046762684,"threshold_uncertainty_score":0.9997488},"labels":[],"label_agreement":null},{"id":"W3134041587","doi":"10.1007/s10589-020-00249-0","title":"Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates","year":2021,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":24,"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; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Mathematics; Mathematical optimization; Probabilistic logic; Martingale (probability theory); Convergence (economics); Extension (predicate logic); Stochastic optimization; Convergence of random variables; Algorithm; Random variable; Computer science; Applied mathematics","score_opus":0.04944740018437694,"score_gpt":0.32636244376584844,"score_spread":0.2769150435814715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134041587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014414188,0.00020079513,0.9969376,0.00063496194,0.000072420895,0.0013507021,0.00007349008,0.00019712692,0.0005184996],"genre_scores_gemma":[0.013026917,0.00003402122,0.9855106,0.0001707916,0.00009078368,0.00048208388,0.00046109813,0.000034649656,0.00018905893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977848,0.00012832192,0.00048429385,0.0007857541,0.00048753872,0.0003293149],"domain_scores_gemma":[0.9961775,0.0010136828,0.00016062094,0.00039449977,0.0020270068,0.00022670794],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035867275,0.0002318586,0.00028206746,0.00025155168,0.00064663816,0.00047535513,0.00035711064,0.00009338559,0.000091432266],"category_scores_gemma":[0.00029946462,0.00025735452,0.000072235634,0.0012810118,0.00015270017,0.00047837774,0.00022546762,0.00012365816,0.000008137973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006448512,0.000120128905,0.000009724434,0.00003315979,0.000034738932,9.848978e-7,0.00008408283,0.89706415,0.000006516256,0.10101197,0.00006209787,0.0015659997],"study_design_scores_gemma":[0.0005891702,0.00003785143,0.00002449634,0.000023091605,0.000037089605,0.00003289234,0.00004622953,0.9940661,0.000055032662,0.0047441847,0.00008232024,0.00026153878],"about_ca_topic_score_codex":0.0000033509373,"about_ca_topic_score_gemma":5.05727e-7,"teacher_disagreement_score":0.097001955,"about_ca_system_score_codex":0.00011615598,"about_ca_system_score_gemma":0.000591589,"threshold_uncertainty_score":0.99998784},"labels":[],"label_agreement":null},{"id":"W3136625733","doi":"10.1007/978-3-662-63170-6_7","title":"Novel Hybrid GWO-WOA and BAT-PSO Algorithms for Solving Design Optimization Problems","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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; Mathematical optimization; Optimization algorithm; Algorithm; Mathematics","score_opus":0.05171167206694603,"score_gpt":0.2819092111108858,"score_spread":0.23019753904393975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136625733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.3868718e-7,0.0008559678,0.9951645,0.0007834066,0.0013053106,0.0014150336,0.00001976883,0.00015436487,0.00030122406],"genre_scores_gemma":[0.00026111398,0.00023138465,0.99769354,0.00050517585,0.00036192042,0.00006210414,0.000028939929,0.000065120395,0.00079071306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99458426,0.00006190259,0.00076238264,0.0022906943,0.0013866024,0.0009141343],"domain_scores_gemma":[0.9953987,0.0014872543,0.00037298398,0.0012831454,0.0011396856,0.0003182279],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0022897644,0.0006205087,0.0007055079,0.0010171657,0.00051313,0.0017920383,0.0024793032,0.00027796588,0.000040875297],"category_scores_gemma":[0.0007605308,0.00061107933,0.00012188157,0.00083236664,0.0005978388,0.0008805745,0.0016998982,0.00067972083,0.0000061805995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030436931,0.000030101137,0.0000012399672,0.000069678594,0.0000146648,0.000023956518,0.00017152095,0.7598254,0.00008257904,0.0025639588,0.000025675998,0.23718816],"study_design_scores_gemma":[0.00055749976,0.00017203628,0.0000036076158,0.00032504316,0.000012694374,0.00017369949,2.0423036e-7,0.98496115,0.00097340497,0.011733399,0.00043991653,0.0006473683],"about_ca_topic_score_codex":0.000012823194,"about_ca_topic_score_gemma":0.000005778979,"teacher_disagreement_score":0.2365408,"about_ca_system_score_codex":0.0002856601,"about_ca_system_score_gemma":0.0012054337,"threshold_uncertainty_score":0.999634},"labels":[],"label_agreement":null},{"id":"W3139149903","doi":"10.1007/978-3-662-63170-6_5","title":"A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Evolutionary algorithm; Benchmark (surveying); Convergence (economics); Computer science; Evolutionary computation; Metaheuristic; Premature convergence; Process (computing); Global optimization; Mathematics; Genetic algorithm","score_opus":0.02199607920956767,"score_gpt":0.2639593538183612,"score_spread":0.24196327460879355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139149903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.571668e-7,0.0005734447,0.9932517,0.001233595,0.0018611576,0.0014411147,0.000030184328,0.0002163493,0.0013915051],"genre_scores_gemma":[0.0010351184,0.000096918586,0.9947942,0.0006099602,0.0004881977,0.000037603968,0.000065582215,0.00006069424,0.0028117145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946052,0.00007268382,0.0008759882,0.0021381767,0.0013814472,0.0009265411],"domain_scores_gemma":[0.9952742,0.001106642,0.00044019666,0.001542044,0.001210862,0.0004260559],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010833938,0.00062736886,0.0008317125,0.0010592118,0.0003135167,0.0014511864,0.0031371452,0.00027103222,0.00024210219],"category_scores_gemma":[0.0010005077,0.0006195017,0.00019694232,0.0009702314,0.0005807208,0.00049606676,0.0011309743,0.0006624588,0.000023206385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004660733,0.000029507166,0.0000033041583,0.00007594215,0.000024081042,0.00008926614,0.00013632316,0.61076283,0.000027325817,0.01435178,0.0001838301,0.37431115],"study_design_scores_gemma":[0.00070067553,0.00020816007,0.0000021865224,0.0002826914,0.000016619073,0.00015249646,1.3702734e-7,0.96796066,0.0005053752,0.027611723,0.0019266399,0.0006326586],"about_ca_topic_score_codex":0.000019550687,"about_ca_topic_score_gemma":0.000012789407,"teacher_disagreement_score":0.3736785,"about_ca_system_score_codex":0.00029725122,"about_ca_system_score_gemma":0.002386277,"threshold_uncertainty_score":0.9996256},"labels":[],"label_agreement":null},{"id":"W3140713005","doi":"10.1007/978-3-030-68514-0_1","title":"Metaheuristic Optimization Algorithms","year":2021,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University","funders":"","keywords":"Metaheuristic; Parallel metaheuristic; Computer science; Mathematical optimization; Population; Algorithm; Optimization algorithm; State (computer science); Mathematics; Meta-optimization; Sociology","score_opus":0.027981821199219586,"score_gpt":0.2673447543037582,"score_spread":0.23936293310453863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140713005","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.2926526e-8,0.001352276,0.7715533,0.00039195523,0.000998898,0.00066542847,0.000019222998,0.00031785303,0.224701],"genre_scores_gemma":[0.000016687201,0.0032142068,0.76894855,0.00020858028,0.00026488947,0.00006447197,0.00039110027,0.00017249254,0.22671902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940569,0.0002123318,0.0014841915,0.0018587302,0.0016133541,0.0007744963],"domain_scores_gemma":[0.9955349,0.00032771422,0.00069190975,0.002047698,0.0010743566,0.00032344784],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011049238,0.0008351062,0.0010565353,0.0013727131,0.00021503554,0.0008193455,0.0017661473,0.00078422937,0.0028407676],"category_scores_gemma":[0.0007962039,0.0009879917,0.00027551458,0.0008477979,0.0001609389,0.0008849475,0.0010620268,0.001072305,0.0001494563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005872498,0.000057573798,0.000003966448,0.00007586092,0.000068530404,0.00019984858,0.00006189374,0.86756045,0.0000012171773,0.12399527,0.00039093813,0.007578604],"study_design_scores_gemma":[0.00064561045,0.000052768042,0.000004960361,0.0002968334,0.000049416267,0.00004232992,0.000004146511,0.98135346,0.000029974752,0.0016993898,0.014926094,0.0008949916],"about_ca_topic_score_codex":0.000023358023,"about_ca_topic_score_gemma":0.0000088796605,"teacher_disagreement_score":0.12229587,"about_ca_system_score_codex":0.00060826016,"about_ca_system_score_gemma":0.0007363632,"threshold_uncertainty_score":0.999257},"labels":[],"label_agreement":null},{"id":"W3142950774","doi":"10.5430/air.v10n1p1","title":"Comparison of Centralized and Distributed Intelligent Particle Multi-Swarm Optimization on Search Performance","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Computer science; Metaheuristic; Task (project management); Swarm behaviour; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.33676114848408845,"score_gpt":0.47563296381230696,"score_spread":0.13887181532821852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142950774","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":[0.09385429,0.00026049418,0.9040722,0.0010551483,0.00013841325,0.00039701807,0.000012020541,0.000058316567,0.00015204989],"genre_scores_gemma":[0.9054267,0.0007386955,0.09353783,0.000020696356,0.000034743047,0.000029761439,0.000027526952,0.000016036425,0.00016801829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99493206,0.0010046833,0.00082007574,0.0007371844,0.0016460968,0.0008599126],"domain_scores_gemma":[0.99580526,0.0010190204,0.00009202683,0.00084078667,0.0018825848,0.00036029678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027134428,0.00018142031,0.00036104195,0.00029761545,0.00040989532,0.0003906941,0.00087491266,0.000111525784,0.00021137869],"category_scores_gemma":[0.0018282852,0.00017623436,0.00006320789,0.0027121445,0.00046749134,0.00034859142,0.00070621615,0.0006211465,0.00014949919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009713188,0.0013954783,0.0032191793,0.00008907144,0.00004079503,0.000030114668,0.0028718961,0.7563245,0.003760848,0.054888416,0.000084267944,0.17719829],"study_design_scores_gemma":[0.000053843738,0.00017521209,0.000221564,0.000034700213,0.0000021877977,0.0000032137273,0.0006599654,0.60165524,0.39677513,0.0002580543,0.000062727624,0.000098143115],"about_ca_topic_score_codex":0.000056538054,"about_ca_topic_score_gemma":0.000015502781,"teacher_disagreement_score":0.8115724,"about_ca_system_score_codex":0.00012970192,"about_ca_system_score_gemma":0.00036565817,"threshold_uncertainty_score":0.71866286},"labels":[],"label_agreement":null},{"id":"W3153604172","doi":"10.1017/s0263574721000199","title":"Simultaneous task placement and sequence optimization in an inspection robotic cell","year":2021,"lang":"en","type":"article","venue":"Robotica","topic":"Metaheuristic Optimization Algorithms Research","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; École de Technologie Supérieure","funders":"","keywords":"Workcell; Task (project management); Artificial intelligence; Process (computing); Computer science; Particle swarm optimization; Computer vision; Sequence (biology); Robot; Travelling salesman problem; Optimization problem; Simulation; Engineering; Algorithm","score_opus":0.02615639491630307,"score_gpt":0.28872806143471186,"score_spread":0.2625716665184088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153604172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007899451,0.00010517175,0.99743,0.0005978037,0.00015870863,0.00018076376,0.0000010029645,0.00011682837,0.000619748],"genre_scores_gemma":[0.3939945,0.000117721625,0.605381,0.00011719731,0.000024427747,0.000009633712,0.000015855954,0.000010971461,0.00032866967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982759,0.0002560981,0.00026691056,0.00053860317,0.00037723,0.00028528937],"domain_scores_gemma":[0.99884874,0.00021669036,0.000056069795,0.0004653849,0.00024932972,0.00016377677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029189335,0.00012445773,0.00016394355,0.00015903155,0.00011762913,0.00030364064,0.00028766412,0.00006872879,0.000060664075],"category_scores_gemma":[0.0003541421,0.00013456619,0.000016548474,0.0007916896,0.000049085535,0.00044757174,0.00021133797,0.00018249042,0.000022997956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003306475,0.00012760297,0.000118716736,0.000018662166,0.0000026158605,0.00015044378,0.00025627672,0.99579954,0.00062082376,0.0015136615,0.0000136816925,0.0013746503],"study_design_scores_gemma":[0.00035295254,0.00008387268,0.000111032365,0.000014557057,0.0000042666434,0.00003781236,0.000055066437,0.9984864,0.00057469413,0.0001150871,0.000020118981,0.00014413115],"about_ca_topic_score_codex":0.000012480082,"about_ca_topic_score_gemma":0.000018655714,"teacher_disagreement_score":0.39320457,"about_ca_system_score_codex":0.00011849756,"about_ca_system_score_gemma":0.0001834033,"threshold_uncertainty_score":0.54874504},"labels":[],"label_agreement":null},{"id":"W3156628753","doi":"10.1049/ell2.12176","title":"A greedy non‐hierarchical grey wolf optimizer for real‐world optimization","year":2021,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Metaheuristic Optimization Algorithms Research","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 Guelph","funders":"","keywords":"Benchmark (surveying); Hierarchy; Computer science; Greedy algorithm; Perspective (graphical); Mathematical optimization; Optimization algorithm; Algorithm; Artificial intelligence; Mathematics; Geography","score_opus":0.01522472528538423,"score_gpt":0.27776692319398144,"score_spread":0.26254219790859723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156628753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032258345,0.0001462025,0.97518206,0.021859767,0.00034189483,0.00049620157,0.0000069837356,0.00017798039,0.0014663034],"genre_scores_gemma":[0.0022664238,0.00028626935,0.9892702,0.004372845,0.00024129669,0.00017509283,0.000111873065,0.000056750003,0.003219258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683076,0.00021509714,0.0004505268,0.0008580026,0.00066765066,0.0009779698],"domain_scores_gemma":[0.99785507,0.00037897,0.00012810856,0.0009417117,0.00045966357,0.0002364754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006833422,0.00025788628,0.0003304809,0.00034276178,0.00027671593,0.00050639303,0.0010056508,0.000086319735,0.0001567506],"category_scores_gemma":[0.00034253747,0.0002745571,0.00017006918,0.0015327595,0.00007653748,0.00048287795,0.0002986867,0.00046353549,0.00003583807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006250027,0.00022764257,0.00008364285,0.000059279388,0.00016346268,0.0001130854,0.00028380277,0.90056926,0.0043547982,0.051631223,0.029067557,0.013383748],"study_design_scores_gemma":[0.0010254298,0.000080367194,0.000048541788,0.000012532303,0.000016664424,0.000023658396,0.000003466458,0.98494387,0.0030430115,0.0006402851,0.009847818,0.00031432958],"about_ca_topic_score_codex":0.000012068628,"about_ca_topic_score_gemma":0.000026721193,"teacher_disagreement_score":0.08437464,"about_ca_system_score_codex":0.00027633065,"about_ca_system_score_gemma":0.00063704606,"threshold_uncertainty_score":0.9999707},"labels":[],"label_agreement":null},{"id":"W3157047073","doi":"10.22266/ijies2021.0630.46","title":"Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Particle swarm optimization; Meta-optimization; Multi-swarm optimization; Test functions for optimization; Continuous optimization; Population; Set (abstract data type); Algorithm; Mathematics","score_opus":0.022163953912274766,"score_gpt":0.2679632357895824,"score_spread":0.24579928187730765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157047073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008655689,0.0012382854,0.9954428,0.00022216904,0.0027280373,0.00018917823,0.0000085337015,0.0000478803,0.00003656014],"genre_scores_gemma":[0.038870025,0.00043439164,0.9599875,0.000035038276,0.00043002953,0.000024237008,0.000022103824,0.000028527369,0.00016815039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980251,0.00005264477,0.00076373905,0.0002433506,0.0007004915,0.0002146305],"domain_scores_gemma":[0.9966417,0.00031678655,0.00033478453,0.00017573457,0.0023885593,0.00014244774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085934886,0.00015761131,0.0002609535,0.00041441267,0.00005406938,0.0007534134,0.0005433564,0.000074693446,0.000014056668],"category_scores_gemma":[0.0006197635,0.00015352602,0.00010403502,0.00026765073,0.000014516034,0.00042898714,0.00009489504,0.00016957783,0.0000011124422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004292839,0.00004997873,0.000016129461,0.00005138113,0.00011781067,0.00004110782,0.00015152965,0.98437774,0.00013394008,0.0011180012,0.000026970023,0.013911098],"study_design_scores_gemma":[0.00050302746,0.00006927709,0.000005067702,0.00029101185,0.000012173231,0.0003278448,0.00004011048,0.9962119,0.001302212,0.000017619814,0.0010738502,0.00014594311],"about_ca_topic_score_codex":0.0000076464,"about_ca_topic_score_gemma":2.3596222e-7,"teacher_disagreement_score":0.03878347,"about_ca_system_score_codex":0.00016376484,"about_ca_system_score_gemma":0.0001937951,"threshold_uncertainty_score":0.72651833},"labels":[],"label_agreement":null},{"id":"W3160834095","doi":"10.1016/j.rico.2021.100015","title":"Improving the search pattern of Rooted Tree Optimisation algorithm to solve complex problems","year":2021,"lang":"en","type":"article","venue":"Results in Control and Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Montréal","funders":"","keywords":"Tabu search; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Mathematics; Metaheuristic; Tree (set theory); Convergence (economics); Algorithm; Computer science","score_opus":0.027757472522184867,"score_gpt":0.27076265565816093,"score_spread":0.24300518313597608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160834095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014387499,0.00013365615,0.9948689,0.0037622862,0.0001080961,0.0006349282,0.000039060917,0.00003908151,0.0002701485],"genre_scores_gemma":[0.111308366,0.00016401282,0.88781536,0.00034179515,0.00006506198,0.00005381593,0.00008578521,0.000017907087,0.00014788103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976748,0.0002994393,0.0006231801,0.00051493256,0.00056656485,0.00032105803],"domain_scores_gemma":[0.9980013,0.0004679725,0.00015878008,0.00054365874,0.0007138386,0.00011444899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013171061,0.00014988669,0.00026640986,0.00022639743,0.00013784542,0.00025246292,0.000450705,0.00008001996,0.000016563212],"category_scores_gemma":[0.0006682755,0.0001206483,0.00004088665,0.0010730738,0.00006004356,0.0002908649,0.0002568018,0.00018043305,0.000003229965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018184219,0.0000491753,0.000026540605,0.000017535956,0.000011489199,0.0000046735995,0.0005762382,0.70229405,0.00035146935,0.0001577538,0.000040472896,0.29645243],"study_design_scores_gemma":[0.0023823464,0.00008605691,0.0016093112,0.00003647734,0.0000072258204,0.000008270951,0.000078275785,0.9951192,0.00042444715,0.000037275837,0.000082823026,0.00012826943],"about_ca_topic_score_codex":0.0002236042,"about_ca_topic_score_gemma":0.00006713502,"teacher_disagreement_score":0.29632416,"about_ca_system_score_codex":0.000057765148,"about_ca_system_score_gemma":0.00017198114,"threshold_uncertainty_score":0.4919895},"labels":[],"label_agreement":null},{"id":"W3168883534","doi":"","title":"A Scalable Deterministic Global Optimization Algorithm for Clustering Problems","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Cluster analysis; Scalability; Algorithm design; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence","score_opus":0.04968482956191144,"score_gpt":0.3361670436158939,"score_spread":0.28648221405398244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168883534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002107953,0.000037885333,0.98465484,0.0018700019,0.00065883034,0.00023923161,0.000038256185,0.00017240831,0.012307448],"genre_scores_gemma":[0.04893806,0.000083896186,0.94532627,0.0003105329,0.00015547976,0.00010919477,0.00023451466,0.000022925286,0.0048191403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786824,0.00017343069,0.00036508,0.00061249256,0.00064844865,0.00033233126],"domain_scores_gemma":[0.99819005,0.00018166743,0.00016566695,0.00029915464,0.0010303544,0.00013312914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004244377,0.00019256413,0.00020316495,0.0001306018,0.0002314857,0.0008091657,0.0008328333,0.00007340019,0.00054980675],"category_scores_gemma":[0.0008505819,0.0002015185,0.000082672566,0.00038063747,0.00003597108,0.0003653814,0.00038793337,0.00027619337,0.000057840327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009974502,0.00008522125,0.00018780078,0.000018467757,0.000043092663,0.00003414524,0.00006292875,0.7576644,0.0000586262,0.03298883,0.000046085483,0.20880044],"study_design_scores_gemma":[0.000706825,0.00012360341,0.00005558081,0.000063906315,0.0000051623488,0.00006146462,0.000017843182,0.99511373,0.00007092163,0.001423194,0.0021559342,0.00020185176],"about_ca_topic_score_codex":0.000029569297,"about_ca_topic_score_gemma":0.000015662528,"teacher_disagreement_score":0.23744932,"about_ca_system_score_codex":0.00016066867,"about_ca_system_score_gemma":0.00026691408,"threshold_uncertainty_score":0.8217686},"labels":[],"label_agreement":null},{"id":"W3169904086","doi":"","title":"Nonmyopic Multifidelity Acitve Search","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science","score_opus":0.08054011375428007,"score_gpt":0.36158431784632106,"score_spread":0.281044204092041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169904086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003192524,0.00003999909,0.932309,0.0087559335,0.00059870456,0.00009227303,0.000009935359,0.0001712211,0.054830458],"genre_scores_gemma":[0.89876086,0.00011411267,0.085340165,0.00048749213,0.00012725411,0.00001456599,0.00008601212,0.000015309699,0.015054215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736464,0.00040824665,0.00029598232,0.00058539794,0.0010500287,0.0002956874],"domain_scores_gemma":[0.99799997,0.00023405431,0.00008059321,0.00043714076,0.0010997072,0.00014854126],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062590855,0.00015392953,0.00016617969,0.00020739443,0.00017939777,0.00060575025,0.0011288895,0.00006163615,0.0029455258],"category_scores_gemma":[0.0011805726,0.00015248865,0.000072189156,0.000378329,0.000048547816,0.00033472528,0.0005825642,0.0007644012,0.0006900303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032790893,0.00028235288,0.008570781,0.000021313888,0.00012267967,0.00039346633,0.0007241982,0.054403104,0.0038522624,0.7216989,0.00026576425,0.20963238],"study_design_scores_gemma":[0.00038174598,0.000044384975,0.0022164374,0.000023787323,0.0000014189446,0.000026439035,0.000041895535,0.98958135,0.0015759324,0.0010782828,0.0048768055,0.00015152877],"about_ca_topic_score_codex":0.00009422586,"about_ca_topic_score_gemma":0.000016668952,"teacher_disagreement_score":0.9351782,"about_ca_system_score_codex":0.000093645445,"about_ca_system_score_gemma":0.00027728782,"threshold_uncertainty_score":0.99796593},"labels":[],"label_agreement":null},{"id":"W3173855118","doi":"10.1609/icaps.v31i1.15978","title":"Iterative-deepening Bidirectional Heuristic Search with Restricted Memory","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Israel Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Heuristics; Iterative deepening depth-first search; Heuristic; Incremental heuristic search; Beam search; Bidirectional search; Search algorithm; Node (physics); Exploit; Algorithm; Artificial intelligence","score_opus":0.04752449624311907,"score_gpt":0.308609240293454,"score_spread":0.26108474405033494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173855118","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":[0.902222,0.00015239771,0.05990585,0.005387003,0.00055710203,0.00026561547,0.000015890735,0.0006525544,0.030841613],"genre_scores_gemma":[0.9029922,0.000016810447,0.09611549,0.00008939992,0.000040042094,0.000009576762,0.0000055476876,0.00001009113,0.0007208776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982295,0.00003539764,0.00026492865,0.0003978302,0.00087210623,0.00020023294],"domain_scores_gemma":[0.99787647,0.00018853368,0.00016391664,0.00012016574,0.00156783,0.00008309166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036051287,0.00014175274,0.00017039465,0.00021397539,0.00023198774,0.0005637203,0.00070207426,0.000055868564,0.000044866683],"category_scores_gemma":[0.0006505038,0.00010496222,0.00003249364,0.00059813936,0.000085354404,0.00031509393,0.00032380896,0.00035311893,0.0000044850226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060133403,0.0006367383,0.044541564,0.0004301369,0.001076704,0.00023983003,0.008514477,0.14654447,0.20403768,0.57993644,0.0016298346,0.011810801],"study_design_scores_gemma":[0.00036493354,0.000054180804,0.004084228,0.00044339133,0.0000066244957,0.00012136247,0.00033810487,0.9675822,0.026387207,0.00047055152,0.000022825167,0.00012433714],"about_ca_topic_score_codex":0.000009678407,"about_ca_topic_score_gemma":3.2603742e-7,"teacher_disagreement_score":0.82103777,"about_ca_system_score_codex":0.000043474356,"about_ca_system_score_gemma":0.00025414155,"threshold_uncertainty_score":0.54359674},"labels":[],"label_agreement":null},{"id":"W3174204509","doi":"10.1609/aaai.v35i18.17873","title":"Logic Guided Genetic Algorithms (Student Abstract)","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Perimeter Institute; University of Waterloo; University of Toronto","funders":"","keywords":"Conjunction (astronomy); Computer science; A priori and a posteriori; Set (abstract data type); Algorithm; Genetic algorithm; Function (biology); Fraction (chemistry); Symbolic regression; Data set; State (computer science); Artificial intelligence; Machine learning; Genetic programming; Programming language","score_opus":0.14011594457512389,"score_gpt":0.3638528095070106,"score_spread":0.22373686493188671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174204509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043654285,0.00027332403,0.8440613,0.02098462,0.0024022067,0.0014005036,0.000017434488,0.00036721505,0.08683907],"genre_scores_gemma":[0.85798055,0.00021461537,0.1393615,0.00052114186,0.00012794066,0.0000446739,0.0000010961885,0.000021577393,0.0017269284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967118,0.000036466077,0.00081543636,0.0007402569,0.0012163661,0.0004796836],"domain_scores_gemma":[0.99647355,0.00014310748,0.00037101473,0.0005984921,0.0022414252,0.00017241231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006503672,0.0002526453,0.00032258543,0.00014789327,0.00022735423,0.0005847248,0.0029390207,0.00010611568,0.00057036435],"category_scores_gemma":[0.0010951065,0.00019682334,0.00015161502,0.0012209206,0.0002554827,0.0002959721,0.00096232875,0.0003846207,0.0003069057],"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":[0.000010835925,0.0004228841,0.00017878035,0.00005145954,0.00003700983,0.000014766063,0.0007403513,0.00094479223,0.020653864,0.8545587,0.00071996887,0.121666566],"study_design_scores_gemma":[0.0000636901,0.00013599018,0.0022351786,0.00014822584,0.000017000079,0.000045439094,0.0004461119,0.29767922,0.53621614,0.16226296,0.00035678147,0.00039324886],"about_ca_topic_score_codex":0.000020359812,"about_ca_topic_score_gemma":0.0000034046554,"teacher_disagreement_score":0.8143262,"about_ca_system_score_codex":0.00007066318,"about_ca_system_score_gemma":0.0003243401,"threshold_uncertainty_score":0.80262226},"labels":[],"label_agreement":null},{"id":"W3174209100","doi":"10.1609/aaai.v35i5.16485","title":"Necessary and Sufficient Conditions for Avoiding Reopenings in Best First Suboptimal Search with General Bounding Functions","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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; Canadian Institute for Advanced Research","keywords":"Bounded function; Bounding overwatch; Constant (computer programming); Computer science; Heuristic; Differentiable function; Piecewise; Mathematical optimization; Piecewise linear function; Incremental heuristic search; Function (biology); Mathematics; Search algorithm; Beam search; Artificial intelligence","score_opus":0.09174042424760195,"score_gpt":0.3268076675045277,"score_spread":0.23506724325692577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174209100","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":[0.52279574,0.000049240327,0.46400487,0.008082308,0.0002974596,0.00089653104,0.000027772812,0.000057074383,0.0037890289],"genre_scores_gemma":[0.9655903,0.000052667012,0.03328377,0.00007295092,0.000045425728,0.00009355496,0.0000037862278,0.000014852656,0.000842721],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978839,0.00004053758,0.00046327175,0.0006232001,0.00054974685,0.0004392998],"domain_scores_gemma":[0.9978004,0.00036914763,0.00016887381,0.00025201886,0.0012835461,0.00012597852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009428575,0.00018437095,0.00025584048,0.0002625182,0.000660958,0.0006338028,0.00082851085,0.00006732184,0.000068098976],"category_scores_gemma":[0.000869561,0.0001494502,0.000057716894,0.0013431932,0.0003167513,0.00049704366,0.00042777936,0.000396943,0.0000142497665],"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":[0.00010478548,0.0003874511,0.0021489451,0.00014582017,0.000029370218,0.000005681484,0.0029650417,0.008130536,0.015114023,0.96523166,0.00016383234,0.0055728294],"study_design_scores_gemma":[0.00012644127,0.00031345675,0.0004913938,0.00033767163,0.000015525988,0.000032264626,0.0027974902,0.8680571,0.11972037,0.007752941,0.00010472468,0.00025062478],"about_ca_topic_score_codex":0.000058932575,"about_ca_topic_score_gemma":0.00006554861,"teacher_disagreement_score":0.95747876,"about_ca_system_score_codex":0.000078369085,"about_ca_system_score_gemma":0.00030789984,"threshold_uncertainty_score":0.61117744},"labels":[],"label_agreement":null},{"id":"W3175206867","doi":"10.22266/ijies2021.0831.34","title":"Mixed Best Members Based Optimizer for Solving Various Optimization Problems","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Mathematical optimization; Computer science; Particle swarm optimization; Meta-optimization; Optimization problem; Population; Multi-swarm optimization; Derivative-free optimization; Continuous optimization; Set (abstract data type); Test functions for optimization; Metaheuristic; Algorithm; Mathematics","score_opus":0.03768490655135273,"score_gpt":0.2725425201303167,"score_spread":0.23485761357896398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175206867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004007401,0.0010781974,0.99452597,0.00038818398,0.003353834,0.00014858917,0.0000065867334,0.000027194159,0.000070711176],"genre_scores_gemma":[0.16050045,0.00044990415,0.83806443,0.000044686563,0.00040103766,0.00002676377,0.000015696152,0.00002955461,0.00046749096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828184,0.000047575475,0.0006549473,0.00020054796,0.0006333741,0.00018174596],"domain_scores_gemma":[0.9970304,0.00037670077,0.00025686112,0.00016151827,0.0020347834,0.00013977718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083197973,0.00013295775,0.00023806113,0.0003331662,0.0000453684,0.0005712976,0.0005296229,0.00006137852,0.000020187512],"category_scores_gemma":[0.00080509885,0.00012349198,0.00010467308,0.00019505601,0.000013825292,0.0003082431,0.00008351792,0.00014696307,0.0000018425098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007762326,0.000055453213,0.000021505082,0.000056870573,0.00013895927,0.000045441055,0.000116311356,0.9938793,0.0002921448,0.0033666547,0.000120884,0.0018987064],"study_design_scores_gemma":[0.00044666865,0.00006676905,0.000003752497,0.00025129996,0.0000128213815,0.00030204048,0.00005889841,0.99398947,0.0015029939,0.000022185011,0.0032245615,0.00011855645],"about_ca_topic_score_codex":0.000009846438,"about_ca_topic_score_gemma":5.496901e-7,"teacher_disagreement_score":0.1600997,"about_ca_system_score_codex":0.00011797354,"about_ca_system_score_gemma":0.00017413321,"threshold_uncertainty_score":0.5509036},"labels":[],"label_agreement":null},{"id":"W3175911291","doi":"10.22266/ijies2021.0831.41","title":"MLBO: Mixed Leader Based Optimizer for Solving Optimization Problems","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Computer science; Mathematical optimization; Meta-optimization; Optimization problem; Multi-swarm optimization; Particle swarm optimization; Population; Continuous optimization; Metaheuristic; Test functions for optimization; Derivative-free optimization; Algorithm; Mathematics","score_opus":0.03547555667392628,"score_gpt":0.27581750178400033,"score_spread":0.24034194511007406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175911291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002711739,0.0012638266,0.9945666,0.00062207854,0.0030593164,0.00013653055,0.0000055573328,0.000029929934,0.000044977864],"genre_scores_gemma":[0.1700954,0.0003763008,0.8283326,0.00006531156,0.00052334205,0.00002790116,0.000016708089,0.00003115333,0.00053126866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983415,0.000046251236,0.00063289184,0.0001934661,0.0006088684,0.00017703814],"domain_scores_gemma":[0.9971023,0.00034923494,0.00023452275,0.00015521923,0.0020355256,0.00012315658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008196654,0.00012576653,0.00022757196,0.00031911733,0.0000400124,0.0005293423,0.00050944806,0.000058960577,0.000017183078],"category_scores_gemma":[0.00080198457,0.00011473309,0.00010222463,0.0001801667,0.000013707971,0.00032115594,0.00007580149,0.00014313836,0.0000018677172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072463577,0.00004060881,0.000027003123,0.000059189268,0.00011556866,0.00003080801,0.000107472544,0.99443567,0.00046872185,0.002661501,0.00023043,0.0018157761],"study_design_scores_gemma":[0.0004321516,0.000049219154,0.000010036761,0.00023748362,0.000008723376,0.00023331505,0.000059156697,0.9929216,0.0026016235,0.00001928058,0.0033153137,0.000112132555],"about_ca_topic_score_codex":0.000003291788,"about_ca_topic_score_gemma":2.0737298e-7,"teacher_disagreement_score":0.16982423,"about_ca_system_score_codex":0.00009519096,"about_ca_system_score_gemma":0.00015458591,"threshold_uncertainty_score":0.510446},"labels":[],"label_agreement":null},{"id":"W3181892836","doi":"10.1007/978-3-030-78743-1_21","title":"Two Modified NichePSO Algorithms for Multimodal Optimization","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Computer science; Benchmark (surveying); Particle swarm optimization; Heuristic; Mathematical optimization; Algorithm; Evolutionary algorithm; Metaheuristic; Optimization algorithm; Multi-swarm optimization; Genetic algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.040345808245364365,"score_gpt":0.3135425495727767,"score_spread":0.27319674132741234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181892836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011165594,0.00039744534,0.9930326,0.00085227937,0.0022231732,0.0010785313,0.000024584988,0.00020036104,0.0021899552],"genre_scores_gemma":[0.0008682809,0.000088408,0.9957723,0.0006941329,0.0006012836,0.000060512684,0.000061073675,0.00006615112,0.0017878481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99404943,0.000088134315,0.0008249624,0.002375076,0.001695694,0.0009666776],"domain_scores_gemma":[0.9948589,0.0011107939,0.0003652681,0.0019260303,0.0014097657,0.00032920353],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016100167,0.00064052094,0.0007574066,0.0011443162,0.00040556345,0.0012362865,0.00383197,0.00037907672,0.00008425973],"category_scores_gemma":[0.0006876152,0.0006403853,0.00022653925,0.0011256792,0.00054465083,0.00073143933,0.0017172622,0.0008127862,0.00002038078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044402905,0.000030526422,0.0000013422907,0.000028926743,0.00001318809,0.00003703149,0.00011236821,0.7218797,0.000015425017,0.013703566,0.000015734444,0.26415774],"study_design_scores_gemma":[0.00087287405,0.00011071625,0.000003108481,0.00013929879,0.0000107219485,0.00004408235,1.4981444e-7,0.978325,0.00060248695,0.018835148,0.00040399685,0.00065240054],"about_ca_topic_score_codex":0.000023671271,"about_ca_topic_score_gemma":0.000017008508,"teacher_disagreement_score":0.26350534,"about_ca_system_score_codex":0.00042929713,"about_ca_system_score_gemma":0.0014866468,"threshold_uncertainty_score":0.9998005},"labels":[],"label_agreement":null},{"id":"W3181997525","doi":"10.3390/app11136201","title":"Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Particle swarm optimization; Dimension (graph theory); Maxima and minima; Meta-optimization; Mathematical optimization; Benchmark (surveying); Differential evolution; Multi-swarm optimization; Computer science; Convergence (economics); Algorithm; Imperialist competitive algorithm; Mathematics","score_opus":0.09690670564451638,"score_gpt":0.36220047255427756,"score_spread":0.2652937669097612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181997525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020139279,0.00031878176,0.9660314,0.0010992442,0.00008261214,0.00022155115,0.000001295426,0.000060101152,0.012045745],"genre_scores_gemma":[0.6862927,0.000025105412,0.31336987,0.00014768324,0.000013111899,0.000030203313,0.0000056911485,0.000001309976,0.0001143035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977015,0.00019559164,0.00022981988,0.00060360803,0.001030006,0.00023947406],"domain_scores_gemma":[0.9987244,0.0002778753,0.00008327165,0.00035305676,0.00042657906,0.00013482488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015639117,0.00009844861,0.00019907032,0.00016073984,0.00044797952,0.0004898587,0.00038673822,0.00002894394,0.00025475628],"category_scores_gemma":[0.00014968487,0.00008583706,0.000033371736,0.0043415832,0.00025961493,0.00036530168,0.00027802316,0.000065550936,0.00002884354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017301024,0.00005242286,0.00045331387,0.0000021984602,0.00005775314,0.0000029426042,0.00083355344,0.9235996,0.00032359632,0.07053111,0.000108037784,0.004033719],"study_design_scores_gemma":[0.00021664058,0.00001750914,0.00091641134,0.0000016752296,0.000057907608,0.0000030771648,0.00032077308,0.9913343,0.006107973,0.000830768,0.00008409086,0.000108881075],"about_ca_topic_score_codex":0.000005630285,"about_ca_topic_score_gemma":0.000008064747,"teacher_disagreement_score":0.66615343,"about_ca_system_score_codex":0.000026624793,"about_ca_system_score_gemma":0.00025316657,"threshold_uncertainty_score":0.47237185},"labels":[],"label_agreement":null},{"id":"W3182786887","doi":"10.1145/3449726.3463186","title":"Population-based coordinate descent algorithm with majority voting","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Population; Coordinate descent; Voting; Mathematical optimization; Algorithm; Scale (ratio); Mathematics","score_opus":0.018907877567900123,"score_gpt":0.24380350571475212,"score_spread":0.224895628146852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182786887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09278137,0.00013394181,0.90519696,0.00126498,0.00014684805,0.00023913807,0.0000053963568,0.000062304745,0.00016904002],"genre_scores_gemma":[0.6435984,0.000010545928,0.35625336,0.000042936594,0.000021727472,0.000007992505,0.0000090399135,0.00000611112,0.000049938277],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983316,0.000059436872,0.00034874977,0.00042847937,0.00060428877,0.00022741978],"domain_scores_gemma":[0.9976628,0.00010517711,0.00026532062,0.00013270725,0.0017311281,0.000102857404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022740463,0.00015974701,0.00021849466,0.0001078007,0.00033454492,0.00018723181,0.0004146887,0.000051806008,0.000016471515],"category_scores_gemma":[0.000101417536,0.00012860297,0.000044359134,0.00073136436,0.000104945575,0.00026384962,0.00029233497,0.00015220737,0.0000024283886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004413459,0.0007360636,0.42172748,0.00069159054,0.00015849198,0.000010322596,0.000881305,0.19361365,0.0016024525,0.083305165,0.0010965727,0.29613277],"study_design_scores_gemma":[0.0003334618,0.00003633011,0.3104559,0.00007508605,0.000010493079,0.000036531495,0.00004402233,0.6860195,0.00038513687,0.0024733872,0.000021128819,0.00010907151],"about_ca_topic_score_codex":0.00003874334,"about_ca_topic_score_gemma":0.0000018425166,"teacher_disagreement_score":0.55081695,"about_ca_system_score_codex":0.00007030239,"about_ca_system_score_gemma":0.00028225046,"threshold_uncertainty_score":0.5244277},"labels":[],"label_agreement":null},{"id":"W3187712750","doi":"10.1155/2021/5526127","title":"Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Sine; Convergence (economics); Computer science; Trigonometric functions; Mathematical optimization; Optimization problem; Population; Wireless sensor network; Algorithm; Mathematics","score_opus":0.021482831399074472,"score_gpt":0.2792236875496082,"score_spread":0.2577408561505337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187712750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08617043,0.00039834066,0.9127185,0.0003730267,0.00009314863,0.00018257688,0.000041571104,0.000016526277,0.0000059066656],"genre_scores_gemma":[0.53374803,0.000058167614,0.4661275,0.000006494872,0.000020683652,0.0000020503792,0.000020489686,0.000008139168,0.000008398292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984388,0.00007989106,0.00062748534,0.00019806933,0.0004735975,0.00018218538],"domain_scores_gemma":[0.99767405,0.0002270784,0.00046735138,0.00015219557,0.0013297469,0.00014956943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004230262,0.00012524704,0.0003605228,0.00015474112,0.00007766318,0.000080623555,0.00019317114,0.00003961673,0.000002639889],"category_scores_gemma":[0.000041633553,0.000102807266,0.00007270627,0.0004057945,0.00005017264,0.000906784,0.0000027340968,0.00013769315,1.0860854e-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":[0.00037427948,0.00063883595,0.0025028451,0.0013428163,0.00035991156,0.0004609052,0.0032710498,0.3827688,0.037312306,0.009735323,0.000011944317,0.561221],"study_design_scores_gemma":[0.00977428,0.0011146253,0.12321561,0.00074811006,0.00011572531,0.0003651553,0.002011919,0.8415992,0.020113982,0.00033086722,0.00025031745,0.00036016348],"about_ca_topic_score_codex":0.000008052851,"about_ca_topic_score_gemma":0.000031484742,"teacher_disagreement_score":0.5608608,"about_ca_system_score_codex":0.000038207556,"about_ca_system_score_gemma":0.00018372122,"threshold_uncertainty_score":0.41923586},"labels":[],"label_agreement":null},{"id":"W3189183394","doi":"10.1109/cec45853.2021.9504760","title":"Visualizing and Characterizing the Parameter Configuration Landscape of Particle Swarm Optimization using Physical Landform Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Computer science; Swarm behaviour; Heuristic; Multi-swarm optimization; Landform; Mathematical optimization; Scheme (mathematics); Artificial intelligence; Machine learning; Mathematics; Geology","score_opus":0.06668862446659618,"score_gpt":0.33267334479366245,"score_spread":0.26598472032706627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189183394","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":[0.15483248,0.000029613357,0.8438272,0.0007517748,0.000051431693,0.00010859302,0.0000010260426,0.000031180163,0.00036673484],"genre_scores_gemma":[0.8958611,0.0000351289,0.103827275,0.000115130446,0.00004108127,0.000007920476,0.000011904267,0.0000064367096,0.00009403372],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989718,0.0001462776,0.00022692497,0.0002284136,0.00028202927,0.00014450662],"domain_scores_gemma":[0.99901676,0.00024785238,0.00011201363,0.00029374845,0.00027948245,0.000050112732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032236718,0.00007542282,0.00012585988,0.000039749175,0.00012854091,0.0002437432,0.00016048383,0.000030107683,0.0000354348],"category_scores_gemma":[0.00023042712,0.000054420765,0.000027383634,0.00042985016,0.000045394587,0.00046590075,0.00010519796,0.00007142222,0.0000032636588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053742187,0.0007706845,0.008497597,0.00020139285,0.00018658917,0.000019507952,0.008846335,0.14776807,0.41356537,0.32736644,0.00013922474,0.09258505],"study_design_scores_gemma":[0.00018233927,0.000016969356,0.0022660044,0.000008821601,0.000008176432,0.000009595129,0.00014532352,0.93872833,0.05844148,0.00009534101,0.000034396417,0.00006320685],"about_ca_topic_score_codex":0.0000066309203,"about_ca_topic_score_gemma":6.9016676e-7,"teacher_disagreement_score":0.79096025,"about_ca_system_score_codex":0.0000140521815,"about_ca_system_score_gemma":0.000068896,"threshold_uncertainty_score":0.23504212},"labels":[],"label_agreement":null},{"id":"W3189323750","doi":"10.1109/cec45853.2021.9504762","title":"Memetic Differential Evolution Using Coordinate Descent","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Memetic algorithm; Coordinate descent; Benchmark (surveying); Local search (optimization); Differential evolution; Mathematical optimization; Computer science; Population; Local optimum; Algorithm; Guided Local Search; Mathematics","score_opus":0.03685512702895182,"score_gpt":0.2925236321785362,"score_spread":0.2556685051495844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189323750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049759783,0.00005634336,0.9922411,0.00041107534,0.00044906655,0.000077384815,7.188762e-7,0.000095396295,0.0016929705],"genre_scores_gemma":[0.52851,0.000010017836,0.46889785,0.000055780998,0.000047404697,0.0000031081609,0.0000028025072,0.0000069676494,0.0024661096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861157,0.00016747644,0.00018448953,0.000327221,0.0004468409,0.00026239725],"domain_scores_gemma":[0.9989991,0.00004854021,0.000035826168,0.00044250663,0.00035695758,0.000117056545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017134774,0.00008458135,0.00011610519,0.000102932354,0.00012230752,0.00024087426,0.00037296017,0.0000350832,0.000837722],"category_scores_gemma":[0.00017850683,0.00007816455,0.00004670866,0.00065730984,0.00002788377,0.0002225637,0.0004131626,0.00009278458,0.00007870067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013297831,0.0009844318,0.001736227,0.00012326967,0.00017833777,0.0004288995,0.0004233583,0.026634736,0.06541601,0.81441325,0.0032277997,0.08642039],"study_design_scores_gemma":[0.00023335511,0.000010451752,0.0009985935,0.000006657146,0.000005436991,0.000029749031,0.000011907267,0.989608,0.00767712,0.0011151553,0.00020567111,0.000097924676],"about_ca_topic_score_codex":0.000026102161,"about_ca_topic_score_gemma":0.0000029115188,"teacher_disagreement_score":0.96297324,"about_ca_system_score_codex":0.00011774737,"about_ca_system_score_gemma":0.0002080592,"threshold_uncertainty_score":0.9172468},"labels":[],"label_agreement":null},{"id":"W3190760168","doi":"10.1109/cec45853.2021.9504905","title":"Machine Learning for Determining the Transition Point in Hybrid Metaheuristics","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Prince Edward Island","funders":"","keywords":"Metaheuristic; Benchmark (surveying); Computer science; Task (project management); Relay; Artificial intelligence; Point (geometry); Transition (genetics); Hybrid algorithm (constraint satisfaction); Machine learning; Mathematical optimization; Mathematics; Engineering","score_opus":0.03133504659907127,"score_gpt":0.2886239191119007,"score_spread":0.25728887251282945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190760168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003912269,0.00008884797,0.9947759,0.002944358,0.00013543488,0.00021010906,0.0000033999263,0.000061737475,0.0013890216],"genre_scores_gemma":[0.33743337,0.00007546699,0.6596953,0.0008078267,0.00006270292,0.000061078965,0.000044956934,0.000019099009,0.0018002139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860036,0.00025475133,0.0002765058,0.00031121765,0.00029696943,0.00026018804],"domain_scores_gemma":[0.99863315,0.00065692974,0.000047214475,0.00032253336,0.00028232933,0.00005787008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009313025,0.00009647564,0.00015064476,0.00009767355,0.00015698266,0.00023001584,0.0004165218,0.00002262118,0.00009489378],"category_scores_gemma":[0.0011275813,0.00007197853,0.00006355441,0.000423797,0.00002591667,0.00020594157,0.00013013992,0.00020880188,0.000013645208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000539675,0.000749219,0.0019221384,0.00021272378,0.00014361404,0.0004435153,0.003970541,0.26504207,0.0006576352,0.14031948,0.0025275704,0.58395755],"study_design_scores_gemma":[0.0005124041,0.0000371208,0.0003009581,0.0000063440034,0.00000544011,0.000020669828,0.000031301613,0.9928106,0.0015585105,0.001729169,0.0028937717,0.00009372361],"about_ca_topic_score_codex":0.000010274733,"about_ca_topic_score_gemma":0.000013431494,"teacher_disagreement_score":0.7277685,"about_ca_system_score_codex":0.00003203323,"about_ca_system_score_gemma":0.00010126637,"threshold_uncertainty_score":0.2935199},"labels":[],"label_agreement":null},{"id":"W3190982209","doi":"10.1109/cec45853.2021.9505006","title":"Predicting Particle Swarm Optimization Control Parameters From Fitness Landscape Characteristics","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Benchmark (surveying); Fitness landscape; Generalization; Computer science; Selection (genetic algorithm); Suite; Mathematical optimization; Control (management); Test suite; Metaheuristic; Field (mathematics); Machine learning; Optimization problem; Artificial intelligence; Test case; Algorithm; Mathematics; Population","score_opus":0.01980432618358108,"score_gpt":0.25438106389650383,"score_spread":0.23457673771292276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190982209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029384868,0.00005998336,0.9677865,0.0011293816,0.00042779767,0.0001523345,0.000028696733,0.00024234045,0.00078810396],"genre_scores_gemma":[0.5086559,0.000042261352,0.4899698,0.00053065707,0.00008787741,0.000026038175,0.00006886421,0.000015606975,0.00060299604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979935,0.00024032907,0.00039573666,0.0005024778,0.00051067444,0.0003572769],"domain_scores_gemma":[0.9980617,0.0005272421,0.00011371819,0.0006527593,0.000439309,0.00020526975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033846457,0.00014264604,0.00024119165,0.000052618758,0.00014718143,0.00053917,0.0005012696,0.000070773,0.00065054046],"category_scores_gemma":[0.0009245619,0.00013595125,0.000056187902,0.0005838222,0.000034468016,0.00045065072,0.00021409284,0.00014952193,0.00008533416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037156078,0.00042377762,0.0649068,0.000030343488,0.000213367,0.00028114693,0.00073526776,0.8950974,0.0007435704,0.006313298,0.0008727188,0.030345192],"study_design_scores_gemma":[0.00081953726,0.00002272721,0.003450398,0.000010478679,0.00001419242,0.000007972041,0.000046237954,0.9910706,0.0041918764,0.00011189084,0.00009855345,0.000155497],"about_ca_topic_score_codex":0.00003302295,"about_ca_topic_score_gemma":0.000004025298,"teacher_disagreement_score":0.47927105,"about_ca_system_score_codex":0.000028910266,"about_ca_system_score_gemma":0.00015257191,"threshold_uncertainty_score":0.7122962},"labels":[],"label_agreement":null},{"id":"W3191597211","doi":"10.1109/cec45853.2021.9504812","title":"An Exploration-only Exploitation-only Hybrid for Large Scale Global Optimization","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Computer science; Mathematical optimization; Selection (genetic algorithm); Population; Convergence (economics); Metaheuristic; Scale (ratio); Artificial intelligence; Mathematics; Geography; Economics","score_opus":0.037143198323096926,"score_gpt":0.3303448506157609,"score_spread":0.293201652292664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191597211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026919742,0.000048466478,0.99317145,0.0021843582,0.00045720203,0.00045547562,0.00008196567,0.00032779085,0.0030040897],"genre_scores_gemma":[0.015293482,0.000044984838,0.9813671,0.000699071,0.00014329277,0.000110663954,0.00044109792,0.000020755739,0.0018795152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974863,0.00021855792,0.0004392491,0.00074406166,0.0006526815,0.00045916403],"domain_scores_gemma":[0.9969849,0.00016255067,0.000106301784,0.00088396907,0.0015829713,0.00027931255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060329953,0.00018371515,0.0002120235,0.00010945071,0.00033972866,0.00081898586,0.0007411576,0.00006132989,0.00050134974],"category_scores_gemma":[0.0004916173,0.000191395,0.00008669308,0.0010205543,0.000025718447,0.0035192138,0.00017537548,0.000078013945,0.0000822918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027165936,0.0010201829,0.00094086595,0.00005738129,0.00005823608,0.00003801415,0.0007813715,0.50362843,0.00007475833,0.43256456,0.011053486,0.04975555],"study_design_scores_gemma":[0.0008044964,0.00009267768,0.00010444272,0.000006372193,0.000007635076,0.000039046052,0.00032603138,0.99013627,0.0008398157,0.0050348225,0.002376047,0.00023235279],"about_ca_topic_score_codex":0.0000069963953,"about_ca_topic_score_gemma":0.000049445476,"teacher_disagreement_score":0.48650783,"about_ca_system_score_codex":0.00010316667,"about_ca_system_score_gemma":0.0008984814,"threshold_uncertainty_score":0.78975},"labels":[],"label_agreement":null},{"id":"W3192162026","doi":"10.1007/s43069-021-00068-x","title":"Review on Nature-Inspired Algorithms","year":2021,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":59,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Variety (cybernetics); Process (computing); Set (abstract data type); Engineering optimization; Class (philosophy); Optimization problem; Optimization algorithm; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.0741606981322478,"score_gpt":0.4232050557761675,"score_spread":0.3490443576439197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192162026","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056900783,0.032356083,0.763772,0.1513978,0.0008355463,0.0015915137,0.000046504494,0.0003001503,0.04964351],"genre_scores_gemma":[0.010016564,0.063617215,0.8239211,0.025124408,0.00048609928,0.0011702948,0.00036864393,0.00010530573,0.07519036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950325,0.001091144,0.00039022128,0.0007993567,0.0018570111,0.0008297664],"domain_scores_gemma":[0.9949064,0.00037663002,0.00001941156,0.0017790453,0.0025810811,0.00033743025],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024683934,0.00016594635,0.000255583,0.00038718944,0.0009381186,0.0008437899,0.0014656557,0.00014165846,0.0008183386],"category_scores_gemma":[0.004517947,0.00014709654,0.000099622855,0.0033919052,0.00011907708,0.0005845781,0.00085449853,0.0012757775,0.0015772652],"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":[0.0000056381336,0.00060549605,0.000020609918,0.00027284908,0.00006694319,0.0003351245,0.00009463616,0.004261764,0.00058121147,0.49100295,0.3465004,0.15625238],"study_design_scores_gemma":[0.0003998989,0.00018005444,0.000061008865,0.00056947645,0.000003643612,0.00006192591,0.00004794818,0.6004558,0.004883293,0.00092432124,0.39214662,0.00026601972],"about_ca_topic_score_codex":0.000027563892,"about_ca_topic_score_gemma":0.000053389133,"teacher_disagreement_score":0.596194,"about_ca_system_score_codex":0.00016461102,"about_ca_system_score_gemma":0.001167298,"threshold_uncertainty_score":0.9992001},"labels":[],"label_agreement":null},{"id":"W3195734723","doi":"10.22266/ijies2021.1031.12","title":"TIMBO: Three Influential Members Based Optimizer","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Computer science; Population; Optimization problem; Genetic algorithm; Meta-optimization; Derivative-free optimization; Algorithm; Mathematics","score_opus":0.02104300154923021,"score_gpt":0.27077550879937656,"score_spread":0.24973250725014634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195734723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038049915,0.00096091436,0.99121743,0.0003705603,0.0033645614,0.0000469479,0.0000028878233,0.000024306368,0.00020738656],"genre_scores_gemma":[0.75627387,0.00030927066,0.24204381,0.00011022137,0.0007383991,0.000007137678,0.0000058989535,0.000026822603,0.0004845809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830306,0.00005100654,0.00053641555,0.00015211063,0.00081389025,0.00014352723],"domain_scores_gemma":[0.998072,0.00019084041,0.00016877601,0.00017187085,0.0012513825,0.00014511793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005920622,0.0001079163,0.00019583871,0.00029061132,0.00002422299,0.0004579066,0.0006112981,0.000046639467,0.00006347744],"category_scores_gemma":[0.00044445717,0.00009615881,0.00009134976,0.00017920471,0.000015752012,0.00025541213,0.00011861168,0.00018687453,0.000008624006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000094163215,0.000040914132,0.0001841133,0.000022150554,0.00018548407,0.00030252888,0.0000781171,0.9888291,0.00042651285,0.005246467,0.00028383682,0.004391398],"study_design_scores_gemma":[0.00030148128,0.000029558696,0.00011005883,0.00012240522,0.000006749712,0.00045978444,0.000020976426,0.98959327,0.0018811027,0.000024856607,0.007357358,0.00009239847],"about_ca_topic_score_codex":0.000010917468,"about_ca_topic_score_gemma":6.2584553e-7,"teacher_disagreement_score":0.7524689,"about_ca_system_score_codex":0.00007722756,"about_ca_system_score_gemma":0.00014777717,"threshold_uncertainty_score":0.4415604},"labels":[],"label_agreement":null},{"id":"W3197047861","doi":"10.1109/tcyb.2021.3101880","title":"A Promotive Particle Swarm Optimizer With Double Hierarchical Structures","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Korea Electric Power Corporation; National Natural Science Foundation of China","keywords":"Hierarchy; Particle swarm optimization; Benchmark (surveying); Swarm behaviour; Computer science; Convergence (economics); Set (abstract data type); Mathematical optimization; Local optimum; Operator (biology); Hierarchical organization; Mathematics; Artificial intelligence; Algorithm; Biology","score_opus":0.026172741535246874,"score_gpt":0.2747791986209256,"score_spread":0.24860645708567872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197047861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068955934,0.00003158739,0.9897121,0.0013663105,0.00027770246,0.00027023337,0.000011093208,0.00016094014,0.0012744239],"genre_scores_gemma":[0.5172958,0.000046605033,0.47850135,0.00019720594,0.000034420755,0.00006015795,0.0000025029947,0.000027144812,0.0038348057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786526,0.00015926031,0.00025873314,0.0005649902,0.0007305707,0.00042120507],"domain_scores_gemma":[0.99837416,0.00018125371,0.000050839775,0.000747143,0.0003753267,0.0002712569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015607849,0.00019755469,0.00021197872,0.00009601244,0.00021190662,0.00029840434,0.0004819718,0.00008639581,0.0003002315],"category_scores_gemma":[0.0000139678805,0.00016667637,0.000074003874,0.00089401624,0.00014707977,0.00021161897,0.000009765993,0.0004557099,0.0000888095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022372151,0.0011369636,0.000026680047,0.00003395659,0.0002356611,0.00038881082,0.001938284,0.9250574,0.00074811035,0.022795888,0.00029049208,0.04712402],"study_design_scores_gemma":[0.0025773356,0.0004124336,0.00015830873,0.000026686599,0.000042084874,0.00017579945,0.00005908507,0.7071893,0.28644556,0.0015057962,0.0010227845,0.00038484216],"about_ca_topic_score_codex":0.000018090555,"about_ca_topic_score_gemma":0.000029982064,"teacher_disagreement_score":0.51121074,"about_ca_system_score_codex":0.000062063,"about_ca_system_score_gemma":0.00031486846,"threshold_uncertainty_score":0.67968655},"labels":[],"label_agreement":null},{"id":"W3197111254","doi":"10.1609/socs.v12i1.18581","title":"Iterative-Deepening Bidirectional Heuristic Search with Restricted Memory","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Heuristic; Computer science; Incremental heuristic search; Null-move heuristic; Iterative deepening depth-first search; Search algorithm; Algorithm; Beam search; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.017984615461359425,"score_gpt":0.2674510381562879,"score_spread":0.24946642269492847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197111254","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":[0.6922798,0.00014465589,0.032932807,0.051243797,0.016301986,0.0022765181,0.00008089937,0.00061317283,0.20412637],"genre_scores_gemma":[0.9817689,0.000042367108,0.012894836,0.0001544003,0.00054596853,0.00005157295,0.000010337904,0.000037906688,0.004493695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9951669,0.00010805707,0.00043153242,0.0006764635,0.0032128277,0.0004042118],"domain_scores_gemma":[0.9947268,0.00050150626,0.00016392584,0.00032662236,0.004111547,0.0001696233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008355313,0.00021679899,0.00026035449,0.00025312358,0.00034602065,0.0006455289,0.0023535516,0.0000918796,0.00014679556],"category_scores_gemma":[0.0007766058,0.00016531533,0.00011338182,0.0016685949,0.00017124828,0.0005047654,0.0011504415,0.0006461969,0.000035254718],"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":[0.0007023808,0.0014056691,0.006170795,0.00014007677,0.00049788225,0.00006672754,0.0013244515,0.006437938,0.10681913,0.8696534,0.004065186,0.002716364],"study_design_scores_gemma":[0.004485299,0.0008144083,0.005268644,0.00042867704,0.000031077558,0.00025237855,0.00023392196,0.1965841,0.77959526,0.008128526,0.003513094,0.0006646087],"about_ca_topic_score_codex":0.00006371935,"about_ca_topic_score_gemma":0.0000013096458,"teacher_disagreement_score":0.8615249,"about_ca_system_score_codex":0.0003145778,"about_ca_system_score_gemma":0.0005051717,"threshold_uncertainty_score":0.67413634},"labels":[],"label_agreement":null},{"id":"W3199114070","doi":"10.1007/s00366-021-01487-4","title":"An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems","year":2021,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":42,"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":"Maxima and minima; Chaotic; Mathematical optimization; Convergence (economics); Heuristics; Particle swarm optimization; Mathematics; Algorithm; Engineering optimization; Metaheuristic; Engineering design process; Computer science; Optimization problem; Engineering; Artificial intelligence","score_opus":0.008656017236220927,"score_gpt":0.21614573020336264,"score_spread":0.20748971296714172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199114070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041072498,0.00016973066,0.9977634,0.00030590585,0.00049403234,0.00040690575,0.000003269925,0.00043936534,0.000006655001],"genre_scores_gemma":[0.040706333,0.000016767252,0.9589101,0.000058919693,0.00010677456,0.00008355567,0.000023648712,0.000056274224,0.00003764592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982366,0.000026451457,0.00028956696,0.00066129846,0.000287186,0.0004988958],"domain_scores_gemma":[0.9985437,0.00027997774,0.00006647565,0.00052619295,0.0002819677,0.00030169912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029345194,0.00026851095,0.00030362146,0.0002110812,0.000116329466,0.0005854791,0.00044478194,0.000077454446,0.00000829342],"category_scores_gemma":[0.00018426732,0.00026754837,0.00004661958,0.0005876685,0.000018882385,0.00063349877,0.00017853566,0.0002054428,0.0000011538406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003466488,0.000035208468,0.000020430856,0.00010129624,0.00004654039,0.00001533901,0.00022209447,0.99654186,0.0006325575,0.00063461374,0.000034327237,0.0017122509],"study_design_scores_gemma":[0.00075480953,0.00016430415,0.00013920802,0.000086283035,0.000012240734,0.000074784315,0.000006860051,0.9976748,0.0005213319,0.0000043168957,0.00021921638,0.000341807],"about_ca_topic_score_codex":0.0000045498045,"about_ca_topic_score_gemma":2.3911042e-7,"teacher_disagreement_score":0.04029561,"about_ca_system_score_codex":0.000067248235,"about_ca_system_score_gemma":0.00010126779,"threshold_uncertainty_score":0.99997765},"labels":[],"label_agreement":null},{"id":"W3205411000","doi":"10.1007/978-3-030-70296-0_79","title":"An Adaptive Tribal Topology for Particle Swarm Optimization","year":2021,"lang":"en","type":"book-chapter","venue":"Transactions on computational science and computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Swarm behaviour; Particle swarm optimization; Maxima and minima; Metaheuristic; Multi-swarm optimization; Mathematical optimization; Computer science; Benchmark (surveying); Fitness function; Swarm intelligence; Population; Mathematics; Geography; Genetic algorithm","score_opus":0.0640853099857278,"score_gpt":0.3385461339797257,"score_spread":0.2744608239939979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205411000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007763448,0.000113150665,0.9937063,0.0011091032,0.00038039606,0.00062722043,0.00010403403,0.00013008656,0.0038219797],"genre_scores_gemma":[0.038737975,0.00009346613,0.95087236,0.0008428398,0.00009812612,0.00010170977,0.00013908978,0.00003890613,0.00907551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958145,0.00007735433,0.00064881693,0.0013125046,0.0017050772,0.00044175607],"domain_scores_gemma":[0.9944267,0.001134989,0.00024144616,0.00042289167,0.0033849666,0.00038897793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009488097,0.00037166785,0.00037305895,0.00052161753,0.001047016,0.00059610244,0.0010261392,0.00017859746,0.00030406372],"category_scores_gemma":[0.00009738059,0.0003992213,0.00012053135,0.00064485986,0.00089628005,0.00092764356,0.00004585744,0.00036197223,0.00004338492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022046164,0.00007279822,1.0077854e-7,0.000006738769,0.00002075268,0.0000040041846,0.00007662431,0.55346835,9.969143e-7,0.3999544,0.000018564635,0.046354603],"study_design_scores_gemma":[0.00018881392,0.00056516833,0.0000074077993,0.00003628728,0.000021050215,0.000048550617,0.00003831493,0.86743927,0.00013007376,0.13078938,0.00038357172,0.0003520946],"about_ca_topic_score_codex":0.000008356222,"about_ca_topic_score_gemma":0.0000042241177,"teacher_disagreement_score":0.31397092,"about_ca_system_score_codex":0.00027626308,"about_ca_system_score_gemma":0.0017980301,"threshold_uncertainty_score":0.999846},"labels":[],"label_agreement":null},{"id":"W3206167478","doi":"10.1155/2021/6642009","title":"An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Metaheuristic Optimization Algorithms Research","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":"National Natural Science Foundation of China","keywords":"Travelling salesman problem; Ant colony optimization algorithms; Mathematical optimization; Heuristic; Computer science; Population; Path (computing); Algorithm; Local optimum; Mathematics","score_opus":0.023403336667281678,"score_gpt":0.3020226378480316,"score_spread":0.27861930118074996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206167478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029760527,0.000051131046,0.9955314,0.0005170518,0.00034436656,0.0004897765,0.0000481491,0.000030816835,0.000011249341],"genre_scores_gemma":[0.39485243,0.000043205688,0.60480356,0.00012433699,0.0000802953,0.000019624793,0.000044845976,0.000017283612,0.000014381451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981271,0.00020710345,0.00063897506,0.00031304194,0.00048507444,0.00022869254],"domain_scores_gemma":[0.99681455,0.0005739762,0.0005103796,0.00034844727,0.0015837763,0.00016888729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066863484,0.00015915124,0.00023964781,0.00013334522,0.00022017428,0.00015512158,0.00047358315,0.00006279047,0.000025018458],"category_scores_gemma":[0.00020192988,0.0001229853,0.000109655564,0.00045688302,0.00003433242,0.0010009812,0.0000029933547,0.00023859635,4.6669933e-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":[0.00029141406,0.00021066566,0.00001616255,0.000021504582,0.0000229284,0.000021575703,0.00074686954,0.9769302,0.0026774334,0.0006687031,0.0000029822413,0.018389547],"study_design_scores_gemma":[0.0015480919,0.0015723206,0.0052016373,0.000046697387,0.000036814206,0.000006046088,0.00021769779,0.988576,0.0022316163,0.00035125355,0.00007199506,0.00013984725],"about_ca_topic_score_codex":0.0000023585599,"about_ca_topic_score_gemma":0.000024800142,"teacher_disagreement_score":0.3918764,"about_ca_system_score_codex":0.00007999911,"about_ca_system_score_gemma":0.00046272148,"threshold_uncertainty_score":0.5015195},"labels":[],"label_agreement":null},{"id":"W3206780878","doi":"10.1115/omae2021-62304","title":"Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Constraint (computer-aided design); Convergence (economics); Multi-objective optimization; Mathematical optimization; Exploit; Artificial neural network; Task (project management); Artificial intelligence; Pareto principle; Optimization problem; Genetic algorithm; Machine learning; Algorithm; Engineering; Mathematics","score_opus":0.05955576062711624,"score_gpt":0.3081879165762,"score_spread":0.24863215594908375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206780878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010099739,0.00015494098,0.9973736,0.00010281405,0.000048668047,0.00009361795,0.000006281016,0.00003489611,0.0011752414],"genre_scores_gemma":[0.2809558,0.00002368923,0.71882486,0.000034688386,0.000006405291,0.0000010727891,0.00002167354,0.0000061040887,0.00012570259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985871,0.00025980282,0.00032575164,0.0002903332,0.00034257374,0.00019448108],"domain_scores_gemma":[0.9991603,0.00016414799,0.000106723026,0.00019845557,0.000308021,0.00006237495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031426476,0.00009634197,0.00020380017,0.00015164465,0.00007177807,0.000091646514,0.00026088496,0.00004380448,0.0005622993],"category_scores_gemma":[0.00033006244,0.00009442841,0.000029631516,0.00069658016,0.00006922964,0.00019256904,0.0002211939,0.00017500255,0.0000010868741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031247514,0.000027085078,0.00041841262,0.000008415757,0.000010668036,0.000011723709,0.0001398155,0.9857993,0.0028704628,0.0066594137,0.000014146012,0.0040374463],"study_design_scores_gemma":[0.00038076637,0.00002255834,0.00006838121,0.000013229113,0.0000019761933,0.000016719358,0.000108107284,0.9933285,0.005766163,0.00017074768,0.000030176154,0.000092645234],"about_ca_topic_score_codex":0.00019702416,"about_ca_topic_score_gemma":0.000017995353,"teacher_disagreement_score":0.27994582,"about_ca_system_score_codex":0.000047015423,"about_ca_system_score_gemma":0.00017029095,"threshold_uncertainty_score":0.6156783},"labels":[],"label_agreement":null},{"id":"W3207923187","doi":"10.1007/978-3-030-70296-0_89","title":"Simulated Annealing Embedded Within Personal Velocity Update of Particle Swarm Optimization","year":2021,"lang":"en","type":"book-chapter","venue":"Transactions on computational science and computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Simulated annealing; Particle swarm optimization; Swarm behaviour; Computer science; Materials science; Algorithm; Artificial intelligence","score_opus":0.04466950687585346,"score_gpt":0.30835453249913924,"score_spread":0.2636850256232858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207923187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017460577,0.00014649547,0.99606806,0.00055814075,0.00028690463,0.0003404527,0.00007250989,0.00010235544,0.002250455],"genre_scores_gemma":[0.31491354,0.00015180498,0.67757964,0.0005893101,0.00005365938,0.000014721434,0.00013642816,0.000046036082,0.0065148575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99483013,0.000087031454,0.00095805165,0.0010946604,0.002661009,0.0003691374],"domain_scores_gemma":[0.99436057,0.0007952884,0.00045578936,0.00033585788,0.0037366974,0.00031582772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011257528,0.00038474912,0.00044284528,0.00059178856,0.0007258334,0.00041440345,0.00086716627,0.00016504027,0.00044483785],"category_scores_gemma":[0.00013856454,0.0004114694,0.00013174283,0.001133032,0.00097473967,0.0007296912,0.00008331927,0.0004982779,0.000049361348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017648048,0.00007423229,5.4032324e-7,0.000026885036,0.000047833608,0.000010151382,0.00047888502,0.81764585,0.0000039151682,0.16487452,0.000011803407,0.016807707],"study_design_scores_gemma":[0.00018055919,0.0001346331,0.000015405523,0.00012735873,0.000025680714,0.00004258116,0.00006296915,0.9704223,0.00056148384,0.028018545,0.000040432173,0.0003680499],"about_ca_topic_score_codex":0.000010691098,"about_ca_topic_score_gemma":0.000001984329,"teacher_disagreement_score":0.31848842,"about_ca_system_score_codex":0.00022261454,"about_ca_system_score_gemma":0.0018774351,"threshold_uncertainty_score":0.9998337},"labels":[],"label_agreement":null},{"id":"W3207992307","doi":"10.1109/ccece53047.2021.9569035","title":"A Multi-Agent Krill Herd Algorithm","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Computer science; Metaheuristic; Multi-agent system; Algorithm; Task (project management); Mathematical optimization; Krill; Artificial intelligence; Mathematics; Engineering","score_opus":0.04246127212241364,"score_gpt":0.3131368173121361,"score_spread":0.2706755451897225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207992307","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002126739,0.00017419725,0.9908889,0.0015430421,0.00041906093,0.00010160268,0.0000027141662,0.00020104827,0.0066481456],"genre_scores_gemma":[0.0003979725,0.00006392394,0.96347624,0.00059277256,0.00005145424,0.000013835365,0.000005384087,0.0000095981395,0.035388824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982974,0.00014833068,0.00022471487,0.00047544108,0.00052616553,0.0003279563],"domain_scores_gemma":[0.99847436,0.00009090295,0.00003391159,0.0007748659,0.00041586373,0.00021008277],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031584533,0.00011096048,0.00014578296,0.00008933502,0.0000995944,0.0002942306,0.0006621105,0.000046721776,0.0012012417],"category_scores_gemma":[0.0002314222,0.000099506804,0.000065335655,0.00078212406,0.000029452603,0.00025287157,0.00059700746,0.00012824697,0.0009241144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012918512,0.0005824214,0.00013686786,0.00002215013,0.000078290686,0.00092196144,0.00035463006,0.0014185791,0.0009321555,0.020004945,0.012553633,0.9629931],"study_design_scores_gemma":[0.00037367782,0.000014749212,0.0003244082,0.000003513258,0.0000018510184,0.000052781797,0.00002427504,0.9674723,0.0032969976,0.00015738404,0.028144857,0.00013321199],"about_ca_topic_score_codex":0.000018028853,"about_ca_topic_score_gemma":0.0000049495584,"teacher_disagreement_score":0.9660537,"about_ca_system_score_codex":0.000044661654,"about_ca_system_score_gemma":0.00022166112,"threshold_uncertainty_score":0.9998538},"labels":[],"label_agreement":null},{"id":"W3208187808","doi":"10.1109/esscirc53450.2021.9567809","title":"The Power of Parallelism in Stochastic Search for Global Optimum: Keynote Paper","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Parallelism (grammar); Computer science; Local optimum; Markov chain Monte Carlo; Population; Mathematical optimization; Parallel computing; Markov chain; Parallel tempering; Data parallelism; Hamming distance; Algorithm; Mathematics; Artificial intelligence; Machine learning; Bayesian