{"meta":{"query_hash":"ad6c7d98a363","filters":{"venue":"IEEE Robotics and Automation Letters"},"cohort_total":275,"direct_labels_cover":0,"predictions_cover":275,"exported":275,"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/ad6c7d98a363","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Robotics+and+Automation+Letters"},"results":[{"id":"W2292954498","doi":"10.1109/lra.2016.2522096","title":"Mixed-Integer and Constraint Programming Techniques for Mobile Robot Task Planning","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Constraint programming; Robot; Integer programming; Task (project management); Computer science; Constraint (computer-aided design); Set (abstract data type); Mathematical optimization; Plan (archaeology); Mobile robot; Integer (computer science); Motion planning; Artificial intelligence; Mathematics; Algorithm; Stochastic programming; Engineering; Programming language","score_opus":0.016166400033941094,"score_gpt":0.2481487157063605,"score_spread":0.2319823156724194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292954498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062821787,0.000021611371,0.9880998,0.0046733287,0.00021636192,0.00037481906,0.000003566111,0.00031005713,0.000018250506],"genre_scores_gemma":[0.61104596,0.000011981883,0.38833028,0.0004918389,0.000037962232,0.000059204962,0.0000023068492,0.0000070190904,0.000013450525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992736,0.000023164086,0.00019610689,0.00024640266,0.00009547103,0.00016523115],"domain_scores_gemma":[0.9995391,0.0001278595,0.00010128909,0.00011965351,0.000050141698,0.0000619874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017163994,0.00011070253,0.00011211569,0.00010630021,0.0001338365,0.00018775537,0.00008100375,0.000049167,0.0000017182858],"category_scores_gemma":[0.00002109604,0.00008574772,0.000028655631,0.00007625024,0.000093386254,0.0003742772,0.000026253798,0.0000416477,0.000001250013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035647586,0.000017788148,0.000873252,0.00004241603,0.00002282807,0.0000034840803,0.00039900566,0.01420589,0.040052056,0.01408275,0.0006767472,0.9296202],"study_design_scores_gemma":[0.0012472502,0.00023390287,0.0031520335,0.00041861343,0.00003399717,0.0001383933,0.00018287534,0.968584,0.018300774,0.0010143089,0.0059755915,0.0007182563],"about_ca_topic_score_codex":0.0000019396957,"about_ca_topic_score_gemma":0.0000010360345,"teacher_disagreement_score":0.9543781,"about_ca_system_score_codex":0.00002615921,"about_ca_system_score_gemma":0.000017469883,"threshold_uncertainty_score":0.34966907},"labels":[],"label_agreement":null},{"id":"W2295130897","doi":"10.1109/lra.2016.2528294","title":"Design Principles for a Family of Direct-Drive Legged Robots","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":318,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Laboratory; Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Computer science; Robustness (evolution); Transparency (behavior); Legged robot; Simulation; Artificial intelligence; Computer security; Biology","score_opus":0.02253485156345155,"score_gpt":0.21416822966249943,"score_spread":0.19163337809904787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295130897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019347295,0.00005008964,0.97805476,0.001554031,0.00032339606,0.00033829242,0.0000064786364,0.00019192997,0.00013373655],"genre_scores_gemma":[0.95418406,0.000044289758,0.045294728,0.00026331746,0.0000656419,0.00004203933,0.0000021106416,0.000025704141,0.00007811717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931914,0.00002597805,0.00026482347,0.00012303823,0.00010300069,0.00016399594],"domain_scores_gemma":[0.99955124,0.00016970704,0.000065026354,0.00012424793,0.00004074459,0.00004901027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013168616,0.000120633464,0.00018922101,0.00007819991,0.000041711588,0.000022398617,0.00006968436,0.00004828809,0.0000037426678],"category_scores_gemma":[0.000020386316,0.00009108381,0.00005053555,0.000060348073,0.00003651197,0.00011243494,0.000005994435,0.000028610506,0.000005919012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049899613,0.0000093574745,0.000033665394,0.000038995437,0.00003969441,4.960104e-7,0.000081440536,0.756054,0.23685002,0.00070419174,0.0009622832,0.0052208896],"study_design_scores_gemma":[0.0015984267,0.000058564907,0.003946074,0.000130098,0.000048737787,0.0000024517685,0.000014965271,0.9715046,0.02179639,0.00013265215,0.0005097632,0.00025725018],"about_ca_topic_score_codex":0.0000012613731,"about_ca_topic_score_gemma":6.7256155e-7,"teacher_disagreement_score":0.93483675,"about_ca_system_score_codex":0.000030629577,"about_ca_system_score_gemma":0.000009527473,"threshold_uncertainty_score":0.37142903},"labels":[],"label_agreement":null},{"id":"W2295622322","doi":"10.1109/lra.2016.2530859","title":"Generalized Predictive Control of a Surgical Robot for Beating-Heart Surgery Under Delayed and Slowly-Sampled Ultrasound Image Data","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Cardiac and Coronary Surgery Techniques","field":"Medicine","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Division of Orthopaedic Surgery, University of Alberta; Canada Foundation for Innovation","keywords":"Computer vision; Artificial intelligence; Computer science; Robot; Robotics; Surgical robot; Teleoperation; Model predictive control; Medicine; Surgery; Control (management)","score_opus":0.022740670263735593,"score_gpt":0.27664402073862143,"score_spread":0.2539033504748858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295622322","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":[0.3832098,0.00013860335,0.60632205,0.009383739,0.0001268304,0.00046034207,0.0002519239,0.000099345976,0.0000073424553],"genre_scores_gemma":[0.9806042,0.00020817891,0.01781225,0.0009903744,0.00015278898,0.00003416009,0.00015239624,0.000024026365,0.000021624284],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99880534,0.000089313464,0.00040095663,0.0003097134,0.00018974069,0.00020491214],"domain_scores_gemma":[0.9974694,0.0018584451,0.00015359283,0.0003109589,0.00010599061,0.00010156601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005284079,0.00015841491,0.00051162555,0.00010586529,0.0000684639,0.000025958594,0.00004893108,0.00008161495,0.000011905805],"category_scores_gemma":[0.00012235151,0.00011563809,0.00011863514,0.00007215484,0.00013550784,0.00021327015,0.000023165954,0.00005466928,5.8550984e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017141723,0.00020069788,0.034674156,0.00029323454,0.0008374622,0.000031632793,0.00011394465,0.0010480814,0.91320777,0.0009014159,0.03692989,0.0100475345],"study_design_scores_gemma":[0.035355087,0.001663101,0.6677364,0.0022295925,0.0038220303,0.0020874892,0.00019737236,0.22824544,0.041473318,0.0021999618,0.012487647,0.0025025755],"about_ca_topic_score_codex":0.000028607643,"about_ca_topic_score_gemma":0.0000017202327,"teacher_disagreement_score":0.87173444,"about_ca_system_score_codex":0.00002765057,"about_ca_system_score_gemma":0.000051605595,"threshold_uncertainty_score":0.47155845},"labels":[],"label_agreement":null},{"id":"W2296757716","doi":"10.1109/lra.2016.2528301","title":"Mechanics of Tissue Cutting During Needle Insertion in Biological Tissue","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":71,"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":"Alberta Innovates - Health Solutions","keywords":"Biomedical engineering; Soft tissue; Biological tissue; Materials science; Puncturing; Viscoelasticity; Displacement (psychology); Biomechanics; Anatomy; Composite material; Computer science; Surgery; Engineering; Medicine","score_opus":0.012289584014512179,"score_gpt":0.22104234782449372,"score_spread":0.20875276380998153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296757716","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":[0.75400037,0.000026560569,0.244846,0.00079441443,0.00010483492,0.00010235126,0.0000031100508,0.000103012186,0.00001933297],"genre_scores_gemma":[0.99406624,0.000067925306,0.0057449955,0.000044598735,0.000042508604,0.00001121256,0.0000029173896,0.000013313519,0.0000063062735],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942803,0.000011434115,0.00024259003,0.00011104663,0.00006947692,0.00013743601],"domain_scores_gemma":[0.99973965,0.000051883795,0.000059209702,0.00010140425,0.0000152239445,0.000032656022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007163952,0.0000892704,0.000121553254,0.000095286596,0.00003284505,0.000013601075,0.00005752291,0.00006129153,0.0000068584814],"category_scores_gemma":[0.000018351455,0.00007242278,0.000014242904,0.00013747356,0.000017872313,0.0000826048,0.000012687629,0.000048831203,0.000008796381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.143055e-7,0.00000948896,0.0006508153,0.000031791093,0.0000049423684,0.0000010347819,0.00006913253,0.16032217,0.8341629,0.0012356158,0.00003386892,0.0034774141],"study_design_scores_gemma":[0.0011362572,0.000058481033,0.063476525,0.0003127562,0.000020845597,0.000017964172,0.00006365722,0.3578791,0.57463765,0.0015022191,0.00036292846,0.0005316238],"about_ca_topic_score_codex":0.00000614036,"about_ca_topic_score_gemma":0.0000024959634,"teacher_disagreement_score":0.25952524,"about_ca_system_score_codex":0.000034497407,"about_ca_system_score_gemma":0.000002900298,"threshold_uncertainty_score":0.29533154},"labels":[],"label_agreement":null},{"id":"W2323973715","doi":"10.1109/lra.2016.2527065","title":"Adaptive Quasi-Static Modelling of Needle Deflection During Steering in Soft Tissue","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Deflection (physics); Soft tissue; Imaging phantom; Cantilever; Vibration; Stiffness; Materials science; Biomedical engineering; Displacement (psychology); Acoustics; Optics; Physics; Engineering; Surgery; Composite material; Medicine","score_opus":0.019049462610281336,"score_gpt":0.2163234037373264,"score_spread":0.19727394112704505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323973715","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":[0.38778013,0.000028311726,0.6117139,0.00023924062,0.00006724447,0.000078857054,0.0000016709956,0.00007496585,0.000015679481],"genre_scores_gemma":[0.98110414,0.00005097839,0.018753158,0.00001926223,0.000030249921,0.000010346092,0.0000011954313,0.000020981539,0.000009670825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941975,0.000008422227,0.00022285672,0.0001149177,0.00008773601,0.00014632221],"domain_scores_gemma":[0.9997439,0.0000595145,0.0000462005,0.0000975425,0.000018605637,0.000034277906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057866433,0.000096841235,0.00012752334,0.00010991685,0.00004287,0.000017165328,0.00004595017,0.000039376926,0.000004325928],"category_scores_gemma":[0.0000039165643,0.00009202283,0.000018849356,0.00012653634,0.000024430356,0.00012983583,0.00000753109,0.000053854044,0.0000074279637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012714002,0.000009342209,0.00015202312,0.00004575084,0.000008569006,6.632768e-7,0.0002743932,0.87277144,0.12423415,0.00018653656,0.000017045082,0.002298787],"study_design_scores_gemma":[0.00026995348,0.000012311199,0.0019267234,0.00012467473,0.000009244614,0.0000029021521,0.000046733916,0.9874418,0.009747431,0.0002835695,0.000008691571,0.00012598427],"about_ca_topic_score_codex":0.000030189365,"about_ca_topic_score_gemma":0.000013623949,"teacher_disagreement_score":0.593324,"about_ca_system_score_codex":0.000050375387,"about_ca_system_score_gemma":0.000004628215,"threshold_uncertainty_score":0.3752582},"labels":[],"label_agreement":null},{"id":"W2329446256","doi":"10.1109/lra.2016.2528293","title":"Sliding-Based Switching Control for Image-Guided Needle Steering in Soft Tissue","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":30,"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":"Alberta Innovates - Health Solutions","keywords":"Bevel; Kinematics; Deflection (physics); Imaging phantom; Control theory (sociology); Materials science; Biomedical engineering; Rotation (mathematics); Computer science; Acoustics; Engineering; Physics; Artificial intelligence; Optics; Mechanical engineering","score_opus":0.012094038467956548,"score_gpt":0.2376443083337794,"score_spread":0.22555026986582286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329446256","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":[0.07307348,0.00002738281,0.9210976,0.005054521,0.000186208,0.00030501102,0.00000851444,0.00023196952,0.000015321555],"genre_scores_gemma":[0.9676313,0.000005824882,0.031794686,0.00038488075,0.00008427587,0.00005242467,0.0000043035793,0.000034447807,0.000007902804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930805,0.000007781927,0.0002332189,0.00015002243,0.00008087865,0.00022002011],"domain_scores_gemma":[0.9995542,0.00019338401,0.00004202187,0.00013158491,0.00002506612,0.00005375583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012935576,0.0001279053,0.00015250333,0.0000927005,0.000075184915,0.0000694734,0.00007550037,0.0000493011,0.0000057149587],"category_scores_gemma":[0.000024225375,0.00011591063,0.00003230045,0.00007988569,0.000014084008,0.00012603858,0.000005064628,0.000052579213,0.000012399146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001078593,0.0000062250983,0.00015202572,0.00004336879,0.000007992129,8.6325934e-7,0.00004880818,0.6329744,0.36247358,0.000283394,0.0006716334,0.003336637],"study_design_scores_gemma":[0.0012008536,0.000010349682,0.0015116811,0.000087311855,0.000015587815,0.0000021942392,0.000009725956,0.987986,0.008391454,0.0002061117,0.00038036125,0.00019832797],"about_ca_topic_score_codex":0.0000082948945,"about_ca_topic_score_gemma":0.0000072118937,"teacher_disagreement_score":0.8945578,"about_ca_system_score_codex":0.00005070293,"about_ca_system_score_gemma":0.000008252537,"threshold_uncertainty_score":0.47266984},"labels":[],"label_agreement":null},{"id":"W2335696576","doi":"10.1109/lra.2016.2528295","title":"Multiactuator Haptic Feedback on the Wrist for Needle Steering Guidance in Brachytherapy","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":40,"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":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Health Solutions","keywords":"Haptic technology; Computer science; Actuator; Brachytherapy; Audio feedback; Simulation; Wrist; Trajectory; Human–computer interaction; Biomedical engineering; Artificial intelligence; Engineering; Medicine; Surgery","score_opus":0.014738492846203818,"score_gpt":0.221677578512029,"score_spread":0.20693908566582517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335696576","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":[0.3872002,0.000049683473,0.5997244,0.012201628,0.00024317797,0.00037638392,0.000010841237,0.00014909859,0.000044545708],"genre_scores_gemma":[0.99275744,0.00003165143,0.00618186,0.0007874155,0.00009056125,0.00009184856,0.0000018359697,0.000028149556,0.000029263054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994351,0.000008674215,0.00018640878,0.00012452957,0.00007639814,0.00016884682],"domain_scores_gemma":[0.9994661,0.00028532522,0.000034526307,0.0001662226,0.000015326717,0.000032466953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010506095,0.00011281588,0.00009979551,0.000052596795,0.00007353112,0.000051018615,0.00008778194,0.000035996207,0.0000048730203],"category_scores_gemma":[0.000018385233,0.00007525332,0.000031347263,0.00007777622,0.000030463825,0.00007123877,0.000005125458,0.000052644347,0.000016477157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053992417,0.000025836107,0.00047579815,0.000048357055,0.000029410876,6.8604425e-7,0.00026868482,0.78116083,0.19220924,0.009460944,0.003804163,0.012510659],"study_design_scores_gemma":[0.0007764149,0.000024752591,0.012338605,0.000146499,0.000009125782,0.0000017159194,0.000026261043,0.9787638,0.005162479,0.0004810165,0.002014268,0.00025502293],"about_ca_topic_score_codex":0.0000029577864,"about_ca_topic_score_gemma":0.0000040940213,"teacher_disagreement_score":0.6055572,"about_ca_system_score_codex":0.000048075934,"about_ca_system_score_gemma":0.0000040202935,"threshold_uncertainty_score":0.30687416},"labels":[],"label_agreement":null},{"id":"W2558693562","doi":"10.1109/lra.2016.2633623","title":"Design of a Passive Vertical Takeoff and Landing Aquatic UAV","year":2016,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Takeoff; Takeoff and landing; Wing; Marine engineering; Aerospace engineering; Aeronautics; Flight control surfaces; Simulation; Engineering; Environmental science; Computer science; Automotive engineering; Aerodynamics","score_opus":0.019433332558858755,"score_gpt":0.20780571768595998,"score_spread":0.18837238512710122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558693562","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":[0.24200568,0.000044096232,0.75644374,0.0013336892,0.00003682657,0.00006794259,7.8080757e-7,0.00005593815,0.0000113324795],"genre_scores_gemma":[0.99396986,0.000060294697,0.00585075,0.00007832077,0.000018240167,0.000005813609,6.0742394e-7,0.000009849167,0.000006249704],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999566,0.00003325795,0.00017198353,0.00006495275,0.00007360875,0.00009019252],"domain_scores_gemma":[0.99971265,0.00011921874,0.000023880222,0.00009874078,0.000009887476,0.000035653113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007557658,0.000066683475,0.000105046565,0.000045616493,0.000032173262,0.000026162279,0.000046654957,0.000030144412,0.000002253629],"category_scores_gemma":[0.000002977076,0.00004798666,0.000011844693,0.00004116136,0.000036575406,0.000086420005,0.000009909413,0.000028639875,0.0000047412714],"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":[0.0000032927642,0.000008312334,0.0007415023,0.00007208001,0.00004543958,0.0000012982388,0.00055392995,0.025005866,0.96283555,0.00031994763,0.0002277071,0.010185059],"study_design_scores_gemma":[0.00077578105,0.000040433482,0.0033133668,0.00026407998,0.00003315641,0.000015140169,0.00006363541,0.94347876,0.051252104,0.00031344962,0.00023157433,0.00021853975],"about_ca_topic_score_codex":0.0000031264344,"about_ca_topic_score_gemma":6.444949e-7,"teacher_disagreement_score":0.9184729,"about_ca_system_score_codex":0.000016419033,"about_ca_system_score_gemma":0.0000030823235,"threshold_uncertainty_score":0.19568393},"labels":[],"label_agreement":null},{"id":"W2577484822","doi":"10.1109/lra.2017.2651945","title":"Modeling Grasp Motor Imagery Through Deep Conditional Generative Models","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"GRASP; Computer science; Artificial intelligence; Generative model; Process (computing); Generative grammar; Object (grammar); Task (project management); Action (physics); Human–computer interaction; Deep learning; Machine learning; Engineering; Systems