probability","score_opus":0.02712710892445458,"score_gpt":0.3225874359842625,"score_spread":0.2954603270598079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208187808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055818364,0.00024783553,0.99111634,0.00355672,0.00018161604,0.00028697835,0.0000050782123,0.00002312817,0.004024144],"genre_scores_gemma":[0.38751075,0.00008493747,0.6053392,0.00048113274,0.000040155624,0.000084432766,0.000006305765,0.000014693456,0.0064383917],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832517,0.00015239492,0.00030803686,0.0003143624,0.0005307889,0.0003692626],"domain_scores_gemma":[0.9980808,0.0006570307,0.000035447854,0.0006267501,0.0005102989,0.00008969017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083915854,0.000088339766,0.00015518682,0.000041659776,0.00009417165,0.0001570696,0.0008211567,0.000046935602,0.00013897789],"category_scores_gemma":[0.00048552483,0.00006233836,0.000063753316,0.00073030195,0.00007832143,0.00017007304,0.00041101678,0.00010359838,0.000020049265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004676533,0.00020249779,0.00017489825,0.000022156592,0.00004711638,0.000029958474,0.00041531835,0.16114718,0.00015144641,0.8041223,0.0021812432,0.031459115],"study_design_scores_gemma":[0.0007988238,0.000065652224,0.0014676763,0.000010536811,0.0000020928844,0.000010636676,0.00010760366,0.9890223,0.00039735893,0.006638219,0.0013659677,0.00011311774],"about_ca_topic_score_codex":0.000023105984,"about_ca_topic_score_gemma":0.00002723106,"teacher_disagreement_score":0.82787514,"about_ca_system_score_codex":0.000050576262,"about_ca_system_score_gemma":0.00038051815,"threshold_uncertainty_score":0.25420845},"labels":[],"label_agreement":null},{"id":"W3210481527","doi":"10.1155/2021/5538296","title":"Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Metaheuristic Optimization Algorithms Research","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":"Universidad Politécnica de Madrid; Comunidad de Madrid","keywords":"Metaheuristic; Computer science; PID controller; Mathematical optimization; Fitness function; Algorithm; Function (biology); Optimization problem; Controller (irrigation); Mathematics; Control engineering; Engineering; Genetic algorithm","score_opus":0.015592075538055914,"score_gpt":0.2863815943135784,"score_spread":0.2707895187755225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210481527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004420488,0.0003026431,0.9934025,0.00087204773,0.0005340203,0.00025364716,0.000016945587,0.000025640435,0.00017209594],"genre_scores_gemma":[0.1910312,0.0002285561,0.80807656,0.00009369394,0.00009846975,0.000014404834,0.00009262812,0.00001804231,0.00034644993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972481,0.0002314795,0.001134639,0.0003192068,0.00082949526,0.0002370938],"domain_scores_gemma":[0.996162,0.00027868737,0.00069875544,0.00018524085,0.0025349527,0.00014036175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058786117,0.00018559651,0.00044117257,0.00047113214,0.00009353195,0.00009516444,0.00030800185,0.00007167992,0.00012549937],"category_scores_gemma":[0.00073243503,0.00016274994,0.00015345139,0.0015573596,0.000060510047,0.0014803921,0.000010642207,0.00040174273,0.0000055738146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008862012,0.0002484858,0.00035701704,0.000014210893,0.000077650504,0.00021513834,0.0010364422,0.98406124,0.00094702764,0.0036919517,0.000047384015,0.009214824],"study_design_scores_gemma":[0.0068989033,0.0004991089,0.0436408,0.00020741373,0.00009553119,0.00016629805,0.0020668074,0.9352968,0.004192249,0.005826744,0.0006563524,0.00045302178],"about_ca_topic_score_codex":0.000004113605,"about_ca_topic_score_gemma":0.000020334934,"teacher_disagreement_score":0.18661071,"about_ca_system_score_codex":0.00016131483,"about_ca_system_score_gemma":0.0005938076,"threshold_uncertainty_score":0.663675},"labels":[],"label_agreement":null},{"id":"W3212356453","doi":"","title":"Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms","year":2021,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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":"Algorithm; Stochastic gradient descent; Ergodic theory; Generalization; Dynamical systems theory; Mathematics; Computer science; Hessian matrix; Fractal; Artificial neural network; Bounded function; Hyperparameter; Invariant measure; Mathematical optimization; Artificial intelligence; Applied mathematics","score_opus":0.015430659584609402,"score_gpt":0.22833262797161744,"score_spread":0.21290196838700803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212356453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010108975,0.00080527714,0.98541516,0.0025206069,0.00008635875,0.00017244816,0.000012530885,0.000084750645,0.00079390476],"genre_scores_gemma":[0.4100996,0.00013340745,0.58835363,0.000044966488,0.00001044065,0.0000091459215,0.000072442876,0.000015536654,0.0012608239],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643606,0.0020404172,0.00036294814,0.00046839393,0.00048454705,0.00020762531],"domain_scores_gemma":[0.9953449,0.00030922136,0.00021560014,0.0008972279,0.0031071894,0.00012590847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012789117,0.00014796281,0.00020389605,0.00015135275,0.00021467215,0.000328238,0.0005586846,0.000091008435,0.000105884224],"category_scores_gemma":[0.001946481,0.00014695936,0.000043856402,0.0007876422,0.00015150353,0.00041738804,0.00049695413,0.00014880732,0.0000027644437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021399126,0.0011390853,0.00097045064,0.00045120783,0.0002211984,0.000025225521,0.021367716,0.25273687,0.08085365,0.41331235,0.0004481545,0.2284527],"study_design_scores_gemma":[0.00030844175,3.897283e-7,0.00023600667,0.00014778232,0.000008462529,0.00002388843,0.000033516917,0.89680433,0.10166344,0.0005428733,0.00009531522,0.00013552544],"about_ca_topic_score_codex":0.00007902679,"about_ca_topic_score_gemma":0.00004042067,"teacher_disagreement_score":0.6440675,"about_ca_system_score_codex":0.000033305507,"about_ca_system_score_gemma":0.00024796804,"threshold_uncertainty_score":0.5992828},"labels":[],"label_agreement":null},{"id":"W3213339069","doi":"10.32920/ryerson.14647074.v1","title":"RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Toronto Metropolitan University","funders":"","keywords":"Rank (graph theory); Genetic programming; Computer science; Ranking (information retrieval); Learning to rank; Artificial intelligence; Machine learning; Evolutionary algorithm; Evolutionary programming; Genetic algorithm; Mathematics","score_opus":0.028395259418108748,"score_gpt":0.2993532365904498,"score_spread":0.27095797717234105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213339069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055419067,0.00015409978,0.9910781,0.00023371192,0.00032145958,0.0020694225,0.0000091542925,0.00029917658,0.00029297202],"genre_scores_gemma":[0.031113576,0.000025875592,0.96821606,0.00007521845,0.00010169144,0.00010137226,0.00015541285,0.000024689665,0.00018607805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689245,0.00021046822,0.0006256392,0.0006732663,0.0010545006,0.0005436672],"domain_scores_gemma":[0.9968863,0.0001960602,0.00027919988,0.00070190727,0.001717742,0.00021876497],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007580426,0.00032415555,0.00040547297,0.0005468512,0.00032657373,0.0028970775,0.0009619639,0.00013376887,0.000041249943],"category_scores_gemma":[0.0006088214,0.00029954608,0.00011837899,0.00082322053,0.000055081615,0.0013047552,0.0012825511,0.0005600607,0.000012007491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007422773,0.000034401317,0.00003642773,0.00040615644,0.00008123077,0.000017060776,0.0011168335,0.972645,0.000034521763,0.0007780245,0.000071319715,0.024704823],"study_design_scores_gemma":[0.0005372643,0.00017449383,0.00011850323,0.00018176943,0.000026755817,0.00006729873,0.0007874393,0.9950483,0.0002224502,0.00018804862,0.0022510828,0.00039660133],"about_ca_topic_score_codex":0.00008456064,"about_ca_topic_score_gemma":0.000003183069,"teacher_disagreement_score":0.02557167,"about_ca_system_score_codex":0.00023877156,"about_ca_system_score_gemma":0.0023315363,"threshold_uncertainty_score":0.99994564},"labels":[],"label_agreement":null},{"id":"W3213436692","doi":"10.5267/j.dsl.2021.8.004","title":"Binary social group optimization algorithm for solving 0-1 knapsack problem","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Knapsack problem; Continuous knapsack problem; Binary number; Algorithm; Mathematical optimization; Optimization problem; Group (periodic table); Mathematics; Transformation (genetics); Continuous optimization; Polynomial-time approximation scheme; Computer science; Multi-swarm optimization","score_opus":0.029899433794663356,"score_gpt":0.31840861658667674,"score_spread":0.2885091827920134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213436692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005730251,0.000038214144,0.9911454,0.0066662463,0.00079720997,0.0004028241,0.000008539335,0.00013812105,0.00023045592],"genre_scores_gemma":[0.0015299333,0.000018082483,0.9953968,0.0026521946,0.00016741529,0.00006417079,0.000016313677,0.000018828898,0.00013625684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99551845,0.00009273006,0.0005104959,0.0011688777,0.0019525766,0.0007568446],"domain_scores_gemma":[0.9974583,0.0005606751,0.00016990564,0.00070307177,0.00087419373,0.0002338622],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025724377,0.00019864677,0.0002543025,0.0005632506,0.0012024496,0.0014961112,0.0020349878,0.0000737861,0.00009696405],"category_scores_gemma":[0.0008659185,0.00019227763,0.00012570102,0.0040199603,0.00036251606,0.0019655784,0.0008972154,0.00018680502,0.00005023877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000892445,0.00016123458,0.000028079088,0.000015980277,0.000013836724,0.00009353665,0.00059482834,0.10096574,0.020200832,0.00612537,0.016082404,0.85570925],"study_design_scores_gemma":[0.00055084843,0.000040095354,0.00017615428,0.000019093643,0.000004086834,0.000028566412,0.000038745,0.9939572,0.001486971,0.0008374486,0.002620043,0.00024069668],"about_ca_topic_score_codex":0.0000029305218,"about_ca_topic_score_gemma":5.358833e-7,"teacher_disagreement_score":0.89299154,"about_ca_system_score_codex":0.0001889642,"about_ca_system_score_gemma":0.00036932708,"threshold_uncertainty_score":0.99954045},"labels":[],"label_agreement":null},{"id":"W3217452736","doi":"10.1080/09540091.2021.2002266","title":"A conditional opposition-based particle swarm optimisation for feature selection","year":2021,"lang":"en","type":"article","venue":"Connection Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Particle swarm optimization; Metaheuristic; Computer science; Feature selection; Swarm behaviour; Benchmark (surveying); Artificial intelligence; Feature (linguistics); Machine learning; Selection (genetic algorithm); Multi-swarm optimization; Convergence (economics); Mathematical optimization; Data mining; Mathematics","score_opus":0.0388511532403716,"score_gpt":0.3306135890364989,"score_spread":0.2917624357961273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217452736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004662625,0.000024929097,0.9889226,0.0050327517,0.00051569106,0.0002858036,0.000014312615,0.00017736538,0.00036393668],"genre_scores_gemma":[0.6515935,0.0000029287814,0.34674925,0.00076090934,0.0001036711,0.00012191727,0.000054733482,0.000007532745,0.0006055501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795204,0.00009804506,0.0002070317,0.00064569886,0.0007541533,0.0003429982],"domain_scores_gemma":[0.99700934,0.000344207,0.000100190955,0.0003308037,0.0020266348,0.00018884437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010462116,0.00010593797,0.00011518513,0.00020015243,0.0008531093,0.00065236253,0.00043027083,0.00005592311,0.00010969941],"category_scores_gemma":[0.001464055,0.00011233649,0.00006220331,0.0025655369,0.00018437399,0.0010781704,0.000074680465,0.00011314184,0.000039790364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004186332,0.00046820505,0.0006647252,0.000050803865,0.000028742674,0.000013946693,0.00031667494,0.36057198,0.26016605,0.35070467,0.0058274274,0.021144927],"study_design_scores_gemma":[0.0004207398,0.000058345588,0.0008525904,0.0000050445697,0.0000038007906,0.000026585809,0.00001906386,0.7196536,0.27568898,0.0018349497,0.0013322539,0.00010400567],"about_ca_topic_score_codex":0.000004419789,"about_ca_topic_score_gemma":0.0000041561357,"teacher_disagreement_score":0.6469309,"about_ca_system_score_codex":0.0002117506,"about_ca_system_score_gemma":0.0012592014,"threshold_uncertainty_score":0.6561513},"labels":[],"label_agreement":null},{"id":"W34283273","doi":"10.1007/s00484-021-02167-0","title":"Classification system optimization with multi-objective genetic algorithms","year":2006,"lang":"en","type":"article","venue":"International Conference on Frontiers in Handwriting Recognition","topic":"Metaheuristic Optimization Algorithms Research","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":"Classifier (UML); Computer science; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Genetic algorithm; Statistical classification; Machine learning; Random subspace method; Data mining; Algorithm","score_opus":0.04635011991833306,"score_gpt":0.2815833445060461,"score_spread":0.23523322458771306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34283273","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015049792,0.000025139898,0.980774,0.00033431582,0.000782104,0.0004529695,0.000028070936,0.00020084773,0.015897613],"genre_scores_gemma":[0.38610393,0.000035913097,0.61323696,0.00003024231,0.000114071605,0.00013771505,0.00015743996,0.00001695484,0.00016674813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755025,0.00021051556,0.0005085778,0.00067595736,0.0007476512,0.0003070672],"domain_scores_gemma":[0.99830514,0.00008149665,0.00027647734,0.00027608656,0.0009892782,0.00007150092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041693635,0.00021905478,0.00022002937,0.00076199765,0.00014100235,0.00048142817,0.0006008163,0.00011415265,0.00004207768],"category_scores_gemma":[0.00011006046,0.00021731708,0.000043824482,0.00060018076,0.000082656814,0.0006070494,0.00006295281,0.00026268896,0.00003720145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046532665,0.0016455116,0.025653346,0.00019340731,0.00022849739,0.000244958,0.0007508937,0.37404078,0.00060577196,0.06431779,0.001215486,0.5306382],"study_design_scores_gemma":[0.0013226479,0.00009029025,0.0048338403,0.0002869041,0.0000070358515,0.00003144164,0.0006836252,0.9912449,0.0004153944,0.0008026087,0.000019344014,0.0002619825],"about_ca_topic_score_codex":0.00011908862,"about_ca_topic_score_gemma":0.000022170443,"teacher_disagreement_score":0.6172041,"about_ca_system_score_codex":0.0004722876,"about_ca_system_score_gemma":0.00013171119,"threshold_uncertainty_score":0.88619334},"labels":[],"label_agreement":null},{"id":"W36751344","doi":"10.1007/978-3-642-37192-9_20","title":"On the Utility of Trading Criteria Based Retraining in Forex Markets","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Retraining; Foreign exchange market; Business; Foreign exchange; Computer science; Financial economics; Economics; Monetary economics; International trade","score_opus":0.048440032444271454,"score_gpt":0.2936856222458886,"score_spread":0.24524558980161718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36751344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000100317,0.000056203673,0.971869,0.0012984729,0.0006605418,0.0005983989,0.0000055657647,0.00004424613,0.02536726],"genre_scores_gemma":[0.59271675,0.0000060952166,0.40627852,0.000781447,0.00006703324,0.000014140316,0.0000020955256,0.000020041603,0.00011387813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568033,0.00021376302,0.0007104207,0.0011962476,0.0015800451,0.0006191864],"domain_scores_gemma":[0.99432933,0.0032877426,0.00029472457,0.0016483068,0.0003071306,0.00013276681],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004217525,0.00038663775,0.0005231334,0.0010710309,0.00015661978,0.00046494338,0.0042137946,0.00023162228,0.0018423576],"category_scores_gemma":[0.0012369475,0.00028716523,0.00010939881,0.0010693617,0.0008142229,0.00034908106,0.0007452373,0.0009906407,0.00001749097],"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":[0.000032466076,0.00011105399,0.00005855309,0.00015764666,0.000015921478,0.0000980734,0.0014787514,0.0064607705,0.000108948225,0.026156345,0.0014261672,0.9638953],"study_design_scores_gemma":[0.00019448367,0.000082270824,0.0005784558,0.00038031643,0.0000015772878,0.000006049191,2.010733e-7,0.9219571,0.0004706399,0.07601691,0.00006980405,0.00024221919],"about_ca_topic_score_codex":0.00001997088,"about_ca_topic_score_gemma":0.000011776084,"teacher_disagreement_score":0.9636531,"about_ca_system_score_codex":0.00015938295,"about_ca_system_score_gemma":0.00059157336,"threshold_uncertainty_score":0.99995804},"labels":[],"label_agreement":null},{"id":"W404655571","doi":"10.1007/s00500-015-1716-3","title":"Novel self-adaptive particle swarm optimization methods","year":2015,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Université Laval","funders":"","keywords":"Swarm behaviour; Particle swarm optimization; Multi-swarm optimization; Computer science; Metaheuristic; Mathematical optimization; Premature convergence; Algorithm; Mathematics; Artificial intelligence","score_opus":0.09085828163086822,"score_gpt":0.37004264148158894,"score_spread":0.2791843598507207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W404655571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025397236,0.0000922502,0.9960355,0.00040747516,0.0005460664,0.00022012876,7.9137374e-7,0.0006481591,0.0017956585],"genre_scores_gemma":[0.0320798,0.0000023382993,0.96741843,0.00020086055,0.00013775362,0.0000057365123,0.0000024791273,0.000019713842,0.00013289372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766207,0.0003814554,0.0003816796,0.00051333336,0.00058560586,0.0004758533],"domain_scores_gemma":[0.9978116,0.00049518904,0.00015864307,0.0005435871,0.0006486795,0.00034226512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002558371,0.00016788726,0.000219691,0.00012007572,0.0001986522,0.00031859902,0.0008602722,0.00006714566,0.000011872373],"category_scores_gemma":[0.0009785998,0.0001671396,0.000052976484,0.0011221236,0.000043518383,0.00045988537,0.0007136204,0.0002065959,0.00007654372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004455328,0.0001239574,0.00008120862,0.0000060081284,0.000027414766,0.0000058697083,0.0015432206,0.940142,0.000072421375,0.011849433,0.00024799453,0.04589605],"study_design_scores_gemma":[0.0005905384,0.00007280706,0.000058268874,0.000009559699,0.000006673997,0.00002384594,0.00008565512,0.996746,0.0012511587,0.00046591443,0.00049707317,0.00019250218],"about_ca_topic_score_codex":0.000022991937,"about_ca_topic_score_gemma":3.995121e-7,"teacher_disagreement_score":0.05660404,"about_ca_system_score_codex":0.00013559846,"about_ca_system_score_gemma":0.00027728127,"threshold_uncertainty_score":0.6815755},"labels":[],"label_agreement":null},{"id":"W4200615498","doi":"10.1002/9781119794929.ch8","title":"Incorporating LP and Hybridizing It with Meta‐heuristic Algorithms","year":2021,"lang":"en","type":"other","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Heuristic; Linear programming; Convergence (economics); Algorithm; Meta heuristic; Mathematical optimization; Evolutionary algorithm; Computer science; Rate of convergence; Mathematics","score_opus":0.05414899837175338,"score_gpt":0.29534885222674084,"score_spread":0.24119985385498746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200615498","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.08317934e-7,0.0018934904,0.6674766,0.00047974527,0.00013404057,0.00024828134,0.0000075193616,0.00023740908,0.32952285],"genre_scores_gemma":[0.000019325724,0.00012430645,0.5981864,0.00022406307,0.00011849835,0.000038540977,0.000022335518,0.00017184009,0.4010947],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996964,0.00024410489,0.00036826052,0.0010861891,0.00091229076,0.00042520376],"domain_scores_gemma":[0.99793357,0.00024647324,0.00030621412,0.0010253849,0.00021330836,0.00027505093],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055822125,0.00044377547,0.00077803346,0.00046512732,0.00015524606,0.0009648658,0.0008553778,0.0001551434,0.0033921036],"category_scores_gemma":[0.0002155893,0.00033149237,0.0000837594,0.00083404203,0.00018491491,0.00014024277,0.0007742223,0.00039399927,0.000051419138],"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":[0.000009290351,0.0003306137,0.00009232203,0.0014454758,0.0073120315,0.0034548633,0.00037625423,0.0007214516,0.00004709179,0.09878237,0.7997347,0.08769354],"study_design_scores_gemma":[0.00087697204,0.00016905477,0.000015456599,0.00040522276,0.0005422375,0.00045365834,0.000065472,0.64809686,0.00012638544,0.0007400763,0.34711367,0.0013949518],"about_ca_topic_score_codex":0.00036604822,"about_ca_topic_score_gemma":0.00009506524,"teacher_disagreement_score":0.6473754,"about_ca_system_score_codex":0.00003240641,"about_ca_system_score_gemma":0.00044082798,"threshold_uncertainty_score":0.9999137},"labels":[],"label_agreement":null},{"id":"W4205998440","doi":"10.5267/j.ijiec.2021.11.001","title":"A new hybrid algorithm based on MVO and SA for function optimization","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","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":"Simulated annealing; Benchmark (surveying); Hybrid algorithm (constraint satisfaction); Algorithm; Metaheuristic; Optimization algorithm; Computer science; Mathematical optimization; Mathematics","score_opus":0.030199681041480582,"score_gpt":0.27547507868005605,"score_spread":0.24527539763857548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205998440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000082065555,0.000024568826,0.9939412,0.00239821,0.003249386,0.00019882538,0.000034722703,0.000042852487,0.000028210407],"genre_scores_gemma":[0.03836397,0.0000049034547,0.9603843,0.00021424798,0.0008656052,0.00002301558,0.00004621297,0.000020108484,0.00007762444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837536,0.00006607074,0.0004358862,0.00017448718,0.0008127456,0.0001354714],"domain_scores_gemma":[0.99841356,0.00053256954,0.00025664165,0.0001112755,0.0005501273,0.00013582507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005722686,0.0001101353,0.00014833201,0.0007643009,0.00013639877,0.00022984602,0.00057224394,0.000029763569,0.00007364422],"category_scores_gemma":[0.00051441527,0.00011997102,0.00008251324,0.0003460514,0.000009337112,0.00029601698,0.00011869231,0.00032263092,9.274604e-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":[0.000041168514,0.00004609794,0.0000059250247,0.000001154574,0.00005419497,0.0000109487,0.000027635424,0.9015967,0.000008116972,0.0021498436,0.0023249655,0.09373324],"study_design_scores_gemma":[0.0020952602,0.00042189693,0.000024095132,0.00001974049,0.000013221323,0.000085166896,0.0000094340485,0.99100304,0.000049794704,0.00033472897,0.005842375,0.000101257516],"about_ca_topic_score_codex":0.000005833738,"about_ca_topic_score_gemma":5.2578617e-8,"teacher_disagreement_score":0.09363198,"about_ca_system_score_codex":0.0002014746,"about_ca_system_score_gemma":0.00041838113,"threshold_uncertainty_score":0.4892276},"labels":[],"label_agreement":null},{"id":"W4206995338","doi":"10.1090/mbk/139/32","title":"The Pollard Rho factorisaction algorithm","year":2021,"lang":"en","type":"book-chapter","venue":"American Mathematical Society eBooks","topic":"Metaheuristic Optimization Algorithms Research","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 Laval","funders":"","keywords":"Computer science; Algorithm","score_opus":0.02215236016215544,"score_gpt":0.2742301322675225,"score_spread":0.2520777721053671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206995338","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.7264733e-7,0.00012993273,0.6609222,0.0007213883,0.00022550445,0.0003023402,0.000013307381,0.00017686601,0.3375082],"genre_scores_gemma":[0.000013483373,0.0003541394,0.41059476,0.00042042497,0.00025034684,0.000041767154,0.000010507864,0.000075581476,0.588239],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99610627,0.00011275766,0.0007050115,0.0007991689,0.001628202,0.0006485919],"domain_scores_gemma":[0.9957215,0.0013386735,0.000510572,0.0016219754,0.00045986124,0.00034736918],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072787225,0.00050197594,0.000746322,0.00004724496,0.0006255076,0.000770948,0.0015474319,0.00024827215,0.00031318387],"category_scores_gemma":[0.00019523402,0.00036851116,0.0007465287,0.0001221489,0.0010372985,0.0000878104,0.00079237507,0.0010002,0.00043465375],"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":[0.0000011243922,0.000021039772,1.4659543e-7,0.00003697553,0.00030183909,0.00001804924,0.00036975826,0.00000388027,0.0000050140156,0.51000506,0.0067920065,0.48244512],"study_design_scores_gemma":[0.00028392088,0.00016537574,0.000002679876,0.00013999375,0.00010555536,0.00008951771,0.00032019438,0.20473163,0.00011227103,0.21568274,0.5773609,0.0010051712],"about_ca_topic_score_codex":0.000012062649,"about_ca_topic_score_gemma":0.00000120054,"teacher_disagreement_score":0.570569,"about_ca_system_score_codex":0.00027388375,"about_ca_system_score_gemma":0.00043702143,"threshold_uncertainty_score":0.9998767},"labels":[],"label_agreement":null},{"id":"W4207030585","doi":"10.36227/techrxiv.16860049.v1","title":"Adaptability of Improved NEAT in Variable Environments","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Python (programming language); Adaptability; Computer science; Programming language; Management","score_opus":0.024624348800726014,"score_gpt":0.26988654375425397,"score_spread":0.24526219495352797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207030585","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020342346,0.00011164562,0.99313235,0.00012779428,0.00033050674,0.0004054992,0.000008417506,0.00002718485,0.003822371],"genre_scores_gemma":[0.05557495,0.00009431081,0.9421282,0.000033786317,0.000015766127,0.00005578333,0.00002752808,0.000010951626,0.0020587428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975038,0.00030886522,0.0005740689,0.00080141355,0.00053383067,0.00027801047],"domain_scores_gemma":[0.99773,0.00016248757,0.00015217043,0.001761504,0.00009175681,0.00010207883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010482847,0.0001772631,0.00042145283,0.00015786594,0.000019371944,0.00014168065,0.0014381523,0.00020054472,0.0008342442],"category_scores_gemma":[0.00035037083,0.0001746264,0.00007375731,0.0004040216,0.000060184586,0.00016959835,0.0043606777,0.00048816486,0.000008593867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007436476,0.0067321802,0.015067176,0.0026865765,0.0005535072,0.00016673471,0.0032729967,0.73263156,0.01811489,0.048035797,0.00065423246,0.17200996],"study_design_scores_gemma":[0.00023656539,0.000017245899,0.0028300937,0.000028015304,0.0000032308008,9.624764e-7,0.000018844137,0.99315196,0.002228912,0.0010097217,0.0003224884,0.00015198618],"about_ca_topic_score_codex":0.0006192233,"about_ca_topic_score_gemma":0.000018475788,"teacher_disagreement_score":0.26052034,"about_ca_system_score_codex":0.00014087473,"about_ca_system_score_gemma":0.00050133286,"threshold_uncertainty_score":0.9134389},"labels":[],"label_agreement":null},{"id":"W4207051291","doi":"10.36227/techrxiv.16860049","title":"Adaptability of Improved NEAT in Variable Environments","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Python (programming language); Adaptability; Computer science; Research data; Data science; Programming language; Biology; Ecology","score_opus":0.024624348800726014,"score_gpt":0.26988654375425397,"score_spread":0.24526219495352797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207051291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020342346,0.00011164562,0.99313235,0.00012779428,0.00033050674,0.0004054992,0.000008417506,0.00002718485,0.003822371],"genre_scores_gemma":[0.05557495,0.00009431081,0.9421282,0.000033786317,0.000015766127,0.00005578333,0.00002752808,0.000010951626,0.0020587428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975038,0.00030886522,0.0005740689,0.00080141355,0.00053383067,0.00027801047],"domain_scores_gemma":[0.99773,0.00016248757,0.00015217043,0.001761504,0.00009175681,0.00010207883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010482847,0.0001772631,0.00042145283,0.00015786594,0.000019371944,0.00014168065,0.0014381523,0.00020054472,0.0008342442],"category_scores_gemma":[0.00035037083,0.0001746264,0.00007375731,0.0004040216,0.000060184586,0.00016959835,0.0043606777,0.00048816486,0.000008593867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007436476,0.0067321802,0.015067176,0.0026865765,0.0005535072,0.00016673471,0.0032729967,0.73263156,0.01811489,0.048035797,0.00065423246,0.17200996],"study_design_scores_gemma":[0.00023656539,0.000017245899,0.0028300937,0.000028015304,0.0000032308008,9.624764e-7,0.000018844137,0.99315196,0.002228912,0.0010097217,0.0003224884,0.00015198618],"about_ca_topic_score_codex":0.0006192233,"about_ca_topic_score_gemma":0.000018475788,"teacher_disagreement_score":0.26052034,"about_ca_system_score_codex":0.00014087473,"about_ca_system_score_gemma":0.00050133286,"threshold_uncertainty_score":0.9134389},"labels":[],"label_agreement":null},{"id":"W4210373550","doi":"10.1155/2022/8858756","title":"The Train Delay Model Developed by the Genetic Programming Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Metaheuristic Optimization Algorithms Research","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":"European Social Fund; European Regional Development Fund; Univerzita Pardubice","keywords":"Genetic programming; Algorithm; Symbolic regression; Genetic algorithm; Computer science; A priori and a posteriori; Function (biology); Exponential function; Mathematical optimization; Machine learning; Mathematics","score_opus":0.01738930336743416,"score_gpt":0.28013132535090934,"score_spread":0.2627420219834752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210373550","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039822087,0.00072375656,0.9928412,0.0018476442,0.00030132112,0.00025907787,0.000010165546,0.000018450439,0.000016210413],"genre_scores_gemma":[0.044052154,0.00024100824,0.95509845,0.00015119294,0.00003861669,0.0000626876,0.000009562317,0.000015963224,0.0003303686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977704,0.00017759694,0.0006051858,0.0001597376,0.001038673,0.0002483942],"domain_scores_gemma":[0.99869657,0.00020912784,0.00042292837,0.00023380294,0.0003549793,0.0000825917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011534011,0.0001035818,0.00013107593,0.0000786189,0.00073621096,0.0001261793,0.0010745061,0.000018327257,0.00001063074],"category_scores_gemma":[0.000054768043,0.000065156986,0.000085925254,0.0006267323,0.00005266732,0.00036076398,0.000019474943,0.00039945566,8.6357574e-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":[0.000012306467,0.000027990653,0.0000062298336,0.0000017948478,0.000016588418,0.000015957095,0.0011427151,0.51620877,0.00010784478,0.0004057311,0.00019495879,0.4818591],"study_design_scores_gemma":[0.00080635905,0.0001931325,0.0010685822,0.0000056981125,0.000019828294,0.000095899704,0.0006554381,0.968385,0.00020540346,0.0023412814,0.026089557,0.00013386302],"about_ca_topic_score_codex":0.00000236051,"about_ca_topic_score_gemma":0.000005489482,"teacher_disagreement_score":0.48172525,"about_ca_system_score_codex":0.00008262494,"about_ca_system_score_gemma":0.00030241042,"threshold_uncertainty_score":0.5662414},"labels":[],"label_agreement":null},{"id":"W4220855481","doi":"10.1007/s00366-021-01591-5","title":"A boosted chimp optimizer for numerical and engineering design optimization challenges","year":2022,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":46,"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":"Mitacs; Simon Fraser University; University of Calgary","keywords":"Benchmark (surveying); Maxima and minima; Mathematical optimization; Convergence (economics); Dimension (graph theory); Global optimization; Computer science; Optimization problem; Algorithm; Mathematics","score_opus":0.02153520288673531,"score_gpt":0.21542051356170663,"score_spread":0.19388531067497133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220855481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004363018,0.0003782437,0.9973442,0.0007904539,0.00042544046,0.00058177765,0.000003537314,0.00042067992,0.000012029791],"genre_scores_gemma":[0.0113905715,0.000041678228,0.98806363,0.00006146879,0.000055352746,0.0003027769,0.000008367185,0.00004679004,0.000029363202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984289,0.000063987216,0.00021504104,0.000493847,0.00040119392,0.00039702217],"domain_scores_gemma":[0.99878067,0.00055533793,0.000059470214,0.0003512512,0.000081679864,0.00017160331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047334217,0.00021937989,0.00024566773,0.0003166487,0.00017807462,0.00015830933,0.00054324116,0.000033166674,0.000011638857],"category_scores_gemma":[0.00010723289,0.00022284736,0.00003755648,0.00049172714,0.0000132422665,0.00024981107,0.0003686233,0.00021573808,8.415333e-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":[0.000016720638,0.000027262344,0.000001823904,0.000037018322,0.000044784934,0.000012324865,0.00028951824,0.99376625,0.000015217722,0.0023372585,0.00015962371,0.0032922062],"study_design_scores_gemma":[0.0008008274,0.00034050093,0.000049505106,0.000018774199,0.000007786087,0.00008988035,0.000010868227,0.9968757,0.0000396008,0.0000060114075,0.0014852313,0.00027530693],"about_ca_topic_score_codex":0.0000013460149,"about_ca_topic_score_gemma":1.7879385e-8,"teacher_disagreement_score":0.011346941,"about_ca_system_score_codex":0.0000903335,"about_ca_system_score_gemma":0.000057145036,"threshold_uncertainty_score":0.90874517},"labels":[],"label_agreement":null},{"id":"W4220924655","doi":"10.1155/2022/9864064","title":"Corrigendum to “The Fourth-Party Logistics Routing Problem Using Ant Colony System-Improved Grey Wolf Optimization”","year":2022,"lang":"en","type":"erratum","venue":"Journal of Advanced Transportation","topic":"Metaheuristic Optimization Algorithms Research","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 Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Section (typography); Table (database); Adjacency list; Function (biology); Ant colony optimization algorithms; Routing (electronic design automation); Combinatorics; Mathematics; Computer science; Algorithm; Data mining","score_opus":0.03257004666300837,"score_gpt":0.2868427415935368,"score_spread":0.2542726949305284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220924655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016935555,0.0003345013,0.9609202,0.00057657645,0.03661482,0.0010476983,0.00011793742,0.00007532271,0.00029601084],"genre_scores_gemma":[0.0013195256,0.00031193477,0.9865025,0.00019192789,0.0012052403,0.00005567831,0.0002975723,0.0001018706,0.0100137135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99450004,0.00038480366,0.0020341845,0.00059644715,0.0018962089,0.0005883176],"domain_scores_gemma":[0.9936226,0.0001620562,0.002841581,0.0006963337,0.0023552969,0.00032211342],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018028715,0.00043916702,0.00081627077,0.0006346912,0.00066878105,0.000378992,0.0018283913,0.0002349037,0.00009885461],"category_scores_gemma":[0.00050487614,0.000366518,0.00026913427,0.0016994625,0.00005851018,0.00082312286,0.00007618924,0.001659201,0.000004415112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064398264,0.00006721532,0.000010340014,0.0002022045,0.00009428228,0.00018308252,0.0011077215,0.9761997,0.000039010327,0.0006662405,0.019656885,0.0017089513],"study_design_scores_gemma":[0.0007298719,0.0004161633,0.00011769674,0.00038709398,0.00016730954,0.00013263081,0.00055745564,0.9403519,0.000017622247,0.00004954548,0.056663323,0.00040937666],"about_ca_topic_score_codex":0.00003810565,"about_ca_topic_score_gemma":0.000048686834,"teacher_disagreement_score":0.037006438,"about_ca_system_score_codex":0.0008804813,"about_ca_system_score_gemma":0.0016661789,"threshold_uncertainty_score":0.9998787},"labels":[],"label_agreement":null},{"id":"W4220995982","doi":"10.18280/ria.360101","title":"Enhanced Black Widow Algorithm for Numerical Functions Optimization","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Metaheuristic Optimization Algorithms Research","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":"Direction Générale de la Recherche Scientifique et du Développement Technologique","keywords":"Initialization; Crossover; Mathematical optimization; Benchmark (surveying); Metaheuristic; Algorithm; Computer science; Convergence (economics); Local optimum; Mathematics; Artificial intelligence","score_opus":0.04153612484385261,"score_gpt":0.29781834474197355,"score_spread":0.25628221989812094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220995982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015922878,0.00006368069,0.9947644,0.0012878985,0.0008722494,0.0007334085,0.00003566897,0.0002097597,0.0020170317],"genre_scores_gemma":[0.030104833,0.000041388394,0.9516332,0.0003359162,0.00018332753,0.0007472855,0.00009827738,0.000041116204,0.01681465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975847,0.00019071781,0.00052278215,0.0007152393,0.00049608276,0.00049046654],"domain_scores_gemma":[0.9981245,0.00039187327,0.00015909733,0.0008047653,0.00035242198,0.00016733295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00077198306,0.0001782753,0.0002307793,0.0002535273,0.0007926595,0.0001957798,0.0011435046,0.000047619156,0.0017278732],"category_scores_gemma":[0.00028159237,0.00020182536,0.00014252967,0.0016408009,0.000083769344,0.0003130619,0.0004946779,0.00027713663,0.00029754758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058399246,0.00016212577,9.693775e-7,0.000008701259,0.000012212291,0.0000028736845,0.000379889,0.8443924,0.00010769616,0.0042108535,0.0017618113,0.14895461],"study_design_scores_gemma":[0.000082555154,0.00024079057,8.7190284e-7,0.000004800345,0.0000075993,0.000015951291,0.00038409713,0.978699,0.004382901,0.00065805507,0.0152988555,0.00022447233],"about_ca_topic_score_codex":0.0000089356445,"about_ca_topic_score_gemma":3.4665686e-7,"teacher_disagreement_score":0.14873014,"about_ca_system_score_codex":0.00016050786,"about_ca_system_score_gemma":0.00014858987,"threshold_uncertainty_score":0.99918467},"labels":[],"label_agreement":null},{"id":"W4224036513","doi":"10.1016/j.aml.2022.108125","title":"A Vanka-type multigrid solver for complex-shifted Laplacian systems from diagonalization-based parallel-in-time algorithms","year":2022,"lang":"en","type":"article","venue":"Applied Mathematics Letters","topic":"Metaheuristic Optimization Algorithms Research","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 British Columbia","funders":"","keywords":"Multigrid method; Solver; Smoothing; Mathematics; Type (biology); Sequence (biology); Algorithm; Laplacian smoothing; Applied mathematics; Laplace operator; Partial differential equation; Mathematical optimization; Mathematical analysis; Finite element method","score_opus":0.03460324402762935,"score_gpt":0.26525686558098854,"score_spread":0.2306536215533592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224036513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019891209,0.00002769212,0.9936012,0.0016560673,0.00032011978,0.0018832934,0.00011670338,0.00021844463,0.00018736393],"genre_scores_gemma":[0.013324915,0.000002654326,0.98224294,0.0022062,0.0000982577,0.0013373191,0.0005627864,0.00006965597,0.0001552768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675816,0.00013068697,0.0007083199,0.00066905876,0.0011692398,0.00056452595],"domain_scores_gemma":[0.9971818,0.001372369,0.00029677615,0.00088152033,0.00011669246,0.00015084576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009587339,0.0002862962,0.0005055373,0.00035315406,0.00033339224,0.00032706265,0.001632686,0.00006267352,0.00026286868],"category_scores_gemma":[0.00013129858,0.00031640462,0.00009322675,0.0009497832,0.00006844499,0.000120334254,0.00039908916,0.00025371616,0.0001102553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009259047,0.0011356184,0.000100317346,0.0003139961,0.00019553046,0.00006107204,0.0025361886,0.9153151,0.008004754,0.043800883,0.026876945,0.0015670037],"study_design_scores_gemma":[0.0017526817,0.000037361984,0.00006290064,0.000015959124,0.000016401,0.0000024885107,0.000069330876,0.99251056,0.00009522562,0.0017709024,0.0033118932,0.00035431157],"about_ca_topic_score_codex":0.000046199366,"about_ca_topic_score_gemma":0.0000028894758,"teacher_disagreement_score":0.07719544,"about_ca_system_score_codex":0.00022059625,"about_ca_system_score_gemma":0.00013280365,"threshold_uncertainty_score":0.99992883},"labels":[],"label_agreement":null},{"id":"W4225134652","doi":"10.1007/s10898-022-01162-y","title":"Parallel algorithm portfolios with adaptive resource allocation strategy","year":2022,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University","funders":"European Social Fund","keywords":"Resource allocation; Mathematical optimization; Process (computing); Algorithm; Computer science; Swarm intelligence; Mathematics; Particle swarm optimization","score_opus":0.01724348718280352,"score_gpt":0.2643682714715833,"score_spread":0.2471247842887798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225134652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007587449,0.00019909494,0.99571323,0.0010238938,0.000175316,0.00023422862,0.000013182821,0.000042931268,0.0025222441],"genre_scores_gemma":[0.027362645,0.000048455255,0.9719697,0.00023112992,0.00009865308,0.000015233014,0.000016681119,0.000014890481,0.00024261668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685913,0.00040458448,0.00061861635,0.00027990714,0.0015511009,0.00028666964],"domain_scores_gemma":[0.99769074,0.000058610152,0.00081329996,0.00034197842,0.00089225185,0.0002031196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009717269,0.00016818657,0.00026490836,0.00019826807,0.00029958534,0.00020458506,0.0009930955,0.000045017925,0.00018220232],"category_scores_gemma":[0.000078629324,0.00014817358,0.00007919079,0.0017067271,0.00004978902,0.00084031385,0.00024552678,0.0003238212,0.0000041843605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009117118,0.00014541468,0.00006784234,0.0000022650986,0.00005563867,0.00010207441,0.00005751152,0.96921957,0.0000012738353,0.006464346,0.001883985,0.021908931],"study_design_scores_gemma":[0.0010673573,0.0011162737,0.00027643883,0.000009944044,0.00002285955,0.0007960215,0.0002297593,0.9943706,0.0000066960547,0.0003902649,0.0015500938,0.00016371452],"about_ca_topic_score_codex":0.000013499278,"about_ca_topic_score_gemma":0.000001017875,"teacher_disagreement_score":0.02728677,"about_ca_system_score_codex":0.00050750875,"about_ca_system_score_gemma":0.0006900015,"threshold_uncertainty_score":0.60423434},"labels":[],"label_agreement":null},{"id":"W4225154779","doi":"10.1162/evco_a_00311","title":"Active Sets for Explicitly Constrained Evolutionary Optimization","year":2022,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"","keywords":"Set (abstract data type); Active set method; Computer science; Mathematical optimization; Evolutionary algorithm; Optimization problem; Black box; Constrained optimization problem; Evolution strategy; Mathematics; Algorithm; Artificial intelligence; Nonlinear programming","score_opus":0.029664006408876446,"score_gpt":0.3041633975792627,"score_spread":0.27449939117038624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225154779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030355406,0.0001425088,0.99370545,0.0022919476,0.00080132333,0.0011414022,0.00018008112,0.00036069655,0.0010730432],"genre_scores_gemma":[0.14006644,0.000009124254,0.8570597,0.00031002198,0.00010955994,0.00077061733,0.0011541116,0.000028018267,0.00049235317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971606,0.0004177878,0.00044824547,0.00065481843,0.0009218501,0.0003967006],"domain_scores_gemma":[0.99812394,0.0005470977,0.00024059099,0.00033495412,0.00061682286,0.00013657162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049744494,0.00019787943,0.00020616964,0.00046994854,0.0012547758,0.00008736319,0.00066986284,0.00005522307,0.00032947218],"category_scores_gemma":[0.00022890016,0.00024305806,0.000114003975,0.0011571884,0.000084138934,0.0008194528,0.000462552,0.00022708821,0.000026235133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005442771,0.00017318748,0.000019417226,0.000011610236,0.000030417561,0.0000051394495,0.00024012507,0.9548139,0.000033987002,0.021564445,0.012051986,0.011001371],"study_design_scores_gemma":[0.0010312385,0.00024855105,0.0010069169,0.000004312076,0.000008982462,0.0000811502,0.00014997815,0.98417383,0.000018610623,0.010177897,0.0028461674,0.00025235937],"about_ca_topic_score_codex":0.000011730974,"about_ca_topic_score_gemma":2.9817622e-7,"teacher_disagreement_score":0.1397629,"about_ca_system_score_codex":0.0006374611,"about_ca_system_score_gemma":0.000578603,"threshold_uncertainty_score":0.99116206},"labels":[],"label_agreement":null},{"id":"W4226445767","doi":"10.1007/978-981-16-8248-3_8","title":"An Improved SHO Technique for Mathematical and Multidisciplinary Engineering Applications","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Hyena; Benchmark (surveying); Metaheuristic; Computer science; Mathematical optimization; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Geography; Cartography","score_opus":0.010276293095107733,"score_gpt":0.26041220460087006,"score_spread":0.2501359115057623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226445767","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000018138996,0.0005098582,0.9968006,0.00013503872,0.000054628235,0.001833039,0.000015639314,0.0002873625,0.000362035],"genre_scores_gemma":[0.0046798103,0.0000949705,0.9922359,0.0000262619,0.00017014709,0.0021958905,0.00005128075,0.00013905016,0.0004066808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795073,0.000016272676,0.00045026475,0.00075284275,0.0003339863,0.00049592374],"domain_scores_gemma":[0.9980491,0.00093923294,0.00007763707,0.00066048943,0.00007791383,0.0001956309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042887032,0.00039496596,0.000473032,0.0006819563,0.00009888286,0.0001125614,0.00079971243,0.00034404226,0.000049776612],"category_scores_gemma":[0.00043180815,0.00041791413,0.000093965195,0.00036921827,0.000022188719,0.00015351463,0.00031349718,0.0011428959,0.0000016726535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015070607,0.000113602524,0.000001635764,0.00051936734,0.0000678921,0.00003036148,0.0001358033,0.55787283,0.016923584,0.36957812,0.00000568299,0.05473602],"study_design_scores_gemma":[0.00018957623,0.00016416624,0.0000035936853,0.000033671073,0.000012952086,0.000048725844,1.169842e-7,0.98138297,0.00067391386,0.014050592,0.0030182654,0.00042145813],"about_ca_topic_score_codex":0.0000017384339,"about_ca_topic_score_gemma":5.4448117e-7,"teacher_disagreement_score":0.4235101,"about_ca_system_score_codex":0.00025479082,"about_ca_system_score_gemma":0.000095256124,"threshold_uncertainty_score":0.99982727},"labels":[],"label_agreement":null},{"id":"W4229942708","doi":"10.1007/978-3-319-41192-7","title":"Search and Optimization by Metaheuristics","year":2016,"lang":"en","type":"book","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":296,"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":"Metaheuristic; Computer science; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.022259608063322885,"score_gpt":0.27432474020655084,"score_spread":0.25206513214322795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229942708","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0251303e-8,0.00050331437,0.7007668,0.0006836121,0.00015870405,0.000263118,0.000045414297,0.00015385922,0.29742518],"genre_scores_gemma":[7.79841e-7,0.0012134778,0.3798523,0.00012593079,0.00008018948,0.000009514169,0.000053786516,0.00003349888,0.6186305],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706763,0.00016802689,0.0004261391,0.0008445357,0.0010613272,0.0004323523],"domain_scores_gemma":[0.99766874,0.00042993386,0.00010282291,0.00095004716,0.00052058004,0.00032784857],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007104636,0.00033327824,0.0004166429,0.00034861334,0.00014132996,0.00048881024,0.0011983706,0.00030992477,0.001019511],"category_scores_gemma":[0.0002246321,0.00025510063,0.00006146015,0.00019472896,0.00017964326,0.00033620858,0.0008964563,0.00034801348,0.0002890026],"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":[0.0000045300662,0.000041435065,0.000001987784,0.00010599979,0.00012704957,0.000029781952,0.000049052473,0.00461248,0.000009214042,0.17236562,0.76026255,0.06239028],"study_design_scores_gemma":[0.00032129473,0.000060597464,4.1285102e-7,0.000040202445,0.000017860053,0.000018149387,9.0052066e-7,0.77757466,0.000069968715,0.0023186977,0.21918155,0.0003956977],"about_ca_topic_score_codex":0.000004716768,"about_ca_topic_score_gemma":3.4474976e-7,"teacher_disagreement_score":0.77296215,"about_ca_system_score_codex":0.00014519092,"about_ca_system_score_gemma":0.0005949134,"threshold_uncertainty_score":0.9999901},"labels":[],"label_agreement":null},{"id":"W4234022368","doi":"10.22215/etd/2014-10580","title":"Swarm Optimization Using Agents Modeled as Distributions","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Perspective (graphical); Metaheuristic; Computer science; Heuristic; Swarm behaviour; Mathematical optimization; Field (mathematics); Black box; Artificial intelligence; Machine learning; Mathematics","score_opus":0.04076039808083713,"score_gpt":0.3485973512389364,"score_spread":0.3078369531580993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234022368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004265082,0.000039699837,0.9750499,0.00009125135,0.0011485086,0.0004957103,0.000019550398,0.00027275452,0.022456102],"genre_scores_gemma":[0.006346158,0.00016624908,0.9409285,0.00009550649,0.00016575449,0.00007077835,0.0042028083,0.000067508976,0.0479567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702674,0.0002150863,0.0005490955,0.00076572393,0.0009919831,0.0004513756],"domain_scores_gemma":[0.9975461,0.00009497396,0.00029781158,0.0009459667,0.000865293,0.00024985752],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042205074,0.00033953605,0.00038937113,0.00041077557,0.00035996435,0.0005877863,0.0013177465,0.0003191857,0.00081971905],"category_scores_gemma":[0.00042738282,0.00033974234,0.00014219759,0.00095720706,0.000025430243,0.0004530931,0.00016330132,0.00032865023,0.00029082844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010304101,0.00010955242,0.00000735795,0.00006995973,0.000068845904,0.00000631599,0.00013250503,0.9481539,0.000020492505,0.04437078,0.0020624762,0.0049875206],"study_design_scores_gemma":[0.0002024598,0.000028190218,0.000023584353,0.00003969009,0.000031062787,0.0000027589272,0.000020773748,0.9968337,0.0005414898,0.00097356556,0.0009604437,0.0003423019],"about_ca_topic_score_codex":0.00016310209,"about_ca_topic_score_gemma":0.000009121357,"teacher_disagreement_score":0.04867979,"about_ca_system_score_codex":0.00019204553,"about_ca_system_score_gemma":0.0005268914,"threshold_uncertainty_score":0.99990547},"labels":[],"label_agreement":null},{"id":"W4234932457","doi":"10.23952/asvao.1.2019.3.06","title":"Duality in vector optimization with domination structures","year":2019,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Duality (order theory); Vector optimization; Vector (molecular biology); Mathematics; Computer science; Mathematical optimization; Optimization problem; Combinatorics; Biology; Recombinant DNA; Genetics; Multi-swarm optimization","score_opus":0.01001554667937715,"score_gpt":0.2573089136681643,"score_spread":0.24729336698878712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234932457","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055978,0.00003205779,0.99222153,0.00018232648,0.000048320424,0.0005682542,0.000005814703,0.0000872034,0.0012566905],"genre_scores_gemma":[0.44278073,0.00005790601,0.55668694,0.00008373588,0.000017740027,0.000033890254,0.00021293771,0.000015287063,0.00011079864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975506,0.00019915603,0.00048029926,0.0007765492,0.0006838874,0.000309485],"domain_scores_gemma":[0.99863267,0.00011853296,0.0002574937,0.00060749246,0.000264694,0.00011909871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076866464,0.00023892777,0.00044924088,0.00096234656,0.00012956288,0.0003926972,0.00037017264,0.00012743546,0.0002987001],"category_scores_gemma":[0.00006233686,0.00020501955,0.000057540114,0.0040227347,0.00005476032,0.0005404487,0.00013324461,0.00016959007,0.00000877079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032003187,0.000038507882,0.0015910264,0.000018720533,0.00012813826,0.0000015038615,0.00029756027,0.9745237,0.000029402723,0.021645973,0.00000762966,0.001685826],"study_design_scores_gemma":[0.0010321875,0.00004344875,0.0057938104,0.0000063720727,0.000109916946,0.0000017136051,0.00007766976,0.99226254,0.00016688285,0.00023835941,0.000010869549,0.0002562172],"about_ca_topic_score_codex":0.000067682326,"about_ca_topic_score_gemma":0.000025602581,"teacher_disagreement_score":0.43718293,"about_ca_system_score_codex":0.00008932058,"about_ca_system_score_gemma":0.00007790387,"threshold_uncertainty_score":0.83604544},"labels":[],"label_agreement":null},{"id":"W4236332704","doi":"10.32920/ryerson.14664405.v1","title":"Implementation of a novel reactive navigation algorithm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dijkstra's algorithm; Robot; Computer science; Traverse; Algorithm; Obstacle; Graph; Mobile robot; Hash function; Terrain; Holonomic; Process (computing); Artificial intelligence; Shortest path problem; Theoretical computer science; Geography","score_opus":0.04493479840962592,"score_gpt":0.3703739824579284,"score_spread":0.32543918404830247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236332704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015239896,0.000049081536,0.9959336,0.00032179613,0.0005010597,0.000492346,0.00004114087,0.000085610154,0.0010513737],"genre_scores_gemma":[0.015519269,0.00003481697,0.9836923,0.000041677667,0.000055819888,0.0000741215,0.00030915978,0.000014278159,0.00025857182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753475,0.00013575572,0.00055966794,0.00066331425,0.00088684465,0.0002196874],"domain_scores_gemma":[0.9972667,0.00012136708,0.00040719422,0.0009208589,0.0011904662,0.000093426104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005985532,0.00018945274,0.000325592,0.00023845048,0.00004561135,0.00027613924,0.0008930346,0.00015294869,0.00022805954],"category_scores_gemma":[0.000069410744,0.00019416382,0.000116589,0.00053562684,0.000039974515,0.00035631837,0.001836748,0.00037506505,0.0000062578038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037023387,0.0006013046,0.00014606175,0.00034562583,0.00035413075,0.000033600987,0.003795025,0.0081531685,0.0032220834,0.020195507,0.00052582455,0.96262395],"study_design_scores_gemma":[0.00048530006,0.00003382661,0.00073496473,0.000069528825,0.000014911573,0.000011656727,0.00054804893,0.9702964,0.026953379,0.00057134905,0.00006524786,0.0002153928],"about_ca_topic_score_codex":0.0009570356,"about_ca_topic_score_gemma":0.0000138507185,"teacher_disagreement_score":0.9624086,"about_ca_system_score_codex":0.00012679366,"about_ca_system_score_gemma":0.0008184148,"threshold_uncertainty_score":0.7917771},"labels":[],"label_agreement":null},{"id":"W4240258315","doi":"10.1504/ijcse.2016.10001035","title":"Constraint handling in probability collectives using a modified feasibility-based rule","year":2016,"lang":"en","type":"article","venue":"International Journal of Computational Science and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Variety (cybernetics); Heuristic; Computer science; Constraint (computer-aided design); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.04244383824893212,"score_gpt":0.31466687842316077,"score_spread":0.27222304017422866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240258315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2565167,0.00001715636,0.74236834,0.00074826187,0.00023646372,0.000063371415,0.0000018060706,0.000009022778,0.00003890456],"genre_scores_gemma":[0.72017527,0.0000024061737,0.27976125,0.000026178723,0.000029719276,0.0000011236568,1.1040201e-7,0.0000021486303,0.0000017840866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981192,0.00003663893,0.00037703634,0.00020015008,0.001097788,0.00016921102],"domain_scores_gemma":[0.9977331,0.00043112808,0.00013040823,0.00007775308,0.0015133808,0.00011419204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019198955,0.00007727493,0.00012652234,0.00065597333,0.00006033003,0.00020395515,0.0006340577,0.000020996753,0.000007486681],"category_scores_gemma":[0.0015295267,0.000057690377,0.000031350948,0.0006028409,0.00019558873,0.0008430012,0.00010989607,0.0000919586,4.4224248e-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":[0.00001628547,0.000052817697,0.0017817896,0.000005360488,0.000010050432,0.000018921533,0.000108088054,0.97755826,0.005247602,0.0078553315,0.0000022058698,0.0073432866],"study_design_scores_gemma":[0.0007382616,0.000030036214,0.012056275,0.0000908579,0.0000010848404,0.00007216294,0.000007795558,0.97634846,0.00063025585,0.00994156,0.000010722129,0.00007255362],"about_ca_topic_score_codex":0.0000052994283,"about_ca_topic_score_gemma":5.520622e-7,"teacher_disagreement_score":0.46365857,"about_ca_system_score_codex":0.00036329098,"about_ca_system_score_gemma":0.0009653164,"threshold_uncertainty_score":0.23525453},"labels":[],"label_agreement":null},{"id":"W4240609839","doi":"10.1515/iupac.85.0388","title":"Continuous Dynode Particle Multiplier","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Metaheuristic Optimization Algorithms Research","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; National Research Council Canada","funders":"","keywords":"Chemical nomenclature; Terminology; Mass spectrometry; Chemistry; Analytical Chemistry (journal); Chromatography; Organic chemistry","score_opus":0.019870117094674216,"score_gpt":0.40609520557054896,"score_spread":0.3862250884758747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240609839","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":[0.0000028762292,0.00021975879,0.30464545,0.0009818721,0.00062814535,0.00027609532,0.6930893,0.00012319615,0.00003328572],"genre_scores_gemma":[0.000008483063,0.00049869996,0.01947217,0.00035151414,0.0004928063,0.00003632357,0.9769604,0.000037148486,0.0021424172],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99532837,0.00026032896,0.0006261778,0.0008832448,0.0021511775,0.00075072824],"domain_scores_gemma":[0.9957848,0.00031517565,0.00027193676,0.0021109006,0.0011259178,0.00039123397],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011949915,0.0004058219,0.0006357799,0.00022013414,0.00016283979,0.0003745999,0.002174531,0.00027659314,0.0024074304],"category_scores_gemma":[0.0020159532,0.00030713985,0.00014429966,0.00049490406,0.00017546292,0.0002827613,0.0009580823,0.000484201,0.000037854756],"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":[0.000022076405,0.00019035985,0.0000046075115,0.000036152986,0.00005593934,0.00012111798,0.000011900307,0.000024623745,0.0000064022397,0.00011987088,0.9738374,0.02556958],"study_design_scores_gemma":[0.0010762147,0.0001225979,0.000012269412,0.00009392657,0.000021690046,0.000017939909,0.0000033069434,0.017228376,0.00006497122,0.00021179538,0.98075235,0.00039457265],"about_ca_topic_score_codex":0.00005363262,"about_ca_topic_score_gemma":0.00006350065,"teacher_disagreement_score":0.28517327,"about_ca_system_score_codex":0.0003048376,"about_ca_system_score_gemma":0.00095263345,"threshold_uncertainty_score":0.9999381},"labels":[],"label_agreement":null},{"id":"W4242271313","doi":"10.1017/s0021900200006070","title":"Geometric Convergence of Genetic Algorithms Under Tempered Random Restart","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Mathematics; Algorithm; Convergence (economics); Population; Genetic algorithm; Rate of convergence; Mutation; Class (philosophy); Mathematical optimization; Computer science; Key (lock); Artificial intelligence","score_opus":0.02888184872187093,"score_gpt":0.27749243825187647,"score_spread":0.24861058953000553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242271313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05367514,0.00023824484,0.94380265,0.0006160874,0.00024646602,0.00045828838,0.0000024717842,0.000024786368,0.0009358648],"genre_scores_gemma":[0.539588,0.000095255404,0.46013087,0.00008956841,0.000058412763,0.000003397989,4.1376597e-7,0.000005416682,0.000028651624],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966648,0.00018501212,0.001254981,0.00034145435,0.0012269744,0.00032673727],"domain_scores_gemma":[0.9969588,0.00040683436,0.0007188958,0.00073840824,0.00096814503,0.00020895063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027995163,0.00017181791,0.0005503677,0.00046954895,0.00007814661,0.000081677,0.0013121231,0.00009404038,0.00012118653],"category_scores_gemma":[0.00053186395,0.0001390053,0.0001615135,0.0018722046,0.00015334642,0.00024727205,0.00012989523,0.00034772066,0.0000126037985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023730246,0.004734866,0.0043024207,0.00052478205,0.00051057554,0.00014146457,0.0020192408,0.2983475,0.013065525,0.06950227,0.0044740993,0.60000426],"study_design_scores_gemma":[0.0124555705,0.0022987844,0.11938039,0.00009787108,0.000113625036,0.00025382984,0.00012385169,0.35383856,0.030793132,0.47841558,0.0012654711,0.00096333266],"about_ca_topic_score_codex":0.0000037152636,"about_ca_topic_score_gemma":3.9448506e-7,"teacher_disagreement_score":0.59904087,"about_ca_system_score_codex":0.00011818648,"about_ca_system_score_gemma":0.00044556468,"threshold_uncertainty_score":0.56684715},"labels":[],"label_agreement":null},{"id":"W4244270277","doi":"10.22215/etd/2011-07154","title":"Exchange inlet design optimization by genetic algorithm","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science; Algorithm","score_opus":0.03229938946203255,"score_gpt":0.2758832408898755,"score_spread":0.24358385142784295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244270277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011590237,0.0012966591,0.98153317,0.000039622533,0.0010738062,0.0008765708,0.000017997192,0.0003526581,0.014808364],"genre_scores_gemma":[0.000004074365,0.001299188,0.89192116,0.0001220408,0.000095401185,0.00020231745,0.00077971024,0.000074295436,0.10550183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639744,0.0003920129,0.000612408,0.0010094031,0.0010139549,0.00057480135],"domain_scores_gemma":[0.99749887,0.00013682316,0.00032957905,0.001075336,0.0006676804,0.0002917028],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005337408,0.00046780176,0.0004376982,0.0005481036,0.00018460106,0.0004343763,0.0019941574,0.00045866595,0.0032868541],"category_scores_gemma":[0.00014373194,0.0004590191,0.00010226134,0.00094537844,0.000035074725,0.00044278477,0.00017272952,0.00038480342,0.00045588813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002022996,0.00021726517,0.000002440095,0.0001532415,0.00012609904,0.000050176288,0.0012585022,0.023310952,0.00002224494,0.0006731459,0.107654415,0.8665113],"study_design_scores_gemma":[0.00031779803,0.0001242207,0.000017396325,0.000028582297,0.000024051797,0.000008811549,0.000028968929,0.9931187,0.0012868675,0.00037572425,0.0041361167,0.00053273083],"about_ca_topic_score_codex":0.00008159013,"about_ca_topic_score_gemma":0.000005582372,"teacher_disagreement_score":0.9698078,"about_ca_system_score_codex":0.00008245867,"about_ca_system_score_gemma":0.00035280944,"threshold_uncertainty_score":0.99978614},"labels":[],"label_agreement":null},{"id":"W4244439700","doi":"10.32920/ryerson.14647050.v1","title":"Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Meta-optimization; Metaheuristic; Imperialist competitive algorithm; Multi-swarm optimization; Derivative-free optimization; Algorithm; Hybrid algorithm (constraint satisfaction); Mathematical optimization; Computer science; Optimization problem; Particle swarm optimization; Test functions for optimization; Continuous optimization; Benchmark (surveying); Bees algorithm; Convergence (economics); Cuckoo search; Parallel metaheuristic; Ant colony optimization algorithms; Mathematics; Artificial intelligence; Constraint satisfaction; Probabilistic logic","score_opus":0.04353849727071869,"score_gpt":0.2966537671727526,"score_spread":0.2531152699020339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244439700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021568323,0.00047970488,0.9927912,0.0003385002,0.003587639,0.0015120683,0.00017703958,0.00061470957,0.0004776071],"genre_scores_gemma":[0.0023226042,0.0007067679,0.9936228,0.00014128038,0.00031150528,0.0005970901,0.0010419282,0.00006146509,0.0011945807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952008,0.0002933958,0.0010198135,0.0017469604,0.0011222657,0.00061674335],"domain_scores_gemma":[0.9951912,0.00024503842,0.00025390985,0.0017031046,0.002301173,0.0003055468],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012605513,0.00048679597,0.00066345965,0.0004454562,0.00027635545,0.0012811569,0.0018765001,0.00028060112,0.00034047518],"category_scores_gemma":[0.00078048505,0.0005141356,0.00031933372,0.00053103303,0.000052487685,0.00065430405,0.0021719688,0.0006281759,0.00001770734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023727764,0.00007949841,0.0000015735114,0.00012063205,0.00010840309,0.000014140984,0.00006114956,0.97117156,0.000003569087,0.001094286,0.00047305107,0.026869752],"study_design_scores_gemma":[0.00040509953,0.0000448437,0.0000011330239,0.0000719556,0.00007081343,0.000024944284,0.00002426469,0.9971999,0.0002920568,0.0013178064,0.000102628444,0.00044455714],"about_ca_topic_score_codex":0.00007387979,"about_ca_topic_score_gemma":0.0000017571488,"teacher_disagreement_score":0.026425194,"about_ca_system_score_codex":0.00021119947,"about_ca_system_score_gemma":0.0009822394,"threshold_uncertainty_score":0.9997556},"labels":[],"label_agreement":null},{"id":"W4244442967","doi":"10.1007/978-3-319-41192-7_5","title":"Evolutionary Strategies","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"","keywords":"CMA-ES; Evolution strategy; Adaptation (eye); Construct (python library); Mathematical optimization; Evolutionary algorithm; Quadratic equation; Point (geometry); Mathematics; Covariance matrix; Matrix (chemical analysis); Computer science; Algorithm; Physics; Materials science; Geometry","score_opus":0.0298019123276116,"score_gpt":0.27284737817835575,"score_spread":0.24304546585074416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244442967","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":[2.8404619e-9,0.00014914716,0.48755312,0.00051192817,0.00017734927,0.00008060824,0.000005850656,0.00013971595,0.5113823],"genre_scores_gemma":[0.000011286539,0.00019155136,0.16926599,0.00008148411,0.00014848531,0.0000057539123,0.000004913658,0.0000196176,0.83027095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998291,0.000020844815,0.00026277133,0.0004909917,0.0006953016,0.00023904831],"domain_scores_gemma":[0.99844795,0.00014404785,0.000094966475,0.0008737355,0.00030747417,0.00013184085],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016771996,0.00021524722,0.00021140733,0.000249642,0.000079392696,0.0002207114,0.0011971905,0.00018667562,0.00638606],"category_scores_gemma":[0.00003008565,0.0001549657,0.000089314744,0.00004016445,0.000106084524,0.00045251916,0.0004985265,0.00019507772,0.0043100407],"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":[6.5028973e-7,0.0000034218504,1.2982454e-7,0.0000068142494,0.000018904244,0.000020734014,0.000005207826,0.00001517242,0.0000013094401,0.9582306,0.020797178,0.02089984],"study_design_scores_gemma":[0.00011348865,0.000033015935,0.00000519697,0.000039663737,0.0000036150489,0.000020978523,0.0000012741186,0.017051622,0.0000038672606,0.45600018,0.5264512,0.00027590006],"about_ca_topic_score_codex":0.0000015941016,"about_ca_topic_score_gemma":7.4772896e-7,"teacher_disagreement_score":0.50565404,"about_ca_system_score_codex":0.0000790643,"about_ca_system_score_gemma":0.0005428119,"threshold_uncertainty_score":0.9964652},"labels":[],"label_agreement":null},{"id":"W4247450063","doi":"10.32920/ryerson.14647074","title":"RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Toronto Metropolitan University","funders":"","keywords":"Rank (graph theory); Genetic programming; Computer science; Ranking (information retrieval); Learning to rank; Artificial intelligence; Evolutionary algorithm; Machine learning; Evolutionary programming; Genetic algorithm; Mathematics","score_opus":0.028395259418108748,"score_gpt":0.2993532365904498,"score_spread":0.27095797717234105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247450063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055419067,0.00015409978,0.9910781,0.00023371192,0.00032145958,0.0020694225,0.0000091542925,0.00029917658,0.00029297202],"genre_scores_gemma":[0.031113576,0.000025875592,0.96821606,0.00007521845,0.00010169144,0.00010137226,0.00015541285,0.000024689665,0.00018607805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689245,0.00021046822,0.0006256392,0.0006732663,0.0010545006,0.0005436672],"domain_scores_gemma":[0.9968863,0.0001960602,0.00027919988,0.00070190727,0.001717742,0.00021876497],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007580426,0.00032415555,0.00040547297,0.0005468512,0.00032657373,0.0028970775,0.0009619639,0.00013376887,0.000041249943],"category_scores_gemma":[0.0006088214,0.00029954608,0.00011837899,0.00082322053,0.000055081615,0.0013047552,0.0012825511,0.0005600607,0.000012007491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007422773,0.000034401317,0.00003642773,0.00040615644,0.00008123077,0.000017060776,0.0011168335,0.972645,0.000034521763,0.0007780245,0.000071319715,0.024704823],"study_design_scores_gemma":[0.0005372643,0.00017449383,0.00011850323,0.00018176943,0.000026755817,0.00006729873,0.0007874393,0.9950483,0.0002224502,0.00018804862,0.0022510828,0.00039660133],"about_ca_topic_score_codex":0.00008456064,"about_ca_topic_score_gemma":0.000003183069,"teacher_disagreement_score":0.02557167,"about_ca_system_score_codex":0.00023877156,"about_ca_system_score_gemma":0.0023315363,"threshold_uncertainty_score":0.99994564},"labels":[],"label_agreement":null},{"id":"W4248360727","doi":"10.1109/ijcnn.2006.1716775","title":"Virtual Reality Visual Data Mining via Neural Networks obtained from Multi-objective Evolutionary Optimization: Application to Geophysical Prospecting","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Metaheuristic Optimization Algorithms Research","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":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Artificial neural network; Artificial intelligence; Principal component analysis; Set (abstract data type); Genetic algorithm; Multi-objective optimization; Linear programming; Pattern recognition (psychology); Data mining; Machine learning; Algorithm","score_opus":0.05396050701975837,"score_gpt":0.31641636383682803,"score_spread":0.2624558568170697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248360727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008016547,0.000019149962,0.9828083,0.0056297807,0.0011845822,0.0010351308,0.00004560136,0.0003610126,0.00089992164],"genre_scores_gemma":[0.91385615,0.0000096042695,0.08125915,0.0007355268,0.0031768414,0.00016230522,0.0003851487,0.00004323394,0.00037201776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545217,0.00015149373,0.0008711898,0.0014599521,0.0013445535,0.0007206558],"domain_scores_gemma":[0.99678653,0.0003763955,0.0005295502,0.0007178957,0.0013767561,0.00021286825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082251354,0.000451224,0.00040278782,0.00018966277,0.0006041195,0.00083463895,0.0029859305,0.00014282392,0.00005394213],"category_scores_gemma":[0.00027210033,0.0003846283,0.00009932881,0.0011359204,0.00017738128,0.0012015245,0.001206443,0.00065733155,0.000044467895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121798126,0.00020376513,0.00067185273,0.0000030028452,0.000047674006,0.0000060531634,0.00012842959,0.97545165,0.00022331595,0.007949972,0.00509492,0.010097565],"study_design_scores_gemma":[0.0005349201,0.00016495031,0.0067319665,0.000046912115,0.000017707676,0.000021997492,0.000051667717,0.99088585,0.00006994809,0.0010067923,0.00007417485,0.00039308213],"about_ca_topic_score_codex":0.00055950246,"about_ca_topic_score_gemma":0.00003655492,"teacher_disagreement_score":0.9058396,"about_ca_system_score_codex":0.00027133437,"about_ca_system_score_gemma":0.00010517902,"threshold_uncertainty_score":0.9998606},"labels":[],"label_agreement":null},{"id":"W4248426926","doi":"10.1109/ccece.2018.8447836","title":"IIR Filter Design Using Multiobjective Artificial Bee Colony Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Infinite impulse response; Artificial bee colony algorithm; Mathematical optimization; Convergence (economics); Computer science; Filter (signal processing); Passband; Algorithm; Global optimization; Mathematics; Digital filter; Engineering; Band-pass filter","score_opus":0.09843313491562941,"score_gpt":0.3416223917468706,"score_spread":0.24318925683124118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248426926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018752961,0.00000933843,0.99608696,0.00027072083,0.00047425716,0.0004043716,0.0000040611244,0.00019786155,0.00236488],"genre_scores_gemma":[0.00532893,0.000002234415,0.99206495,0.00034662176,0.0003717148,0.000018335873,0.0000013338855,0.000016284921,0.0018495917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977946,0.00031078444,0.00030522968,0.00054922793,0.0005720711,0.00046807894],"domain_scores_gemma":[0.99833316,0.00026663108,0.00008131012,0.0004531482,0.0006909678,0.00017476395],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00079225743,0.00016308397,0.00018633765,0.00024270809,0.00033654514,0.00034967987,0.00078081404,0.00008645083,0.0010742195],"category_scores_gemma":[0.00030218408,0.0001469274,0.00005233635,0.00084602245,0.0001983501,0.00050219806,0.00037749982,0.00014931668,0.00064796035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006995527,0.00065351155,0.000048961803,0.000014153246,0.00017792678,0.0001291329,0.0036809358,0.010759274,0.014734377,0.017912552,0.017345343,0.9344739],"study_design_scores_gemma":[0.00022123044,0.00016423834,0.000063835476,0.000004749673,0.0000049122937,0.000017908817,0.000019420251,0.96862954,0.027977401,0.0017573335,0.0009566509,0.00018275192],"about_ca_topic_score_codex":0.000091267466,"about_ca_topic_score_gemma":0.000006282751,"teacher_disagreement_score":0.9578703,"about_ca_system_score_codex":0.00010542237,"about_ca_system_score_gemma":0.00025102668,"threshold_uncertainty_score":0.99983895},"labels":[],"label_agreement":null},{"id":"W4248646680","doi":"10.23952/jano.2.2020.1.01","title":"A special issue focused on superiorization versus constrained optimization: analysis and applications","year":2020,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Psychology","score_opus":0.01826649990284673,"score_gpt":0.2627202259413634,"score_spread":0.24445372603851667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248646680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025917067,0.000025498524,0.9931126,0.0025214234,0.00013548376,0.0002875742,0.000005140959,0.00003895691,0.0038474002],"genre_scores_gemma":[0.024346525,0.00036567377,0.9721754,0.0006227196,0.0023992555,0.00001928258,0.000029520628,0.00002116817,0.00002044233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982753,0.00007486086,0.00058103807,0.00035637757,0.0005312463,0.000181195],"domain_scores_gemma":[0.9985106,0.00023658545,0.00036999644,0.00017725023,0.00033985052,0.00036571966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024745293,0.00017735628,0.00041860418,0.00036186934,0.00017682585,0.00029842916,0.0002977693,0.000092296796,0.0002684752],"category_scores_gemma":[0.00017395346,0.00016125166,0.00007616816,0.0019965065,0.000093251925,0.00034414636,0.00009375289,0.00020814402,0.000005695111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002533699,0.00006113631,0.000021354053,0.000010590124,0.00015328586,0.0000038699386,0.00029931887,0.95696074,0.00001498844,0.0073030307,0.00020683196,0.03471147],"study_design_scores_gemma":[0.0016930279,0.00036919344,0.000052509622,0.0000038279422,0.00015214665,0.0000051617108,0.000050784656,0.9937484,0.00007193038,0.00006590209,0.003626805,0.00016027692],"about_ca_topic_score_codex":8.5029285e-7,"about_ca_topic_score_gemma":6.8833614e-8,"teacher_disagreement_score":0.03678768,"about_ca_system_score_codex":0.000036273417,"about_ca_system_score_gemma":0.00009566705,"threshold_uncertainty_score":0.65756524},"labels":[],"label_agreement":null},{"id":"W4249797588","doi":"10.1007/978-1-4419-1665-5_2","title":"Tabu Search","year":2010,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":50,"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 Montréal; Polytechnique Montréal","funders":"","keywords":"Tabu search; Guided Local Search; Hill climbing; Diversification (marketing strategy); Computer science; Heuristic; Space (punctuation); Incremental heuristic search; Beam search; Theoretical computer science; Search algorithm; Algorithm; Artificial intelligence","score_opus":0.06679086680121286,"score_gpt":0.41150676708795514,"score_spread":0.34471590028674226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249797588","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":[0.00068503723,0.000116719944,0.02923548,0.029063143,0.005434699,0.0055817044,0.00015982665,0.00027618746,0.9294472],"genre_scores_gemma":[0.035127554,0.008685044,0.20138994,0.00027662222,0.00060692866,0.0020747227,0.00033977246,0.00015476564,0.7513446],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96560085,0.0006730749,0.0024103953,0.0048105437,0.023092644,0.0034125086],"domain_scores_gemma":[0.9858925,0.00043765898,0.00014205661,0.004121123,0.008497735,0.00090895203],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"category_scores_codex":[0.036019858,0.0009854385,0.0007624888,0.026566263,0.0053089415,0.013289397,0.026936384,0.00043262838,0.0049167676],"category_scores_gemma":[0.0027186964,0.0010371114,0.00022197321,0.011298994,0.015062729,0.0149776945,0.01944273,0.0050312155,0.0018215355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009230632,0.00041888846,0.000082418286,0.000073486575,0.0001092804,0.00063667865,0.0007993515,0.06974421,0.00081348803,0.9181811,0.0010247856,0.008024017],"study_design_scores_gemma":[0.0023743133,0.00043943795,0.0028922542,0.0010431742,0.000018924093,0.00016878177,0.0029733921,0.5250876,0.0017121232,0.05854915,0.4024623,0.0022785235],"about_ca_topic_score_codex":0.0009157369,"about_ca_topic_score_gemma":0.005021536,"teacher_disagreement_score":0.85963196,"about_ca_system_score_codex":0.006262109,"about_ca_system_score_gemma":0.0026173731,"threshold_uncertainty_score":0.9992079},"labels":[],"label_agreement":null},{"id":"W4251848796","doi":"10.32920/ryerson.14664405","title":"Implementation of a novel reactive navigation algorithm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Dijkstra's algorithm; Robot; Computer science; Traverse; Obstacle; Algorithm; Hash function; Graph; Mobile robot; Terrain; Process (computing); Holonomic; Artificial intelligence; Shortest path problem; Theoretical computer science; Geography","score_opus":0.04493479840962592,"score_gpt":0.3703739824579284,"score_spread":0.32543918404830247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251848796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015239896,0.000049081536,0.9959336,0.00032179613,0.0005010597,0.000492346,0.00004114087,0.000085610154,0.0010513737],"genre_scores_gemma":[0.015519269,0.00003481697,0.9836923,0.000041677667,0.000055819888,0.0000741215,0.00030915978,0.000014278159,0.00025857182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753475,0.00013575572,0.00055966794,0.00066331425,0.00088684465,0.0002196874],"domain_scores_gemma":[0.9972667,0.00012136708,0.00040719422,0.0009208589,0.0011904662,0.000093426104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005985532,0.00018945274,0.000325592,0.00023845048,0.00004561135,0.00027613924,0.0008930346,0.00015294869,0.00022805954],"category_scores_gemma":[0.000069410744,0.00019416382,0.000116589,0.00053562684,0.000039974515,0.00035631837,0.001836748,0.00037506505,0.0000062578038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037023387,0.0006013046,0.00014606175,0.00034562583,0.00035413075,0.000033600987,0.003795025,0.0081531685,0.0032220834,0.020195507,0.00052582455,0.96262395],"study_design_scores_gemma":[0.00048530006,0.00003382661,0.00073496473,0.000069528825,0.000014911573,0.000011656727,0.00054804893,0.9702964,0.026953379,0.00057134905,0.00006524786,0.0002153928],"about_ca_topic_score_codex":0.0009570356,"about_ca_topic_score_gemma":0.0000138507185,"teacher_disagreement_score":0.9624086,"about_ca_system_score_codex":0.00012679366,"about_ca_system_score_gemma":0.0008184148,"threshold_uncertainty_score":0.7917771},"labels":[],"label_agreement":null},{"id":"W4254212488","doi":"10.1007/978-3-540-68830-3_6","title":"Opposition-Based Differential Evolution","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":123,"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":"Ode; Initialization; Opposition (politics); Differential evolution; Suite; Test suite; Mathematical optimization; Benchmark (surveying); Population; Computer science; Algorithm; Mathematics; Applied mathematics; Test case; Law","score_opus":0.12689775363702605,"score_gpt":0.3686390694557175,"score_spread":0.24174131581869146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254212488","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011450693,0.001708505,0.96471226,0.0005117204,0.0010267621,0.00038085665,0.00002525925,0.00011676685,0.031516705],"genre_scores_gemma":[0.1483319,0.00411195,0.74868065,0.00076636643,0.0009136054,0.00025055584,0.00040407374,0.00016778085,0.09637315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967087,0.00009298199,0.00078850094,0.00081713585,0.0012553277,0.00033737763],"domain_scores_gemma":[0.9969315,0.0013147509,0.0002743372,0.00047391772,0.0009016595,0.00010381821],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000268476,0.0003946717,0.000499978,0.0007293806,0.0002626088,0.00008221509,0.0010480536,0.00018380243,0.0001891677],"category_scores_gemma":[0.0002458347,0.00040971424,0.00015352452,0.0002664045,0.0005816121,0.0001880228,0.00049714785,0.00053068064,0.00032697667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074612963,0.00004828108,0.0000051820584,0.000056581765,0.00008322006,0.00009943623,0.00019413789,0.40401223,3.2072148e-7,0.5885158,0.0023852757,0.0045920596],"study_design_scores_gemma":[0.00010970251,0.00007018727,0.000027077873,0.00022192171,0.000008269545,0.000023739995,0.000012398987,0.7629595,0.000010315054,0.23516582,0.0010464253,0.00034468807],"about_ca_topic_score_codex":0.0000074387394,"about_ca_topic_score_gemma":0.0000073839483,"teacher_disagreement_score":0.35894725,"about_ca_system_score_codex":0.00092953624,"about_ca_system_score_gemma":0.0005122474,"threshold_uncertainty_score":0.9998355},"labels":[],"label_agreement":null},{"id":"W4254975740","doi":"10.4018/978-1-59904-996-0.ch017","title":"Graph Based Evolutionary Algorithms","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Metaheuristic Optimization Algorithms Research","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":"Evolutionary algorithm; Computer science; A priori and a posteriori; Evolutionary computation; Graph; Computation; Algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.024928301964779652,"score_gpt":0.27080694542834866,"score_spread":0.245878643463569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254975740","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":[7.088253e-8,0.0003479886,0.4094956,0.0002571951,0.00040931487,0.00032082954,0.000078292665,0.0003137334,0.588777],"genre_scores_gemma":[0.0004246723,0.000021002448,0.6408507,0.0020016572,0.0005160619,0.00003648449,0.000033650864,0.00007253125,0.35604322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99588996,0.00007968895,0.000615009,0.0011179622,0.0016504336,0.00064694247],"domain_scores_gemma":[0.9970005,0.00010016039,0.00027711265,0.0016216129,0.00054928334,0.0004512882],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032930798,0.0005891411,0.00057155505,0.0003422502,0.00022136071,0.00030548658,0.0021348542,0.0005051064,0.00017742191],"category_scores_gemma":[0.000067122615,0.0006065706,0.00036393455,0.0001308824,0.00018015907,0.00014347157,0.0004171271,0.00054452044,0.0007056214],"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":[0.000007026867,0.00002142126,0.0000011383597,0.000014683915,0.000043415097,0.00020217407,0.000005713712,0.00032105678,7.670224e-7,0.9092197,0.011015244,0.079147674],"study_design_scores_gemma":[0.0006462618,0.00020991999,0.000050832543,0.00013370634,0.000037483143,0.00009471163,7.192706e-7,0.14859602,0.000012716712,0.74404055,0.10530663,0.00087043515],"about_ca_topic_score_codex":0.00002327302,"about_ca_topic_score_gemma":0.0000037915265,"teacher_disagreement_score":0.23273374,"about_ca_system_score_codex":0.00041383997,"about_ca_system_score_gemma":0.0009732628,"threshold_uncertainty_score":0.99963856},"labels":[],"label_agreement":null},{"id":"W4255316375","doi":"10.35940/ijitee.a4215.119119","title":"BOSA: Binary Orientation Search Algorithm","year":2019,"lang":"en","type":"article","venue":"International Journal of Innovative Technology and Exploring Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":84,"is_retracted":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":"Benchmark (surveying); Orientation (vector space); Binary number; Algorithm; Computer science; Binary search algorithm; Optimization algorithm; Search algorithm; Mathematical optimization; Mathematics; Arithmetic","score_opus":0.02446113224111164,"score_gpt":0.2801468312143471,"score_spread":0.25568569897323545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255316375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21914874,0.00008654325,0.77832454,0.0010603943,0.001184846,0.000054359647,0.0000011565553,0.000063458836,0.00007595321],"genre_scores_gemma":[0.64494914,0.00011537778,0.3547352,0.000028719764,0.000095448784,0.000006901673,0.00000153437,0.000009567481,0.000058137666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988958,0.00001622797,0.00033385,0.00016016322,0.00043600923,0.00015791589],"domain_scores_gemma":[0.99829686,0.0000858343,0.0001256394,0.00014002115,0.0013131454,0.00003851865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047872955,0.00009474143,0.00015016824,0.0018883883,0.000026886353,0.00007318469,0.0006908813,0.000057947032,0.000016311136],"category_scores_gemma":[0.00021632829,0.00008903942,0.000022131117,0.0014599086,0.000035401812,0.0009269521,0.00028594947,0.00039487449,0.00001168847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044982662,0.00014835532,0.011653159,0.000044345306,0.00058141956,0.00044645084,0.0013573045,0.035592638,0.03503128,0.36776248,0.00014637782,0.5471912],"study_design_scores_gemma":[0.0024716957,0.0007096423,0.012764062,0.00033959543,0.0000061803003,0.0009786821,0.0007564153,0.91369545,0.05815048,0.003014496,0.0066195335,0.00049373496],"about_ca_topic_score_codex":0.0000011804664,"about_ca_topic_score_gemma":1.9276882e-8,"teacher_disagreement_score":0.87810284,"about_ca_system_score_codex":0.000079242156,"about_ca_system_score_gemma":0.000061529216,"threshold_uncertainty_score":0.3630922},"labels":[],"label_agreement":null},{"id":"W4255726574","doi":"10.32920/ryerson.14647050","title":"Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Meta-optimization; Metaheuristic; Imperialist competitive algorithm; Multi-swarm optimization; Derivative-free optimization; Algorithm; Mathematical optimization; Hybrid algorithm (constraint satisfaction); Computer science; Optimization problem; Particle swarm optimization; Test functions for optimization; Continuous optimization; Benchmark (surveying); Convergence (economics); Bees algorithm; Cuckoo search; Ant colony optimization algorithms; Mathematics; Artificial intelligence; Probabilistic logic; Constraint satisfaction","score_opus":0.04353849727071869,"score_gpt":0.2966537671727526,"score_spread":0.2531152699020339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255726574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021568323,0.00047970488,0.9927912,0.0003385002,0.003587639,0.0015120683,0.00017703958,0.00061470957,0.0004776071],"genre_scores_gemma":[0.0023226042,0.0007067679,0.9936228,0.00014128038,0.00031150528,0.0005970901,0.0010419282,0.00006146509,0.0011945807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952008,0.0002933958,0.0010198135,0.0017469604,0.0011222657,0.00061674335],"domain_scores_gemma":[0.9951912,0.00024503842,0.00025390985,0.0017031046,0.002301173,0.0003055468],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012605513,0.00048679597,0.00066345965,0.0004454562,0.00027635545,0.0012811569,0.0018765001,0.00028060112,0.00034047518],"category_scores_gemma":[0.00078048505,0.0005141356,0.00031933372,0.00053103303,0.000052487685,0.00065430405,0.0021719688,0.0006281759,0.00001770734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023727764,0.00007949841,0.0000015735114,0.00012063205,0.00010840309,0.000014140984,0.00006114956,0.97117156,0.000003569087,0.001094286,0.00047305107,0.026869752],"study_design_scores_gemma":[0.00040509953,0.0000448437,0.0000011330239,0.0000719556,0.00007081343,0.000024944284,0.00002426469,0.9971999,0.0002920568,0.0013178064,0.000102628444,0.00044455714],"about_ca_topic_score_codex":0.00007387979,"about_ca_topic_score_gemma":0.0000017571488,"teacher_disagreement_score":0.026425194,"about_ca_system_score_codex":0.00021119947,"about_ca_system_score_gemma":0.0009822394,"threshold_uncertainty_score":0.9997556},"labels":[],"label_agreement":null},{"id":"W4280625329","doi":"10.23919/date54114.2022.9774576","title":"Efficient Traveling Salesman Problem Solvers using the Ising Model with Simulated Bifurcation","year":2022,"lang":"en","type":"article","venue":"2022 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":21,"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; China Scholarship Council","keywords":"Travelling salesman problem; Solver; Computer science; Mathematical optimization; Simulated annealing; Benchmark (surveying); Ising model; Spins; Applied mathematics; Mathematics; Algorithm; Physics; Statistical physics","score_opus":0.0955018404612106,"score_gpt":0.30279289995429143,"score_spread":0.20729105949308083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280625329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044626363,0.00003411756,0.95153403,0.00084715197,0.00016604146,0.0015722215,0.000041602543,0.00054370123,0.00063479587],"genre_scores_gemma":[0.52227485,0.000027427415,0.47523248,0.00028746517,0.000038300666,0.00013779469,0.0004805899,0.00007953697,0.0014415549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933775,0.0017921788,0.0011042617,0.001156126,0.0018357731,0.0007341246],"domain_scores_gemma":[0.99578434,0.0007590704,0.0007017704,0.0013005297,0.0012606448,0.00019366461],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0035084237,0.00046036905,0.0003969636,0.000775204,0.0013614495,0.0009060677,0.0013407933,0.000097594064,0.00064400036],"category_scores_gemma":[0.0008729772,0.00041412364,0.000071391965,0.00438033,0.00018500371,0.00063767075,0.0005638485,0.00082064717,0.00020441813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029531322,0.0002804781,0.00008709844,0.000033770055,0.000016325488,0.0000069379034,0.0029565678,0.9758617,0.016678594,0.0021904542,0.00036113375,0.0014973675],"study_design_scores_gemma":[0.00085519353,0.00006906714,0.00024369346,0.00011121275,0.000025735242,0.00006596486,0.00009067688,0.9953138,0.00031075982,0.0004627587,0.0019464601,0.000504636],"about_ca_topic_score_codex":0.00008504065,"about_ca_topic_score_gemma":0.00004997593,"teacher_disagreement_score":0.47764847,"about_ca_system_score_codex":0.0003867507,"about_ca_system_score_gemma":0.0010913854,"threshold_uncertainty_score":0.99993867},"labels":[],"label_agreement":null},{"id":"W4283025888","doi":"10.1145/3544489","title":"Algorithm 1027: <tt>NOMAD</tt> Version 4: Nonlinear Optimization with the MADS Algorithm","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Mathematical Software","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Algorithm; Component (thermodynamics); Software; Code (set theory); Parallel computing; Programming language; Set (abstract data type)","score_opus":0.015497266875545528,"score_gpt":0.25223856608583045,"score_spread":0.23674129921028492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283025888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002648895,0.00003386267,0.9935939,0.004642112,0.00023486353,0.00071709463,0.00013573056,0.0005114848,0.00010450964],"genre_scores_gemma":[0.000145956,0.000022148257,0.99663955,0.00053637405,0.000055165077,0.00033830712,0.00005122696,0.000058137746,0.0021531205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598634,0.00040553196,0.00045453856,0.00073919323,0.0018384848,0.00057590863],"domain_scores_gemma":[0.99639237,0.001104525,0.00015309145,0.001796707,0.00029534785,0.0002579753],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00085414835,0.0003445314,0.000351481,0.0002940157,0.001695573,0.00031666667,0.002377717,0.000096564996,0.003610841],"category_scores_gemma":[0.00020046516,0.00025090144,0.00016764255,0.0017534441,0.00018304244,0.0004521262,0.0002111501,0.0008757723,0.00030294893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023902074,0.0006524585,0.0000021429867,0.000031734387,0.00008841424,0.00004693143,0.0004274515,0.3967057,0.0000018867302,0.00020747178,0.0003340523,0.60147786],"study_design_scores_gemma":[0.00081932236,0.0004201384,0.000008293515,0.000022153976,0.00005333702,0.00015754577,0.00024392687,0.98656774,0.000113879534,0.0067628785,0.0044870065,0.0003437986],"about_ca_topic_score_codex":0.000011166865,"about_ca_topic_score_gemma":7.582864e-7,"teacher_disagreement_score":0.60113406,"about_ca_system_score_codex":0.00024700837,"about_ca_system_score_gemma":0.00021778114,"threshold_uncertainty_score":0.99999434},"labels":[],"label_agreement":null},{"id":"W4283212329","doi":"10.1109/iemtronics55184.2022.9795756","title":"A Data-Centric Machine Learning Approach for Controlling Exploration in Estimation of Distribution Algorithms","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","topic":"Metaheuristic Optimization Algorithms Research","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 Prince Edward Island","funders":"","keywords":"Computer science; Randomness; Machine learning; Artificial intelligence; Convergence (economics); Heuristic; Algorithm; Metaheuristic; Ideal (ethics); Fitness function; Process (computing); Mathematical optimization; Mathematics; Genetic algorithm","score_opus":0.0582568992309601,"score_gpt":0.3149985670819831,"score_spread":0.256741667851023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283212329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018546704,0.0020152442,0.99297124,0.0008234324,0.00042318914,0.0010863376,0.00069360033,0.000063464606,0.00006884028],"genre_scores_gemma":[0.87790996,0.002870621,0.11087158,0.00005143473,0.00008057148,0.00079048506,0.007077297,0.000040142255,0.00030789158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628043,0.0003174333,0.0007948813,0.00091987156,0.0010455904,0.0006418162],"domain_scores_gemma":[0.9980962,0.00028031864,0.000517491,0.0006062576,0.00039456086,0.00010514487],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026886286,0.0002661424,0.0004178544,0.00043571365,0.00035661488,0.00023769919,0.0018012819,0.00009002288,0.00005589661],"category_scores_gemma":[0.00036111087,0.00030323336,0.00008651018,0.0007002778,0.000049558537,0.0007793918,0.0008089563,0.0008457801,0.000001516292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092885275,0.00026933727,0.00003120197,0.000032819833,0.00008547248,0.000001740321,0.00017780726,0.62107795,0.00037317426,0.32942832,0.00006435934,0.04836494],"study_design_scores_gemma":[0.0019736649,0.00036956472,0.00000508759,0.000010099518,0.000023801167,0.000013554135,0.00013048832,0.9778543,0.000344178,0.009228273,0.009787465,0.00025950588],"about_ca_topic_score_codex":0.000058360067,"about_ca_topic_score_gemma":0.000032475225,"teacher_disagreement_score":0.8820996,"about_ca_system_score_codex":0.00077766937,"about_ca_system_score_gemma":0.00086778344,"threshold_uncertainty_score":0.999942},"labels":[],"label_agreement":null},{"id":"W4283828779","doi":"10.1007/s00500-022-07280-9","title":"Self-adaptive salp swarm algorithm for optimization problems","year":2022,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":27,"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":"Benchmark (surveying); Computer science; Tuner; Convergence (economics); Algorithm; Mathematical optimization; Population; Metaheuristic; Swarm intelligence; Range (aeronautics); Genetic algorithm; Particle swarm optimization; Mathematics; Engineering; Geography","score_opus":0.024475305420277746,"score_gpt":0.270610781612458,"score_spread":0.24613547619218024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283828779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002033676,0.00012513054,0.9964277,0.00036676004,0.00074853736,0.0009940608,0.000015081331,0.00066984346,0.00063251297],"genre_scores_gemma":[0.004125965,0.0000054340444,0.9949602,0.0002305647,0.00017445306,0.00013375693,0.000033568824,0.00003514132,0.00030090383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729156,0.0002767687,0.00042657845,0.000680142,0.0007667417,0.0005581987],"domain_scores_gemma":[0.9982359,0.0005213761,0.00023302752,0.00047528133,0.00039631518,0.00013813208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014808963,0.00019229944,0.00023920761,0.00026405355,0.0011048274,0.00026321027,0.0012337479,0.000042258056,0.000047983518],"category_scores_gemma":[0.0001409972,0.00021652182,0.000098662815,0.0011342422,0.000029819856,0.00027951848,0.0012877076,0.0003052279,0.00001320958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002243024,0.00010482239,0.000008981317,0.000018305256,0.000030549192,0.0000046224554,0.00073953107,0.86831355,0.0000022415197,0.003555591,0.0005014261,0.12671812],"study_design_scores_gemma":[0.0006276915,0.00021630837,0.0000074590566,0.000006968133,0.000007855404,0.000026159414,0.00008547997,0.9925337,0.000042611005,0.00080175546,0.005399342,0.0002446601],"about_ca_topic_score_codex":0.0000100997995,"about_ca_topic_score_gemma":2.3261937e-7,"teacher_disagreement_score":0.12647346,"about_ca_system_score_codex":0.00023796191,"about_ca_system_score_gemma":0.0002625244,"threshold_uncertainty_score":0.8829503},"labels":[],"label_agreement":null},{"id":"W4285805441","doi":"10.1145/3520304.3528811","title":"Particle swarm optimization with average-fitness based selection","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"","keywords":"Particle swarm optimization; Selection (genetic algorithm); Computer science; Multi-swarm optimization; Mathematical optimization; Position (finance); Swarm behaviour; Metaheuristic; Space (punctuation); Fitness landscape; Modal; Artificial intelligence; Machine learning; Mathematics; Population","score_opus":0.02056954485432722,"score_gpt":0.23725766189540073,"score_spread":0.21668811704107352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285805441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10090504,0.0000648285,0.89716333,0.0011263891,0.00011648339,0.00036022812,0.0000037919604,0.00007570891,0.000184182],"genre_scores_gemma":[0.8133602,0.000008506274,0.18640706,0.000060124046,0.000016553353,0.00005268595,0.0000055998976,0.000008143746,0.000081132304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837464,0.00008059617,0.00028566312,0.00035979456,0.0007002661,0.00019903261],"domain_scores_gemma":[0.99872583,0.00006803457,0.0002568972,0.00010259785,0.00077202416,0.00007461428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029177175,0.00013092515,0.00014991751,0.00012489158,0.0006665881,0.000121140976,0.0004923348,0.000025894746,0.000064886015],"category_scores_gemma":[0.000039701932,0.00010999255,0.000031651543,0.0009945706,0.00011239155,0.0002934177,0.00036055173,0.00016201653,0.0000013320958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028086484,0.000088730216,0.007495143,0.000028060489,0.000010450587,1.9681578e-7,0.00018687182,0.98354584,0.00021152574,0.0058196117,0.00015655583,0.002428901],"study_design_scores_gemma":[0.00054661714,0.0001881735,0.027381975,0.000016598267,0.0000107877595,0.00004414178,0.00009315743,0.9694746,0.0005296999,0.0015084529,0.000074501746,0.0001312623],"about_ca_topic_score_codex":0.000015552294,"about_ca_topic_score_gemma":4.1545331e-7,"teacher_disagreement_score":0.71245515,"about_ca_system_score_codex":0.00009116795,"about_ca_system_score_gemma":0.00021640366,"threshold_uncertainty_score":0.5126924},"labels":[],"label_agreement":null},{"id":"W4293023448","doi":"10.1145/3489517.3530595","title":"Solving traveling salesman problems via a parallel fully connected ising machine","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 59th ACM/IEEE Design Automation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Travelling salesman problem; Simulated annealing; Computer science; Ising model; Exponential function; Combinatorial optimization; Medoid; Mathematical optimization; Cluster analysis; Adaptive simulated annealing; Annealing (glass); Algorithm; Mathematics; Artificial intelligence; Materials science; Physics; Statistical physics","score_opus":0.05894507042106219,"score_gpt":0.26434047508557257,"score_spread":0.2053954046645104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293023448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052782525,0.00006245123,0.989927,0.0020906385,0.00036065828,0.001179043,0.000007665526,0.00041642497,0.0006778891],"genre_scores_gemma":[0.67800677,0.000012241956,0.32133505,0.00015235656,0.000029484007,0.00017640501,0.0000040595296,0.000025267998,0.0002583524],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963846,0.0002018591,0.00081147556,0.0006570969,0.0014193665,0.0005255954],"domain_scores_gemma":[0.99691576,0.00039445492,0.0008466223,0.0005535556,0.0011458541,0.00014376963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024267067,0.00029020986,0.00038153058,0.0003111119,0.0008829904,0.0004996751,0.0038569092,0.000072774434,0.00018237418],"category_scores_gemma":[0.0013416637,0.0002558243,0.00011174028,0.0016063094,0.00012137805,0.00083877426,0.001126287,0.00053689914,0.000016100419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021815834,0.0009165673,0.0018011874,0.0011918619,0.00037842029,0.000012083298,0.021100335,0.3791285,0.38550293,0.11926391,0.0051718378,0.08531422],"study_design_scores_gemma":[0.00058562495,0.0001282592,0.00045149337,0.000080678205,0.000018773735,0.000060360657,0.000101477344,0.97433496,0.010280988,0.013556554,0.00012010118,0.00028073022],"about_ca_topic_score_codex":0.000033286513,"about_ca_topic_score_gemma":0.0000010965164,"teacher_disagreement_score":0.67272854,"about_ca_system_score_codex":0.00017378418,"about_ca_system_score_gemma":0.0003793579,"threshold_uncertainty_score":0.9999894},"labels":[],"label_agreement":null},{"id":"W4293075356","doi":"10.1007/s11063-022-10876-9","title":"Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering","year":2022,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Fuzzy clustering; Computer science; Premature convergence; Canopy clustering algorithm; Population; CURE data clustering algorithm; Correlation clustering; Data mining; Algorithm; Fuzzy logic; Artificial intelligence; Genetic algorithm; Machine learning","score_opus":0.04514023152022985,"score_gpt":0.29188349604611546,"score_spread":0.24674326452588563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293075356","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004021506,0.00008431887,0.9926136,0.0020057287,0.00048373736,0.00054224004,0.000011883765,0.00022054164,0.00001642914],"genre_scores_gemma":[0.14911546,0.0000014813361,0.84860164,0.0017756236,0.00010825254,0.00024757255,0.000021089683,0.000032119126,0.000096790274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978289,0.00018678805,0.0003254023,0.00059900887,0.00058524025,0.00047464448],"domain_scores_gemma":[0.9991998,0.000094225965,0.00017323603,0.0003077594,0.00011546838,0.00010952787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029136788,0.00018479236,0.00019623454,0.00022132767,0.0008691789,0.00030794728,0.0008229941,0.000028951868,0.000015974472],"category_scores_gemma":[0.00006269963,0.0001992278,0.00007425483,0.0005545179,0.000044722932,0.00046428302,0.00045827733,0.0002629873,0.000004032931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009801459,0.000038799426,0.0000526481,0.000028882952,0.000010332666,0.00002094453,0.0007080222,0.3310649,0.0022970405,0.000013720121,0.00019824499,0.66555667],"study_design_scores_gemma":[0.0006195394,0.00007772078,0.0013068499,0.000007657366,0.000007613449,0.000038797556,0.00006697327,0.997278,0.00012010786,0.0000664385,0.00018563654,0.00022471548],"about_ca_topic_score_codex":0.000033596545,"about_ca_topic_score_gemma":0.0000026389985,"teacher_disagreement_score":0.66621304,"about_ca_system_score_codex":0.00016534001,"about_ca_system_score_gemma":0.00006872614,"threshold_uncertainty_score":0.8124274},"labels":[],"label_agreement":null},{"id":"W4294811381","doi":"10.1109/cec55065.2022.9870429","title":"Clustering Center-based Differential Evolution","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Differential evolution; Cluster analysis; Computer science; Sampling (signal processing); Population; Centroid; Mathematical optimization; Algorithm; Heuristic; Data mining; Artificial intelligence; Mathematics","score_opus":0.023721451100349716,"score_gpt":0.2766270093293811,"score_spread":0.2529055582290314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294811381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002701343,0.00006715681,0.98658115,0.001286383,0.007250303,0.0006033144,0.00014261068,0.00047316062,0.00089458533],"genre_scores_gemma":[0.96407175,0.00000582886,0.03327259,0.00036178168,0.00021905995,0.00028991012,0.00038593233,0.000047263427,0.0013458612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949314,0.0009833956,0.00061150943,0.00093062595,0.0019745936,0.00056844944],"domain_scores_gemma":[0.99810344,0.000378928,0.00031542638,0.0006645118,0.0003086248,0.00022908978],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00049680163,0.00031410457,0.0003018379,0.0008593976,0.0013252363,0.00021999727,0.0012706124,0.00007044722,0.0009551171],"category_scores_gemma":[0.00009816538,0.0003690393,0.00015317564,0.0014729638,0.000115470175,0.0004493888,0.00065033534,0.0005839733,0.0001422548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009460253,0.0006306479,0.00033031483,0.00002565001,0.0000397916,0.000050233066,0.00005702112,0.9615552,0.00022442515,0.0090720225,0.024110701,0.0038094288],"study_design_scores_gemma":[0.0016468557,0.00033867327,0.0025696084,0.000016763892,0.000008540896,0.00004463492,0.000037902795,0.9909501,0.00005559199,0.0013419434,0.0026293672,0.00036002448],"about_ca_topic_score_codex":0.000027072363,"about_ca_topic_score_gemma":0.000003410472,"teacher_disagreement_score":0.9613704,"about_ca_system_score_codex":0.0013958425,"about_ca_system_score_gemma":0.00040047025,"threshold_uncertainty_score":0.9999749},"labels":[],"label_agreement":null},{"id":"W4294811509","doi":"10.1109/cec55065.2022.9870413","title":"Methods to Detect and Address Stall in Particle Swarm Optimization","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Mathematical optimization; Computer science; Swarm behaviour; Stall (fluid mechanics); Multi-swarm optimization; Modal; Regular polygon; Mathematics; Engineering; Aerospace engineering","score_opus":0.035768465796337474,"score_gpt":0.34474380056316767,"score_spread":0.3089753347668302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294811509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062537487,0.00024838955,0.9892977,0.0014525629,0.0014188644,0.0007732065,0.000039540377,0.00018698514,0.00032898888],"genre_scores_gemma":[0.17488213,0.00003816887,0.823026,0.00067331525,0.00006112627,0.00046360824,0.000059748763,0.00003906724,0.0007567994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956444,0.0015580719,0.000544672,0.00082303793,0.0009841534,0.00044569813],"domain_scores_gemma":[0.9981356,0.0007341962,0.00016653983,0.00044247753,0.00026710966,0.0002540754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001468635,0.00022174774,0.00027688965,0.0006675759,0.0005161781,0.00017820022,0.0006969104,0.000052076997,0.00027475136],"category_scores_gemma":[0.00034394895,0.00026142923,0.00004663035,0.0022628526,0.00007157581,0.00044933913,0.00065925904,0.00037806568,0.000029145196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005903848,0.00010518475,0.00009282923,0.000011100173,0.00001541946,0.00003194405,0.0002633127,0.9656952,0.00026069614,0.001878584,0.0029455342,0.02864113],"study_design_scores_gemma":[0.00088094576,0.00032540946,0.0021158368,0.000011301302,0.0000051284915,0.000030178895,0.00008284848,0.99259466,0.0005151616,0.0011590602,0.0019996136,0.0002798483],"about_ca_topic_score_codex":0.000042516433,"about_ca_topic_score_gemma":0.000005434768,"teacher_disagreement_score":0.16862838,"about_ca_system_score_codex":0.00039312776,"about_ca_system_score_gemma":0.00019521944,"threshold_uncertainty_score":0.9999838},"labels":[],"label_agreement":null},{"id":"W4295866169","doi":"10.1007/s11047-022-09920-3","title":"Is integration of mechanisms a way to enhance a nature-inspired algorithm?","year":2022,"lang":"en","type":"article","venue":"Natural Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Université de Sherbrooke","funders":"","keywords":"Firefly algorithm; Computer science; Novelty; Premature convergence; Convergence (economics); Theory of computation; Particle swarm optimization; Space (punctuation); Algorithm; Artificial intelligence; Mathematical optimization; Mathematics; Psychology","score_opus":0.015484774354612912,"score_gpt":0.3239023054742745,"score_spread":0.3084175311196616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295866169","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045932923,0.00020403936,0.9918208,0.0013367721,0.0012077786,0.0003633178,0.000010581099,0.00015242706,0.000311001],"genre_scores_gemma":[0.4991024,0.0000010845793,0.49976772,0.0007830818,0.000037761874,0.000009988702,0.0000063699604,0.000008710171,0.0002828561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971532,0.00025989886,0.00042874602,0.0005823982,0.0012041631,0.00037159462],"domain_scores_gemma":[0.99850863,0.00021524464,0.00020301472,0.00054004736,0.00041906166,0.00011399596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010433093,0.0001689027,0.00025951266,0.00037722767,0.00038530017,0.000117857264,0.001499715,0.00006725215,0.000086170956],"category_scores_gemma":[0.00033031183,0.0001651311,0.000096225405,0.0019437613,0.000020539046,0.00020127087,0.0014804262,0.0009249722,0.00001865288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015419993,0.00009465512,0.000006709423,0.00002011479,0.000034203687,0.000021294758,0.0023961982,0.00745664,0.018002812,0.025885437,0.0017053895,0.94436115],"study_design_scores_gemma":[0.00015011053,0.00014866781,0.00018524454,0.000018157756,0.0000029147204,0.000018319355,0.000078791854,0.95252836,0.044638854,0.0014312172,0.0006238474,0.00017553673],"about_ca_topic_score_codex":0.00003408206,"about_ca_topic_score_gemma":0.000001435847,"teacher_disagreement_score":0.9450717,"about_ca_system_score_codex":0.00016890245,"about_ca_system_score_gemma":0.00008770947,"threshold_uncertainty_score":0.6733851},"labels":[],"label_agreement":null},{"id":"W4296715628","doi":"10.18280/mmep.090419","title":"An Overview of Self-Adaptive Differential Evolution Algorithms with Mutation Strategy","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Metaheuristic Optimization Algorithms Research","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":"","keywords":"Computer science; Differential evolution; Adaptive mutation; Dependability; Mutation; Scheduling (production processes); Simplicity; Artificial neural network; Analogy; Algorithm; Mathematical optimization; Artificial intelligence; Machine learning; Genetic algorithm; Mathematics","score_opus":0.05385649436757183,"score_gpt":0.26581087624628075,"score_spread":0.2119543818787089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296715628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061170007,0.00020166063,0.993229,0.000018792638,0.00002976476,0.00021157337,0.0000037286054,0.00014730442,0.00004117745],"genre_scores_gemma":[0.5370083,0.000016502594,0.4628886,0.0000013658021,0.000008928781,0.000048596263,0.0000034189588,0.0000113861615,0.000012930502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987698,0.00006050395,0.00025244136,0.00025953015,0.00046037018,0.00019732416],"domain_scores_gemma":[0.9994435,0.00007583422,0.00006448481,0.00023769878,0.00008307037,0.00009545196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003422088,0.0001208644,0.00019381069,0.00012875244,0.00010546218,0.00007245148,0.00025036585,0.000027268432,0.000022332606],"category_scores_gemma":[0.0000073745664,0.00010522785,0.000023053155,0.00031649994,0.000017947896,0.00020939985,0.000094397445,0.00017430911,0.0000012182511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027367046,0.00011018152,8.055246e-7,0.00016836017,0.00001843511,0.0000023725356,0.00049861905,0.86558414,0.000047164038,0.13262233,5.6262553e-7,0.00094432355],"study_design_scores_gemma":[0.00021853053,0.0003549204,0.000011076608,0.000039210554,0.000012360813,0.00003192973,0.000034155615,0.9864827,0.000046407076,0.012641127,0.000004484989,0.0001231159],"about_ca_topic_score_codex":0.000011739894,"about_ca_topic_score_gemma":9.187728e-8,"teacher_disagreement_score":0.53089124,"about_ca_system_score_codex":0.000046727728,"about_ca_system_score_gemma":0.000038904498,"threshold_uncertainty_score":0.42910674},"labels":[],"label_agreement":null},{"id":"W4312060289","doi":"10.5281/zenodo.7080764","title":"Invited paper: A Review of Thresheld Convergence","year":2015,"lang":"en","type":"review","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metaheuristic Optimization Algorithms Research","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":"Convergence (economics); Computer science; Economics","score_opus":0.1112920629197347,"score_gpt":0.33568790035667717,"score_spread":0.22439583743694247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312060289","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":[2.2218465e-8,0.92997235,0.04914922,0.00065531745,0.00019230024,0.0013731238,0.0002468978,0.0005613199,0.017849471],"genre_scores_gemma":[3.0160274e-7,0.99028456,0.006051748,0.00046629566,0.00008799822,3.3174547e-7,0.0016380913,0.0010633289,0.00040736585],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941411,0.0018159309,0.0011282208,0.0009147356,0.0014554439,0.0005445502],"domain_scores_gemma":[0.9940424,0.00011983701,0.0006821217,0.0020032818,0.0027097177,0.00044264022],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0035052956,0.00038934607,0.0013056606,0.00062871637,0.0007043361,0.000618608,0.0061278907,0.00018698037,0.008046275],"category_scores_gemma":[0.0036301583,0.00035548242,0.00027716497,0.00320347,0.00024955007,0.0005277465,0.0046665533,0.0006713925,0.006027639],"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":[0.0000016909612,0.00006201691,7.0580928e-9,0.02280798,0.000058375164,0.000013883066,0.000056655972,0.0000022135052,6.591849e-7,0.0020073066,0.29369128,0.6812979],"study_design_scores_gemma":[0.00016891486,0.00013518019,1.9959815e-7,0.013465217,0.00008078802,0.00019248185,0.0000061322617,0.00093309,0.000001393136,0.000091056754,0.9846124,0.00031313443],"about_ca_topic_score_codex":0.0000066576144,"about_ca_topic_score_gemma":3.5621312e-8,"teacher_disagreement_score":0.6909211,"about_ca_system_score_codex":0.00025526423,"about_ca_system_score_gemma":0.00005345731,"threshold_uncertainty_score":0.99988973},"labels":[],"label_agreement":null},{"id":"W4312513771","doi":"10.23952/jnva.6.2022.6.02","title":"A steepest descent-like method for vector optimization problems with variable domination structure","year":2022,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Metaheuristic Optimization Algorithms Research","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":"Ministerio de Ciencia, Tecnología y Medio Ambiente","keywords":"Descent (aeronautics); Method of steepest descent; Gradient descent; Variable (mathematics); Mathematical optimization; Mathematics; Computer science; Artificial intelligence; Physics; Mathematical analysis; Artificial neural network","score_opus":0.012543719876499338,"score_gpt":0.2745541484731804,"score_spread":0.26201042859668106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312513771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014262216,0.000056320154,0.99815345,0.001225868,0.00010851371,0.00019371914,0.000092453934,0.000009218365,0.000017812996],"genre_scores_gemma":[0.00790399,0.000012955569,0.99153197,0.00011860548,0.00011891514,0.0000151914965,0.00010641483,0.000009026467,0.00018290548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805874,0.00026571078,0.000456487,0.00023324417,0.00083669525,0.00014914639],"domain_scores_gemma":[0.9976805,0.00036064076,0.00055463705,0.00015767937,0.0011455058,0.0001010721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014266011,0.00010957455,0.00029421755,0.00067064154,0.0003342746,0.00017868257,0.00035460264,0.000034131976,0.00025835336],"category_scores_gemma":[0.00017221033,0.00008700508,0.000104794555,0.0020234243,0.000013865538,0.00041384462,0.00010342912,0.00018963628,1.5555472e-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":[0.000062032144,0.000110029214,0.00020685815,0.000014618055,0.0006699691,0.0000022013087,0.00016925331,0.9906634,0.00009044058,0.006783815,0.000056263154,0.0011710837],"study_design_scores_gemma":[0.0009605966,0.00026478985,0.0007631246,0.0000038385897,0.00045234672,0.00006186902,0.000026045254,0.9953657,0.000016078488,0.00076439825,0.0012190775,0.000102155915],"about_ca_topic_score_codex":0.0000151506865,"about_ca_topic_score_gemma":0.000004296946,"teacher_disagreement_score":0.0077613676,"about_ca_system_score_codex":0.00010232922,"about_ca_system_score_gemma":0.00032439252,"threshold_uncertainty_score":0.3547964},"labels":[],"label_agreement":null},{"id":"W4312533570","doi":"10.1109/tevc.2022.3227440","title":"Low-Dimensional Space Modeling-Based Differential Evolution for Large-Scale Global Optimization Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal","funders":"HORIZON EUROPE Marie Sklodowska-Curie Actions; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; European Regional Development Fund; Science Foundation Ireland","keywords":"Differential evolution; Curse of dimensionality; Computer science; Mathematical optimization; Artificial intelligence; Dimensionality reduction; Metaheuristic; Global optimization; Algorithm; Mathematics","score_opus":0.017629956348911797,"score_gpt":0.2634041218648854,"score_spread":0.24577416551597359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312533570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064303615,0.000039627736,0.994034,0.0012202855,0.00175964,0.0013440097,0.00046074582,0.00045301593,0.00004564751],"genre_scores_gemma":[0.67996246,0.0000019438294,0.31888086,0.00011030309,0.000057302303,0.0005696409,0.0002874035,0.000026233487,0.00010386373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962321,0.00042987117,0.0005679659,0.0008627225,0.0013673089,0.0005400372],"domain_scores_gemma":[0.9984308,0.00020698812,0.0001940923,0.00041760472,0.00056490616,0.00018564446],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00048025858,0.00029700095,0.00025769958,0.0004787885,0.0018573531,0.00012680482,0.0005451538,0.00010528511,0.00019313964],"category_scores_gemma":[0.000017930164,0.00035371838,0.00022685529,0.0014154549,0.00004655782,0.00056102633,0.000023128629,0.0003395723,0.000022539807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013713568,0.0011565519,0.0000048383486,0.00003214,0.000032427466,0.0000020110642,0.00006405681,0.9936221,0.00004506059,0.0038445645,0.00047965048,0.00057941064],"study_design_scores_gemma":[0.002152489,0.00045923918,0.00004084512,0.000018208517,0.00002971494,0.000025835685,0.00003825721,0.9927595,0.0000542714,0.004041769,0.000039929946,0.0003398898],"about_ca_topic_score_codex":0.000025052837,"about_ca_topic_score_gemma":0.000012011157,"teacher_disagreement_score":0.6793194,"about_ca_system_score_codex":0.0018306663,"about_ca_system_score_gemma":0.0006430816,"threshold_uncertainty_score":0.99989146},"labels":[],"label_agreement":null},{"id":"W4312621041","doi":"10.1609/icaps.v32i1.19805","title":"Beam Search: Faster and Monotonic","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Metaheuristic Optimization Algorithms Research","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":"National Science Foundation","keywords":"Beam search; Satisficing; Beam (structure); Computation; Monotonic function; Heuristic; Mathematical optimization; Beam diameter; Computer science; Search algorithm; Mathematics; Algorithm; Physics; Optics; Artificial intelligence; Mathematical analysis","score_opus":0.060167967661227346,"score_gpt":0.3081002708569113,"score_spread":0.24793230319568393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312621041","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":[0.9806648,0.00009076447,0.0054849423,0.0047228485,0.0003046357,0.00020681976,0.000011900442,0.00024708622,0.008266208],"genre_scores_gemma":[0.9814885,0.000014715926,0.017976904,0.00015199903,0.0000143580855,0.000016522454,0.000001369617,0.0000063383045,0.00032927556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871665,0.000017610468,0.00019281595,0.0002744462,0.00064365554,0.00015483292],"domain_scores_gemma":[0.99940693,0.00007575258,0.00011995794,0.00008412517,0.00025480194,0.00005840729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121223,0.00009346368,0.00011225376,0.00018633687,0.0002777945,0.00030505587,0.0008973821,0.000024609852,0.000039161227],"category_scores_gemma":[0.00015338795,0.00007610048,0.000022715456,0.00021883857,0.00006108979,0.00020152968,0.0009796747,0.00030910675,0.0000015330078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002628519,0.0003414452,0.03353148,0.00022258003,0.00040503562,0.000021378944,0.013475703,0.11519952,0.073838525,0.7427279,0.0016976753,0.018275937],"study_design_scores_gemma":[0.00021706143,0.00005988667,0.0016625064,0.000060154147,0.0000028412414,0.000036891488,0.00052694464,0.9932662,0.0031379785,0.0008635749,0.000084801824,0.00008116001],"about_ca_topic_score_codex":0.000008782685,"about_ca_topic_score_gemma":4.602064e-8,"teacher_disagreement_score":0.87806666,"about_ca_system_score_codex":0.000033188233,"about_ca_system_score_gemma":0.00006269188,"threshold_uncertainty_score":0.31032875},"labels":[],"label_agreement":null},{"id":"W4312671093","doi":"10.1007/978-3-031-20176-9_22","title":"An Extension of the iMOACO$$\\mathbb {_R}$$ Algorithm Based on Layer-Set Selection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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; Brandon University","funders":"","keywords":"Selection (genetic algorithm); Fitness proportionate selection; Extension (predicate logic); Rank (graph theory); Layer (electronics); Benchmark (surveying); Algorithm; Reduction (mathematics); Set (abstract data type); Computer science; Mechanism (biology); Ant colony optimization algorithms; Mathematics; Probabilistic logic; Combinatorics; Mathematical optimization; Artificial intelligence; Fitness function; Geography; Physics","score_opus":0.02784172113464203,"score_gpt":0.28892666405086365,"score_spread":0.26108494291622164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312671093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002362537,0.000053218137,0.995438,0.00072265416,0.001809277,0.0006341153,0.000024478699,0.000120690536,0.001173941],"genre_scores_gemma":[0.036650512,0.000025296036,0.96083194,0.0017097692,0.00031218067,0.00002808334,0.000018411689,0.00006285078,0.00036094646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99380565,0.00029762925,0.00063469814,0.0017121859,0.0029440473,0.0006058057],"domain_scores_gemma":[0.9956344,0.0006829951,0.00046100115,0.0024484254,0.00058368227,0.0001895036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022002757,0.0004765816,0.00051239994,0.0011555268,0.00057878566,0.00035935626,0.004977938,0.00023627256,0.000251496],"category_scores_gemma":[0.00025539045,0.00037025168,0.00017648691,0.0019008764,0.0005674896,0.00047471764,0.0013537289,0.0013016285,0.000015152787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007531138,0.00007885556,0.00001696576,0.000018769522,0.00000550072,0.000019086861,0.00016615815,0.5638732,0.00018793813,0.0043541775,0.000040490442,0.43123135],"study_design_scores_gemma":[0.00027138065,0.00046747798,0.00016865403,0.00010792574,0.000006868577,0.00003228413,1.76112e-7,0.98518056,0.0021882318,0.010010624,0.001197522,0.00036832722],"about_ca_topic_score_codex":0.000049087113,"about_ca_topic_score_gemma":0.000016398828,"teacher_disagreement_score":0.43086302,"about_ca_system_score_codex":0.00044205174,"about_ca_system_score_gemma":0.0012141367,"threshold_uncertainty_score":0.99987495},"labels":[],"label_agreement":null},{"id":"W4312877245","doi":"10.1109/cloudsummit54781.2022.00004","title":"Table of Contents","year":2022,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Table (database); Computer science; Database","score_opus":0.03872965839526196,"score_gpt":0.28411123660206455,"score_spread":0.2453815782068026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312877245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022161008,0.000025645399,0.9683252,0.0004352875,0.00014043013,0.000071042734,0.0000027986164,0.000035143545,0.030742873],"genre_scores_gemma":[0.46494532,0.0000065765394,0.48865786,0.00039166742,0.000010604902,0.00004155495,0.0000034103061,0.000006133787,0.04593686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992232,0.00006531923,0.000107285494,0.000111458925,0.0003959394,0.00009683784],"domain_scores_gemma":[0.9995418,0.0000433976,0.000030993186,0.0002685407,0.00008143382,0.00003384231],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002617475,0.000025397992,0.000060238013,0.000068788824,0.00007030386,0.000018243396,0.00064062956,0.0000042750557,0.0022229222],"category_scores_gemma":[0.000056856476,0.000023646124,0.000014216604,0.00047200808,0.000012549701,0.00008665386,0.00059653755,0.000052701336,0.000018753964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011260996,0.00046285047,0.0018003408,0.000020314053,0.000043791944,0.000028288348,0.00043555256,0.029600415,0.0023507678,0.85704476,0.067546815,0.040654853],"study_design_scores_gemma":[0.00025483136,0.00006719531,0.00024474962,4.4053357e-7,6.2562367e-7,0.0000061586006,0.000038832488,0.96868086,0.0021160047,0.000841816,0.02770058,0.000047890764],"about_ca_topic_score_codex":0.000035668727,"about_ca_topic_score_gemma":2.7654482e-7,"teacher_disagreement_score":0.9390805,"about_ca_system_score_codex":0.0000105777735,"about_ca_system_score_gemma":0.000041160412,"threshold_uncertainty_score":0.9986892},"labels":[],"label_agreement":null},{"id":"W4312966805","doi":"10.1115/detc2022-88722","title":"Comparative Study of First-Order Moving Asymptotes Optimizers for the Moving Morphable Components Topology Optimization Framework","year":2022,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Kinova (Canada); Polytechnique Montréal","funders":"","keywords":"Asymptote; Solver; Convergence (economics); Network topology; Topology optimization; Mathematical optimization; Computer science; Local optimum; Topology (electrical circuits); Mathematics; Finite element method; Engineering","score_opus":0.05434033578714452,"score_gpt":0.3284808397542143,"score_spread":0.2741405039670698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312966805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028687308,0.000103610604,0.9929246,0.0012233562,0.0005160906,0.0017872619,0.000009905337,0.00009597719,0.00047052137],"genre_scores_gemma":[0.31172422,0.000013420373,0.6870816,0.00014298798,0.000027969485,0.0004832234,0.000010128144,0.00001620368,0.0005002518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727166,0.0004657436,0.0005369032,0.0005339551,0.00078881637,0.0004029295],"domain_scores_gemma":[0.9951563,0.0031711874,0.00028402786,0.0008205434,0.00048658001,0.00008138067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011081684,0.00019402312,0.00041001439,0.0002470958,0.0012599342,0.00015433451,0.0017427095,0.00004689661,0.0008781491],"category_scores_gemma":[0.00049256976,0.00015690665,0.00007316346,0.0012572821,0.00010356692,0.0002972759,0.0013537958,0.00031725006,0.0000033061538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036043803,0.00051040674,0.0006334634,0.000010472977,0.00011953674,0.0000017848873,0.0031618308,0.98959833,0.000009910451,0.0051613203,0.00047649094,0.00028041276],"study_design_scores_gemma":[0.000973356,0.0005073004,0.00017407956,0.0000054385,0.000022085518,0.0000046519385,0.004783092,0.99272794,0.000049197097,0.0003669734,0.00022361487,0.0001622849],"about_ca_topic_score_codex":0.00034726746,"about_ca_topic_score_gemma":0.000014187953,"teacher_disagreement_score":0.3088555,"about_ca_system_score_codex":0.0001044736,"about_ca_system_score_gemma":0.00009336229,"threshold_uncertainty_score":0.96905226},"labels":[],"label_agreement":null},{"id":"W4313034700","doi":"10.1609/socs.v15i1.21758","title":"Optimal Search with Neural Networks: Challenges and Approaches","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Israel Science Foundation; United States-Israel Binational Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Heuristics; Computer science; Machine learning; Artificial neural network; Heuristic; Artificial intelligence; Implementation; Classifier (UML); Beam search; Incremental heuristic search; Search algorithm; Algorithm","score_opus":0.040927950380743636,"score_gpt":0.2525866672370791,"score_spread":0.21165871685633547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313034700","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":[0.82292205,0.0010254331,0.009628237,0.1037064,0.008347123,0.0032670866,0.00003685459,0.0003760376,0.050690804],"genre_scores_gemma":[0.9962807,0.00011408051,0.002892153,0.00006294272,0.00022396399,0.00009491174,0.000002052974,0.000022178465,0.00030698604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963713,0.00008587979,0.00025920948,0.0005438263,0.00238391,0.0003558301],"domain_scores_gemma":[0.9987809,0.0002298237,0.00011740819,0.00022986894,0.0005186058,0.00012338057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014960689,0.00016904165,0.00019609698,0.00017290413,0.00039393085,0.00027094022,0.0028615813,0.000042113443,0.00003008093],"category_scores_gemma":[0.00007861282,0.00012688956,0.000060985025,0.00048762432,0.00018152298,0.00033630835,0.0028053648,0.0006445181,0.0000014860201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048760683,0.00055936316,0.0009175203,0.000054914926,0.00015024288,0.000005333641,0.0016877365,0.12828848,0.00053979456,0.85657203,0.00032331067,0.010413647],"study_design_scores_gemma":[0.0011714192,0.00078941206,0.0009090505,0.000019370455,0.0000067448627,0.000061076724,0.00039559076,0.9921149,0.0026424741,0.00092445395,0.000777774,0.000187721],"about_ca_topic_score_codex":0.000025219759,"about_ca_topic_score_gemma":3.2905945e-7,"teacher_disagreement_score":0.86382645,"about_ca_system_score_codex":0.00017026061,"about_ca_system_score_gemma":0.00007351946,"threshold_uncertainty_score":0.5317578},"labels":[],"label_agreement":null},{"id":"W4313595208","doi":"10.1007/s00500-022-07780-8","title":"An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"","keywords":"Cluster analysis; Swarm intelligence; Algorithm; Metaheuristic; Computer science; Particle swarm optimization; Harmony search; Maxima and minima; Firefly algorithm; Benchmark (surveying); Mathematical optimization; Hybrid algorithm (constraint satisfaction); Optimization problem; Meta-optimization; Multi-swarm optimization; Imperialist competitive algorithm; Cuckoo search; Mathematics; Artificial intelligence; Constraint satisfaction","score_opus":0.03421044503841995,"score_gpt":0.30782170338721,"score_spread":0.27361125834879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313595208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011733476,0.00012680299,0.9961373,0.00008817777,0.0008340536,0.0008511559,0.000009831863,0.0007496706,0.00002962664],"genre_scores_gemma":[0.054208796,0.000027810755,0.9452937,0.000032552925,0.000258498,0.00003541573,0.000041956664,0.000039604824,0.00006164469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751204,0.00010837229,0.00052908744,0.00075061363,0.00048473108,0.00061517366],"domain_scores_gemma":[0.9981638,0.00055046345,0.00018238489,0.00048072432,0.0004139107,0.00020872705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019603304,0.00020912055,0.0002646432,0.00036801753,0.0005448854,0.0008864676,0.00075711904,0.000055551533,0.0000020174266],"category_scores_gemma":[0.00030129246,0.00021661013,0.000045837012,0.0009325604,0.00005798364,0.00028840895,0.00062545115,0.00016861483,0.000015528167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.808872e-7,0.000026235391,0.000013352028,0.00009360674,0.00000936704,0.00000620718,0.00043958696,0.8594064,0.000020105135,0.00019374107,0.000016973594,0.13977346],"study_design_scores_gemma":[0.00019975512,0.000093321294,0.000011275054,0.00010818393,0.0000049341324,0.000034542656,0.00012497959,0.99887747,0.00016989111,0.00004912695,0.00008662632,0.00023991404],"about_ca_topic_score_codex":0.000023866376,"about_ca_topic_score_gemma":5.333933e-7,"teacher_disagreement_score":0.13953353,"about_ca_system_score_codex":0.00006523312,"about_ca_system_score_gemma":0.00008884144,"threshold_uncertainty_score":0.88331044},"labels":[],"label_agreement":null},{"id":"W4313681586","doi":"10.1007/s00521-022-08179-0","title":"Velocity pausing particle swarm optimization: a novel variant for global optimization","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Metaheuristic; Mathematical optimization; Computer science; Convergence (economics); Multi-swarm optimization; Local optimum; Premature convergence; Population; MATLAB; Algorithm; Mathematics","score_opus":0.05096111637006901,"score_gpt":0.33579487884874304,"score_spread":0.28483376247867404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313681586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006056886,0.00002912164,0.99573237,0.002114516,0.000110082954,0.00066976936,0.000019894416,0.000547205,0.00017136199],"genre_scores_gemma":[0.121877864,0.000020780244,0.8773769,0.0002446423,0.00017240226,0.00014480107,0.00006495816,0.000014725799,0.00008290386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853927,0.000047725527,0.00030481245,0.0005051742,0.0002506507,0.00035236115],"domain_scores_gemma":[0.9988211,0.000277935,0.00010991041,0.00035905113,0.0002810358,0.00015098666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042954762,0.00013350922,0.00015016928,0.000065636734,0.0007003841,0.0003828507,0.00039002483,0.000054694392,0.000004105474],"category_scores_gemma":[0.00014806719,0.00013738815,0.000041039493,0.0016460606,0.000056915076,0.00021104592,0.00030072228,0.00008565267,0.000008802161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021950918,0.00003895476,0.00003301269,0.000016129601,0.0000074275986,8.4894185e-7,0.000052803396,0.94716424,0.00002672576,0.027965166,0.00010864019,0.024583856],"study_design_scores_gemma":[0.00042191724,0.000027371343,0.00022210872,0.000007208843,0.00000864944,0.000022487473,0.000017810591,0.9979574,0.000060901526,0.00051943737,0.0005922593,0.00014246763],"about_ca_topic_score_codex":0.0000087632025,"about_ca_topic_score_gemma":6.511837e-7,"teacher_disagreement_score":0.12127218,"about_ca_system_score_codex":0.00004220134,"about_ca_system_score_gemma":0.00006490213,"threshold_uncertainty_score":0.5602526},"labels":[],"label_agreement":null},{"id":"W4317629412","doi":"10.22541/au.167419576.67789895/v1","title":"CBWO: Chaotic Beluga Whale Optimizer for Numerical and Engineering Optimization Problems","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Benchmark (surveying); Chaotic; Computer science; Convergence (economics); Optimization algorithm; Heuristics; Rate of convergence; Mathematical optimization; Algorithm; Optimization problem; Whale; Code (set theory); Metaheuristic; Artificial intelligence; Mathematics; Key (lock)","score_opus":0.0534391344587794,"score_gpt":0.28947496647265875,"score_spread":0.23603583201387934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317629412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009538441,0.00018036943,0.99403375,0.0020937547,0.0009792409,0.0016515761,0.000020826368,0.0008106122,0.00022032192],"genre_scores_gemma":[0.0007295804,0.00033467283,0.99416465,0.00006816877,0.00015402751,0.0006976456,0.00010276894,0.00008318757,0.0036653145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971041,0.000072854775,0.0005520941,0.00113187,0.0005758289,0.0005632273],"domain_scores_gemma":[0.997796,0.00047242187,0.00016132531,0.0008912674,0.00040477727,0.00027420605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008227959,0.000371467,0.0005011764,0.00045971258,0.00013012855,0.0008458944,0.0010914363,0.0002943074,0.00006943836],"category_scores_gemma":[0.00069498416,0.0003619139,0.000118144984,0.00053874694,0.000039269515,0.0002899871,0.002322928,0.0004607455,0.000032898588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025387471,0.000032725253,0.000008250356,0.0003224377,0.000051409123,0.0000041005546,0.00011640404,0.99344933,0.0000040361447,0.0034180395,0.0010065213,0.0015841847],"study_design_scores_gemma":[0.0003909975,0.000053141088,0.00003081092,0.000088728324,0.000017796156,0.000008373705,0.000005656044,0.9978928,0.000033519373,0.0006022142,0.0004896615,0.00038632445],"about_ca_topic_score_codex":0.000033006687,"about_ca_topic_score_gemma":8.0362514e-7,"teacher_disagreement_score":0.0044434243,"about_ca_system_score_codex":0.00008924979,"about_ca_system_score_gemma":0.00018923919,"threshold_uncertainty_score":0.9998833},"labels":[],"label_agreement":null},{"id":"W4317795292","doi":"10.2316/j.2023.203-0428","title":"3D ELECTRIC FIELD COMPUTATION OF OPTIMISED HIGH-VOLTAGE INSULATOR USING PSO-FEM COUPLED ALGORITHM, 1-6.","year":2023,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Finite element method; Particle swarm optimization; Insulator (electricity); Computation; Electric field; Algorithm; Metaheuristic; Swarm behaviour; Voltage; Computer science; Materials science; Engineering; Mathematical optimization; Mathematics; Electrical engineering; Physics; Structural engineering","score_opus":0.016781871881028615,"score_gpt":0.28819385873980286,"score_spread":0.2714119868587742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317795292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020773206,0.000331169,0.9760552,0.00017100772,0.0024746894,0.000040579394,0.000004227678,0.0000272697,0.00012266965],"genre_scores_gemma":[0.96329105,0.00017848695,0.036044918,0.000056092096,0.00022636128,0.0000021025696,0.0000044973217,0.000012541196,0.00018393606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779004,0.00010985473,0.0007088871,0.00017782446,0.0010332682,0.0001801329],"domain_scores_gemma":[0.9977622,0.0002625791,0.0005131883,0.00013343374,0.0012076185,0.000120956465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068229524,0.000119408745,0.00028578218,0.00080225524,0.000052738415,0.00020134379,0.00062360766,0.000072802955,0.000015137911],"category_scores_gemma":[0.00017017988,0.00010649114,0.00007386973,0.00063060806,0.00002004549,0.00038821826,0.00014032889,0.00012849453,0.000002851217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013226067,0.0002604472,0.00048953766,0.00006941902,0.00097192486,0.00058884744,0.00097462867,0.88148636,0.015862832,0.018582499,0.0016844769,0.07889675],"study_design_scores_gemma":[0.00076016644,0.00015000223,0.0004024614,0.00007735861,0.000008751367,0.0001886308,0.000037818754,0.99693024,0.00068597664,0.00021303355,0.00044809998,0.00009746001],"about_ca_topic_score_codex":0.00027000337,"about_ca_topic_score_gemma":5.055082e-7,"teacher_disagreement_score":0.9425179,"about_ca_system_score_codex":0.00007451007,"about_ca_system_score_gemma":0.00016863493,"threshold_uncertainty_score":0.43425825},"labels":[],"label_agreement":null},{"id":"W4318323524","doi":"10.1016/j.ins.2023.01.103","title":"PSO-ELPM: PSO with elite learning, enhanced parameter updating, and exponential mutation operator","year":2023,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":100,"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":"Particle swarm optimization; Mathematical optimization; Benchmark (surveying); Operator (biology); Exponential function; Mutation; Population; Mathematics; Wilcoxon signed-rank test; Computer science; Statistics","score_opus":0.020217666523122513,"score_gpt":0.2964951871606678,"score_spread":0.2762775206375453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318323524","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":[0.11643689,0.000011466401,0.8765875,0.00065007864,0.00019343042,0.0002609938,0.0000024916583,0.0002948437,0.0055622687],"genre_scores_gemma":[0.8830028,0.00008224045,0.11597562,0.00037245074,0.00003646499,0.000064565786,0.0000314161,0.000005495726,0.00042895792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799067,0.00011545232,0.00035692623,0.00025736095,0.00097108504,0.00030851792],"domain_scores_gemma":[0.9989954,0.00020450553,0.00019023643,0.00019440679,0.00030119554,0.00011425744],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001363457,0.000114777766,0.00011856292,0.00048473288,0.0005389483,0.0015028787,0.00052976847,0.000039997212,0.0000733334],"category_scores_gemma":[0.0006747872,0.000087580636,0.00001823542,0.0019878913,0.00022623444,0.0049572336,0.00016091502,0.00012939899,0.00040676226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003721246,0.000047223126,0.0019919744,0.00011069346,0.00004586869,0.000015285157,0.031169271,0.18669194,0.0012067389,0.027948057,0.0025994438,0.7481363],"study_design_scores_gemma":[0.0003482823,0.00015821143,0.0022417675,0.000019818897,0.0000022356624,0.000014008808,0.001076397,0.9897795,0.002480386,0.00021606452,0.003500351,0.00016299722],"about_ca_topic_score_codex":0.000025371211,"about_ca_topic_score_gemma":0.0000028456677,"teacher_disagreement_score":0.80308753,"about_ca_system_score_codex":0.000018478406,"about_ca_system_score_gemma":0.00015887788,"threshold_uncertainty_score":0.99953365},"labels":[],"label_agreement":null},{"id":"W4318332884","doi":"10.23952/jano.5.2023.1.11","title":"Some topological properties of solution sets in partially ordered set optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Set (abstract data type); Mathematics; Topology (electrical circuits); Solution set; Pure mathematics; Computer science; Combinatorics","score_opus":0.042646845383208565,"score_gpt":0.28389829597127375,"score_spread":0.24125145058806519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318332884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004366095,0.000116360825,0.993839,0.001183772,0.00014372548,0.00020300185,0.0000014699597,0.00004074962,0.000105776926],"genre_scores_gemma":[0.5844632,0.0008366894,0.41449183,0.000085886524,0.00007001568,0.000010567574,0.000009188718,0.000012358195,0.000020261883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981542,0.00013223337,0.0007401393,0.00021578486,0.0005176193,0.00024001372],"domain_scores_gemma":[0.9989501,0.00009719053,0.00040021454,0.00015457773,0.00028169338,0.000116220275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086095737,0.00012571824,0.0003480655,0.00043989305,0.000072564704,0.00008243387,0.00031207583,0.000106077234,0.000023874914],"category_scores_gemma":[0.0002867424,0.00009967108,0.00004662824,0.0011254717,0.00008429417,0.0005012974,0.00015332503,0.00018686967,0.0000034170757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009478741,0.00007971699,0.000051530744,0.000024403837,0.000013139121,0.0000071992304,0.0003039195,0.9924944,0.00058469805,0.0030019202,0.00006931044,0.0032749563],"study_design_scores_gemma":[0.00072604895,0.00015803956,0.00030662966,0.000028067927,0.000007655549,0.0000126063815,0.000052584295,0.9966615,0.00085336727,0.0010610927,0.000027535742,0.00010486717],"about_ca_topic_score_codex":0.0000047271956,"about_ca_topic_score_gemma":2.2392312e-7,"teacher_disagreement_score":0.5800971,"about_ca_system_score_codex":0.000043393404,"about_ca_system_score_gemma":0.00012293404,"threshold_uncertainty_score":0.40644687},"labels":[],"label_agreement":null},{"id":"W4318603845","doi":"10.1109/ssci51031.2022.10022110","title":"A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Decomposition; Benchmark (surveying); Metaheuristic; Mathematical optimization; Multi-swarm optimization; Computer science; Optimization problem; Function (biology); Global optimization; Scale (ratio); Mathematics; Biology; Ecology; Physics","score_opus":0.030943502908614934,"score_gpt":0.33097788794344296,"score_spread":0.30003438503482804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318603845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055203587,0.00006552995,0.9927153,0.0028836504,0.0012979866,0.0011425863,0.00039845574,0.00031658463,0.0006278618],"genre_scores_gemma":[0.26167515,0.00009524074,0.7336795,0.0011842848,0.00023519329,0.0010975732,0.0010780982,0.00006622649,0.0008887594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954886,0.00046679826,0.000809653,0.0010462207,0.0014856642,0.00070307043],"domain_scores_gemma":[0.99770004,0.0003751142,0.00033461823,0.00056874356,0.0007590083,0.0002624983],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010642,0.00035198472,0.00033144894,0.0002441282,0.0015128293,0.00059817865,0.0012792057,0.00008660677,0.0006195064],"category_scores_gemma":[0.00008641328,0.00041192977,0.00017128982,0.00196789,0.00009404078,0.0011370836,0.000416855,0.00029330485,0.000051307292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017160927,0.00048599284,0.000034004843,0.000023837114,0.000038075472,0.000008874597,0.0003317153,0.9366696,0.00003437231,0.059993282,0.00077672524,0.0014319365],"study_design_scores_gemma":[0.0004658971,0.0010485952,0.000022862652,0.000011167659,0.0000170933,0.00006229674,0.0003605265,0.99015343,0.0011658816,0.005775812,0.0005030399,0.00041340812],"about_ca_topic_score_codex":0.000015435331,"about_ca_topic_score_gemma":0.000008500404,"teacher_disagreement_score":0.26112312,"about_ca_system_score_codex":0.0006073331,"about_ca_system_score_gemma":0.00042044075,"threshold_uncertainty_score":0.9998332},"labels":[],"label_agreement":null},{"id":"W4318947280","doi":"10.1088/2632-2153/acb895","title":"Supplementing recurrent neural networks with annealing to solve combinatorial optimization problems","year":2023,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Metaheuristic Optimization Algorithms Research","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":"Vector Institute; University of Waterloo","funders":"Vector Institute","keywords":"Travelling salesman problem; Simulated annealing; Mathematical optimization; Combinatorial optimization; Computer science; Markov chain; Optimization problem; Heuristic; Artificial neural network; Extremal optimization; Scheduling (production processes); Convergence (economics); Mathematics; Algorithm; Artificial intelligence; Machine learning","score_opus":0.012655573042973819,"score_gpt":0.27166090448910435,"score_spread":0.2590053314461305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318947280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009729776,0.00004365764,0.9825656,0.0059943576,0.00037350354,0.00036546265,8.57159e-7,0.0008179776,0.00010882923],"genre_scores_gemma":[0.7105924,0.00005472029,0.2889659,0.00010127594,0.00006779846,0.00008552076,0.000019128294,0.000023109671,0.000090125905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775845,0.000045885437,0.00022330863,0.0006589329,0.0006538804,0.0006595488],"domain_scores_gemma":[0.9989417,0.00008079448,0.00010061715,0.00032154267,0.00041486576,0.00014048957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018672488,0.0001396682,0.00016776752,0.0012206554,0.0008659597,0.00043627885,0.0009406648,0.000063696425,0.000011395135],"category_scores_gemma":[0.00085460016,0.000118385025,0.000011000575,0.008526829,0.000293415,0.00038080776,0.0009974389,0.00044274976,0.000008253266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025865834,0.000012160453,0.004668598,0.0000070460255,0.000003879895,0.000010109592,0.00016485975,0.9358701,0.0000676568,0.010652142,0.0000443136,0.048496544],"study_design_scores_gemma":[0.00032001562,0.0003503976,0.00011489521,0.000016772805,0.0000025747104,0.000018770148,0.000048359132,0.99786407,0.00008505138,0.0002265574,0.0008079491,0.0001445776],"about_ca_topic_score_codex":0.000025508163,"about_ca_topic_score_gemma":0.000004292925,"teacher_disagreement_score":0.70086265,"about_ca_system_score_codex":0.00004639956,"about_ca_system_score_gemma":0.000098293895,"threshold_uncertainty_score":0.66603494},"labels":[],"label_agreement":null},{"id":"W4319299347","doi":"10.1002/ett.4739","title":"A levy flight based strategy to improve the exploitation capability of arithmetic optimization algorithm for engineering global optimization problems","year":2023,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Lévy flight; Algorithm; Meta-optimization; Firefly algorithm; Metaheuristic; Mathematical optimization; Derivative-free optimization; Engineering optimization; Population-based incremental learning; Multi-swarm optimization; Optimization problem; Imperialist competitive algorithm; Maxima and minima; Continuous optimization; Global optimization; Computer science; Particle swarm optimization; Cuckoo search; Mathematics; Genetic