engineering","score_opus":0.03599566429412324,"score_gpt":0.2506917033094166,"score_spread":0.21469603901529336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577484822","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":[0.08466089,0.000052133255,0.91314226,0.0011138794,0.0003663656,0.00009248828,0.0000020124428,0.00019794682,0.00037202198],"genre_scores_gemma":[0.9707351,0.000034669563,0.02837878,0.0005506503,0.0002055285,0.000010180456,0.000032526972,0.000026420752,0.000026194331],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932194,0.000017944696,0.00019901601,0.00015047698,0.00014743052,0.00016322045],"domain_scores_gemma":[0.99966246,0.000016750972,0.000058496487,0.00017907412,0.00003648652,0.000046743364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006835726,0.00013858736,0.00013406169,0.000053828164,0.00044073508,0.00029237155,0.00009454541,0.000054837852,0.000015245947],"category_scores_gemma":[0.000012012273,0.0001471734,0.000044082244,0.00002354385,0.000044027092,0.00086088694,0.0000149550115,0.00013120144,0.000017845015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010671985,0.0000038077462,0.000028915147,0.000018692612,0.000023617327,0.000003222237,0.00028466975,0.9881779,0.008041389,0.002881918,0.00024638596,0.00028841264],"study_design_scores_gemma":[0.00022046748,0.000004509101,0.0009421579,0.00001910776,0.0000127399235,0.000004883405,0.000022211354,0.9964469,0.00022147848,0.0019124846,0.000015999936,0.0001770676],"about_ca_topic_score_codex":0.000015095912,"about_ca_topic_score_gemma":0.00000331653,"teacher_disagreement_score":0.8860742,"about_ca_system_score_codex":0.000034214143,"about_ca_system_score_gemma":0.0000057322773,"threshold_uncertainty_score":0.6001557},"labels":[],"label_agreement":null},{"id":"W2581993098","doi":"10.1109/lra.2017.2657879","title":"Reliable Grasping of Three-Dimensional Untethered Mobile Magnetic Microgripper for Autonomous Pick-and-Place","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":127,"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":"GRASP; Grippers; Computer science; Micrometer; Controller (irrigation); Magnetorheological fluid; Mechanical engineering; Control engineering; Engineering","score_opus":0.010976050879303445,"score_gpt":0.239070307330799,"score_spread":0.22809425645149556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581993098","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":[0.9373078,0.00023112247,0.060144305,0.0013297228,0.0003740273,0.00048051644,0.000030168922,0.000028886037,0.00007346412],"genre_scores_gemma":[0.97950494,0.0000064177675,0.019915937,0.00017429354,0.00012587638,0.000024708388,0.00001976961,0.000022647226,0.00020538566],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920535,0.00001113487,0.00026739584,0.00021865178,0.00008681576,0.00021066688],"domain_scores_gemma":[0.9993129,0.000061279105,0.0002512608,0.0002579803,0.000058630467,0.00005797013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012848998,0.00015338612,0.00023046177,0.000049146227,0.0003500763,0.00011326077,0.00012250905,0.00004662427,0.000027978229],"category_scores_gemma":[0.0000045674415,0.00014643607,0.00006890994,0.000025377389,0.00012584594,0.00013307782,0.000043832097,0.00007542687,0.0000042815436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008407069,0.00033430467,0.047737923,0.00048452916,0.00031347087,0.0000036404663,0.0006818613,0.44553077,0.44874945,0.009041093,0.010434763,0.03660412],"study_design_scores_gemma":[0.00566942,0.0005379026,0.029953195,0.00050235254,0.00042227906,0.000013669977,0.0001416027,0.90899956,0.04446198,0.0032994824,0.0047441786,0.0012543681],"about_ca_topic_score_codex":0.00011547181,"about_ca_topic_score_gemma":0.0000072087473,"teacher_disagreement_score":0.46346882,"about_ca_system_score_codex":0.000012859776,"about_ca_system_score_gemma":0.000031227177,"threshold_uncertainty_score":0.59714895},"labels":[],"label_agreement":null},{"id":"W2587859519","doi":"10.1109/lra.2017.2670678","title":"Design of a Self-Adaptive Robotic Leg Using a Triggered Compliant Element","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","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":"Polytechnique Montréal","funders":"","keywords":"Traverse; Kinematics; Computer science; Control theory (sociology); Robustness (evolution); Swing; Obstacle; Mechanism (biology); Pantograph; Robot; Simulation; Control engineering; Engineering; Artificial intelligence; Control (management); Mechanical engineering; Physics","score_opus":0.03338128779337101,"score_gpt":0.24008727887265663,"score_spread":0.2067059910792856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587859519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025329215,0.000043654476,0.9731134,0.0005832917,0.00039485036,0.00033354468,0.0000018891059,0.00015257973,0.000047548845],"genre_scores_gemma":[0.90823734,0.000019933046,0.09153706,0.00011828051,0.00005003503,0.0000076176466,0.0000013894074,0.000021413634,0.0000069424473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913716,0.000039974446,0.0003163068,0.00013989492,0.00016607573,0.00020059434],"domain_scores_gemma":[0.99941206,0.000039738457,0.00016613974,0.00027492968,0.000041355273,0.00006578211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015481401,0.00015893548,0.00026515496,0.000087372195,0.00018977364,0.000113895185,0.00014631732,0.000049098297,0.000007861438],"category_scores_gemma":[0.000007968186,0.00015676841,0.000049483446,0.000042432697,0.000049851587,0.0001920132,0.000016600441,0.00008181216,0.0000066526413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004772238,0.000024411382,0.00005762101,0.00004850771,0.00009277944,0.0000048440593,0.00018557119,0.9700816,0.02798223,0.0003681967,0.00015411472,0.0009953149],"study_design_scores_gemma":[0.0008719071,0.00003141135,0.0020903891,0.00007225835,0.000060093895,0.000008428968,0.000018276252,0.99514383,0.0014708136,0.000056246965,0.000008431194,0.0001679046],"about_ca_topic_score_codex":0.000013382023,"about_ca_topic_score_gemma":0.0000010799416,"teacher_disagreement_score":0.8829081,"about_ca_system_score_codex":0.00006856597,"about_ca_system_score_gemma":0.00001682851,"threshold_uncertainty_score":0.639283},"labels":[],"label_agreement":null},{"id":"W2625808923","doi":"10.1109/lra.2017.2715398","title":"Vibration-Reducing End Effector for Automation of Drilling Tasks in Aircraft Manufacturing","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Robot end effector; Fuselage; Drill; Robot; Actuator; Task (project management); Payload (computing); Drilling; Robotics; Engineering; Mechanical engineering; Drill bit; Computer science; Artificial intelligence; Systems engineering","score_opus":0.01063088880098428,"score_gpt":0.22721863297212835,"score_spread":0.21658774417114407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625808923","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":[0.36052048,0.00000964347,0.63837665,0.00030929377,0.00047424075,0.00020730637,0.000006230023,0.00006840856,0.000027757096],"genre_scores_gemma":[0.8570295,0.000012029867,0.14277327,0.000045019613,0.00007896291,0.000017246626,0.000016140528,0.000021893591,0.0000059465046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924576,0.000011750237,0.00031343754,0.00014494115,0.00011230116,0.00017180742],"domain_scores_gemma":[0.9995034,0.00009043567,0.0001369237,0.00021334268,0.000017308274,0.000038602193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018576112,0.00013229785,0.00019672526,0.00012650728,0.00014861878,0.00012240319,0.00010127948,0.00007282874,0.0000040758396],"category_scores_gemma":[0.000030560303,0.00014079701,0.000043717824,0.000025126825,0.000025996935,0.0003204269,0.000012653316,0.00007225761,0.000002174739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018519813,0.000005838007,0.00007975881,0.00023442302,0.0000135688415,8.6193927e-7,0.00015497049,0.9260185,0.06758493,0.000627761,0.00004715545,0.005230387],"study_design_scores_gemma":[0.0003933321,0.000015382435,0.0097408295,0.00014231558,0.00001530275,0.0000018135918,0.000010293488,0.965144,0.024139535,0.00024579224,0.000006696937,0.0001447156],"about_ca_topic_score_codex":0.000019900102,"about_ca_topic_score_gemma":0.000012587952,"teacher_disagreement_score":0.496509,"about_ca_system_score_codex":0.000045178094,"about_ca_system_score_gemma":0.000006529145,"threshold_uncertainty_score":0.5741536},"labels":[],"label_agreement":null},{"id":"W2732463748","doi":"10.1109/lra.2017.2723926","title":"The Raincouver Scene Parsing Benchmark for Self-Driving in Adverse Weather and at Night","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parsing; Computer science; Benchmark (surveying); Benchmarking; Focus (optics); Artificial intelligence; Segmentation; Dusk; Task (project management); Pixel; Computer vision; Machine learning; Cartography; Geography; Engineering","score_opus":0.01155486080503709,"score_gpt":0.2505481348100118,"score_spread":0.23899327400497472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732463748","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":[0.23432037,0.00005373859,0.73962057,0.02505407,0.0003567041,0.00042617175,0.0000010037554,0.00009259467,0.00007476992],"genre_scores_gemma":[0.8724481,0.00006702778,0.12651409,0.0007872916,0.00007387184,0.00003365149,9.497893e-7,0.000009305257,0.00006574472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993552,0.000019746878,0.0001444436,0.0002144664,0.0000864572,0.0001796623],"domain_scores_gemma":[0.9992537,0.00019807395,0.0001408562,0.00034849203,0.000018389164,0.00004050672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001654975,0.000085626525,0.00008095881,0.00002970461,0.0009978543,0.00018786479,0.00025807336,0.000026796955,3.337084e-7],"category_scores_gemma":[0.00001886512,0.00006793874,0.000020076415,0.000047903974,0.00006907744,0.00037819365,0.000099883735,0.000053442116,0.0000013358273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003629449,0.00018731006,0.054649282,0.00018200393,0.00014035916,0.000031552972,0.0067761512,0.5022342,0.059505448,0.08496367,0.025137708,0.26615602],"study_design_scores_gemma":[0.0003538638,0.000008863504,0.032180235,0.000032316435,0.0000057534166,0.000005092599,0.000006627278,0.9625971,0.00029148668,0.0014236403,0.0029700114,0.00012503196],"about_ca_topic_score_codex":0.0000023355612,"about_ca_topic_score_gemma":0.000037884583,"teacher_disagreement_score":0.6381277,"about_ca_system_score_codex":0.000047057514,"about_ca_system_score_gemma":0.000007358815,"threshold_uncertainty_score":0.76747894},"labels":[],"label_agreement":null},{"id":"W2754329383","doi":"10.1109/lra.2017.2778765","title":"DPC-Net: Deep Pose Correction for Visual Localization","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Vision and Imaging","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 Toronto","funders":"","keywords":"Odometry; Artificial intelligence; Visual odometry; Computer science; Estimator; Ground truth; Pose; Deep learning; Pipeline (software); Computer vision; Convolutional neural network; Rotation (mathematics); Translation (biology); Pattern recognition (psychology); Algorithm; Mathematics; Robot; Mobile robot","score_opus":0.012968469212039585,"score_gpt":0.2900750068234628,"score_spread":0.27710653761142323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754329383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002438746,0.000012767842,0.99065477,0.004468699,0.002114309,0.00014366141,4.5864783e-7,0.00012198674,0.00004458966],"genre_scores_gemma":[0.8236759,0.000019031422,0.17233047,0.0036078973,0.0002300625,0.0000113200385,0.00000823073,0.000014564735,0.00010253044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937475,0.0000151570075,0.00014627374,0.00020894555,0.000115560535,0.00013931787],"domain_scores_gemma":[0.9994542,0.00003964986,0.00016658846,0.00023176617,0.000059097387,0.000048691465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010622486,0.00008692796,0.00008691149,0.000066154804,0.00058906275,0.00048236,0.0001937633,0.000029135681,0.0000013782949],"category_scores_gemma":[0.000054311757,0.00008575898,0.000029334124,0.00004596613,0.000039266306,0.0009160822,0.000040359846,0.00004424431,0.000007188974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000921935,0.000057183366,0.0010903148,0.000043965094,0.000017342725,0.000003933443,0.00058266293,0.26909825,0.015361546,0.0055427067,0.0071010957,0.70109177],"study_design_scores_gemma":[0.00030292312,0.00002781703,0.0032533093,0.000023853096,0.0000045703127,0.0000058297423,0.000006930081,0.99310625,0.0016934279,0.00042258014,0.0010391084,0.00011339863],"about_ca_topic_score_codex":0.0000049513783,"about_ca_topic_score_gemma":0.0000025228678,"teacher_disagreement_score":0.82123715,"about_ca_system_score_codex":0.00002443488,"about_ca_system_score_gemma":0.000008825793,"threshold_uncertainty_score":0.46514085},"labels":[],"label_agreement":null},{"id":"W2754539945","doi":"10.1109/lra.2018.2801471","title":"An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots With Non-Minimum Phase Dynamics","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Iterative Learning Control Systems","field":"Engineering","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":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impromptu; Computer science; Minimum phase; Trajectory; Feed forward; Instability; Inverse dynamics; Inverse; Inversion (geology); Control theory (sociology); Robot; Stability (learning theory); Tracking (education); Artificial intelligence; Phase (matter); Mathematics; Machine learning; Control engineering; Engineering; Physics; Control (management)","score_opus":0.007585371385330385,"score_gpt":0.22362014717818954,"score_spread":0.21603477579285915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754539945","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":[0.43441418,0.0000065999807,0.5650396,0.000036022866,0.00011418856,0.00024503478,0.000004026382,0.00012669792,0.000013650735],"genre_scores_gemma":[0.94364107,2.4933792e-7,0.05597834,0.00007143615,0.00017237791,0.000023748478,0.000054640797,0.000053450847,0.000004717011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990374,0.00004774502,0.00028696554,0.00022874166,0.00015670397,0.00024245508],"domain_scores_gemma":[0.9994384,0.00007248427,0.00015976327,0.00014951086,0.000112950314,0.000066900306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002477327,0.00019458303,0.00025846536,0.00017158134,0.00016012687,0.00009248693,0.00009520173,0.00008029385,0.0000013067657],"category_scores_gemma":[0.000016146081,0.00018565581,0.000045194847,0.00013158575,0.00008331649,0.00026548622,0.000004104239,0.0001609131,6.4269443e-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":[0.000023595137,0.000027083122,0.0004476351,0.000272624,0.000025953494,6.804311e-7,0.0005596474,0.73071325,0.2639606,0.00001094871,0.000016017442,0.0039419867],"study_design_scores_gemma":[0.0019000885,0.0004737903,0.00053047406,0.000070650254,0.0000435467,0.0000024362437,0.00010068189,0.9893575,0.0072890823,8.352563e-7,0.0000059084214,0.00022505724],"about_ca_topic_score_codex":0.00001020254,"about_ca_topic_score_gemma":0.0000054431794,"teacher_disagreement_score":0.50922686,"about_ca_system_score_codex":0.000084520085,"about_ca_system_score_gemma":0.00002307749,"threshold_uncertainty_score":0.7570824},"labels":[],"label_agreement":null},{"id":"W2756159298","doi":"10.1109/lra.2018.2799741","title":"How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Visual odometry; Deep learning; Computer vision; Encoder; Odometry; Context (archaeology); Metric (unit); Pattern recognition (psychology); Mobile robot; Robot","score_opus":0.017275147571148737,"score_gpt":0.24030883461941316,"score_spread":0.22303368704826443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756159298","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":[0.041252937,0.0000249172,0.9526481,0.0049129697,0.00030840616,0.0005227922,0.000009008572,0.000268493,0.000052355517],"genre_scores_gemma":[0.9931555,0.000024825964,0.005220621,0.0010186763,0.000332668,0.00006966527,0.00009535141,0.00004100533,0.0000416901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991446,0.000034760298,0.00022936857,0.00018260763,0.00016760395,0.00024104185],"domain_scores_gemma":[0.9996465,0.00004014125,0.000046376193,0.00007973508,0.00009635748,0.00009088751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014629937,0.00015993552,0.00015744653,0.00017698055,0.00024367524,0.00017037298,0.00004814091,0.00008842436,0.0000022488848],"category_scores_gemma":[0.000027532233,0.00017316203,0.000042351297,0.00028160922,0.000050152677,0.00031560927,0.0000039284473,0.00005979461,0.00000585407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048768193,0.000012773164,0.000026750016,0.000093052695,0.000017434453,2.732483e-7,0.0013069985,0.97942686,0.01207408,0.0009230946,0.00052851357,0.0055852733],"study_design_scores_gemma":[0.0003400535,0.00009058718,0.0006263591,0.00006669491,0.000026285421,0.0000020612513,0.00009957167,0.9933182,0.0025656107,0.000027217013,0.0026142274,0.00022311643],"about_ca_topic_score_codex":0.000011140738,"about_ca_topic_score_gemma":0.00006106861,"teacher_disagreement_score":0.95190257,"about_ca_system_score_codex":0.00009223356,"about_ca_system_score_gemma":0.000011265772,"threshold_uncertainty_score":0.70613426},"labels":[],"label_agreement":null},{"id":"W2763529930","doi":"10.1109/lra.2017.2759789","title":"Cooperative Continuum Robots: Concept, Modeling, and Workspace Analysis","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workspace; Reachability; Robot; Computer science; Mathematical optimization; Simulation; Control theory (sociology); Mathematics; Control (management); Artificial intelligence; Algorithm","score_opus":0.012682778095525139,"score_gpt":0.23965062885245192,"score_spread":0.22696785075692677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763529930","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":[0.15372765,0.00015317503,0.8426423,0.0028902888,0.00014900012,0.00012992982,0.000008809613,0.00015266922,0.00014614845],"genre_scores_gemma":[0.98516166,0.000105932864,0.014313933,0.00023918977,0.00007680386,0.000013854253,0.000014899365,0.00001808217,0.00005565581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938166,0.000010269805,0.0001787524,0.00018230715,0.00008804205,0.00015899088],"domain_scores_gemma":[0.9994922,0.000035573714,0.000065114444,0.00028373668,0.00004324907,0.0000801057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066616114,0.00014264352,0.00021108791,0.000082244915,0.00035498734,0.00038749096,0.000107978354,0.00005862569,0.0000055589867],"category_scores_gemma":[0.000015449798,0.00014322638,0.00004161308,0.000096569114,0.00009465315,0.00019250817,0.000022926146,0.000102086204,0.0000042959123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.8802843e-7,0.0000049590626,0.00067213754,0.000007952845,0.00015221244,0.0000010634063,0.0002206102,0.9949135,0.001495084,0.0010837996,0.00091729464,0.00053088414],"study_design_scores_gemma":[0.00021274762,0.0000049079367,0.004461067,0.000016177986,0.00016716859,0.0000014883302,0.000035600413,0.9945869,0.00019128744,0.00007787349,0.00006740094,0.00017738112],"about_ca_topic_score_codex":0.000022315819,"about_ca_topic_score_gemma":0.00004000031,"teacher_disagreement_score":0.831434,"about_ca_system_score_codex":0.000017805101,"about_ca_system_score_gemma":0.0000045270745,"threshold_uncertainty_score":0.58406025},"labels":[],"label_agreement":null},{"id":"W2766503163","doi":"10.1109/lra.2017.2768122","title":"Surgeon-in-the-Loop 3-D Needle Steering Through Ultrasound-Guided Feedback Control","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Loop (graph theory); Feedback control; Feedback loop; Control (management); Computer science; Control theory (sociology); Surgery; Medicine; Engineering; Control engineering; Artificial intelligence; Mathematics; Computer