algorithm","score_opus":0.031272383800057736,"score_gpt":0.29909990819877014,"score_spread":0.2678275243987124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299347","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006743185,0.00006476798,0.9882407,0.0075514084,0.0001496024,0.0017539142,0.00009183433,0.0020281454,0.00005215614],"genre_scores_gemma":[0.070592366,0.00021549733,0.92703265,0.000025724405,0.000006168774,0.0019984522,0.00005401764,0.000026665044,0.000048445512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978793,0.00014079336,0.00064115215,0.000500564,0.00042375101,0.0004144183],"domain_scores_gemma":[0.9962864,0.0007477182,0.00019689395,0.0020316066,0.0006788195,0.00005854803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094682863,0.00024167728,0.00026101124,0.00078995153,0.00058672926,0.00016469957,0.0020401236,0.00015608086,0.000016814205],"category_scores_gemma":[0.00059923145,0.00021821451,0.0001273568,0.005119004,0.00013500413,0.0004055368,0.00008274504,0.00029253596,0.000009913996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033575243,0.000110910456,0.0000015921745,0.00002832888,0.00002765373,1.0530747e-7,0.00009922471,0.80628717,0.00007461792,0.001294898,0.000039179948,0.19203295],"study_design_scores_gemma":[0.00034699787,0.00024008528,0.000023476918,0.000043468204,0.000023831053,0.0000012115573,0.00040576857,0.99487466,0.0030212197,0.0006163954,0.00019745299,0.00020544403],"about_ca_topic_score_codex":0.000038988455,"about_ca_topic_score_gemma":0.000011916713,"teacher_disagreement_score":0.19182749,"about_ca_system_score_codex":0.00021349099,"about_ca_system_score_gemma":0.00017223481,"threshold_uncertainty_score":0.88985294},"labels":[],"label_agreement":null},{"id":"W4319309759","doi":"10.1145/3571697.3571711","title":"A Novel Cooperative Parallel Multi-Population Optimization Algorithm","year":2022,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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; Probabilistic logic; Heuristic; Convergence (economics); Population; Evolutionary algorithm; Algorithm; Evolutionary computation; Mathematical optimization; Parametric statistics; Consistency (knowledge bases); Mathematics; Artificial intelligence","score_opus":0.04000195567319357,"score_gpt":0.29989982024325557,"score_spread":0.259897864570062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319309759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008224957,0.000030465022,0.99736875,0.0006605128,0.0003137648,0.00044301862,0.000017201422,0.00025354404,0.0009045241],"genre_scores_gemma":[0.0020853407,0.000011664569,0.99242634,0.00037141194,0.000030634586,0.00018707567,0.00011004139,0.0000159117,0.004761582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979145,0.00022963452,0.00031989923,0.0004982472,0.0007485771,0.00028915814],"domain_scores_gemma":[0.9989628,0.00009054891,0.00009987697,0.00047540743,0.0002514279,0.000119891025],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000569611,0.00014179258,0.00016234093,0.00018934543,0.0005989334,0.00022072376,0.0008091505,0.00003178511,0.0013387811],"category_scores_gemma":[0.00013105369,0.00014092869,0.000046675137,0.0011819515,0.000024827954,0.00054755574,0.0007465694,0.00022285477,0.000035043773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002709298,0.00019748282,0.000030512067,0.0000016110388,0.000012251384,0.0000045110132,0.00015586165,0.97218,0.000022808834,0.0099424375,0.00042080707,0.017029017],"study_design_scores_gemma":[0.0008757175,0.000073377145,0.00026885583,0.0000010101148,0.00000259109,0.000027364853,0.000057031684,0.9976738,0.000021949305,0.000051367217,0.0007684556,0.00017849133],"about_ca_topic_score_codex":0.00013418421,"about_ca_topic_score_gemma":0.0000037797986,"teacher_disagreement_score":0.025493799,"about_ca_system_score_codex":0.00016099987,"about_ca_system_score_gemma":0.00011252212,"threshold_uncertainty_score":0.9995741},"labels":[],"label_agreement":null},{"id":"W4320149352","doi":"10.1007/978-3-031-24866-5_7","title":"Binary Black Widow Optimization Algorithm for Feature Selection Problems","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Algorithm; Computer science; Binary number; Optimization algorithm; Feature (linguistics); Artificial intelligence; Mathematical optimization; Mathematics; Arithmetic; Philosophy","score_opus":0.020966405893147997,"score_gpt":0.26736875263416693,"score_spread":0.24640234674101893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320149352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.8144137e-7,0.00027102907,0.992992,0.0015148745,0.0018061724,0.0018205422,0.00004508143,0.00029136214,0.0012585638],"genre_scores_gemma":[0.000080292,0.00014534142,0.99563205,0.0006231451,0.00051293377,0.000110203655,0.00012342993,0.00007760969,0.0026949667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99427265,0.000109440305,0.00062285963,0.0021936381,0.001889775,0.0009116619],"domain_scores_gemma":[0.99664736,0.0006062984,0.000450457,0.001184914,0.0008637834,0.00024720366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017523046,0.000628498,0.00062845513,0.0016789647,0.0007218688,0.0008929928,0.0034446307,0.00040108024,0.00022164031],"category_scores_gemma":[0.00021583798,0.0006330128,0.00018795638,0.0019666916,0.00053730956,0.0010342295,0.0016139433,0.0012673633,0.000020161024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003107409,0.000032955744,0.0000018593914,0.00003457467,0.000012185874,0.000011894672,0.00015994445,0.67874223,0.000010627029,0.0023228684,0.00018635807,0.31848136],"study_design_scores_gemma":[0.00045067907,0.0004492029,0.0000055790165,0.0000841812,0.000012036298,0.00005413716,1.5159175e-7,0.97736424,0.00012692354,0.012870996,0.007934925,0.00064695574],"about_ca_topic_score_codex":0.000015228749,"about_ca_topic_score_gemma":0.0000087675835,"teacher_disagreement_score":0.3178344,"about_ca_system_score_codex":0.00080845907,"about_ca_system_score_gemma":0.0011560837,"threshold_uncertainty_score":0.9996121},"labels":[],"label_agreement":null},{"id":"W4320149378","doi":"10.1007/978-3-031-24866-5","title":"Learning and Intelligent Optimization","year":2022,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University","funders":"University of Ioannina; Far Eastern Federal University; SBA Research; Hefei University of Technology; Hefei University; Università di Bologna; Université de Lorraine; Jyväskylän Yliopisto; Univerzita Karlova v Praze; Università degli Studi di Genova; University of Crete; Università degli Studi di Cagliari","keywords":"Computer science; Artificial intelligence","score_opus":0.018030999264643233,"score_gpt":0.28231403016628837,"score_spread":0.2642830309016451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320149378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000212226,0.000501333,0.99508417,0.00043260763,0.000990899,0.00037281675,0.0000016888047,0.00015101157,0.0024633477],"genre_scores_gemma":[0.0005910326,0.00037734467,0.99472225,0.00037752758,0.00019202179,0.000025063993,0.000018525743,0.000032184773,0.0036640659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99587923,0.00021358312,0.00044545316,0.0014194845,0.001469251,0.00057299936],"domain_scores_gemma":[0.9978559,0.00074145454,0.00022287597,0.00072499795,0.00025629599,0.00019849217],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017918559,0.00033575844,0.0003805877,0.001237132,0.0004974354,0.0008766271,0.0023816046,0.00015073761,0.00026469186],"category_scores_gemma":[0.0005653447,0.00033742702,0.00005403505,0.0017914743,0.00045430253,0.00045959995,0.0034063559,0.0012857886,0.000014486818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011408041,0.000012238931,0.000017032738,0.000017297867,0.000003374452,0.000026783304,0.000352735,0.66526,0.0000014401189,0.00059820904,0.000032202188,0.3336775],"study_design_scores_gemma":[0.00012486722,0.00016732661,0.000006677215,0.000048533915,0.0000031060908,0.000052376752,3.7745028e-7,0.98793536,0.000054164975,0.005959639,0.0053119594,0.000335621],"about_ca_topic_score_codex":0.000007990025,"about_ca_topic_score_gemma":0.0000029404641,"teacher_disagreement_score":0.33334187,"about_ca_system_score_codex":0.0005778056,"about_ca_system_score_gemma":0.0012555227,"threshold_uncertainty_score":0.9999078},"labels":[],"label_agreement":null},{"id":"W4321504838","doi":"10.1007/978-3-031-26504-4_6","title":"A Learning Metaheuristic Algorithm for a Scheduling Application","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Tabu search; Metaheuristic; Computer science; Parallel metaheuristic; Guided Local Search; Hyper-heuristic; Artificial intelligence; Scheduling (production processes); Mathematical optimization; Search-based software engineering; Machine learning; Algorithm; Mathematics; Software","score_opus":0.02921644708812989,"score_gpt":0.2975934123230751,"score_spread":0.26837696523494525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321504838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.5880373e-7,0.00016279556,0.9959399,0.000494556,0.0011562812,0.0011754483,0.000010465535,0.00049847283,0.0005612301],"genre_scores_gemma":[0.0006609633,0.000075578384,0.9958241,0.00022677261,0.0005185655,0.00014726119,0.000029762528,0.000084280415,0.002432713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947181,0.000069708854,0.00072501163,0.0020678453,0.0015181364,0.0009012448],"domain_scores_gemma":[0.9953442,0.0018632283,0.0003991078,0.0013197797,0.000816502,0.00025714654],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002737419,0.0005250312,0.00065264606,0.0015956041,0.0005032582,0.0008599952,0.0034530994,0.00032727377,0.000012145951],"category_scores_gemma":[0.0010204274,0.00052415865,0.00018671837,0.0015177903,0.00043991904,0.0004468087,0.0014130982,0.0010768047,0.00017721498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015663259,0.000012199648,0.000002007962,0.000038467748,0.000014430431,0.000017740567,0.00014546791,0.19969909,0.00002048185,0.010199851,0.000005708031,0.78984296],"study_design_scores_gemma":[0.00027680316,0.00014355319,0.0000062437666,0.00012243711,0.000012447877,0.000021367154,1.9400979e-7,0.9055358,0.00015002061,0.091993235,0.0012604743,0.00047738853],"about_ca_topic_score_codex":0.00001309033,"about_ca_topic_score_gemma":0.00000761417,"teacher_disagreement_score":0.7893656,"about_ca_system_score_codex":0.00031074756,"about_ca_system_score_gemma":0.00079027936,"threshold_uncertainty_score":0.999721},"labels":[],"label_agreement":null},{"id":"W4323342267","doi":"10.5267/j.ijiec.2023.1.002","title":"Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm algorithm enhanced by arithmetic optimization algorithm","year":2023,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","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":"Algorithm; Benchmark (surveying); Benchmarking; Context (archaeology); Swarm behaviour; Optimization algorithm; Computer science; Swarm intelligence; Metaheuristic; Mathematics; Particle swarm optimization; Mathematical optimization; Artificial intelligence","score_opus":0.02614145670597141,"score_gpt":0.2875177971073566,"score_spread":0.2613763404013852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323342267","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000074049356,0.000080978185,0.9908815,0.001830719,0.005839642,0.0007448939,0.00020337122,0.00031714595,0.000027711718],"genre_scores_gemma":[0.0010935218,0.0001280384,0.99624974,0.000055200806,0.001836411,0.00009092308,0.00035669073,0.0000715393,0.00011791699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99554163,0.00014060555,0.0014833092,0.00054196676,0.0016660853,0.0006263747],"domain_scores_gemma":[0.9950564,0.000966456,0.0007851611,0.000293987,0.0025681467,0.0003298746],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016654072,0.00038849743,0.00052408664,0.0016338384,0.00023448384,0.00086598424,0.0018198931,0.00018069813,0.000028229808],"category_scores_gemma":[0.0009815262,0.00041049655,0.00028367594,0.001382918,0.00006340261,0.0012121527,0.00034035862,0.0006954021,0.000015420032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057500924,0.000085907224,7.7086e-7,0.000007673888,0.0002139834,0.000027349772,0.000121709185,0.6827782,0.0001115204,0.00027321244,0.0024756517,0.31389832],"study_design_scores_gemma":[0.0024871556,0.000283762,0.0000031404643,0.0002131407,0.00003797812,0.00014714671,0.000035914763,0.99133354,0.0024683778,0.0006614665,0.0019450313,0.00038332873],"about_ca_topic_score_codex":0.000014001104,"about_ca_topic_score_gemma":1.8279603e-7,"teacher_disagreement_score":0.31351498,"about_ca_system_score_codex":0.00042858362,"about_ca_system_score_gemma":0.0008518361,"threshold_uncertainty_score":0.9998347},"labels":[],"label_agreement":null},{"id":"W4324137481","doi":"10.1109/icssit55814.2023.10060911","title":"Sparrow Search Optimizer for Constrained Engineering Optimal Designs","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Consistency (knowledge bases); Mathematical optimization; Optimal design; Value (mathematics); Artificial intelligence; Machine learning; Mathematics","score_opus":0.08495236879765003,"score_gpt":0.321829225325992,"score_spread":0.23687685652834198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324137481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039074873,0.000009455006,0.99547976,0.0014244989,0.00023497132,0.00052630936,0.000008103584,0.00078407413,0.001142065],"genre_scores_gemma":[0.014055821,0.000012383769,0.9807135,0.0000714816,0.00007217417,0.00011774062,0.000015460477,0.000023973047,0.004917506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981525,0.000051095656,0.00024727048,0.00043591013,0.00047842486,0.0006347787],"domain_scores_gemma":[0.99830747,0.00070707634,0.000023532994,0.00046756564,0.00026630392,0.00022807826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001165712,0.00014374327,0.00018554316,0.00038929543,0.00012501789,0.00029270197,0.0008714849,0.00006347391,0.0002531666],"category_scores_gemma":[0.00048766055,0.00013482884,0.000080026744,0.0011925793,0.00004658228,0.0002855548,0.00029257807,0.00012957906,0.00032203642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000854941,0.000027682085,0.000012495474,0.00003659494,0.00003757896,0.000027836986,0.00021758144,0.91888654,0.00129259,0.06553534,0.007541492,0.0063757147],"study_design_scores_gemma":[0.00052953465,0.00006298413,0.00004161697,0.0000054885736,0.0000024421709,0.000009363013,0.00003713506,0.99423444,0.0037279227,0.000078475394,0.0011072047,0.00016341047],"about_ca_topic_score_codex":0.000006680761,"about_ca_topic_score_gemma":2.2163891e-7,"teacher_disagreement_score":0.07534788,"about_ca_system_score_codex":0.00003558863,"about_ca_system_score_gemma":0.00017853185,"threshold_uncertainty_score":0.549816},"labels":[],"label_agreement":null},{"id":"W4324141208","doi":"10.3390/biomimetics8010121","title":"Green Anaconda Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":62,"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; Univerzita Hradec Králové","keywords":"Metaheuristic; Test suite; Suite; Algorithm; Computer science; Mathematical optimization; Evolutionary algorithm; Optimization problem; Optimization algorithm; Mathematics; Test case; Machine learning","score_opus":0.04784962429236665,"score_gpt":0.2954097904869883,"score_spread":0.24756016619462162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324141208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007751123,0.00020140472,0.9940015,0.0020492962,0.0011637302,0.0013463991,0.000059421298,0.0009784023,0.00019207169],"genre_scores_gemma":[0.00018345495,0.00023480157,0.9918465,0.0001659998,0.00032893283,0.00014509323,0.00029194175,0.00008160663,0.006721669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964277,0.00013427202,0.0008027712,0.0009438648,0.0008512132,0.00084018783],"domain_scores_gemma":[0.9969832,0.00047921686,0.0003094185,0.0010184553,0.00075396115,0.0004557762],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010269905,0.0003762116,0.00046426515,0.0009856136,0.00042346268,0.0006638108,0.0014541987,0.00020686825,0.00014761301],"category_scores_gemma":[0.00078113726,0.0003824973,0.00018052683,0.004220649,0.00010542284,0.0006244061,0.0005438184,0.00016183003,0.00015381724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054485386,0.00007873364,0.00001396451,0.000070848044,0.00010560035,0.000014017715,0.00019263246,0.89924806,0.00009371051,0.0016548645,0.007912036,0.09061008],"study_design_scores_gemma":[0.0012429284,0.00017831141,0.000016474283,0.000030094721,0.000054570042,0.000010868282,0.000016619493,0.9904284,0.0004689179,0.0006951295,0.0064231376,0.0004345705],"about_ca_topic_score_codex":0.00008919043,"about_ca_topic_score_gemma":0.0000029272549,"teacher_disagreement_score":0.09118032,"about_ca_system_score_codex":0.00011986289,"about_ca_system_score_gemma":0.00044039998,"threshold_uncertainty_score":0.9998627},"labels":[],"label_agreement":null},{"id":"W4366193823","doi":"10.3390/math11081890","title":"Editorial for the Special Issue “Advances in Machine Learning and Mathematical Modeling for Optimization Problems”","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; De facto; Artificial intelligence; Deep learning; Range (aeronautics); Image processing; Machine learning; Image (mathematics); Engineering","score_opus":0.034403071202319016,"score_gpt":0.3257775433512778,"score_spread":0.2913744721489588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366193823","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005637482,0.00009080637,0.99480045,0.0006920308,0.0028080014,0.00112004,0.0000062785293,0.00010222505,0.0003745348],"genre_scores_gemma":[0.000065137036,0.00043133696,0.9811974,0.000011153278,0.016412733,0.00043276697,0.000015204705,0.000030896063,0.0014033401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866885,0.00003951634,0.00037863554,0.00024686384,0.00038163346,0.00028452347],"domain_scores_gemma":[0.9978683,0.0016144024,0.000089835594,0.00020389221,0.00017508362,0.000048473856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016257393,0.00012033049,0.00020867244,0.00014457182,0.0002259229,0.00024071228,0.00037923455,0.00006421606,0.00004919777],"category_scores_gemma":[0.0020542124,0.00008781885,0.00003979256,0.00045379263,0.000033828765,0.0002695184,0.00017639095,0.00013637531,0.000024392164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071603004,0.000037598078,0.0000018164717,0.00032699577,0.000006793048,2.682493e-7,0.000997379,0.97329974,0.000002256049,0.015677053,0.0025497274,0.007093211],"study_design_scores_gemma":[0.00046592933,0.000053955733,7.4670275e-8,0.00003660392,0.000006967588,0.0000014077658,0.00009328936,0.9352609,0.000006130494,0.03695731,0.027022773,0.000094671195],"about_ca_topic_score_codex":8.91525e-7,"about_ca_topic_score_gemma":0.0000018695505,"teacher_disagreement_score":0.038038854,"about_ca_system_score_codex":0.000023732175,"about_ca_system_score_gemma":0.000036166457,"threshold_uncertainty_score":0.3581149},"labels":[],"label_agreement":null},{"id":"W4377199999","doi":"10.1007/978-3-031-32726-1_13","title":"Designing Optimization Problems with Diverse Solutions","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 British Columbia","funders":"","keywords":"Computer science; Mathematical optimization; Mathematics","score_opus":0.05251928756141984,"score_gpt":0.2676739807437541,"score_spread":0.21515469318233427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377199999","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0203804e-7,0.000074215895,0.9950556,0.0005981808,0.0007854738,0.00067089027,0.0000058970027,0.0004216695,0.0023876622],"genre_scores_gemma":[0.0006832529,0.0000884773,0.9968844,0.0002098256,0.00017728824,0.000027514918,0.000014603042,0.00005586518,0.0018587823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952988,0.00006942703,0.00047085356,0.0015499777,0.0017390013,0.0008719557],"domain_scores_gemma":[0.9969619,0.0005656913,0.00027255106,0.0012800188,0.0006765632,0.00024329219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014741052,0.00045206476,0.00042499273,0.00150635,0.0005866203,0.0009077237,0.0028736673,0.00023619013,0.000052268744],"category_scores_gemma":[0.00023684272,0.00039332753,0.00007288697,0.0019276384,0.00072456815,0.0008258764,0.001725087,0.00076099014,0.00012723454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021020126,0.000014363209,0.000008083172,0.000023615341,0.000011237462,0.00006723796,0.00028417123,0.91663533,0.000008560627,0.0068875444,0.000022965454,0.07603479],"study_design_scores_gemma":[0.00025837927,0.00014853351,0.0000099777435,0.00029552582,0.000008594464,0.000040146875,2.8217408e-7,0.9846339,0.00007633634,0.013933365,0.00012763144,0.0004673052],"about_ca_topic_score_codex":0.00002195418,"about_ca_topic_score_gemma":0.000040242376,"teacher_disagreement_score":0.075567484,"about_ca_system_score_codex":0.00032053536,"about_ca_system_score_gemma":0.0009844897,"threshold_uncertainty_score":0.9998519},"labels":[],"label_agreement":null},{"id":"W4378419870","doi":"10.1007/978-3-031-34020-8_20","title":"Binary Black Widow with Hill Climbing Algorithm for Feature Selection","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University","funders":"","keywords":"Hill climbing; Computer science; Algorithm; Feature selection; Metaheuristic; Binary number; Convergence (economics); Set (abstract data type); Ideal (ethics); Extension (predicate logic); Domain (mathematical analysis); Artificial intelligence; Mathematics","score_opus":0.04667246751670973,"score_gpt":0.3125757170518372,"score_spread":0.26590324953512745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378419870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001835491,0.00006357111,0.9689488,0.0015841548,0.00023529754,0.0008696741,0.000038510716,0.00020557742,0.028052608],"genre_scores_gemma":[0.00017432334,0.001452995,0.98933226,0.00047390917,0.000060331164,0.00008396732,0.00019060708,0.000022979042,0.008208645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778277,0.000058329708,0.00059897744,0.00044641484,0.00074707053,0.00036643285],"domain_scores_gemma":[0.9960912,0.0005429978,0.00038157243,0.0016356289,0.0011916653,0.00015692972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022103596,0.00026608634,0.0003060357,0.0016598479,0.00071930554,0.001034044,0.0030303556,0.0001770793,0.0000044507156],"category_scores_gemma":[0.000108057095,0.0002471123,0.00005103292,0.0012911925,0.0008549691,0.0053568296,0.0018100714,0.00059835665,0.000066768785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004020851,0.000021458152,0.000010512099,0.00005264228,0.000016651751,8.1163387e-7,0.00090614817,0.004743462,0.0000017888972,0.28211734,0.0017522412,0.7103729],"study_design_scores_gemma":[0.0003701519,0.00014228819,0.00018313227,0.00013943855,0.000006174811,0.00001950851,0.000013739299,0.95590806,0.000006458035,0.0025958738,0.040333,0.00028216545],"about_ca_topic_score_codex":0.0000064590045,"about_ca_topic_score_gemma":0.0000066477764,"teacher_disagreement_score":0.9511646,"about_ca_system_score_codex":0.00019931802,"about_ca_system_score_gemma":0.00054797035,"threshold_uncertainty_score":0.9999981},"labels":[],"label_agreement":null},{"id":"W4379259372","doi":"10.5267/j.dsl.2023.4.010","title":"A novel crossover operator for genetic algorithm: Stas crossover","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Genetic algorithm; Mathematical optimization; Population; Operator (biology); Selection (genetic algorithm); Computer science; Algorithm; Mathematics; Artificial intelligence","score_opus":0.04398463197428079,"score_gpt":0.3550297463470003,"score_spread":0.31104511437271953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379259372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022563681,0.000016306989,0.97164226,0.0033503487,0.0014471766,0.0006095096,0.000053462583,0.0002549253,0.000062341205],"genre_scores_gemma":[0.0044957385,0.000013599504,0.98967946,0.0049758623,0.00014637508,0.000117102885,0.000005181794,0.000027457505,0.0005392063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994319,0.000038805832,0.00053530565,0.0013274271,0.0026521215,0.0011273331],"domain_scores_gemma":[0.9966029,0.00086681655,0.000121802885,0.0013612994,0.0006243438,0.00042284915],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025886986,0.00024042962,0.0002666608,0.00095071166,0.0009549311,0.0026329646,0.0038061838,0.000067385474,0.00008461355],"category_scores_gemma":[0.0019851662,0.00020823165,0.00012378459,0.00615627,0.0006491709,0.0015247393,0.0011539761,0.0001788355,0.0008344879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006795724,0.00019984343,0.00033635265,0.000023369348,0.000032075925,0.00014913468,0.0011866558,0.060326036,0.12520036,0.0057566883,0.11193507,0.6947864],"study_design_scores_gemma":[0.0013676367,0.00005392366,0.0071777417,0.000014265549,0.0000029824694,0.000025115283,0.0000179816,0.96751094,0.0028869938,0.0006562701,0.019969776,0.00031634967],"about_ca_topic_score_codex":0.000013210884,"about_ca_topic_score_gemma":7.613404e-7,"teacher_disagreement_score":0.9071849,"about_ca_system_score_codex":0.0001590244,"about_ca_system_score_gemma":0.0004102747,"threshold_uncertainty_score":0.9999435},"labels":[],"label_agreement":null},{"id":"W4382807941","doi":"10.1007/978-981-99-3428-7_5","title":"IGA: An Improved Genetic Algorithm for Real-Optimization Problem","year":2023,"lang":"en","type":"book-chapter","venue":"Springer tracts in nature-inspired computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"","keywords":"Crossover; Wilcoxon signed-rank test; Algorithm; Rate of convergence; Convergence (economics); Genetic algorithm; Mathematical optimization; Rank (graph theory); Computer science; Mutation; Selection (genetic algorithm); Mathematics; Statistics; Key (lock); Artificial intelligence","score_opus":0.02316120077301969,"score_gpt":0.29264962122112126,"score_spread":0.26948842044810156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382807941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014291094,0.0003240249,0.9735251,0.00017054602,0.0019599723,0.0024558615,0.000043413416,0.0011507303,0.02035606],"genre_scores_gemma":[0.0004967605,0.00036257613,0.9755781,0.00017062774,0.00088301726,0.00007973769,0.00019119971,0.00032103522,0.021916939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937322,0.00013534159,0.001540842,0.0022123465,0.0011648816,0.0012143697],"domain_scores_gemma":[0.9954916,0.00068430504,0.0009117595,0.0016524226,0.00086205365,0.00039781857],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0017762802,0.00087806897,0.0010073339,0.0014423303,0.00037188997,0.0007359203,0.002524215,0.0015161552,0.00001789895],"category_scores_gemma":[0.0003064675,0.00098163,0.00028019582,0.0006758869,0.00010534257,0.00054325815,0.0008572007,0.0023659812,0.000036873804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019272584,0.00014781758,0.000011085614,0.00031176698,0.00014585763,0.00016362662,0.00035098707,0.4004204,0.00003304376,0.0537429,0.00014679274,0.5445065],"study_design_scores_gemma":[0.0010560653,0.00021910384,0.00020703,0.0003870787,0.000035863137,0.000018471937,0.0000054825273,0.98903203,0.000063461215,0.006098625,0.0019197286,0.00095705333],"about_ca_topic_score_codex":0.00005292242,"about_ca_topic_score_gemma":0.00002712507,"teacher_disagreement_score":0.58861166,"about_ca_system_score_codex":0.00046908684,"about_ca_system_score_gemma":0.0006326655,"threshold_uncertainty_score":0.9999356},"labels":[],"label_agreement":null},{"id":"W4382866836","doi":"10.1609/icaps.v33i1.27175","title":"W-restrained Bidirectional Bounded-Suboptimal Heuristic Search","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Metaheuristic Optimization Algorithms Research","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":"United States-Israel Binational Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Bounded function; Heuristic; Property (philosophy); Search algorithm; Mathematical optimization; Computer science; Algorithm; Mathematics; Discrete mathematics; Combinatorics; Mathematical analysis; Philosophy","score_opus":0.0726040891907504,"score_gpt":0.3367700420719623,"score_spread":0.2641659528812119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382866836","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":[0.9575037,0.000033190037,0.013522121,0.007943454,0.00096119154,0.00028617564,0.000023162434,0.0015741825,0.018152857],"genre_scores_gemma":[0.96592605,0.000022975346,0.033070497,0.00007174347,0.000060192597,0.0000133865815,0.000006860485,0.000011394772,0.0008169224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981355,0.000019688854,0.00029256177,0.00036428883,0.0009220506,0.0002659024],"domain_scores_gemma":[0.9988183,0.00017864529,0.00014157938,0.00011344762,0.00065594335,0.00009209955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008319244,0.00014028458,0.00015493286,0.0003736123,0.0002467165,0.00050163426,0.0011162356,0.00006734204,0.000033444077],"category_scores_gemma":[0.00070896285,0.0001118506,0.00004878519,0.0007194297,0.0001105543,0.00026663337,0.0004261502,0.000302328,0.000029605148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017885679,0.00019690838,0.01743549,0.0002190603,0.00035743567,0.00003463581,0.002823078,0.075709075,0.046526678,0.8450784,0.004250721,0.007189674],"study_design_scores_gemma":[0.00024395772,0.00004288448,0.0073824655,0.0001816641,0.0000037950178,0.00002712137,0.00016625934,0.9862662,0.003004163,0.002508057,0.0000609062,0.00011250362],"about_ca_topic_score_codex":0.00001258605,"about_ca_topic_score_gemma":1.3131883e-7,"teacher_disagreement_score":0.91055715,"about_ca_system_score_codex":0.000041087027,"about_ca_system_score_gemma":0.00014357112,"threshold_uncertainty_score":0.48372707},"labels":[],"label_agreement":null},{"id":"W4383721094","doi":"10.1007/s10462-023-10542-z","title":"AFOX: a new adaptive nature-inspired optimization algorithm","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Regina","funders":"","keywords":"Metaheuristic; Meta-optimization; Computer science; Mathematical optimization; Multi-swarm optimization; Derivative-free optimization; Particle swarm optimization; Imperialist competitive algorithm; Convergence (economics); Optimization problem; Algorithm; Local optimum; Parallel metaheuristic; Benchmark (surveying); Continuous optimization; Engineering optimization; Optimization algorithm; Mathematics","score_opus":0.09369246705703665,"score_gpt":0.37165255071036646,"score_spread":0.2779600836533298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383721094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.136472e-7,0.013599266,0.9786362,0.0045496924,0.00066091324,0.0008211643,0.0000059024605,0.0005835581,0.0011427024],"genre_scores_gemma":[0.00024751358,0.07459037,0.9217798,0.0016337526,0.00028035234,0.00009820984,0.000043021166,0.000037460788,0.0012895031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966167,0.00030285597,0.00080642605,0.0007675606,0.00090700417,0.00059940474],"domain_scores_gemma":[0.9977174,0.00030525393,0.00020719378,0.0009507828,0.00046966242,0.00034973794],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014316447,0.00026619754,0.00044810944,0.00034026228,0.00021076089,0.00026131005,0.0014913182,0.0001544835,0.00054749224],"category_scores_gemma":[0.0011051301,0.0002461681,0.00017082153,0.005122401,0.00007962032,0.00060730433,0.00042339627,0.00044979932,0.0037278603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001952474,0.000032683347,6.711312e-7,0.00007310216,0.000018766028,0.000032365846,0.000062414714,0.024539823,0.000004551267,0.053443313,0.004889334,0.91690105],"study_design_scores_gemma":[0.000020380276,0.000069695445,0.000003176741,0.00050914485,0.000016834445,0.000012322021,0.000018501014,0.97730863,0.0006483011,0.010448052,0.010680372,0.00026456147],"about_ca_topic_score_codex":0.00004275079,"about_ca_topic_score_gemma":0.000004661005,"teacher_disagreement_score":0.95276886,"about_ca_system_score_codex":0.000083425424,"about_ca_system_score_gemma":0.00039709228,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W4383722638","doi":"10.36227/techrxiv.16860049.v2","title":"Adaptability of Improved NEAT in Variable Environments","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Python (programming language); Adaptability; Computer science; Data science; Programming language; Management","score_opus":0.05412458107371051,"score_gpt":0.2969718087178417,"score_spread":0.24284722764413116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383722638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047417215,0.000026166843,0.99604315,0.00023841501,0.00047321327,0.00055530295,0.000020582584,0.00008328679,0.0020856857],"genre_scores_gemma":[0.018737443,0.00010177686,0.97094434,0.00002638899,0.00002626301,0.00011894791,0.000029458879,0.000023557403,0.00999181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751526,0.00022845344,0.0005951934,0.000787447,0.00056026474,0.0003134003],"domain_scores_gemma":[0.9977475,0.00025593126,0.000157191,0.0016813552,0.000058625348,0.000099425924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014885667,0.00017810016,0.00038531818,0.00026332692,0.000021139243,0.000084626,0.0017534452,0.0001992586,0.00029383248],"category_scores_gemma":[0.0004267464,0.00017078203,0.000065130334,0.0005053245,0.00006579541,0.00012275022,0.0046281912,0.0004693785,0.00007249762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006537662,0.0024470806,0.01250261,0.0018055538,0.0003399645,0.00006964374,0.0014143762,0.8388854,0.0040174304,0.06623158,0.0023947114,0.06982623],"study_design_scores_gemma":[0.0002121192,0.00002058412,0.004754374,0.000022307846,0.0000025941035,3.1901646e-7,0.0000070346127,0.98654413,0.00055602024,0.0072971424,0.0004415627,0.00014181947],"about_ca_topic_score_codex":0.000904085,"about_ca_topic_score_gemma":0.000020585512,"teacher_disagreement_score":0.14765868,"about_ca_system_score_codex":0.00013629063,"about_ca_system_score_gemma":0.0003030804,"threshold_uncertainty_score":0.69642895},"labels":[],"label_agreement":null},{"id":"W4385488238","doi":"10.1109/ecai58194.2023.10194001","title":"Chaotic American zebra search optimization algorithm for benchmark challenges","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Chaotic; Benchmark (surveying); Truss; Computer science; Metaheuristic; Swarm intelligence; Algorithm; Swarm behaviour; Bar (unit); Modal; Mathematical optimization; CHAOS (operating system); Artificial intelligence; Mathematics; Engineering; Particle swarm optimization","score_opus":0.05494683518197453,"score_gpt":0.32366129423621354,"score_spread":0.268714459054239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385488238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002557792,0.00008407489,0.9894286,0.005939048,0.00028416692,0.0006406476,0.000010402409,0.0006950073,0.0028924881],"genre_scores_gemma":[0.001207132,0.0010326236,0.99325997,0.00016936725,0.00014750253,0.00017790224,0.000057724417,0.000032710002,0.003915092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99741644,0.00016630352,0.00030530302,0.0006806153,0.0007541281,0.00067718426],"domain_scores_gemma":[0.9979288,0.00058270775,0.00007181037,0.00071856217,0.00045734187,0.00024075291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012194149,0.00017934918,0.0002625907,0.00058441487,0.00022587636,0.00025393275,0.0010937088,0.000054303207,0.00015555811],"category_scores_gemma":[0.00026667496,0.00016632327,0.00008560245,0.0021588819,0.00011448334,0.00039418216,0.00042472972,0.00013317225,0.0002641104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026341172,0.00005790758,0.0000070486667,0.000029397861,0.000031585456,0.000009150539,0.00037435413,0.11709372,0.000007379128,0.019312054,0.0030144693,0.8600603],"study_design_scores_gemma":[0.00034030213,0.00017068179,0.00021344321,0.0000064047126,0.0000034744528,0.0000046108876,0.000207437,0.9964839,0.00012763345,0.00053474936,0.0017087236,0.00019862734],"about_ca_topic_score_codex":0.00002623878,"about_ca_topic_score_gemma":0.000002659385,"teacher_disagreement_score":0.8793902,"about_ca_system_score_codex":0.000056595694,"about_ca_system_score_gemma":0.00014351316,"threshold_uncertainty_score":0.6782466},"labels":[],"label_agreement":null},{"id":"W4385763925","doi":"10.24963/ijcai.2023/625","title":"Front-to-End Bidirectional Heuristic Search with Consistent Heuristics: Enumerating and Evaluating Algorithms and Bounds","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Heuristics; Heuristic; Computer science; Set (abstract data type); Algorithm; Bidirectional search; Mathematical optimization; Search algorithm; Beam search; Theoretical computer science; Incremental heuristic search; Mathematics; Artificial intelligence","score_opus":0.06058572995949063,"score_gpt":0.34199767726924996,"score_spread":0.2814119473097593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385763925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020863611,0.00019009133,0.97350675,0.0018089324,0.00025551525,0.0005523258,0.000016761682,0.00042349845,0.0023825301],"genre_scores_gemma":[0.0987923,0.00011222373,0.89331293,0.00032451397,0.00015697391,0.0000977858,0.000019616678,0.000042189535,0.007141465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667275,0.0002956128,0.00037498202,0.00084533275,0.0012263615,0.0005849766],"domain_scores_gemma":[0.99753064,0.001018554,0.00006227242,0.00042240304,0.0005184944,0.00044761156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020528466,0.00022996649,0.00028957837,0.00047954085,0.0006146069,0.0009077701,0.00033553335,0.000058033085,0.00018319504],"category_scores_gemma":[0.00093737926,0.00019434288,0.000024931676,0.0010411905,0.00019200129,0.00027199808,0.0006792984,0.0002637403,0.0001827985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010644738,0.00022110475,0.010957355,0.00038645163,0.00039622656,0.0004758757,0.0053923363,0.027736621,0.0012161435,0.02019072,0.008412577,0.92450815],"study_design_scores_gemma":[0.0005373075,0.0003445137,0.0077741602,0.00003968036,0.000012266331,0.00013556467,0.00018962433,0.9896651,0.00013601263,0.0003445587,0.000556043,0.00026514966],"about_ca_topic_score_codex":0.00016829299,"about_ca_topic_score_gemma":0.000023962786,"teacher_disagreement_score":0.9619285,"about_ca_system_score_codex":0.00006319518,"about_ca_system_score_gemma":0.0002802523,"threshold_uncertainty_score":0.87536484},"labels":[],"label_agreement":null},{"id":"W4386170085","doi":"10.1016/j.swevo.2023.101387","title":"Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":64,"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":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Differential evolution; Benchmark (surveying); Evolutionary algorithm; Artificial intelligence; Convergence (economics); Neuroevolution; Evolutionary computation; Artificial neural network; Machine learning; Population; Flexibility (engineering); Generality; Mathematical optimization; Mathematics","score_opus":0.02676152662992749,"score_gpt":0.2939925407969376,"score_spread":0.2672310141670101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386170085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013904484,0.00011871716,0.99521786,0.0007818021,0.0005005171,0.0007950107,0.000008521616,0.0006884235,0.000498684],"genre_scores_gemma":[0.74435425,0.00009835776,0.25231013,0.0000448242,0.0001967597,0.00018449828,0.0019129438,0.00002829136,0.0008699613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974626,0.00023719517,0.0005297573,0.0006153149,0.00066725415,0.0004879067],"domain_scores_gemma":[0.9983867,0.0004374767,0.00021813443,0.00020750765,0.00056606,0.00018416905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047016048,0.00023651092,0.0002420919,0.00064308726,0.00092404836,0.00017739127,0.0002769818,0.00013698883,0.000045151235],"category_scores_gemma":[0.00031975933,0.00025825563,0.000094910574,0.0010866012,0.00014836281,0.0006951207,0.00018536382,0.00018313572,0.000060247206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041076204,0.00005023773,0.00020569518,0.00005315762,0.000038950453,0.0000044792623,0.00017970157,0.9823917,0.00010095445,0.010091767,0.0018923552,0.0049499017],"study_design_scores_gemma":[0.001330506,0.00021343205,0.011751057,0.00002216179,0.00001843872,0.000041484804,0.000109732806,0.9822653,0.000016890834,0.003678813,0.0002811084,0.0002711075],"about_ca_topic_score_codex":0.000012779841,"about_ca_topic_score_gemma":9.672463e-7,"teacher_disagreement_score":0.7429638,"about_ca_system_score_codex":0.00028940826,"about_ca_system_score_gemma":0.00024334584,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W4386536666","doi":"10.1063/5.0162724","title":"Arithmetic optimizer algorithm: A comprehensive survey of its results, variants, and applications","year":2023,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Icon; Citation; Computer science; Information retrieval; Search algorithm; Download; World Wide Web; Algorithm; Programming language","score_opus":0.07211304030383141,"score_gpt":0.3124150870787077,"score_spread":0.24030204677487627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386536666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058940786,0.0001491616,0.9951206,0.0008932481,0.00008524206,0.00077470957,0.00013680654,0.00027830133,0.0019725559],"genre_scores_gemma":[0.34270665,0.0034723983,0.6493517,0.00025412903,0.00011163278,0.00058986293,0.00017599513,0.00006434583,0.00327325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761766,0.00006834817,0.0005456963,0.0007295514,0.0006104042,0.0004283417],"domain_scores_gemma":[0.9958666,0.0004859581,0.00024717947,0.00034482422,0.002834226,0.00022121365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011714282,0.00020834629,0.00036951623,0.00042857477,0.00016496603,0.00033114434,0.001028304,0.000105242994,0.000023824978],"category_scores_gemma":[0.0008022774,0.00020385113,0.000033193952,0.0025652112,0.00017700878,0.00050541863,0.0007119307,0.00023380395,0.000115277864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002046417,0.0008711746,0.009131688,0.0014183286,0.00057372067,0.00005568894,0.009067099,0.00059428136,0.009233874,0.24291328,0.022218555,0.70371765],"study_design_scores_gemma":[0.0006855736,0.00008891065,0.04225385,0.000040773866,0.000009778392,0.000015608157,0.00010663033,0.9536719,0.000268212,0.0012146236,0.0014171953,0.00022695445],"about_ca_topic_score_codex":0.00010601856,"about_ca_topic_score_gemma":0.0000021802098,"teacher_disagreement_score":0.9530776,"about_ca_system_score_codex":0.000023898132,"about_ca_system_score_gemma":0.00023608803,"threshold_uncertainty_score":0.83128077},"labels":[],"label_agreement":null},{"id":"W4387005180","doi":"10.1109/cec53210.2023.10254079","title":"Block Differential Evolution","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University; Ontario Tech University","funders":"","keywords":"Computer science; Blocking (statistics); Benchmark (surveying); Block (permutation group theory); Dimension (graph theory); Mathematical optimization; Optimization problem; Differential evolution; Crossover; Convergence (economics); Heuristic; Algorithm; Mathematics; Artificial intelligence","score_opus":0.025059917013054843,"score_gpt":0.285770877123561,"score_spread":0.26071096011050615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387005180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017015075,0.0000039402166,0.9880576,0.00097419764,0.00034489235,0.00007573439,6.112533e-7,0.0006146025,0.008226922],"genre_scores_gemma":[0.8491121,0.000016920361,0.11092898,0.00007479059,0.00014176531,0.000023837438,0.0000076070446,0.000011062024,0.03968296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905425,0.00004892708,0.000106718784,0.00020601826,0.00037165883,0.00021241613],"domain_scores_gemma":[0.9994314,0.00005982569,0.00001609035,0.00033573553,0.00007969173,0.00007731217],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016918172,0.000050945193,0.000059567115,0.00019249524,0.00007907437,0.00011650174,0.00049489475,0.000025306506,0.00024828606],"category_scores_gemma":[0.00010214914,0.000043117434,0.000026472286,0.0010200507,0.000017093658,0.00015142748,0.00030351136,0.000057863486,0.002375133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003520015,0.00009508379,0.0005534245,0.000017652548,0.000030212606,0.000038975348,0.00018108332,0.005890214,0.0020012488,0.8658641,0.07940547,0.045919016],"study_design_scores_gemma":[0.00012146893,0.000013190384,0.0043858998,0.0000011342897,7.19899e-7,0.0000028866302,0.0000043334862,0.9914064,0.00033381052,0.0021801253,0.001491816,0.000058252568],"about_ca_topic_score_codex":0.000010892011,"about_ca_topic_score_gemma":0.0000010262162,"teacher_disagreement_score":0.98551613,"about_ca_system_score_codex":0.000024226902,"about_ca_system_score_gemma":0.00004238636,"threshold_uncertainty_score":0.99840164},"labels":[],"label_agreement":null},{"id":"W4387005219","doi":"10.1109/cec53210.2023.10254195","title":"A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Particle swarm optimization; Machine learning; Heuristic; Artificial intelligence; Fitness landscape; Mathematical optimization; Control (management); Sampling (signal processing); Mathematics; Population","score_opus":0.10276649256352458,"score_gpt":0.33188698367722597,"score_spread":0.2291204911137014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387005219","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011585711,0.00010478421,0.9874109,0.00018632963,0.00010823353,0.00022785953,0.000014841468,0.0002904174,0.000070904556],"genre_scores_gemma":[0.3028704,0.00014637435,0.6955506,0.000046716188,0.00004472001,0.000118134245,0.00004084587,0.000015510066,0.0011666883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998421,0.00012799402,0.00027679087,0.00046367646,0.00038317093,0.000327359],"domain_scores_gemma":[0.99823546,0.0010333962,0.00008938504,0.00035690315,0.00017046383,0.000114401075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009945895,0.00014448175,0.0003469034,0.0006689074,0.0002999397,0.00032588333,0.00031528962,0.00007285817,0.00020551852],"category_scores_gemma":[0.0009377617,0.000113724345,0.00013543894,0.0029776117,0.000035730358,0.0003492099,0.00022661476,0.00019611802,0.000014250879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003258561,0.000060065082,0.063580394,0.00019137486,0.0040736976,0.000010898298,0.0005114171,0.8927226,0.000033731714,0.011110112,0.00022405281,0.027449073],"study_design_scores_gemma":[0.00016713823,0.00006084141,0.005015945,0.0000046333635,0.0006540729,0.0000044269937,0.0000105582685,0.9927962,0.00006786025,0.00090824615,0.00019212191,0.00011798786],"about_ca_topic_score_codex":0.00002331928,"about_ca_topic_score_gemma":0.0000023573318,"teacher_disagreement_score":0.29186028,"about_ca_system_score_codex":0.000015163542,"about_ca_system_score_gemma":0.00003085355,"threshold_uncertainty_score":0.46375442},"labels":[],"label_agreement":null},{"id":"W4387005463","doi":"10.1109/cec53210.2023.10254096","title":"A Data-Centric Approach to Parameter Tuning, an Application to Differential Evolution","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Differential evolution; Benchmark (surveying); Crossover; Computer science; Suite; Differential (mechanical device); Population; Process (computing); Evolutionary computation; Algorithm; Artificial intelligence; Machine learning; Engineering","score_opus":0.07405581540004094,"score_gpt":0.3362113741045886,"score_spread":0.26215555870454765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387005463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011679203,0.0000030210622,0.9953882,0.0005909702,0.00014701295,0.0007244739,0.000018487255,0.0005145245,0.0014453593],"genre_scores_gemma":[0.21147946,0.0000027570106,0.7852969,0.00026906334,0.00013529879,0.00021847176,0.00036965625,0.000019405143,0.0022089952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997649,0.00014523538,0.00023978378,0.00087928225,0.00067985296,0.000406861],"domain_scores_gemma":[0.9973731,0.00008425202,0.00003644242,0.0019565977,0.00015234397,0.00039725492],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005975036,0.00012124137,0.00013781777,0.00056433206,0.0001275611,0.00034209155,0.0023038143,0.000047996877,0.000044069402],"category_scores_gemma":[0.00033338263,0.00010894094,0.000020506654,0.0030717358,0.000014315775,0.00052174024,0.0014751209,0.0000976534,0.0014334437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044955228,0.0013047338,0.00083934417,0.00006930841,0.00007618224,0.000008726058,0.0014354256,0.09292528,0.0025533384,0.3176601,0.09569795,0.48738468],"study_design_scores_gemma":[0.00013675081,0.000047740406,0.003662372,0.0000015505292,0.0000032730416,0.0000022382571,0.000019094143,0.9910871,0.000051251445,0.0003240648,0.004523791,0.00014077898],"about_ca_topic_score_codex":0.00006637371,"about_ca_topic_score_gemma":0.0000055823148,"teacher_disagreement_score":0.8981618,"about_ca_system_score_codex":0.00007081334,"about_ca_system_score_gemma":0.00006676959,"threshold_uncertainty_score":0.99934405},"labels":[],"label_agreement":null},{"id":"W4387195735","doi":"10.1007/978-3-031-43085-5_2","title":"Low-Dimensional Space Modeling-Based Differential Evolution: A Scalability Perspective on bbob-largescale suite","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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":"Scalability; Computer science; Curse of dimensionality; Differential evolution; Dimensionality reduction; Mathematical optimization; Suite; Global optimization; Artificial intelligence; Algorithm; Mathematics; Database","score_opus":0.024141184410859277,"score_gpt":0.28105012722075134,"score_spread":0.25690894280989207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387195735","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006511261,0.000063859676,0.9919815,0.0030480358,0.00248252,0.00075486605,0.000030558895,0.00039038138,0.0011831656],"genre_scores_gemma":[0.35778353,0.00001726908,0.63566136,0.0012071563,0.0013963123,0.000074126314,0.000042952684,0.00017925698,0.0036380438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919592,0.00017854443,0.0006773083,0.002860452,0.0032721483,0.001052359],"domain_scores_gemma":[0.9947019,0.0012499312,0.00024295278,0.00216124,0.0012233545,0.00042058114],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015341407,0.0007217476,0.00072913646,0.0016393075,0.00053198135,0.00068068405,0.003328082,0.00044191006,0.00011696946],"category_scores_gemma":[0.0008126486,0.00066479744,0.00026907772,0.001590432,0.0009995289,0.00039203864,0.0017260482,0.0015376881,0.00026271195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025739455,0.00012661405,0.000009336367,0.0000348177,0.000014620003,0.00006716945,0.00014595625,0.8651715,0.000022102768,0.12756254,0.000028685314,0.0067909337],"study_design_scores_gemma":[0.00041893046,0.00020468299,0.00004988879,0.0002506464,0.0000067633728,0.000009270908,2.8864625e-7,0.8218262,0.0001689089,0.17652191,0.0000135055525,0.00052895653],"about_ca_topic_score_codex":0.00006112791,"about_ca_topic_score_gemma":0.000059488248,"teacher_disagreement_score":0.3577184,"about_ca_system_score_codex":0.002301428,"about_ca_system_score_gemma":0.0019071585,"threshold_uncertainty_score":0.9995803},"labels":[],"label_agreement":null},{"id":"W4387265282","doi":"10.3390/biomimetics8060468","title":"OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Calgary","funders":"University of Calgary","keywords":"Population; Metaheuristic; Mathematical optimization; Computer science; Optimization problem; Process (computing); Continuous optimization; Algorithm; Engineering optimization; Test suite; Multi-objective optimization; Test functions for optimization; Multi-swarm optimization; Machine learning; Mathematics; Test case","score_opus":0.053851505432997965,"score_gpt":0.307190129778605,"score_spread":0.25333862434560706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387265282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009024202,0.000128327,0.9958785,0.0011342287,0.0010886609,0.0008573197,0.000022129596,0.00067820953,0.00020355664],"genre_scores_gemma":[0.00021629968,0.00013448054,0.9918054,0.00009244501,0.00022157203,0.000097424985,0.00008186588,0.000045922185,0.007304636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758846,0.000074745076,0.00048101437,0.00061082264,0.00061457587,0.00063035823],"domain_scores_gemma":[0.9979193,0.00049876363,0.0001565921,0.00072146935,0.0004007549,0.00030312035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010192905,0.00022205821,0.0002869556,0.0005816933,0.00024182473,0.00048477497,0.001062872,0.000115596515,0.00006121379],"category_scores_gemma":[0.00090475637,0.00021790565,0.00012073055,0.0024537353,0.00005498276,0.00032438157,0.0004047172,0.00011620655,0.00025519775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008199229,0.00018970734,0.000030163626,0.00014860166,0.00019553713,0.000029166102,0.0004961841,0.37573913,0.0005335059,0.016490897,0.063481174,0.54265773],"study_design_scores_gemma":[0.0006813968,0.00010779307,0.000023109656,0.0000162874,0.000023868908,0.000007158807,0.000008970048,0.9834791,0.00059837016,0.002131036,0.01267269,0.00025024868],"about_ca_topic_score_codex":0.000027481292,"about_ca_topic_score_gemma":9.339819e-7,"teacher_disagreement_score":0.6077399,"about_ca_system_score_codex":0.000066636,"about_ca_system_score_gemma":0.00032847008,"threshold_uncertainty_score":0.8885935},"labels":[],"label_agreement":null},{"id":"W4387632706","doi":"","title":"Mixing Techniques to Compute Derivatives of semi-numerical models: Application to Magnetic Nano Switch Optimization","year":2011,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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":"Center for Diagnosis and Research on Alzheimer's Disease","funders":"","keywords":"Nano-; Mixing (physics); Computer science; Materials science; Physics; Composite material","score_opus":0.023032758546071178,"score_gpt":0.24493156363230273,"score_spread":0.22189880508623155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387632706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006605802,0.00007545763,0.9812915,0.002863652,0.000045153087,0.0007459548,0.0000064830806,0.0003026558,0.014008566],"genre_scores_gemma":[0.18117528,0.000044579545,0.81777805,0.0001621047,0.000007469915,0.00012406708,0.000020297313,0.00002445774,0.00066370796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959371,0.001815464,0.000588814,0.00071319524,0.0005914596,0.00035394848],"domain_scores_gemma":[0.9945038,0.0005551352,0.00025298842,0.0016785732,0.0026990932,0.00031041427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028522715,0.0002173174,0.00029487102,0.00044057664,0.00021386115,0.00017772685,0.0019278757,0.00009638415,0.00007021955],"category_scores_gemma":[0.00086320215,0.00023324564,0.00007431588,0.0019490956,0.00009319668,0.00043266127,0.0010038418,0.00015839169,0.000033878197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038089078,0.0014150152,0.0005051131,0.00011885834,0.000057501427,0.0000041471926,0.033605132,0.06948233,0.02212704,0.39145437,0.0009175865,0.48027483],"study_design_scores_gemma":[0.00015029474,0.0000037990346,0.00030145334,0.00015883819,0.000006082152,0.0000047335607,0.00003666637,0.8733431,0.12328385,0.0016517491,0.00083328946,0.00022613019],"about_ca_topic_score_codex":0.00036219665,"about_ca_topic_score_gemma":0.000027533644,"teacher_disagreement_score":0.8038608,"about_ca_system_score_codex":0.00007827493,"about_ca_system_score_gemma":0.000138999,"threshold_uncertainty_score":0.9511481},"labels":[],"label_agreement":null},{"id":"W4387736682","doi":"10.36227/techrxiv.24320203.v1","title":"Self-Adaptive Spherical Search with Constrained Multi-Operator Differential Evolution (SASS-CMODE) for nonlinear programming problems","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Sass; Mathematical optimization; Computer science; Differential evolution; Operator (biology); Metaheuristic; Nonlinear system; Algorithm; Mathematics","score_opus":0.06809769907260968,"score_gpt":0.3194999808892484,"score_spread":0.2514022818166387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387736682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003100701,0.000030825297,0.99070865,0.00055775675,0.00053443597,0.005661656,0.00011563876,0.0019914212,0.00008954276],"genre_scores_gemma":[0.010873274,0.000019353287,0.98537207,0.000020428724,0.00027421248,0.0014581137,0.0001996996,0.000105402316,0.0016774206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99444723,0.00036192112,0.0007463278,0.0018610276,0.001479222,0.0011042482],"domain_scores_gemma":[0.99600047,0.00038451946,0.0002267765,0.001135448,0.001797141,0.000455657],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00100484,0.0006026105,0.0007439493,0.0003294049,0.00034467792,0.0011336285,0.0018333452,0.00043501798,0.000043583772],"category_scores_gemma":[0.00022449512,0.00047960642,0.00021786113,0.0008706406,0.00024972172,0.00028872772,0.0022633022,0.0010964504,0.000083017265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013382931,0.01372787,0.0017642529,0.010580698,0.006927752,0.0004950642,0.012414929,0.64462477,0.0010664265,0.11115057,0.0033789044,0.19253047],"study_design_scores_gemma":[0.001784327,0.00048168458,0.00008622567,0.000117077456,0.000043201726,0.000010691642,0.00011361724,0.9960803,0.0001963565,0.0001510972,0.00033720114,0.00059821474],"about_ca_topic_score_codex":0.00015861161,"about_ca_topic_score_gemma":0.000089351495,"teacher_disagreement_score":0.35145554,"about_ca_system_score_codex":0.0004575907,"about_ca_system_score_gemma":0.0021414119,"threshold_uncertainty_score":0.99990326},"labels":[],"label_agreement":null},{"id":"W4387873795","doi":"10.3390/biomimetics8060507","title":"Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":107,"is_retracted":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":"Metaheuristic; Algorithm; Benchmark (surveying); Computer science; Mathematical optimization; Test suite; Optimization problem; Suite; Mathematics; Test case; Machine learning","score_opus":0.04427160285548675,"score_gpt":0.2948410349910398,"score_spread":0.25056943213555305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387873795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000042801116,0.00021907166,0.9932562,0.001270656,0.0017989059,0.0017732547,0.00008433464,0.0014491264,0.00014417175],"genre_scores_gemma":[0.000082268445,0.00043178993,0.99377537,0.00014611328,0.00040395584,0.00021434574,0.0004403884,0.00012190572,0.004383884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954159,0.00018125793,0.0010142026,0.0011891214,0.0011070258,0.0010924948],"domain_scores_gemma":[0.9963588,0.00059982436,0.00041011247,0.0011628272,0.00092410576,0.00054429553],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013885833,0.0004946839,0.0005801333,0.0012517276,0.0005187574,0.00092466513,0.0016098118,0.00028617855,0.00010951339],"category_scores_gemma":[0.0010927932,0.00050692447,0.00023476877,0.00468684,0.00012099987,0.0007584966,0.0006117552,0.00021499429,0.00021022675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004578083,0.000107427644,0.00000631448,0.00004463487,0.000098972334,0.000012224483,0.00016993775,0.77364147,0.00010018234,0.00088255305,0.0066619217,0.21826978],"study_design_scores_gemma":[0.0015460881,0.00022613951,0.000012925995,0.000042395975,0.000073185554,0.000015000132,0.000021894393,0.99045837,0.0008607406,0.0007854574,0.005387867,0.0005699502],"about_ca_topic_score_codex":0.000052694933,"about_ca_topic_score_gemma":0.0000013646861,"teacher_disagreement_score":0.21769984,"about_ca_system_score_codex":0.0001852504,"about_ca_system_score_gemma":0.00048898486,"threshold_uncertainty_score":0.9997382},"labels":[],"label_agreement":null},{"id":"W4387917828","doi":"10.1007/978-3-031-44505-7","title":"Learning and Intelligent Optimization","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":6,"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":"University of Ioannina; Università degli Studi di Ferrara; Consejo Superior de Investigaciones Científicas; Università di Bologna; Università degli Studi di Cagliari; Centre National de la Recherche Scientifique; Universite Angers; Sapienza Università di Roma; Khalifa University of Science, Technology and Research; Brown University; Università degli Studi di Trento; Universität Bielefeld; Università degli Studi di Milano-Bicocca; Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences; Universitat Pompeu Fabra; University of Crete; University of Toronto; Indian Council of Agricultural Research; Wilfrid Laurier University; Università degli Studi di Milano; Université de Lorraine; University of Pittsburgh; Università degli Studi di Udine; Università della Calabria; Università degli Studi di Camerino; KU Leuven; Technische Universiteit Eindhoven; Université de Lille; University of Patras; University of Southern California","keywords":"Computer science; Artificial intelligence; Optimization problem; Mathematical optimization; Algorithm; Mathematics","score_opus":0.023102495083191107,"score_gpt":0.2945752562375587,"score_spread":0.2714727611543676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387917828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000020270757,0.00023103124,0.99626094,0.0004908294,0.0011549201,0.0003498637,0.0000013241053,0.00030963492,0.0011994157],"genre_scores_gemma":[0.00038835526,0.00050262676,0.98976517,0.00023253405,0.00028573832,0.000016182552,0.000014607342,0.000045085293,0.008749687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613106,0.00013696984,0.00045784135,0.0014089767,0.0012382988,0.0006268391],"domain_scores_gemma":[0.9975572,0.0009671587,0.0001962737,0.00069761823,0.00036438424,0.00021736193],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017238262,0.00034611498,0.00039426086,0.0014106549,0.00030239703,0.0010453127,0.0020339666,0.00023137612,0.00002806501],"category_scores_gemma":[0.00090654724,0.00033189775,0.000052466286,0.0022310058,0.00051150133,0.0004508411,0.0022607858,0.00095902965,0.000076092045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.0175136e-7,0.000007291692,0.00001662021,0.00002531435,0.0000038216635,0.000030244602,0.00028510962,0.6544293,0.0000018634704,0.00047186823,0.00003973501,0.344688],"study_design_scores_gemma":[0.000111802845,0.00010832198,0.000014465493,0.00014145242,0.0000029739267,0.000026154199,2.3544506e-7,0.98726916,0.00008107969,0.011138272,0.0007802168,0.00032589227],"about_ca_topic_score_codex":0.0000067806136,"about_ca_topic_score_gemma":0.0000062564673,"teacher_disagreement_score":0.3443621,"about_ca_system_score_codex":0.0003462119,"about_ca_system_score_gemma":0.0010268169,"threshold_uncertainty_score":0.9999917},"labels":[],"label_agreement":null},{"id":"W4388101121","doi":"10.1007/s00500-023-09289-0","title":"Evolutionary ensembles based on prioritized aggregation operator","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"","keywords":"Operator (biology); Computer science; Evolutionary computation; Evolutionary algorithm; Artificial intelligence; Biology","score_opus":0.025724361350440123,"score_gpt":0.29538116785600416,"score_spread":0.269656806505564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388101121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060088,0.00003223321,0.98937833,0.0015202739,0.0005717185,0.00026268297,0.0000031664795,0.0010286849,0.0011940831],"genre_scores_gemma":[0.61839664,0.0000082214365,0.38001734,0.0005928663,0.0002607699,0.000014729563,0.00003663365,0.000027443408,0.00064537436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783677,0.00024046405,0.00029278843,0.00048959226,0.0007315319,0.0004088436],"domain_scores_gemma":[0.9981894,0.0008184563,0.00008102136,0.00051640096,0.00026754296,0.00012717296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009114059,0.00014298338,0.00016442515,0.00039664854,0.000405176,0.00024365718,0.00066630024,0.000060428283,0.00003222867],"category_scores_gemma":[0.00095571246,0.00014386738,0.000061781015,0.00169311,0.00003983527,0.00018981479,0.0002929623,0.00018362394,0.0006023164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022644743,0.00014405607,0.0018163268,0.00007905929,0.000026489211,0.00013307451,0.00047217138,0.71005267,0.0006646749,0.022787264,0.013343139,0.25045842],"study_design_scores_gemma":[0.00046985617,0.000041511772,0.0037137931,0.000057283265,0.0000015976092,0.00000388222,0.0000117687005,0.9935329,0.00023260208,0.00051803124,0.0012636834,0.00015309993],"about_ca_topic_score_codex":0.000005868921,"about_ca_topic_score_gemma":2.4306325e-7,"teacher_disagreement_score":0.61238784,"about_ca_system_score_codex":0.0000862968,"about_ca_system_score_gemma":0.00024397537,"threshold_uncertainty_score":0.77417594},"labels":[],"label_agreement":null},{"id":"W4388294700","doi":"10.1016/j.eswa.2023.122335","title":"Lévy Arithmetic Algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Metaheuristic; Mathematics; Benchmark (surveying); Mathematical optimization; Computer science; Arithmetic; Optimization problem","score_opus":0.015276394716650077,"score_gpt":0.28143076615970714,"score_spread":0.26615437144305704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388294700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027546053,0.0004872956,0.99449503,0.0005349705,0.0001590305,0.0030014236,0.000034423563,0.0011495475,0.00011074752],"genre_scores_gemma":[0.010239824,0.00025329177,0.9766819,0.00010818702,0.00032016364,0.01140417,0.00019587537,0.00009020493,0.0007064025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674046,0.00013597099,0.00056817214,0.0011553173,0.0008087653,0.00059130637],"domain_scores_gemma":[0.9971664,0.00021752587,0.00016827467,0.0012152395,0.0006242856,0.00060825096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067360216,0.00035156537,0.00040158225,0.0007526096,0.00038951953,0.00047815975,0.0009794321,0.00012121847,0.000009774766],"category_scores_gemma":[0.00009742689,0.00033367422,0.000036522484,0.0036017024,0.000036643025,0.0006669726,0.00025890165,0.00018173637,0.0002902669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004333082,0.00013462055,0.000002337605,0.00007608162,0.00006937142,0.0000074886093,0.00084475835,0.814134,0.0018424498,0.014190545,0.000244394,0.16844961],"study_design_scores_gemma":[0.0003245218,0.00010276576,0.00004983155,0.000030768686,0.000012360147,0.000037030946,0.000108738765,0.98963505,0.0008168512,0.00003405585,0.008446431,0.00040159197],"about_ca_topic_score_codex":0.000060294784,"about_ca_topic_score_gemma":0.0000012923485,"teacher_disagreement_score":0.17550103,"about_ca_system_score_codex":0.00011986552,"about_ca_system_score_gemma":0.00011575627,"threshold_uncertainty_score":0.99991155},"labels":[],"label_agreement":null},{"id":"W4388533824","doi":"10.7717/peerj-cs.1557","title":"An improved hybrid whale optimization algorithm for global optimization and engineering design problems","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Metaheuristic Optimization Algorithms Research","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":"McMaster University","funders":"","keywords":"Benchmark (surveying); Computer science; Differential evolution; Particle swarm optimization; Mathematical optimization; Evolutionary algorithm; Engineering optimization; Metaheuristic; Population; Multi-objective optimization; Algorithm; Optimization problem; Machine learning; Mathematics","score_opus":0.026084071971882544,"score_gpt":0.2807383630520047,"score_spread":0.25465429108012216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388533824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056779114,0.000030768668,0.9970413,0.00036109952,0.0007610505,0.00096615497,0.000014052064,0.000757659,0.000011168532],"genre_scores_gemma":[0.0019592221,0.000037238347,0.9975791,0.00008314655,0.0001333737,0.00011728116,0.000030319303,0.000019746612,0.000040565235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693763,0.000083751314,0.00036328696,0.0011020391,0.00077426276,0.0007390488],"domain_scores_gemma":[0.9978259,0.0001819253,0.00011792627,0.0006967415,0.00079044513,0.00038705012],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0023134656,0.00023806925,0.00023119301,0.0004427867,0.0004857193,0.001634867,0.0016392507,0.000052594874,0.000004965875],"category_scores_gemma":[0.00019742179,0.00024184248,0.000039928487,0.0029785514,0.0001704027,0.0023808589,0.00058565347,0.000099721336,0.000007007164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011777512,0.000028526061,0.0000057716607,0.000015236874,0.0000048186494,0.0000032033165,0.00008849384,0.8871977,0.000107631524,0.00050517474,0.00008959169,0.11195272],"study_design_scores_gemma":[0.00046093905,0.00024960292,0.00008033342,0.000015819905,0.000004715054,0.00003717436,0.0000034082975,0.9981863,0.00040986258,0.00021256534,0.000057501667,0.0002817632],"about_ca_topic_score_codex":0.000008985164,"about_ca_topic_score_gemma":2.4279697e-7,"teacher_disagreement_score":0.11167096,"about_ca_system_score_codex":0.00013081795,"about_ca_system_score_gemma":0.00031858188,"threshold_uncertainty_score":0.9994015},"labels":[],"label_agreement":null},{"id":"W4388667916","doi":"10.1007/s11721-023-00229-0","title":"Decomposition and merging cooperative particle swarm optimization with random grouping for large-scale optimization problems","year":2023,"lang":"en","type":"article","venue":"Swarm Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Mathematics; Scaling; Metaheuristic; Decomposition; Mathematical optimization; Variable (mathematics); Combinatorics; Scale (ratio); Algorithm; Physics","score_opus":0.02775602502805102,"score_gpt":0.31142738325262886,"score_spread":0.28367135822457784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388667916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096932234,0.000110267545,0.996366,0.0006887244,0.0001974661,0.0011824472,0.000010501527,0.00035842595,0.00011680169],"genre_scores_gemma":[0.11324801,0.00049230526,0.8851105,0.000144656,0.0000848219,0.0004388424,0.000106952335,0.000042364558,0.0003315307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978373,0.0001595441,0.0004121112,0.00064704457,0.00042542745,0.0005185562],"domain_scores_gemma":[0.9980423,0.00041897187,0.00014058448,0.00034430926,0.00088727183,0.00016658759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010726909,0.00020745721,0.00024721472,0.00022065896,0.000515031,0.0004347211,0.0004016873,0.00006789145,0.000028661954],"category_scores_gemma":[0.0003813268,0.0001845633,0.000039953036,0.0016346363,0.00007652468,0.00089038874,0.00019740105,0.00013233282,0.000022793907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005235944,0.000051356004,0.000058542017,0.00004704957,0.000022316633,0.0000028843274,0.0014153473,0.99189734,0.00006190573,0.0032889834,0.000061639235,0.003040265],"study_design_scores_gemma":[0.00076424784,0.00019761744,0.000015009578,0.00007185593,0.000014453373,0.000008088145,0.00029774744,0.98784167,0.010118259,0.000323572,0.000107833504,0.00023964899],"about_ca_topic_score_codex":0.000010595263,"about_ca_topic_score_gemma":0.0000093025155,"teacher_disagreement_score":0.11227869,"about_ca_system_score_codex":0.000059056416,"about_ca_system_score_gemma":0.00008407583,"threshold_uncertainty_score":0.75262725},"labels":[],"label_agreement":null},{"id":"W4389671032","doi":"10.1016/j.asoc.2023.111141","title":"Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Computer science; Dimensionality reduction; Feature selection; Binary number; Support vector machine; Classifier (UML); Curse of dimensionality; Binary classification; Artificial intelligence; Pattern recognition (psychology); Local optimum; Differential evolution; Feature vector; Machine learning; Data mining; Algorithm; Mathematics","score_opus":0.017518211048133732,"score_gpt":0.26815059418388637,"score_spread":0.25063238313575265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389671032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1404445,0.000013006954,0.85762995,0.00033964,0.00024794368,0.0008302833,0.0000047510284,0.00047714554,0.000012761211],"genre_scores_gemma":[0.84692574,0.000001520961,0.15265381,0.0000137093675,0.0001511466,0.00005050475,0.000061025425,0.000025216106,0.00011730045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792624,0.00009931893,0.00022914501,0.0007472762,0.0004992171,0.000498782],"domain_scores_gemma":[0.99888456,0.00030909383,0.000109695,0.0002764806,0.00028125066,0.00013890132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007808628,0.00020381782,0.00022996044,0.00033894627,0.00082270877,0.0001866851,0.00027822904,0.000117682335,0.0000031925204],"category_scores_gemma":[0.000048355527,0.00018709524,0.00003791056,0.0012274367,0.000072332376,0.000174056,0.00042507102,0.00030776756,0.000008048832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045998025,0.00029213462,0.000067045854,0.00033818834,0.00020595854,0.00000937331,0.0013445016,0.44495264,0.287732,0.2233133,0.000846233,0.04043865],"study_design_scores_gemma":[0.00078791135,0.00021495503,0.0015487538,0.000021042135,0.00001471972,0.000028150911,0.00008523877,0.99268514,0.0014938583,0.0029090114,0.0000037098696,0.0002075134],"about_ca_topic_score_codex":0.000018245204,"about_ca_topic_score_gemma":7.544659e-7,"teacher_disagreement_score":0.7064813,"about_ca_system_score_codex":0.00012333498,"about_ca_system_score_gemma":0.00014563416,"threshold_uncertainty_score":0.76295227},"labels":[],"label_agreement":null},{"id":"W4389923272","doi":"10.3390/biomimetics8080619","title":"Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Metaheuristic; Armadillo; Mathematical optimization; Computer science; Optimization algorithm; Algorithm; Mathematics; Biology; Ecology","score_opus":0.04705691919722536,"score_gpt":0.2941826078632756,"score_spread":0.24712568866605025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389923272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000474597,0.00023657073,0.9935532,0.0019551322,0.0015310335,0.0014295944,0.000053662126,0.0010314202,0.00020461439],"genre_scores_gemma":[0.00018525956,0.00038605204,0.99410915,0.00016731379,0.00031270678,0.00016561484,0.00026041118,0.00008607621,0.0043274076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962614,0.00013510227,0.0008497784,0.00095854484,0.0008886876,0.0009064507],"domain_scores_gemma":[0.9969369,0.0004930091,0.0003192905,0.0010277336,0.00073059427,0.000492427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011081154,0.000393487,0.00049219606,0.0009634937,0.00044296304,0.00077587896,0.0014147903,0.00020198405,0.00014281976],"category_scores_gemma":[0.0010229085,0.00039175028,0.00019398326,0.003977518,0.00010799656,0.0005657842,0.00051853823,0.00017226403,0.0001825579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052093073,0.000087578235,0.000005740703,0.000057493868,0.00008950234,0.00001517236,0.0001588798,0.9236454,0.0001661406,0.0018681989,0.008802214,0.06509847],"study_design_scores_gemma":[0.0012717564,0.000171801,0.000013646238,0.00003671309,0.000055633147,0.000016051114,0.000017287015,0.9901308,0.00083245005,0.00050853955,0.0064868517,0.00045846507],"about_ca_topic_score_codex":0.000027071135,"about_ca_topic_score_gemma":0.0000010104798,"teacher_disagreement_score":0.066485405,"about_ca_system_score_codex":0.0001331606,"about_ca_system_score_gemma":0.0004423877,"threshold_uncertainty_score":0.99985343},"labels":[],"label_agreement":null},{"id":"W4390071945","doi":"10.1109/iccsm60247.2023.00019","title":"Hybrid Differential Evolution and Particle Swarm Optimization for Speech Emotion Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Moncton","funders":"","keywords":"Particle swarm optimization; Differential evolution; Computer science; Differential (mechanical device); Artificial intelligence; Speech recognition; Multi-swarm optimization; Metaheuristic; Machine learning; Engineering; Aerospace engineering","score_opus":0.048281520975011205,"score_gpt":0.3028147372842428,"score_spread":0.2545332163092316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390071945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023944987,0.000006636466,0.9731399,0.0015517403,0.00031714272,0.00046223405,0.0000051130596,0.00041112758,0.00016112743],"genre_scores_gemma":[0.6987853,0.00005120278,0.29954162,0.000033803728,0.000099992474,0.00008771926,0.00008374684,0.000014443708,0.0013021788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871546,0.00008039594,0.00023204552,0.0003856031,0.00032482587,0.00026168235],"domain_scores_gemma":[0.99914634,0.00013463509,0.000065392844,0.00030413165,0.00024642886,0.00010307971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043691567,0.000092785325,0.00010148988,0.0001695413,0.00019983706,0.00024888667,0.00023524307,0.00003941713,0.00005670482],"category_scores_gemma":[0.0002827407,0.00008841197,0.000032306936,0.0005617187,0.000029659153,0.00047480443,0.00012691553,0.000052926996,0.00006542839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006756098,0.0003527949,0.0010707557,0.00016966187,0.00006867869,0.000008287671,0.00044421887,0.186297,0.011298227,0.47564363,0.011855239,0.31272393],"study_design_scores_gemma":[0.00043374108,0.00005132406,0.0043553053,0.000003931129,0.000005474382,0.0000037038094,0.000023187693,0.9895029,0.002833921,0.0025665811,0.000119599295,0.00010031987],"about_ca_topic_score_codex":0.000007264911,"about_ca_topic_score_gemma":7.285251e-7,"teacher_disagreement_score":0.8032059,"about_ca_system_score_codex":0.000058613514,"about_ca_system_score_gemma":0.0000436371,"threshold_uncertainty_score":0.36053357},"labels":[],"label_agreement":null},{"id":"W4390481469","doi":"10.1109/ssci52147.2023.10372026","title":"Opposition-Based Crossover Operation for Differential Evolution Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Crossover; Differential evolution; Benchmark (surveying); Computer science; Mathematical optimization; Algorithm; Population; Evolutionary algorithm; Artificial intelligence; Mathematics","score_opus":0.02681508219606496,"score_gpt":0.30992994011503455,"score_spread":0.2831148579189696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390481469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020815207,0.0000044721264,0.9965838,0.0013014734,0.0005325723,0.0004990812,0.000026211505,0.0004720454,0.00037215403],"genre_scores_gemma":[0.095229656,0.000004025114,0.8976211,0.0002543639,0.00023886698,0.0003123899,0.00029359132,0.00002231859,0.006023721],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855727,0.000069937654,0.00021928088,0.00037272274,0.00046670626,0.0003140758],"domain_scores_gemma":[0.9989786,0.00020460536,0.000039873517,0.00036058354,0.00031151995,0.000104812476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033836765,0.00010536222,0.0001111369,0.00025720464,0.00029563674,0.00044603625,0.00044264906,0.000062070045,0.00024603526],"category_scores_gemma":[0.00012446285,0.000096143216,0.00006985554,0.0006650259,0.000036944737,0.00039902865,0.000108434935,0.000063058185,0.00034927874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006909431,0.00055084843,0.00016499638,0.00012467027,0.00010653524,0.000022095744,0.00029822538,0.17272843,0.01220865,0.4280747,0.078282654,0.3073691],"study_design_scores_gemma":[0.0008553278,0.00006329292,0.0006169954,0.000003689019,0.0000033548927,9.556212e-7,0.0000046277164,0.9928473,0.003160549,0.0016357884,0.0006864058,0.00012170011],"about_ca_topic_score_codex":0.000019403846,"about_ca_topic_score_gemma":0.0000021289445,"teacher_disagreement_score":0.8201189,"about_ca_system_score_codex":0.00008857317,"about_ca_system_score_gemma":0.00017171534,"threshold_uncertainty_score":0.44893882},"labels":[],"label_agreement":null},{"id":"W4391145932","doi":"10.3390/biomimetics9020065","title":"Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Biomimetics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":133,"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":"Benchmark (surveying); Metaheuristic; Algorithm; Test suite; Computer science; Evolutionary algorithm; Optimization algorithm; Optimization problem; Mathematical optimization; Artificial intelligence; Test case; Mathematics; Machine learning","score_opus":0.0328331190701672,"score_gpt":0.2881612102099395,"score_spread":0.2553280911397723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391145932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000017027913,0.0016921684,0.9907334,0.001471072,0.002986779,0.0016780556,0.00010041945,0.0011360719,0.00020032778],"genre_scores_gemma":[0.00015916798,0.00048847473,0.99436224,0.00014629071,0.00058956083,0.00019139361,0.00028364564,0.00012527673,0.0036539454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563825,0.00016319069,0.0009805803,0.0013196913,0.0009981958,0.00090010936],"domain_scores_gemma":[0.99692863,0.0006038862,0.00021840619,0.0009970693,0.0007460833,0.0005059154],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001152829,0.00051460863,0.00053565874,0.0010226396,0.00036438255,0.002191273,0.0013928869,0.00029209902,0.00018180083],"category_scores_gemma":[0.00065417006,0.00050562934,0.0002607902,0.0029239682,0.00011881999,0.0010759919,0.00043732728,0.0002854778,0.00009308519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004197872,0.0001334139,0.000002080598,0.00012765008,0.0001809972,0.000023750536,0.00019540888,0.5535012,0.00009095391,0.0027521064,0.0065389355,0.4364493],"study_design_scores_gemma":[0.000839763,0.00024995097,0.0000025921124,0.000108795386,0.00012236384,0.000036056437,0.000011224222,0.9841058,0.0009220221,0.0009776832,0.012053205,0.0005705226],"about_ca_topic_score_codex":0.000045389144,"about_ca_topic_score_gemma":9.712397e-7,"teacher_disagreement_score":0.43587878,"about_ca_system_score_codex":0.00025393654,"about_ca_system_score_gemma":0.000674017,"threshold_uncertainty_score":0.9997395},"labels":[],"label_agreement":null},{"id":"W4391249768","doi":"10.1109/la-cci58595.2023.10409454","title":"Footprinting the Behaviour of Particle Swarm Optimization with Increasing Dimensionality","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"","keywords":"Curse of dimensionality; Particle swarm optimization; Footprinting; Mathematical optimization; Multi-swarm optimization; Computer science; Swarm behaviour; Materials science; Algorithm; Artificial intelligence; Mathematics; Chemistry; DNA","score_opus":0.03357183161493484,"score_gpt":0.29060736759970845,"score_spread":0.25703553598477363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391249768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19413678,0.0000051415645,0.80384016,0.0011288979,0.000038272672,0.0001426955,3.983169e-7,0.00014553728,0.00056210934],"genre_scores_gemma":[0.717851,0.0000047848484,0.2817416,0.00004366082,0.000009859995,0.000009732219,0.0000017050123,0.0000061060678,0.00033158253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985883,0.0002129317,0.00022307337,0.0002193422,0.00054490275,0.00021145187],"domain_scores_gemma":[0.99870247,0.00040833364,0.00008533329,0.0004623102,0.00028018333,0.00006135704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015808919,0.000069308306,0.000098545344,0.000064140884,0.0001606201,0.00009514153,0.00045443545,0.000020535648,0.000036807316],"category_scores_gemma":[0.000351266,0.000042109805,0.00002461461,0.001346526,0.00007197897,0.00018904959,0.00034909885,0.000078483085,0.000016580561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014234443,0.000074429576,0.03642977,0.000012694025,0.000021983546,0.00000915931,0.00037598895,0.9154233,0.00033267983,0.04233352,0.00010614259,0.0048661013],"study_design_scores_gemma":[0.0001668012,0.000026883923,0.03024281,0.000008423963,0.0000043890454,0.0000071311474,0.000063877545,0.9643751,0.0049293116,0.00010637699,0.000010888632,0.000058005007],"about_ca_topic_score_codex":0.00013693936,"about_ca_topic_score_gemma":0.000003224217,"teacher_disagreement_score":0.5237142,"about_ca_system_score_codex":0.000014622591,"about_ca_system_score_gemma":0.00007230379,"threshold_uncertainty_score":0.17171879},"labels":[],"label_agreement":null},{"id":"W4391266734","doi":"10.1109/la-cci58595.2023.10409325","title":"The Danger of Metaphors for Metaheuristic Design","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"York University","funders":"","keywords":"Metaheuristic; Metaphor; Computer science; Parallel metaheuristic; Evolutionary computation; Swarm behaviour; Artificial intelligence; Swarm intelligence; Code (set theory); Machine learning; Particle swarm optimization; Programming language; Philosophy","score_opus":0.08316847308036736,"score_gpt":0.33462481932125543,"score_spread":0.2514563462408881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391266734","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016866335,0.00010037562,0.99625313,0.0012224991,0.00026471907,0.00047610258,0.000003207183,0.00014804953,0.0015150283],"genre_scores_gemma":[0.0056983796,0.0001571627,0.97117233,0.000071025024,0.000035608522,0.00023570537,0.0000036564222,0.000017409422,0.022608701],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854916,0.00018891548,0.00027935745,0.00023507944,0.0004471405,0.00030031597],"domain_scores_gemma":[0.9959323,0.0029693632,0.000074617084,0.00062942953,0.00032232227,0.00007199887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025835014,0.0000825949,0.00015164055,0.0001613608,0.00018832268,0.00012230524,0.00114788,0.000027177459,0.000028883705],"category_scores_gemma":[0.0016806049,0.00004969381,0.00008309667,0.0012687271,0.0000753319,0.000117457086,0.00022312898,0.000051673844,0.00010537155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026898244,0.00006032473,0.0000129505415,0.000049490784,0.00022536208,0.000007828198,0.0003534804,0.017477538,0.001103547,0.6748131,0.13284548,0.17302401],"study_design_scores_gemma":[0.00016014173,0.000054684686,0.0000680198,0.0000016910759,0.0000094762445,0.0000011302415,0.00001684758,0.9654054,0.0042928066,0.00927559,0.020648357,0.00006585219],"about_ca_topic_score_codex":0.0000046515297,"about_ca_topic_score_gemma":9.3487665e-7,"teacher_disagreement_score":0.9479279,"about_ca_system_score_codex":0.000010703335,"about_ca_system_score_gemma":0.00009685349,"threshold_uncertainty_score":0.21330656},"labels":[],"label_agreement":null},{"id":"W4391270090","doi":"10.1007/s10489-023-05073-7","title":"A Memetic Approach to Multi-Disciplinary Design and Numerical Optimization Problems using Intensify Slime Mould Optimizer","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Mount Royal University; University of Calgary","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Memetic algorithm; Convergence (economics); Parallel metaheuristic; Local search (optimization); Algorithm; Meta-optimization; Mathematics","score_opus":0.12489492499090206,"score_gpt":0.33802603347124355,"score_spread":0.2131311084803415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391270090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020697882,0.00040868405,0.99640954,0.000302123,0.0002234175,0.0014069163,0.0000020666887,0.0003640227,0.00086256344],"genre_scores_gemma":[0.04532929,0.00007386895,0.95384246,0.00018974916,0.000046814206,0.00021785981,0.000005821641,0.00004899632,0.00024514555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692726,0.00015200832,0.0005452359,0.0012061286,0.0006065311,0.00056282204],"domain_scores_gemma":[0.99835354,0.00032994171,0.0000622869,0.00065564946,0.00024156302,0.00035701896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010486617,0.00032775474,0.00034050824,0.00049526995,0.0002165333,0.0007933885,0.0009640551,0.00014159798,0.000052360392],"category_scores_gemma":[0.00017216959,0.00029515836,0.00005579689,0.002097506,0.00013648669,0.0003952092,0.00095432496,0.00040979363,0.00013447001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012577899,0.00010411951,0.0000010505573,0.00007272964,0.0000319431,0.000010545765,0.0015021097,0.9692538,0.00055548124,0.010706705,0.00011933094,0.017629638],"study_design_scores_gemma":[0.0000824699,0.00007209721,0.0000022434463,0.00005801248,0.000019014022,0.0000707285,0.00012471242,0.99650675,0.0018326172,0.00078800274,0.00009647026,0.0003468568],"about_ca_topic_score_codex":0.000016163629,"about_ca_topic_score_gemma":6.891435e-8,"teacher_disagreement_score":0.045308594,"about_ca_system_score_codex":0.00012515661,"about_ca_system_score_gemma":0.0001663156,"threshold_uncertainty_score":0.99995005},"labels":[],"label_agreement":null},{"id":"W4391407400","doi":"10.1007/978-3-031-53025-8","title":"Optimization, Learning Algorithms and Applications","year":2024,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Metaheuristic Optimization Algorithms Research","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":"Uniwersytet Opolski; Slovenská technická univerzita v Bratislave; Université de Reims Champagne-Ardenne; Kauno Technologijos Universitetas; Instituto Politécnico de Bragança; Universidade de Trás-os-Montes e Alto Douro; Instituto Politécnico do Porto; Universidade do Porto; Università degli Studi di Genova; Hanzehogeschool Groningen; Université de Sherbrooke; Universidade do Minho; Università degli Studi di Parma; Universidad de León; Politechnika Opolska; Universitatea Tehnică „Gheorghe Asachi” din Iaşi; Université de Lorraine","keywords":"Computer science; Artificial intelligence; Machine learning; Information retrieval","score_opus":0.030866000400886245,"score_gpt":0.3185919237356132,"score_spread":0.2877259233347269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391407400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.1628054e-7,0.0012298254,0.8949389,0.0008912397,0.0001554006,0.0005455705,0.000008974013,0.00018326861,0.10204662],"genre_scores_gemma":[0.00013207746,0.011369701,0.9742891,0.00043433195,0.00009104955,0.00022504397,0.00015027907,0.000018741295,0.013289643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772435,0.00009319129,0.00073381833,0.00048482095,0.00066418183,0.0002996394],"domain_scores_gemma":[0.9967988,0.00037028428,0.00023414863,0.0017170047,0.0006913249,0.00018842901],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017600309,0.00024045308,0.00026565307,0.0018461647,0.0006868962,0.0021890202,0.0029673267,0.00014529625,0.000011008132],"category_scores_gemma":[0.00012564956,0.0002459959,0.000031651645,0.0018726353,0.0010475673,0.005799602,0.0043200958,0.0008145468,0.00010029964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.961569e-7,0.000018988274,0.000010791031,0.00011479109,0.000010080957,6.8138166e-7,0.0012566238,0.033310976,2.193836e-7,0.40857685,0.0012534002,0.555446],"study_design_scores_gemma":[0.00010736775,0.000020910878,0.000028371729,0.00007823425,0.000004630969,0.000025523277,0.00001257611,0.7544595,6.993419e-7,0.0033670627,0.24170664,0.00018849366],"about_ca_topic_score_codex":0.000005297549,"about_ca_topic_score_gemma":0.0000011314166,"teacher_disagreement_score":0.7211485,"about_ca_system_score_codex":0.00021601909,"about_ca_system_score_gemma":0.0009312659,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W4391488220","doi":"10.1007/978-3-031-53036-4","title":"Optimization, Learning Algorithms and Applications","year":2024,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"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":"Uniwersytet Opolski; Slovenská technická univerzita v Bratislave; Université de Reims Champagne-Ardenne; Kauno Technologijos Universitetas; Instituto Politécnico de Bragança; Universidade de Trás-os-Montes e Alto Douro; Instituto Politécnico do Porto; Universidade do Porto; Università degli Studi di Genova; Hanzehogeschool Groningen; Université de Sherbrooke; Universidade do Minho; Università degli Studi di Parma; Universidad de León; Politechnika Opolska; Universitatea Tehnică „Gheorghe Asachi” din Iaşi; Université de Lorraine","keywords":"Computer science; Artificial intelligence; Algorithm","score_opus":0.030866000400886245,"score_gpt":0.3185919237356132,"score_spread":0.2877259233347269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391488220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.1628054e-7,0.0012298254,0.8949389,0.0008912397,0.0001554006,0.0005455705,0.000008974013,0.00018326861,0.10204662],"genre_scores_gemma":[0.00013207746,0.011369701,0.9742891,0.00043433195,0.00009104955,0.00022504397,0.00015027907,0.000018741295,0.013289643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772435,0.00009319129,0.00073381833,0.00048482095,0.00066418183,0.0002996394],"domain_scores_gemma":[0.9967988,0.00037028428,0.00023414863,0.0017170047,0.0006913249,0.00018842901],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017600309,0.00024045308,0.00026565307,0.0018461647,0.0006868962,0.0021890202,0.0029673267,0.00014529625,0.000011008132],"category_scores_gemma":[0.00012564956,0.0002459959,0.000031651645,0.0018726353,0.0010475673,0.005799602,0.0043200958,0.0008145468,0.00010029964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.961569e-7,0.000018988274,0.000010791031,0.00011479109,0.000010080957,6.8138166e-7,0.0012566238,0.033310976,2.193836e-7,0.40857685,0.0012534002,0.555446],"study_design_scores_gemma":[0.00010736775,0.000020910878,0.000028371729,0.00007823425,0.000004630969,0.000025523277,0.00001257611,0.7544595,6.993419e-7,0.0033670627,0.24170664,0.00018849366],"about_ca_topic_score_codex":0.000005297549,"about_ca_topic_score_gemma":0.0000011314166,"teacher_disagreement_score":0.7211485,"about_ca_system_score_codex":0.00021601909,"about_ca_system_score_gemma":0.0009312659,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W4391890132","doi":"10.1007/978-3-031-49295-2_3","title":"Metaheuristics Methods","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Canadore College","funders":"","keywords":"Metaheuristic; Computer science; Artificial intelligence","score_opus":0.0593214670761827,"score_gpt":0.37705185919371054,"score_spread":0.31773039211752785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391890132","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":[3.9493703e-10,0.001025879,0.50082666,0.0004390464,0.00083985826,0.00013304241,0.000008010545,0.0003367126,0.49639076],"genre_scores_gemma":[1.796445e-7,0.0002582177,0.4889769,0.00012209773,0.00012044909,0.000006168204,0.000008018921,0.00004481376,0.5104632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971862,0.00006619431,0.0005627413,0.0009564428,0.00087375136,0.00035470573],"domain_scores_gemma":[0.99714357,0.00052982464,0.00012178852,0.0015760395,0.00036686094,0.00026194687],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012114123,0.00042217487,0.00053657586,0.0006011593,0.000073877956,0.00064904045,0.001919596,0.0003289572,0.003874871],"category_scores_gemma":[0.00020936885,0.00035430593,0.00025712885,0.00017621582,0.000101758524,0.00014967081,0.0013878699,0.00080451585,0.0072167097],"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":[4.0597857e-7,0.0000046631426,1.6188258e-8,0.000048188922,0.00011648308,0.00009850701,0.00001536396,0.000033661774,0.0000018254261,0.908123,0.01902866,0.07252919],"study_design_scores_gemma":[0.00003141274,0.000019148525,8.0723275e-8,0.00002280292,0.000035767724,0.000022777349,3.6002203e-7,0.17101325,0.00003251406,0.28716567,0.5414247,0.00023149754],"about_ca_topic_score_codex":0.0000033051092,"about_ca_topic_score_gemma":7.3648647e-7,"teacher_disagreement_score":0.6209574,"about_ca_system_score_codex":0.00009150503,"about_ca_system_score_gemma":0.0002600233,"threshold_uncertainty_score":0.99989086},"labels":[],"label_agreement":null},{"id":"W4391912930","doi":"10.1145/3583131.3590467","title":"Evolutionary Mixed-Integer Optimization with Explicit Constraints","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Integer (computer science); Mathematical optimization; Integer programming; Computer science; Mathematics; Programming language","score_opus":0.023124006418148123,"score_gpt":0.24577984532345756,"score_spread":0.22265583890530943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391912930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016315429,0.00008570312,0.979468,0.0017737849,0.00017005469,0.00038779542,0.000009351801,0.00017365774,0.0016162258],"genre_scores_gemma":[0.7295087,0.00008941758,0.26993704,0.000043618093,0.000028636518,0.000035134988,0.000008104104,0.000010276946,0.00033909362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982812,0.000034788034,0.0003398749,0.00043100998,0.0006426954,0.00027046842],"domain_scores_gemma":[0.998201,0.00012691725,0.00022293774,0.00014697819,0.001194948,0.00010723061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002444258,0.00016559889,0.00018014124,0.0002441922,0.00029360523,0.00011683958,0.0006052371,0.00006372702,0.00003727339],"category_scores_gemma":[0.00014624243,0.0001270881,0.00003869809,0.0012781501,0.00035037598,0.00039019162,0.00041657104,0.00013336999,0.000018279732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042939402,0.00011025776,0.011053557,0.00017433622,0.00008098867,0.0000032140317,0.000849204,0.85096276,0.0003616577,0.109166026,0.008965317,0.018229757],"study_design_scores_gemma":[0.0003262252,0.00007512047,0.048851393,0.000066234665,0.000010486735,0.00006762941,0.00018016361,0.94311184,0.00008567371,0.007001948,0.00007595525,0.00014731266],"about_ca_topic_score_codex":0.000006723518,"about_ca_topic_score_gemma":2.862548e-7,"teacher_disagreement_score":0.71319324,"about_ca_system_score_codex":0.00004203542,"about_ca_system_score_gemma":0.00022795232,"threshold_uncertainty_score":0.5182502},"labels":[],"label_agreement":null},{"id":"W4392190020","doi":"10.1016/j.swevo.2024.101517","title":"Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":114,"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":"Shaanxi Key Science and Technology Innovation Team Project","keywords":"Reinforcement learning; Computer science; Evolutionary algorithm; Evolutionary computation; Artificial intelligence; Machine learning; Adaptation (eye); Optimization problem; Population; Algorithm","score_opus":0.1356344590218789,"score_gpt":0.36211721861281404,"score_spread":0.22648275959093514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392190020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025333297,0.0050822007,0.98818403,0.0016991147,0.00043440005,0.0003509753,0.000014995719,0.00034005666,0.0013608863],"genre_scores_gemma":[0.78356373,0.0031307326,0.20393527,0.000090875306,0.00024411535,0.00010438503,0.0006118117,0.000041069947,0.008278029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656975,0.00094885903,0.0003852727,0.00064199377,0.0010239203,0.00043022775],"domain_scores_gemma":[0.9976955,0.0010714284,0.000051049745,0.00020230123,0.00073978415,0.00023991561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024300506,0.0001823216,0.00018408519,0.00080196135,0.0007272944,0.0005344496,0.00025979415,0.000099650795,0.000035214583],"category_scores_gemma":[0.00026962624,0.00018163411,0.000035938327,0.0011275641,0.00031080414,0.00086779956,0.0005128406,0.00045591212,0.000055864217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000579821,0.00018712199,0.0011490163,0.00040774504,0.00019783413,0.00025631,0.0021017108,0.091415435,0.00005037306,0.053701915,0.04835261,0.80212194],"study_design_scores_gemma":[0.00023420593,0.00020727527,0.033682078,0.00005401674,0.0000050620647,0.00014430757,0.00013508344,0.9518425,0.0000041662106,0.0068846624,0.0066185053,0.00018813253],"about_ca_topic_score_codex":0.00020431243,"about_ca_topic_score_gemma":0.000003204774,"teacher_disagreement_score":0.8604271,"about_ca_system_score_codex":0.00017972231,"about_ca_system_score_gemma":0.00047875967,"threshold_uncertainty_score":0.7406824},"labels":[],"label_agreement":null},{"id":"W4392200299","doi":"10.18280/isi.290132","title":"Optimizing Task Scheduling in Cloud Computing Using Discrete Tuna Swarm Optimization","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Metaheuristic Optimization Algorithms Research","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":"Tuna; Computer science; Swarm behaviour; Cloud computing; Scheduling (production processes); Distributed computing; Mathematical optimization; Task (project management); Artificial intelligence; Fishery; Fish <Actinopterygii>; Mathematics; Engineering; Operating system; Biology; Systems engineering","score_opus":0.024648121327758895,"score_gpt":0.28696990142598044,"score_spread":0.26232178009822155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392200299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009102625,0.00042263223,0.98645973,0.000096657364,0.0011074021,0.00043571915,0.0000068947647,0.0005145544,0.001853756],"genre_scores_gemma":[0.41620284,0.000042920215,0.5834939,0.00006083689,0.00010780004,0.000011733964,0.000044640678,0.000018312134,0.00001699746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997169,0.00018963458,0.0010466432,0.0003664592,0.00064993976,0.00057831063],"domain_scores_gemma":[0.99866784,0.0002215756,0.0002200993,0.00042550528,0.00032398358,0.00014097398],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015857331,0.00026548532,0.00029377258,0.0011395124,0.0003457274,0.00232099,0.00066795165,0.0001419362,0.00002467761],"category_scores_gemma":[0.00055130967,0.0002726107,0.000085903695,0.0024093736,0.00009930961,0.007021545,0.00040863254,0.00038247305,0.00006954143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047615385,0.000009180458,0.000049539467,0.000272387,0.000018375893,0.000014013963,0.005943049,0.957612,0.00004568704,0.005635558,0.000015653932,0.030379789],"study_design_scores_gemma":[0.00026849913,0.000028389017,0.000041427335,0.00060293585,0.000008133123,0.0000582293,0.0003686102,0.9971235,0.00027832092,0.00073213613,0.0001945469,0.0002952956],"about_ca_topic_score_codex":0.00009113638,"about_ca_topic_score_gemma":0.0000022263325,"teacher_disagreement_score":0.40710023,"about_ca_system_score_codex":0.0006599086,"about_ca_system_score_gemma":0.00031365964,"threshold_uncertainty_score":0.9999726},"labels":[],"label_agreement":null},{"id":"W4392925322","doi":"10.20944/preprints202403.0898.v1","title":"Frilled Lizard Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Optimization algorithm; Algorithm; Mathematics","score_opus":0.10584569754794139,"score_gpt":0.36148834430700055,"score_spread":0.2556426467590592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392925322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006651904,0.00159408,0.98228353,0.0016650205,0.0049736192,0.0049051307,0.00023342298,0.0016764281,0.00260225],"genre_scores_gemma":[0.007880779,0.00072700344,0.9829096,0.000288338,0.00080431503,0.0025468701,0.0006107309,0.00028150837,0.003950824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9906613,0.0003094551,0.0019446162,0.0039265,0.0018874446,0.0012706511],"domain_scores_gemma":[0.99171513,0.00066222856,0.00095933717,0.0037309483,0.0023585134,0.0005738435],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0031702686,0.0011772896,0.0014142649,0.0013058167,0.0005167003,0.0011247565,0.0044278554,0.0014611981,0.00062154746],"category_scores_gemma":[0.0029032263,0.001229176,0.00073142804,0.001780558,0.00018818384,0.0005530945,0.010568776,0.0029980028,0.00043219587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016380509,0.00030729183,0.00010220576,0.0009571032,0.0005573891,0.000024891826,0.0005016385,0.99012524,0.000095480675,0.0033245373,0.0002897137,0.0036981166],"study_design_scores_gemma":[0.0011323842,0.000055807086,0.00007170686,0.0005721457,0.00025783683,0.000037733927,0.000017152734,0.99009186,0.001077126,0.0032591196,0.0022520493,0.0011750516],"about_ca_topic_score_codex":0.000051106963,"about_ca_topic_score_gemma":0.0000029151713,"teacher_disagreement_score":0.00781426,"about_ca_system_score_codex":0.00060219486,"about_ca_system_score_gemma":0.0014968002,"threshold_uncertainty_score":0.99991214},"labels":[],"label_agreement":null},{"id":"W4392943001","doi":"10.1109/icmla58977.2023.00112","title":"A New Self-Adaptive Hybrid Approach Based on History-Driven Methods for Improving Metaheuristics","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Computer science; Artificial intelligence","score_opus":0.0715653130843526,"score_gpt":0.34193750891847857,"score_spread":0.270372195834126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010953295,0.00004309544,0.9874709,0.00055292895,0.00063618924,0.0009148367,0.000007800136,0.001138916,0.009234233],"genre_scores_gemma":[0.00025233589,0.00000518258,0.9894132,0.00053891836,0.00012817576,0.00017479145,0.000022479348,0.000045515764,0.009419385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969372,0.00042915568,0.00042481755,0.00091561483,0.0006589949,0.0006342],"domain_scores_gemma":[0.9964029,0.0015219869,0.00015603931,0.0010477948,0.0004938641,0.00037738503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002169439,0.00027415552,0.0003813496,0.0006351827,0.00016478416,0.00017421102,0.0014081717,0.000074675736,0.00010451374],"category_scores_gemma":[0.0015954637,0.00024659385,0.00017898764,0.0009732786,0.000036535388,0.00024495344,0.00035419437,0.00024656107,0.0001301259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006714818,0.00034672944,0.0000049752916,0.0001874821,0.0001552276,0.000032952088,0.0005764805,0.07725365,0.00027762944,0.14567122,0.16166213,0.61376435],"study_design_scores_gemma":[0.00073107035,0.00023317618,0.0000122650135,0.0000038218363,0.000027010356,0.000003283576,0.000014479968,0.9641747,0.0006363054,0.001034774,0.032854646,0.00027448975],"about_ca_topic_score_codex":0.00004242185,"about_ca_topic_score_gemma":4.9008196e-7,"teacher_disagreement_score":0.88692105,"about_ca_system_score_codex":0.00043398287,"about_ca_system_score_gemma":0.001039298,"threshold_uncertainty_score":0.9999986},"labels":[],"label_agreement":null},{"id":"W4393261328","doi":"10.18280/mmep.110326","title":"A New Metaheuristic Algorithm Called Treble Opposite Algorithm and Its Application to Solve Portfolio Selection","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Metaheuristic Optimization Algorithms Research","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":"Universitas Telkom","keywords":"Algorithm; Selection (genetic algorithm); Computer science; Portfolio; Metaheuristic; Artificial intelligence; Economics; Finance","score_opus":0.01904998732109859,"score_gpt":0.24773555978621178,"score_spread":0.2286855724651132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393261328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006479306,0.0012110063,0.9971019,0.0003546236,0.00010010326,0.00053915073,0.0000038121357,0.0005218113,0.000102797385],"genre_scores_gemma":[0.0037858817,0.00017868279,0.99489146,0.000021422438,0.00009826189,0.00012751062,0.0000048227644,0.000041814306,0.00085013383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981983,0.000017764989,0.0003938066,0.00061135186,0.00038515162,0.00039366237],"domain_scores_gemma":[0.9990194,0.00018544817,0.000029627556,0.00022540022,0.00009578009,0.00044436872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000554382,0.00024268177,0.00030335793,0.00033163244,0.00009766714,0.0005396703,0.00023468908,0.0000973736,0.000016851882],"category_scores_gemma":[0.000053657273,0.00022193557,0.000045243385,0.00071848586,0.0000103212,0.00028921952,0.00015456762,0.0002550917,0.00006439622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012373836,0.000033299693,1.4981674e-7,0.00046475435,0.00006594003,0.000009149976,0.00058645726,0.78693336,0.00035601162,0.053414073,0.00011958846,0.158016],"study_design_scores_gemma":[0.00013226617,0.00006213127,9.376428e-7,0.0001568883,0.00003178203,0.00009146068,0.0000026359023,0.98642033,0.00025161824,0.011536795,0.0010717878,0.00024139074],"about_ca_topic_score_codex":0.00002077035,"about_ca_topic_score_gemma":1.5441316e-7,"teacher_disagreement_score":0.19948697,"about_ca_system_score_codex":0.000046349458,"about_ca_system_score_gemma":0.00005694624,"threshold_uncertainty_score":0.905027},"labels":[],"label_agreement":null},{"id":"W4393342946","doi":"10.1016/j.asoc.2024.111566","title":"Dynamic metaheuristic selection via Thompson Sampling for online optimization","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Office of Energy Research and Development","keywords":"Metaheuristic; Benchmark (surveying); Computer science; Heuristic; Heuristics; Mathematical optimization; Sampling (signal processing); Hyper-heuristic; Selection (genetic algorithm); Set (abstract data type); Range (aeronautics); Consistency (knowledge bases); Algorithm; Artificial intelligence; Mathematics; Engineering","score_opus":0.02826939667858403,"score_gpt":0.3276748048447225,"score_spread":0.2994054081661385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393342946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022072619,0.00024615115,0.9961807,0.00039695422,0.0007936823,0.00072061445,0.000008496337,0.0011705607,0.00026207327],"genre_scores_gemma":[0.14336032,0.000012927767,0.8559948,0.00011747021,0.00017159292,0.00003616586,0.00010522903,0.000051079893,0.00015038617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975037,0.000069826485,0.0005364302,0.00087498163,0.00047347284,0.00054159545],"domain_scores_gemma":[0.998032,0.0010996895,0.000120989636,0.0003650219,0.00025113966,0.00013121123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010027303,0.0002600859,0.00029910306,0.00041747445,0.00040609646,0.00072368147,0.00065917266,0.000110852,0.000030476116],"category_scores_gemma":[0.00023124226,0.00026707427,0.00010674432,0.0014247589,0.000038639282,0.00025599846,0.00027695845,0.0003231253,0.000037510043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054927427,0.0000445675,0.0000023732412,0.000116332834,0.000045896148,0.0000019514628,0.00014338535,0.7909895,0.000530331,0.012425504,0.00007542601,0.19561924],"study_design_scores_gemma":[0.0002561541,0.000042656204,0.000030057081,0.000044196495,0.000029345001,0.000022758075,0.000013862973,0.99523574,0.000118389726,0.0026702129,0.0012641671,0.00027246636],"about_ca_topic_score_codex":0.0000053076064,"about_ca_topic_score_gemma":0.0000020207528,"teacher_disagreement_score":0.20424622,"about_ca_system_score_codex":0.00017802148,"about_ca_system_score_gemma":0.00015860941,"threshold_uncertainty_score":0.9999781},"labels":[],"label_agreement":null},{"id":"W4393616711","doi":"10.23952/jano.6.2024.2.08","title":"Implicit augmented Lagrangian and generalized optimization","year":2024,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Augmented Lagrangian method; Lagrangian; Lagrangian relaxation; Mathematical optimization; Applied mathematics; Computer science; Mathematics; Calculus (dental); Medicine","score_opus":0.010526543568980144,"score_gpt":0.2630617335023203,"score_spread":0.2525351899333402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393616711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015458734,0.00091890915,0.9962783,0.0012603755,0.00020174946,0.00015025215,0.0000018233814,0.000062418156,0.00097158446],"genre_scores_gemma":[0.040865872,0.0017632487,0.95689255,0.00022293818,0.00013613536,0.0000062372637,0.000008385946,0.000021034133,0.000083568455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862325,0.000059458904,0.00047037343,0.00026335786,0.00039598328,0.00018757964],"domain_scores_gemma":[0.9991757,0.00012471805,0.0001593288,0.00013877459,0.00019209494,0.00020937836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046228082,0.00014313866,0.0002590847,0.00033926452,0.00010428153,0.0005160947,0.00021532817,0.00007700858,0.00007050312],"category_scores_gemma":[0.00004978553,0.00011493796,0.000047207894,0.0006679308,0.00004669589,0.0004949336,0.000106896725,0.00018971061,0.0000027784283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036565634,0.000034178556,0.000005477307,0.00003194076,0.00004874649,0.000015173951,0.0001953495,0.93902725,0.00022342466,0.018912064,0.00034217266,0.041127667],"study_design_scores_gemma":[0.00052680104,0.0000999048,0.000025284431,0.000025045973,0.000026579253,0.00009349643,0.000015141854,0.9969933,0.00013221503,0.0007056609,0.00123474,0.000121850586],"about_ca_topic_score_codex":0.0000024553563,"about_ca_topic_score_gemma":3.3500562e-8,"teacher_disagreement_score":0.05796604,"about_ca_system_score_codex":0.000038870974,"about_ca_system_score_gemma":0.00007585538,"threshold_uncertainty_score":0.49767128},"labels":[],"label_agreement":null},{"id":"W4396600523","doi":"","title":"An approach to learn drivers' implicit knowledge in the Traveling Salesman Problem with Time Windows","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metaheuristic Optimization Algorithms Research","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":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Computer science; Algorithm","score_opus":0.014752765440267147,"score_gpt":0.2408290827436372,"score_spread":0.22607631730337005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396600523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012023381,0.00005344651,0.90755826,0.004259003,0.000025361003,0.00076767546,0.000007762951,0.00014162203,0.07516348],"genre_scores_gemma":[0.5104464,0.00001053018,0.48306432,0.00020933119,0.000011558236,0.00027947058,0.00008410353,0.000029083252,0.0058651776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9903875,0.007505056,0.0003131562,0.00068252784,0.00070277136,0.00040896764],"domain_scores_gemma":[0.9964252,0.0007510938,0.00012544196,0.0018050923,0.00071652845,0.00017664615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008729698,0.00017290843,0.00019435746,0.00026625535,0.00071655534,0.0005005621,0.0034072134,0.000039584964,0.00007796508],"category_scores_gemma":[0.00019829934,0.0001462452,0.000048862967,0.0020803919,0.000093848415,0.00028978495,0.0008053809,0.00047176902,0.000077755205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004910603,0.0061085625,0.0030424385,0.00009195453,0.000081476486,0.000032081563,0.21631,0.07313734,0.00437068,0.52707636,0.0027941952,0.16690584],"study_design_scores_gemma":[0.00068625074,0.0000046662944,0.0029203666,0.000053965214,0.00000693617,0.000043131146,0.0008374465,0.98307145,0.00085713767,0.00053396024,0.010690696,0.00029400934],"about_ca_topic_score_codex":0.00018227345,"about_ca_topic_score_gemma":0.00013217467,"teacher_disagreement_score":0.9099341,"about_ca_system_score_codex":0.00012064214,"about_ca_system_score_gemma":0.00026953564,"threshold_uncertainty_score":0.6331507},"labels":[],"label_agreement":null},{"id":"W4396600600","doi":"10.1016/j.trpro.2024.12.220","title":"Learning implicit multiple time windows in the Traveling Salesman Problem","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Metaheuristic Optimization Algorithms Research","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":"HEC Montréal","funders":"","keywords":"Travelling salesman problem; Computer science; Algorithm","score_opus":0.03531422629747824,"score_gpt":0.3491913571771389,"score_spread":0.3138771308796606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396600600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056410067,0.00017408155,0.92042875,0.007417657,0.00009401144,0.003700231,0.000011790812,0.00051099213,0.011252388],"genre_scores_gemma":[0.92479324,0.000090762216,0.07127681,0.00018990814,0.000050813636,0.00065076887,0.00006457706,0.00002798862,0.0028551088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996423,0.0005424254,0.00050584285,0.0005622672,0.0012834409,0.00068301527],"domain_scores_gemma":[0.9977251,0.001085899,0.000060732942,0.00042118545,0.00060966436,0.000097383905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044012424,0.0001497055,0.00019448737,0.00061073474,0.00039513383,0.00030859618,0.0014831445,0.00009836,0.000043799475],"category_scores_gemma":[0.00066037104,0.00012088472,0.000055358127,0.0034692525,0.0001184699,0.0004150061,0.00003720718,0.0010723777,0.00015221229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027701593,0.001646089,0.1342222,0.0017855262,0.00019446867,0.0004258359,0.07467326,0.13490596,0.009827619,0.3773037,0.011098978,0.25363937],"study_design_scores_gemma":[0.001657841,0.00017826649,0.19417515,0.00015263342,0.0000069828993,0.0000025696968,0.00090223097,0.78895414,0.0008296054,0.0066232,0.006242203,0.0002751715],"about_ca_topic_score_codex":0.00015194701,"about_ca_topic_score_gemma":0.00021858307,"teacher_disagreement_score":0.86838317,"about_ca_system_score_codex":0.0000710258,"about_ca_system_score_gemma":0.0004870274,"threshold_uncertainty_score":0.49295357},"labels":[],"label_agreement":null},{"id":"W4396670306","doi":"10.1007/s44196-024-00497-6","title":"Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems","year":2024,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Brandon University","funders":"","keywords":"Artificial bee colony algorithm; Convergence (economics); Firefly algorithm; Mathematical optimization; Computer science; Genetic algorithm; Algorithm; Ant colony optimization algorithms; Optimization algorithm; Artificial intelligence; Mathematics; Particle swarm optimization","score_opus":0.048125763745224856,"score_gpt":0.342416545532792,"score_spread":0.2942907817875671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396670306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001904082,0.0005930973,0.9930514,0.0008863341,0.004291817,0.0007517424,0.00012204449,0.00007738728,0.000035779944],"genre_scores_gemma":[0.09423051,0.000054441272,0.9040955,0.0002099074,0.0011759867,0.000045739547,0.00004451923,0.00005170227,0.00009174746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956173,0.00019316048,0.0014891326,0.00058782985,0.0017462433,0.00036638122],"domain_scores_gemma":[0.9937178,0.0012638592,0.0005704725,0.00015474716,0.0040488117,0.00024434007],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019989675,0.00032337094,0.00042431385,0.0012114814,0.00022678808,0.002346321,0.00110195,0.000108302775,0.00003556439],"category_scores_gemma":[0.00051883946,0.00029983092,0.00019900402,0.0005303785,0.00013098723,0.0012463295,0.00015556625,0.00039430888,0.000011747223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028002733,0.00010875402,0.0000048961965,0.00005397367,0.00017924246,0.00006980273,0.00016341396,0.89663374,0.0001390802,0.0046718814,0.00019830272,0.097748905],"study_design_scores_gemma":[0.00030378855,0.0003317142,0.000005338968,0.00058096356,0.000028368544,0.0004233817,0.00006431034,0.99301475,0.00076561124,0.0036947054,0.0005029586,0.00028413083],"about_ca_topic_score_codex":0.00004611393,"about_ca_topic_score_gemma":0.0000011656482,"teacher_disagreement_score":0.09746477,"about_ca_system_score_codex":0.00047363763,"about_ca_system_score_gemma":0.0010687952,"threshold_uncertainty_score":0.9999454},"labels":[],"label_agreement":null},{"id":"W4396920847","doi":"10.31857/s0005117924030037","title":"Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Automation and Remote Control","topic":"Metaheuristic Optimization Algorithms Research","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 à Montréal","funders":"","keywords":"Crossover; Metaheuristic; Benchmark (surveying); Genetic algorithm; Computer science; Combinatorial optimization; Algorithm; Mathematical optimization; Convergence (economics); Heuristic; Randomness; Exploit; Quality control and genetic algorithms; Premature convergence; Meta-optimization; Mathematics; Artificial intelligence","score_opus":0.009434068072688354,"score_gpt":0.24828592730209176,"score_spread":0.2388518592294034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396920847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054902146,0.0010577149,0.9944773,0.0003406051,0.0019560992,0.0012359802,0.000025034227,0.00082193053,0.000030464822],"genre_scores_gemma":[0.014271266,0.00008568047,0.9850134,0.000061371036,0.0003369161,0.000054656346,0.00003616584,0.000045483834,0.000095064825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772716,0.0001188378,0.00053018506,0.0006695644,0.00052544376,0.0004288336],"domain_scores_gemma":[0.9984982,0.00041901667,0.00010213777,0.00039041718,0.00034876956,0.00024147754],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009478187,0.00025962616,0.00031620014,0.00042664277,0.00024669903,0.0010497753,0.00039287668,0.00013209182,0.00002011396],"category_scores_gemma":[0.0002606354,0.00024096252,0.00009014214,0.00057732424,0.00004290914,0.0004460989,0.00008372076,0.00017778591,0.000013265928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001582624,0.000034514323,3.43756e-7,0.00008202472,0.000050657185,0.00000868397,0.00014628195,0.49704975,0.00007315782,0.0022681747,0.000038753304,0.50024605],"study_design_scores_gemma":[0.001357844,0.00014816517,0.000060351907,0.000073501666,0.00005316288,0.000038550996,0.000006521367,0.99660265,0.000047985828,0.00052443205,0.0008113658,0.00027544223],"about_ca_topic_score_codex":0.000017272723,"about_ca_topic_score_gemma":2.5506137e-7,"teacher_disagreement_score":0.49997064,"about_ca_system_score_codex":0.00008866486,"about_ca_system_score_gemma":0.00015321147,"threshold_uncertainty_score":0.99998724},"labels":[],"label_agreement":null},{"id":"W4396927647","doi":"10.1007/s12532-024-00253-z","title":"Progressively strengthening and tuning MIP solvers for reoptimization","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Parallel computing; Theory of computation; Operations management; Operations research; Mathematical optimization; Mathematics; Algorithm; Economics","score_opus":0.03303405857604129,"score_gpt":0.3292207379324631,"score_spread":0.2961866793564218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396927647","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037248273,0.00020036181,0.9968648,0.0007507458,0.00015007218,0.0008105586,0.0000010575384,0.0006039552,0.00024596293],"genre_scores_gemma":[0.08276449,0.000005599068,0.9168085,0.000018995052,0.000055732908,0.0001449555,0.000020918227,0.000023913233,0.00015687908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984037,0.00007420351,0.00036986664,0.00045967827,0.0003797253,0.00031278888],"domain_scores_gemma":[0.9985914,0.0008575394,0.00007687893,0.00015592697,0.00019569222,0.00012256755],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000820865,0.00015379798,0.00018015344,0.00022745722,0.00020532534,0.0012937264,0.00022454593,0.00006621142,0.0000074572513],"category_scores_gemma":[0.0005559293,0.00013872184,0.000055736025,0.00051896315,0.00007559974,0.00050886755,0.00013067982,0.00013516162,0.000015728025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004567558,0.000059333317,0.0000065176378,0.00064278237,0.000039591083,0.000010822586,0.001668605,0.008392298,0.0000321599,0.14280869,0.0001559427,0.8461787],"study_design_scores_gemma":[0.00020688829,0.000105277475,0.0000064164137,0.00018394655,0.00001697534,0.000020730633,0.000056438956,0.9673411,0.00005780472,0.03088472,0.0009728216,0.00014687185],"about_ca_topic_score_codex":7.234061e-7,"about_ca_topic_score_gemma":1.0068143e-7,"teacher_disagreement_score":0.9589488,"about_ca_system_score_codex":0.00004667383,"about_ca_system_score_gemma":0.00006778147,"threshold_uncertainty_score":0.99974304},"labels":[],"label_agreement":null},{"id":"W4399178934","doi":"10.18280/mmep.110515","title":"Optimizing Northern Goshawk Algorithm with Fuzzy Logic and Whale Algorithm Strategies","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Metaheuristic Optimization Algorithms Research","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":"Whale; Fuzzy logic; Algorithm; Computer science; Fishery; Artificial intelligence; Biology","score_opus":0.02405201819786652,"score_gpt":0.2361042286266223,"score_spread":0.2120522104287558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399178934","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023412627,0.0023558356,0.9958314,0.00026000038,0.000070319,0.00022566508,0.0000036106692,0.00052534736,0.00049372675],"genre_scores_gemma":[0.010237654,0.00028969868,0.9891291,0.00001127009,0.000050635324,0.0000497727,0.0000030976355,0.000040877465,0.00018789349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983675,0.000022587032,0.000294943,0.00052678876,0.00036763554,0.00042054872],"domain_scores_gemma":[0.99918693,0.00021480068,0.00002671924,0.00028357585,0.000082002276,0.00020598952],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00046506044,0.00026002704,0.00028502653,0.00018574006,0.000107067994,0.001286265,0.00025739643,0.0000854145,0.0000073933334],"category_scores_gemma":[0.000016871925,0.00019691432,0.000032701864,0.00037436472,0.00006713675,0.00053718744,0.00015695061,0.00032009513,0.000017081924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.8034675e-7,0.00002441069,3.212407e-7,0.00056825776,0.000043225533,0.000041375268,0.0011486975,0.8986479,0.000015482541,0.055511605,0.0000032861553,0.04399483],"study_design_scores_gemma":[0.00013166643,0.00007970472,3.604603e-7,0.0003643386,0.000015050002,0.0001505474,0.000057686768,0.97294056,0.00002464743,0.025842726,0.00013676252,0.00025593248],"about_ca_topic_score_codex":0.000009509554,"about_ca_topic_score_gemma":4.586426e-7,"teacher_disagreement_score":0.07429265,"about_ca_system_score_codex":0.000025916637,"about_ca_system_score_gemma":0.00005272023,"threshold_uncertainty_score":0.9997505},"labels":[],"label_agreement":null},{"id":"W4399181335","doi":"10.1007/s10732-024-09529-y","title":"Mathematical models and solving methods for diversity and equity optimization","year":2024,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Metaheuristic Optimization Algorithms Research","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":"HEC Montréal","funders":"Junta de Comunidades de Castilla-La Mancha; Generalitat Valenciana; Ministerio de Ciencia, Innovación y Universidades","keywords":"Equity (law); Diversity (politics); Mathematical optimization; Mathematics; Computer science; Mathematical economics; Management science; Economics; Sociology; Political science","score_opus":0.12590444482944654,"score_gpt":0.428268988258312,"score_spread":0.30236454342886543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399181335","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012552623,0.0012284787,0.99763995,0.0004285535,0.00025727556,0.0001062419,0.000003071966,0.000022091504,0.0001888281],"genre_scores_gemma":[0.01693493,0.0005186718,0.98235565,0.000033815475,0.00007245293,9.706149e-7,3.377661e-7,0.000007770538,0.00007538556],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989494,0.00011298866,0.00033153308,0.00015704114,0.0002950223,0.00015399493],"domain_scores_gemma":[0.9981245,0.0011296801,0.00010578935,0.00012516428,0.00036251216,0.00015239537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023014273,0.000083716695,0.0002063434,0.00018009929,0.00025710242,0.00034776772,0.00029867096,0.000049389215,0.000008650624],"category_scores_gemma":[0.0011423589,0.000068707544,0.000045086967,0.00017077733,0.0000602594,0.000515604,0.0011847822,0.00016966609,4.3126678e-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":[0.000036774658,0.00012461617,0.000045304143,0.001076307,0.00018940284,0.00011138289,0.0038340562,0.21208279,0.00007108052,0.4896278,0.0014852626,0.2913152],"study_design_scores_gemma":[0.0001582301,0.000081035825,0.00000851389,0.00004914946,0.000034774068,0.0001360754,0.000018399982,0.8998428,0.000028214738,0.09940519,0.00017551938,0.00006208334],"about_ca_topic_score_codex":6.365903e-7,"about_ca_topic_score_gemma":3.8896307e-8,"teacher_disagreement_score":0.68776006,"about_ca_system_score_codex":0.000036904454,"about_ca_system_score_gemma":0.00007959723,"threshold_uncertainty_score":0.33535323},"labels":[],"label_agreement":null},{"id":"W4399291550","doi":"10.1038/s41598-024-62686-9","title":"Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metaheuristic Optimization Algorithms Research","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 Moncton","funders":"University of Engineering and Technology, Lahore","keywords":"Initialization; Particle swarm optimization; Metaheuristic; Computer science; Algorithm; Resampling; Weibull distribution; Mathematical optimization; Mathematics; Statistics","score_opus":0.07307720255715129,"score_gpt":0.3800802182928686,"score_spread":0.3070030157357173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399291550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008420365,0.00028694357,0.9864767,0.0004814471,0.0014377568,0.0024164862,0.00000795344,0.00014654148,0.00032576945],"genre_scores_gemma":[0.6117552,0.000032651085,0.38696355,0.000005803526,0.0000218488,0.0002905533,0.00007376691,0.000013862614,0.0008427638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622434,0.0004131203,0.00088136044,0.0010610953,0.0010343731,0.0003857147],"domain_scores_gemma":[0.99672794,0.0006821108,0.00026745987,0.0005113376,0.0016503206,0.00016083986],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005384587,0.00019479022,0.0005341581,0.00040076612,0.00033593577,0.0015265781,0.00028778633,0.00007208035,0.00002363965],"category_scores_gemma":[0.00027999646,0.00016108672,0.00011123564,0.0017298767,0.00038312122,0.00079407694,0.00022056838,0.00017570204,0.000002019351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009754802,0.0013368562,0.00030145337,0.0011768177,0.00091520674,0.00015336947,0.01768166,0.8430295,0.016387915,0.07710032,0.006431534,0.0345099],"study_design_scores_gemma":[0.0038204435,0.000085765925,0.00009325682,0.000054832566,0.000039127288,0.000027955148,0.000073666,0.96108025,0.03075654,0.0037309572,0.00008084647,0.00015637188],"about_ca_topic_score_codex":0.000013853259,"about_ca_topic_score_gemma":0.0000016387371,"teacher_disagreement_score":0.60333484,"about_ca_system_score_codex":0.00013304075,"about_ca_system_score_gemma":0.0006610546,"threshold_uncertainty_score":0.99950993},"labels":[],"label_agreement":null},{"id":"W4399557311","doi":"10.1016/j.dsp.2024.104637","title":"Behaviors of first-order optimizers in the context of sparse data and sparse models: A comparative study","year":2024,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Metaheuristic Optimization Algorithms Research","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":"Bishop's University; Université du Québec à Trois-Rivières","funders":"","keywords":"Context (archaeology); Computer science; Sparse approximation; Order (exchange); Sparse matrix; Mathematical optimization; Artificial intelligence; Mathematics; Biology","score_opus":0.1313547994423052,"score_gpt":0.3537208359567656,"score_spread":0.22236603651446044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399557311","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":[0.04097505,0.0011200953,0.9558609,0.00014441497,0.000029376753,0.0005736411,0.000062969295,0.00003290224,0.0012006563],"genre_scores_gemma":[0.99186355,0.0000092739865,0.008044376,0.00001223787,0.000008105035,0.000014992058,0.000013675987,0.000007973531,0.000025813388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830914,0.00008611784,0.00041403723,0.00043212727,0.00057746423,0.00018111954],"domain_scores_gemma":[0.9989228,0.0003215486,0.00009815363,0.00041383435,0.00019284795,0.000050793977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067352667,0.0001328771,0.0002662018,0.00018647421,0.000059825208,0.0006469743,0.0011175757,0.00002800385,0.0000072063376],"category_scores_gemma":[0.000063530475,0.00009484395,0.000020637186,0.0009966986,0.00019441376,0.002049712,0.0005463636,0.00016894567,0.0000017139082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016154398,0.0035810897,0.007325037,0.00083916064,0.00018723526,0.00033128867,0.15301004,0.10602541,0.000052863666,0.0032434294,0.0005964239,0.72464645],"study_design_scores_gemma":[0.0003226891,0.00012353576,0.00008643386,0.000119213524,0.000012617234,0.0000088861925,0.006629868,0.9921772,0.000022067385,0.00036200573,0.00003997163,0.00009551378],"about_ca_topic_score_codex":0.00005085538,"about_ca_topic_score_gemma":0.000016850843,"teacher_disagreement_score":0.9508885,"about_ca_system_score_codex":0.000015197585,"about_ca_system_score_gemma":0.00016095754,"threshold_uncertainty_score":0.62387884},"labels":[],"label_agreement":null},{"id":"W4399634105","doi":"10.5267/j.dsl.2024.4.001","title":"A hybrid genetic-simulated annealing algorithm for multiple traveling salesman problems","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Bisha","keywords":"Travelling salesman problem; Simulated annealing; Genetic algorithm; Mathematical optimization; Computer science; 2-opt; Algorithm; Mathematics","score_opus":0.03378102675463943,"score_gpt":0.3191322940027633,"score_spread":0.2853512672481238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399634105","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021794729,0.00019399796,0.97365594,0.00179329,0.0014303129,0.00073325454,0.000019380945,0.0003489202,0.000030160854],"genre_scores_gemma":[0.11073847,0.000024311168,0.8880438,0.000908858,0.000146892,0.00004144255,0.0000058654377,0.000028046075,0.000062320265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995351,0.00006514773,0.0006113728,0.0013671427,0.0017489851,0.0008563232],"domain_scores_gemma":[0.9969587,0.0013843765,0.00008104048,0.0008521578,0.0003909031,0.00033284802],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0028544087,0.00023557311,0.00024454444,0.00085634965,0.00058420806,0.0023425128,0.0024320877,0.000046448327,0.000021166012],"category_scores_gemma":[0.0010123989,0.00020425001,0.00013040335,0.0026446057,0.00032531377,0.0011542958,0.00043541007,0.00024086775,0.0001301534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000333162,0.000021831864,0.00003129492,0.000019956544,0.000010730975,0.00008844723,0.00035611904,0.15425754,0.011354377,0.00030058372,0.0014339171,0.83212185],"study_design_scores_gemma":[0.0003884613,0.000045845347,0.00019421634,0.00009258982,0.0000050296007,0.00005790717,0.000008799429,0.98930556,0.0033576794,0.001245626,0.005039638,0.00025866678],"about_ca_topic_score_codex":0.000014379886,"about_ca_topic_score_gemma":7.1430634e-7,"teacher_disagreement_score":0.835048,"about_ca_system_score_codex":0.00014812271,"about_ca_system_score_gemma":0.0002709271,"threshold_uncertainty_score":0.99869317},"labels":[],"label_agreement":null},{"id":"W4399680902","doi":"10.5267/j.ijiec.2024.5.004","title":"Effects of crossover operator combined with mutation operator in genetic algorithms for the generalized travelling salesman problem","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","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":"Al-Imam Muhammad Ibn Saud Islamic University","keywords":"Crossover; Travelling salesman problem; Operator (biology); Genetic algorithm; Mutation; Mathematical optimization; Algorithm; Mathematics; Computer science; Artificial intelligence; Biology; Genetics; Gene","score_opus":0.025199216016296072,"score_gpt":0.29227710258205736,"score_spread":0.26707788656576126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399680902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019760199,0.00032251835,0.97670496,0.0010322633,0.0015872711,0.00054678525,0.000011702657,0.000028763097,0.000005533134],"genre_scores_gemma":[0.6594172,0.00003657522,0.33994108,0.000028290058,0.00046390644,0.00005285922,0.000007392301,0.000025617246,0.00002704875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982903,0.00006615851,0.00067859853,0.0001784853,0.0006214174,0.0001650517],"domain_scores_gemma":[0.9974746,0.0013358385,0.00016589527,0.000104039136,0.0008477954,0.000071809074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052669895,0.00013973533,0.00022385157,0.0005412459,0.000047099915,0.00043307483,0.00075143477,0.000066712455,0.000005683135],"category_scores_gemma":[0.000337858,0.0001010374,0.00009092546,0.00063402276,0.00003620659,0.00037054118,0.000045448644,0.00030882886,0.000001288745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059608617,0.000060703696,0.000016958336,0.00003285482,0.00023776422,0.00008819897,0.00061799213,0.98043627,0.00048445028,0.00564362,0.00007715426,0.0122444555],"study_design_scores_gemma":[0.0028636581,0.00025539417,0.00023497728,0.00032178336,0.000029013248,0.000094497744,0.000018040262,0.99251074,0.003044893,0.00025779172,0.00026288707,0.000106343265],"about_ca_topic_score_codex":0.000015888163,"about_ca_topic_score_gemma":0.0000016324611,"teacher_disagreement_score":0.639657,"about_ca_system_score_codex":0.00013094337,"about_ca_system_score_gemma":0.0004774011,"threshold_uncertainty_score":0.41761506},"labels":[],"label_agreement":null},{"id":"W4399681197","doi":"10.5267/j.ijiec.2024.4.004","title":"An improved black widow optimization (IBWO) algorithm for solving global optimization problems","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematical optimization; Optimization algorithm; Algorithm; Global optimization; Computer science; Optimization problem; Mathematics","score_opus":0.03040651770898991,"score_gpt":0.3082248744484339,"score_spread":0.277818356739444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399681197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000494548,0.00011855045,0.99264413,0.0015252087,0.0049454733,0.0004057282,0.000067943154,0.00021164985,0.00003185614],"genre_scores_gemma":[0.015229242,0.000043609154,0.98283756,0.000054513992,0.0016517484,0.000021986933,0.000096152646,0.00003451659,0.000030666535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976349,0.000059847294,0.00089998485,0.0003412288,0.0007796087,0.00028447196],"domain_scores_gemma":[0.99703103,0.0003698499,0.00029049063,0.00020389732,0.0018837763,0.00022096463],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000871099,0.00021869157,0.00025195163,0.00066973903,0.000090391644,0.0014557631,0.0012288755,0.00015627897,0.000034695997],"category_scores_gemma":[0.0006106907,0.00022178893,0.00016953608,0.0008917921,0.00003741944,0.0018409735,0.00010764594,0.000352485,0.0000041268727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007758538,0.00007654263,0.000003670997,0.000010879564,0.00018614996,0.000017268741,0.00013443462,0.9234543,0.000048589292,0.0023748747,0.00043314803,0.07325238],"study_design_scores_gemma":[0.0010183516,0.00022060443,0.000004784959,0.00018289156,0.00002977302,0.00011841187,0.000016313414,0.9970047,0.00007210144,0.0003055446,0.0008237628,0.00020279526],"about_ca_topic_score_codex":0.0000122880665,"about_ca_topic_score_gemma":4.8337165e-7,"teacher_disagreement_score":0.073550366,"about_ca_system_score_codex":0.0004410139,"about_ca_system_score_gemma":0.000616852,"threshold_uncertainty_score":0.9995808},"labels":[],"label_agreement":null},{"id":"W4399693827","doi":"10.1007/978-3-031-55964-8_5","title":"Optimization by Monte Carlo Methods","year":2024,"lang":"en","type":"book-chapter","venue":"Undergraduate texts in mathematics","topic":"Metaheuristic Optimization Algorithms Research","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":"Acadia University","funders":"","keywords":"Monte Carlo method; Computer science; Statistical physics; Mathematics; Statistics; Physics","score_opus":0.031636050712801934,"score_gpt":0.3297636773453508,"score_spread":0.2981276266325488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399693827","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.270656e-8,0.0013124975,0.6756356,0.0010686509,0.0004059451,0.0004681224,0.000023329578,0.0002586502,0.32082722],"genre_scores_gemma":[0.0000048529982,0.0008922678,0.52874124,0.000044998673,0.000036929494,0.000022224418,0.000017410364,0.00009457347,0.47014546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621135,0.000116817144,0.0011231946,0.0009591332,0.0010788272,0.0005106978],"domain_scores_gemma":[0.99698114,0.000712545,0.0003795986,0.0014763507,0.00025240015,0.00019795196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014622718,0.0006184185,0.00080104754,0.00084405195,0.000089341935,0.0006161127,0.0016427677,0.00044770178,0.0002248331],"category_scores_gemma":[0.00029234265,0.0005884634,0.00019410872,0.00039247895,0.00013529357,0.0002845388,0.00080915814,0.00093899964,0.00046654325],"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":[0.0000012524309,0.000072491806,1.2683489e-7,0.00046375088,0.000108898595,0.00007658757,0.0003459053,0.05940455,0.000003568826,0.9106272,0.017691312,0.011204359],"study_design_scores_gemma":[0.00012132495,0.000023367316,1.6404018e-8,0.00025833317,0.000028467666,0.000023542316,0.00000484005,0.59145147,0.000013329996,0.38555107,0.022188755,0.0003354977],"about_ca_topic_score_codex":0.000007792263,"about_ca_topic_score_gemma":0.0000053160707,"teacher_disagreement_score":0.5320469,"about_ca_system_score_codex":0.00033692588,"about_ca_system_score_gemma":0.00020396379,"threshold_uncertainty_score":0.9996567},"labels":[],"label_agreement":null},{"id":"W4399970954","doi":"10.28924/2291-8639-22-2024-105","title":"Application of Ant Colony Programming Approach for Solving Systems of Stochastic Differential Equations","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Mathematics; Mathematical optimization; Ant colony optimization algorithms; Ant colony; Stochastic differential equation; Stochastic programming; Applied mathematics","score_opus":0.0215332210920512,"score_gpt":0.3266769948467153,"score_spread":0.3051437737546641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399970954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035699474,0.00057188555,0.9984052,0.00013688055,0.00008478034,0.00036375748,0.000041246793,0.0000119260885,0.000027288186],"genre_scores_gemma":[0.8672359,0.000035764777,0.1323786,0.0000026827342,0.0001324268,0.00013215045,0.00003684431,0.000005473277,0.000040143314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842376,0.000031505504,0.00071834034,0.00019129057,0.00054311636,0.000091970636],"domain_scores_gemma":[0.9974633,0.00044947397,0.0004884349,0.00018497923,0.0013497897,0.000064046435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005415644,0.00007804406,0.00026645267,0.00083003734,0.000054498596,0.00019556606,0.00058974506,0.000036438825,0.0000040087853],"category_scores_gemma":[0.00010867237,0.000066420966,0.00019343194,0.0009438458,0.00006420324,0.0002014555,0.000082260776,0.00008477631,3.934559e-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":[0.000026473681,0.00064157875,0.00036406756,0.0002583553,0.0041775443,0.0000011997804,0.0005594547,0.30830163,0.0065307454,0.5302764,0.00007425399,0.14878827],"study_design_scores_gemma":[0.000150154,0.000040412993,0.00018859384,0.000022857399,0.00031794666,0.000008701692,0.000064149935,0.99814737,0.00018697939,0.0004943661,0.0003248126,0.00005365532],"about_ca_topic_score_codex":0.00003081186,"about_ca_topic_score_gemma":0.0000016129643,"teacher_disagreement_score":0.8668789,"about_ca_system_score_codex":0.000040385272,"about_ca_system_score_gemma":0.00010771355,"threshold_uncertainty_score":0.27085683},"labels":[],"label_agreement":null},{"id":"W4399989754","doi":"10.1109/tevc.2024.3418470","title":"A Prediction and Weak Coevolution-Based Dynamic Constrained Multiobjective Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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","funders":"High-end Foreign Experts Recruitment Plan of China; National Natural Science Foundation of China","keywords":"Coevolution; Computer science; Evolutionary computation; Mathematical optimization; Artificial intelligence; Evolutionary algorithm; Machine learning; Mathematics; Biology","score_opus":0.014773459700807139,"score_gpt":0.2681553874437499,"score_spread":0.25338192774294277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399989754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023463492,0.0002711711,0.994721,0.001126939,0.0013779656,0.0006917116,0.00014489029,0.0010841654,0.00034753105],"genre_scores_gemma":[0.692299,0.000066859175,0.30706286,0.000061526305,0.000035488578,0.00012047604,0.000078845354,0.000026779835,0.00024814473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746156,0.00028838948,0.00046303478,0.0008023847,0.000667428,0.000317189],"domain_scores_gemma":[0.99845743,0.0006053689,0.00007990761,0.0002788106,0.00040587512,0.00017262007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036365737,0.00027024804,0.00019901781,0.0009960985,0.0005270126,0.00031629423,0.00021949732,0.00015542135,0.00007203299],"category_scores_gemma":[0.00003380196,0.00029269158,0.00010553298,0.0014379119,0.00020506277,0.0010412812,0.000005338998,0.00037342022,0.00007216803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027838229,0.00015815264,0.0000035494572,0.0000508602,0.000053719275,0.000007737736,0.00013754613,0.9662651,0.00013544301,0.0011965305,0.00020596186,0.03175759],"study_design_scores_gemma":[0.0007068689,0.00024819368,0.00045142602,0.00009839967,0.00003414124,0.000070028225,0.00003991942,0.99702245,0.00012869829,0.0008568026,0.00010079988,0.00024229895],"about_ca_topic_score_codex":0.000024202323,"about_ca_topic_score_gemma":0.0000049246964,"teacher_disagreement_score":0.69206434,"about_ca_system_score_codex":0.0005265058,"about_ca_system_score_gemma":0.0004987288,"threshold_uncertainty_score":0.9999525},"labels":[],"label_agreement":null},{"id":"W4400184785","doi":"10.1007/s43069-024-00336-6","title":"One-Shot Learning for MIPs with SOS1 Constraints","year":2024,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"Institut de Valorisation des Données","keywords":"Shot (pellet); Computer science; Artificial intelligence; Chemistry","score_opus":0.16772378563297602,"score_gpt":0.42246039914045613,"score_spread":0.2547366135074801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400184785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025097266,0.00025552732,0.9787889,0.012178668,0.00013742333,0.0010075641,0.00001796835,0.00022171582,0.007141267],"genre_scores_gemma":[0.34976462,0.00018631716,0.61857194,0.00014005358,0.000204043,0.0010375157,0.00010092319,0.000061747785,0.029932832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967722,0.00034987944,0.00025636665,0.00063994055,0.0011498055,0.0008317714],"domain_scores_gemma":[0.9973773,0.00072207896,0.000009577322,0.0005266105,0.0011145642,0.00024989052],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0023604303,0.00012876844,0.00015746037,0.00066333596,0.0010961884,0.0024306488,0.0008465942,0.00007191457,0.00037187804],"category_scores_gemma":[0.0010751657,0.000111677866,0.000054616597,0.0016805365,0.00037125117,0.00084343215,0.00031391962,0.0006705533,0.00037955277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025838803,0.00017864794,0.000060576127,0.00013604338,0.00013810414,0.000055067514,0.0009388493,0.0675058,0.0017802264,0.68592906,0.013353153,0.22989862],"study_design_scores_gemma":[0.000278037,0.0003625119,0.00001988923,0.0000675326,0.0000032401067,0.0000203854,0.00025115852,0.93863827,0.0011433113,0.0006998833,0.05837058,0.00014518475],"about_ca_topic_score_codex":0.000035444347,"about_ca_topic_score_gemma":0.000086093954,"teacher_disagreement_score":0.8711325,"about_ca_system_score_codex":0.00011963144,"about_ca_system_score_gemma":0.0011387183,"threshold_uncertainty_score":0.99860495},"labels":[],"label_agreement":null},{"id":"W4400412426","doi":"10.1145/3638529.3654012","title":"Direct Augmented Lagrangian Evolution Strategies","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","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":"Lagrangian; Augmented Lagrangian method; Computer science; Applied mathematics; Mathematics; Algorithm","score_opus":0.01818831382712729,"score_gpt":0.25866533924153157,"score_spread":0.24047702541440427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400412426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017971918,0.0014912012,0.97211534,0.0014297831,0.00035198475,0.00027155908,0.0000065862955,0.00016866748,0.0061929617],"genre_scores_gemma":[0.9256625,0.000084201136,0.0737564,0.000016083279,0.00003764582,0.000014802998,0.0000017648406,0.0000064913165,0.00042012302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987675,0.000025990417,0.00025952485,0.0003430814,0.00042679586,0.00017710312],"domain_scores_gemma":[0.9991625,0.00008273428,0.00008234441,0.0000923774,0.0005148911,0.00006516872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022125944,0.0001198642,0.00012429652,0.00016939938,0.00017160385,0.00035544383,0.00045487873,0.000046878475,0.000017879658],"category_scores_gemma":[0.000062404935,0.000092755115,0.00004712003,0.0007216647,0.00016506933,0.00053149543,0.00025945684,0.000107805914,0.000010885038],"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":[0.000020454723,0.00010685953,0.0022787796,0.0006719903,0.00012805419,0.0000027982574,0.0017778345,0.02004301,0.006466456,0.9059216,0.0067029507,0.055879205],"study_design_scores_gemma":[0.000107791355,0.000046424982,0.034759976,0.00010236022,0.000013058303,0.000028812674,0.00014018867,0.9186284,0.00022463841,0.045487534,0.0003535066,0.00010728935],"about_ca_topic_score_codex":0.000021959331,"about_ca_topic_score_gemma":7.203781e-7,"teacher_disagreement_score":0.9076906,"about_ca_system_score_codex":0.00005308894,"about_ca_system_score_gemma":0.00026763696,"threshold_uncertainty_score":0.37824437},"labels":[],"label_agreement":null},{"id":"W4400776009","doi":"10.1007/s00500-024-09823-8","title":"A sophisticated solution to numerical and engineering optimization problems using Chaotic Beluga Whale Optimizer","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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 Calgary","funders":"","keywords":"Whale; Beluga Whale; Chaotic; Computer science; Optimization problem; Mathematical optimization; Fishery; Artificial intelligence; Algorithm; Mathematics; Oceanography; Biology; Geology; Arctic","score_opus":0.028952735283182585,"score_gpt":0.279551159684461,"score_spread":0.2505984244012784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400776009","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000771982,0.00047937792,0.9962952,0.00085507584,0.00053358765,0.0003692409,0.0000015978278,0.0006372267,0.00005670297],"genre_scores_gemma":[0.276547,0.000007420911,0.72318435,0.00005725375,0.0001230101,0.000006909633,0.0000048667666,0.000028899809,0.00004024481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980117,0.00007840643,0.0003704232,0.0006491579,0.0004039023,0.00048640522],"domain_scores_gemma":[0.99897176,0.00027305834,0.000049018086,0.00029414037,0.00017216173,0.00023985871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006252468,0.00020101828,0.00022477806,0.00041445796,0.00022376826,0.0007508941,0.00036770877,0.00007439869,0.00002029412],"category_scores_gemma":[0.00041246455,0.00020951142,0.0000421342,0.0014212711,0.000029952496,0.0003765495,0.00055225735,0.00024190395,0.000031179236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013688111,0.000017427183,0.000014735991,0.000093353585,0.000019581508,0.000017141634,0.00038433942,0.98799276,0.0003895368,0.002066579,0.00004958181,0.008953588],"study_design_scores_gemma":[0.00013246592,0.000040486986,0.00003333187,0.00018715409,0.00001195019,0.000071899514,0.000005336119,0.9989886,0.000053452328,0.00006138991,0.00018686279,0.00022701787],"about_ca_topic_score_codex":0.000016913526,"about_ca_topic_score_gemma":1.3224839e-7,"teacher_disagreement_score":0.27577505,"about_ca_system_score_codex":0.00014363627,"about_ca_system_score_gemma":0.00011054218,"threshold_uncertainty_score":0.8543627},"labels":[],"label_agreement":null},{"id":"W4401011606","doi":"10.1007/s00500-024-09896-5","title":"A co-evolutionary algorithm with adaptive penalty function for constrained optimization","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Verafin (Canada)","funders":"Università degli Studi di Trento","keywords":"Metaheuristic; Mathematical optimization; Penalty method; Computer science; Evolutionary algorithm; Optimization problem; Constrained optimization; Benchmark (surveying); Algorithm; Mathematics","score_opus":0.02337172264000821,"score_gpt":0.28811614323647,"score_spread":0.2647444205964618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401011606","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019314974,0.00027304565,0.9963265,0.00041493707,0.0005271694,0.0006198854,0.000025276842,0.00069912325,0.0010947648],"genre_scores_gemma":[0.039478246,0.0000053258454,0.95959586,0.000117181415,0.00028865878,0.000037626716,0.00007931863,0.000029742096,0.00036803546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798226,0.00011468685,0.00031545412,0.0006593275,0.00051428116,0.0004140074],"domain_scores_gemma":[0.99829537,0.0007136074,0.00008983938,0.00029097675,0.0004771839,0.00013303309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068461377,0.00019817112,0.00019936837,0.00029497256,0.00038340822,0.0004387538,0.000386788,0.000073572926,0.000052295196],"category_scores_gemma":[0.000118205106,0.00017603165,0.00007642459,0.00091360573,0.000112888956,0.00051464356,0.000094596144,0.00021808811,0.000035996272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003545623,0.000055956716,0.000020780806,0.00007598325,0.00012158167,0.000029867588,0.0003593497,0.6951996,0.000017407814,0.029104596,0.0021674973,0.27281195],"study_design_scores_gemma":[0.0004740884,0.00031460568,0.000047984904,0.000090819536,0.000018588775,0.00007535675,0.00006370902,0.9963079,0.0000350171,0.00091159943,0.001442327,0.00021801404],"about_ca_topic_score_codex":0.0000070942706,"about_ca_topic_score_gemma":3.952623e-7,"teacher_disagreement_score":0.3011083,"about_ca_system_score_codex":0.00012686358,"about_ca_system_score_gemma":0.00046983513,"threshold_uncertainty_score":0.71783626},"labels":[],"label_agreement":null},{"id":"W4401013477","doi":"10.1016/b978-0-443-15533-8.00004-7","title":"Emerging