security","score_opus":0.019620906022090573,"score_gpt":0.24426942036375998,"score_spread":0.2246485143416694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766503163","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":[0.74178207,0.000092619826,0.24724331,0.008347321,0.00073095504,0.0004385377,0.0000127214325,0.0002653562,0.0010871141],"genre_scores_gemma":[0.99640566,0.000060547092,0.0025349073,0.00078673015,0.00012722086,0.000030445504,0.0000073111305,0.000025319312,0.000021835118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999182,0.000016574048,0.0002575981,0.00015180322,0.00014836814,0.00024369276],"domain_scores_gemma":[0.99927074,0.00016911913,0.00007790439,0.00042223505,0.000021537418,0.000038447077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017395726,0.00015405832,0.00017016509,0.000043501997,0.00031471334,0.0003806749,0.00025677512,0.000057176032,0.000005807898],"category_scores_gemma":[0.00003056164,0.00013870721,0.000047147074,0.00007198463,0.000067414156,0.00025170864,0.000010337577,0.00013907443,0.00002330178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.7943783e-7,0.000017963679,0.003926182,0.00003790683,0.000029869387,0.0000050226463,0.00044453144,0.9694217,0.017038181,0.0025049788,0.0061508943,0.00042187513],"study_design_scores_gemma":[0.001824962,0.000017021915,0.17670916,0.00011378859,0.00007797971,0.000079531455,0.00016630026,0.8151045,0.00189244,0.0008776092,0.0024422896,0.00069444085],"about_ca_topic_score_codex":0.000054014727,"about_ca_topic_score_gemma":0.000016170046,"teacher_disagreement_score":0.25462362,"about_ca_system_score_codex":0.000030620966,"about_ca_system_score_gemma":0.000006220875,"threshold_uncertainty_score":0.56563157},"labels":[],"label_agreement":null},{"id":"W2789888630","doi":"10.1109/lra.2018.2803815","title":"The Programmable Permanent Magnet Actuator: A Paradigm Shift in Efficiency for Low-Speed Torque-Holding Robotic Applications","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Actuator; Torque; Magnet; Stator; Rotary actuator; Power (physics); Electrical engineering; Mechanical engineering; Computer science; Control theory (sociology); Automotive engineering; Engineering; Physics; Control (management); Artificial intelligence","score_opus":0.009975075587193367,"score_gpt":0.23491909653315562,"score_spread":0.22494402094596225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789888630","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":[0.115093224,0.00023481442,0.8761883,0.0057157837,0.00041914135,0.0018320638,0.00000968225,0.00041122915,0.00009577792],"genre_scores_gemma":[0.99357194,0.00006405514,0.005473411,0.0002161725,0.00022479055,0.00038184333,0.000019996352,0.000036025747,0.000011793313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989697,0.000012393961,0.0003207546,0.00021551223,0.00012793652,0.00035372912],"domain_scores_gemma":[0.9994179,0.00015898517,0.000060555652,0.00026363318,0.000024548088,0.00007439886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001814457,0.00016396532,0.00014420075,0.000083650375,0.00040108841,0.00021505335,0.00017738696,0.000058775466,0.000002966588],"category_scores_gemma":[0.00000904015,0.00014331989,0.000045793684,0.00025971726,0.00011620727,0.000100946476,0.00001582134,0.00009991115,0.000020972768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040847435,0.000065699045,0.0001457377,0.00011354883,0.000026740388,6.0464924e-7,0.00062707736,0.95780283,0.008703737,0.025659787,0.0016544438,0.005195728],"study_design_scores_gemma":[0.0003632259,0.000031888394,0.0019468863,0.000037933398,0.000025325258,0.0000030058861,0.000039567476,0.9913899,0.0004817058,0.0016993148,0.0037463636,0.00023490132],"about_ca_topic_score_codex":0.0000075245757,"about_ca_topic_score_gemma":0.000025566278,"teacher_disagreement_score":0.8784787,"about_ca_system_score_codex":0.00007648462,"about_ca_system_score_gemma":0.000015928212,"threshold_uncertainty_score":0.58444154},"labels":[],"label_agreement":null},{"id":"W2790141348","doi":"10.1109/lra.2018.2794618","title":"Improving Industrial Grippers With Adhesion-Controlled Friction","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adhesion, Friction, and Surface Interactions","field":"Engineering","cited_by":50,"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; Ford Motor Company","keywords":"Grippers; Slipping; Shear force; Mandrel; Adhesive; Tactile sensor; Contact force; Fabrication; Materials science; Mechanical engineering; Contact area; Robot; Computer science; Nanotechnology; Engineering; Composite material; Artificial intelligence; Physics; Classical mechanics","score_opus":0.011306428954489366,"score_gpt":0.20315635078098293,"score_spread":0.19184992182649357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790141348","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":[0.8087417,0.000022705273,0.18748292,0.0007382334,0.0018884809,0.00025610824,0.000003142963,0.00049096096,0.00037577457],"genre_scores_gemma":[0.99593717,0.000020219419,0.0031137269,0.00017523482,0.0006233984,0.000016998467,0.000006247024,0.000028599869,0.00007837878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992092,0.000026860154,0.00024986643,0.00016305683,0.00015816343,0.00019281713],"domain_scores_gemma":[0.99960035,0.00006834126,0.00007209014,0.0001235371,0.00006340669,0.000072286944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010344914,0.00015982645,0.00018179223,0.00014562324,0.00027407502,0.00010114645,0.000050098082,0.000090490306,0.000031631327],"category_scores_gemma":[0.000019489133,0.00013468809,0.00004395663,0.00017363338,0.000049817714,0.0003495012,0.000005283203,0.00015198917,0.000032411623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023384868,0.000074736745,0.0043870495,0.000072182396,0.00033608475,0.000010360501,0.0018703926,0.62675726,0.3283612,0.00034471715,0.02430574,0.013246415],"study_design_scores_gemma":[0.0030347037,0.00016878844,0.0026818148,0.00007879054,0.00010565275,0.000027123702,0.00025130284,0.9822984,0.009247993,0.000019620124,0.0017014324,0.00038437854],"about_ca_topic_score_codex":0.000036997582,"about_ca_topic_score_gemma":0.00002495368,"teacher_disagreement_score":0.35554114,"about_ca_system_score_codex":0.00007457877,"about_ca_system_score_gemma":0.000015530615,"threshold_uncertainty_score":0.5492421},"labels":[],"label_agreement":null},{"id":"W2791219751","doi":"10.1109/lra.2018.2809512","title":"Free Head Movement Eye Gaze Contingent Ultrasound Interfaces for the da Vinci Surgical System","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Gaze Tracking and Assistive Technology","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Modality (human–computer interaction); Computer vision; Eye tracking; Eye movement; Computer science; Artificial intelligence; Eye tracking on the ISS; Pupil; Psychology","score_opus":0.01946166577728276,"score_gpt":0.26577884498804233,"score_spread":0.24631717921075957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791219751","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":[0.23711015,0.00005965429,0.7462034,0.015310489,0.00079094735,0.00020824987,0.000006337914,0.0002736111,0.0000371619],"genre_scores_gemma":[0.98353773,0.000005243184,0.0154206855,0.00080558774,0.00016381781,0.00002448671,0.000001459818,0.000007469048,0.000033507513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990789,0.000037731555,0.00023264777,0.00026275165,0.0001610629,0.0002268711],"domain_scores_gemma":[0.99909604,0.00027986703,0.00012895085,0.00038243906,0.00007644985,0.00003623146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032730115,0.00012469545,0.00014341151,0.00005622115,0.00028664203,0.00026036927,0.0005061419,0.000050242503,0.0000014621426],"category_scores_gemma":[0.000031122785,0.000088032015,0.000042738906,0.000109225344,0.00016582993,0.000112569476,0.000079810765,0.00008044434,0.00000801735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006440514,0.0003695044,0.008370919,0.0005675205,0.00070525485,0.000058538757,0.0037852726,0.037381854,0.14246875,0.6568862,0.052102875,0.09723887],"study_design_scores_gemma":[0.0017162007,0.00035224875,0.016338386,0.00025360828,0.000060588663,0.000048249603,0.00023473319,0.9464119,0.024886638,0.00097530894,0.008243031,0.0004791212],"about_ca_topic_score_codex":0.000022186125,"about_ca_topic_score_gemma":0.00001313513,"teacher_disagreement_score":0.90903,"about_ca_system_score_codex":0.000052060746,"about_ca_system_score_gemma":0.000011465304,"threshold_uncertainty_score":0.35898414},"labels":[],"label_agreement":null},{"id":"W2794440733","doi":"10.1109/lra.2018.2812910","title":"A High-Bandwidth Back-Drivable Hydrostatic Power Distribution System for Exoskeletons Based on Magnetorheological Clutches","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Magnetorheological fluid; Clutch; Bandwidth (computing); Hydrostatic equilibrium; Inertia; Torque; Haptic technology; Software portability; Computer science; Simulation; Engineering; Mechanical engineering; Control engineering; Telecommunications; Physics","score_opus":0.006617426519723881,"score_gpt":0.2004994355896199,"score_spread":0.19388200906989603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794440733","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":[0.19886972,0.000015374051,0.79641086,0.002823793,0.0009815358,0.000429858,0.00008857303,0.00029490676,0.0000853666],"genre_scores_gemma":[0.9707588,0.00000304945,0.028494636,0.0004447361,0.00010832927,0.000034169687,0.00009914961,0.000028691189,0.000028428125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900055,0.000033358057,0.00030679515,0.00021321987,0.00017186376,0.00027419248],"domain_scores_gemma":[0.9993759,0.00020108308,0.00006254517,0.00019617673,0.00007433622,0.00008996424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017769534,0.00019083646,0.0002096557,0.00007544016,0.00017081368,0.00009485673,0.00008914918,0.000104819905,0.000012908523],"category_scores_gemma":[0.000031856707,0.00016733463,0.00006693578,0.00013047583,0.00013651566,0.0000754855,0.000008909367,0.00008621152,0.0000459119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015703305,0.00004010665,0.00011529326,0.00026622944,0.000020312178,0.0000013998227,0.000089004,0.9785714,0.0069047897,0.007391229,0.0062747784,0.00030975428],"study_design_scores_gemma":[0.0007169947,0.00033193195,0.0016196471,0.00010238306,0.000028685976,0.000002926796,0.000024626539,0.9937603,0.0022249916,0.00017620019,0.0007723729,0.0002389492],"about_ca_topic_score_codex":0.000003200944,"about_ca_topic_score_gemma":0.00000165785,"teacher_disagreement_score":0.7718891,"about_ca_system_score_codex":0.00011365431,"about_ca_system_score_gemma":0.000013097292,"threshold_uncertainty_score":0.6823708},"labels":[],"label_agreement":null},{"id":"W2797154185","doi":"10.1109/lra.2018.2825473","title":"OREO: An Open-Hardware Robotic Head That Supports Practical Saccades and Accommodation","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Stereopsis; Machine vision; Focus (optics); Robotics; Robot","score_opus":0.05903658796968672,"score_gpt":0.3491818060752113,"score_spread":0.2901452181055246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797154185","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":[0.25557888,0.0000059723097,0.73028576,0.013085371,0.0005965264,0.00020921371,9.794875e-7,0.00016899874,0.00006831527],"genre_scores_gemma":[0.94838464,0.000011123289,0.049010076,0.0024117783,0.00011601031,0.000008622856,0.000011520094,0.000010967744,0.000035246063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987846,0.00012466565,0.00023094317,0.0004062238,0.00024334574,0.00021020525],"domain_scores_gemma":[0.99931175,0.000046421283,0.00014476116,0.00028450112,0.00007605366,0.0001364961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034001452,0.00014512711,0.00015507152,0.00012239625,0.00035812196,0.0010245522,0.00025459,0.00007020004,0.000016682541],"category_scores_gemma":[0.000026681799,0.0001350297,0.000019776595,0.00019592707,0.000100637015,0.002582795,0.00013974641,0.0001110082,0.000025376816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015269469,0.0018149196,0.029628664,0.00045713788,0.00029773123,0.00018069478,0.011575583,0.032198247,0.30722156,0.20981772,0.028613573,0.37804145],"study_design_scores_gemma":[0.00063614466,0.0003551234,0.097190134,0.000054570606,0.000027115253,0.00020214243,0.00008857146,0.8958097,0.0032489193,0.0014226037,0.0005753362,0.00038960643],"about_ca_topic_score_codex":0.000032986558,"about_ca_topic_score_gemma":0.00003623476,"teacher_disagreement_score":0.86361146,"about_ca_system_score_codex":0.000028951907,"about_ca_system_score_gemma":0.000027525699,"threshold_uncertainty_score":0.98797804},"labels":[],"label_agreement":null},{"id":"W2809555803","doi":"10.1109/lra.2018.2849553","title":"Local Positioning System Using UWB Range Measurements for an Unmanned Blimp","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","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":"Université de Montréal","funders":"","keywords":"Robustness (evolution); Computer science; Estimator; Kalman filter; Control theory (sociology); Extended Kalman filter; Gyroscope; Simulation; Real-time computing; Engineering; Artificial intelligence; Aerospace engineering","score_opus":0.03067398227360072,"score_gpt":0.2332395021778549,"score_spread":0.2025655199042542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809555803","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":[0.27642554,0.00002826777,0.72208303,0.00005426511,0.00087547995,0.00010200463,0.0000032433932,0.00039269033,0.000035481116],"genre_scores_gemma":[0.986387,8.6924905e-7,0.013110195,0.00005422263,0.0003788246,0.000012203533,0.000010643349,0.000040747444,0.000005269856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993218,0.000016889395,0.00018613803,0.00013359604,0.00013000434,0.00021152521],"domain_scores_gemma":[0.9997092,0.000013449005,0.000035373945,0.00011893373,0.000049548486,0.00007353134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016329777,0.0001364991,0.00014359617,0.000079221,0.00015557211,0.00008502174,0.00005389222,0.00006426692,6.042222e-7],"category_scores_gemma":[0.0000029938642,0.00014525652,0.000030690273,0.00008831008,0.000029032435,0.00019089575,0.0000043066534,0.000043711752,0.0000043291434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024681592,0.0000033756146,0.000052636828,0.00014727577,0.000032370222,5.277756e-7,0.00018180447,0.946487,0.052498646,0.00012781477,0.00024048789,0.00022560869],"study_design_scores_gemma":[0.0003283719,0.000032074226,0.00023029777,0.00015906231,0.000028190132,0.000012878018,0.00008289523,0.99451923,0.004380036,0.0000018382575,0.000051095358,0.00017402369],"about_ca_topic_score_codex":0.000021250195,"about_ca_topic_score_gemma":0.0000072764537,"teacher_disagreement_score":0.7099615,"about_ca_system_score_codex":0.00012194582,"about_ca_system_score_gemma":0.0000046974847,"threshold_uncertainty_score":0.59233886},"labels":[],"label_agreement":null},{"id":"W2889728390","doi":"10.1109/lra.2018.2890444","title":"Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Observability; Calibration; Set (abstract data type); Variety (cybernetics); Quadratic growth; Quadratic equation; Quadratic programming; Optimization problem","score_opus":0.006673337120281541,"score_gpt":0.18505756346715205,"score_spread":0.1783842263468705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889728390","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":[0.49435365,0.00004417625,0.5015183,0.0021796487,0.0009791879,0.00023343731,0.000009967708,0.00039130994,0.00029029208],"genre_scores_gemma":[0.98958004,0.000020362697,0.009223104,0.0007679972,0.0001779573,0.000007536728,0.00005317269,0.000029265384,0.00014056334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990771,0.000035346344,0.00027389615,0.00020558096,0.00021090105,0.00019719696],"domain_scores_gemma":[0.9996197,0.000036572466,0.0000534013,0.0001854255,0.000026931804,0.0000779662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068929556,0.00017095027,0.00018262646,0.000085880405,0.00006812905,0.00021723137,0.00008459297,0.00008825814,0.00013434702],"category_scores_gemma":[0.0000049960686,0.00017521548,0.000053731033,0.00011586456,0.000020115393,0.00033148646,0.000010114031,0.00010681324,0.00015916777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037851532,0.000012791065,0.00031105478,0.000023242867,0.000031507494,0.00000203425,0.000106551895,0.9144865,0.08086091,0.00035326832,0.00123273,0.002575592],"study_design_scores_gemma":[0.0006377308,0.000019936495,0.004833963,0.000029339339,0.000025999472,0.0000047677554,0.00003500531,0.9921764,0.0016026624,0.00004572968,0.0003729,0.00021555333],"about_ca_topic_score_codex":0.000020753032,"about_ca_topic_score_gemma":0.0000029205619,"teacher_disagreement_score":0.49522638,"about_ca_system_score_codex":0.00006260386,"about_ca_system_score_gemma":0.000008706869,"threshold_uncertainty_score":0.714508},"labels":[],"label_agreement":null},{"id":"W2891174905","doi":"10.1109/lra.2018.2883408","title":"There's No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs During GPS Failure","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":59,"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; Defence Research and Development Canada","keywords":"Visual odometry; Global Positioning System; Computer science; Multirotor; Gimbal; Computer vision; Real-time computing; Artificial intelligence; Simulation; Aeronautics; Engineering; Aerospace engineering; Robot; Telecommunications","score_opus":0.005881904702428882,"score_gpt":0.21588744021684048,"score_spread":0.2100055355144116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891174905","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":[0.8378394,0.000054491764,0.16072775,0.0003626578,0.0005597764,0.00030158233,0.000010033931,0.00012744172,0.00001681124],"genre_scores_gemma":[0.9881739,0.00006587018,0.011286259,0.00006373038,0.0002658428,0.000012173498,0.000020173655,0.000042535587,0.00006948331],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991765,0.000019992127,0.00030839266,0.00018466216,0.00012507907,0.00018538392],"domain_scores_gemma":[0.99960047,0.000033666147,0.0000824365,0.0001327856,0.00009024804,0.000060405942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009502186,0.00015899367,0.00017916782,0.000095171155,0.00011617093,0.00003646103,0.00005310186,0.00009115543,0.000012336674],"category_scores_gemma":[0.000020277213,0.00015625157,0.000040166095,0.00009207154,0.00005154087,0.0001253194,0.000011640968,0.00007222522,0.0000032336827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004135359,0.000044042816,0.0033200474,0.00094324804,0.00010729737,0.0000027877754,0.0012235532,0.47136465,0.5159733,0.000142199,0.005828952,0.0010085361],"study_design_scores_gemma":[0.0006171188,0.00008014391,0.0044333357,0.00006867986,0.00003062384,0.0000040907685,0.000050883784,0.984119,0.010060794,0.000016815833,0.000295441,0.00022309153],"about_ca_topic_score_codex":0.000007156325,"about_ca_topic_score_gemma":0.000010439798,"teacher_disagreement_score":0.5127543,"about_ca_system_score_codex":0.000026027856,"about_ca_system_score_gemma":0.0000051237716,"threshold_uncertainty_score":0.6371754},"labels":[],"label_agreement":null},{"id":"W2891214310","doi":"10.1109/lra.2018.2890572","title":"Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Model predictive control; Computer science; Point (geometry); Control (management); Control theory (sociology); Artificial