trends in computational swarm intelligence: A comprehensive overview","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Metaheuristic Optimization Algorithms Research","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":"Swarm intelligence; Swarm behaviour; Computer science; Data science; Artificial intelligence; Machine learning; Particle swarm optimization","score_opus":0.0532703291114881,"score_gpt":0.33852510771358535,"score_spread":0.28525477860209725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401013477","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":[5.5368025e-7,0.0072800843,0.041884124,0.0006398143,0.0008338271,0.00038134074,0.000031197116,0.00019540709,0.94875365],"genre_scores_gemma":[0.00006878882,0.00046326357,0.046894237,0.00036728272,0.00017061824,0.0000443361,0.00006655913,0.000082737155,0.9518422],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962223,0.00008084403,0.00093992613,0.0010879098,0.00118844,0.00048055657],"domain_scores_gemma":[0.9981331,0.00025869455,0.00021998056,0.0008198898,0.00035897066,0.00020936721],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043539633,0.00053670123,0.00072154036,0.0016675866,0.000096864234,0.00036852714,0.0013669931,0.0002594391,0.00070570904],"category_scores_gemma":[0.000021247613,0.00053568784,0.00029025215,0.0002831416,0.0001562486,0.00013414123,0.0010142635,0.0010544717,0.0010233598],"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":[0.0000019760485,0.000008588174,2.5740798e-7,0.000116716685,0.000059100927,0.00019083517,0.00029631864,0.0035584432,1.9804197e-7,0.2453597,0.0001755252,0.75023234],"study_design_scores_gemma":[0.000116535484,0.000038389742,0.00000719401,0.000566893,0.00002211047,0.000043209628,0.000006478928,0.22166069,0.000003087929,0.14286643,0.6342219,0.0004470849],"about_ca_topic_score_codex":0.0000014517168,"about_ca_topic_score_gemma":0.000009362287,"teacher_disagreement_score":0.74978524,"about_ca_system_score_codex":0.00024047097,"about_ca_system_score_gemma":0.00026720774,"threshold_uncertainty_score":0.9997545},"labels":[],"label_agreement":null},{"id":"W4401013509","doi":"10.1016/b978-0-443-15533-8.00009-6","title":"Conclusion and future research directions","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Metaheuristic Optimization Algorithms Research","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":"Lakehead University","funders":"","keywords":"Computer science","score_opus":0.03794060541817349,"score_gpt":0.3289472627806413,"score_spread":0.2910066573624678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401013509","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":[2.1507891e-7,0.012646795,0.0008584146,0.0018192411,0.0013144292,0.00060605095,0.000020142214,0.00023348902,0.9825012],"genre_scores_gemma":[0.000004002783,0.005092186,0.013596031,0.00009042533,0.0011649451,0.000048390917,0.000008951867,0.00006835725,0.9799267],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968417,0.00011914411,0.0003760687,0.000960339,0.0012678532,0.00043489021],"domain_scores_gemma":[0.99780124,0.00024851717,0.0000719816,0.0010368215,0.0005505612,0.0002908893],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014567056,0.00031386112,0.00038520855,0.0008122957,0.00047763402,0.0006216489,0.0008928964,0.00036584077,0.0003095873],"category_scores_gemma":[0.000039953746,0.00027595673,0.00011283367,0.00012697911,0.00030719273,0.00010400451,0.0019899772,0.0015203755,0.00083349686],"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":[0.0000012592724,0.0000028448055,8.0537035e-8,0.000060301987,0.000030931722,0.0000673868,0.00018587585,1.8556706e-7,0.000002895513,0.27653137,0.00067998125,0.7224369],"study_design_scores_gemma":[0.00009142859,0.00005369598,0.0000015077854,0.00015337695,0.00001675233,0.000059373673,0.0000066815765,0.0032865792,0.000009381694,0.05757664,0.93849885,0.00024572652],"about_ca_topic_score_codex":7.965679e-7,"about_ca_topic_score_gemma":0.00000542791,"teacher_disagreement_score":0.9378189,"about_ca_system_score_codex":0.00014026638,"about_ca_system_score_gemma":0.00030159688,"threshold_uncertainty_score":0.99996924},"labels":[],"label_agreement":null},{"id":"W4401023659","doi":"10.24963/ijcai.2024/766","title":"Boost Embodied AI Models with Robust Compression Boundary","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Eusko Jaurlaritza","keywords":"Reinforcement learning; Computer science; Travelling salesman problem; Combinatorial optimization; Solver; Set (abstract data type); Optimization problem; Local optimum; Domain (mathematical analysis); Artificial intelligence; Mathematical optimization; Theoretical computer science; Algorithm; Mathematics","score_opus":0.05116237900306967,"score_gpt":0.30245592558821743,"score_spread":0.25129354658514774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401023659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017902465,0.00074271497,0.88591963,0.0037494842,0.0009505119,0.0006467477,0.000025442223,0.0007843672,0.107163206],"genre_scores_gemma":[0.011442452,0.00017478691,0.9658834,0.0007847481,0.00020147974,0.00015242111,0.00008726949,0.000081765844,0.021191692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99581534,0.00020354273,0.0004939983,0.0014890429,0.0014604648,0.00053762336],"domain_scores_gemma":[0.9968354,0.00011915771,0.00013010015,0.0020418318,0.0005556984,0.00031778016],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005818661,0.0004598451,0.000520366,0.00048310473,0.00020597345,0.002579263,0.0025159458,0.00029311547,0.0003379593],"category_scores_gemma":[0.000035149533,0.0003324138,0.00012306581,0.0005413056,0.00015703533,0.00035253575,0.008952613,0.0017933131,0.00035897794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001645931,0.00009224673,0.000003686732,0.00035536618,0.0001095752,0.00016298884,0.00028139263,0.83943343,0.000004464173,0.11490148,0.035575446,0.009063473],"study_design_scores_gemma":[0.00021880033,0.000045057248,0.000004837212,0.00030000063,0.00001944457,0.00002444115,0.00000852991,0.9143243,0.00007630083,0.08228051,0.0022979854,0.00039978005],"about_ca_topic_score_codex":0.00011913933,"about_ca_topic_score_gemma":0.000015418043,"teacher_disagreement_score":0.08597151,"about_ca_system_score_codex":0.00014670538,"about_ca_system_score_gemma":0.001283411,"threshold_uncertainty_score":0.9999128},"labels":[],"label_agreement":null},{"id":"W4401367936","doi":"10.1134/s000511792403007x","title":"Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems","year":2024,"lang":"en","type":"article","venue":"Automation and Remote Control","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Montréal","funders":"","keywords":"Metaheuristic; Genetic algorithm; Parallel metaheuristic; Computer science; Mathematical optimization; Algorithm; Meta-optimization; Optimization algorithm; Mathematics","score_opus":0.009434068072688354,"score_gpt":0.24828592730209176,"score_spread":0.2388518592294034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401367936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000054902146,0.0010577149,0.9944773,0.0003406051,0.0019560992,0.0012359802,0.000025034227,0.00082193053,0.000030464822],"genre_scores_gemma":[0.014271266,0.00008568047,0.9850134,0.000061371036,0.0003369161,0.000054656346,0.00003616584,0.000045483834,0.000095064825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772716,0.0001188378,0.00053018506,0.0006695644,0.00052544376,0.0004288336],"domain_scores_gemma":[0.9984982,0.00041901667,0.00010213777,0.00039041718,0.00034876956,0.00024147754],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009478187,0.00025962616,0.00031620014,0.00042664277,0.00024669903,0.0010497753,0.00039287668,0.00013209182,0.00002011396],"category_scores_gemma":[0.0002606354,0.00024096252,0.00009014214,0.00057732424,0.00004290914,0.0004460989,0.00008372076,0.00017778591,0.000013265928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001582624,0.000034514323,3.43756e-7,0.00008202472,0.000050657185,0.00000868397,0.00014628195,0.49704975,0.00007315782,0.0022681747,0.000038753304,0.50024605],"study_design_scores_gemma":[0.001357844,0.00014816517,0.000060351907,0.000073501666,0.00005316288,0.000038550996,0.000006521367,0.99660265,0.000047985828,0.00052443205,0.0008113658,0.00027544223],"about_ca_topic_score_codex":0.000017272723,"about_ca_topic_score_gemma":2.5506137e-7,"teacher_disagreement_score":0.49997064,"about_ca_system_score_codex":0.00008866486,"about_ca_system_score_gemma":0.00015321147,"threshold_uncertainty_score":0.99998724},"labels":[],"label_agreement":null},{"id":"W4401416376","doi":"10.1109/cec60901.2024.10612083","title":"Opposition-based Multi-Objective ADAM Optimizer (OMAdam) for Training ANNs","year":2024,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University; Ontario Tech University","funders":"","keywords":"Opposition (politics); Training (meteorology); Computer science; Artificial neural network; Artificial intelligence; Operations research; Engineering; Political science; Law; Meteorology","score_opus":0.0872323117370008,"score_gpt":0.3510949414992215,"score_spread":0.2638626297622207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401416376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027190898,0.00011908993,0.9928396,0.0022183168,0.0005623139,0.0006052771,0.00003005376,0.0005741272,0.0030240547],"genre_scores_gemma":[0.045054253,0.0000038456774,0.95006514,0.00048994354,0.000098604396,0.000223065,0.000023041957,0.00002661551,0.0040154858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981883,0.00009664553,0.0002674724,0.00061796134,0.00041288583,0.00041674564],"domain_scores_gemma":[0.9982093,0.00086524355,0.000030809868,0.0004035369,0.00030633112,0.00018476977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006709545,0.000167789,0.00018796998,0.00032520617,0.00022844056,0.00093235687,0.0006111856,0.00007243594,0.00026303862],"category_scores_gemma":[0.00027191621,0.0001460304,0.00013224014,0.0007045994,0.000071298724,0.00045980242,0.00009927707,0.00015983809,0.00009869861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075815275,0.0006917929,0.000027097087,0.0004305931,0.00046187048,0.0002698954,0.008243364,0.3812174,0.0030509122,0.21619128,0.036996223,0.35234377],"study_design_scores_gemma":[0.0006021636,0.00009874567,0.000020603933,0.000033188237,0.0000087949,0.000007505245,0.0000813874,0.9928413,0.0018682318,0.00045390066,0.0037934226,0.0001907277],"about_ca_topic_score_codex":0.000022332659,"about_ca_topic_score_gemma":0.000005870757,"teacher_disagreement_score":0.61162394,"about_ca_system_score_codex":0.000088980814,"about_ca_system_score_gemma":0.000682061,"threshold_uncertainty_score":0.8990739},"labels":[],"label_agreement":null},{"id":"W4401596732","doi":"10.1109/tevc.2024.3443913","title":"Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"National Natural Science Foundation of China","keywords":"Algorithm; Bridging (networking); Computer science; Reinforcement learning; Evolutionary algorithm; Evolutionary computation; Artificial intelligence; Machine learning","score_opus":0.03684696776184181,"score_gpt":0.2988468254451176,"score_spread":0.26199985768327577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401596732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015771072,0.0005451646,0.99339485,0.0008759809,0.0018395393,0.0006397283,0.0000762518,0.0008208419,0.00023052766],"genre_scores_gemma":[0.91985744,0.0003667143,0.07740355,0.00022200673,0.00016485689,0.000107524684,0.00023254038,0.00006014175,0.0015852039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957109,0.0006767772,0.0006425041,0.0011376312,0.0012806851,0.0005514669],"domain_scores_gemma":[0.9974232,0.0011954565,0.00011834169,0.00040862523,0.0005670499,0.00028735012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006062642,0.0004233522,0.00034187883,0.0011013302,0.0008612578,0.00041715548,0.0004098123,0.00011663868,0.00007230909],"category_scores_gemma":[0.00004574554,0.00045139142,0.00015095579,0.0013860305,0.00018947494,0.00090694986,0.00002375271,0.0008411498,0.00034896508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004437559,0.00015538292,0.000012093831,0.00004619752,0.000104364,0.00006322889,0.00016224974,0.84732455,0.000038695016,0.00041152464,0.0028149413,0.14882241],"study_design_scores_gemma":[0.0005842137,0.00056749064,0.005215617,0.00013110801,0.000022664131,0.00017972494,0.000020809952,0.9909534,0.00027216834,0.0006252541,0.0009959507,0.0004315972],"about_ca_topic_score_codex":0.00018553973,"about_ca_topic_score_gemma":0.0000030637411,"teacher_disagreement_score":0.91828036,"about_ca_system_score_codex":0.000533688,"about_ca_system_score_gemma":0.00035649832,"threshold_uncertainty_score":0.99979377},"labels":[],"label_agreement":null},{"id":"W4401879610","doi":"10.1109/compsac61105.2024.00285","title":"Synchronous Set-Based Particle Swarm Optimization: Heuristics for Portfolio Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Particle swarm optimization; Multi-swarm optimization; Computer science; Metaheuristic; Mathematical optimization; Heuristics; Portfolio optimization; Set (abstract data type); Portfolio; Algorithm; Mathematics","score_opus":0.030721164745771863,"score_gpt":0.31134818103077777,"score_spread":0.2806270162850059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401879610","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001178006,0.00032300467,0.9925814,0.0022078538,0.00081716786,0.0007204751,0.000025290648,0.0009880799,0.002324907],"genre_scores_gemma":[0.018953744,0.00006324641,0.9771004,0.00043010357,0.00018018582,0.00017575531,0.00010208803,0.000050260314,0.0029442168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975201,0.00009871208,0.00052434776,0.0007312043,0.00059791986,0.0005276994],"domain_scores_gemma":[0.9978938,0.0005255429,0.00007227068,0.0007210897,0.0005238438,0.00026343775],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007178374,0.00023594583,0.00023451299,0.00026636515,0.00023126927,0.0010914325,0.00074601994,0.00010729135,0.000869763],"category_scores_gemma":[0.00043445543,0.0002202051,0.000117671065,0.0013935323,0.0000721815,0.00061552104,0.00015391149,0.00014288066,0.00010720383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072255048,0.00006580361,0.000008833798,0.00008131115,0.000026578507,0.000025526397,0.00005014765,0.9572512,0.0000048668257,0.030910468,0.0063683325,0.005199726],"study_design_scores_gemma":[0.00044577915,0.000133218,0.0000029145463,0.000026934424,0.000022766988,0.000014481209,0.000011769957,0.9931235,0.0009891846,0.00028521803,0.0046725874,0.00027165862],"about_ca_topic_score_codex":0.000009767426,"about_ca_topic_score_gemma":8.8143196e-7,"teacher_disagreement_score":0.035872307,"about_ca_system_score_codex":0.00013397362,"about_ca_system_score_gemma":0.0005691021,"threshold_uncertainty_score":0.9999455},"labels":[],"label_agreement":null},{"id":"W4401931292","doi":"10.18280/jesa.570406","title":"Decentralized Control Design for Heating System in Multi-Zone Buildings Based on Whale Optimization Algorithm","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Metaheuristic Optimization Algorithms Research","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":"PID controller; Control theory (sociology); MATLAB; Controller (irrigation); Heating system; Computer science; Normalization (sociology); Energy (signal processing); Temperature control; Electric energy consumption; Minification; Energy consumption; Control engineering; Engineering; Control (management); Mathematics; Mechanical engineering","score_opus":0.03776886121649152,"score_gpt":0.30222774619282144,"score_spread":0.2644588849763299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401931292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009213227,0.001026423,0.9955784,0.00031988497,0.0009507223,0.0012972777,0.000023273067,0.00063618826,0.00007567795],"genre_scores_gemma":[0.03872,0.00008112803,0.9605327,0.000113589784,0.00012500602,0.0001283565,0.0000062675754,0.00008025621,0.00021269136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952334,0.0012374937,0.001145581,0.00063521776,0.0009719522,0.00077635667],"domain_scores_gemma":[0.99675006,0.0016462959,0.00031622176,0.0004283974,0.00052972604,0.00032928074],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0038515215,0.00035893614,0.00057230284,0.0010344262,0.00041925043,0.0022247606,0.0009128247,0.00012752434,0.000045824065],"category_scores_gemma":[0.0009969794,0.0003138013,0.00019984026,0.0014000278,0.000064448934,0.0009346809,0.00007649088,0.0004447891,0.00003979285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025151829,0.00008757378,0.00001832341,0.00029036144,0.000046541278,0.00025549575,0.00022577829,0.77502036,0.00006713093,0.0011999265,0.0002789941,0.22248437],"study_design_scores_gemma":[0.0028970302,0.0002914413,0.00039713318,0.0015629434,0.000029887457,0.00038524892,0.00004892878,0.9936201,0.00015969029,0.0000798224,0.0002244437,0.00030332227],"about_ca_topic_score_codex":0.00002539838,"about_ca_topic_score_gemma":0.0000012891034,"teacher_disagreement_score":0.22218105,"about_ca_system_score_codex":0.0008972578,"about_ca_system_score_gemma":0.00049891,"threshold_uncertainty_score":0.9999314},"labels":[],"label_agreement":null},{"id":"W4402464079","doi":"10.11159/cist24.174","title":"Feature Selection and Classification Performance: A Multi-Dataset Comparative Analysis Using Boruta Algorithm and Random Forest","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Centre National pour la Recherche Scientifique et Technique","keywords":"Random forest; Computer science; Feature selection; Selection (genetic algorithm); Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Statistical classification; Algorithm; Data mining","score_opus":0.022222570938692208,"score_gpt":0.2777503685963036,"score_spread":0.25552779765761136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464079","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":[0.14477308,0.0019805566,0.8516368,0.00029827506,0.00061475293,0.0005443018,0.000018773408,0.00011021667,0.000023187817],"genre_scores_gemma":[0.9544019,0.00012006083,0.045261372,0.000012000486,0.000059186546,0.000014992919,0.0000014865958,0.0000052030528,0.00012379487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986981,0.00001511683,0.00018307242,0.0004942,0.00037751842,0.00023195238],"domain_scores_gemma":[0.99937826,0.00013071984,0.00007731748,0.000095069925,0.00019326257,0.00012537846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065346586,0.00014848029,0.00027119758,0.0006617505,0.0002674543,0.0010357532,0.00030240495,0.00003642615,1.3498956e-7],"category_scores_gemma":[0.0000308329,0.00010270219,0.000023053859,0.002903945,0.00014952128,0.00049492193,0.00020263948,0.00022491999,1.2441667e-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":[0.00019238827,0.00041710024,0.03953813,0.0038698579,0.0019394208,0.000015351714,0.0043344875,0.39946154,0.017189257,0.20806071,0.0036513978,0.32133037],"study_design_scores_gemma":[0.00023090624,0.00006368941,0.0070495554,0.0001256392,0.000041084033,0.000049611055,0.000006604185,0.9917925,0.00018321589,0.0000053510353,0.0003344922,0.00011733452],"about_ca_topic_score_codex":0.000010950585,"about_ca_topic_score_gemma":0.0000011416611,"teacher_disagreement_score":0.80962884,"about_ca_system_score_codex":0.00003728983,"about_ca_system_score_gemma":0.00003865982,"threshold_uncertainty_score":0.9987791},"labels":[],"label_agreement":null},{"id":"W4402474306","doi":"10.1109/ccece59415.2024.10667062","title":"Exploring Common Patterns in Well-Known Metaheuristic Optimization Algorithms","year":2024,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Wilfrid Laurier University; Brock University","funders":"","keywords":"Metaheuristic; Computer science; Parallel metaheuristic; Algorithm; Mathematical optimization; Meta-optimization; Mathematics","score_opus":0.10430084415151553,"score_gpt":0.3160400967171695,"score_spread":0.21173925256565396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046814396,0.0002888938,0.98892355,0.001082265,0.001157399,0.00031221774,0.000004710336,0.0006112214,0.0071515916],"genre_scores_gemma":[0.21757726,0.0010124593,0.7769412,0.00021518354,0.0002498779,0.00028644595,0.000041332838,0.00007408928,0.0036021934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972339,0.00024008138,0.000554283,0.00075123005,0.0007105056,0.00050999527],"domain_scores_gemma":[0.99855703,0.00040080972,0.00004196932,0.0007059597,0.00011131763,0.00018293464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009465294,0.00023468482,0.00029008836,0.0008715519,0.00008807474,0.0008593367,0.0009925512,0.00006282237,0.0007551039],"category_scores_gemma":[0.0001293279,0.00021246505,0.0000814371,0.001917677,0.00003489125,0.0017000737,0.00044115173,0.00036338068,0.00045555504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063972648,0.00021239166,0.0006448056,0.00023459981,0.00007437899,0.0007299047,0.0010208675,0.70744777,0.000029536803,0.06066966,0.0010431595,0.2278865],"study_design_scores_gemma":[0.00020237296,0.00004004162,0.00045164494,0.00007679781,0.0000069397024,0.000023335755,0.00003763193,0.9959561,0.00027710095,0.00038871335,0.0022946019,0.0002447007],"about_ca_topic_score_codex":0.00022941057,"about_ca_topic_score_gemma":0.000025464398,"teacher_disagreement_score":0.28850833,"about_ca_system_score_codex":0.00014216534,"about_ca_system_score_gemma":0.00011217245,"threshold_uncertainty_score":0.86640733},"labels":[],"label_agreement":null},{"id":"W4403251249","doi":"10.2139/ssrn.4981246","title":"Providing a Method for Efficient Crossover in Genetic Algorithms with X-Method Coding and its Constraints","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Coding (social sciences); Computer science; Algorithm; Genetic algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.023717874585135888,"score_gpt":0.3455430069609815,"score_spread":0.3218251323758456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403251249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010585232,0.0071769007,0.9888767,0.0008989209,0.0004707094,0.0012998838,0.00002015962,0.00007419697,0.00012403014],"genre_scores_gemma":[0.033187144,0.0013003298,0.96425426,0.000053165204,0.00023923832,0.00016761528,0.0000039021984,0.0000740769,0.00072027603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99391675,0.00054650765,0.0007270711,0.0012123625,0.0008403188,0.0027570142],"domain_scores_gemma":[0.9978891,0.0006433124,0.0003585312,0.00042924457,0.00044463025,0.00023519967],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008911214,0.00044900944,0.0006201548,0.0008115212,0.00025096606,0.0012486934,0.001151654,0.00025496975,0.000011015323],"category_scores_gemma":[0.00046869487,0.0003769007,0.00014382339,0.0006420052,0.00008424616,0.00011111902,0.0013074258,0.0049287328,0.0000061323176],"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":false,"study_design_scores_codex":[0.00011680096,0.00015601674,0.00003874607,0.0007069886,0.0006138879,0.0001764824,0.001785896,0.14636268,0.00018832585,0.28551942,0.000028886941,0.5643059],"study_design_scores_gemma":[0.001162565,0.00023368197,0.000023139886,0.00032295712,0.000054010026,0.0017157579,0.0001935239,0.8981224,0.000222361,0.09751525,0.000051950377,0.00038244034],"about_ca_topic_score_codex":0.00003230918,"about_ca_topic_score_gemma":0.000057324163,"teacher_disagreement_score":0.7517597,"about_ca_system_score_codex":0.0013840565,"about_ca_system_score_gemma":0.00875848,"threshold_uncertainty_score":0.9998683},"labels":[],"label_agreement":null},{"id":"W4403947118","doi":"10.18280/mmep.111025","title":"Stochastic Gradient Descents Optimizer and Its Variants: Performance of the Optimizers for Multinomial Logistic Models on Large Data Sets by Simulation","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Metaheuristic Optimization Algorithms Research","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":"Multinomial distribution; Multinomial logistic regression; Computer science; Logistic regression; Mathematical optimization; Econometrics; Mathematics; Machine learning","score_opus":0.10319558823282901,"score_gpt":0.2943643076832303,"score_spread":0.1911687194504013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403947118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032632204,0.00032468725,0.9955096,0.00007955093,0.00012803217,0.0005476492,0.00006178577,0.000075047974,0.00001038932],"genre_scores_gemma":[0.7484718,0.00004381807,0.25136864,0.0000062944887,0.000011143498,0.000030454688,0.000009606974,0.000018618657,0.000039619008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987279,0.000022009193,0.00030915084,0.0003997769,0.00026811758,0.00027301378],"domain_scores_gemma":[0.9988715,0.0005467128,0.000040815525,0.00039026584,0.0000602152,0.000090457004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006519501,0.00015554579,0.00019980296,0.00008052799,0.00009217757,0.00015479639,0.00042290823,0.00006063151,0.0000018915932],"category_scores_gemma":[0.00018110382,0.00011225239,0.00002646627,0.00015793474,0.000025016165,0.00028598917,0.00026426374,0.00015558129,0.0000015375784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066604075,0.000044214772,1.2745947e-7,0.00076726795,0.000021473135,2.5038378e-7,0.0002479753,0.98702514,0.00001616452,0.011448533,0.0000141872315,0.00040800442],"study_design_scores_gemma":[0.0003357705,0.00004821186,4.1968252e-7,0.000457559,0.00002132157,0.0000034811515,0.000002138216,0.99706423,0.000020000361,0.0019170722,0.0000085444935,0.00012125916],"about_ca_topic_score_codex":0.000001078158,"about_ca_topic_score_gemma":1.8844652e-8,"teacher_disagreement_score":0.74520856,"about_ca_system_score_codex":0.000022159827,"about_ca_system_score_gemma":0.000021312511,"threshold_uncertainty_score":0.45775196},"labels":[],"label_agreement":null},{"id":"W4403979562","doi":"10.5206/mt.v4i3.21093","title":"The Extended Watson—Wong—Wyman Lemma in Maple","year":2024,"lang":"en","type":"article","venue":"Maple Transactions","topic":"Metaheuristic Optimization Algorithms Research","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":"Western University","funders":"","keywords":"Maple; Watson; Lemma (botany); Mathematics; Computer science; Botany; Artificial intelligence; Biology","score_opus":0.016079056700768653,"score_gpt":0.28409697189768535,"score_spread":0.2680179151969167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403979562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012701497,0.0004949973,0.9836717,0.010443463,0.0009010487,0.00026105027,0.00001155904,0.00030491396,0.003784231],"genre_scores_gemma":[0.776404,0.0009795346,0.15195812,0.00027831315,0.00022960713,0.00050154736,0.000015991647,0.00008235411,0.06955053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982218,0.0001598642,0.00029971002,0.00041900823,0.0004716782,0.00042790367],"domain_scores_gemma":[0.99873185,0.00041496727,0.000018055549,0.00065274135,0.00006860197,0.00011379276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007114551,0.00013299556,0.00012363234,0.00024599038,0.00036678495,0.00072375586,0.0008652337,0.00005749426,0.0004895465],"category_scores_gemma":[0.00003876705,0.00010031269,0.00009589808,0.0013377907,0.000083469116,0.00040021172,0.000033764354,0.00040350322,0.00044183974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017031798,0.00028426692,0.0000267027,0.00010183176,0.000118916185,0.00024893336,0.0027166475,0.017986955,0.0004403426,0.14583327,0.008843687,0.8233814],"study_design_scores_gemma":[0.00019680195,0.000031096515,0.00050333946,0.000019068431,0.0000067231244,0.000029583942,0.00008255745,0.84791076,0.0004574,0.0044353157,0.14618038,0.00014696477],"about_ca_topic_score_codex":0.000118623786,"about_ca_topic_score_gemma":0.00030868177,"teacher_disagreement_score":0.8317136,"about_ca_system_score_codex":0.00009516899,"about_ca_system_score_gemma":0.00016883793,"threshold_uncertainty_score":0.6979194},"labels":[],"label_agreement":null},{"id":"W4404460122","doi":"10.1007/s00500-024-10328-7","title":"Shuffled multi-evolutionary algorithm with linear population size reduction","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Reduction (mathematics); Algorithm; Evolutionary algorithm; Population; Mathematics; Computer science; Mathematical optimization","score_opus":0.020844793063182227,"score_gpt":0.29947190413810953,"score_spread":0.2786271110749273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404460122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015198026,0.0003559465,0.9951395,0.00055915705,0.000999193,0.00027199922,0.0000025878594,0.0009831127,0.00016869201],"genre_scores_gemma":[0.15752108,0.000007686689,0.8410169,0.000034551875,0.00041752582,0.0000069344705,0.000021900209,0.00002637198,0.0009470651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978383,0.00015853111,0.00034184192,0.00066278555,0.0006190536,0.000379473],"domain_scores_gemma":[0.99880177,0.00033867534,0.00007647698,0.00040488289,0.00025019998,0.00012797455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000606634,0.0001855059,0.00018198171,0.0002301769,0.0003121929,0.00034613194,0.00044931768,0.00007697731,0.000032614145],"category_scores_gemma":[0.0002223338,0.00016471052,0.00006200614,0.0012941619,0.00005386527,0.0005640982,0.00025717734,0.00033117394,0.00010806283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011292095,0.00017355607,0.0006885847,0.00014524054,0.00011462672,0.00014280682,0.0011103282,0.15915772,0.00021880535,0.00832452,0.00089768623,0.82901484],"study_design_scores_gemma":[0.00026445472,0.00005719868,0.0029208532,0.00009495219,0.000008272867,0.00016012033,0.000023645638,0.9950417,0.00006323837,0.00034659513,0.0008157636,0.0002031923],"about_ca_topic_score_codex":0.00005656515,"about_ca_topic_score_gemma":9.22077e-7,"teacher_disagreement_score":0.835884,"about_ca_system_score_codex":0.000154234,"about_ca_system_score_gemma":0.0001675409,"threshold_uncertainty_score":0.67167},"labels":[],"label_agreement":null},{"id":"W4404944255","doi":"10.1007/s12206-024-1125-6","title":"Principle and performance validation of search and rescue team algorithm","year":2024,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Knapsack problem; Algorithm; Robustness (evolution); Metaheuristic; Mathematical optimization; Computer science; Modal; Optimization problem; Mathematics","score_opus":0.01922730556347673,"score_gpt":0.3107953147575139,"score_spread":0.29156800919403714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404944255","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":[0.48237687,0.0008336445,0.5134662,0.0029612272,0.00016333481,0.00010276829,9.508589e-7,0.00003196481,0.000063041436],"genre_scores_gemma":[0.8110523,0.0011385999,0.18773516,0.000013622234,0.000020527501,0.0000012286902,5.139693e-8,0.000002859007,0.000035603156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985746,0.000035865574,0.000295419,0.00023969433,0.0006569959,0.00019740967],"domain_scores_gemma":[0.99906564,0.00008382885,0.0000473587,0.000165986,0.0005165415,0.000120645214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031849847,0.00006290295,0.00016285869,0.0007410651,0.00011482432,0.0001536461,0.0005189759,0.00007651575,0.000004738948],"category_scores_gemma":[0.0005366914,0.00004558552,0.000011825196,0.0016215042,0.00049296196,0.00062688655,0.00055855495,0.00029837686,0.0000010531779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004871217,0.000030794377,0.00015714884,0.000055512013,0.0000105790095,0.000020255182,0.000083042185,0.000036766327,0.01530788,0.09487467,0.000018382314,0.8894001],"study_design_scores_gemma":[0.00018332484,0.0006878801,0.00029326853,0.00006787418,0.0000057817592,0.00065599545,0.000042690695,0.9164587,0.07750165,0.0035262809,0.0005135494,0.000062968385],"about_ca_topic_score_codex":0.0000033983474,"about_ca_topic_score_gemma":3.502035e-7,"teacher_disagreement_score":0.91642195,"about_ca_system_score_codex":0.000030624597,"about_ca_system_score_gemma":0.00035152465,"threshold_uncertainty_score":0.18589236},"labels":[],"label_agreement":null},{"id":"W4405245753","doi":"10.1145/3707465","title":"An Evolutionary Algorithm for Expensive Mixed-Integer Black-Box Optimization with Explicit Constraints","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Metaheuristic Optimization Algorithms Research","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Black box; Mathematical optimization; Integer (computer science); Evolutionary algorithm; Set (abstract data type); Algorithm; Exploit; Computer science; Constraint (computer-aided design); Function (biology); Optimization problem; Integer programming; Constrained optimization problem; Process (computing); Mathematics; Artificial intelligence","score_opus":0.014629037312522528,"score_gpt":0.2775491560978298,"score_spread":0.2629201187853073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405245753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024419236,0.00039461168,0.99609846,0.0010961244,0.00043542075,0.00081881136,0.00006828701,0.00085956533,0.00020430054],"genre_scores_gemma":[0.031779356,0.0005949206,0.9656926,0.00009250573,0.00012722694,0.00028731913,0.00037044034,0.00007589939,0.0009797462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969026,0.00034319473,0.00046751538,0.0011269266,0.0006569723,0.0005027985],"domain_scores_gemma":[0.9974801,0.00070973777,0.00011933435,0.00060185714,0.0007992046,0.00028977793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043860194,0.00038955823,0.0003016589,0.0007806774,0.00094561133,0.00047924477,0.00050410355,0.00022255597,0.00028775996],"category_scores_gemma":[0.00016346155,0.00037408032,0.00010881765,0.0011529566,0.00031572132,0.0018003287,0.000028219474,0.00053239695,0.000029378074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004367092,0.00016039913,0.000008814065,0.00003746413,0.00008572525,0.000013205871,0.00036956064,0.916205,0.0000100803145,0.0016643918,0.00022411467,0.08117758],"study_design_scores_gemma":[0.0007607612,0.0007922386,0.000029878449,0.0001331239,0.000059055532,0.00015020315,0.0004786323,0.9957651,0.000059342918,0.00031375204,0.0010141217,0.00044379005],"about_ca_topic_score_codex":0.000014279715,"about_ca_topic_score_gemma":0.0000012385555,"teacher_disagreement_score":0.08073379,"about_ca_system_score_codex":0.0002412519,"about_ca_system_score_gemma":0.00038651994,"threshold_uncertainty_score":0.99987113},"labels":[],"label_agreement":null},{"id":"W4405384256","doi":"10.1007/s11227-024-06803-5","title":"Publisher Correction: Cooperative, collaborative, coevolutionary multi-objective optimization on CPU-GPU multi-core","year":2024,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Computer science; Core (optical fiber); Multi-core processor; Parallel computing; Many core; Telecommunications","score_opus":0.041707348238248565,"score_gpt":0.3120371033853171,"score_spread":0.27032975514706853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405384256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009780795,0.0011451178,0.9890103,0.0013043763,0.0063082213,0.00033919263,0.0000037721459,0.00013138006,0.0007795605],"genre_scores_gemma":[0.25633365,0.00035754166,0.73847014,0.00046804632,0.0008083333,0.000009826159,0.000012342521,0.00006747115,0.0034726802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969066,0.0007727422,0.0007233383,0.00034517213,0.0008972439,0.00035491274],"domain_scores_gemma":[0.99582195,0.0012979549,0.00021019383,0.00036582013,0.0021381385,0.00016593274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025008677,0.000252383,0.00032155114,0.00054231193,0.00058571954,0.00079299335,0.000978962,0.00009983768,0.0000875955],"category_scores_gemma":[0.00091238285,0.00017141287,0.00011190822,0.0025144734,0.00016206889,0.0017172828,0.00028237514,0.00091666327,0.000047845973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003507379,0.000187036,0.00011254072,0.000015865793,0.000117818534,0.000060568764,0.0033578477,0.96599185,0.000089094014,0.0008794201,0.021358764,0.0077941315],"study_design_scores_gemma":[0.0006630564,0.00028064093,0.00054231135,0.00019720999,0.000022364766,0.00036448313,0.0008525957,0.99570465,0.000079901125,0.000048942504,0.0010615475,0.00018231486],"about_ca_topic_score_codex":0.000019774348,"about_ca_topic_score_gemma":0.000004750433,"teacher_disagreement_score":0.25535557,"about_ca_system_score_codex":0.00031860865,"about_ca_system_score_gemma":0.0007067339,"threshold_uncertainty_score":0.7646853},"labels":[],"label_agreement":null},{"id":"W4405791888","doi":"10.18280/isi.290606","title":"Performance Assessment of Three Swarm Intelligence Algorithms in Combinatorial Problem","year":2024,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Metaheuristic Optimization Algorithms Research","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":"Swarm intelligence; Computer science; Swarm behaviour; Algorithm; Artificial intelligence; Particle swarm optimization","score_opus":0.03855522446078999,"score_gpt":0.307209874404058,"score_spread":0.26865464994326804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405791888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006542743,0.0017393443,0.9634138,0.0004102497,0.006042568,0.0010160772,0.000044062177,0.00015615618,0.02063504],"genre_scores_gemma":[0.708945,0.0012770933,0.2886978,0.000037307218,0.00023284835,0.00016396717,0.000066617475,0.000029703237,0.00054965506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956429,0.00023655983,0.0018193026,0.00037832547,0.0012322398,0.00069067883],"domain_scores_gemma":[0.99760705,0.00035302926,0.00036161244,0.0005637375,0.000944591,0.00016995381],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029821456,0.0003421385,0.0004765576,0.0010311515,0.00016844299,0.0011489196,0.0009897759,0.0002638949,0.00017593995],"category_scores_gemma":[0.00032389018,0.00035726486,0.00011622219,0.0031150656,0.0004704284,0.0082048625,0.0004698111,0.0006553782,0.00022316055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011104469,0.000110093446,0.0011969358,0.004001655,0.000054518317,0.00001826025,0.005387598,0.060468853,0.0000063256634,0.2109735,0.00018416057,0.717587],"study_design_scores_gemma":[0.00029315776,0.00035508885,0.003785009,0.0019848763,0.000018026614,0.000058829723,0.00016615132,0.9696211,0.0005024397,0.019016873,0.0038756465,0.00032276512],"about_ca_topic_score_codex":0.00042942597,"about_ca_topic_score_gemma":0.000019177578,"teacher_disagreement_score":0.90915227,"about_ca_system_score_codex":0.00096862984,"about_ca_system_score_gemma":0.0014387461,"threshold_uncertainty_score":0.999888},"labels":[],"label_agreement":null},{"id":"W4406161035","doi":"10.1016/j.eswa.2025.126398","title":"t-SNE-PSO: Optimizing t-SNE using particle swarm optimization","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Université du Québec à Trois-Rivières; Bishop's University","funders":"","keywords":"Particle swarm optimization; Computer science; Metaheuristic; Multi-swarm optimization; Mathematical optimization; Particle (ecology); Algorithm; Mathematics","score_opus":0.02750459388104861,"score_gpt":0.31741090542930855,"score_spread":0.28990631154825997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406161035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000118110875,0.0011815294,0.9934902,0.0007521154,0.00021699109,0.0015173012,0.000004537846,0.0003927174,0.0023264922],"genre_scores_gemma":[0.054420374,0.00008966347,0.9408653,0.00025208062,0.00014634663,0.0020138235,0.00001873621,0.00003443679,0.0021592563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976261,0.00016800487,0.0005302361,0.00069393707,0.0005254588,0.00045624102],"domain_scores_gemma":[0.9976433,0.00016996455,0.00018205572,0.0012903834,0.0005185784,0.0001957486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044113144,0.00022700406,0.0002948742,0.00026421333,0.00055771595,0.0005786171,0.0009799331,0.00009291519,0.000022327333],"category_scores_gemma":[0.0000591262,0.00020296735,0.00004900138,0.002197063,0.00009081734,0.0005409315,0.00024158366,0.00015764972,0.000049874747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000683162,0.00012823261,0.000099291006,0.00003885762,0.00005088091,0.0000031810803,0.0003729573,0.95393485,0.00053395313,0.041918874,0.00062717346,0.002284948],"study_design_scores_gemma":[0.00045564503,0.000022781896,0.0000145366985,0.000068826004,0.0000110421715,0.000019933665,0.00019342285,0.98878825,0.0013411934,0.000055340093,0.00881001,0.0002190456],"about_ca_topic_score_codex":0.00015975484,"about_ca_topic_score_gemma":0.0000020926288,"teacher_disagreement_score":0.054302264,"about_ca_system_score_codex":0.000199271,"about_ca_system_score_gemma":0.00031994825,"threshold_uncertainty_score":0.82767683},"labels":[],"label_agreement":null},{"id":"W4406411297","doi":"10.1016/j.amc.2025.129278","title":"A new mathematical model and solution method for the asymmetric traveling salesman problem with replenishment arcs","year":2025,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Travelling salesman problem; Mathematical optimization; Computer science; Applied mathematics; Mathematics","score_opus":0.03024387723599984,"score_gpt":0.3162004486415981,"score_spread":0.2859565714055982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406411297","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000058782432,0.000065951186,0.9955294,0.0012800518,0.000012968727,0.0011075506,0.0000010943473,0.000059183683,0.0018849889],"genre_scores_gemma":[0.008976363,0.000029964674,0.99048865,0.00009628241,0.0000116582005,0.00015003463,0.0000032982507,0.000010334988,0.00023340179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988892,0.000016745045,0.0003116723,0.00031782582,0.00028054588,0.0001840147],"domain_scores_gemma":[0.9985045,0.0009670503,0.00011737312,0.00022919264,0.00011365665,0.000068235415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091302756,0.00013064333,0.00020021926,0.00013204876,0.000249355,0.00030002077,0.00020827792,0.000044803448,8.582029e-7],"category_scores_gemma":[0.000053804884,0.00009013834,0.000022112707,0.0004592127,0.000034108532,0.00007755797,0.0001407649,0.00009209701,0.000001099925],"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":[0.0000070565466,0.000039406368,3.1519997e-7,0.00020359282,0.000032430562,1.5932291e-7,0.0006809337,0.036860913,0.000063886924,0.71785426,0.00019404049,0.24406299],"study_design_scores_gemma":[0.0003944917,0.000027820099,0.000006650078,0.000028691615,0.000032638265,0.000006369089,0.000053580727,0.6649236,0.0001018485,0.3343348,0.000023347453,0.000066181354],"about_ca_topic_score_codex":0.0000036985718,"about_ca_topic_score_gemma":0.0000013414624,"teacher_disagreement_score":0.62806267,"about_ca_system_score_codex":0.000024565206,"about_ca_system_score_gemma":0.00011292475,"threshold_uncertainty_score":0.36757347},"labels":[],"label_agreement":null},{"id":"W4406852867","doi":"10.1016/j.ins.2025.121908","title":"Adaptive knowledge transfer based on machine learning method for evolutionary multitasking optimization","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","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 Victoria","funders":"National Defense Basic Scientific Research Program of China; Northwestern Polytechnical University; National Natural Science Foundation of China","keywords":"Human multitasking; Computer science; Transfer of learning; Artificial intelligence; Knowledge transfer; Machine learning; Distributed computing; Theoretical computer science; Knowledge management; Psychology; Cognitive psychology","score_opus":0.03902144589697563,"score_gpt":0.3539665320585347,"score_spread":0.31494508616155903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406852867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003792769,0.000045422443,0.980896,0.0010005613,0.0003103094,0.0004777817,0.000011830049,0.00014577643,0.017108548],"genre_scores_gemma":[0.03209536,0.000007909259,0.96691364,0.00052987185,0.000016336602,0.00010398909,0.000033581637,0.0000030911333,0.00029622111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983702,0.00021666041,0.00039318838,0.0002568578,0.00050452066,0.00025862473],"domain_scores_gemma":[0.9980505,0.0010639942,0.00007888218,0.00019491861,0.000550917,0.0000607598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020831835,0.00012528004,0.00013443819,0.0009166217,0.00075993943,0.00038415744,0.000725905,0.000058716796,0.000043070977],"category_scores_gemma":[0.00094893103,0.00010893158,0.00006451038,0.0018176074,0.00009674399,0.0021106063,0.0000768779,0.00014164987,0.000029967707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000135221735,0.000024631414,0.000028610828,0.000016375756,0.0000046031614,7.1303e-8,0.0003598669,0.90945435,0.0000027815236,0.03281241,0.0002268768,0.057055913],"study_design_scores_gemma":[0.00054055767,0.0001410335,0.00009101353,0.000035475598,0.000003990422,7.0492547e-7,0.00009928135,0.99178207,0.00016835911,0.0003807883,0.0066421917,0.0001145055],"about_ca_topic_score_codex":0.000021570966,"about_ca_topic_score_gemma":0.00000221743,"teacher_disagreement_score":0.08232776,"about_ca_system_score_codex":0.000097250166,"about_ca_system_score_gemma":0.0004153807,"threshold_uncertainty_score":0.58449167},"labels":[],"label_agreement":null},{"id":"W4407093524","doi":"10.1002/cjce.25323","title":"Issue Highlights","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Political science","score_opus":0.008186961107944415,"score_gpt":0.22668534184266673,"score_spread":0.2184983807347223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407093524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032117937,0.00071817543,0.9667483,0.025367815,0.0013873652,0.00008036297,9.82384e-7,0.000026458541,0.0024587724],"genre_scores_gemma":[0.93099684,0.0000052320133,0.06736132,0.00040777493,0.0002460559,0.0000018259861,3.0388588e-7,0.00000881774,0.00097184296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993529,0.000015334617,0.00019700686,0.00006459929,0.00017212609,0.00019806194],"domain_scores_gemma":[0.99921095,0.00011808015,0.000037564885,0.00021077476,0.00016558956,0.00025706383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032551604,0.00005996091,0.000105985964,0.00021120804,0.000050696653,0.00014201821,0.0009980592,0.00003496134,0.00003929923],"category_scores_gemma":[0.00042511447,0.000042498195,0.000042579686,0.00046044524,0.000029786068,0.00010706725,0.000036212128,0.00024142726,0.000009480304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010132552,0.00003515136,0.000078814766,0.00013732616,0.00037325843,0.000506183,0.0013941161,0.20362152,0.036974594,0.6457472,0.07397086,0.037150856],"study_design_scores_gemma":[0.00034114032,0.000016539712,0.00006915114,0.000100549805,0.0000137502075,0.00011586764,0.0000032350072,0.7606301,0.07458788,0.0018523065,0.16211449,0.00015500627],"about_ca_topic_score_codex":0.00017716779,"about_ca_topic_score_gemma":0.000016103619,"teacher_disagreement_score":0.92778504,"about_ca_system_score_codex":0.00012054663,"about_ca_system_score_gemma":0.0006022244,"threshold_uncertainty_score":0.18546589},"labels":[],"label_agreement":null},{"id":"W4407362332","doi":"10.1109/isgteurope62998.2024.10863283","title":"Topology and Parameter Identification in Electrical Distribution Systems using Spatial Priors","year":2024,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"University of Waterloo; Mitacs","keywords":"Prior probability; Topology (electrical circuits); Identification (biology); Computer science; Distribution (mathematics); Artificial intelligence; Mathematics; Bayesian probability; Mathematical analysis; Combinatorics","score_opus":0.02728989919175039,"score_gpt":0.3195673994291534,"score_spread":0.292277500237403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407362332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012231349,0.00026803702,0.9863661,0.0004144138,0.0003981898,0.00018433113,0.0000022351776,0.00007489226,0.00006045279],"genre_scores_gemma":[0.9768287,0.00002676982,0.022800758,0.000010886408,0.000050779123,0.000013110717,0.000010455282,0.0000044143126,0.00025410397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988999,0.00015681316,0.00024201602,0.00031496157,0.00020598853,0.00018036738],"domain_scores_gemma":[0.9994976,0.00019761089,0.000022399918,0.00017750535,0.000053720752,0.000051198145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000506016,0.000061225626,0.00009477338,0.00017343592,0.000039323062,0.000456045,0.00016976733,0.000056830988,0.000011174304],"category_scores_gemma":[0.00017814498,0.000053535965,0.000014015482,0.00060245296,0.00003591245,0.00025753825,0.00008845233,0.000115134855,0.00001941018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011545688,0.000113850176,0.002303824,0.00014800396,0.000036371115,0.0001222861,0.0004423046,0.0072990605,0.0026567446,0.7666604,0.00053580716,0.2196698],"study_design_scores_gemma":[0.000063046304,0.00001769486,0.0018785539,0.0000083036975,0.0000022769377,0.000036343863,0.00000521971,0.9964152,0.00024748908,0.0009525515,0.00031439547,0.00005891333],"about_ca_topic_score_codex":0.00033558917,"about_ca_topic_score_gemma":0.0000057963593,"teacher_disagreement_score":0.98911613,"about_ca_system_score_codex":0.00010364931,"about_ca_system_score_gemma":0.00007359034,"threshold_uncertainty_score":0.43976524},"labels":[],"label_agreement":null},{"id":"W4407598381","doi":"10.1016/j.swevo.2024.101839","title":"Evolutionary algorithm based on multi-probability distribution model for stochastic optimization","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"National Natural Science Foundation of China","keywords":"Computer science; Estimation of distribution algorithm; Algorithm; Evolutionary algorithm; Stochastic optimization; Probability distribution; Mathematical optimization; Artificial intelligence; Mathematics; Statistics","score_opus":0.029423580042932097,"score_gpt":0.2983921817003596,"score_spread":0.2689686016574275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407598381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006974206,0.00014235733,0.99612683,0.0016505307,0.00038598047,0.0011087034,0.00018517971,0.00025447033,0.00007618127],"genre_scores_gemma":[0.096835636,0.000009737617,0.9013636,0.00021533092,0.00004235491,0.00021653633,0.0010275905,0.000012255379,0.0002769408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781334,0.00017721325,0.00045202233,0.00075331435,0.000452173,0.00035191554],"domain_scores_gemma":[0.99809444,0.00051432155,0.0001292829,0.00034986567,0.00078225625,0.00012985693],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005341178,0.00023655142,0.00022668499,0.00033061692,0.00062793016,0.0001261387,0.00031573416,0.00013745301,0.000005172024],"category_scores_gemma":[0.00045630112,0.00025126364,0.00008934227,0.00080216234,0.0001322578,0.0004681238,0.00014100812,0.00016116255,0.0000062263794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004384434,0.00027687525,0.000022735087,0.000049985156,0.0000138522755,4.864611e-7,0.00003314232,0.97235805,0.0000022556658,0.008225408,0.0018192182,0.01715415],"study_design_scores_gemma":[0.0014134962,0.00010958383,0.0014423758,0.000043291417,0.000018914243,0.0000025951795,0.0000074719696,0.97404677,0.0000059287972,0.022646388,0.000047658465,0.00021550468],"about_ca_topic_score_codex":0.000010422884,"about_ca_topic_score_gemma":7.31956e-7,"teacher_disagreement_score":0.09676589,"about_ca_system_score_codex":0.00043197535,"about_ca_system_score_gemma":0.00053885777,"threshold_uncertainty_score":0.999994},"labels":[],"label_agreement":null},{"id":"W4407860886","doi":"10.1007/s00521-025-11074-z","title":"The firefighter algorithm for optimization problems","year":2025,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Computational Science and Engineering; Computer science; Algorithm; Mathematical optimization; Mathematics; Computational science","score_opus":0.016961697811060925,"score_gpt":0.30561413653053726,"score_spread":0.2886524387194763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407860886","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013738486,0.00027821356,0.9943933,0.003637107,0.00010325789,0.00074497075,0.0000032448495,0.00013046766,0.00069568906],"genre_scores_gemma":[0.008834857,0.000097177865,0.9881001,0.00045159744,0.00012174368,0.0004583331,0.000016749236,0.000009460455,0.0019099723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913776,0.000045809007,0.00020451588,0.00029506916,0.00011910774,0.00019773854],"domain_scores_gemma":[0.9987925,0.0005596187,0.000060082653,0.00033853107,0.0002031719,0.000046089448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003076853,0.00008213296,0.000081716935,0.000060901737,0.0010241106,0.00041422705,0.00048009725,0.000031570053,0.0000010588655],"category_scores_gemma":[0.000046239773,0.00006016347,0.000029424438,0.00048431323,0.000061013132,0.00008390666,0.00019983192,0.00009217349,0.0000022960746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.6892467e-7,0.000016600483,0.000011282518,0.000011504719,0.000007904011,5.2478207e-8,0.000030097157,0.064197674,0.0000056790523,0.045970306,0.00087641564,0.888872],"study_design_scores_gemma":[0.0001614538,0.000012857891,0.000053252938,0.0000062971735,0.000003915482,0.0000020220891,0.0000048402785,0.96037245,0.000030918596,0.0036050382,0.035688728,0.00005823593],"about_ca_topic_score_codex":0.0000031820828,"about_ca_topic_score_gemma":4.9185235e-7,"teacher_disagreement_score":0.8961748,"about_ca_system_score_codex":0.000014362275,"about_ca_system_score_gemma":0.000036758298,"threshold_uncertainty_score":0.7876734},"labels":[],"label_agreement":null},{"id":"W4408862731","doi":"10.1109/icce63647.2025.10929970","title":"An Ising Machine-Based Hybrid Optimization Method Using Constraint Conversion and Correction","year":2025,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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 Institutes for Quantum and Radiological Science and Technology; Council for Science, Technology and Innovation; Swine Innovation Porc","keywords":"Constraint (computer-aided design); Computer science; Ising model; Mathematical optimization; Mathematics; Statistical physics; Physics","score_opus":0.021023189146535014,"score_gpt":0.3378188546855835,"score_spread":0.3167956655390485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408862731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004243964,0.000028158756,0.9968984,0.00035335743,0.0005703582,0.00021536557,0.0000018751243,0.00017068228,0.0013374164],"genre_scores_gemma":[0.058523674,0.000007724183,0.94081086,0.0004147239,0.00001192732,0.0000026261846,0.000011616335,0.0000062374834,0.00021058698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985881,0.00035232402,0.00022201591,0.0004135148,0.00024326345,0.00018076717],"domain_scores_gemma":[0.99900305,0.0002451739,0.00006715744,0.0003365635,0.0002447446,0.000103314094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008135042,0.000114434304,0.00014345461,0.00038496015,0.00024787494,0.00035903405,0.00025592733,0.000043787895,0.00011672532],"category_scores_gemma":[0.00017849878,0.00011078382,0.000024958614,0.000569735,0.00006299869,0.0004662702,0.00010317158,0.00012039682,0.0000020761358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000816647,0.00003937576,0.00018192356,0.000015388312,0.000008226695,0.0000044067997,0.000024992469,0.94848,0.0005116043,0.0018087495,0.00010535183,0.048811793],"study_design_scores_gemma":[0.00045031685,0.00003734509,0.000041069954,0.000020856609,0.000009237405,0.000018803192,0.000028628201,0.9906847,0.008435628,0.00008929039,0.000076859484,0.00010724755],"about_ca_topic_score_codex":0.00013720877,"about_ca_topic_score_gemma":0.0000014639267,"teacher_disagreement_score":0.058099277,"about_ca_system_score_codex":0.00008392637,"about_ca_system_score_gemma":0.00022005403,"threshold_uncertainty_score":0.4517633},"labels":[],"label_agreement":null},{"id":"W4409084041","doi":"10.18280/mmep.120329","title":"Fast Slow Optimization: An Adaptive Stochastic Optimization that Employs Both Short and Long Step Size Searches","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Metaheuristic Optimization Algorithms Research","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":"Universitas Telkom","keywords":"Stochastic optimization; Mathematical optimization; Computer science; Adaptive optimization; Mathematics","score_opus":0.04504432079004283,"score_gpt":0.25884124973230527,"score_spread":0.21379692894226243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409084041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030296322,0.00028130275,0.9983036,0.00017304797,0.00006461606,0.0004446682,0.0000025098188,0.0002817402,0.00014557796],"genre_scores_gemma":[0.093002476,0.0000977214,0.90642583,0.000024691153,0.000021966945,0.00006580247,0.00000506815,0.000029437962,0.00032697778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982639,0.000062326326,0.00034132253,0.00053982873,0.00039885513,0.00039378012],"domain_scores_gemma":[0.9987258,0.000472079,0.00003684534,0.0003900563,0.00013793107,0.00023729767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062613416,0.0002528937,0.00032241337,0.00021843807,0.00018269329,0.0005133724,0.0003404424,0.00011307435,0.000018527837],"category_scores_gemma":[0.00015157124,0.0002370051,0.000032518445,0.0004428814,0.00007461945,0.000504479,0.00023832048,0.00025549522,0.000001776845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004766223,0.00005345932,0.000005453102,0.0002261454,0.00003591427,0.0000024120757,0.0003208402,0.98229605,0.000003411182,0.01562956,0.0000045079955,0.0014174791],"study_design_scores_gemma":[0.00021395275,0.00007457461,0.000009798614,0.00025299392,0.00002256013,0.000013731666,0.000026639653,0.99738103,0.000011340913,0.0017656239,0.000002826342,0.00022490439],"about_ca_topic_score_codex":0.0000040935565,"about_ca_topic_score_gemma":2.2087853e-7,"teacher_disagreement_score":0.09269951,"about_ca_system_score_codex":0.00004129631,"about_ca_system_score_gemma":0.00004982103,"threshold_uncertainty_score":0.9664787},"labels":[],"label_agreement":null},{"id":"W4409119625","doi":"10.1038/s41598-025-89458-3","title":"An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear engineering problems","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metaheuristic Optimization Algorithms Research","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":"Benchmark (surveying); Computer science; Adaptability; Mathematical optimization; Zoom; Local optimum; Artificial intelligence; Nonlinear system; Optimization problem; Machine learning; Algorithm; Mathematics; Engineering","score_opus":0.02055967211135816,"score_gpt":0.29492641017782983,"score_spread":0.27436673806647166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409119625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004595391,0.00023666164,0.9893588,0.00031332622,0.00410092,0.001238868,0.000009751417,0.000097717275,0.00004856912],"genre_scores_gemma":[0.10865677,0.0000052326486,0.8885843,0.000030349664,0.000039388313,0.00018792064,0.0000473918,0.00001738226,0.002431217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714893,0.00009847143,0.00071042177,0.0009019778,0.0007653856,0.00037479596],"domain_scores_gemma":[0.9964367,0.00032759624,0.00030230792,0.0015077814,0.0013127835,0.00011280062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00304487,0.00017875296,0.0002504102,0.00043760805,0.00043802633,0.00043632204,0.000791399,0.000048416663,0.000016646582],"category_scores_gemma":[0.0011726543,0.00012277423,0.0001257278,0.0012646584,0.00025105922,0.00026056508,0.0003100777,0.00017603346,0.0000035791797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000615486,0.00018304406,0.00002499414,0.00003343683,0.000031183572,0.000014003455,0.00026786097,0.990865,0.004222962,0.0006492643,0.0018854669,0.0018166448],"study_design_scores_gemma":[0.00023324408,0.000039144088,0.000064633896,0.00008927763,0.000013928557,0.000022549242,0.00002518089,0.98174745,0.013967023,0.00016911226,0.0035121446,0.00011630016],"about_ca_topic_score_codex":0.000024286684,"about_ca_topic_score_gemma":0.0000041842586,"teacher_disagreement_score":0.10406138,"about_ca_system_score_codex":0.000087873814,"about_ca_system_score_gemma":0.00045586185,"threshold_uncertainty_score":0.5006588},"labels":[],"label_agreement":null},{"id":"W4409209732","doi":"10.1007/s42979-025-03889-3","title":"Enhancing Grey Wolf Optimization with Applications and Innovations","year":2025,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Metaheuristic Optimization Algorithms Research","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":"Vale (Canada)","funders":"","keywords":"Computer science","score_opus":0.012394233747254681,"score_gpt":0.2815383089642552,"score_spread":0.2691440752170005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409209732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016651966,0.000038934013,0.995598,0.0014965147,0.00017304777,0.00038974526,9.849001e-7,0.00016997788,0.0019662343],"genre_scores_gemma":[0.027807185,0.000014594573,0.97128916,0.00050249917,0.000036825888,0.000073765805,0.000002351858,0.000004719145,0.00026888083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981927,0.00004022034,0.00025408546,0.000677477,0.0005152584,0.00032021964],"domain_scores_gemma":[0.99805397,0.00012739938,0.00008029042,0.0007130455,0.00091042626,0.000114895076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077879085,0.0001222778,0.00012743789,0.0007508635,0.00067994726,0.00092888053,0.0012226431,0.000030440071,0.000007939749],"category_scores_gemma":[0.00007739414,0.000107205844,0.0000119622255,0.0068166438,0.0004187296,0.0011174999,0.0007183041,0.00013156877,0.0000093810395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031081152,0.000098672404,0.0006893327,0.000042303294,0.000017143459,0.000004163221,0.00035523495,0.20776603,0.00044626943,0.5992521,0.00023521374,0.19109045],"study_design_scores_gemma":[0.00018949797,0.00003469469,0.0009847996,0.000027122189,0.000002744522,0.0000111218715,0.000006476727,0.99540955,0.001009553,0.0013745114,0.0008265775,0.0001233279],"about_ca_topic_score_codex":0.000008014376,"about_ca_topic_score_gemma":0.0000037294942,"teacher_disagreement_score":0.78764355,"about_ca_system_score_codex":0.00006562502,"about_ca_system_score_gemma":0.00063306885,"threshold_uncertainty_score":0.8957216},"labels":[],"label_agreement":null},{"id":"W4409361896","doi":"10.1609/aaai.v39i25.34911","title":"Anchor Search: A Unified Framework for Suboptimal Bidirectional Search","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Alliance de recherche numérique du Canada; Israel Science Foundation; Israel National Road Safety Authority; Canadian Institute for Advanced Research","keywords":"Computer science","score_opus":0.15076007786207432,"score_gpt":0.3779439235649356,"score_spread":0.2271838457028613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409361896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059922836,0.00002545077,0.9738107,0.009659647,0.0006244393,0.000944961,0.000014126862,0.00010980775,0.0088186385],"genre_scores_gemma":[0.7376186,0.00005413222,0.25928056,0.00027793294,0.00010862578,0.00012189116,0.0000013365398,0.000016285167,0.0025206187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972898,0.000050284034,0.00057223404,0.0006749713,0.00087122986,0.00054148975],"domain_scores_gemma":[0.9963351,0.00068097067,0.00015383892,0.00043201438,0.0022687807,0.00012930104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014480877,0.00021974902,0.00029654868,0.00042211305,0.0004023787,0.00049705774,0.0027245358,0.00014658066,0.00013624193],"category_scores_gemma":[0.0021566674,0.00017629481,0.00015869629,0.0020899149,0.0003840402,0.00031423205,0.00063215976,0.00054262334,0.000056030283],"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":[0.000112192356,0.00015005212,0.00007812382,0.00007074357,0.000029006065,2.3520612e-7,0.00034929617,0.00074364897,0.003224162,0.94521827,0.0003412084,0.04968307],"study_design_scores_gemma":[0.000040985,0.00014030594,0.0001265781,0.00017461828,0.000007808612,0.0000014057717,0.00022827146,0.50083816,0.24608529,0.2519876,0.0002212288,0.0001477554],"about_ca_topic_score_codex":0.000030835246,"about_ca_topic_score_gemma":0.0000023576606,"teacher_disagreement_score":0.73162633,"about_ca_system_score_codex":0.00009201411,"about_ca_system_score_gemma":0.00052497763,"threshold_uncertainty_score":0.7189093},"labels":[],"label_agreement":null},{"id":"W4409362866","doi":"10.1609/aaai.v39i25.34905","title":"Suboptimal Search with Dynamic Distribution of Suboptimality","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","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":"Distribution (mathematics); Mathematics","score_opus":0.05123822905305393,"score_gpt":0.3277991023609898,"score_spread":0.2765608733079359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409362866","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":[0.10852998,0.000019352958,0.8792162,0.0035464857,0.00016501128,0.0005812545,0.000021288222,0.00006609681,0.007854334],"genre_scores_gemma":[0.9782065,0.000030902,0.021203162,0.00003360197,0.000008001275,0.00001991064,0.0000024422216,0.000006721462,0.00048873323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768835,0.000044799883,0.00058948615,0.0004935312,0.00082726596,0.0003565478],"domain_scores_gemma":[0.99701166,0.00015680924,0.0002728734,0.00044797952,0.002032881,0.0000778046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009954114,0.00018898731,0.0003071288,0.00018629785,0.00016508478,0.00017974208,0.0022021274,0.00008118456,0.000051398056],"category_scores_gemma":[0.0005520466,0.00013389431,0.000088812354,0.0018786888,0.0005815822,0.00031786071,0.0005292526,0.0003375562,0.000017189941],"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":[0.0001443747,0.0002287799,0.00053406524,0.00011404581,0.00003081423,4.619327e-7,0.00023558518,0.0017552692,0.00601624,0.95976835,0.00005672539,0.03111528],"study_design_scores_gemma":[0.000048993643,0.00021432556,0.0017888697,0.00020722277,0.000013149127,0.0000022418867,0.00018861769,0.67889494,0.29670385,0.021781407,0.000018233673,0.0001381491],"about_ca_topic_score_codex":0.00005762172,"about_ca_topic_score_gemma":0.0000058531364,"teacher_disagreement_score":0.93798697,"about_ca_system_score_codex":0.00008557353,"about_ca_system_score_gemma":0.00035702254,"threshold_uncertainty_score":0.5460052},"labels":[],"label_agreement":null},{"id":"W4409393577","doi":"10.1007/s10115-025-02389-3","title":"Atom search optimization: a systematic review of current variants and applications","year":2025,"lang":"en","type":"review","venue":"Knowledge and Information Systems","topic":"Metaheuristic Optimization Algorithms Research","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 Chicoutimi","funders":"","keywords":"Current (fluid); Computer science; Atom (system on chip); Algorithm; Theoretical computer science; Mathematics; Engineering; Parallel computing; Electrical engineering","score_opus":0.04351094629363648,"score_gpt":0.3644960439534498,"score_spread":0.3209850976598133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409393577","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":[1.6318981e-10,0.5221266,0.47189298,0.000005091087,0.00014535288,0.0030489168,0.000037159818,0.000031579657,0.0027122989],"genre_scores_gemma":[1.0856919e-7,0.9941581,0.0037018117,0.000016000471,0.00004653805,0.0015529657,0.0001622167,0.00000875373,0.00035352272],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99616337,0.0006953427,0.0021207419,0.00030754233,0.00049692584,0.00021607314],"domain_scores_gemma":[0.99630684,0.0005764286,0.0008623727,0.00088312436,0.0012103046,0.00016094305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019730628,0.00031301653,0.0020036274,0.00083125866,0.00015217585,0.00038852452,0.00079058245,0.00015515044,0.000008359215],"category_scores_gemma":[0.00032307918,0.0002426614,0.00013317943,0.0018803718,0.000058753834,0.0013501978,0.0005388192,0.00024236285,0.000080370024],"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":[1.2435163e-7,0.00001510846,6.420402e-8,0.81315166,0.000046624296,9.968296e-8,0.00008158216,0.000024141675,3.446734e-10,0.035524398,0.0003368447,0.15081939],"study_design_scores_gemma":[0.000083677056,0.000010410522,4.9542496e-8,0.48568586,0.00021220035,0.000032337677,0.000010029978,0.051349387,1.8244647e-8,0.0000026990574,0.46246338,0.00014996719],"about_ca_topic_score_codex":0.000002164479,"about_ca_topic_score_gemma":5.994736e-8,"teacher_disagreement_score":0.47203147,"about_ca_system_score_codex":0.00007775316,"about_ca_system_score_gemma":0.00086639187,"threshold_uncertainty_score":0.98954445},"labels":[],"label_agreement":null},{"id":"W4409648117","doi":"10.23977/jnca.2025.100107","title":"A Nature-inspired Fully Enhanced Hybrid Algorithm Based on Intra-group Competition Mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Network Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Mechanism (biology); Group (periodic table); Competition (biology); Computer science; Algorithm; Artificial intelligence; Chemistry; Biology; Ecology; Physics","score_opus":0.006311926803255178,"score_gpt":0.2730461447773535,"score_spread":0.26673421797409835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409648117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003382279,0.00021126235,0.99625653,0.0015935065,0.0003696856,0.00028023514,0.000002334786,0.00006570653,0.0008824887],"genre_scores_gemma":[0.39906174,0.000058749465,0.59911716,0.001178201,0.0004991415,0.0000147440805,0.0000067945743,0.000008884944,0.000054584943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983392,0.00016915699,0.0005285541,0.00029559387,0.00040361108,0.00026389904],"domain_scores_gemma":[0.9981671,0.0005532003,0.00036553162,0.00035883562,0.00042103807,0.00013429849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009877002,0.00014628173,0.0002697777,0.000275242,0.000388258,0.00023668534,0.0006265515,0.000078757395,0.000006641145],"category_scores_gemma":[0.00006682311,0.00013587889,0.0000822646,0.00090518343,0.000048818656,0.00010335486,0.00013506932,0.00057879306,0.000004591662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019957935,0.00028818427,0.000014813011,0.000036590012,0.000064432745,0.000013008744,0.000032068052,0.08636928,0.00026662447,0.39874026,0.0021405867,0.51201415],"study_design_scores_gemma":[0.0005777955,0.00013076534,0.00018351979,0.00015807628,0.000015283966,0.000023840796,0.0000068161685,0.96682155,0.0004909064,0.026487969,0.00498845,0.00011504598],"about_ca_topic_score_codex":0.0000010853104,"about_ca_topic_score_gemma":3.188739e-7,"teacher_disagreement_score":0.8804522,"about_ca_system_score_codex":0.000064692715,"about_ca_system_score_gemma":0.00014596261,"threshold_uncertainty_score":0.554098},"labels":[],"label_agreement":null},{"id":"W4410790146","doi":"10.1002/wics.70028","title":"A Review of Benchmark and Test Functions for Global Optimization Algorithms and Metaheuristics","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Metaheuristic Optimization Algorithms Research","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 Manitoba","funders":"","keywords":"Metaheuristic; Benchmark (surveying); Computer science; Algorithm; Global optimization; Test functions for optimization; Mathematical optimization; Mathematics; Optimization problem; Multi-swarm optimization","score_opus":0.04656249920066885,"score_gpt":0.3987294413001376,"score_spread":0.35216694209946875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410790146","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":[4.442748e-10,0.4972821,0.49721307,0.000085877546,0.0002331853,0.0019086824,0.003157472,0.000025241814,0.00009436464],"genre_scores_gemma":[8.268027e-9,0.5269803,0.47057754,0.00008793968,0.000045633897,0.00035834953,0.001769947,0.000016948869,0.00016338495],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949201,0.00058855093,0.0025235002,0.001063498,0.000524405,0.00037995656],"domain_scores_gemma":[0.99014574,0.0063406434,0.0014371505,0.00067162974,0.0011481678,0.0002566595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014459139,0.00069469644,0.002972453,0.00034387404,0.00035071484,0.00020206008,0.00087133195,0.00020027369,0.000046387955],"category_scores_gemma":[0.0044962363,0.0006018364,0.00036115525,0.0014141545,0.00027878466,0.00024140529,0.0018196488,0.00031957668,0.000008900359],"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":[0.000001905364,0.00009993841,0.0000016724586,0.18893595,0.00012391774,0.0000039185147,0.000013208848,0.0007409468,1.3294889e-9,0.00932475,0.035679188,0.7650746],"study_design_scores_gemma":[0.00024941578,0.0002508623,0.0000026944267,0.12545912,0.0011896763,0.00013006043,0.0000039667734,0.38819447,5.4652647e-9,0.0043622046,0.47966528,0.0004922616],"about_ca_topic_score_codex":0.0000019423549,"about_ca_topic_score_gemma":0.0000014839177,"teacher_disagreement_score":0.76458234,"about_ca_system_score_codex":0.00017552159,"about_ca_system_score_gemma":0.00081096886,"threshold_uncertainty_score":0.9996433},"labels":[],"label_agreement":null},{"id":"W4410898856","doi":"10.5267/j.dsl.2025.4.004","title":"An improved pelican optimization algorithm for function optimization and constrained engineering design problems","year":2025,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","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":"Pelican; Mathematical optimization; Multi-objective optimization; Function (biology); Optimization algorithm; Engineering optimization; Computer science; Function optimization; Algorithm; Optimization problem; Engineering; Mathematics; Genetic algorithm","score_opus":0.018706768795716353,"score_gpt":0.2875331395107159,"score_spread":0.26882637071499954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410898856","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014276813,0.00002380682,0.99671817,0.001266341,0.00056771265,0.0010625595,0.000004492522,0.00019696177,0.000017172744],"genre_scores_gemma":[0.005609042,0.00001862935,0.9932445,0.00092453667,0.00003616973,0.00012204404,0.000010510132,0.000013229194,0.000021294394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997414,0.000078752404,0.00044008807,0.00093749614,0.0006591471,0.00047050178],"domain_scores_gemma":[0.99779147,0.00054101535,0.00012692902,0.00063029217,0.0006889922,0.00022133047],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025720736,0.00019987095,0.00020828063,0.0010497486,0.000496595,0.0012206481,0.001030457,0.000071131486,0.00001320707],"category_scores_gemma":[0.0010127802,0.00019086481,0.000034660363,0.0031636334,0.00025936734,0.0018861223,0.00018794334,0.00012630144,0.0000015936771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073531587,0.000022438002,0.000004835561,0.000006027919,0.000004969081,5.781541e-7,0.00006167585,0.84778434,0.006059922,0.00060772424,0.00010334066,0.1453368],"study_design_scores_gemma":[0.00072386937,0.0001341023,0.00006659109,0.000028406783,0.000008761858,0.000005784161,0.000022171555,0.99768716,0.0008606519,0.00016710193,0.000103987106,0.00019142492],"about_ca_topic_score_codex":0.000006479902,"about_ca_topic_score_gemma":2.2854346e-7,"teacher_disagreement_score":0.14990282,"about_ca_system_score_codex":0.00011968141,"about_ca_system_score_gemma":0.00027362362,"threshold_uncertainty_score":0.9998162},"labels":[],"label_agreement":null},{"id":"W4411026218","doi":"10.3390/a18060341","title":"S-EPSO: A Socio-Emotional Particle Swarm Optimization Algorithm for Multimodal Search in Low-Dimensional Engineering Applications","year":2025,"lang":"en","type":"article","venue":"Algorithms","topic":"Metaheuristic Optimization Algorithms Research","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":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Mathematical optimization; Algorithm; Computer science; Multi-swarm optimization; Mathematics","score_opus":0.015046567215443863,"score_gpt":0.29457266506491114,"score_spread":0.2795260978494673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411026218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021614347,0.00014370777,0.996081,0.0015599405,0.00031094035,0.0013316128,0.00004362262,0.0002067701,0.000106250896],"genre_scores_gemma":[0.004384146,0.000022122018,0.99331206,0.00021209868,0.00013569149,0.0009871807,0.00010125985,0.000027341828,0.0008180865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972764,0.00010381776,0.00056074886,0.0007575597,0.0006376937,0.0006637381],"domain_scores_gemma":[0.99802244,0.0006098259,0.00006836392,0.00055640657,0.00055988395,0.00018307343],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094043213,0.00024078575,0.0002985459,0.0004770555,0.0002609833,0.00021369815,0.0008268946,0.00014815385,0.000052285577],"category_scores_gemma":[0.00018161772,0.0002636122,0.00011407951,0.0017405372,0.00007385511,0.00047250863,0.00035718875,0.00029620854,0.000044312852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005050773,0.0003019712,0.00005878686,0.000044634748,0.000038354196,0.0000070661235,0.00009376209,0.8735647,0.00007255254,0.009698921,0.00015918637,0.11595502],"study_design_scores_gemma":[0.001445334,0.000036518162,0.0004296092,0.00004823606,0.0000073075516,0.000007419091,0.000023725961,0.9957572,0.00097271096,0.00052952615,0.00050481874,0.00023755725],"about_ca_topic_score_codex":0.00004295922,"about_ca_topic_score_gemma":0.0000016044116,"teacher_disagreement_score":0.12219254,"about_ca_system_score_codex":0.00025895986,"about_ca_system_score_gemma":0.00041820918,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W4411167332","doi":"10.1139/dsa-2024-0066","title":"Enhancing drone swarm efficiency through a high-flexibility biomimetic formation algorithm","year":2025,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Metaheuristic Optimization Algorithms Research","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; Swarm behaviour; Flexibility (engineering); Computer science; Algorithm; Artificial intelligence; Mathematics; Biology","score_opus":0.016924964557906215,"score_gpt":0.2974704846835042,"score_spread":0.28054552012559797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411167332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007724937,0.00087196927,0.9943403,0.0006285561,0.00019812299,0.001309375,0.0000192713,0.00020174863,0.0016581438],"genre_scores_gemma":[0.5900493,0.0001868588,0.40483007,0.00014068504,0.00016139683,0.0017626997,0.000060576727,0.000018985029,0.0027893893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980872,0.00012976065,0.0005641821,0.00055987533,0.00034551288,0.0003134349],"domain_scores_gemma":[0.9984565,0.00018015243,0.00015089347,0.0008589887,0.00025588047,0.00009760127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007479195,0.00016208232,0.00026528462,0.00020638508,0.0004726897,0.00035862793,0.00053553126,0.000083662904,0.0000075378302],"category_scores_gemma":[0.000055944733,0.00015250387,0.000039731138,0.0014002522,0.000101387785,0.00047115627,0.00026195287,0.00013810856,0.000067932204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034991576,0.00043836443,0.000032717715,0.0005446964,0.000053798678,0.000002001619,0.0006668186,0.0034763622,0.00243894,0.8528042,0.00061241374,0.13892622],"study_design_scores_gemma":[0.0005210429,0.000042208605,0.00018151643,0.000057344692,0.000020601681,0.000014362416,0.00017650748,0.972094,0.0048134434,0.007871196,0.013973624,0.0002341299],"about_ca_topic_score_codex":0.00032873958,"about_ca_topic_score_gemma":0.0000061835703,"teacher_disagreement_score":0.9686177,"about_ca_system_score_codex":0.00011035517,"about_ca_system_score_gemma":0.00012559429,"threshold_uncertainty_score":0.62189275},"labels":[],"label_agreement":null},{"id":"W4411232503","doi":"10.1109/alife-ciscompanion65078.2025.11031002","title":"Comparative Study of Decomposition and Merging Evolutionary Algorithms for Large-Scale Optimization Problems","year":2025,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decomposition; Scale (ratio); Evolutionary algorithm; Evolutionary computation; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.03223074014930341,"score_gpt":0.35265965600355254,"score_spread":0.32042891585424915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411232503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017072577,0.00011874728,0.99526,0.00027410057,0.00012214911,0.0013093487,0.000007656555,0.00007147784,0.0011292512],"genre_scores_gemma":[0.14827397,0.000017792792,0.8509263,0.000032531538,0.00001152601,0.00015348358,0.000018910929,0.000004767724,0.00056075276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866956,0.00013733529,0.00035373663,0.0003765611,0.00026987906,0.00019290367],"domain_scores_gemma":[0.99882746,0.00023134315,0.00009732349,0.0002711265,0.00051969674,0.000053038333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046041736,0.00010859406,0.0002419772,0.00031038764,0.00022301018,0.000080996,0.00030144223,0.000038123122,0.000019179513],"category_scores_gemma":[0.00003991496,0.00010180239,0.000027989792,0.0007168197,0.000030035557,0.00036821558,0.00024044543,0.00006531027,9.781371e-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":[0.000032125732,0.0016248355,0.0010655441,0.00011217164,0.00013468634,8.050817e-7,0.004196347,0.96575665,0.00013170969,0.021775704,0.0012523854,0.0039170342],"study_design_scores_gemma":[0.0012831466,0.00023241177,0.0009709247,0.000020712649,0.000014429572,0.0000013334396,0.00083240453,0.99552625,0.00024532384,0.00067426957,0.00010859454,0.00009021981],"about_ca_topic_score_codex":0.000019418041,"about_ca_topic_score_gemma":0.0000110991805,"teacher_disagreement_score":0.14656672,"about_ca_system_score_codex":0.000039695995,"about_ca_system_score_gemma":0.00007362935,"threshold_uncertainty_score":0.4151381},"labels":[],"label_agreement":null},{"id":"W4411673831","doi":"10.2139/ssrn.5324689","title":"The Effect of Elitist Fitness-Based Selection on the Escape from Local Optima","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Metaheuristic Optimization Algorithms Research","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":"Selection (genetic algorithm); Local optimum; Mathematical optimization; Computer science; Artificial intelligence; Mathematics","score_opus":0.008578061897256059,"score_gpt":0.2791942430786236,"score_spread":0.27061618118136754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411673831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015971882,0.0017947138,0.9890213,0.005248421,0.0009331314,0.00058267114,0.000018742427,0.000058365626,0.00074544526],"genre_scores_gemma":[0.9714721,0.0060851322,0.015351702,0.00034053347,0.0010566359,0.00029920888,0.000057198813,0.0000848465,0.0052526863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99381083,0.0019901427,0.0006415028,0.0005751392,0.0012522292,0.001730136],"domain_scores_gemma":[0.9944353,0.0034276897,0.0005425892,0.0010559078,0.0004424453,0.000096034746],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006993781,0.00036775885,0.0004498324,0.00024590694,0.00073703856,0.0005780309,0.0034212286,0.00023654671,0.00005015227],"category_scores_gemma":[0.00094149384,0.0002082758,0.00033316112,0.0006517356,0.00021955934,0.00008899524,0.00064265414,0.0061867232,0.000022681676],"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":false,"study_design_scores_codex":[0.00041722736,0.00011638958,0.00025201938,0.00007749321,0.0009595933,0.000009235254,0.00007631381,0.3950758,0.000033748594,0.20403206,0.0016607865,0.39728934],"study_design_scores_gemma":[0.000690279,0.0007378052,0.000114270224,0.00017161257,0.00006002658,0.000023625942,0.000030011139,0.92625827,0.0020476312,0.068946056,0.0007076713,0.00021274287],"about_ca_topic_score_codex":0.00024195248,"about_ca_topic_score_gemma":0.00018674578,"teacher_disagreement_score":0.97366965,"about_ca_system_score_codex":0.0012865824,"about_ca_system_score_gemma":0.007172525,"threshold_uncertainty_score":0.9984559},"labels":[],"label_agreement":null},{"id":"W4411959463","doi":"10.31449/inf.v49i23.8270","title":"Adaptive Strategy-Enhanced NSGA-II for Multi-Objective Optimization with Improved Convergence and Diversity Control","year":2025,"lang":"en","type":"article","venue":"Informatica","topic":"Metaheuristic Optimization Algorithms Research","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":"Convergence (economics); Diversity (politics); Mathematical optimization; Control (management); Computer science; Mathematics; Artificial intelligence; Economics; Political science; Economic growth","score_opus":0.02154016471860877,"score_gpt":0.2727647767679153,"score_spread":0.2512246120493065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411959463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003451929,0.000017449902,0.99657434,0.00015733474,0.000090114016,0.0011408397,0.000024347979,0.00009139874,0.0015589831],"genre_scores_gemma":[0.40818417,0.00001502398,0.5910259,0.00021945353,0.000006241884,0.00008884278,0.0000056224158,0.000004010223,0.00045075684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893534,0.000052800387,0.0002757621,0.00023817415,0.0002273107,0.00027059717],"domain_scores_gemma":[0.99855,0.0002452111,0.00013808713,0.0002830091,0.0006827693,0.00010090487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034930647,0.00014739251,0.00021466315,0.00016673429,0.00058330287,0.00015890526,0.00045051484,0.00006214169,0.000017507075],"category_scores_gemma":[0.0002674192,0.00012228427,0.00002955903,0.00044476113,0.000119439734,0.0010990617,0.00044786205,0.0001198043,0.0000040406585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013905782,0.0004946369,0.00055006065,0.00054001075,0.00093063543,0.0000063246416,0.016848166,0.80050844,0.00017191679,0.094624944,0.00084868947,0.08308558],"study_design_scores_gemma":[0.0027189497,0.0003357728,0.00043317286,0.000025363279,0.00002006435,0.0000014011886,0.0003075794,0.9952591,0.0004950703,0.00024012178,0.000023120789,0.00014029087],"about_ca_topic_score_codex":0.00002406015,"about_ca_topic_score_gemma":0.0000069698817,"teacher_disagreement_score":0.40783897,"about_ca_system_score_codex":0.00005932039,"about_ca_system_score_gemma":0.00022386457,"threshold_uncertainty_score":0.49866077},"labels":[],"label_agreement":null},{"id":"W4412083216","doi":"10.1007/s00521-025-11423-y","title":"Training neural networks with a self-adaptive ant colony algorithm","year":2025,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Metaheuristic Optimization Algorithms Research","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":"Brandon University","funders":"","keywords":"Computational Science and Engineering; Computer science; Artificial neural network; Ant colony optimization algorithms; Artificial intelligence; Training (meteorology); ANT; Ant colony; Algorithm; Machine learning; Computer network","score_opus":0.022858832679765236,"score_gpt":0.2948385559920372,"score_spread":0.27197972331227194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412083216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009235179,0.00017694663,0.99569744,0.0008539536,0.00008020216,0.00051125174,0.000002606595,0.00038355807,0.0013705353],"genre_scores_gemma":[0.45481622,0.00001597306,0.54425305,0.0005074277,0.00012442638,0.00008936464,0.000006351211,0.000011251991,0.00017591579],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852633,0.0001103008,0.00024529637,0.0005370893,0.00021902603,0.00036194376],"domain_scores_gemma":[0.99880236,0.0003947329,0.00009286519,0.00038936577,0.00018996609,0.00013072212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026028955,0.00016551229,0.00020556762,0.00014387279,0.00058266235,0.00029513252,0.000521859,0.00005284866,0.000001995225],"category_scores_gemma":[0.00001797962,0.00014340835,0.000031742995,0.0011663812,0.000093257426,0.00013331002,0.00030039158,0.00031442745,0.0000024075407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004739304,0.00006591336,0.0001077205,0.000011108152,0.000035969253,0.0000075560474,0.00033099373,0.0643019,0.000006135019,0.022182956,0.00017839532,0.91276664],"study_design_scores_gemma":[0.0003191223,0.00007716382,0.00077861815,0.000014374729,0.000012775017,0.000036060315,0.00006619381,0.99658436,0.0000096918,0.00028102475,0.0016797708,0.00014083911],"about_ca_topic_score_codex":0.000015538593,"about_ca_topic_score_gemma":0.0000019669387,"teacher_disagreement_score":0.93228245,"about_ca_system_score_codex":0.00003230575,"about_ca_system_score_gemma":0.00008585783,"threshold_uncertainty_score":0.5848023},"labels":[],"label_agreement":null},{"id":"W4412405415","doi":"10.1109/otcon65728.2025.11071003","title":"A Fuzzy-Based Harmony Search Method for Optimizing the Traveling Salesman Problem","year":2025,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Future Earth","funders":"","keywords":"Harmony search; Travelling salesman problem; Harmony (color); Computer science; Mathematical optimization; Fuzzy logic; 2-opt; Artificial intelligence; Mathematics","score_opus":0.04410457393262727,"score_gpt":0.3626913088847308,"score_spread":0.3185867349521035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412405415","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008301162,0.0000631699,0.9627181,0.018786492,0.0001435527,0.0011169189,0.0000032482367,0.00019297477,0.016967196],"genre_scores_gemma":[0.0017869817,0.000008164258,0.99071646,0.0016960147,0.00003663215,0.00024312326,0.00000482378,0.000014303557,0.005493526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997672,0.00038290507,0.00036857324,0.00056004035,0.0004947623,0.0005216926],"domain_scores_gemma":[0.99689376,0.001689563,0.000051199742,0.0007847315,0.00047643995,0.00010427498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034844084,0.00016190959,0.00021160288,0.00024684673,0.00047988188,0.0005744884,0.0016997206,0.00006721917,0.000036271445],"category_scores_gemma":[0.00029141022,0.00011117133,0.0001222422,0.0012653065,0.000062221574,0.00017725717,0.00032328945,0.00026070717,0.000020088499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032295236,0.00012413869,0.000025364721,0.0001765962,0.00009271822,0.000005199861,0.00053066487,0.2628829,0.0009990799,0.52148056,0.00783436,0.20581616],"study_design_scores_gemma":[0.0006331241,0.000039230625,0.00002245524,0.000025584863,0.000010284245,0.0000018601933,0.000058059537,0.9833729,0.006268618,0.0059175477,0.0035266406,0.00012366916],"about_ca_topic_score_codex":0.00004340715,"about_ca_topic_score_gemma":0.0000056140693,"teacher_disagreement_score":0.72049004,"about_ca_system_score_codex":0.00006534151,"about_ca_system_score_gemma":0.0005788993,"threshold_uncertainty_score":0.5539805},"labels":[],"label_agreement":null},{"id":"W4412508466","doi":"10.1609/socs.v18i1.36008","title":"Suboptimal Search with Dynamic Distribution of Suboptimality (Extended Abstract)","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Metaheuristic Optimization Algorithms Research","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":"Alliance de recherche numérique du Canada; Canadian Institute for Advanced Research","keywords":"Distribution (mathematics); Computer science; Environmental science; Mathematics; Mathematical analysis","score_opus":0.010847928656989114,"score_gpt":0.2811817900214269,"score_spread":0.27033386136443777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412508466","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":[0.9283686,0.000025958578,0.023792207,0.014094519,0.002894666,0.0012636923,0.00009498021,0.00012426279,0.029341115],"genre_scores_gemma":[0.99604875,0.0000255121,0.0031187742,0.000029507748,0.00005101284,0.000026805694,0.000013601844,0.0000124997705,0.0006735218],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99622375,0.00004089162,0.0005560501,0.00052318105,0.0022863215,0.0003698215],"domain_scores_gemma":[0.9961376,0.00029245496,0.00023280135,0.0003672456,0.0028719876,0.000097884935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001587849,0.00019984679,0.00029519366,0.00021773922,0.00017433804,0.00023349913,0.0032561342,0.00010886207,0.0000333455],"category_scores_gemma":[0.00035324463,0.00014952931,0.00013300795,0.0012257429,0.00031089646,0.00041415857,0.0009914273,0.00049946527,0.000007202113],"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":[0.0008377849,0.0010821498,0.0054825996,0.00021052094,0.0002447651,0.0000021983963,0.0001987936,0.0031640928,0.023904536,0.9623443,0.00065290375,0.0018753709],"study_design_scores_gemma":[0.005014711,0.0010654114,0.09413222,0.0006143382,0.00004956683,0.000018437142,0.00014798179,0.40674055,0.47401914,0.016739266,0.00091128016,0.0005471112],"about_ca_topic_score_codex":0.00014901847,"about_ca_topic_score_gemma":0.0000014446371,"teacher_disagreement_score":0.94560504,"about_ca_system_score_codex":0.00040077968,"about_ca_system_score_gemma":0.0003534517,"threshold_uncertainty_score":0.60976285},"labels":[],"label_agreement":null},{"id":"W4412732533","doi":"10.1007/s10462-025-11291-x","title":"Cuckoo catfish optimizer: a new meta-heuristic optimization algorithm","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","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":"Ministry of Education and Child Care","funders":"","keywords":"Meta heuristic; Computer science; Cuckoo; Cuckoo search; Catfish; Heuristic; Optimization algorithm; Mathematical optimization; Algorithm; Metaheuristic; Artificial intelligence; Fish <Actinopterygii>; Mathematics; Fishery; Biology; Zoology; Particle swarm optimization","score_opus":0.10654809245438929,"score_gpt":0.3755850223123636,"score_spread":0.26903692985797434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412732533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.545373e-8,0.061212674,0.9235848,0.009202515,0.0006696659,0.0010629689,0.0000056743957,0.00027453425,0.003987049],"genre_scores_gemma":[0.000060790026,0.07784916,0.91501176,0.0031982912,0.000113185306,0.0001866509,0.000024930478,0.000026452202,0.0035287964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563724,0.0004990611,0.0013991538,0.0010144867,0.0007945903,0.00065544184],"domain_scores_gemma":[0.99657995,0.0005245829,0.0002907662,0.0015358945,0.0007129545,0.00035587922],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001777265,0.00041417536,0.0009981695,0.0004075426,0.00027818055,0.00050905306,0.0021466184,0.000121503246,0.0018564808],"category_scores_gemma":[0.0021148913,0.00036286487,0.00042181398,0.003712572,0.00012969617,0.00064993434,0.00054432737,0.00038044728,0.00071491016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027833155,0.00011536557,7.835028e-7,0.0003358035,0.0002935863,0.000019238338,0.000044776603,0.05226994,0.0000023272946,0.09854915,0.010632363,0.83773386],"study_design_scores_gemma":[0.00003746519,0.00004409551,7.7716e-7,0.000502784,0.00054624025,0.000014697756,0.000009149119,0.95045716,0.00058018946,0.018378714,0.029082812,0.00034594815],"about_ca_topic_score_codex":0.00013099238,"about_ca_topic_score_gemma":0.000006040799,"teacher_disagreement_score":0.89818716,"about_ca_system_score_codex":0.000121501165,"about_ca_system_score_gemma":0.0007652857,"threshold_uncertainty_score":0.99988234},"labels":[],"label_agreement":null},{"id":"W4412742817","doi":"10.1109/rait65068.2025.11088949","title":"Optimized Travelling Salesman Problem Solution Using 1-Tree Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Future Earth","funders":"","keywords":"Travelling salesman problem; Computer science; Mathematical optimization; Tree (set theory); Algorithm; Mathematics; Combinatorics","score_opus":0.043600074494963136,"score_gpt":0.3047839233212758,"score_spread":0.2611838488263127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412742817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005159806,0.000057106576,0.9106504,0.00041113058,0.00014431826,0.00037227097,4.849656e-7,0.00022897655,0.088083744],"genre_scores_gemma":[0.006128986,0.000018643654,0.9876829,0.000116556366,0.00002700952,0.000020777456,0.000005271728,0.000008888856,0.005990993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981723,0.00017052155,0.0003530575,0.0005118795,0.00040515486,0.00038712402],"domain_scores_gemma":[0.99896497,0.00010003663,0.00006682915,0.0005582386,0.00021429226,0.000095611525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081206363,0.00014649451,0.00021045363,0.00037709373,0.00024056694,0.00032153024,0.0008802676,0.000081506514,0.00004151168],"category_scores_gemma":[0.00007459002,0.00013350377,0.00006760219,0.0012261225,0.00005830776,0.00036713798,0.00033710754,0.0001811246,0.000023882661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021006741,0.00032931418,0.00007589752,0.00013660663,0.000101270976,0.000008783533,0.0003588674,0.58234864,0.0014743478,0.33793625,0.001686364,0.07552264],"study_design_scores_gemma":[0.00054443954,0.000010419562,0.000035640565,0.000017007673,0.000008154368,0.0000061230417,0.000017774271,0.9960829,0.0008409376,0.0020099229,0.0002939902,0.00013269376],"about_ca_topic_score_codex":0.00004334877,"about_ca_topic_score_gemma":0.0000011886634,"teacher_disagreement_score":0.41373423,"about_ca_system_score_codex":0.00009715853,"about_ca_system_score_gemma":0.00024920813,"threshold_uncertainty_score":0.54441255},"labels":[],"label_agreement":null},{"id":"W4412799004","doi":"10.1016/j.cma.2025.118208","title":"Holistic swarm optimization: A novel metaphor-less algorithm guided by whole population information for addressing exploration-exploitation dilemma","year":2025,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"McMaster University","funders":"","keywords":"Dilemma; Metaphor; Population; Swarm behaviour; Mathematical optimization; Computer science; Optimization algorithm; Algorithm; Artificial intelligence; Theoretical computer science; Mathematics; Sociology","score_opus":0.07542285910518817,"score_gpt":0.3552998725752396,"score_spread":0.27987701347005145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412799004","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013852407,0.000080913735,0.99797255,0.0001800544,0.000764795,0.0007616747,0.000017285254,0.00016920482,0.000039663464],"genre_scores_gemma":[0.0010624636,0.000038277674,0.9980934,0.00014718215,0.00005691787,0.00036533872,0.00020303507,0.000019574729,0.000013774953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982778,0.00006510472,0.0006687346,0.00041342402,0.0002600696,0.00031488226],"domain_scores_gemma":[0.9987491,0.00042369665,0.0001569935,0.0003469177,0.00023114565,0.00009218246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015190919,0.00023771712,0.00033717477,0.0006648377,0.00015874414,0.00058399,0.00039076636,0.000133145,0.0000014397233],"category_scores_gemma":[0.00023305758,0.0002675895,0.00004402435,0.0010600003,0.0000076105957,0.0010098447,0.00025173888,0.00017798667,5.9890993e-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":[0.0000028566433,0.000021305372,2.8982168e-7,0.0000910173,0.000016528435,1.5470917e-7,0.00016622085,0.67824036,0.0003724682,0.10014761,0.00010922321,0.22083199],"study_design_scores_gemma":[0.00089323136,0.00001952134,0.000009362091,0.00006635935,0.000014704709,0.0000018800723,0.00004325753,0.9906484,0.0005684101,0.0056716087,0.0018216667,0.00024163612],"about_ca_topic_score_codex":0.000011145286,"about_ca_topic_score_gemma":3.5485377e-7,"teacher_disagreement_score":0.31240803,"about_ca_system_score_codex":0.00012479474,"about_ca_system_score_gemma":0.000048953705,"threshold_uncertainty_score":0.99997765},"labels":[],"label_agreement":null},{"id":"W4413235444","doi":"10.23952/jnva.9.2025.6.05","title":"An embedding result for a class of epigraphical sets and applications to set optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Metaheuristic Optimization Algorithms Research","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":"Embedding; Class (philosophy); Set (abstract data type); Mathematics; Computer science; Artificial intelligence; Programming language","score_opus":0.022637645492269143,"score_gpt":0.37031479268057205,"score_spread":0.3476771471883029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413235444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011639262,0.000051716128,0.9962865,0.0022422923,0.000022955852,0.00013837568,0.000057715042,0.0000049637392,0.000031571282],"genre_scores_gemma":[0.04838715,0.000052434076,0.95127255,0.00014549922,0.00006175854,0.000010276378,0.000029428842,0.0000028856523,0.00003800537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988527,0.000088164146,0.00049446226,0.00017901984,0.00029709673,0.00008854535],"domain_scores_gemma":[0.99797934,0.00040524264,0.00023941672,0.00016188766,0.0010925868,0.00012152394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009780799,0.000067316934,0.0002521174,0.000981319,0.00010620811,0.00010289792,0.0002411357,0.000047627374,0.00000829431],"category_scores_gemma":[0.0003399231,0.00005837403,0.00009032704,0.0018229451,0.000025346,0.0001794465,0.00005597847,0.00007746781,1.3198478e-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":[0.000040795203,0.000110113535,0.0011399206,0.000021885442,0.0004968946,5.096054e-7,0.00012943307,0.96751535,0.000090180365,0.025211772,0.000068036024,0.005175096],"study_design_scores_gemma":[0.0003417875,0.000081080645,0.004412207,0.0000081475255,0.00023083176,0.0000032934843,0.000023093735,0.9928039,0.000019447512,0.0016293573,0.00039690413,0.000049953225],"about_ca_topic_score_codex":0.0000060631087,"about_ca_topic_score_gemma":0.000003027545,"teacher_disagreement_score":0.047223225,"about_ca_system_score_codex":0.000016508498,"about_ca_system_score_gemma":0.0001342073,"threshold_uncertainty_score":0.23804238},"labels":[],"label_agreement":null},{"id":"W4413553287","doi":"10.1109/ojies.2025.3602363","title":"GMPP Estimator as a Global Solution for MPPT Algorithms Under Partial Shading Conditions","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Industrial Electronics Society","topic":"Metaheuristic Optimization Algorithms Research","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":"Shading; Estimator; Mathematics; Computer science; Algorithm; Mathematical optimization; Applied mathematics; Statistics","score_opus":0.07208998828905328,"score_gpt":0.3880696760607032,"score_spread":0.3159796877716499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413553287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010292047,0.00018186997,0.9796651,0.015209318,0.0024979967,0.000895553,0.000028108636,0.000020620437,0.0004722206],"genre_scores_gemma":[0.3235319,0.0004138792,0.6571269,0.006029562,0.0039457846,0.00029854832,0.000025029312,0.00009525646,0.008533129],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974475,0.00029575522,0.0007059086,0.00030144004,0.0006591823,0.00059024076],"domain_scores_gemma":[0.9977607,0.00037754735,0.0005691424,0.0004561952,0.0006751462,0.00016125584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024741532,0.00018561173,0.00036725454,0.000053245145,0.000874787,0.00092651224,0.0032272302,0.0002362631,0.000031309173],"category_scores_gemma":[0.0007244038,0.00014557551,0.0004570345,0.0011695375,0.00012718061,0.0007650724,0.0005104569,0.00080273976,0.0000055429705],"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":false,"study_design_scores_codex":[0.00031950034,0.000637938,0.00013824226,0.000029333452,0.0018584869,0.00000906831,0.00029043478,0.09958025,0.0020303854,0.37892705,0.4727234,0.04345591],"study_design_scores_gemma":[0.004806728,0.00037730794,0.000024317773,0.0001283631,0.00015406348,0.00012080723,0.00008753696,0.90609604,0.004706225,0.062155057,0.021096643,0.000246899],"about_ca_topic_score_codex":0.00004092771,"about_ca_topic_score_gemma":0.0000051223165,"teacher_disagreement_score":0.8065158,"about_ca_system_score_codex":0.0011301183,"about_ca_system_score_gemma":0.005662215,"threshold_uncertainty_score":0.9999748},"labels":[],"label_agreement":null},{"id":"W4413629940","doi":"10.21203/rs.3.rs-7292808/v1","title":"A distributed multi-objective optimization algorithm with time-varying priorities for multi-agent systems","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Metaheuristic Optimization Algorithms Research","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 de Sherbrooke","funders":"","keywords":"Computer science; Optimization algorithm; Multi-agent system; Distributed computing; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.0863825949788198,"score_gpt":0.39041298362542387,"score_spread":0.3040303886466041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413629940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010021303,0.0012713686,0.9858946,0.0003336112,0.00065033784,0.0084598055,0.0026268715,0.0005133192,0.00024001695],"genre_scores_gemma":[0.0005004967,0.0004098044,0.9844599,0.000017353552,0.00023435052,0.004358831,0.0013076451,0.000083049075,0.008628617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99062884,0.0018770744,0.0009013744,0.0021510173,0.002859538,0.0015821384],"domain_scores_gemma":[0.9877491,0.0018014343,0.0003489534,0.0021231086,0.007484162,0.0004932608],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003986528,0.00067344325,0.0009875386,0.0016824977,0.0010254193,0.002395702,0.0029199487,0.0005876878,0.00003950051],"category_scores_gemma":[0.0024362388,0.00061197754,0.00025032103,0.0023459247,0.00032813475,0.00051043625,0.004008787,0.0019111921,0.000045985817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008211214,0.0004490806,0.00007200963,0.0025942016,0.00037510134,0.00007044673,0.000819532,0.983147,0.000006487464,0.00093378284,0.0011587256,0.010291505],"study_design_scores_gemma":[0.0021129632,0.0002978365,0.00009052867,0.0016153594,0.000031461637,0.000010630713,0.00022402569,0.993911,0.00012478846,0.00005957279,0.0009229677,0.00059883285],"about_ca_topic_score_codex":0.00044157106,"about_ca_topic_score_gemma":0.000009757641,"teacher_disagreement_score":0.010764017,"about_ca_system_score_codex":0.0014285757,"about_ca_system_score_gemma":0.0035290676,"threshold_uncertainty_score":0.99963313},"labels":[],"label_agreement":null},{"id":"W4413776701","doi":"10.2139/ssrn.5407188","title":"An Adaptive Balance Search  Based Complementary Heterogeneous Particle Swarm Optimization Architecture","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"","keywords":"Particle swarm optimization; Architecture; Balance (ability); Computer science; Mathematical optimization; Multi-swarm optimization; Swarm behaviour; Particle (ecology); Artificial intelligence; Mathematics; Geography; Biology; Ecology","score_opus":0.022248709559298564,"score_gpt":0.3040933373732065,"score_spread":0.2818446278139079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413776701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010650882,0.0010736989,0.9942792,0.0021106584,0.00044035228,0.0006528577,0.000057794958,0.00016557911,0.00015473993],"genre_scores_gemma":[0.3647905,0.001729619,0.631278,0.0007137168,0.0004721613,0.00008522529,0.00020011725,0.00006326908,0.0006674538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99274707,0.0013032884,0.0006836813,0.0009961659,0.0012871254,0.0029826942],"domain_scores_gemma":[0.9972289,0.00018900976,0.00030513806,0.0012609299,0.00066870503,0.00034727345],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0031809441,0.00044312782,0.00048048032,0.0004608578,0.00047575033,0.00067969714,0.0032166494,0.00021052151,0.000111940666],"category_scores_gemma":[0.00008596621,0.0004348901,0.00021327993,0.0006892335,0.00009865776,0.00028327483,0.0009770719,0.005049659,0.000013732482],"study_design_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":[0.00007748554,0.00019286547,0.00008733017,0.00002737579,0.00018586354,0.000023268505,0.00014673079,0.96683896,0.000013087786,0.010887689,0.000032884294,0.021486457],"study_design_scores_gemma":[0.0008072023,0.0005606464,0.000015029611,0.00006644293,0.00003248433,0.00011362142,0.00008164132,0.9746693,0.00053692545,0.022666179,0.000081813436,0.00036875697],"about_ca_topic_score_codex":0.00012997499,"about_ca_topic_score_gemma":0.00014497065,"teacher_disagreement_score":0.3637254,"about_ca_system_score_codex":0.0016466532,"about_ca_system_score_gemma":0.009389198,"threshold_uncertainty_score":0.9998103},"labels":[],"label_agreement":null},{"id":"W4413872209","doi":"10.5267/j.ijiec.2025.6.001","title":"An enhanced dung beetle optimization algorithm based-on multi-strategies for solving global optimization problems","year":2025,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Metaheuristic Optimization Algorithms Research","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":"National Natural Science Foundation of China","keywords":"Optimization algorithm; Dung beetle; Mathematical optimization; Computer science; Optimization problem; Algorithm; Mathematics; Ecology; Biology","score_opus":0.03418458907752006,"score_gpt":0.327242333226351,"score_spread":0.2930577441488309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413872209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000549403,0.000025699044,0.99540484,0.0010330937,0.002859204,0.00039098397,0.000038577175,0.000108586406,0.00008407494],"genre_scores_gemma":[0.06294017,0.000009954959,0.93640864,0.00012871169,0.00038120057,0.000032403408,0.000064168686,0.000016135851,0.000018644296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979509,0.00007322099,0.0007816025,0.00029015302,0.0006653021,0.00023878721],"domain_scores_gemma":[0.99674195,0.0004105864,0.00037543618,0.00021601497,0.002125837,0.00013020077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063573243,0.00020028098,0.00024773856,0.0007544909,0.00013288949,0.0008077542,0.0011328289,0.00014235932,0.000015699747],"category_scores_gemma":[0.00081235054,0.00020989375,0.0001278677,0.0008116473,0.000028506878,0.0011104112,0.000071358634,0.00027765235,0.000001187399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022301534,0.00019275282,0.000009322298,0.000007987046,0.00008983396,0.000005063659,0.00005714945,0.9760392,0.00006182543,0.004622609,0.00015142144,0.018740585],"study_design_scores_gemma":[0.0022773931,0.00019502634,0.000022045087,0.0002040725,0.00001865364,0.000010061847,0.000028913772,0.9964351,0.00025772708,0.00025328703,0.000136591,0.00016110585],"about_ca_topic_score_codex":0.00001067078,"about_ca_topic_score_gemma":8.5120246e-7,"teacher_disagreement_score":0.06288523,"about_ca_system_score_codex":0.00037218988,"about_ca_system_score_gemma":0.0008919557,"threshold_uncertainty_score":0.85592186},"labels":[],"label_agreement":null},{"id":"W4413953215","doi":"10.1007/s10586-025-05445-3","title":"A multi-strategy improved gazelle optimization algorithm for solving numerical optimization and engineering applications","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Concordia University","funders":"","keywords":"Computer science; Optimization algorithm; Engineering optimization; Mathematical optimization; Optimization problem; Algorithm; Mathematics","score_opus":0.01689102579222413,"score_gpt":0.2881112256679542,"score_spread":0.2712201998757301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413953215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007208174,0.00019575996,0.99778736,0.00028839931,0.00022116768,0.0011323276,0.000004306975,0.00029210068,0.000071375194],"genre_scores_gemma":[0.003836881,0.000018105118,0.9954551,0.00015878933,0.00009181834,0.0001403929,0.000030043224,0.000023725477,0.00024517914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983004,0.0000531676,0.00045349984,0.0006216882,0.00016447275,0.0004067959],"domain_scores_gemma":[0.9985446,0.00044898666,0.00012839344,0.0003909026,0.00036520767,0.00012193083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050612254,0.00020508304,0.00024407922,0.00031260916,0.00039418408,0.0005087317,0.0005000636,0.000104193474,0.000005531015],"category_scores_gemma":[0.00020079291,0.00022517965,0.000057449146,0.000846486,0.000042547334,0.00033034774,0.0005036556,0.00017536043,0.000001801374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014351401,0.000042389456,0.000010899332,0.0000547418,0.000021028576,4.0169107e-7,0.00008152415,0.91813916,0.00006489536,0.00061451126,0.000045519108,0.080923505],"study_design_scores_gemma":[0.00092661066,0.000029211737,0.000023972729,0.000037409085,0.000013051719,0.0000067886153,0.000023650353,0.9983766,0.000088365174,0.000027368425,0.00024580225,0.00020116143],"about_ca_topic_score_codex":0.000005469046,"about_ca_topic_score_gemma":1.9815637e-7,"teacher_disagreement_score":0.08072234,"about_ca_system_score_codex":0.00008235326,"about_ca_system_score_gemma":0.00011462092,"threshold_uncertainty_score":0.918256},"labels":[],"label_agreement":null},{"id":"W4414034432","doi":"10.1007/s10845-025-02675-5","title":"A robust optimization model for the supplier portfolio selection problem using a combination of artificial fish swarm algorithm and simulated annealing","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Metaheuristic Optimization Algorithms Research","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":"Laurentian University","funders":"","keywords":"Simulated annealing; Swarm behaviour; Selection (genetic algorithm); Mathematical optimization; Portfolio; Computer science; Fish <Actinopterygii>; Algorithm; Artificial intelligence; Mathematics; Fishery; Business; Biology","score_opus":0.0503925827841007,"score_gpt":0.3050126222267949,"score_spread":0.25462003944269423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414034432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057383757,0.00007236014,0.9930529,0.00042332406,0.00021478784,0.0004637335,0.0000033728304,0.00001606088,0.000015060963],"genre_scores_gemma":[0.120516166,0.000121585916,0.879059,0.00006348071,0.000059743346,0.0000048425504,0.0000033649808,0.000013703908,0.0001581029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834377,0.00006672529,0.00080157147,0.00019395191,0.00039037253,0.0002036399],"domain_scores_gemma":[0.9981062,0.00031372166,0.0005610723,0.0001485917,0.0008122045,0.00005820354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011449408,0.00013177966,0.00024260282,0.0005301759,0.00022431296,0.00021965776,0.00035718523,0.00007486072,0.000009181327],"category_scores_gemma":[0.00015957918,0.00010363282,0.00009262401,0.0003805315,0.00003955993,0.00041645876,0.000113407055,0.00021735697,1.08637465e-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":[0.00003309477,0.00006135908,0.000011847252,0.00004846064,0.00007715252,0.0000010788403,0.00024545856,0.9680189,0.000091579655,0.0004362224,0.000061192724,0.03091364],"study_design_scores_gemma":[0.0002919367,0.00006095462,0.000015733685,0.000081930266,0.00005133436,0.000020357844,0.00006004834,0.95721364,0.04066944,0.0014213745,0.000031714128,0.00008153174],"about_ca_topic_score_codex":0.00001817565,"about_ca_topic_score_gemma":0.0000018241722,"teacher_disagreement_score":0.11477779,"about_ca_system_score_codex":0.0001282283,"about_ca_system_score_gemma":0.00018353794,"threshold_uncertainty_score":0.42260236},"labels":[],"label_agreement":null},{"id":"W4414156966","doi":"10.3390/math13182909","title":"Machine Learning for Enhancing Metaheuristics in Global Optimization: A Comprehensive Review","year":2025,"lang":"en","type":"review","venue":"Mathematics","topic":"Metaheuristic Optimization Algorithms Research","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 Prince Edward Island","funders":"","keywords":"Metaheuristic; Flexibility (engineering); Hyper-heuristic; Key (lock); Search-based software engineering; Global optimization; Resource (disambiguation)","score_opus":0.07026562065125108,"score_gpt":0.3910868932276872,"score_spread":0.3208212725764361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414156966","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":[1.3970744e-10,0.49950352,0.49799472,0.000049840502,0.00014557586,0.0015690972,0.00003207695,0.0000767294,0.00062844413],"genre_scores_gemma":[2.2570719e-9,0.54226774,0.45650703,0.00007087778,0.000026142383,0.00029889186,0.00010396064,0.000022527247,0.00070283865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571645,0.0005183509,0.0018448513,0.0007508383,0.0006380563,0.00053144526],"domain_scores_gemma":[0.99479604,0.0026140239,0.0008180792,0.001066391,0.00057082716,0.000134652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012855212,0.0005887965,0.0029865506,0.00038073058,0.00014717711,0.0002528187,0.0017883064,0.00024228098,0.00006434473],"category_scores_gemma":[0.004853181,0.0005159169,0.00048591496,0.0026350035,0.000045008826,0.00014587832,0.000792996,0.00054562767,0.000044339555],"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":[6.304476e-7,0.00013877441,1.5589747e-7,0.4279386,0.00013785239,0.000025496702,0.000042506712,0.011884481,3.727323e-9,0.014701135,0.00087071443,0.54425967],"study_design_scores_gemma":[0.00014655128,0.000022708566,9.387086e-9,0.050908353,0.00028960084,0.000030355332,0.0000029175549,0.4757383,5.590493e-8,0.0005562263,0.4720164,0.00028855825],"about_ca_topic_score_codex":0.000003645323,"about_ca_topic_score_gemma":0.0000037919701,"teacher_disagreement_score":0.54397106,"about_ca_system_score_codex":0.00032370232,"about_ca_system_score_gemma":0.00086610264,"threshold_uncertainty_score":0.9997292},"labels":[],"label_agreement":null},{"id":"W4414223321","doi":"10.1609/icaps.v35i1.36134","title":"New Exact Methods for Solving Quadratic Traveling Salesman Problem","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Metaheuristic Optimization Algorithms Research","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":"Alliance de recherche numérique du Canada; University of Toronto; Innovation, Science and Economic Development Canada","keywords":"Travelling salesman problem; Integer programming; Generalization; Quadratic programming; Quadratic equation; Branch and bound; Scalability; Integer (computer science); Linear programming","score_opus":0.05569454548912666,"score_gpt":0.38933066774580644,"score_spread":0.33363612225667977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414223321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008737129,0.00008704761,0.97848964,0.0036102694,0.00038271162,0.0003286728,0.0000021969354,0.0002723115,0.008090023],"genre_scores_gemma":[0.276573,0.000010873882,0.7225749,0.00010767893,0.000026250407,0.000018938907,0.0000015930957,0.0000071093687,0.0006797026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870354,0.000018207316,0.0003929481,0.0003603699,0.00031160977,0.0002133182],"domain_scores_gemma":[0.9985814,0.00037564864,0.00024556665,0.00011355736,0.00061522203,0.00006856695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011444264,0.00014990674,0.00021033108,0.00023649212,0.00021004977,0.00054370594,0.0011793501,0.00007372729,0.0000092463515],"category_scores_gemma":[0.001048027,0.0001183544,0.00005997344,0.00034447148,0.000038189908,0.0002749279,0.000266284,0.00022338849,9.676484e-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":[0.00006952796,0.000054301356,0.0013064628,0.00027708698,0.00022112107,6.4523545e-7,0.0017557724,0.010272434,0.055113822,0.87096524,0.0009258459,0.059037734],"study_design_scores_gemma":[0.0003500557,0.00003435806,0.00033272366,0.0007524361,0.000012308176,0.0000049240257,0.00016971977,0.9623834,0.01752592,0.01822778,0.00009895015,0.00010743331],"about_ca_topic_score_codex":0.000012560519,"about_ca_topic_score_gemma":2.2344516e-7,"teacher_disagreement_score":0.95211095,"about_ca_system_score_codex":0.00004312636,"about_ca_system_score_gemma":0.00023458003,"threshold_uncertainty_score":0.5242969},"labels":[],"label_agreement":null},{"id":"W4415058972","doi":"10.1016/j.asoc.2025.114066","title":"The effect of elitist fitness-based selection on the escape from local optima","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Local optimum; Metaheuristic; Local search (optimization); Guided Local Search; Selection (genetic algorithm); Iterated local search; Task (project management); Key (lock)","score_opus":0.0070331794154838085,"score_gpt":0.2648644223046214,"score_spread":0.2578312428891376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415058972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070148553,0.000050862916,0.9877775,0.0011714357,0.00026327785,0.000442698,0.0000016574047,0.00012874352,0.003149006],"genre_scores_gemma":[0.96046716,0.0000017989031,0.039092857,0.00026840586,0.000051833587,0.000030544066,0.000004883681,0.000010423227,0.0000720633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809986,0.00034709246,0.000338775,0.000398133,0.0005007346,0.00031541724],"domain_scores_gemma":[0.9909263,0.008160809,0.00014340675,0.00060385535,0.00012130588,0.000044270317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015385952,0.00016313749,0.00021514288,0.00009161244,0.00068541424,0.00025385717,0.00117102,0.00006291518,0.000015784957],"category_scores_gemma":[0.00038637707,0.000099485966,0.000069382666,0.00095059414,0.00018194316,0.00003350668,0.00030803523,0.00032873795,0.000031085678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007866893,0.000041424944,0.00038093686,0.000035893856,0.0000753455,0.0000020025288,0.000090871275,0.44686973,0.00037231753,0.089462265,0.0017093385,0.4608812],"study_design_scores_gemma":[0.00044954845,0.0000725717,0.0007217585,0.000038931885,0.000008532081,3.8072258e-7,0.000014387747,0.9833223,0.013653426,0.0011108743,0.0005217202,0.00008553009],"about_ca_topic_score_codex":0.00004968618,"about_ca_topic_score_gemma":0.0000033060473,"teacher_disagreement_score":0.95345235,"about_ca_system_score_codex":0.00006765164,"about_ca_system_score_gemma":0.00013268144,"threshold_uncertainty_score":0.52717215},"labels":[],"label_agreement":null},{"id":"W4415502605","doi":"10.1093/jcde/qwaf112","title":"AR-RBMO: An enhanced red-billed blue magpie optimizer with attraction-repulsion and dynamic balancing strategies for global optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"National Natural Science Foundation of China","keywords":"Benchmark (surveying); Global optimization; Metaheuristic; Flexibility (engineering); Convergence (economics); Population; Robustness (evolution); Swarm intelligence; Optimization problem; Wilcoxon signed-rank test","score_opus":0.010517179620667231,"score_gpt":0.2738454120282298,"score_spread":0.2633282324075626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415502605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053545814,0.00017312929,0.9937232,0.00029782284,0.00017406032,0.00021650735,0.0000014752338,0.00003480415,0.000024378298],"genre_scores_gemma":[0.27856892,0.00005398161,0.7213016,0.000024449206,0.000020516827,0.0000062624213,0.0000038384637,0.0000062038257,0.000014204351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989577,0.000053862983,0.00035441841,0.00019620023,0.00028280987,0.00015499089],"domain_scores_gemma":[0.9987854,0.00034804095,0.0001558982,0.00008800354,0.0005170208,0.00010566595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004888595,0.0001326392,0.00021292371,0.00022061451,0.000114145594,0.0003622787,0.00016126114,0.00004993131,0.00000295342],"category_scores_gemma":[0.00009755179,0.00011477067,0.000028675511,0.00033253361,0.0000236758,0.0009217534,0.0000326875,0.00011578897,8.738183e-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":[0.00007532337,0.000024617848,0.000008742735,0.000043465498,0.00004747898,0.000004835906,0.00005794509,0.99127156,0.00031145776,0.002764681,0.000022896209,0.005366977],"study_design_scores_gemma":[0.0010556962,0.00021492294,0.0012027298,0.00010051887,0.000019677334,0.000095139345,0.00004730004,0.9947332,0.000052214175,0.0023498142,0.0000160865,0.000112664566],"about_ca_topic_score_codex":9.899902e-7,"about_ca_topic_score_gemma":2.2890937e-7,"teacher_disagreement_score":0.27321434,"about_ca_system_score_codex":0.0000815904,"about_ca_system_score_gemma":0.0002358113,"threshold_uncertainty_score":0.4680212},"labels":[],"label_agreement":null},{"id":"W4415556772","doi":"10.61091/jcmcc128-08","title":"Small hard-to-color graphs for the Sl algorithm","year":2025,"lang":"","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Metaheuristic Optimization Algorithms Research","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":"Graph; Vertex (graph theory); Graph coloring; Graph power; Graph algorithms; Efficient algorithm","score_opus":0.03372772032434005,"score_gpt":0.297906942643309,"score_spread":0.26417922231896895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415556772","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039647217,0.0018400267,0.91603655,0.0039100177,0.071565494,0.002269744,0.000014711664,0.000047765763,0.00035099193],"genre_scores_gemma":[0.1526291,0.0008626363,0.84022826,0.000641995,0.0048907134,0.00006888706,0.0000028913805,0.00015118073,0.0005243048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992426,0.0005223706,0.0033396792,0.0008000422,0.001749252,0.001162696],"domain_scores_gemma":[0.9828795,0.00841315,0.0021854935,0.0011485704,0.0047178604,0.00065539166],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009922528,0.00078917,0.0018958,0.0010738734,0.0017279972,0.0026105978,0.003738007,0.00042530012,0.000014682505],"category_scores_gemma":[0.0050222804,0.0006211524,0.000743059,0.002707109,0.00033688816,0.00037281984,0.0018809374,0.0013659274,0.000007902365],"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":[0.0001829957,0.001010281,0.00001811779,0.00052012416,0.00079704326,0.000024370058,0.001641792,0.001064387,0.000069286994,0.8974664,0.0031055773,0.094099574],"study_design_scores_gemma":[0.005860405,0.0013692137,0.000054855664,0.00075047597,0.00041290492,0.000056816447,0.00028605788,0.5795718,0.00032741838,0.4027293,0.008142783,0.00043802345],"about_ca_topic_score_codex":0.000020673202,"about_ca_topic_score_gemma":5.9327675e-7,"teacher_disagreement_score":0.57850736,"about_ca_system_score_codex":0.0002774993,"about_ca_system_score_gemma":0.0014911025,"threshold_uncertainty_score":0.99962395},"labels":[],"label_agreement":null},{"id":"W4415649049","doi":"10.1007/978-3-032-04777-9_8","title":"A New Hybrid History-Driven Metaheuristic Approach for Continuous Optimization","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Metaheuristic Optimization Algorithms Research","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 Regina","funders":"","keywords":"Metaheuristic; Tree (set theory); Hybrid system; Optimization problem; Tree traversal; Local search (optimization); Beam search; Hybrid algorithm (constraint satisfaction)","score_opus":0.02245640978012536,"score_gpt":0.23888850119716973,"score_spread":0.21643209141704436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415649049","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0569889e-8,0.022894919,0.94981647,0.00009451962,0.0016130958,0.0017894461,0.00001773425,0.00010900984,0.023664813],"genre_scores_gemma":[0.0010839402,0.0014961127,0.835492,0.0002624425,0.0012875436,0.0002313584,0.0003677891,0.00011388015,0.15966493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971367,0.00013838033,0.0007713207,0.001037396,0.00044913785,0.0004670328],"domain_scores_gemma":[0.99736786,0.0009446819,0.00039348888,0.0007885292,0.0003094746,0.00019595095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062844204,0.000500525,0.0010515237,0.00047914448,0.00010380772,0.00034710238,0.0008092974,0.00047977216,0.000034836634],"category_scores_gemma":[0.00034006647,0.0004643435,0.00015443937,0.00014587425,0.00006769558,0.00011648708,0.00025049987,0.0006081782,0.0000014815726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015330175,0.000011207641,0.0000039267698,0.00023302158,0.00008806176,0.000015513582,0.00004661965,0.93217814,1.4150514e-7,0.033748727,0.009176234,0.024483068],"study_design_scores_gemma":[0.0005429309,0.00007021019,4.5115323e-7,0.00025021215,0.000060843537,0.000030540694,5.533498e-7,0.9477765,3.4781084e-7,0.0011377366,0.04973975,0.00038991356],"about_ca_topic_score_codex":0.00007278462,"about_ca_topic_score_gemma":0.000006665724,"teacher_disagreement_score":0.13600013,"about_ca_system_score_codex":0.0003380191,"about_ca_system_score_gemma":0.00039224702,"threshold_uncertainty_score":0.99978083},"labels":[],"label_agreement":null},{"id":"W4416513214","doi":"10.1109/tsmc.2025.3628274","title":"A New Explicit Penalty Method for Evolutionary Multimodal Optimization","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Metaheuristic Optimization Algorithms Research","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":"Hunan Provincial Postdoctoral Science Foundation; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Penalty method; Leverage (statistics); Flexibility (engineering); Local optimum; Optimization problem; Population; Evolutionary algorithm; Function (biology)","score_opus":0.024429797815307533,"score_gpt":0.3061023963866196,"score_spread":0.2816725985713121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416513214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013476396,0.004288374,0.97746736,0.0006438044,0.009947996,0.005320456,0.00026436872,0.00023344264,0.0018207537],"genre_scores_gemma":[0.20079653,0.0016453033,0.6387157,0.0001945064,0.0007654701,0.0018113538,0.000037563153,0.00017378843,0.15585978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925215,0.0013575905,0.001980632,0.0018238056,0.0012807299,0.0010357199],"domain_scores_gemma":[0.9946913,0.0013001253,0.0005653465,0.0014770469,0.001229696,0.0007364488],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018628257,0.0008222627,0.0012170371,0.0013188792,0.0010953777,0.0018431034,0.0011229736,0.00063470256,0.0000761781],"category_scores_gemma":[0.00007168551,0.00087024586,0.00034679813,0.0017585482,0.00011767174,0.00051767,0.000027382957,0.0006458353,0.00005952212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014577126,0.00030213964,0.0000055993855,0.001220966,0.00042560155,0.000007229799,0.0006167792,0.9521433,0.00005549249,0.014964836,0.004885745,0.025226552],"study_design_scores_gemma":[0.0029017099,0.0004471455,0.000017045142,0.0010656102,0.0002631455,0.0001008153,0.00055407867,0.98522216,0.00015849555,0.000082433246,0.008489836,0.0006975512],"about_ca_topic_score_codex":0.00223807,"about_ca_topic_score_gemma":0.000023162307,"teacher_disagreement_score":0.33875164,"about_ca_system_score_codex":0.00052956305,"about_ca_system_score_gemma":0.0011070508,"threshold_uncertainty_score":0.9993748},"labels":[],"label_agreement":null},{"id":"W52929814","doi":"10.5220/0002253403140317","title":"A NEW HYBRID GENETIC ALGORITHM FOR MAXIMUM INDEPENDENT SET PROBLEM","year":2009,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Crossover; Genetic algorithm; Heuristic; Algorithm; Computer science; Set (abstract data type); Mutation; Mathematical optimization; Population-based incremental learning; Operator (biology); Meta-optimization; Variety (cybernetics); Mathematics; Artificial intelligence; Biology","score_opus":0.023428074667953578,"score_gpt":0.28873536671678873,"score_spread":0.26530729204883513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W52929814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010205109,0.00007719701,0.99316114,0.0023436167,0.0001697838,0.0008014569,0.000007958161,0.00022507814,0.0032035827],"genre_scores_gemma":[0.00048212602,0.000020940033,0.9885164,0.00066639244,0.00013240175,0.000033343254,0.000009305905,0.000011950316,0.010127141],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786836,0.000070725524,0.00033916187,0.0005652814,0.00064794073,0.0005085029],"domain_scores_gemma":[0.9986138,0.000096824995,0.00007209604,0.00064010423,0.00023341809,0.00034380003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048008206,0.00017191537,0.00019648034,0.00019500301,0.00011125984,0.00038292469,0.0011706026,0.000048671973,0.00027051888],"category_scores_gemma":[0.00006373292,0.00015241177,0.00008143892,0.0004084607,0.000014679372,0.00027957637,0.00017750659,0.00012715596,0.00015540396],"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":[0.0000019769595,0.000041713665,0.0000037710315,0.000004291493,0.000010519197,0.000018058325,0.00005107459,0.0008295999,0.000019676272,0.0040094564,0.021880688,0.97312915],"study_design_scores_gemma":[0.0007627304,0.00024562827,0.00021684475,0.000005245129,0.0000049003197,0.000068603666,0.000004618018,0.9429497,0.0011510847,0.042076558,0.012302065,0.00021199025],"about_ca_topic_score_codex":0.000028941637,"about_ca_topic_score_gemma":0.0000013227534,"teacher_disagreement_score":0.9729172,"about_ca_system_score_codex":0.000056277277,"about_ca_system_score_gemma":0.000319862,"threshold_uncertainty_score":0.6215172},"labels":[],"label_agreement":null},{"id":"W61930541","doi":"","title":"A preliminary study for multiple ant colony system with new communication strategies","year":2005,"lang":"en","type":"article","venue":"International Conference on Communications","topic":"Metaheuristic Optimization Algorithms Research","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":"Ant colony optimization algorithms; Travelling salesman problem; Weighting; Ant colony; Computer science; Scheme (mathematics); ANT; Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Computer network","score_opus":0.1261040882049941,"score_gpt":0.38162791936840496,"score_spread":0.2555238311634108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W61930541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012291204,0.00009345099,0.95260864,0.016093079,0.000103611856,0.0014897241,0.00003373403,0.00027841286,0.028070198],"genre_scores_gemma":[0.71438557,0.000049985727,0.28394175,0.000082087594,0.000039869148,0.00044963168,0.00006925104,0.0000127644025,0.000969083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981708,0.000256741,0.00042906072,0.00036438528,0.0005640158,0.00021498598],"domain_scores_gemma":[0.99509734,0.0008625087,0.00023792525,0.0026945325,0.00097595423,0.00013172375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000523553,0.00018004533,0.00019252382,0.000248893,0.0004287805,0.000703998,0.005188283,0.00005255195,0.0000442648],"category_scores_gemma":[0.00018059563,0.0001623606,0.000048468697,0.00030125483,0.000119275304,0.0008098655,0.0007495595,0.00026977743,0.000071706185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014888041,0.0010689457,0.00041073136,0.000010250774,0.00013445571,0.0000022614056,0.002242938,0.006822419,0.000044915538,0.9664266,0.0012086133,0.021479003],"study_design_scores_gemma":[0.001489027,0.0006116062,0.0017293547,0.00008506407,0.000013277504,0.000022825921,0.0024961338,0.98528826,0.000043684737,0.0007556828,0.0072670723,0.00019801361],"about_ca_topic_score_codex":0.00024535228,"about_ca_topic_score_gemma":0.0006986556,"teacher_disagreement_score":0.97846586,"about_ca_system_score_codex":0.00022294499,"about_ca_system_score_gemma":0.0006051276,"threshold_uncertainty_score":0.9641207},"labels":[],"label_agreement":null},{"id":"W6890210412","doi":"10.34943/64984621-555d-41fb-ae45-3745eeac3e15","title":"China Creek Hydrophone Deployed 2023-09-16","year":2023,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Metaheuristic Optimization Algorithms Research","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":"Hydrophone; Underwater; Sound (geography); Data logger; Inlet; Waveform","score_opus":0.00985496403637428,"score_gpt":0.23027306343393691,"score_spread":0.22041809939756263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890210412","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":[0.0000020893337,0.00057361054,0.1873358,0.0010492249,0.0042464947,0.0005508895,0.8058981,0.00030140922,0.00004239589],"genre_scores_gemma":[0.00001533485,0.0033068387,0.009397404,0.0013886591,0.0012662001,0.000030209078,0.9769258,0.00011870844,0.007550854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99399304,0.00030306083,0.0007516068,0.0013633033,0.0021986251,0.0013903386],"domain_scores_gemma":[0.9958781,0.00039195182,0.00039805708,0.0023594098,0.0002475457,0.0007249115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009845488,0.00062464917,0.0007779468,0.00009069847,0.0006321887,0.00073427946,0.0038881346,0.00055104203,0.00013306095],"category_scores_gemma":[0.00021146976,0.0006895396,0.0003688834,0.0025120426,0.0001613316,0.00020217565,0.0014660761,0.0018037412,0.00006219825],"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":[0.0000018869991,0.000030199835,0.0000028169168,0.00007381394,0.00019569573,0.00017768578,0.000010998959,0.03628668,4.07579e-8,0.000007445019,0.9619959,0.0012168599],"study_design_scores_gemma":[0.00023684319,0.000023322176,0.000063762134,0.000042086227,0.000027366577,0.000011467324,0.000010612274,0.39630747,2.6060744e-7,0.000030363106,0.6027934,0.0004530365],"about_ca_topic_score_codex":0.789535,"about_ca_topic_score_gemma":0.64770776,"teacher_disagreement_score":0.3600208,"about_ca_system_score_codex":0.0010845504,"about_ca_system_score_gemma":0.003458646,"threshold_uncertainty_score":0.9995556},"labels":[],"label_agreement":null},{"id":"W6901527556","doi":"10.60692/qc1ye-n4s07","title":"Invited paper: A Review of Thresheld Convergence","year":2015,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Convergence (economics); Particle swarm optimization; Local search (optimization); Differential evolution; Attraction; Population; Structural basin; Stability (learning theory)","score_opus":0.0748210566638181,"score_gpt":0.26234279261719234,"score_spread":0.18752173595337424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901527556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007335147,0.00019954272,0.99190795,0.0009918828,0.00049533794,0.0005358622,0.000024704645,0.00020880974,0.0049023805],"genre_scores_gemma":[0.91096437,0.000060677445,0.08104226,0.0074038096,0.00007852355,0.00016157396,0.00003334652,0.000016623797,0.00023880774],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978362,0.000154344,0.0008138055,0.00014716803,0.00084967236,0.0001988224],"domain_scores_gemma":[0.9975945,0.000013528053,0.00038202107,0.00075772306,0.0010584093,0.00019383634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013205792,0.00012306593,0.0002778814,0.0002333226,0.000041729196,0.000107961394,0.0007369557,0.000054138953,0.00003680956],"category_scores_gemma":[0.0002890285,0.000098390556,0.000056339246,0.00085779204,0.000033369426,0.0014904225,0.00022199203,0.000080907244,0.00062416017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023611558,0.00007557598,0.12902763,0.12740538,0.00069413875,0.00009787938,0.369915,0.0069208806,0.0000128639285,0.06855168,0.26107523,0.0359876],"study_design_scores_gemma":[0.0028951599,0.00024720468,0.004438463,0.008186285,0.000042554304,0.00019725104,0.0034654269,0.9556466,0.0009798842,0.000025122506,0.023078933,0.0007971352],"about_ca_topic_score_codex":0.0000058230653,"about_ca_topic_score_gemma":2.2121132e-8,"teacher_disagreement_score":0.9487257,"about_ca_system_score_codex":0.00006848108,"about_ca_system_score_gemma":0.000096969095,"threshold_uncertainty_score":0.8022524},"labels":[],"label_agreement":null},{"id":"W6901589623","doi":"10.60692/sn2bp-er609","title":"Velocity pausing particle swarm optimization: a novel variant for global optimization","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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":"Ericsson (Canada)","funders":"","keywords":"Particle swarm optimization; Metaheuristic; Benchmark (surveying); Multi-swarm optimization; Local optimum; Convergence (economics); Premature convergence; Global optimization; Population","score_opus":0.06976714388031968,"score_gpt":0.27188545616456816,"score_spread":0.20211831228424848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901589623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00067127956,0.0000013419939,0.9953254,0.00039028173,0.00072229415,0.0009107594,0.00012994691,0.0010559211,0.000792811],"genre_scores_gemma":[0.35604703,4.887064e-7,0.64314216,0.00018222988,0.000105110405,0.00025453643,0.00010750078,0.000015661974,0.00014526649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975019,0.000089588946,0.00082754396,0.0003474789,0.00070748123,0.0005260127],"domain_scores_gemma":[0.9979237,0.000040004063,0.00034434246,0.0006389375,0.0008495909,0.00020343276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011658032,0.00021920775,0.0002639449,0.0002502059,0.00043571932,0.0010143234,0.0006236214,0.00012605917,0.000018098806],"category_scores_gemma":[0.00025367067,0.00020667758,0.00008856392,0.0022383018,0.000034076038,0.0019937542,0.00027401184,0.00007490187,0.00029789042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001699953,0.000004416185,0.0003217183,0.000124361,0.00002708313,0.0000029437902,0.0041755196,0.98821086,3.0461987e-7,0.0063082427,0.00013449768,0.0006730474],"study_design_scores_gemma":[0.0013107627,0.00003719119,0.0005249373,0.000042700176,0.0000131611905,0.000043222313,0.0006722632,0.9968943,0.00009385036,0.0000058281853,0.0001363546,0.00022544678],"about_ca_topic_score_codex":0.0000046922846,"about_ca_topic_score_gemma":6.929403e-8,"teacher_disagreement_score":0.35537577,"about_ca_system_score_codex":0.00027195225,"about_ca_system_score_gemma":0.00017701348,"threshold_uncertainty_score":0.97811437},"labels":[],"label_agreement":null},{"id":"W6902300727","doi":"10.6084/m9.figshare.26628502.v1","title":"Additional file 1 of Artificial Intelligence based wrapper for high dimensional feature selection","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Metaheuristic Optimization Algorithms Research","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":"Feature selection; Selection (genetic algorithm); Feature (linguistics); High dimensional; Pattern recognition (psychology)","score_opus":0.0461467105856483,"score_gpt":0.2934908961387054,"score_spread":0.2473441855530571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902300727","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":[7.3918486e-8,0.000019595016,0.13997081,0.00028364666,0.00007400879,0.00017805221,0.859249,0.00009398991,0.00013082265],"genre_scores_gemma":[0.00012882016,8.4299906e-8,0.2749918,0.00007932202,0.00015289738,0.0008262375,0.7225117,0.0000104302,0.0012987298],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891716,0.000035534555,0.00015979493,0.0003189776,0.000399085,0.00016941577],"domain_scores_gemma":[0.99682486,0.0024714922,0.0000450257,0.00015925673,0.000436918,0.0000624269],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000519005,0.00009181385,0.00009561458,0.00017424027,0.00008040478,0.00013801712,0.00031066092,0.000074285505,0.98157007],"category_scores_gemma":[0.0029081947,0.000084740685,0.00007296362,0.0006796864,0.00000930367,0.00019545862,0.00008031826,0.00013760665,0.0014591274],"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":[0.0000037084355,0.000023014316,9.696201e-9,0.00007561514,0.000008380999,0.0000026667826,0.0000054287307,0.0021119656,0.000009165327,0.0010135097,0.9699172,0.026829347],"study_design_scores_gemma":[0.000011630434,0.000038693426,0.000012402199,0.00041027233,0.0000010582048,0.0000030514975,6.4970754e-7,0.67081696,0.0007601898,0.0007082728,0.32717323,0.00006358161],"about_ca_topic_score_codex":7.46042e-7,"about_ca_topic_score_gemma":0.000001167031,"teacher_disagreement_score":0.98011094,"about_ca_system_score_codex":0.000033969493,"about_ca_system_score_gemma":0.0004212504,"threshold_uncertainty_score":0.99931836},"labels":[],"label_agreement":null},{"id":"W6920233024","doi":"10.60692/kd67p-mxk81","title":"Invited paper: A Review of Thresheld Convergence","year":2015,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Convergence (economics); Particle swarm optimization; Local search (optimization); Differential evolution; Attraction; Population; Structural basin; Stability (learning theory)","score_opus":0.0748210566638181,"score_gpt":0.26234279261719234,"score_spread":0.18752173595337424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920233024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007335147,0.00019954272,0.99190795,0.0009918828,0.00049533794,0.0005358622,0.000024704645,0.00020880974,0.0049023805],"genre_scores_gemma":[0.91096437,0.000060677445,0.08104226,0.0074038096,0.00007852355,0.00016157396,0.00003334652,0.000016623797,0.00023880774],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978362,0.000154344,0.0008138055,0.00014716803,0.00084967236,0.0001988224],"domain_scores_gemma":[0.9975945,0.000013528053,0.00038202107,0.00075772306,0.0010584093,0.00019383634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013205792,0.00012306593,0.0002778814,0.0002333226,0.000041729196,0.000107961394,0.0007369557,0.000054138953,0.00003680956],"category_scores_gemma":[0.0002890285,0.000098390556,0.000056339246,0.00085779204,0.000033369426,0.0014904225,0.00022199203,0.000080907244,0.00062416017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023611558,0.00007557598,0.12902763,0.12740538,0.00069413875,0.00009787938,0.369915,0.0069208806,0.0000128639285,0.06855168,0.26107523,0.0359876],"study_design_scores_gemma":[0.0028951599,0.00024720468,0.004438463,0.008186285,0.000042554304,0.00019725104,0.0034654269,0.9556466,0.0009798842,0.000025122506,0.023078933,0.0007971352],"about_ca_topic_score_codex":0.0000058230653,"about_ca_topic_score_gemma":2.2121132e-8,"teacher_disagreement_score":0.9487257,"about_ca_system_score_codex":0.00006848108,"about_ca_system_score_gemma":0.000096969095,"threshold_uncertainty_score":0.8022524},"labels":[],"label_agreement":null},{"id":"W6927339931","doi":"10.3389/fneur.2021.757665.s003","title":"Table_3_Antegrade or Retrograde Approach for the Management of Tandem Occlusions in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.DOCX","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Metaheuristic Optimization Algorithms Research","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":"Modified Rankin Scale; Cochrane Library; Odds ratio; Intracerebral hemorrhage; Stroke (engine); Concomitant","score_opus":0.10515946588784852,"score_gpt":0.3454269154334845,"score_spread":0.240267449545636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6927339931","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":[5.1413773e-10,0.04706546,0.010330053,0.00023229736,0.000013170215,0.0048201918,0.93748885,0.000019874104,0.000030097204],"genre_scores_gemma":[1.5083215e-7,0.0065929797,0.021832557,0.0002676287,0.000005610469,0.009166252,0.9600555,0.000015872132,0.0020634695],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99634236,0.00042286084,0.0009882994,0.00078183337,0.0010859894,0.00037865524],"domain_scores_gemma":[0.99607295,0.0007617091,0.0007699122,0.002120959,0.00017475235,0.000099689976],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010684878,0.0003727308,0.0023639537,0.0004509476,0.000202655,0.00019112849,0.003291591,0.00011127753,0.12239049],"category_scores_gemma":[0.0007049194,0.00019352745,0.00075558625,0.0029286235,0.000017606128,0.00013249868,0.0022578049,0.00052106543,0.000010894214],"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":[0.0000016525294,0.000053921944,1.5431256e-8,0.12766686,0.0388226,0.000024756702,0.000007578487,0.00014065602,1.2157713e-8,0.00001010936,0.83326614,0.0000057059833],"study_design_scores_gemma":[0.00036618186,0.00008386433,0.0000011432293,0.004627592,0.4538648,0.000044212135,0.0000589601,0.07581811,9.0887943e-7,0.0000052949854,0.46463263,0.00049630867],"about_ca_topic_score_codex":0.000012765115,"about_ca_topic_score_gemma":0.0000090180365,"teacher_disagreement_score":0.4150422,"about_ca_system_score_codex":0.0000788235,"about_ca_system_score_gemma":0.00013372903,"threshold_uncertainty_score":0.87841177},"labels":[],"label_agreement":null},{"id":"W6930119102","doi":"10.5281/zenodo.11975447","title":"Christ pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metaheuristic Optimization Algorithms Research","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":"Circumstantial evidence; Pretext; Subject (documents); Stupidity; Session (web analytics)","score_opus":0.029285596234657416,"score_gpt":0.25944437869932785,"score_spread":0.23015878246467042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930119102","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":[1.3217847e-7,0.000563861,0.24842921,0.0006796105,0.0004088341,0.00040927358,0.0001416735,0.0025624826,0.74680495],"genre_scores_gemma":[0.000058751877,0.00040674442,0.015440542,0.00013608557,0.00051160046,1.0301357e-7,0.00097344187,0.009237309,0.9732354],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99670887,0.00043189267,0.00033097042,0.0010136967,0.0009533938,0.0005611816],"domain_scores_gemma":[0.99766916,0.000023141729,0.0001558609,0.0014172405,0.0004098711,0.00032470387],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008578282,0.00029835757,0.00029103106,0.0010413247,0.0007262656,0.003232362,0.003994976,0.00020341101,0.17836736],"category_scores_gemma":[0.00061627926,0.00030850642,0.00010150695,0.0014145927,0.00019368537,0.00019141872,0.0039071613,0.00063486706,0.42560494],"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":[0.000002468278,0.000052826603,1.8681941e-8,0.00015929263,0.00006516243,0.000060943017,0.00013412167,0.000010303758,0.000021272499,0.015027775,0.9317619,0.052703872],"study_design_scores_gemma":[0.00020749401,0.00008557413,0.0000016981887,0.0001168428,0.000013582837,0.00011683904,0.000018102448,0.0074498584,0.000019451996,0.00045779473,0.9912076,0.0003051468],"about_ca_topic_score_codex":0.000025032063,"about_ca_topic_score_gemma":2.7963398e-7,"teacher_disagreement_score":0.24723758,"about_ca_system_score_codex":0.0001607762,"about_ca_system_score_gemma":0.0000128711945,"threshold_uncertainty_score":0.9999367},"labels":[],"label_agreement":null},{"id":"W6930301617","doi":"10.5281/zenodo.11689976","title":"2023 dinghy towing guide pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metaheuristic Optimization Algorithms Research","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":"Work (physics); Articular cartilage damage; Limiting; Frame (networking)","score_opus":0.0341866335488046,"score_gpt":0.2747473078308493,"score_spread":0.24056067428204467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930301617","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":[1.8917451e-7,0.0004611388,0.28540093,0.00067771215,0.0004982923,0.0003996222,0.000114076865,0.0020648474,0.7103832],"genre_scores_gemma":[0.000026904494,0.00035062758,0.022125062,0.00014534548,0.0005481548,1.2771748e-7,0.0007186932,0.007393708,0.96869135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99622315,0.00046651615,0.00043412574,0.0011182952,0.0010977008,0.000660212],"domain_scores_gemma":[0.9975718,0.00003806277,0.00017187564,0.0014106245,0.0004631469,0.00034445655],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001258352,0.00033035956,0.0003267087,0.0012096455,0.00083508494,0.0035183283,0.0041396236,0.00021070191,0.15497997],"category_scores_gemma":[0.0011563131,0.0003461149,0.0001214036,0.0017073292,0.00016114877,0.00026250305,0.0048784306,0.0006658746,0.329032],"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":[0.0000022491251,0.000040754192,3.7958674e-8,0.00014929737,0.00007978995,0.00007881497,0.00014777097,0.000027216713,0.000047259975,0.009258801,0.9350765,0.055091504],"study_design_scores_gemma":[0.00020715367,0.00007728832,0.0000014990092,0.0001830583,0.00001657143,0.00009703216,0.000031660005,0.0113870455,0.000027377797,0.0003600578,0.9872704,0.00034086476],"about_ca_topic_score_codex":0.000044179444,"about_ca_topic_score_gemma":4.2968932e-7,"teacher_disagreement_score":0.2632759,"about_ca_system_score_codex":0.00022966032,"about_ca_system_score_gemma":0.00001677757,"threshold_uncertainty_score":0.9998991},"labels":[],"label_agreement":null},{"id":"W6930364649","doi":"10.5281/zenodo.12003806","title":"cours