intelligence; Mathematics; Physics; Geometry","score_opus":0.0073714221293537325,"score_gpt":0.19939613191935335,"score_spread":0.1920247097899996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891214310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031219093,0.000020880932,0.9651322,0.0013352566,0.0004321599,0.0013421059,0.0002205234,0.00028965005,0.000008132688],"genre_scores_gemma":[0.96639436,0.0000032186408,0.032576166,0.00049473264,0.00011488744,0.00017824894,0.00019596548,0.000035214016,0.000007180388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992021,0.000021467282,0.00028536204,0.00019481326,0.00011167413,0.00018462037],"domain_scores_gemma":[0.9996276,0.000040538547,0.00005201267,0.00013417618,0.00007064229,0.0000750413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089971836,0.00015442555,0.00018939348,0.000092386195,0.00007506343,0.00004252743,0.000044027176,0.000064480206,0.0000026463695],"category_scores_gemma":[0.000009424849,0.0001679439,0.000055249042,0.00007861457,0.000010572884,0.00027654733,0.0000022794347,0.00006246038,0.000007655054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008691,0.000008226701,0.0000062668073,0.00003352177,0.00003454283,1.8580153e-7,0.00020561529,0.753161,0.2459261,0.00007599014,0.0003337885,0.00020606317],"study_design_scores_gemma":[0.0013544462,0.000036425154,0.00015563444,0.000023887624,0.000044593577,0.0000019259098,0.00001903514,0.996686,0.0014499044,0.00003407296,0.000013642061,0.00018044471],"about_ca_topic_score_codex":0.0000012665693,"about_ca_topic_score_gemma":0.000003285823,"teacher_disagreement_score":0.9351753,"about_ca_system_score_codex":0.00013141567,"about_ca_system_score_gemma":0.000008208193,"threshold_uncertainty_score":0.6848554},"labels":[],"label_agreement":null},{"id":"W2891275937","doi":"10.1109/lra.2019.2891492","title":"A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on &lt;italic&gt;SE(3)&lt;/italic&gt;","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":41,"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":"Jerk; Acceleration; White noise; Trajectory; Noise (video); Constant (computer programming); Additive white Gaussian noise; Computer science; Motion (physics); Mathematics; Control theory (sociology); Artificial intelligence; Statistics; Physics; Classical mechanics","score_opus":0.011453242008918181,"score_gpt":0.22883046124037043,"score_spread":0.21737721923145226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891275937","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":[0.4488201,0.000009129671,0.5445665,0.00388515,0.00092666026,0.0008814961,0.000020519514,0.00037408574,0.000516333],"genre_scores_gemma":[0.9731293,0.000005499537,0.021855993,0.003906442,0.00023864197,0.00007774824,0.00012152906,0.000038890063,0.000626013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981591,0.0000874257,0.0004517398,0.00055390265,0.00041847787,0.0003293271],"domain_scores_gemma":[0.9988667,0.00019325876,0.00032632254,0.00038480552,0.00012098445,0.00010793603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003192936,0.00028732241,0.00030163402,0.00032965286,0.00026191142,0.00043133655,0.00023812635,0.00013219887,0.000044376506],"category_scores_gemma":[0.000038909504,0.0002843818,0.00013142015,0.00021557669,0.000048906113,0.0008680862,0.00002354911,0.00013327338,0.000477132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001137136,0.0007542729,0.00038014332,0.00048437377,0.00021583388,0.0000139014155,0.0029928721,0.52633864,0.22902611,0.029649204,0.035933048,0.17409788],"study_design_scores_gemma":[0.0013619701,0.00040838716,0.0071787895,0.0001804018,0.000036213558,0.000012647542,0.000012250933,0.98449695,0.004363203,0.00048390607,0.0010084885,0.00045677013],"about_ca_topic_score_codex":0.0000016909329,"about_ca_topic_score_gemma":0.0000013611992,"teacher_disagreement_score":0.5243091,"about_ca_system_score_codex":0.00011039521,"about_ca_system_score_gemma":0.00003108092,"threshold_uncertainty_score":0.99996084},"labels":[],"label_agreement":null},{"id":"W2897861898","doi":"10.1109/lra.2019.2901638","title":"Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems With Unknown and Changing Dynamics Performing Repetitive Tasks","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":56,"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":"Model predictive control; Computer science; Gaussian process; Process (computing); Bayesian optimization; Regression; Artificial intelligence; Bayesian probability; Control theory (sociology); Term (time); Robot; Machine learning; Gaussian; Control (management); Mathematics; Statistics","score_opus":0.002388532783694127,"score_gpt":0.17173024311333582,"score_spread":0.16934171032964168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897861898","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":[0.09599627,0.00010368816,0.90209746,0.0001668211,0.00033703257,0.00091273495,0.000017045162,0.00023508414,0.00013385585],"genre_scores_gemma":[0.9946231,0.000019864232,0.0047920938,0.000055907112,0.00011568456,0.000081350634,0.000041234314,0.000055342483,0.00021537438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990859,0.00003219821,0.0002423024,0.00022447712,0.00013296484,0.00028216228],"domain_scores_gemma":[0.99948674,0.00014979203,0.00013169373,0.00009896383,0.00007864081,0.000054192784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019484994,0.00019059663,0.0002759892,0.00015252025,0.00015433307,0.00009651983,0.000040220275,0.00007378346,4.4496008e-7],"category_scores_gemma":[0.000020037833,0.00018258792,0.000026232337,0.00010466677,0.000025761121,0.00037246544,0.000008154701,0.00014402819,0.0000013217567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022560776,0.000002474274,0.0009865483,0.00025157296,0.00007732661,0.0000010637938,0.00048792607,0.9940459,0.0018175662,0.0007093587,0.000007601583,0.0015900839],"study_design_scores_gemma":[0.0013984777,0.000111870264,0.0003329322,0.00026091954,0.00005166441,0.000017765897,0.0005927659,0.9968607,0.000040678697,0.0000070687934,0.00010646767,0.00021873288],"about_ca_topic_score_codex":0.0000037053237,"about_ca_topic_score_gemma":0.0000034372558,"teacher_disagreement_score":0.89862686,"about_ca_system_score_codex":0.0001253449,"about_ca_system_score_gemma":0.000007784417,"threshold_uncertainty_score":0.74457186},"labels":[],"label_agreement":null},{"id":"W2901737534","doi":"10.1109/lra.2018.2881433","title":"Fast and Efficient Aerial Climbing of Vertical Surfaces Using Fixed-Wing UAVs","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Biomimetic flight and propulsion mechanisms","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Climb; Climbing; Thrust; Drag; Aerospace engineering; Wing; Aerodynamics; Drone; Reduction (mathematics); Marine engineering; Fixed wing; Simulation; Automotive engineering; Engineering; Computer science; Structural engineering; Geometry; Mathematics","score_opus":0.013681317016470606,"score_gpt":0.22113332254009196,"score_spread":0.20745200552362136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901737534","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":[0.89685863,0.0000391914,0.10197603,0.00020150736,0.0007733071,0.00006420346,0.0000020057053,0.00006344687,0.000021698657],"genre_scores_gemma":[0.98442453,0.0000075534117,0.015366539,0.00006913423,0.00011624179,5.727426e-7,0.0000012825876,0.000012885505,0.0000012879723],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942446,0.000017762326,0.00019664293,0.00010680451,0.000111069894,0.00014323395],"domain_scores_gemma":[0.9998036,0.00003169413,0.000023975004,0.000071794304,0.000021106038,0.000047811092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011340623,0.000094921124,0.00013155903,0.00006409974,0.000081819344,0.00004391015,0.0000368134,0.000057011403,0.000010112551],"category_scores_gemma":[0.00000713319,0.00008833039,0.000019495594,0.000077766446,0.000078191966,0.0000500403,0.000016785409,0.000050729675,0.0000029827409],"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":[0.0000048271063,0.0000065443805,0.00009137281,0.00006776844,0.000014842924,9.681345e-7,0.00048933615,0.13215484,0.8652119,0.00020189153,0.00006492255,0.0016908093],"study_design_scores_gemma":[0.00021498774,0.00002531311,0.0005091149,0.000055247587,0.000020056175,0.000004292026,0.000024763658,0.8291261,0.1698831,0.000025661182,0.000014732153,0.000096612326],"about_ca_topic_score_codex":0.0000060920065,"about_ca_topic_score_gemma":7.3823816e-7,"teacher_disagreement_score":0.6969713,"about_ca_system_score_codex":0.000013792969,"about_ca_system_score_gemma":0.0000039995793,"threshold_uncertainty_score":0.36020085},"labels":[],"label_agreement":null},{"id":"W2903857705","doi":"10.1109/lra.2018.2885584","title":"Robot Cooperative Behavior Learning Using Single-Shot Learning From Demonstration and Parallel Hidden Markov Models","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Robot; Computer science; Artificial intelligence; Robustness (evolution); Hidden Markov model; Human–computer interaction; Task (project management); Robot learning; Machine learning; Mobile robot; Engineering","score_opus":0.04922757461608525,"score_gpt":0.24655176605841717,"score_spread":0.19732419144233193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903857705","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":[0.4995239,0.000051886167,0.4997091,0.00010954618,0.00014537052,0.000114655064,4.065554e-7,0.0002285441,0.000116576],"genre_scores_gemma":[0.9653288,0.00002654563,0.03416399,0.00013347306,0.00022070766,0.0000075109797,0.00004051948,0.000045918197,0.000032546086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988956,0.000106389256,0.0003159019,0.00026750355,0.00017349457,0.00024111102],"domain_scores_gemma":[0.99957526,0.00007509604,0.0001021147,0.00009134751,0.00006446117,0.00009171131],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012854626,0.00022338818,0.00021180413,0.00013332626,0.000399834,0.00028144702,0.000054050906,0.00011327816,0.000029251276],"category_scores_gemma":[0.000022397295,0.00024786367,0.000030997384,0.00013981009,0.00009407448,0.0005725434,0.000020483818,0.00031294164,0.00000892922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004432896,0.000008057497,0.0021863435,0.000011686588,0.000023822578,0.000003370975,0.00091027224,0.8595726,0.13368061,0.00007297626,0.00002756132,0.003498275],"study_design_scores_gemma":[0.00036883415,0.000048680788,0.007549701,0.000063588945,0.000055890192,0.000012188412,0.00019435356,0.990445,0.00092194934,0.000023877134,0.000025079482,0.00029084092],"about_ca_topic_score_codex":0.00003885944,"about_ca_topic_score_gemma":0.000014792961,"teacher_disagreement_score":0.46580487,"about_ca_system_score_codex":0.000064669584,"about_ca_system_score_gemma":0.000009948792,"threshold_uncertainty_score":0.9999974},"labels":[],"label_agreement":null},{"id":"W2905074778","doi":"10.1109/lra.2018.2885197","title":"Toward Robot-Assisted Diagnosis of Developmental Coordination Disorder","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Children's Physical and Motor Development","field":"Psychology","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Standardization; Normative; Motor coordination; Psychology; Physical medicine and rehabilitation; Set (abstract data type); Motor function; Motor skill; Medicine; Developmental psychology; Computer science; Psychiatry","score_opus":0.02128925372853042,"score_gpt":0.2638670778632834,"score_spread":0.24257782413475298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905074778","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":[0.96569586,0.000021180136,0.02733424,0.0054260413,0.00072850805,0.00021387622,0.000007829429,0.00006968831,0.00050276687],"genre_scores_gemma":[0.991762,0.000005097168,0.0070856675,0.0008244694,0.0001226327,0.000037349975,0.000026135025,0.0000136956005,0.00012294974],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991375,0.000037719314,0.00027905102,0.000216707,0.00015936093,0.00016966493],"domain_scores_gemma":[0.9996101,0.000055416345,0.00012582818,0.00009294868,0.0000613064,0.000054380525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007792537,0.00012827119,0.00016457024,0.00009070506,0.00009058977,0.000024027197,0.0000798966,0.000054850916,0.00014201498],"category_scores_gemma":[0.00001105898,0.00011934071,0.000040799685,0.00016987164,0.0001227393,0.000082219005,0.000026154941,0.00005715813,0.000066858294],"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":[0.00014743209,0.0020943356,0.20239235,0.00024760666,0.0010233893,0.000022514812,0.022232575,0.0016751513,0.07988955,0.019793037,0.08499458,0.5854875],"study_design_scores_gemma":[0.0010288708,0.00009866723,0.99003553,0.00005519009,0.000036728972,0.000011922372,0.00014141777,0.001066949,0.00485411,0.00016202575,0.002229797,0.0002787686],"about_ca_topic_score_codex":0.00004978659,"about_ca_topic_score_gemma":0.0000092285345,"teacher_disagreement_score":0.7876432,"about_ca_system_score_codex":0.0000406486,"about_ca_system_score_gemma":0.000016225393,"threshold_uncertainty_score":0.4866573},"labels":[],"label_agreement":null},{"id":"W2907949433","doi":"10.1109/lra.2018.2890209","title":"Play Me Back: A Unified Training Platform for Robotic and Laparoscopic Surgery","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Training (meteorology); Robotic surgery; Laparoscopic surgery; Medicine; Physical medicine and rehabilitation; Computer science; Surgery; Laparoscopy; Physics","score_opus":0.0636943044043961,"score_gpt":0.2945607904691776,"score_spread":0.2308664860647815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907949433","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":[0.9520239,0.000029438748,0.04258126,0.004259501,0.00041295192,0.0003089817,0.0000022594454,0.00009844835,0.00028326313],"genre_scores_gemma":[0.9911544,0.000008827517,0.005325352,0.0030961153,0.00027055733,0.000007243215,0.000027752234,0.00001827674,0.000091532806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999169,0.000014029103,0.0002673188,0.00019893849,0.00012880348,0.0002218997],"domain_scores_gemma":[0.9991952,0.0004486208,0.000084859945,0.00009097972,0.00004910886,0.00013122706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022387407,0.0001258854,0.0002914792,0.00011743818,0.00014589011,0.000058219426,0.00002172455,0.00007165582,0.000039509385],"category_scores_gemma":[0.00007736494,0.00011078909,0.000051213894,0.0001131848,0.00011332172,0.00011789458,0.0000075504704,0.00007183615,0.000010186079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028800962,0.0009795406,0.1261608,0.0049631777,0.002764834,0.00024815882,0.051042024,0.2399973,0.12493831,0.047487546,0.034100093,0.36443812],"study_design_scores_gemma":[0.0081414,0.00031676958,0.07003114,0.00056400045,0.00023093421,0.00009015156,0.00045473845,0.913602,0.0018393258,0.00043095282,0.0037633006,0.0005352859],"about_ca_topic_score_codex":0.00000305034,"about_ca_topic_score_gemma":0.000002907613,"teacher_disagreement_score":0.67360467,"about_ca_system_score_codex":0.000019604107,"about_ca_system_score_gemma":0.00003036015,"threshold_uncertainty_score":0.45178482},"labels":[],"label_agreement":null},{"id":"W2908355393","doi":"10.1109/lra.2018.2890674","title":"A Therapist-Taught Robotic System for Assistance During Gait Therapy Targeting Foot Drop","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada Foundation for Innovation","keywords":"Physical medicine and rehabilitation; Rehabilitation; Physical therapist; Psychology; Physical therapy; Gait; Medicine","score_opus":0.005809129467535245,"score_gpt":0.195947346193993,"score_spread":0.19013821672645775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908355393","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":[0.68934286,0.0007622828,0.30547997,0.001367077,0.0015804006,0.00082504755,0.000008673178,0.0005328478,0.00010081438],"genre_scores_gemma":[0.9834657,0.000076311284,0.015980719,0.00015518283,0.00012277727,0.00004414025,0.000013378707,0.00006672205,0.00007510897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884593,0.000029029614,0.00038126952,0.00024910315,0.0001818025,0.0003128435],"domain_scores_gemma":[0.9994362,0.00011470849,0.00009912777,0.00021349447,0.00006649488,0.00006994942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001882049,0.00022385266,0.00026755102,0.0001064934,0.00014825488,0.00013218337,0.00011482171,0.000087883695,0.0000037872508],"category_scores_gemma":[0.000008639548,0.00020758099,0.00009601608,0.00013520978,0.000036060766,0.00014507417,0.000009393151,0.00011569955,0.000016949642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008234907,0.00001314362,0.0007112281,0.000613017,0.000052388004,0.0000011100341,0.0003013499,0.83943474,0.15729636,0.00092382747,0.00019756974,0.00044700035],"study_design_scores_gemma":[0.001266492,0.00005762619,0.0050320984,0.0001987336,0.000018970168,0.00000802551,0.00016436922,0.98624074,0.006109305,0.00007525336,0.0004049385,0.00042347223],"about_ca_topic_score_codex":0.000002177721,"about_ca_topic_score_gemma":0.0000014551372,"teacher_disagreement_score":0.2941228,"about_ca_system_score_codex":0.00010981192,"about_ca_system_score_gemma":0.000010449152,"threshold_uncertainty_score":0.8464907},"labels":[],"label_agreement":null},{"id":"W2908523746","doi":"10.1109/lra.2019.2894005","title":"HMFP-DBRNN: Real-Time Hand Motion Filtering and Prediction via Deep Bidirectional RNN","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Neurological disorders and treatments","field":"Medicine","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":"Western University; Concordia University","funders":"","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Noise (video); Ground truth; Compensation (psychology); Motion (physics); Machine learning; Artificial neural network","score_opus":0.008782471070156487,"score_gpt":0.218577169823623,"score_spread":0.2097946987534665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908523746","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":[0.9791115,0.00003202124,0.017378347,0.0026657972,0.00025663906,0.00026574224,0.000005247654,0.00009695613,0.00018776926],"genre_scores_gemma":[0.99678516,0.00008400822,0.0018462893,0.000978225,0.00007674005,0.000008234957,0.000048531667,0.000012642163,0.00016014953],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9993528,0.000022066622,0.00015057108,0.00021643541,0.00013593458,0.00012217641],"domain_scores_gemma":[0.9997372,0.00003124803,0.000056088465,0.00008553399,0.000023098173,0.00006684842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051011702,0.00010890597,0.00014786502,0.000073783,0.00009770954,0.00004427627,0.000014897081,0.00005939773,0.000054799784],"category_scores_gemma":[0.0000067472347,0.00009248925,0.000030874013,0.000066143766,0.000041584564,0.00011495042,0.000011041876,0.00006670281,0.000038395494],"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":[0.00014153708,0.00026983395,0.09374018,0.00020680964,0.00017226518,0.000037852118,0.0002359427,0.017160397,0.8672996,0.00013071344,0.0011174304,0.01948741],"study_design_scores_gemma":[0.0024960986,0.000465452,0.5765688,0.00007365248,0.00013101638,0.00008342068,0.000009564568,0.4170155,0.0024421671,0.00041396244,0.000120148135,0.00018026964],"about_ca_topic_score_codex":0.00001460369,"about_ca_topic_score_gemma":8.815827e-7,"teacher_disagreement_score":0.86485744,"about_ca_system_score_codex":0.00002257621,"about_ca_system_score_gemma":0.000004306494,"threshold_uncertainty_score":0.37716022},"labels":[],"label_agreement":null},{"id":"W2910054127","doi":"10.1109/lra.2019.2891991","title":"Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":378,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs","keywords":"Reinforcement learning; Task (project management); Computer science; Artificial intelligence; Urban search and rescue; Robot; Deep learning; Rescue robot; Search and rescue; Mobile robot; Identification (biology); Frontier; Human–computer interaction; Engineering; Geography","score_opus":0.012679344467080148,"score_gpt":0.21740061789966122,"score_spread":0.20472127343258106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910054127","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":[0.09092907,0.0000562585,0.9076746,0.0005091001,0.00008673487,0.0006675239,5.151452e-7,0.000058146274,0.000018053637],"genre_scores_gemma":[0.9909369,0.00021695312,0.008366577,0.00018291363,0.00004309153,0.00008114498,0.00009371545,0.000027832999,0.000050911774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922615,0.000025017753,0.00026229594,0.00017986986,0.00012997187,0.00017669993],"domain_scores_gemma":[0.99976337,0.00004225873,0.00002572291,0.00011172525,0.000012817951,0.000044082735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001433401,0.00012025171,0.00012868403,0.00013399114,0.00007160639,0.00007257419,0.00004166044,0.000062337625,0.0000035752803],"category_scores_gemma":[0.000005245748,0.0001329402,0.000018746441,0.000095638585,0.000019752213,0.00022228822,0.000010854789,0.0000945374,0.000010324373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000419235,0.0000072315634,0.00037166956,0.00010457299,0.000009954548,2.5617229e-7,0.00024181692,0.9727636,0.023221157,0.0005765147,0.000014641641,0.0026843937],"study_design_scores_gemma":[0.000573962,0.000032224416,0.0010778432,0.00003413088,0.000008903164,8.203211e-7,0.00006223147,0.9962656,0.0014423293,0.000037581885,0.00031596766,0.00014839927],"about_ca_topic_score_codex":0.000007259315,"about_ca_topic_score_gemma":0.000008689943,"teacher_disagreement_score":0.9000078,"about_ca_system_score_codex":0.000071021204,"about_ca_system_score_gemma":0.0000040308887,"threshold_uncertainty_score":0.54211444},"labels":[],"label_agreement":null},{"id":"W2910938323","doi":"10.1109/lra.2019.2891283","title":"Improving User Performance in Haptics-Based Rehabilitation Exercises by Colocation of User's Visual and Motor Axes via a Three-Dimensional Augmented-Reality Display","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Augmented Reality Applications","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Augmented reality; Virtual reality; Rehabilitation; Haptic technology; Task (project management); Human–computer interaction; Computer science; Cognition; User interface; User experience design; Physical medicine and rehabilitation; Simulation; Psychology; Medicine; Physical therapy; Engineering","score_opus":0.006095367838473986,"score_gpt":0.22905483794011047,"score_spread":0.2229594701016365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910938323","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":[0.5509729,0.000009985148,0.4463342,0.0022360848,0.00005480601,0.000349019,0.000007300667,0.000034607496,0.0000011286356],"genre_scores_gemma":[0.976943,0.0000036488582,0.022658966,0.00029310992,0.000008547532,0.000044545734,0.00003264021,0.000008741723,0.000006761873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987952,0.000058033223,0.0003890258,0.0003285339,0.00026941625,0.00015976827],"domain_scores_gemma":[0.9991248,0.0002400627,0.00024884648,0.00025163972,0.000082340004,0.000052257015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032833414,0.00013494995,0.00017936894,0.00014119403,0.00009020987,0.00006748969,0.00014393585,0.00006597072,0.0000016826431],"category_scores_gemma":[0.000021476104,0.0001350178,0.00002613238,0.00023692059,0.0000996513,0.0005336623,0.00005065753,0.00008764796,0.0000032583162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056033787,0.00047302537,0.08109928,0.00074676605,0.00003283362,6.2526146e-7,0.0004993622,0.50808,0.39200377,0.0044853576,0.00035061035,0.012172351],"study_design_scores_gemma":[0.00045551933,0.00010303556,0.1598698,0.000060792296,0.00001040158,9.773521e-7,0.000008352436,0.8370647,0.0022422099,0.000056550663,0.000011274475,0.00011640693],"about_ca_topic_score_codex":0.00017856414,"about_ca_topic_score_gemma":0.000058821322,"teacher_disagreement_score":0.42597017,"about_ca_system_score_codex":0.00008485129,"about_ca_system_score_gemma":0.000043039683,"threshold_uncertainty_score":0.55058664},"labels":[],"label_agreement":null},{"id":"W2911009923","doi":"10.1109/lra.2019.2893440","title":"Design of a Compliant Mechanical Device for Upper Leg Rehabilitation","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pulley; Gait; Actuator; Wearable computer; Mechanism (biology); Computer science; Trajectory; Transparency (behavior); Rehabilitation; Simulation; Control theory (sociology); Engineering; Physical medicine and rehabilitation; Control (management); Structural engineering; Artificial intelligence; Physics; Physical therapy; Computer security","score_opus":0.012651158619170074,"score_gpt":0.22774500344252627,"score_spread":0.2150938448233562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911009923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21543786,0.000035135818,0.7815985,0.0018638367,0.00039288416,0.0005662837,0.000006795476,0.00008544775,0.000013223849],"genre_scores_gemma":[0.7704791,0.000008132522,0.22926219,0.00017467482,0.000018827368,0.00001921113,0.000007654286,0.000021080854,0.000009141221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929386,0.000027231948,0.0002992618,0.00012908946,0.00011757577,0.00013298579],"domain_scores_gemma":[0.99927586,0.0004170962,0.000057602065,0.00013826335,0.00007351373,0.000037664082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022084257,0.000105445906,0.00017604705,0.00008610106,0.000032594195,0.000023198274,0.00006010572,0.000061385836,0.000004126893],"category_scores_gemma":[0.000029459017,0.00010061688,0.000052437015,0.00008619593,0.00003306374,0.00008913488,0.0000069502826,0.00005483851,0.000009920143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005625036,0.000016427575,0.0001311423,0.0002801333,0.000017690016,7.598491e-8,0.000250323,0.8885361,0.106441386,0.0034117296,0.0003695147,0.00053984666],"study_design_scores_gemma":[0.00043889153,0.00013270816,0.0013446551,0.00006867779,0.000019202167,0.0000015308625,0.000050809493,0.9941972,0.0026777212,0.0007732291,0.00015922403,0.00013616054],"about_ca_topic_score_codex":0.0000012742132,"about_ca_topic_score_gemma":3.5936586e-7,"teacher_disagreement_score":0.55504125,"about_ca_system_score_codex":0.00002893835,"about_ca_system_score_gemma":0.0000090518515,"threshold_uncertainty_score":0.41030374},"labels":[],"label_agreement":null},{"id":"W2911143806","doi":"10.1109/lra.2019.2893606","title":"Application of a Redundant Haptic Interface in Enhancing Soft-Tissue Stiffness Discrimination","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Haptic technology; Teleoperation; Redundancy (engineering); Computer science; Interface (matter); Kinematics; Simulation; Virtual reality; Stiffness; Virtual machine; Human–computer interaction; User interface; Robot; Artificial intelligence; Engineering","score_opus":0.00683571181016463,"score_gpt":0.22426927139917838,"score_spread":0.21743355958901375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911143806","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":[0.5306568,0.00003187823,0.46855453,0.00023106157,0.0002352014,0.00018536136,0.0000010639473,0.00006063784,0.000043474713],"genre_scores_gemma":[0.99856293,0.000006416378,0.0013040391,0.000040026833,0.000027516156,0.0000144112255,0.0000070375195,0.000014445041,0.000023200686],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993602,0.000016713157,0.0003041299,0.000114627335,0.00010772945,0.00009662266],"domain_scores_gemma":[0.9997483,0.00003381118,0.00005836207,0.00011266141,0.000023675331,0.000023197928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111370406,0.0000879039,0.0001460599,0.00012245022,0.000016603512,0.000034187186,0.00004935193,0.000041310697,0.0000063385614],"category_scores_gemma":[0.000007337205,0.000090693975,0.000013560455,0.00011107652,0.00001480852,0.00015442392,0.0000061659143,0.000056683384,0.000022270238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012515114,0.000007237281,0.00018804151,0.00017495189,0.0000052323517,2.1184955e-7,0.00088582316,0.45816183,0.5329272,0.0004984345,0.00002017518,0.0071296087],"study_design_scores_gemma":[0.0002606576,0.000010960948,0.008603312,0.000119800105,0.000006880438,0.0000034420702,0.00018483946,0.9674304,0.023168951,0.0000186676,0.00007946104,0.000112619105],"about_ca_topic_score_codex":0.00002975077,"about_ca_topic_score_gemma":0.00003617293,"teacher_disagreement_score":0.50975823,"about_ca_system_score_codex":0.000050487815,"about_ca_system_score_gemma":0.0000055327864,"threshold_uncertainty_score":0.3698393},"labels":[],"label_agreement":null},{"id":"W2911465809","doi":"10.1109/lra.2019.2897143","title":"It Would Make Me Happy if You Used My Guess: Comparing Robot Persuasive Strategies in Social Human–Robot Interaction","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Compliance (psychology); Exploratory research; Warrant; Human–robot interaction; Psychology; Persuasive technology; Social psychology; Human–computer interaction; Social robot; Computer science; Applied psychology; Cognitive psychology; Artificial intelligence; Persuasion; Mobile robot; Robot control; Sociology","score_opus":0.05851957109241265,"score_gpt":0.3643572810340588,"score_spread":0.30583770994164616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911465809","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":[0.95051455,0.000025920352,0.015652893,0.015641183,0.0046946686,0.0006033882,0.000006057379,0.00026818548,0.012593157],"genre_scores_gemma":[0.9956184,0.000004938016,0.00037891857,0.0027877996,0.00044571914,0.000033264878,0.000045221816,0.000040516086,0.00064522994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981671,0.00017133114,0.00055812724,0.00044458045,0.00026882743,0.0003900183],"domain_scores_gemma":[0.99919754,0.00013209111,0.00033572747,0.0001975844,0.00006813229,0.000068938505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020943713,0.00027916487,0.0004222676,0.0003227068,0.00026509236,0.00032569098,0.00016481987,0.00030234185,0.0006484353],"category_scores_gemma":[0.000013378294,0.00031122513,0.00012841771,0.00022593432,0.00009086497,0.0004737249,0.000033676053,0.00069538073,0.00035237323],"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":[0.00042631725,0.001085632,0.057883397,0.00032741137,0.00088490447,0.00009873461,0.16444455,0.43919736,0.21220793,0.08384371,0.034547884,0.0050521693],"study_design_scores_gemma":[0.011737498,0.0005558527,0.6882217,0.00086363015,0.00033129178,0.00011257296,0.15990417,0.12115309,0.0013097719,0.0015280147,0.011098957,0.0031834682],"about_ca_topic_score_codex":0.0002628679,"about_ca_topic_score_gemma":0.00018381845,"teacher_disagreement_score":0.6303383,"about_ca_system_score_codex":0.0001921792,"about_ca_system_score_gemma":0.00002612661,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W2912178342","doi":"10.1109/lra.2019.2897168","title":"Toward Robot-Assisted Photoacoustic Imaging: Implementation Using the da Vinci Research Kit and Virtual Fixtures","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Transducer; Photoacoustic imaging in biomedicine; Computer science; Robot; Ultrasonic sensor; Prostate gland; Computer vision; Acoustics; Artificial intelligence; Biomedical engineering; Prostate; Optics; Medicine; Physics","score_opus":0.036236875252374054,"score_gpt":0.3022668317605973,"score_spread":0.26602995650822325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912178342","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":[0.80666465,0.00013327318,0.19115049,0.0010482919,0.00048189587,0.00032624105,0.000011241915,0.00013310074,0.00005083655],"genre_scores_gemma":[0.99698865,0.000029120341,0.0023390572,0.00048426792,0.00009153439,0.000009362181,0.000013312508,0.000031046722,0.0000136307735],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988577,0.00006307028,0.00023184142,0.00020687003,0.0002989335,0.0003415908],"domain_scores_gemma":[0.9994641,0.0002097544,0.000045839857,0.00016798846,0.000050149556,0.000062165105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038582433,0.00015753628,0.00014532293,0.0001379521,0.0002121212,0.00028256953,0.00010699519,0.00003780891,0.000026150135],"category_scores_gemma":[0.000016940243,0.00013242495,0.000027735427,0.00018363616,0.00010042504,0.000232599,0.000031729127,0.00024553464,0.0000063955636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030524413,0.0000062368613,0.00065135304,0.00008631572,0.000033555047,0.000005503369,0.0009250697,0.4758928,0.5158265,0.00012053787,0.0011101116,0.005338919],"study_design_scores_gemma":[0.0004280956,0.000012404514,0.008915481,0.000054526183,0.00004104764,0.000051610165,0.0011893819,0.98514766,0.0037987612,0.000041097053,0.0001347615,0.00018514969],"about_ca_topic_score_codex":0.00008246418,"about_ca_topic_score_gemma":0.0000038414314,"teacher_disagreement_score":0.51202774,"about_ca_system_score_codex":0.00011099266,"about_ca_system_score_gemma":0.000025683481,"threshold_uncertainty_score":0.54001325},"labels":[],"label_agreement":null},{"id":"W2913653683","doi":"10.1109/lra.2019.2894504","title":"Cable-Less, Magnetically Driven Forceps for Minimally Invasive Surgery","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Invasive surgery; Surgical robot; Forceps; Robot end effector; Decoupling (probability); Magnet; Surgical instrument; Deflection (physics); Robot; Computer science; Wrist; Electromagnetic coil; Simulation; Biomedical engineering; Engineering; Mechanical engineering; Control engineering; Physics; Surgery; Artificial intelligence; Electrical engineering","score_opus":0.011249607800001516,"score_gpt":0.21352459374115448,"score_spread":0.20227498594115298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913653683","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":[0.8700864,0.00002288799,0.12532148,0.002831079,0.0006035357,0.0005786526,0.00004797552,0.00005489444,0.000453134],"genre_scores_gemma":[0.9805781,0.000009140403,0.017442029,0.0009975093,0.0002767452,0.000025332212,0.00010909771,0.00003410462,0.00052794145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989519,0.000026121419,0.00031696938,0.00026701376,0.00012735846,0.00031063647],"domain_scores_gemma":[0.9991286,0.00037595897,0.00015019315,0.00018445187,0.00007346539,0.000087352346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011601686,0.00018457607,0.0002835182,0.000085437816,0.00010301317,0.000108914486,0.000099679746,0.00005376921,0.00011611774],"category_scores_gemma":[0.000008662605,0.00018139422,0.00012927565,0.0000887151,0.00004768744,0.00014893535,0.000024595536,0.000081775426,0.000055062865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008641854,0.00028837033,0.16375557,0.00072506117,0.00039149704,0.0000072765006,0.0008048492,0.35027957,0.3426619,0.032432623,0.09462217,0.013944694],"study_design_scores_gemma":[0.009624203,0.0007421389,0.07032772,0.0011162705,0.0008687413,0.000023374772,0.0013477325,0.82399035,0.061950408,0.0077237994,0.017498447,0.004786821],"about_ca_topic_score_codex":0.000022631206,"about_ca_topic_score_gemma":0.0000032718444,"teacher_disagreement_score":0.47371075,"about_ca_system_score_codex":0.000019097302,"about_ca_system_score_gemma":0.00007494371,"threshold_uncertainty_score":0.7397042},"labels":[],"label_agreement":null},{"id":"W2913737986","doi":"10.1109/lra.2019.2897342","title":"Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preference learning; Equivalence (formal languages); Robustness (evolution); Robot; Intuition; Bayesian probability; Bayesian inference; Multi-task learning; Ranking (information retrieval)","score_opus":0.02587517122558289,"score_gpt":0.2486044096513348,"score_spread":0.2227292384257519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913737986","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":[0.11774833,0.000026050258,0.8805028,0.0006104687,0.0003512247,0.00033598873,0.000001211967,0.00020475315,0.000219164],"genre_scores_gemma":[0.98641574,0.000020862206,0.013208992,0.00011626127,0.00009709349,0.000010475942,0.000024567536,0.000029470553,0.000076562224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993474,0.00003465737,0.0001830822,0.0001660121,0.000105555,0.00016332735],"domain_scores_gemma":[0.9996218,0.000089655485,0.000088562745,0.00009785123,0.0000582747,0.00004386336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008689225,0.000119892116,0.00013029124,0.00011228834,0.00014529975,0.00008406456,0.000049247854,0.000054460503,0.00001375644],"category_scores_gemma":[0.000018386358,0.00013446965,0.000031371605,0.00019401808,0.00002145416,0.00028366398,0.0000060701095,0.00012775467,0.000015673768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037009108,0.0000032053708,0.0002722964,0.0000441855,0.00001699679,2.824371e-7,0.000617991,0.92646825,0.07079557,0.00080781034,0.00017884497,0.0007908466],"study_design_scores_gemma":[0.0002741754,0.000017601496,0.0024083636,0.00005013591,0.000015692694,0.0000024297988,0.0002914233,0.99496233,0.0010000968,0.000048624355,0.0007613013,0.0001678242],"about_ca_topic_score_codex":0.0000020276993,"about_ca_topic_score_gemma":9.0629135e-7,"teacher_disagreement_score":0.86866736,"about_ca_system_score_codex":0.000078321194,"about_ca_system_score_gemma":0.000011450519,"threshold_uncertainty_score":0.54835135},"labels":[],"label_agreement":null},{"id":"W2914856714","doi":"10.1109/lra.2019.2896444","title":"Contactless Robotic Micromanipulation in Air Using a Magneto-Acoustic System","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":41,"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":"Acoustic levitation; Teleoperation; Workspace; Millimeter; Computer science; Process (computing); Orientation (vector space); Automation; Scalability; Levitation; Acoustics; Mechanical engineering; Magnet; Engineering; Artificial intelligence; Robot; Physics; Optics","score_opus":0.010919025415193274,"score_gpt":0.19508908539461814,"score_spread":0.18417005997942487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914856714","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":[0.8432858,0.00012963204,0.15523534,0.00019170994,0.0004925438,0.00017796386,0.0000013557276,0.0004582779,0.000027384098],"genre_scores_gemma":[0.99463797,0.000015515774,0.005171062,0.000110250425,0.000027669304,0.0000014889767,0.0000058632722,0.000021341297,0.000008852649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933904,0.000015397296,0.00023219551,0.00014477759,0.0000832418,0.00018535095],"domain_scores_gemma":[0.9997585,0.000024436942,0.000044104392,0.00013783244,0.000013694376,0.000021446225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007311413,0.00013584603,0.00018667945,0.00016505658,0.000032766522,0.000030239888,0.000059409685,0.00009834505,0.000001976838],"category_scores_gemma":[0.0000032622174,0.00013790633,0.000023563238,0.00014289074,0.000023568542,0.000111226575,0.0000132975065,0.00009968461,0.000018640067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.221765e-7,0.000002804635,0.0006968549,0.0001566304,0.0000049760133,0.000005386522,0.000034142882,0.5564032,0.44236133,0.00009777742,0.0000975687,0.00013842186],"study_design_scores_gemma":[0.0002765806,0.000011105436,0.0064682676,0.00021912977,0.000017590066,0.000034538312,0.00011143382,0.9857181,0.006935416,0.000007649389,0.000018451157,0.00018177438],"about_ca_topic_score_codex":0.000030475967,"about_ca_topic_score_gemma":0.0000041546605,"teacher_disagreement_score":0.4354259,"about_ca_system_score_codex":0.00014319264,"about_ca_system_score_gemma":0.000006423017,"threshold_uncertainty_score":0.5623657},"labels":[],"label_agreement":null},{"id":"W2928530452","doi":"10.1109/lra.2020.2967659","title":"Learning