de docimologie pdf","year":2024,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metaheuristic Optimization Algorithms Research","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":"Passer; Context (archaeology); Limiting","score_opus":0.05812599928387589,"score_gpt":0.28726447013171624,"score_spread":0.22913847084784034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930364649","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":[0.000009070085,0.0010981143,0.32113636,0.004086082,0.00080748025,0.00063909375,0.00020730183,0.0011633413,0.6708532],"genre_scores_gemma":[0.00069303805,0.0010290347,0.026207369,0.00040933298,0.00090018293,2.0537014e-7,0.0008125292,0.0072357045,0.9627126],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99377096,0.0016416273,0.0006036401,0.0014688773,0.0012065008,0.0013083736],"domain_scores_gemma":[0.9960912,0.00012032291,0.00025188143,0.0015335461,0.0013002811,0.0007028013],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0025948246,0.0005017298,0.0004744319,0.00094238855,0.0016831715,0.005865649,0.004988541,0.00042440672,0.47490227],"category_scores_gemma":[0.0032187814,0.0005472225,0.00019262734,0.0019674369,0.0007234655,0.00037170097,0.0055329353,0.0013590156,0.5487016],"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":[0.00001445252,0.00017199734,5.0101e-7,0.00036771165,0.00016255237,0.00032497328,0.00092254573,0.00039262715,0.00014784111,0.06735365,0.7732955,0.1568456],"study_design_scores_gemma":[0.00037886717,0.00027670927,0.00003440422,0.00023973941,0.000054301207,0.0005828089,0.00011563418,0.051155873,0.00008664912,0.0023691878,0.9442127,0.0004931309],"about_ca_topic_score_codex":0.000048767357,"about_ca_topic_score_gemma":5.964053e-7,"teacher_disagreement_score":0.29492897,"about_ca_system_score_codex":0.0008295434,"about_ca_system_score_gemma":0.00006960517,"threshold_uncertainty_score":0.9996979},"labels":[],"label_agreement":null},{"id":"W6931846493","doi":"10.5281/zenodo.7845935","title":"Why You Should Focus on Improving Career Guidance Services","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metaheuristic Optimization Algorithms Research","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; Dysgeusia; Liquation; Emperipolesis; Demotion; Triacetin","score_opus":0.06073825000746619,"score_gpt":0.27310263188605977,"score_spread":0.21236438187859358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931846493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022650564,0.00017221298,0.80879456,0.017890316,0.0007517297,0.0013849642,0.00025699736,0.008375106,0.16010903],"genre_scores_gemma":[0.8532325,0.000823916,0.08809752,0.011189575,0.002353915,0.0000028193062,0.004698769,0.011805267,0.027795685],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99660355,0.000460573,0.00033319253,0.0007960623,0.0011365829,0.00067006995],"domain_scores_gemma":[0.9974224,0.00007594509,0.00012266728,0.0011683367,0.0009190174,0.00029166194],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014736353,0.00020167003,0.00018384197,0.0006127308,0.0019872678,0.0021035634,0.0034863495,0.000083307845,0.0025721188],"category_scores_gemma":[0.0007910703,0.000204464,0.0000648132,0.0024248664,0.00010068648,0.0005961063,0.0031497632,0.0003671324,0.015407311],"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":[0.00004268134,0.00016429038,0.0000074042773,0.00020379128,0.00006281137,0.00012500487,0.0021070512,0.0036832613,0.0029714755,0.034109913,0.36668196,0.58984035],"study_design_scores_gemma":[0.00043841996,0.00022714149,0.0003576635,0.000037060934,0.0000051582097,0.000034906134,0.00013002336,0.20293999,0.0010060382,0.0006420097,0.7939165,0.00026508962],"about_ca_topic_score_codex":0.00005529227,"about_ca_topic_score_gemma":6.848824e-7,"teacher_disagreement_score":0.85096747,"about_ca_system_score_codex":0.00014707225,"about_ca_system_score_gemma":0.000008879743,"threshold_uncertainty_score":0.999312},"labels":[],"label_agreement":null},{"id":"W6939006616","doi":"10.60692/thbcn-2db10","title":"A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes","year":2013,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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 Institute for Advanced Research","funders":"","keywords":"Task (project management); Genetic algorithm; Evolutionary algorithm; Fitness landscape; Variable (mathematics); Evolutionary computation","score_opus":0.033084631035920714,"score_gpt":0.23698456041691668,"score_spread":0.20389992938099596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939006616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020785881,0.0000035889705,0.99073863,0.002070771,0.0004817311,0.0009285435,0.000014504163,0.00016485462,0.003518767],"genre_scores_gemma":[0.97568876,5.57586e-7,0.02360865,0.00020490805,0.00006148566,0.00022938507,0.000011538395,0.000008100168,0.00018664145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980125,0.000156499,0.0006057623,0.00020439387,0.00074506365,0.00027579395],"domain_scores_gemma":[0.9980491,0.000029326306,0.00030276153,0.00080487167,0.00069276645,0.00012117162],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006743901,0.00015912442,0.00020397633,0.00032566529,0.00018038346,0.00036840423,0.00079210254,0.00007984293,0.000080209],"category_scores_gemma":[0.00006509994,0.00010561871,0.00005701535,0.000663685,0.000019773983,0.0011240647,0.00018808818,0.00010139984,0.0014358165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028181035,0.000021695474,0.001114994,0.00031113173,0.000068390094,0.0000012124617,0.061576717,0.9165924,8.19486e-7,0.0069618383,0.0030309581,0.010291672],"study_design_scores_gemma":[0.00037997952,0.000091423564,0.0023321344,0.000069916045,0.000005908652,0.000005809744,0.000971698,0.99565554,0.00006655447,0.0000030915376,0.00028993838,0.0001279915],"about_ca_topic_score_codex":0.000016092243,"about_ca_topic_score_gemma":2.628295e-7,"teacher_disagreement_score":0.97361016,"about_ca_system_score_codex":0.00006703077,"about_ca_system_score_gemma":0.000058771042,"threshold_uncertainty_score":0.99934167},"labels":[],"label_agreement":null},{"id":"W6939106462","doi":"10.60692/wmf9w-wpe97","title":"A Solution to the Challenge of Optimization on ''Golf-Course''-Like Fitness Landscapes","year":2013,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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 Institute for Advanced Research","funders":"","keywords":"Task (project management); Genetic algorithm; Evolutionary algorithm; Fitness landscape; Variable (mathematics); Evolutionary computation","score_opus":0.033084631035920714,"score_gpt":0.23698456041691668,"score_spread":0.20389992938099596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939106462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020785881,0.0000035889705,0.99073863,0.002070771,0.0004817311,0.0009285435,0.000014504163,0.00016485462,0.003518767],"genre_scores_gemma":[0.97568876,5.57586e-7,0.02360865,0.00020490805,0.00006148566,0.00022938507,0.000011538395,0.000008100168,0.00018664145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980125,0.000156499,0.0006057623,0.00020439387,0.00074506365,0.00027579395],"domain_scores_gemma":[0.9980491,0.000029326306,0.00030276153,0.00080487167,0.00069276645,0.00012117162],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006743901,0.00015912442,0.00020397633,0.00032566529,0.00018038346,0.00036840423,0.00079210254,0.00007984293,0.000080209],"category_scores_gemma":[0.00006509994,0.00010561871,0.00005701535,0.000663685,0.000019773983,0.0011240647,0.00018808818,0.00010139984,0.0014358165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028181035,0.000021695474,0.001114994,0.00031113173,0.000068390094,0.0000012124617,0.061576717,0.9165924,8.19486e-7,0.0069618383,0.0030309581,0.010291672],"study_design_scores_gemma":[0.00037997952,0.000091423564,0.0023321344,0.000069916045,0.000005908652,0.000005809744,0.000971698,0.99565554,0.00006655447,0.0000030915376,0.00028993838,0.0001279915],"about_ca_topic_score_codex":0.000016092243,"about_ca_topic_score_gemma":2.628295e-7,"teacher_disagreement_score":0.97361016,"about_ca_system_score_codex":0.00006703077,"about_ca_system_score_gemma":0.000058771042,"threshold_uncertainty_score":0.99934167},"labels":[],"label_agreement":null},{"id":"W6958429649","doi":"10.6084/m9.figshare.26628502","title":"Additional file 1 of Artificial Intelligence based wrapper for high dimensional feature selection","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Metaheuristic Optimization Algorithms Research","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":"Feature selection; Selection (genetic algorithm); Feature (linguistics); High dimensional; Pattern recognition (psychology)","score_opus":0.0461467105856483,"score_gpt":0.2934908961387054,"score_spread":0.2473441855530571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958429649","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":[7.3918486e-8,0.000019595016,0.13997081,0.00028364666,0.00007400879,0.00017805221,0.859249,0.00009398991,0.00013082265],"genre_scores_gemma":[0.00012882016,8.4299906e-8,0.2749918,0.00007932202,0.00015289738,0.0008262375,0.7225117,0.0000104302,0.0012987298],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891716,0.000035534555,0.00015979493,0.0003189776,0.000399085,0.00016941577],"domain_scores_gemma":[0.99682486,0.0024714922,0.0000450257,0.00015925673,0.000436918,0.0000624269],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0000519005,0.00009181385,0.00009561458,0.00017424027,0.00008040478,0.00013801712,0.00031066092,0.000074285505,0.98157007],"category_scores_gemma":[0.0029081947,0.000084740685,0.00007296362,0.0006796864,0.00000930367,0.00019545862,0.00008031826,0.00013760665,0.0014591274],"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":[0.0000037084355,0.000023014316,9.696201e-9,0.00007561514,0.000008380999,0.0000026667826,0.0000054287307,0.0021119656,0.000009165327,0.0010135097,0.9699172,0.026829347],"study_design_scores_gemma":[0.000011630434,0.000038693426,0.000012402199,0.00041027233,0.0000010582048,0.0000030514975,6.4970754e-7,0.67081696,0.0007601898,0.0007082728,0.32717323,0.00006358161],"about_ca_topic_score_codex":7.46042e-7,"about_ca_topic_score_gemma":0.000001167031,"teacher_disagreement_score":0.98011094,"about_ca_system_score_codex":0.000033969493,"about_ca_system_score_gemma":0.0004212504,"threshold_uncertainty_score":0.99931836},"labels":[],"label_agreement":null},{"id":"W6976661708","doi":"10.60692/x846g-r0p96","title":"Supplementing recurrent neural networks with annealing to solve combinatorial optimization problems","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Metaheuristic Optimization Algorithms Research","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":"Vector Institute; University of Waterloo","funders":"","keywords":"Travelling salesman problem; Simulated annealing; Combinatorial optimization; Extremal optimization; Optimization problem; Scheduling (production processes); Recurrent neural network; Cross-entropy method; Artificial neural network","score_opus":0.039404107213096104,"score_gpt":0.24525164028821142,"score_spread":0.20584753307511533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976661708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027667528,9.363458e-7,0.9934428,0.00020487755,0.0014118328,0.0009915687,0.000021427753,0.0007863957,0.00037340526],"genre_scores_gemma":[0.87644994,3.2200057e-7,0.122741126,0.00010271063,0.00020195189,0.0002807361,0.00015194577,0.000022878934,0.00004840884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746186,0.00009889688,0.00072890095,0.00030879525,0.00084420753,0.0005573414],"domain_scores_gemma":[0.99839866,0.000024614703,0.00030280728,0.0005245897,0.00052889605,0.00022040382],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011180305,0.00021026358,0.00023581844,0.0006396401,0.00033851693,0.0010921359,0.0006175085,0.0000734063,0.000016821135],"category_scores_gemma":[0.000055094857,0.00017563645,0.000044677225,0.002151583,0.00001695799,0.0013957635,0.00035100666,0.00015078748,0.00021999283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000117473155,0.0000014876416,0.0022483398,0.00009564693,0.000017512453,0.0000033790166,0.011440924,0.9838856,5.1777825e-8,0.00095975545,0.00026063164,0.0010749489],"study_design_scores_gemma":[0.0008414372,0.000101970574,0.00039756263,0.00009121203,0.000006514464,0.000013147536,0.0009767269,0.9971918,0.000015303636,8.76072e-7,0.00015414626,0.00020928624],"about_ca_topic_score_codex":0.0000056025997,"about_ca_topic_score_gemma":1.1026318e-7,"teacher_disagreement_score":0.87368315,"about_ca_system_score_codex":0.00012460921,"about_ca_system_score_gemma":0.000053798383,"threshold_uncertainty_score":0.9999448},"labels":[],"label_agreement":null},{"id":"W6977527521","doi":"10.6084/m9.figshare.26737995","title":"Additional file 3 of BETTER LIFE- guidelines for chronic disease preventive care for people aged 18–39 years: a literature review","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Metaheuristic Optimization Algorithms Research","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":"Regional Municipality of Durham; University of Toronto; Sunnybrook Health Science Centre; University of Alberta; Lunenfeld-Tanenbaum Research Institute; Women's College Hospital; McMaster University; Centre for Addiction and Mental Health; Ontario Institute for Cancer Research; Memorial University of Newfoundland; Public Health Agency of Canada","funders":"","keywords":"Preventive care; Chronic disease; Chronic care; Preventive healthcare; Disease; MEDLINE","score_opus":0.06200788366481726,"score_gpt":0.36640335456980916,"score_spread":0.3043954709049919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977527521","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":[1.7511574e-10,0.051388297,0.0003228947,0.00026333745,0.00013013698,0.0022951062,0.9455477,0.00004634448,0.0000061703354],"genre_scores_gemma":[1.1863971e-9,0.0007259321,0.026969502,0.00090492686,0.000978905,0.016899824,0.9524655,0.000035615914,0.0010197915],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753356,0.00010314567,0.00058883213,0.0007906456,0.00064399303,0.00033982747],"domain_scores_gemma":[0.99423534,0.0017436288,0.00035119776,0.000994196,0.0024338928,0.00024173748],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009176805,0.00032601325,0.0005691793,0.00023381237,0.00008166908,0.00029505993,0.001502508,0.00018688048,0.9280802],"category_scores_gemma":[0.029363398,0.00030135742,0.0004844242,0.000660714,0.0000103573975,0.00022101875,0.0006555155,0.00033094228,0.002322526],"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":[0.0000044028748,0.000021995984,1.5425828e-9,0.041419163,0.00006477162,0.00001858187,0.000016641652,0.000012553874,7.983152e-9,5.1983676e-7,0.9569834,0.0014579727],"study_design_scores_gemma":[0.000148137,0.000062076164,0.0000030155811,0.12891759,0.00005767983,0.0000025503443,0.0000016433842,0.004866289,2.0780918e-7,0.00003793706,0.865653,0.00024989754],"about_ca_topic_score_codex":7.7426347e-7,"about_ca_topic_score_gemma":0.000010482774,"teacher_disagreement_score":0.9257577,"about_ca_system_score_codex":0.00009846943,"about_ca_system_score_gemma":0.0018600365,"threshold_uncertainty_score":0.99994385},"labels":[],"label_agreement":null},{"id":"W6986700221","doi":"","title":"Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems","year":2021,"lang":"en","type":"dissertation","venue":"e-scholar@UOIT (University of Ontario Institute of Technology)","topic":"Metaheuristic Optimization Algorithms Research","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":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Hyporeflexia; Articular cartilage damage; Proteogenomics","score_opus":0.016421626955853405,"score_gpt":0.29095357744942374,"score_spread":0.2745319504935703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986700221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061709546,0.00038361273,0.9323905,0.00023541733,0.0008693275,0.0025983662,0.000040976232,0.00019560989,0.0015766599],"genre_scores_gemma":[0.00046203952,0.00013884577,0.9921487,0.000010258154,0.000019503272,0.000022269187,0.00062336604,0.00004485235,0.0065301484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99669737,0.00008360379,0.00057186786,0.001283326,0.0006818266,0.0006819911],"domain_scores_gemma":[0.99417657,0.00003397295,0.0008266108,0.0011905733,0.0036132347,0.00015905831],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019399688,0.0004749945,0.001146854,0.0016282275,0.00095709873,0.00016514708,0.0023572256,0.0010326008,0.00014890301],"category_scores_gemma":[0.00077408046,0.00060711894,0.00036043156,0.002147668,0.00030544048,0.0018481035,0.0009390411,0.0018419613,0.00000876308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059631036,0.0016337318,0.00011752395,0.004181485,0.0021424573,0.00025342018,0.052121464,0.17420585,0.016074326,0.013187243,0.00076916406,0.734717],"study_design_scores_gemma":[0.0053807977,0.0010661284,0.00049121364,0.0021239228,0.0005471128,0.000042938434,0.00894539,0.6593799,0.06577694,0.0016055277,0.2529773,0.0016627926],"about_ca_topic_score_codex":0.0019180003,"about_ca_topic_score_gemma":0.0054385066,"teacher_disagreement_score":0.7330542,"about_ca_system_score_codex":0.0011268336,"about_ca_system_score_gemma":0.002637315,"threshold_uncertainty_score":0.999638},"labels":[],"label_agreement":null},{"id":"W6992790387","doi":"","title":"Mendelian and Non-Mendelian&#13;\\nAncestral Repair for Constrained&#13;\\nEvolutionary Optimisation","year":2013,"lang":"en","type":"dissertation","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Metaheuristic Optimization Algorithms Research","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 Waterloo; Irish Research Council; Irish Research Council for Science, Engineering and Technology; Ireland Canada University Foundation","keywords":"Mendelian inheritance; Evolutionary algorithm; Representation (politics); Analogy; Hierarchy; Variety (cybernetics); Permutation (music)","score_opus":0.01992054704670037,"score_gpt":0.2858135342485377,"score_spread":0.26589298720183735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992790387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12476796,0.0032799207,0.83160996,0.007923901,0.0106460415,0.007811569,0.00052490254,0.00297446,0.010461268],"genre_scores_gemma":[0.13600668,0.0012443289,0.85306644,0.00009395274,0.00024227714,0.0011535197,0.001382291,0.00008918801,0.0067212926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99569297,0.000045897676,0.0013197038,0.001400107,0.00078891736,0.00075237645],"domain_scores_gemma":[0.99586457,0.00011480547,0.000924986,0.0013798927,0.0014905392,0.00022523272],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007021993,0.0006233099,0.0008778686,0.002193687,0.00038783427,0.00017841454,0.0018609518,0.0012218666,0.00008879057],"category_scores_gemma":[0.0009928424,0.0006479445,0.0002568434,0.001723925,0.0007372765,0.0014454697,0.00028484655,0.0007419143,0.00004779653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003792472,0.0015965872,0.0010590976,0.0048534023,0.0019285369,0.00018195075,0.0014448208,0.014029145,0.0137579,0.44073263,0.11352308,0.40651363],"study_design_scores_gemma":[0.004280045,0.001153555,0.001786743,0.0010007794,0.0002277214,0.00014467035,0.00087415054,0.89629847,0.0133610405,0.02849993,0.050280035,0.0020928478],"about_ca_topic_score_codex":0.00017791319,"about_ca_topic_score_gemma":0.00014705822,"teacher_disagreement_score":0.8822693,"about_ca_system_score_codex":0.00016062029,"about_ca_system_score_gemma":0.0011456903,"threshold_uncertainty_score":0.9995972},"labels":[],"label_agreement":null},{"id":"W6998725287","doi":"","title":"Appel à contribution - Approches écosystémiques de la santé","year":2011,"lang":"fr","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Metaheuristic Optimization Algorithms Research","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":"Section (typography); Product (mathematics); Context (archaeology); Order (exchange); Subject (documents); Work (physics)","score_opus":0.02197874160009484,"score_gpt":0.2912412740495833,"score_spread":0.26926253244948845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998725287","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017669498,0.0015045566,0.6435859,0.00436094,0.0019739645,0.0015233736,0.000422778,0.0003544613,0.34625635],"genre_scores_gemma":[0.01953131,0.005637074,0.33018535,0.016299376,0.006560667,0.0029576954,0.0051472723,0.0008198671,0.6128614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9900214,0.0036546376,0.0013717955,0.0018575634,0.001614806,0.0014797759],"domain_scores_gemma":[0.9933753,0.0012821027,0.001121069,0.0016345225,0.0016582351,0.0009287724],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0040395986,0.001095521,0.0012111822,0.0008935615,0.0007782809,0.002003789,0.0026334375,0.0014278686,0.091622375],"category_scores_gemma":[0.0025849023,0.0011746951,0.00041013563,0.001287367,0.001085794,0.009909323,0.0009392929,0.0012829028,0.0147913555],"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":[0.00007367571,0.00080789655,0.000053292933,0.00034516066,0.00027264786,0.00024158062,0.00036344014,0.00021837972,0.00014626187,0.8734501,0.08977766,0.03424991],"study_design_scores_gemma":[0.002296962,0.00043253566,0.0071105566,0.0013215556,0.00023204705,0.00048599666,0.000093620285,0.043932308,0.0033372436,0.023614677,0.91533494,0.0018075334],"about_ca_topic_score_codex":0.0002998042,"about_ca_topic_score_gemma":0.00035080392,"teacher_disagreement_score":0.8498354,"about_ca_system_score_codex":0.000867683,"about_ca_system_score_gemma":0.0014566629,"threshold_uncertainty_score":0.9998685},"labels":[],"label_agreement":null},{"id":"W7001309394","doi":"","title":"Interactive information complexity and its applications","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Metaheuristic Optimization Algorithms Research","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":"Communication complexity; Boolean function; Information theory; Function (biology); Computational complexity theory; Coding (social sciences); Communication theory; Worst-case complexity; Coding theory","score_opus":0.027737032787927437,"score_gpt":0.28705673989725367,"score_spread":0.2593197071093262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001309394","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":[0.09507461,0.0013181999,0.019119604,0.00045751384,0.007803388,0.017582,0.007093855,0.0030017295,0.8485491],"genre_scores_gemma":[0.8627946,0.0012195959,0.10167465,0.0010404807,0.00011337999,0.0019109239,0.0066136834,0.00024800366,0.024384692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961431,0.00035323785,0.00090329815,0.00091330876,0.001130754,0.0005562932],"domain_scores_gemma":[0.99591815,0.00048646278,0.0007396777,0.0010568548,0.0014360737,0.0003627991],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008419905,0.00054269924,0.000585313,0.0007765052,0.0008782526,0.00052753324,0.0016064029,0.00045974032,0.00019446106],"category_scores_gemma":[0.0009874271,0.0005886061,0.00014748002,0.0010307445,0.000049975646,0.004314103,0.00053052156,0.0013815816,0.0019022416],"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":[0.000045253346,0.0001088515,0.000002556872,0.00043809126,0.00012011405,0.0000046145155,0.00003784762,0.000110782836,0.00058333226,0.648122,0.0000117808795,0.35041478],"study_design_scores_gemma":[0.004530077,0.0006254103,0.0028883703,0.00093493,0.00030539022,0.0001759487,0.0010156475,0.2992296,0.03837151,0.12759523,0.519143,0.0051848986],"about_ca_topic_score_codex":0.000062296596,"about_ca_topic_score_gemma":0.000046875288,"teacher_disagreement_score":0.8241644,"about_ca_system_score_codex":0.00043972154,"about_ca_system_score_gemma":0.00013047623,"threshold_uncertainty_score":0.99965656},"labels":[],"label_agreement":null},{"id":"W7007630604","doi":"","title":"Adaptive Heterogeneous Multi-Population Cultural Algorithm","year":2014,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Metaheuristic Optimization Algorithms Research","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; National Science Foundation","keywords":"Nucleofection; Genetic algorithm; Filter (signal processing); Term (time); Noise (video); Articular cartilage damage","score_opus":0.032661276843124455,"score_gpt":0.2751165961083047,"score_spread":0.24245531926518024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7007630604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14736833,0.00081804954,0.8419982,0.0003035552,0.0027777145,0.0019353547,0.000262805,0.0006885948,0.003847377],"genre_scores_gemma":[0.30834746,0.00023139182,0.62879497,0.00008719959,0.00025902945,0.000006076586,0.0032154175,0.00014279742,0.05891566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536246,0.00066245557,0.00050277036,0.001266871,0.001531339,0.000674095],"domain_scores_gemma":[0.9961796,0.00016771216,0.00086981733,0.0012211215,0.0011376645,0.00042411702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086116174,0.00057135744,0.00084167696,0.0008138033,0.000787176,0.00020545573,0.0027309924,0.00079940684,0.00044682433],"category_scores_gemma":[0.000247406,0.0006909541,0.00045933152,0.000980345,0.00014538433,0.0013946525,0.0005470712,0.0010060674,0.0004598916],"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":[0.0022492262,0.003057205,0.0149946865,0.0022910198,0.0040683392,0.0015771812,0.0329614,0.02629341,0.0037904123,0.007080998,0.004466109,0.89717],"study_design_scores_gemma":[0.006134033,0.00082020456,0.18277381,0.00069394155,0.00044833327,0.00011002919,0.0019399513,0.79459935,0.003379961,0.0012650109,0.0045820083,0.0032533451],"about_ca_topic_score_codex":0.00033821183,"about_ca_topic_score_gemma":0.0005505783,"teacher_disagreement_score":0.89391667,"about_ca_system_score_codex":0.00038127834,"about_ca_system_score_gemma":0.00021510059,"threshold_uncertainty_score":0.99955416},"labels":[],"label_agreement":null},{"id":"W7021242","doi":"10.13140/2.1.1320.2568","title":"Swarm Intelligence: Concepts, Models and Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":134,"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; Artificial intelligence","score_opus":0.06248270455043398,"score_gpt":0.35855034225201193,"score_spread":0.29606763770157796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021242","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020395724,0.0003793963,0.97179437,0.00030280984,0.000055661647,0.00019972105,8.173429e-7,0.0000997537,0.0271471],"genre_scores_gemma":[0.22213425,0.00025882112,0.77384895,0.00029255613,0.00010606581,0.00010295917,0.0000021029912,0.0000066640755,0.0032476005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991986,0.000038491522,0.0001327159,0.00017675481,0.00022064401,0.00023274892],"domain_scores_gemma":[0.9992244,0.000103614875,0.000024023557,0.00035029455,0.00009647364,0.00020115639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033616944,0.00006292169,0.00007157592,0.000068759735,0.00008622668,0.00010326065,0.00040095177,0.000029361427,0.000111509355],"category_scores_gemma":[0.000026811163,0.000053434414,0.000013529279,0.00032332202,0.00006456001,0.0006194653,0.00024978432,0.00007218243,0.00016111982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2904213e-7,0.000031522526,0.00006662827,0.00000377551,0.0000036449892,1.2981025e-7,0.00022738436,0.0015698385,0.0000043555738,0.8815244,0.0003430716,0.116225064],"study_design_scores_gemma":[0.000040640127,0.000008347277,0.000058146994,0.0000011341956,0.0000015717709,0.000008619902,0.0000496401,0.96753144,0.00072565745,0.016864108,0.014618936,0.00009172824],"about_ca_topic_score_codex":0.000009710741,"about_ca_topic_score_gemma":3.126904e-7,"teacher_disagreement_score":0.96596164,"about_ca_system_score_codex":0.000013117696,"about_ca_system_score_gemma":0.000029262124,"threshold_uncertainty_score":0.21789922},"labels":[],"label_agreement":null},{"id":"W7027892469","doi":"","title":"Dum Dums - S02 E16 Project: Under Gardiner","year":2016,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Metaheuristic Optimization Algorithms Research","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":"Downtown; Space (punctuation); Public space; Selection (genetic algorithm)","score_opus":0.010997362687989924,"score_gpt":0.22727285138315587,"score_spread":0.21627548869516594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027892469","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":[2.4755084e-7,0.00017020426,0.0024486494,0.0006377295,0.00064293185,0.00076437084,0.00009751021,0.00042691024,0.9948115],"genre_scores_gemma":[0.0000138341675,0.00035805677,0.03161393,0.00031552295,0.00025326633,0.000041046264,0.00005930189,0.00013878195,0.96720624],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968661,0.00019536201,0.0008086651,0.00044267465,0.0011371842,0.0005499998],"domain_scores_gemma":[0.99746835,0.00020957743,0.0006663967,0.0010643072,0.00039831543,0.00019304501],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005061536,0.0004458717,0.0005445665,0.00029671617,0.00008783878,0.00021281699,0.0014317143,0.00043858195,0.40103832],"category_scores_gemma":[0.00024904133,0.0003611383,0.00019545785,0.000029945846,0.00018609563,8.325342e-7,0.00046967316,0.0003187145,0.043684162],"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":[0.000014177984,0.000041147934,4.223648e-7,0.00018944492,0.00007446228,0.000019674104,0.000050324696,0.00019447581,1.9691912e-7,0.00086960825,0.9949423,0.0036037646],"study_design_scores_gemma":[0.0006270216,0.00008074485,0.000002560081,0.00018603557,0.000019846992,0.0000712696,0.000017320237,0.0010243411,0.000006551008,0.00005675137,0.9974751,0.0004324516],"about_ca_topic_score_codex":0.0015080073,"about_ca_topic_score_gemma":0.00013569351,"teacher_disagreement_score":0.35735416,"about_ca_system_score_codex":0.00008732631,"about_ca_system_score_gemma":0.00016674762,"threshold_uncertainty_score":0.99988407},"labels":[],"label_agreement":null},{"id":"W7033576020","doi":"","title":"Regularity of the Free Boundary in div(a(x) grad(u(x,y)) )= -(h(x) gamma(u))_x with h'(x)<0","year":2021,"lang":"en","type":"article","venue":"York University Digital Library (York University)","topic":"Metaheuristic Optimization Algorithms Research","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":"Boundary (topology); Free boundary problem; Boundary value problem; Type (biology); Singular boundary method","score_opus":0.00999196240901054,"score_gpt":0.1585666395401504,"score_spread":0.14857467713113987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033576020","genre_codex":"other","genre_gemma":"empirical","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":[0.096115105,0.00047419142,0.20567694,0.011539184,0.00082301174,0.0013658968,0.0012450897,0.0010383222,0.6817223],"genre_scores_gemma":[0.64257956,0.00019619151,0.10429047,0.0003670734,0.00009906802,4.0293267e-7,0.00022701398,0.00008287622,0.25215736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765855,0.00031919585,0.0002032699,0.00067874027,0.00068987487,0.0004503791],"domain_scores_gemma":[0.99767286,0.00022040975,0.0001802656,0.0015399655,0.00014510361,0.00024141345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000841548,0.00024301228,0.00033563463,0.0005519346,0.0003267336,0.0004080002,0.0036331455,0.00013876232,0.00010191474],"category_scores_gemma":[0.00007851625,0.00023449547,0.00018580574,0.0057451883,0.0005302073,0.0037729472,0.0032493,0.00042788926,0.000019007557],"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":[0.0005530157,0.0014786378,0.14732178,0.00020101003,0.00036947624,0.0057358784,0.0012356242,0.00293186,0.00006472617,0.8042431,0.026458163,0.009406732],"study_design_scores_gemma":[0.0055842614,0.0002901465,0.039603937,0.00035447086,0.000075680364,0.0001278827,0.0034848691,0.008255386,0.0020879828,0.004946292,0.9339673,0.0012217952],"about_ca_topic_score_codex":0.00004004886,"about_ca_topic_score_gemma":0.000056889792,"teacher_disagreement_score":0.90750915,"about_ca_system_score_codex":0.00011361521,"about_ca_system_score_gemma":0.001328843,"threshold_uncertainty_score":0.95624477},"labels":[],"label_agreement":null},{"id":"W7034284105","doi":"","title":"Solution Properties Of Polyol Ester Lubricants Designed For Use With R-32 And Related Low GWP Refrigerant Blends","year":2014,"lang":"en","type":"article","venue":"Purdue e-Pubs (Purdue University System)","topic":"Metaheuristic Optimization Algorithms Research","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":"Refrigerant; Lubricant; Gas compressor; Refrigeration; Evaporator; Lubrication; Montreal Protocol; Solubility; Evaporation","score_opus":0.019498149196412802,"score_gpt":0.1957923654498471,"score_spread":0.1762942162534343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034284105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05900983,0.000072317474,0.93818474,0.0003850395,0.00020854945,0.0011912936,0.000024690044,0.00019293459,0.0007306346],"genre_scores_gemma":[0.92338085,0.00001669772,0.07286703,0.00001662385,0.000025957937,0.000008995739,0.000010814952,0.000025506024,0.0036475111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974541,0.00051512435,0.00035970134,0.0006317563,0.0005577814,0.00048153911],"domain_scores_gemma":[0.99778765,0.0002590213,0.00030155675,0.00070729916,0.0006956872,0.00024877785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010693901,0.00024210078,0.000468188,0.0004654388,0.0003372694,0.0001952235,0.00068657,0.00013725547,0.0000060020648],"category_scores_gemma":[0.00018711106,0.00020897819,0.00007753894,0.0007007742,0.00021175407,0.0011251121,0.00026015958,0.00014938823,0.000012921836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016811974,0.0041756416,0.02243727,0.03399665,0.0062789414,0.0015871145,0.06976355,0.035631463,0.12131614,0.48383984,0.011684996,0.19247642],"study_design_scores_gemma":[0.004045376,0.00067279534,0.00100214,0.00091443583,0.000106710046,0.00021040575,0.0007283275,0.9823649,0.007304876,0.000034601282,0.002033019,0.00058240624],"about_ca_topic_score_codex":0.00022210379,"about_ca_topic_score_gemma":0.000019742654,"teacher_disagreement_score":0.9467334,"about_ca_system_score_codex":0.00020748124,"about_ca_system_score_gemma":0.00025264738,"threshold_uncertainty_score":0.85218835},"labels":[],"label_agreement":null},{"id":"W7034774657","doi":"","title":"The Use of Arguments about Myths and Stereotypes to Appeal Sexual Assault Convictions in Canada","year":2023,"lang":"en","type":"article","venue":"York University Digital Library (York University)","topic":"Metaheuristic Optimization Algorithms Research","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":"Conviction; Appeal; Sexual assault; Mythology; Doctrine; Plaintiff; Criminal law; Phenomenon; Derogation","score_opus":0.026798362186952515,"score_gpt":0.18909999317035373,"score_spread":0.16230163098340122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034774657","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":[0.8042122,0.00013759955,0.094426796,0.01307686,0.0014804899,0.0027857362,0.003654301,0.0013971332,0.07882888],"genre_scores_gemma":[0.8252087,0.00026472594,0.012618394,0.000279774,0.00003860008,4.82858e-7,0.00018299166,0.000038759736,0.16136755],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986392,0.00011830164,0.00014123168,0.00036043485,0.00039584568,0.00034493674],"domain_scores_gemma":[0.99878204,0.00044079105,0.00007588698,0.00037818353,0.000059312562,0.00026379764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005314483,0.00013253851,0.00016587254,0.00057259854,0.00029123476,0.00033483736,0.0010229496,0.000051711093,0.000014199689],"category_scores_gemma":[0.000048243626,0.00013922763,0.0000310958,0.0031050374,0.00011676707,0.0022026685,0.0011653975,0.00018693192,0.000027169688],"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":[0.00080416404,0.00046960736,0.35921782,0.00017659037,0.0006402352,0.0034791154,0.0030325581,0.05060933,0.000035832865,0.20115934,0.27227733,0.10809807],"study_design_scores_gemma":[0.00091619627,0.00010965836,0.02722169,0.000043819546,0.000012136625,0.0000059006816,0.005455605,0.04443113,0.000026532449,0.000047539474,0.92138857,0.0003412013],"about_ca_topic_score_codex":0.021348096,"about_ca_topic_score_gemma":0.025589656,"teacher_disagreement_score":0.6491113,"about_ca_system_score_codex":0.0001431905,"about_ca_system_score_gemma":0.0009874637,"threshold_uncertainty_score":0.9921908},"labels":[],"label_agreement":null},{"id":"W7035947327","doi":"","title":"Acoustics Week in Canada 2021 - Post Conference Report","year":2022,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Metaheuristic Optimization Algorithms Research","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":true,"ca_institutions":"","funders":"","keywords":"Noise (video); Physical acoustics","score_opus":0.021318579921741156,"score_gpt":0.2331912884597428,"score_spread":0.21187270853800164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7035947327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02080752,0.00011987296,0.959075,0.0069822334,0.0037459545,0.0006749652,0.00096638827,0.000055599554,0.007572514],"genre_scores_gemma":[0.96085155,0.000024271812,0.033502344,0.0016462641,0.00007536085,0.000049482624,0.0000971236,0.000026297566,0.003727324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971422,0.00015855038,0.00046048788,0.00051532,0.000920832,0.000802633],"domain_scores_gemma":[0.9975239,0.00016099699,0.00011750991,0.00091853045,0.0004561246,0.00082292466],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005544793,0.00017518624,0.00023418546,0.00043015293,0.0003486255,0.00016899359,0.0015190458,0.000043041266,0.0019357357],"category_scores_gemma":[0.0008813209,0.00021854545,0.000027408812,0.0012391999,0.000047521815,0.0001308546,0.00043050037,0.0005923217,0.000025451845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000124961525,0.00014822456,0.0044255676,0.00006342534,0.00005935554,0.05451765,0.0007743289,0.70975167,0.0022061905,0.011946382,0.181526,0.034568693],"study_design_scores_gemma":[0.00018813957,0.000052038307,0.010078451,0.0000060101743,0.00000668294,0.00034307071,0.0004969765,0.97749084,0.000008749185,0.00022000445,0.010786261,0.00032280132],"about_ca_topic_score_codex":0.9822251,"about_ca_topic_score_gemma":0.97350955,"teacher_disagreement_score":0.940044,"about_ca_system_score_codex":0.0036939064,"about_ca_system_score_gemma":0.040425133,"threshold_uncertainty_score":0.99897665},"labels":[],"label_agreement":null},{"id":"W7036112418","doi":"","title":"As Ontario nears a child care deal, can it help end the she-cession?","year":2022,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Metaheuristic Optimization Algorithms Research","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":"Child care; Government (linguistics); Public policy; Plan (archaeology); Health insurance; Coalition government","score_opus":0.006961317961912365,"score_gpt":0.20552495439183566,"score_spread":0.19856363642992328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036112418","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":[0.0000030900255,0.0003778447,0.000031832686,0.001929605,0.00044528622,0.0007982054,0.0001307667,0.00018868006,0.9960947],"genre_scores_gemma":[0.000080104306,0.0001890573,0.009101252,0.0012594436,0.000113033595,0.00006887726,0.00023781275,0.00010041731,0.98885],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99657166,0.00028397274,0.0007030947,0.00041653757,0.0015491913,0.0004755673],"domain_scores_gemma":[0.9975679,0.00019650071,0.00061666174,0.0011368332,0.0002693201,0.00021276901],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00045608066,0.00039940904,0.00043232247,0.00017139642,0.00040824164,0.00031485784,0.0022068261,0.00025670027,0.7327879],"category_scores_gemma":[0.00017374383,0.00032779278,0.00018737833,0.000044131124,0.0001672709,6.3591307e-7,0.0008995702,0.0008473516,0.0059824237],"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":[0.000015087019,0.000031063886,0.0000035589017,0.00014025955,0.000055115717,0.000041061136,0.0018856153,0.0014876582,8.981484e-9,0.00015639466,0.991784,0.0044001834],"study_design_scores_gemma":[0.00041013773,0.00014487098,0.000009282294,0.000100405035,0.000025822885,0.00015334644,0.00050121604,0.00050157506,0.000001462885,0.000009199447,0.99780303,0.00033962954],"about_ca_topic_score_codex":0.0928004,"about_ca_topic_score_gemma":0.08198695,"teacher_disagreement_score":0.72680545,"about_ca_system_score_codex":0.0003080655,"about_ca_system_score_gemma":0.00044613538,"threshold_uncertainty_score":0.9999174},"labels":[],"label_agreement":null},{"id":"W7037208320","doi":"","title":"Dimensions of Critical Social Work Practice in India","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Metaheuristic Optimization Algorithms Research","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":"MacEwan University","funders":"","keywords":"Social change; Oppression; Social philosophy; Social work; Social position; Social order; Emancipation; Social relation; Social inequality","score_opus":0.29437557492255545,"score_gpt":0.6035562392970872,"score_spread":0.3091806643745318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037208320","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":[0.28500736,0.030550493,0.51938313,0.05117511,0.012358594,0.0071760667,0.0003043724,0.0012020054,0.092842884],"genre_scores_gemma":[0.94710624,0.004960392,0.04630799,0.00057845114,0.00021338131,0.00008346088,0.000012490845,0.00006721197,0.00067038013],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9954506,0.0009945558,0.0010083673,0.00048905594,0.0015264202,0.00053100573],"domain_scores_gemma":[0.99399936,0.0038038616,0.00050998875,0.00057332625,0.0008487027,0.00026474654],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004027259,0.0002031209,0.0006325944,0.00172363,0.00029320983,0.0010776309,0.0044113533,0.00012190104,0.0024623598],"category_scores_gemma":[0.009441483,0.00018063466,0.00012309283,0.008317784,0.00024932352,0.0033536984,0.0029805538,0.00060889765,0.000083517014],"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":[0.0007089247,0.005520841,0.17223656,0.0006858608,0.00075454504,0.003169669,0.009382401,0.009045193,0.015053449,0.056117114,0.4885155,0.23880996],"study_design_scores_gemma":[0.001214052,0.000028700755,0.9347051,0.00043604535,0.00006496862,0.00003847094,0.0003072114,0.021043925,0.0032976554,0.023760265,0.014348706,0.0007549508],"about_ca_topic_score_codex":0.00012311332,"about_ca_topic_score_gemma":0.0000028978625,"teacher_disagreement_score":0.76246846,"about_ca_system_score_codex":0.00007520768,"about_ca_system_score_gemma":0.00037175612,"threshold_uncertainty_score":0.99995935},"labels":[],"label_agreement":null},{"id":"W7045088565","doi":"","title":"Ant colony optimization with distributed colonies for dynamic environments on multiple GPUs","year":2024,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Metaheuristic Optimization Algorithms Research","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":"Exploit; Ant colony optimization algorithms; Ant colony; Space (punctuation); Genetic algorithm; Metaheuristic; Local search (optimization)","score_opus":0.011817361018235249,"score_gpt":0.22593788754631325,"score_spread":0.214120526528078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045088565","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016218135,0.00008362942,0.9805001,0.00076065835,0.00048042176,0.0011719225,0.00034783897,0.00011512392,0.00032219736],"genre_scores_gemma":[0.22094649,0.0010002074,0.7318132,0.00005642244,0.00008995043,0.000042334093,0.012436445,0.00019491852,0.033420015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982403,0.000078698766,0.00014005549,0.0006254245,0.0006249381,0.00029056484],"domain_scores_gemma":[0.9986898,0.00027343343,0.0002694566,0.0004814288,0.00018140876,0.00010452557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017211636,0.00025769955,0.00035053515,0.00037082404,0.0002771188,0.00010895845,0.00083807163,0.00020951362,0.000009434867],"category_scores_gemma":[0.00010371501,0.00029005544,0.00010005625,0.00046809076,0.00008770902,0.00023844601,0.0001330515,0.00024300435,0.000040254192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019294447,0.0008911697,0.0013009991,0.0015279134,0.0009252434,0.00029899145,0.0020169723,0.9701819,0.00033203288,0.0028646968,0.012836066,0.00489461],"study_design_scores_gemma":[0.000998724,0.00057180016,0.005760763,0.0001863793,0.00010483644,0.0000026116793,0.0022291061,0.9846981,0.00020293605,0.00004878176,0.004884159,0.00031176387],"about_ca_topic_score_codex":0.00014283408,"about_ca_topic_score_gemma":0.016632691,"teacher_disagreement_score":0.24868685,"about_ca_system_score_codex":0.00035487066,"about_ca_system_score_gemma":0.00017868663,"threshold_uncertainty_score":0.9999552},"labels":[],"label_agreement":null},{"id":"W7045912697","doi":"","title":"Building Virtual Reality Spaces for Visual Data Mining with Hybrid Evolutionary-Classical Optimization: Application to Microarray Gene Expression Data","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Raw data; Representation (politics); Visualization; Virtual reality; Expression (computer science); Synthetic data; Data visualization","score_opus":0.05433768294468588,"score_gpt":0.3434778197108166,"score_spread":0.2891401367661307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045912697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000550528,0.000021581227,0.9927334,0.005206622,0.00012945913,0.0008278144,0.00023827022,0.00016107426,0.0001312383],"genre_scores_gemma":[0.032313142,0.000010939285,0.9657946,0.00014375664,0.00026897457,0.00010598777,0.0012440445,0.000027699652,0.00009084865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968078,0.00011748406,0.00038054414,0.0014455593,0.0008153162,0.0004332593],"domain_scores_gemma":[0.9961285,0.00024716937,0.00016079006,0.002824211,0.00034591122,0.0002934155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009932271,0.00020883452,0.00024016916,0.00018005118,0.00037805794,0.0003541412,0.0034215231,0.00006370336,0.000028634695],"category_scores_gemma":[0.00060475874,0.00018815635,0.00002124543,0.00065520796,0.000097857184,0.0012843052,0.0023262554,0.00013838895,0.000014181577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014993004,0.00034337814,0.00003693328,0.000033833123,0.00004006032,0.0000122108495,0.00016816397,0.8993104,0.039839346,0.0071761343,0.016652863,0.036236715],"study_design_scores_gemma":[0.00077840115,0.00016753815,0.000027354597,0.00004189889,0.000012234943,0.00003079573,0.000023000946,0.9828958,0.010699464,0.00040189983,0.0046753264,0.00024628977],"about_ca_topic_score_codex":0.00001263795,"about_ca_topic_score_gemma":0.0000039511183,"teacher_disagreement_score":0.08358537,"about_ca_system_score_codex":0.00013932047,"about_ca_system_score_gemma":0.00043746608,"threshold_uncertainty_score":0.7672793},"labels":[],"label_agreement":null},{"id":"W7083177629","doi":"10.29173/css310","title":"Daniel L. Duke, (ed). 1995. Teacher Evaluation Policy: From Accountability to Professional Development.","year":2001,"lang":"en","type":"article","venue":"Canadian Social Studies","topic":"Metaheuristic Optimization Algorithms Research","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":"Accountability; Social studies; Educational assessment; Professional development; Evaluation methods; Social accounting; Higher education; Research methodology","score_opus":0.10416017391719383,"score_gpt":0.40682484256155405,"score_spread":0.3026646686443602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083177629","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":[0.4853979,0.005557486,0.14659868,0.18748085,0.012172219,0.008843894,0.00034524515,0.0009301449,0.15267357],"genre_scores_gemma":[0.8841275,0.00012848043,0.08039819,0.007073808,0.0028749856,0.0009586641,0.00012343451,0.000061059676,0.024253888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975874,0.0002738264,0.00028413837,0.0004644472,0.00086749136,0.0005226911],"domain_scores_gemma":[0.99839914,0.00012055703,0.000060449598,0.0002694566,0.0007594043,0.00039102364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00113632,0.00015877375,0.00022711612,0.00024445946,0.0009896306,0.00013553319,0.00062872836,0.000087775974,0.00032227233],"category_scores_gemma":[0.001192444,0.00015506371,0.000037117596,0.00087508844,0.00009607274,0.00024086279,0.00029264222,0.00016577035,0.00023736768],"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":[0.000017647553,0.00015511835,0.020744536,0.000018703742,0.00038263234,0.00002736651,0.060071796,0.000117908334,0.000034246492,0.006437677,0.39366922,0.5183231],"study_design_scores_gemma":[0.00076755387,0.000026345104,0.20140187,0.000030788502,0.000023100194,0.000002119288,0.0038098404,0.010237464,0.00004949638,0.0034107121,0.7796078,0.00063290185],"about_ca_topic_score_codex":0.035206497,"about_ca_topic_score_gemma":0.10916513,"teacher_disagreement_score":0.51769024,"about_ca_system_score_codex":0.002048874,"about_ca_system_score_gemma":0.0034705317,"threshold_uncertainty_score":0.97121817},"labels":[],"label_agreement":null},{"id":"W7084107166","doi":"10.64628/aam.7can3n4np","title":"How Canada can solve its emerging water crisis","year":2019,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"","score_opus":0.013201695508685525,"score_gpt":0.22886325757350334,"score_spread":0.21566156206481782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084107166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00269994,0.000032322867,0.9094893,0.07598865,0.001015791,0.00027701881,0.0000041995004,0.00011885327,0.010373974],"genre_scores_gemma":[0.7687409,0.000014526516,0.13633831,0.0024104074,0.00008279971,0.000016281703,0.000009817995,0.00002245566,0.09236453],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983761,0.00004596162,0.0001298664,0.00035428963,0.0006537115,0.0004401066],"domain_scores_gemma":[0.99898505,0.000041076444,0.00002302361,0.0005452806,0.00023767179,0.0001678784],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022494228,0.00010535944,0.00013119332,0.00009330679,0.000079281446,0.00035718703,0.00081890484,0.000027051967,0.0009558035],"category_scores_gemma":[0.000043030275,0.00007287634,0.000027092661,0.00024961703,0.000006385799,0.00029422084,0.00034904885,0.000108413784,0.00014708148],"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":[0.000011102558,0.0001500819,0.0018518156,0.00024074392,0.0002846818,0.00034353838,0.003526894,0.020351265,0.007411582,0.095959954,0.81484574,0.055022586],"study_design_scores_gemma":[0.0002831994,0.000025261925,0.000117970485,0.0000038640464,0.0000026825667,0.0000089806435,0.0002233823,0.8799578,0.035715573,0.00015105269,0.083232306,0.00027795834],"about_ca_topic_score_codex":0.10281791,"about_ca_topic_score_gemma":0.07339929,"teacher_disagreement_score":0.8596065,"about_ca_system_score_codex":0.000096937125,"about_ca_system_score_gemma":0.00027201115,"threshold_uncertainty_score":0.99995744},"labels":[],"label_agreement":null},{"id":"W7098023222","doi":"","title":"Report 6: Diversity in Evolutionary Ensembles of Artificial Neural Networks 1 Summary","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Diversity (politics); Artificial neural network; Plan (archaeology); Focus (optics); Population; Advice (programming)","score_opus":0.030840359215198464,"score_gpt":0.26791330950453646,"score_spread":0.237072950289338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098023222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015758645,0.00007391402,0.9776487,0.0002999506,0.000226062,0.00012492831,0.0000013840568,0.000060532995,0.005805895],"genre_scores_gemma":[0.8824771,0.000007962326,0.11568328,0.000036666093,0.00008845497,0.0000033491754,0.000017189932,0.0000044991675,0.0016815205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985737,0.000108586646,0.00036980558,0.00028072295,0.00044087446,0.00022628823],"domain_scores_gemma":[0.99920154,0.00012933348,0.00009124336,0.00037461062,0.0001568631,0.000046431895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048019446,0.000075548465,0.00014263148,0.000191424,0.00009955531,0.000029994178,0.00047889503,0.000051239134,0.000065389344],"category_scores_gemma":[0.00008328663,0.0000731597,0.000044601285,0.00066572585,0.00006743264,0.00026397055,0.00081458426,0.00011078883,0.0000045424185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033407017,0.000479743,0.14446661,0.000018702152,0.000013792709,0.0010510418,0.00006694961,0.75115794,0.00011177343,0.06444719,0.019981038,0.01817181],"study_design_scores_gemma":[0.00009195869,0.000015109504,0.06690712,0.0000030596048,0.0000012388768,0.000028643675,0.000004761847,0.9308813,0.000058054247,0.0018620696,0.00007651442,0.00007012712],"about_ca_topic_score_codex":0.0010589225,"about_ca_topic_score_gemma":0.000101398145,"teacher_disagreement_score":0.8667184,"about_ca_system_score_codex":0.000041227922,"about_ca_system_score_gemma":0.00005646749,"threshold_uncertainty_score":0.2983366},"labels":[],"label_agreement":null},{"id":"W7104113099","doi":"","title":"Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Metaheuristic Optimization Algorithms Research","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":"PQ Corporation (Canada)","funders":"","keywords":"Scalability; Evolutionary algorithm; Kernel (algebra); Probabilistic logic; Knapsack problem; Memory management; Memory footprint; Metaheuristic; Evolutionary computation","score_opus":0.049831613670216525,"score_gpt":0.2839702158413225,"score_spread":0.23413860217110596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104113099","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":[0.8573961,0.0006580795,0.13835141,0.0014925658,0.00053517375,0.00073176576,0.000025986217,0.000043244254,0.0007656696],"genre_scores_gemma":[0.96886575,0.0007245743,0.029560417,0.000159999,0.000038470705,0.0000355466,0.00001092894,0.000016671309,0.0005876401],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99551857,0.0006805067,0.0013600052,0.0009998474,0.0009457354,0.00049532525],"domain_scores_gemma":[0.9958851,0.0007826339,0.000534865,0.001389484,0.0011554626,0.00025246924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017484471,0.00035600923,0.0006897299,0.0006188942,0.000280789,0.000059447553,0.00094513915,0.00023837769,0.0001349886],"category_scores_gemma":[0.0013834204,0.00036249557,0.00013256566,0.002315752,0.0013780697,0.000680336,0.0007455914,0.00044076625,0.000035609297],"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":[0.00028175526,0.00040438876,0.9550858,0.0012895967,0.00014953304,0.0000049738806,0.00094863266,0.016674252,0.0016937378,0.009783092,0.0005550293,0.013129221],"study_design_scores_gemma":[0.00069323997,0.00029594483,0.447776,0.0003083558,0.000028415583,0.0000020185714,0.000021266196,0.54495496,0.0053565428,0.00034863735,0.00006226688,0.00015234595],"about_ca_topic_score_codex":0.00007823754,"about_ca_topic_score_gemma":0.000001346389,"teacher_disagreement_score":0.52828074,"about_ca_system_score_codex":0.00016439169,"about_ca_system_score_gemma":0.0010321006,"threshold_uncertainty_score":0.9998827},"labels":[],"label_agreement":null},{"id":"W7110041880","doi":"10.1016/j.swevo.2025.102237","title":"A complementary heterogeneity-driven adaptive balance search method for cognitive-only particle swarm optimization family","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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 Alberta","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Metaheuristic; Balance (ability); Optimization algorithm; Swarm behaviour","score_opus":0.043815077622409535,"score_gpt":0.35555934840301373,"score_spread":0.3117442707806042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110041880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00724886,0.00046600398,0.9890082,0.0014435349,0.00025353342,0.0010452659,0.00007940164,0.0001532408,0.00030199008],"genre_scores_gemma":[0.25925454,0.00006707455,0.73971546,0.0004663913,0.000049529597,0.00009372518,0.00020578272,0.000011835414,0.00013562711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976438,0.00043138777,0.00041541996,0.0006721842,0.0004295337,0.0004076586],"domain_scores_gemma":[0.99797475,0.0008121537,0.00009969694,0.00019997946,0.0007759999,0.00013740413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497042,0.00019893801,0.0002510904,0.00026702968,0.00054388965,0.000165177,0.0003202474,0.0000704903,0.000008443683],"category_scores_gemma":[0.00010502443,0.00021543835,0.00007202048,0.0008408624,0.00010839926,0.0005514741,0.00031172714,0.0001408752,0.0000109293715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013794246,0.0001896874,0.0013926275,0.00006935451,0.00011434407,0.000003922,0.00029536782,0.9449442,0.00011598703,0.015905805,0.0013443526,0.03548642],"study_design_scores_gemma":[0.0016582322,0.00021705487,0.0077359863,0.00004911307,0.000029021754,0.000012131119,0.00018733712,0.9859253,0.00037048885,0.0034452237,0.00017230603,0.0001977844],"about_ca_topic_score_codex":0.000061570005,"about_ca_topic_score_gemma":0.0000056640306,"teacher_disagreement_score":0.2520057,"about_ca_system_score_codex":0.00013546085,"about_ca_system_score_gemma":0.00034563593,"threshold_uncertainty_score":0.8785321},"labels":[],"label_agreement":null},{"id":"W7116060953","doi":"10.2139/ssrn.5940066","title":"A Reinforcement Learning Framework for Shuffled Multi Opposition-Based Learning Evolutionary Algorithm","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Evolutionary algorithm; Reinforcement learning; Population; Benchmark (surveying); Evolutionary computation; Differential evolution; Evolutionary acquisition of neural topologies; Evolutionary programming","score_opus":0.025013386638866122,"score_gpt":0.3242860407468878,"score_spread":0.29927265410802173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116060953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020991807,0.0056694914,0.98577154,0.0027013654,0.0025692983,0.0026980622,0.000025820837,0.0002802899,0.00026312933],"genre_scores_gemma":[0.013892477,0.012804662,0.9425985,0.0003104811,0.0014099999,0.00065790495,0.00033159845,0.00014179014,0.027852587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9814319,0.0023597227,0.0027206116,0.0023102572,0.0028090226,0.00836846],"domain_scores_gemma":[0.9893211,0.0025888886,0.0023195222,0.0013086309,0.0036880383,0.00077379914],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.012612108,0.0013673048,0.0015191454,0.0019592461,0.004223378,0.0017833625,0.00416452,0.0012699462,0.00038959397],"category_scores_gemma":[0.005188247,0.0015237889,0.0013898704,0.0018995024,0.0002983229,0.00068366295,0.0018666196,0.027445935,0.00010787994],"study_design_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":true,"study_design_scores_codex":[0.00016868471,0.00032405305,0.00008474113,0.00020793841,0.00095140893,0.000015843812,0.0003401456,0.7545654,0.000010453429,0.07932212,0.00006770793,0.16394149],"study_design_scores_gemma":[0.0033956035,0.001601917,0.000026815944,0.0012444144,0.00027938955,0.00016891747,0.0005096033,0.90120834,0.00005892443,0.08571333,0.00460991,0.0011828156],"about_ca_topic_score_codex":0.00012195358,"about_ca_topic_score_gemma":0.00001739354,"teacher_disagreement_score":0.16275868,"about_ca_system_score_codex":0.009576561,"about_ca_system_score_gemma":0.04907582,"threshold_uncertainty_score":0.9999078},"labels":[],"label_agreement":null},{"id":"W7124132478","doi":"10.65109/daod4524","title":"Particle Field Optimization: A New Paradigm for Swarm Intelligence","year":2015,"lang":"","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Particle swarm optimization; Multi-swarm optimization; Perspective (graphical); Swarm intelligence; Set (abstract data type); Field (mathematics); Swarm behaviour","score_opus":0.1431479151416756,"score_gpt":0.3633373548475413,"score_spread":0.2201894397058657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124132478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006505277,0.0010779197,0.963847,0.02639757,0.0017394772,0.0013059579,0.0000056088684,0.00018558516,0.0054343916],"genre_scores_gemma":[0.010969347,0.00031045117,0.95227927,0.002005864,0.0006409787,0.000092777474,0.00000850836,0.000042104966,0.03365071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955159,0.00023041682,0.0010066985,0.0010939488,0.0011223283,0.0010307378],"domain_scores_gemma":[0.9946777,0.0009774679,0.00022430725,0.001391157,0.00095598947,0.0017733622],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016062241,0.00038332545,0.00046028575,0.00017479548,0.00025145727,0.0012572148,0.0019928436,0.00021319496,0.0018183774],"category_scores_gemma":[0.003581814,0.0003760284,0.00017096942,0.0017654938,0.00012134226,0.0010675932,0.0006546739,0.0002723253,0.00057842344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008612862,0.00019329991,0.000030684118,0.000028268536,0.000046031288,0.000010830594,0.0011562824,0.728585,7.287905e-7,0.10710515,0.03336036,0.12939724],"study_design_scores_gemma":[0.0009155517,0.00092444953,0.0000014989627,0.000027854237,0.000028039114,0.000022060252,0.000123797,0.9646047,0.004141663,0.009670632,0.0190964,0.00044338877],"about_ca_topic_score_codex":0.00016150005,"about_ca_topic_score_gemma":0.000010766571,"teacher_disagreement_score":0.23601967,"about_ca_system_score_codex":0.00014235867,"about_ca_system_score_gemma":0.0022058696,"threshold_uncertainty_score":0.99986917},"labels":[],"label_agreement":null},{"id":"W7125601733","doi":"10.18280/jesa.581220","title":"Metaheuristic Optimization of Artificial Neural Networks: A Comprehensive Survey of Techniques, Taxonomies, and Trends (2015–2025)","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Metaheuristic Optimization Algorithms Research","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":"Artificial neural network; Metaheuristic; Key (lock); Feature (linguistics); Particle swarm optimization","score_opus":0.04764957865696947,"score_gpt":0.31521428469322416,"score_spread":0.2675647060362547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125601733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026608054,0.01197426,0.98211634,0.0002899806,0.0014757183,0.000682118,0.00012300417,0.00012026875,0.0005574805],"genre_scores_gemma":[0.5563479,0.0059544765,0.43541065,0.000106378706,0.00022872018,0.00003326737,0.000052431547,0.00010592911,0.0017602232],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98828965,0.00477717,0.003843437,0.0009020665,0.0012709303,0.0009167251],"domain_scores_gemma":[0.9892668,0.0019067209,0.0029363045,0.0010994816,0.004339021,0.0004516296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004065152,0.0007243304,0.002018326,0.0025027578,0.00060378166,0.0009789548,0.0016869543,0.00033668,0.00031580057],"category_scores_gemma":[0.0026758427,0.0007045821,0.00031769625,0.0053069345,0.001248949,0.00093615946,0.001139062,0.0009845155,0.0000036137635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015320507,0.00029254047,0.0013292099,0.0005748827,0.00051415595,0.0000613915,0.00018240836,0.3805725,0.00006125058,0.0010724374,0.002255975,0.61293006],"study_design_scores_gemma":[0.0006398754,0.0005102472,0.15389241,0.00068292295,0.00024363109,0.00023829498,0.00003601229,0.84284693,0.00012077819,0.00026588104,0.00013962518,0.00038338357],"about_ca_topic_score_codex":0.0004622454,"about_ca_topic_score_gemma":0.00004598272,"teacher_disagreement_score":0.6125467,"about_ca_system_score_codex":0.00029285983,"about_ca_system_score_gemma":0.0008317328,"threshold_uncertainty_score":0.9995405},"labels":[],"label_agreement":null},{"id":"W7126427103","doi":"10.21428/594757db.ed5be436","title":"Stochastic Grouping and Subspace-Based Initialization inDecomposition and Merging Cooperative Particle SwarmOptimization for Large-Scale Optimization Problems","year":2024,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","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":"Brock University","funders":"","keywords":"Initialization; Particle swarm optimization; Focus (optics); Metaheuristic; Stochastic optimization; Optimization problem; Multi-swarm optimization; Decomposition","score_opus":0.023636840546449232,"score_gpt":0.2934069638802422,"score_spread":0.26977012333379297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126427103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006696794,0.00034526386,0.995833,0.001580018,0.00017359162,0.0009763383,0.0000119549795,0.00033455773,0.00007564439],"genre_scores_gemma":[0.44911912,0.000058005524,0.55012333,0.00018254545,0.00005622031,0.00019091937,0.00012356765,0.000031152013,0.00011512422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983693,0.00014502634,0.00031998547,0.0005673778,0.00028934583,0.0003089694],"domain_scores_gemma":[0.99893975,0.0003235728,0.0000629525,0.00018043211,0.00035432272,0.00013898227],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00068937836,0.00017431025,0.000173213,0.00026170333,0.0003578389,0.0010563353,0.00013181973,0.000078742385,0.000035138026],"category_scores_gemma":[0.00014741685,0.00016600518,0.000026315345,0.0008039759,0.000049865674,0.0011577347,0.00010116928,0.00009757368,0.00000284163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010806975,0.000041405758,0.000027441174,0.000118563396,0.000016695363,0.0000012578769,0.0010213661,0.9687111,0.00020777191,0.027762353,0.0000521125,0.0020291256],"study_design_scores_gemma":[0.00076180464,0.00012902786,0.000029034689,0.000099218276,0.00002167721,0.000008206741,0.00009021978,0.9974931,0.00075929984,0.00036178177,0.000047230154,0.0001994335],"about_ca_topic_score_codex":0.0000064104743,"about_ca_topic_score_gemma":0.000009805379,"teacher_disagreement_score":0.44844946,"about_ca_system_score_codex":0.0000616818,"about_ca_system_score_gemma":0.000104706065,"threshold_uncertainty_score":0.9999807},"labels":[],"label_agreement":null},{"id":"W72737831","doi":"","title":"An Ant Colony Optimization Metaheuristic for the Undirected Rural Postman Problem","year":2007,"lang":"en","type":"article","venue":"IRIS eCampus Telematic University (Università degli Studi eCampus)","topic":"Metaheuristic Optimization Algorithms Research","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":"Université de Montréal","funders":"Ministero dell'Università e della Ricerca; Università della Calabria","keywords":"Metaheuristic; Parallel metaheuristic; Ant colony optimization algorithms; Heuristics; Mathematical optimization; Benchmark (surveying); Heuristic; Set (abstract data type); Computer science; Ant colony; Algorithm; Mathematics; Meta-optimization; Geography","score_opus":0.014616444199647619,"score_gpt":0.25784917914268646,"score_spread":0.24323273494303885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W72737831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063995025,0.00012805109,0.9860732,0.0015166727,0.00045631154,0.0024125173,0.000043117565,0.000557273,0.002413333],"genre_scores_gemma":[0.19755651,0.00021346434,0.7970651,0.00024145465,0.00015836739,0.000009188308,0.00010777188,0.00006982393,0.004578316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958762,0.00048267006,0.0005414171,0.0009334142,0.0010568607,0.0011094668],"domain_scores_gemma":[0.99403363,0.0021338125,0.0005585123,0.0014422062,0.0013357255,0.00049609016],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0023027644,0.00049888785,0.00065824174,0.0009582701,0.0022775978,0.0003262257,0.0032682149,0.00023815766,0.00014689245],"category_scores_gemma":[0.00033592558,0.00047112047,0.0002755843,0.0029272106,0.00041321004,0.0016403086,0.00075943815,0.0004338334,0.000059492122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018009503,0.004685019,0.004254353,0.0009920388,0.0055323173,0.0020176247,0.030797957,0.62231356,0.0011031923,0.196176,0.039642505,0.09068445],"study_design_scores_gemma":[0.003095726,0.00060956506,0.0023557493,0.000055139015,0.00047091368,0.000080180835,0.007834426,0.97676504,0.0000657827,0.00072995,0.007146046,0.0007915057],"about_ca_topic_score_codex":0.0001770149,"about_ca_topic_score_gemma":0.00015852343,"teacher_disagreement_score":0.35445142,"about_ca_system_score_codex":0.000645836,"about_ca_system_score_gemma":0.000369663,"threshold_uncertainty_score":0.99977404},"labels":[],"label_agreement":null},{"id":"W835739657","doi":"10.4018/ijaec.2015010101","title":"A Modified SSLPS Algorithm with Logistic Pseudo-Random Sequence Generator for Improving the Performance of Neka Power Plant","year":2015,"lang":"en","type":"article","venue":"International Journal of Applied Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","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":"Metaheuristic; Pareto principle; Logistic map; Computer science; Mathematical optimization; Chaotic; Tabu search; Heuristic; Multi-objective optimization; Parallel metaheuristic; Context (archaeology); Mathematics; Artificial intelligence; Meta-optimization","score_opus":0.043693870595947,"score_gpt":0.291443186373783,"score_spread":0.24774931577783602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W835739657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021428838,0.00007325256,0.97663516,0.0005456814,0.0007189417,0.000368503,0.000040110393,0.000022799288,0.00016673865],"genre_scores_gemma":[0.5892321,0.000008767213,0.41045845,0.00007164826,0.0001653259,0.000018855068,0.000022943563,0.000008789265,0.000013146496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973982,0.00007440625,0.0006919902,0.00023458722,0.0013992431,0.00020159538],"domain_scores_gemma":[0.9957101,0.00039980872,0.00079165347,0.00018656267,0.0027864117,0.00012550698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011129562,0.00015590397,0.00023922858,0.0003003948,0.00010898517,0.00012333806,0.00111427,0.000052591193,0.00000394878],"category_scores_gemma":[0.00015768313,0.00010861576,0.0000730207,0.00031668547,0.00013642079,0.00050232484,0.00013966096,0.00019997932,0.0000039663123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007675473,0.00013458493,0.00005188588,0.000016793516,0.00013344073,0.000017764014,0.00049206696,0.9624991,0.0007972044,0.012786779,0.0008125399,0.021490306],"study_design_scores_gemma":[0.002484434,0.0004167498,0.000341449,0.000029091134,0.000018428336,0.00029832253,0.00007979815,0.99268585,0.000586885,0.0028142906,0.000121840465,0.00012286959],"about_ca_topic_score_codex":0.00000851616,"about_ca_topic_score_gemma":6.399734e-7,"teacher_disagreement_score":0.56780326,"about_ca_system_score_codex":0.000247484,"about_ca_system_score_gemma":0.0007919467,"threshold_uncertainty_score":0.4429222},"labels":[],"label_agreement":null},{"id":"W937610320","doi":"","title":"Neural network models for combinatorial optimization: A survey of deterministic, stochastic and chaotic approaches","year":2002,"lang":"en","type":"article","venue":"Control and Cybernetics","topic":"Metaheuristic Optimization Algorithms Research","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 Montréal","funders":"","keywords":"Stochastic neural network; Artificial neural network; Computer science; Heuristic; Chaotic; Class (philosophy); Artificial intelligence; Cellular neural network; Mathematical optimization; Stochastic optimization; Combinatorial optimization; Time delay neural network; Algorithm; Mathematics","score_opus":0.07326862679551169,"score_gpt":0.24879682713628098,"score_spread":0.1755282003407693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W937610320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046688202,0.0013492674,0.99709266,0.00015513189,0.00026043577,0.00049444975,0.000022961269,0.000025016223,0.00013318623],"genre_scores_gemma":[0.9220286,0.000072260314,0.077612326,0.000044770455,0.0000855843,0.000039112656,0.0000066255366,0.000013169196,0.000097557495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987075,0.00016164043,0.00031080926,0.00031296446,0.0002310012,0.00027613185],"domain_scores_gemma":[0.99836296,0.0008566597,0.0001203633,0.00026386394,0.0002522167,0.00014392179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048303636,0.00014153656,0.00031495898,0.000045082907,0.00010944549,0.00014011783,0.00028229624,0.00007147443,0.000007138061],"category_scores_gemma":[0.0003506587,0.00013251108,0.000027686825,0.0002013361,0.0001483339,0.000123749,0.0001151565,0.000081191596,5.2808423e-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":[0.000028152323,0.000054807875,0.00009165632,0.00002815987,0.000025161542,0.0000011396528,0.00013099558,0.9645692,4.8617903e-7,0.023840683,0.00009630913,0.011133212],"study_design_scores_gemma":[0.0019892496,0.00021340129,0.00061811716,0.000008488334,0.000025478032,0.000008147229,0.0000033066033,0.99360794,5.778893e-7,0.0033870994,0.000008591109,0.00012960767],"about_ca_topic_score_codex":0.000010302084,"about_ca_topic_score_gemma":0.0000022688714,"teacher_disagreement_score":0.9215617,"about_ca_system_score_codex":0.0000072812777,"about_ca_system_score_gemma":0.000023142084,"threshold_uncertainty_score":0.5403645},"labels":[],"label_agreement":null},{"id":"W964171983","doi":"10.1007/978-3-642-45318-2_14","title":"Incorporating Highly Explorative Methods to Improve the Performance of Variable Neighborhood Search","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","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 Windsor","funders":"","keywords":"Variable neighborhood search; Computer science; Differential evolution; Metaheuristic; Mathematical optimization; Evolutionary algorithm; Population; Job shop scheduling; Scheduling (production processes); Artificial intelligence; Mathematics","score_opus":0.03228913992110071,"score_gpt":0.3096697860617132,"score_spread":0.2773806461406125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W964171983","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000046255154,0.00011449561,0.99236244,0.0010676403,0.0011358774,0.0011619566,0.0000066590605,0.00007261658,0.004032064],"genre_scores_gemma":[0.016618144,0.000022045833,0.981495,0.0006138743,0.0002498434,0.000059014314,0.0000027900608,0.00003678008,0.0009024844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947145,0.00030149662,0.000867705,0.0015002616,0.0018306194,0.0007854382],"domain_scores_gemma":[0.9937361,0.0018635013,0.00043311348,0.0021747323,0.0015141595,0.00027842258],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0048121894,0.0005029969,0.0006786928,0.00096857455,0.00039281393,0.0007050382,0.0058739283,0.00023335726,0.000076640135],"category_scores_gemma":[0.0006077902,0.00035796367,0.00008864114,0.0022537182,0.00083850074,0.0010005891,0.0036775693,0.0011821224,0.000095749965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061728706,0.000028457807,0.000017485243,0.00006775116,0.000021081169,0.000005961962,0.0012918042,0.2304843,0.0013367075,0.061601505,0.000022541435,0.7051162],"study_design_scores_gemma":[0.00015502477,0.00040426888,0.000026522275,0.00017577698,0.000005243844,0.000010309881,8.86924e-7,0.9555938,0.013662277,0.029417733,0.00017080121,0.00037733957],"about_ca_topic_score_codex":0.000055203916,"about_ca_topic_score_gemma":0.000003544887,"teacher_disagreement_score":0.7251095,"about_ca_system_score_codex":0.00026382832,"about_ca_system_score_gemma":0.0015093153,"threshold_uncertainty_score":0.9998872},"labels":[],"label_agreement":null}]}