Matchable Image Transformations for Long-Term Metric Visual Localization","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Metric (unit); Feature (linguistics); Pipeline (software); Pattern recognition (psychology); Context (archaeology); Bridging (networking); Feature extraction","score_opus":0.011859173972142706,"score_gpt":0.2660730566405579,"score_spread":0.2542138826684152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928530452","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":[0.13934897,0.000004816948,0.8534095,0.0067530517,0.000042180014,0.00027597297,0.000008829985,0.000063618034,0.000093051116],"genre_scores_gemma":[0.9950754,0.0000033996453,0.0039053706,0.000690057,0.00013768316,0.000055469034,0.00009977086,0.000010215168,0.00002264633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939144,0.000013565104,0.00018927117,0.00015650046,0.000100691745,0.00014852619],"domain_scores_gemma":[0.99970126,0.00004534007,0.00008207256,0.000049042625,0.000046206635,0.00007605111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006783778,0.00008948665,0.00010044036,0.000064383574,0.0003140015,0.00015152183,0.0000677424,0.000018747529,0.000033084852],"category_scores_gemma":[0.0000049403316,0.00009104863,0.000049246915,0.00035371474,0.000032368218,0.00034483403,0.000008526378,0.000064036576,0.000022663304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011994687,0.00014323244,0.02537742,0.00018200271,0.00007512883,5.997359e-7,0.0031228196,0.85428125,0.053187855,0.013310161,0.0034886017,0.04681895],"study_design_scores_gemma":[0.00040794502,0.00005419568,0.0038933717,0.000011591737,0.00004213655,4.0825793e-7,0.00016716552,0.9906119,0.003915048,0.00020978534,0.0005169031,0.0001695671],"about_ca_topic_score_codex":0.0000051779116,"about_ca_topic_score_gemma":4.952661e-7,"teacher_disagreement_score":0.8557264,"about_ca_system_score_codex":0.000010632442,"about_ca_system_score_gemma":0.000015903135,"threshold_uncertainty_score":0.37128553},"labels":[],"label_agreement":null},{"id":"W2949138173","doi":"10.1109/lra.2019.2923368","title":"Efficient Autonomous Robotic Exploration With Semantic Road Map in Indoor Environments","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Construct (python library); Graph; Semantic mapping; Process (computing); Robot; Road map; Artificial intelligence; Theoretical computer science; Geography","score_opus":0.01045855610812295,"score_gpt":0.20779849621965393,"score_spread":0.19733994011153097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949138173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24525867,0.000019828809,0.75025403,0.003534771,0.0004766051,0.0003371806,5.8186197e-7,0.000101555815,0.000016785722],"genre_scores_gemma":[0.8842375,0.0000025338995,0.115053706,0.00057389395,0.000029451903,0.000016808046,0.000008475404,0.000016265283,0.00006134909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984512,0.00007795935,0.00031961224,0.00045903467,0.00036575718,0.00032643363],"domain_scores_gemma":[0.99929243,0.000052725594,0.00017054577,0.00039328123,0.000012459132,0.00007857973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002389702,0.00020429972,0.00023451896,0.0002328555,0.00007333503,0.00017567286,0.00027376768,0.0000629903,0.0000025245126],"category_scores_gemma":[0.0000061084993,0.00018610577,0.000025819156,0.0002564314,0.00004105772,0.00038112194,0.00005812522,0.00015487421,0.00017719196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002993855,0.000050356884,0.0025861203,0.000026493703,0.000010620579,0.000027279084,0.00071030384,0.9924568,0.0022108601,0.00029045186,0.000036812085,0.0015909072],"study_design_scores_gemma":[0.0006645141,0.000093312796,0.02932518,0.0001060769,0.000008197008,0.000019641015,0.000027055567,0.9692385,0.00022616389,0.00003635592,0.000014648857,0.0002403529],"about_ca_topic_score_codex":0.000019648687,"about_ca_topic_score_gemma":0.0000012636254,"teacher_disagreement_score":0.63897884,"about_ca_system_score_codex":0.00011493224,"about_ca_system_score_gemma":0.00003069361,"threshold_uncertainty_score":0.75891733},"labels":[],"label_agreement":null},{"id":"W2954663339","doi":"10.1109/lra.2019.2924852","title":"Design and Control of a Multifunctional Ankle Exoskeleton Powered by Magnetorheological Actuators to Assist Walking, Jumping, and Landing","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Theratechnologies (Canada); Institut interdisciplinaire d'innovation technologique","funders":"","keywords":"Exoskeleton; Jumping; Magnetorheological fluid; Clutch; Torque; Actuator; Ankle; Powered exoskeleton; Simulation; Computer science; Engineering; Control theory (sociology); Automotive engineering; Control engineering; Damper; Artificial intelligence; Control (management); Physics","score_opus":0.005397410413065643,"score_gpt":0.19622293580677835,"score_spread":0.1908255253937127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954663339","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":[0.48463944,0.000095373434,0.51418024,0.0006263187,0.00017682643,0.00021842381,0.000005907482,0.00004808383,0.000009369642],"genre_scores_gemma":[0.97864044,0.000028284778,0.020972775,0.00029055862,0.000017863184,0.0000072245903,0.0000064707024,0.000014954818,0.000021419815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931055,0.00003442896,0.00022034028,0.00016576167,0.00012333992,0.00014559376],"domain_scores_gemma":[0.99957633,0.00018197225,0.000052003696,0.00007900403,0.000028230583,0.000082461345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017801081,0.00012849775,0.00018911935,0.00009202121,0.000050275343,0.00004897627,0.000037070313,0.000073250696,0.000010107513],"category_scores_gemma":[0.000023276814,0.00011540022,0.000021581205,0.000068095935,0.000054561457,0.00007635866,0.000013153563,0.00007563071,0.0000033113881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034971777,0.000045699304,0.019653225,0.0002980894,0.000091443806,0.0000018905153,0.0008398809,0.6594232,0.30987272,0.0010570312,0.0016380269,0.0070438436],"study_design_scores_gemma":[0.0010691017,0.00018869426,0.029133582,0.00006450313,0.00002984384,0.000007863519,0.000041810716,0.9676852,0.0011070499,0.00011013367,0.00031963765,0.00024257448],"about_ca_topic_score_codex":0.000003755798,"about_ca_topic_score_gemma":4.799057e-7,"teacher_disagreement_score":0.494001,"about_ca_system_score_codex":0.000023260154,"about_ca_system_score_gemma":0.0000047480667,"threshold_uncertainty_score":0.47058842},"labels":[],"label_agreement":null},{"id":"W2955306689","doi":"10.1109/lra.2019.2926666","title":"Intelligent Machining Monitoring Using Sound Signal Processed With the Wavelet Method and a Self-Organizing Neural Network","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SIGNAL (programming language); Wavelet; Computer science; Artificial neural network; Waviness; Artificial intelligence; Microphone; Pattern recognition (psychology); Acoustics; Engineering; Sound pressure","score_opus":0.010748041303294367,"score_gpt":0.23624830294129234,"score_spread":0.22550026163799797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955306689","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":[0.37718362,0.00013073487,0.62213916,0.00015676778,0.00012915803,0.000106740175,2.980849e-7,0.0001461798,0.0000073568954],"genre_scores_gemma":[0.8141723,0.000028317272,0.18549336,0.00013494618,0.00013046277,0.0000031171787,0.000001986442,0.000033514836,0.0000019750987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993477,0.000022122713,0.00014933584,0.000160738,0.00011888515,0.00020123878],"domain_scores_gemma":[0.9996902,0.00009262987,0.00007212525,0.0000771894,0.000029645555,0.00003825704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013847387,0.00015422248,0.00013525205,0.00004042086,0.00018634897,0.00019533552,0.000057039553,0.000036139223,0.0000018061144],"category_scores_gemma":[0.0000031933794,0.000117216274,0.000012559673,0.00017414834,0.000015809057,0.0002931721,0.000017488657,0.00016085838,6.6996404e-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":[0.000003265499,0.0000030281938,0.0013223729,0.00012792829,0.000029973946,0.0000011567674,0.0008876451,0.9920826,0.0031837749,0.00004050399,0.0000047686162,0.002312998],"study_design_scores_gemma":[0.00016720034,0.000020734751,0.0004831446,0.000077408076,0.000039349274,0.00002460697,0.00013553609,0.99787116,0.0009497521,0.000049415783,0.000016236942,0.00016546262],"about_ca_topic_score_codex":0.0000025821728,"about_ca_topic_score_gemma":7.169143e-7,"teacher_disagreement_score":0.4369887,"about_ca_system_score_codex":0.000030725354,"about_ca_system_score_gemma":0.000007880602,"threshold_uncertainty_score":0.47799408},"labels":[],"label_agreement":null},{"id":"W2957186405","doi":"10.1109/lra.2019.2928756","title":"Kinematically Redundant Hybrid Robots With Simple Singularity Conditions and Analytical Inverse Kinematic Solutions","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workspace; Gravitational singularity; Singularity; Simple (philosophy); Kinematics; Notation; Inverse; Robot; Mathematics; Algorithm; Pure mathematics; Computer science; Algebra over a field; Discrete mathematics; Artificial intelligence; Mathematical analysis; Geometry; Physics; Arithmetic","score_opus":0.009008155456594353,"score_gpt":0.2045204944663449,"score_spread":0.19551233900975054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957186405","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":[0.29618776,0.000009878962,0.7017464,0.001271383,0.00014865478,0.00026429608,0.000014018988,0.00019542004,0.00016215624],"genre_scores_gemma":[0.82351017,0.00001603392,0.17573754,0.00055159046,0.00004226375,0.000010912806,0.000059653346,0.000038553466,0.000033289143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896693,0.00002529723,0.00030642783,0.00021163614,0.00019880438,0.00029089503],"domain_scores_gemma":[0.99943686,0.000091370646,0.000056282744,0.0002257392,0.000039907056,0.00014987055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013100711,0.00019720387,0.00027898932,0.00011944941,0.000137419,0.00013697194,0.00006436076,0.00005563556,0.000039808747],"category_scores_gemma":[0.000019590227,0.00017681526,0.00003738051,0.00013441715,0.00009515011,0.00019857475,0.000023873728,0.00015404429,0.000025858746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018872857,0.000021169879,0.00014652686,0.00022026201,0.000052658736,0.000010926706,0.00006072124,0.9679865,0.004121674,0.026662547,0.0006366001,0.000078517696],"study_design_scores_gemma":[0.00042536992,0.00004354403,0.0020735022,0.00009691201,0.00009038906,0.00007674955,0.00003511566,0.99441165,0.000035838268,0.00245629,0.000012130742,0.00024253062],"about_ca_topic_score_codex":0.000007712419,"about_ca_topic_score_gemma":0.000011329593,"teacher_disagreement_score":0.5273224,"about_ca_system_score_codex":0.00004304948,"about_ca_system_score_gemma":0.000016667176,"threshold_uncertainty_score":0.72103167},"labels":[],"label_agreement":null},{"id":"W2963945072","doi":"10.1109/lra.2019.2931221","title":"Haptic Interface for Handshake Emulation","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Motor Control and Adaptation","field":"Neuroscience","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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Handshake; Haptic technology; Trajectory; Interface (matter); Impedance control; Simulation; Hexapod; Computer science; Stiffness; Emulation; Robot; Control theory (sociology); Engineering; Control (management); Artificial intelligence; Asynchronous communication; Physics","score_opus":0.0233205542543973,"score_gpt":0.255150766532253,"score_spread":0.2318302122778557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963945072","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":[0.5846262,0.0000058570417,0.41156447,0.0029181193,0.00044080932,0.00032978284,0.000004133867,0.00006148203,0.000049146634],"genre_scores_gemma":[0.99617016,0.0000030373342,0.0018685613,0.0016433507,0.0000624743,0.000012864572,0.00000360918,0.0000106477155,0.00022531336],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939656,0.00002354944,0.00015153321,0.00019543542,0.00011082664,0.00012211232],"domain_scores_gemma":[0.9996341,0.00013933613,0.00008671519,0.0000896599,0.000019104373,0.000031119293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007283201,0.00008051012,0.00009291407,0.00005708188,0.000076990684,0.000090048496,0.000051865725,0.000029720592,0.000011906598],"category_scores_gemma":[0.00004695628,0.00007509373,0.000032149146,0.0000580636,0.00001789326,0.00021574355,0.0000065789545,0.000039592658,0.0000454355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010286372,0.000008422874,0.000052698153,0.000026104402,0.0000021923902,2.5031846e-7,0.00012199069,0.12661275,0.8692753,0.0019871958,0.00008703176,0.0018157983],"study_design_scores_gemma":[0.00070134155,0.00004910347,0.0021404824,0.000024217177,0.000009682403,0.000002689902,0.000009738916,0.97901714,0.017006729,0.00029185408,0.00063512113,0.0001118945],"about_ca_topic_score_codex":0.0000019723577,"about_ca_topic_score_gemma":9.28472e-7,"teacher_disagreement_score":0.8524044,"about_ca_system_score_codex":0.000016199578,"about_ca_system_score_gemma":0.0000060989955,"threshold_uncertainty_score":0.30622336},"labels":[],"label_agreement":null},{"id":"W2964294887","doi":"10.1109/lra.2019.2931133","title":"Quaternion-Based Smooth Trajectory Generator for Via Poses in $\\boldsymbol{S\\;E(3)}$ Considering Kinematic Limits in Cartesian Space","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","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 Toronto","funders":"","keywords":"Quaternion; Trajectory; Kinematics; Generator (circuit theory); Interpolation (computer graphics); Cartesian coordinate system; Position (finance); Orientation (vector space); Inverse kinematics; Singularity; Computer science; Mathematics; Control theory (sociology); Mathematical analysis; Motion (physics); Artificial intelligence; Geometry; Physics; Classical mechanics","score_opus":0.008976841363979953,"score_gpt":0.20188449455500482,"score_spread":0.19290765319102487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964294887","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":[0.50132847,0.00003638977,0.49727106,0.0005181987,0.00038783601,0.00035325755,0.00000336645,0.00008764758,0.000013743574],"genre_scores_gemma":[0.90879154,0.000007645543,0.09070074,0.0003719196,0.00003958385,0.000023242672,0.000013532357,0.000042293235,0.000009506767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990383,0.00002606608,0.00034507122,0.00020163566,0.000117067386,0.00027186333],"domain_scores_gemma":[0.9995506,0.00015129351,0.00006505303,0.00015810221,0.000016211736,0.000058771435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015436679,0.0001903435,0.00028008688,0.00023531736,0.000026646978,0.000060164042,0.000066266985,0.000086882,0.000009073665],"category_scores_gemma":[0.000016824704,0.00020267081,0.000043934466,0.0001308986,0.00001417673,0.0001105018,0.000004695458,0.00010435577,0.00000878179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027064505,0.000014445798,0.001367992,0.0003080289,0.000007382191,0.0000048552724,0.00019281683,0.92501247,0.07222582,0.00038559677,0.000043576445,0.0004343291],"study_design_scores_gemma":[0.0008196759,0.000027502247,0.0034168777,0.00015820611,0.000009425456,0.0000023510206,0.00003174899,0.9926714,0.0025065748,0.000104376006,0.000010322373,0.00024155778],"about_ca_topic_score_codex":0.00002615602,"about_ca_topic_score_gemma":0.000111289555,"teacher_disagreement_score":0.40746304,"about_ca_system_score_codex":0.00008376007,"about_ca_system_score_gemma":0.000019772633,"threshold_uncertainty_score":0.8264676},"labels":[],"label_agreement":null},{"id":"W2965621489","doi":"10.1109/lra.2019.2932575","title":"Deep Active Localization","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology","keywords":"Reinforcement learning; Computer science; Leverage (statistics); Artificial intelligence; Robustness (evolution); Perception; Robot; Convolutional neural network; Differentiable function; Code (set theory); Machine learning; Programming language; Mathematics","score_opus":0.00511683889865528,"score_gpt":0.18629960016440286,"score_spread":0.1811827612657476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965621489","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":[0.1474847,0.000025415495,0.8508781,0.00037314213,0.00056062796,0.00015138197,0.0000015159732,0.00022990607,0.0002951751],"genre_scores_gemma":[0.99643946,0.000037966627,0.0027212251,0.00064621633,0.00006346357,0.000003135893,0.000039283135,0.000029098353,0.000020149631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941355,0.00001603844,0.00016686083,0.00012877017,0.00012970928,0.0001450887],"domain_scores_gemma":[0.9997428,0.000027108526,0.00003403703,0.00011782899,0.0000308294,0.000047369464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041747116,0.000116682815,0.00011507969,0.000091193106,0.000049272054,0.00006145954,0.000043535514,0.000062019644,0.000024729232],"category_scores_gemma":[0.000004692605,0.00012186652,0.000025276882,0.00013942661,0.000017869115,0.00016859817,0.0000053084495,0.00006668476,0.00007368305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015439097,0.000005448215,0.00049106,0.000047337526,0.000015358904,9.623599e-7,0.0001386994,0.98752725,0.008457589,0.0009395875,0.00040112933,0.001974042],"study_design_scores_gemma":[0.00023344428,0.000012159379,0.0016186684,0.000021361402,0.000011978895,0.000002504444,0.000025408133,0.9951397,0.0023915202,0.00006992891,0.0003180792,0.0001552115],"about_ca_topic_score_codex":0.0000039394963,"about_ca_topic_score_gemma":0.0000027916506,"teacher_disagreement_score":0.84895474,"about_ca_system_score_codex":0.000046006266,"about_ca_system_score_gemma":0.0000041423627,"threshold_uncertainty_score":0.49695724},"labels":[],"label_agreement":null},{"id":"W2972941110","doi":"10.1109/lra.2020.2964159","title":"Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":247,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"","keywords":"Trajectory; Model predictive control; Computer science; Motion planning; Motion (physics); Control (management); Control theory (sociology); Robot; Artificial intelligence; Physics","score_opus":0.05669042353869146,"score_gpt":0.2668804501090223,"score_spread":0.2101900265703308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972941110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01024153,0.000022010498,0.9819596,0.00671391,0.00018111759,0.00045358686,0.000108905064,0.0003184331,9.428289e-7],"genre_scores_gemma":[0.46919176,9.2116375e-7,0.52880794,0.0016884259,0.00014203292,0.000022085786,0.00013313898,0.000011809797,0.0000018683711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883586,0.000049354163,0.0002626168,0.000400952,0.0002209198,0.0002303],"domain_scores_gemma":[0.99938434,0.00006501725,0.00017608734,0.00015152314,0.000097175995,0.00012582494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001201398,0.0001839236,0.00020725731,0.00006429468,0.00017866193,0.00014546546,0.00018472274,0.0000659561,1.6437625e-7],"category_scores_gemma":[0.00003873947,0.00016794675,0.0000378987,0.00013304119,0.000038096012,0.00045601226,0.000016532416,0.00012299634,0.0000010558554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000115847815,0.000041773663,0.00015763707,0.000022619399,0.000033407254,0.0000061343308,0.00081247126,0.98480606,0.013065102,0.000113097056,0.00034440245,0.0005857313],"study_design_scores_gemma":[0.0016143889,0.00014715883,0.0025263815,0.000033678625,0.000036063488,0.000009304936,0.000019521507,0.9949484,0.00045305473,0.000010540805,0.0000025798868,0.00019895252],"about_ca_topic_score_codex":0.0000017592303,"about_ca_topic_score_gemma":3.928038e-7,"teacher_disagreement_score":0.45895025,"about_ca_system_score_codex":0.000049707087,"about_ca_system_score_gemma":0.0000401708,"threshold_uncertainty_score":0.68486696},"labels":[],"label_agreement":null},{"id":"W2976416243","doi":"10.1109/lra.2019.2944060","title":"Homotopic Approach for Robot Allocation Optimization Coupled With Path Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Path (computing); Task (project management); Computer science; Mathematical optimization; Motion planning; Robot; Process (computing); Transformation (genetics); Distributed computing; Homotopy; Artificial intelligence; Mathematics; Engineering; Computer network","score_opus":0.010789557475473064,"score_gpt":0.2124348639474545,"score_spread":0.20164530647198145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976416243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007003454,0.000010593276,0.99019897,0.0015806081,0.00028649892,0.0006677837,0.0000022685851,0.00019283463,0.000057007648],"genre_scores_gemma":[0.21737403,0.0000027162682,0.7819037,0.00055944937,0.00003918882,0.00003201413,0.000043239786,0.000011809398,0.000033857636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893445,0.00003414746,0.00021857746,0.0003669472,0.00022400651,0.0002218516],"domain_scores_gemma":[0.9993245,0.00006452764,0.00016514125,0.00029025032,0.00009188963,0.00006367054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002124613,0.0001540145,0.00017856438,0.00010083347,0.00010890732,0.0002041856,0.00021935887,0.00006157099,0.0000020041857],"category_scores_gemma":[0.000012260211,0.00013749764,0.000027783983,0.00018870636,0.000056022367,0.0004062647,0.000020758747,0.00007426554,0.0000054634484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002495729,0.000025998504,0.0002456936,0.000049593673,0.000018491795,0.0000011861173,0.00019535207,0.994788,0.0013314002,0.0015745154,0.00006701564,0.001700293],"study_design_scores_gemma":[0.0007717227,0.000080908365,0.0016439953,0.000038723323,0.0000138735895,0.000023185636,0.000018293538,0.99707216,0.00011573309,0.00002474918,0.0000048205957,0.00019185235],"about_ca_topic_score_codex":0.0000046682308,"about_ca_topic_score_gemma":7.912613e-8,"teacher_disagreement_score":0.21037059,"about_ca_system_score_codex":0.000039359456,"about_ca_system_score_gemma":0.000043183947,"threshold_uncertainty_score":0.5606991},"labels":[],"label_agreement":null},{"id":"W2978352220","doi":"10.1109/lra.2019.2945468","title":"Workspace Determination and Feedback Control of a Pick-and-Place Parallel Robot: Analysis and Experiments","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","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":"McGill University","funders":"","keywords":"Wrench; Workspace; Robot; Trajectory; SMT placement equipment; Generator (circuit theory); Simulation; Computer science; Tracking (education); Control engineering; Control (management); Engineering; Control theory (sociology); Mechanical engineering; Artificial intelligence","score_opus":0.004830866074782643,"score_gpt":0.20141196221892393,"score_spread":0.1965810961441413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978352220","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":[0.36560237,0.00012982615,0.63373166,0.0002940328,0.00007301298,0.00012276268,0.0000021574326,0.000032347638,0.000011811884],"genre_scores_gemma":[0.88951033,0.00012393315,0.11017046,0.00013068788,0.000012031806,0.000004935687,0.0000050882395,0.000013111017,0.000029413208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993819,0.000019927775,0.0002019025,0.00016227592,0.00010750574,0.00012650157],"domain_scores_gemma":[0.9996741,0.00006548571,0.00007292057,0.00010744344,0.00001780161,0.000062271705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000095367366,0.00013155483,0.00025735312,0.00013408835,0.00003667882,0.00006359707,0.000031454012,0.00006717254,0.0000047919234],"category_scores_gemma":[0.0000050886783,0.00013003671,0.000026873306,0.00011445782,0.000032182328,0.00013919495,0.000013072289,0.00005891398,0.0000011228939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051365137,0.0000065341524,0.002870305,0.000079155216,0.00011073416,8.822314e-7,0.0003010153,0.9815135,0.012837722,0.0004499157,0.000011622185,0.0018134486],"study_design_scores_gemma":[0.0006719809,0.00002174163,0.017927527,0.000030790536,0.00014236828,0.0000040169966,0.00006312741,0.9808168,0.00014299946,0.00004105664,0.0000025914767,0.00013504678],"about_ca_topic_score_codex":0.000008983765,"about_ca_topic_score_gemma":0.000005080407,"teacher_disagreement_score":0.52390796,"about_ca_system_score_codex":0.000015462121,"about_ca_system_score_gemma":0.0000027986296,"threshold_uncertainty_score":0.53027433},"labels":[],"label_agreement":null},{"id":"W2988400224","doi":"10.1109/lra.2019.2952998","title":"Model-Based Robotic Cell Aspiration: Tackling Nonlinear Dynamics and Varying Cell Sizes","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":30,"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":"Pipette; Nonlinear system; Position (finance); Controller (irrigation); Dynamics (music); Biological system; Materials science; Control theory (sociology); Computer science; Biomedical engineering; Chemistry; Artificial intelligence; Physics; Acoustics; Engineering; Biology","score_opus":0.007836357286141106,"score_gpt":0.1875631431608129,"score_spread":0.1797267858746718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988400224","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":[0.5170933,0.00018668745,0.4812399,0.0006503754,0.00020408318,0.00011424525,0.000003075449,0.00040721922,0.00010112584],"genre_scores_gemma":[0.92734563,0.000068424175,0.07208381,0.00038353176,0.000032363787,0.0000015117932,0.000020299209,0.000027597456,0.000036814006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992972,0.000009609097,0.00021003248,0.00018710186,0.00010418766,0.0001918803],"domain_scores_gemma":[0.9996784,0.00004437177,0.000051444127,0.00016565603,0.000023025223,0.00003713871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007368436,0.00017099868,0.00017191248,0.00010638707,0.00008158754,0.000100640274,0.00006878167,0.000115177936,0.0000025613951],"category_scores_gemma":[0.0000041372414,0.00017169114,0.00002977761,0.00009993655,0.000043802047,0.00012574557,0.000018993669,0.00014353223,0.000012110535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010033472,0.0000074423942,0.00017999078,0.0001613069,0.000004218878,0.0000016161127,0.000034152632,0.7984061,0.20007922,0.00004757142,0.0001865292,0.00089085626],"study_design_scores_gemma":[0.0003044704,0.000021681139,0.000051994975,0.000045258475,0.000021149326,0.0000024862413,0.000021211108,0.9588692,0.040400498,0.000034111454,0.000019182295,0.00020876282],"about_ca_topic_score_codex":0.000002551161,"about_ca_topic_score_gemma":0.0000010034789,"teacher_disagreement_score":0.41025236,"about_ca_system_score_codex":0.0000468899,"about_ca_system_score_gemma":0.000011824182,"threshold_uncertainty_score":0.7001361},"labels":[],"label_agreement":null},{"id":"W2995122008","doi":"10.1109/lra.2019.2961051","title":"MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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":"Université de Montréal; Computer Research Institute of Montréal","funders":"Toyota Research Institute","keywords":"Intersection (aeronautics); Computer science; Planner; Global Positioning System; Road map; Fuse (electrical); Scope (computer science); Frame (networking); Computer vision; Scale (ratio); Point (geometry); Plan (archaeology); Navigation system; Artificial intelligence; Path (computing); Autonomous system (mathematics); Real-time computing; Geography; Cartography; Engineering; Telecommunications; Computer network; Mathematics","score_opus":0.00475685519518063,"score_gpt":0.19671742578388277,"score_spread":0.19196057058870214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995122008","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":[0.9008499,0.000039764032,0.09580512,0.0010873515,0.0007864387,0.00025919566,0.0000026122334,0.0010098906,0.00015977008],"genre_scores_gemma":[0.99546003,0.000007777587,0.004120834,0.00019469712,0.00005032745,0.000014189243,0.000019444547,0.000027679394,0.00010502749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992708,0.000017731392,0.00024580132,0.00018474385,0.00008848108,0.00019243169],"domain_scores_gemma":[0.9996766,0.000022426355,0.00006147468,0.00018057475,0.000019194846,0.000039717313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009204892,0.00015340724,0.00016750164,0.00011462649,0.00007556244,0.000045726545,0.00007897385,0.00014144616,0.000021657303],"category_scores_gemma":[0.0000027303524,0.00016124747,0.00003965033,0.000098265504,0.00004070033,0.00021912041,0.000014196529,0.00020317946,0.00019611852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024757783,0.000040971372,0.02392637,0.00046829137,0.00016777439,0.000014474501,0.0011994628,0.7498687,0.18224867,0.0030391973,0.0009267126,0.038074654],"study_design_scores_gemma":[0.00057000265,0.000038677463,0.019389916,0.0000803976,0.00002813182,0.000039163024,0.00005542246,0.97299045,0.005529609,0.00022053107,0.0007623728,0.00029533013],"about_ca_topic_score_codex":0.0000033736203,"about_ca_topic_score_gemma":0.0000032888101,"teacher_disagreement_score":0.22312178,"about_ca_system_score_codex":0.00011285139,"about_ca_system_score_gemma":0.0000091989505,"threshold_uncertainty_score":0.6575481},"labels":[],"label_agreement":null},{"id":"W2995271379","doi":"10.1109/lra.2019.2958734","title":"Design and Control of a Piezo Drill for Robotic Piezo-Driven Cell Penetration","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Cellular Mechanics and Interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Penetration (warfare); Breakage; Materials science; Vibration; Puncturing; Cell membrane; Cell; Biomedical engineering; Computer science; Acoustics; Composite material; Engineering; Chemistry; Physics","score_opus":0.007816181921097532,"score_gpt":0.2148963156624155,"score_spread":0.20708013374131795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995271379","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":[0.27253252,0.00008493005,0.7263436,0.00044849692,0.00018669965,0.00037682673,0.0000041422995,0.0000065115446,0.000016311562],"genre_scores_gemma":[0.9876449,0.00005806211,0.0116099315,0.00047551998,0.000058764566,0.000011680841,0.000044784647,0.000015334997,0.00008102747],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993912,0.000032597363,0.00019119513,0.0001996046,0.00006806972,0.00011732812],"domain_scores_gemma":[0.999604,0.00003769471,0.00012553686,0.00013232796,0.00006288955,0.00003758313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010499203,0.00010386632,0.00013491651,0.00004694309,0.000049414866,0.000032360265,0.000047819653,0.00007259205,0.0000049965142],"category_scores_gemma":[0.000009354881,0.00010393984,0.00004688586,0.00003193757,0.0000209993,0.0000110159335,0.000011624705,0.000038563896,0.0000024163444],"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":[0.000022657201,0.000022342014,0.00011848691,0.000044694694,0.000024652356,1.6256612e-7,0.0000306701,0.1267703,0.8722234,0.0002062214,0.00030480718,0.00023163314],"study_design_scores_gemma":[0.0012009668,0.00038391413,0.00035667847,0.000021726215,0.00005521953,0.000007541461,0.000025932788,0.7529413,0.24425043,0.000062029925,0.0005267944,0.00016747932],"about_ca_topic_score_codex":0.0000033636227,"about_ca_topic_score_gemma":0.0000014489708,"teacher_disagreement_score":0.7151124,"about_ca_system_score_codex":0.0000069455673,"about_ca_system_score_gemma":0.000017515567,"threshold_uncertainty_score":0.42385438},"labels":[],"label_agreement":null},{"id":"W2995670801","doi":"10.1109/lra.2020.2965882","title":"The Complex-Step Derivative Approximation on Matrix Lie Groups","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","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":"Derivative (finance); Matrix (chemical analysis); Algorithm; Function (biology); Mathematics; Computer science; Applied mathematics; Mathematical optimization","score_opus":0.02698179790996239,"score_gpt":0.2435789133491406,"score_spread":0.21659711543917823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995670801","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":[0.055647526,0.000050707167,0.93344414,0.009711129,0.00015807526,0.00021199086,0.000002168851,0.00055608666,0.00021819095],"genre_scores_gemma":[0.9709186,0.00005714569,0.026891876,0.001973548,0.000106632746,0.00001886061,0.000010875927,0.000017916527,0.0000045297957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950296,0.00002071925,0.00014507583,0.00009529535,0.000111107365,0.00012482917],"domain_scores_gemma":[0.9997698,0.000057774316,0.000041513063,0.00007505095,0.000017404278,0.00003847185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007942347,0.00010087171,0.0000929707,0.00002993921,0.00012087974,0.000038666403,0.00006445928,0.000034963614,0.0000020455848],"category_scores_gemma":[0.000018368888,0.00008015161,0.000022299575,0.00008628829,0.000038564234,0.000091924,0.0000068253808,0.00010256302,0.000009393843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028577262,0.000018273255,0.00031077143,0.00012978967,0.0000949405,0.000003652931,0.0007761639,0.5258981,0.40779483,0.021653218,0.024353197,0.018938446],"study_design_scores_gemma":[0.00040775107,0.000087604356,0.002844012,0.000024256486,0.00002238831,0.0000018653416,0.00006048859,0.97229,0.017506484,0.0006451216,0.0058805253,0.00022953584],"about_ca_topic_score_codex":2.6955055e-7,"about_ca_topic_score_gemma":5.726721e-7,"teacher_disagreement_score":0.9152711,"about_ca_system_score_codex":0.00002703466,"about_ca_system_score_gemma":0.000002061766,"threshold_uncertainty_score":0.32684878},"labels":[],"label_agreement":null},{"id":"W2999161835","doi":"10.1109/lra.2020.2966411","title":"<i>mROBerTO 2.0</i> – An Autonomous Millirobot With Enhanced Locomotion for Swarm Robotics","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robot; Robotics; Swarm robotics; Robot locomotion; Computer science; Stepper; Artificial intelligence; Swarm behaviour; Motion control; Control engineering; Motion (physics); Stepper motor; Mechanism (biology); Mobile robot; Robot control; Engineering","score_opus":0.016389941884298775,"score_gpt":0.21383265040731253,"score_spread":0.19744270852301377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999161835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05727188,0.000058145044,0.93772906,0.0037122103,0.00028888264,0.0004300514,0.000009828617,0.000454653,0.000045294822],"genre_scores_gemma":[0.87562144,0.000063246676,0.121709846,0.0021867414,0.00024016778,0.000036698224,0.000054133314,0.0000727389,0.000014970393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888736,0.000020405649,0.00031095775,0.00031141852,0.00016647091,0.00030336564],"domain_scores_gemma":[0.9994476,0.000038000144,0.0000695528,0.00019103744,0.000058946094,0.00019486554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007706401,0.00025512357,0.00025503812,0.000057327718,0.00012280326,0.0001283249,0.00014024935,0.00008492734,0.0000065503255],"category_scores_gemma":[0.0000086642085,0.00023869773,0.00004925373,0.00014772345,0.000049368646,0.00027738014,0.000010748779,0.00013119854,0.000012448899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009522683,0.000018401655,0.000012591162,0.00013519872,0.000032781652,0.0000030913855,0.00046614947,0.9438586,0.049177807,0.0004503287,0.00062317,0.0052123456],"study_design_scores_gemma":[0.00038210093,0.00018794587,0.00010443045,0.000038906852,0.00004530017,0.0000076054944,0.00003119262,0.95797586,0.040323492,0.00005889092,0.00047229795,0.00037196776],"about_ca_topic_score_codex":0.0000053570247,"about_ca_topic_score_gemma":0.000008756243,"teacher_disagreement_score":0.8183496,"about_ca_system_score_codex":0.000038649683,"about_ca_system_score_gemma":0.000013394417,"threshold_uncertainty_score":0.9733811},"labels":[],"label_agreement":null},{"id":"W2999237370","doi":"10.1109/lra.2020.2965907","title":"RSL-Net: Localising in Satellite Images From a Radar on the Ground","year":2020,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Computer science; Radar imaging; Context (archaeology); Remote sensing; Overhead (engineering); Bistatic radar; Radar engineering details; Man-portable radar; Real-time computing; Continuous-wave radar; Radar configurations and types; Computer vision; Artificial intelligence; Geography; Telecommunications","score_opus":0.018637900698961565,"score_gpt":0.2115942832220592,"score_spread":0.19295638252309763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999237370","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":[0.31482202,0.00049809826,0.65974545,0.021931682,0.00161182,0.0006271081,0.00007826658,0.00045260607,0.00023296314],"genre_scores_gemma":[0.9904135,0.00045008594,0.0057787974,0.00276825,0.00030131708,0.000011356592,0.00020040362,0.00007141785,0.000004864761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868417,0.00008210806,0.00041975043,0.00033785595,0.00025609828,0.00022002892],"domain_scores_gemma":[0.9993164,0.00019746344,0.00010012194,0.0002978117,0.000022467631,0.00006574661],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013278719,0.0003146637,0.00030944162,0.00012897419,0.0000733965,0.00034038126,0.00016658285,0.0001812552,0.0000103771845],"category_scores_gemma":[0.00002287236,0.0002754016,0.00007120449,0.00015107881,0.00005546162,0.00007473062,0.000046329307,0.0005273145,0.000018476938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000390944,0.000009581972,0.00005733073,0.00009659616,0.000042592863,0.000019157987,0.00043266194,0.9888646,0.007249083,0.00043025467,0.0011183134,0.0016758953],"study_design_scores_gemma":[0.00021060684,0.000010332395,0.0025430443,0.00033986467,0.000035458645,9.80132e-7,0.000044270026,0.99339855,0.0016037781,0.0012196087,0.00025965847,0.0003338229],"about_ca_topic_score_codex":0.0001286263,"about_ca_topic_score_gemma":0.000018572777,"teacher_disagreement_score":0.67559147,"about_ca_system_score_codex":0.000121077086,"about_ca_system_score_gemma":0.000016397782,"threshold_uncertainty_score":0.99996984},"labels":[],"label_agreement":null},{"id":"W3000530836","doi":"10.1109/lra.2020.2966406","title":"Navigation and Control of Unconventional VTOL UAVs in Forward-Flight With Explicit Wind Velocity Estimation","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Control theory (sociology); Takeoff and landing; Attitude control; Robustness (evolution); Kalman filter; Extended Kalman filter; Quaternion; Computer science; Engineering; Flight control surfaces; Control engineering; Aerodynamics; Artificial intelligence; Aerospace engineering; Control (management); Mathematics","score_opus":0.009010112734286008,"score_gpt":0.19903190408734892,"score_spread":0.1900217913530629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000530836","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":[0.45234475,0.0000462016,0.5458957,0.0014373629,0.000043915017,0.00017188607,0.000008361048,0.000043412925,0.000008390191],"genre_scores_gemma":[0.9930416,0.0000030173826,0.006669651,0.00019418675,0.00005345148,0.000006412302,0.000016944963,0.000013592227,0.0000011733796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993736,0.000022759854,0.00025467525,0.000114665876,0.00014326572,0.000090992085],"domain_scores_gemma":[0.9997326,0.00005209669,0.000083311425,0.000049132173,0.000034647965,0.000048188238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008410296,0.000103426886,0.00018642297,0.000055166052,0.000022943575,0.000023686507,0.000032149288,0.000041729236,0.0000017335657],"category_scores_gemma":[0.000011471146,0.00009911038,0.000017942011,0.00009843599,0.000027330321,0.00020515556,0.0000041268313,0.000073115196,0.0000019914937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001897123,0.0000081380085,0.0024923543,0.00018397842,0.000041710184,0.0000039034167,0.0003860781,0.97130483,0.024006343,0.00036752303,0.000060570634,0.0011255968],"study_design_scores_gemma":[0.0011924469,0.000038607985,0.018549608,0.00010069263,0.000020183308,0.000004499145,0.000027222115,0.9790464,0.0008626979,0.000026058382,0.000029137418,0.000102458536],"about_ca_topic_score_codex":0.00000573211,"about_ca_topic_score_gemma":0.000002487465,"teacher_disagreement_score":0.5406968,"about_ca_system_score_codex":0.00002924479,"about_ca_system_score_gemma":0.0000087098315,"threshold_uncertainty_score":0.4041604},"labels":[],"label_agreement":null},{"id":"W3002270926","doi":"10.1109/lra.2020.2969153","title":"A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on <i>SE(3)</i>","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"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":"Jerk; Trajectory; Odometry; Computer science; Acceleration; White noise; Context (archaeology); Artificial intelligence; Noise (video); Computer vision; Robot; Control theory (sociology); Mobile robot; Geography; Physics","score_opus":0.032000275251698886,"score_gpt":0.2572834345899069,"score_spread":0.225283159338208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002270926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007900867,0.000009427788,0.96316266,0.028185414,0.00015712231,0.00029392884,0.00003136412,0.00022545167,0.00003376874],"genre_scores_gemma":[0.64874786,0.0000058996643,0.34245026,0.008506299,0.00015484997,0.000018783065,0.00008423189,0.000016027932,0.000015769694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989744,0.00002718407,0.0002363503,0.00041016747,0.00017979754,0.00017211448],"domain_scores_gemma":[0.9993235,0.00008765664,0.00015419579,0.00029944337,0.000043311986,0.00009187651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011034138,0.00013588033,0.00016444983,0.000053157662,0.00013702245,0.00028425912,0.00043931557,0.000047232275,0.0000034588506],"category_scores_gemma":[0.0000522955,0.00012837723,0.000030731866,0.00014393058,0.000028713366,0.0006791314,0.00005416402,0.00007655046,0.000036661855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004862019,0.00020521208,0.00015403157,0.00072450214,0.000097115735,0.000018255805,0.0026709354,0.59820646,0.054031413,0.02963436,0.044032723,0.27017638],"study_design_scores_gemma":[0.00037440672,0.00010112017,0.0006281796,0.00003821243,0.000014397765,0.0000035543585,0.000003948543,0.99717957,0.0007899802,0.00026609786,0.0004431381,0.00015742527],"about_ca_topic_score_codex":0.0000015043429,"about_ca_topic_score_gemma":4.712084e-7,"teacher_disagreement_score":0.640847,"about_ca_system_score_codex":0.000018099969,"about_ca_system_score_gemma":0.000030777504,"threshold_uncertainty_score":0.5235072},"labels":[],"label_agreement":null},{"id":"W3005386404","doi":"10.1109/lra.2020.2970944","title":"6-DOF Force Sensing for the Master Tool Manipulator of the da Vinci Surgical System","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Joystick; Surgical robot; Haptic technology; Software; Manipulator (device); Interface (matter); Torque; Impedance control; Simulation; Engineering; Robot; Computer science; Embedded system; Artificial intelligence; Operating system; Physics","score_opus":0.02590618164683748,"score_gpt":0.20330250695503238,"score_spread":0.1773963253081949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005386404","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":[0.40579587,0.000064552405,0.58327746,0.008968619,0.0010225485,0.00058448466,0.000010914619,0.0001863995,0.00008917303],"genre_scores_gemma":[0.99836564,0.0000015304815,0.0008787439,0.0005049009,0.00020407735,0.0000042978427,0.0000015819915,0.000015149492,0.00002410237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994548,0.000020025444,0.0002365884,0.00007822525,0.00011753065,0.000092852635],"domain_scores_gemma":[0.99970186,0.00008885057,0.00004756182,0.00011308952,0.0000231577,0.000025478033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090609705,0.000081791484,0.00012027459,0.000013181104,0.00008196498,0.00006951462,0.00006821611,0.000033198634,0.0000021563071],"category_scores_gemma":[0.000007670419,0.000050829483,0.000054024164,0.00006185117,0.000018250232,0.000051622093,0.00000996189,0.000048594768,0.0000031450757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000382155,0.000001726967,0.00010787436,0.00036207275,0.000045659668,0.0000014258934,0.00063057535,0.97597295,0.017014313,0.003149042,0.0016486007,0.0010619179],"study_design_scores_gemma":[0.00025083133,0.0000063735774,0.00039091756,0.0000485075,0.000024020754,0.000015328531,0.00014556307,0.9964098,0.001407268,0.0000015108245,0.0012302453,0.00006965337],"about_ca_topic_score_codex":0.0000035970193,"about_ca_topic_score_gemma":0.0000015848057,"teacher_disagreement_score":0.59256977,"about_ca_system_score_codex":0.000026745476,"about_ca_system_score_gemma":0.000005452615,"threshold_uncertainty_score":0.20727661},"labels":[],"label_agreement":null},{"id":"W3007799021","doi":"10.1109/lra.2020.2976295","title":"37,000 Human-Planned Robotic Grasps With Six Degrees of Freedom","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"GRASP; Artificial intelligence; Robustness (evolution); Computer vision; Computer science; Grippers; Heuristic; Control theory (sociology); Engineering; Control (management)","score_opus":0.021127762959034548,"score_gpt":0.2148213128735358,"score_spread":0.19369354991450127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007799021","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":[0.3466557,0.0000977974,0.6490162,0.0029536947,0.00023193755,0.00020738362,0.0000017061753,0.00054841675,0.00028717474],"genre_scores_gemma":[0.9906355,0.000008494779,0.008938225,0.0002606792,0.000093938565,0.0000028657873,0.000018419694,0.000030408155,0.000011449006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928766,0.000022213304,0.0002430819,0.00013678549,0.0001605623,0.00014968803],"domain_scores_gemma":[0.9996883,0.000033876313,0.00007340229,0.00009903151,0.000024282244,0.00008106419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000046157522,0.00014367321,0.00019750188,0.0000629274,0.000071564486,0.00004655402,0.000077163066,0.000046608013,0.000027528833],"category_scores_gemma":[0.000007791811,0.00013435831,0.000030576804,0.00016107618,0.00004444703,0.00014215568,0.000008145708,0.00012702156,0.000011585158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042502597,0.0000056635927,0.0022894803,0.00009374604,0.00004035797,0.0000045998636,0.00034326143,0.97669566,0.018877687,0.0004308508,0.0010908267,0.00012360509],"study_design_scores_gemma":[0.00038189962,0.00006612756,0.024528189,0.00004557041,0.000032750788,0.000004215874,0.00003924156,0.9742405,0.000403323,0.0000038116498,0.00008113779,0.0001732533],"about_ca_topic_score_codex":0.000010450753,"about_ca_topic_score_gemma":0.000008978443,"teacher_disagreement_score":0.64397985,"about_ca_system_score_codex":0.000013771834,"about_ca_system_score_gemma":0.0000039655215,"threshold_uncertainty_score":0.54789734},"labels":[],"label_agreement":null},{"id":"W3012888745","doi":"10.1109/lra.2020.3007381","title":"Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","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":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Trajectory; Outlier; Context (archaeology); Artificial intelligence; Machine learning; Algorithm","score_opus":0.01596787004869859,"score_gpt":0.2229417200090569,"score_spread":0.2069738499603583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012888745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06630141,0.000003020683,0.9257395,0.00746481,0.000100592064,0.000106773376,0.0000012874841,0.00024165775,0.000040895804],"genre_scores_gemma":[0.6927553,9.4777494e-7,0.30304244,0.004124817,0.000052180436,0.000006290288,0.000009682706,0.0000058514524,0.000002536413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991429,0.000035437773,0.00016500152,0.0002789029,0.00023066318,0.00014712595],"domain_scores_gemma":[0.9994676,0.00018106912,0.00007806372,0.00012685447,0.00003204846,0.000114331735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089768066,0.000105613566,0.000107023276,0.000052406504,0.00014430494,0.00024047212,0.00016885271,0.000035210287,0.000006181589],"category_scores_gemma":[0.00004060032,0.0000954214,0.000014116509,0.00025247372,0.000020123576,0.00026755704,0.00003725019,0.00013937373,0.00003355409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054469447,0.0000076036486,0.00034537865,0.000009503031,0.000006911582,0.0000020585187,0.0007283715,0.9799509,0.0035019626,0.004329806,0.0006272067,0.010484869],"study_design_scores_gemma":[0.00020685376,0.000051092953,0.00785976,0.000015758642,0.0000056892095,0.0000021923865,0.000008577088,0.99111784,0.00029217362,0.00008340709,0.00021370374,0.00014293725],"about_ca_topic_score_codex":0.0000031881752,"about_ca_topic_score_gemma":5.892273e-7,"teacher_disagreement_score":0.6264538,"about_ca_system_score_codex":0.0000135689625,"about_ca_system_score_gemma":0.000017659862,"threshold_uncertainty_score":0.38911715},"labels":[],"label_agreement":null},{"id":"W3036733903","doi":"10.1109/lra.2020.3003296","title":"Active Vertical Takeoff of an Aquatic UAV","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Takeoff; Takeoff and landing; Aerospace engineering; Aeronautics; Marine engineering; Envelope (radar); Environmental science; Engineering; Simulation; Computer science","score_opus":0.02048109714108993,"score_gpt":0.2161714845096491,"score_spread":0.19569038736855918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036733903","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":[0.7868373,0.000021503276,0.20981975,0.0029939117,0.00004388543,0.00007811899,0.0000023040716,0.00013317945,0.000070043105],"genre_scores_gemma":[0.9954171,0.000007909708,0.0039508427,0.00056099024,0.000040203122,0.00000336643,0.0000061902083,0.000012276353,0.0000011292882],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995352,0.000028474733,0.00018502482,0.00007328141,0.00009571151,0.00008232602],"domain_scores_gemma":[0.99976003,0.00002628564,0.000021562917,0.00011155104,0.000011440157,0.000069106594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003679667,0.00006958152,0.000114589166,0.000024930963,0.000027676078,0.000028306944,0.0000853727,0.000030515877,0.0000043412865],"category_scores_gemma":[0.0000020262155,0.00006804328,0.000020711543,0.00007457869,0.000025879479,0.00013226706,0.000009448976,0.00006328522,0.000010498182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007827077,0.000026553938,0.00015909375,0.0001498246,0.00006689862,0.0000020668253,0.0034471229,0.31524998,0.66815776,0.00034770687,0.00027822546,0.012106949],"study_design_scores_gemma":[0.00020894161,0.00003078053,0.0013133007,0.000018198652,0.000013653034,0.000001972059,0.00011818651,0.9522368,0.04562755,0.000033742446,0.00030251732,0.00009436488],"about_ca_topic_score_codex":0.0000059039276,"about_ca_topic_score_gemma":0.0000015150712,"teacher_disagreement_score":0.6369868,"about_ca_system_score_codex":0.000011899479,"about_ca_system_score_gemma":0.0000038574963,"threshold_uncertainty_score":0.27747244},"labels":[],"label_agreement":null},{"id":"W3042781709","doi":"10.1109/lra.2020.3010218","title":"Enhancement of Force Exertion Capability of a Mobile Manipulator by Kinematic Reconfiguration","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mobile manipulator; Kinematics; Cartesian coordinate system; Control theory (sociology); Trajectory; Computer science; Control reconfiguration; Base (topology); Weighting; Torque; Control engineering; Simulation; Mobile robot; Engineering; Robot; Mathematics; Physics; Artificial intelligence; Control (management); Embedded system","score_opus":0.00930816140964541,"score_gpt":0.1976014524333466,"score_spread":0.18829329102370118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042781709","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":[0.35712996,0.000025191082,0.642279,0.0002448117,0.000087344175,0.00017433593,0.000004848418,0.000037157002,0.000017287075],"genre_scores_gemma":[0.9693142,0.000022118746,0.03046318,0.00012937674,0.000016710983,0.000013627308,0.000021569389,0.000011297116,0.0000078893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993055,0.000013505015,0.0003783498,0.000099768906,0.00012095697,0.00008195453],"domain_scores_gemma":[0.99970764,0.000022512359,0.00010582229,0.000093795265,0.000027250497,0.000042992946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007384263,0.000088671404,0.00018271638,0.000026408141,0.00001690347,0.000010547641,0.000042251228,0.00003970149,0.000016126047],"category_scores_gemma":[0.000009902305,0.000089724774,0.000031793585,0.00007498381,0.000017919507,0.00007312728,0.0000048996408,0.000040332197,0.0000016629201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015967133,0.000008583414,0.000012871148,0.00033387376,0.000009476392,7.813405e-8,0.00021328371,0.53400105,0.46445253,0.00030173332,0.0001176145,0.0005473323],"study_design_scores_gemma":[0.00016126176,0.000056720535,0.00011282362,0.00003938693,0.000018895846,4.7963704e-7,0.000035010962,0.92288446,0.07647114,0.00013714879,0.0000030732167,0.000079594836],"about_ca_topic_score_codex":0.0000040647446,"about_ca_topic_score_gemma":6.4032366e-7,"teacher_disagreement_score":0.6121843,"about_ca_system_score_codex":0.000020902859,"about_ca_system_score_gemma":0.000004480221,"threshold_uncertainty_score":0.36588702},"labels":[],"label_agreement":null},{"id":"W3043334992","doi":"10.1109/lra.2020.3010204","title":"Model-Based Coupling for Co-Simulation of Robotic Contact Tasks","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","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":"CM Labs Simulations (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coupling (piping); Modular design; Interface (matter); Computer science; Representation (politics); Process (computing); Contact force; Stability (learning theory); Iterative and incremental development; Co-simulation; Simulation; Hardware-in-the-loop simulation; Stiffness; Robot; Control engineering; Control theory (sociology); Engineering; Control (management); Artificial intelligence; Mechanical engineering","score_opus":0.020291009624223307,"score_gpt":0.2361071560410523,"score_spread":0.215816146416829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043334992","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":[0.06996879,0.000025728235,0.9284538,0.0010440116,0.00013616506,0.00024167485,0.00001250917,0.000107465465,0.000009844608],"genre_scores_gemma":[0.99478304,0.0000019459296,0.0045763473,0.00053009775,0.00005850781,0.000009583664,0.000019100864,0.000020471336,9.0905417e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941736,0.0000048856955,0.00026109806,0.0001008839,0.00010407266,0.00011166604],"domain_scores_gemma":[0.99968624,0.00009065638,0.00006672056,0.00006471514,0.000031371466,0.000060323415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078246856,0.00009560216,0.00019531079,0.00003095518,0.00003129876,0.000030244466,0.00005143679,0.000049509086,0.0000010284],"category_scores_gemma":[0.000013305725,0.000098319935,0.000054317887,0.000046773806,0.0000075161192,0.00006214402,0.0000024290766,0.000047649733,0.000001315526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066277935,0.000004356033,0.000019323898,0.00016097233,0.00001950017,2.4143114e-7,0.000037171787,0.93153405,0.06728027,0.0006053783,0.000072971074,0.0002591275],"study_design_scores_gemma":[0.0006258735,0.000035311718,0.00005550939,0.00003490353,0.000028279108,1.1288341e-7,0.000004799096,0.9983863,0.0006613945,0.000043206153,0.000017412478,0.00010689074],"about_ca_topic_score_codex":0.0000012738099,"about_ca_topic_score_gemma":6.04686e-7,"teacher_disagreement_score":0.9248142,"about_ca_system_score_codex":0.00002413272,"about_ca_system_score_gemma":0.000008178382,"threshold_uncertainty_score":0.40093705},"labels":[],"label_agreement":null},{"id":"W3043708377","doi":"10.1109/lra.2020.3010208","title":"Improving Multirotor Landing Performance on Inclined Surfaces Using Reverse Thrust","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","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":"Université de Sherbrooke","funders":"","keywords":"Touchdown; Thrust; Landing gear; Payload (computing); Aerospace engineering; Multirotor; Rotor (electric); Marine engineering; Range (aeronautics); Towing; Geology; Simulation; Engineering; Computer science; Mechanical engineering","score_opus":0.018764736997825528,"score_gpt":0.1971320892139577,"score_spread":0.17836735221613217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043708377","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":[0.9099582,0.000024844434,0.08857824,0.0004332019,0.0004937218,0.00008157086,0.0000032600792,0.00040607405,0.000020933048],"genre_scores_gemma":[0.9929499,0.000015400103,0.0065034134,0.00026354432,0.00022124453,0.000003161088,0.0000044581475,0.000031218144,0.000007669377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994284,0.000009761361,0.00016390333,0.00012660363,0.000106741194,0.00016458776],"domain_scores_gemma":[0.99977595,0.000024377932,0.000038418562,0.00007793449,0.00000917239,0.00007417343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006564606,0.00013790913,0.00014031144,0.000041416446,0.0000860849,0.000053811917,0.00005130342,0.000050600538,0.0000023925863],"category_scores_gemma":[0.0000115722905,0.00013615504,0.00002389308,0.00009780974,0.000012668893,0.00014624369,0.000009569401,0.000113963804,0.000009285676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018497012,0.0000016375084,0.00045853067,0.00014399913,0.000011036922,0.0000018657681,0.0002854783,0.8805168,0.1180403,0.000008355345,0.00019597811,0.00033414116],"study_design_scores_gemma":[0.00021358886,0.000017754859,0.0007049791,0.0000821559,0.0000090690355,0.0000020515165,0.00003843626,0.99471927,0.0039186776,1.251787e-7,0.0001287098,0.0001651638],"about_ca_topic_score_codex":0.000018172508,"about_ca_topic_score_gemma":5.811918e-7,"teacher_disagreement_score":0.11420246,"about_ca_system_score_codex":0.000044622167,"about_ca_system_score_gemma":0.0000036601832,"threshold_uncertainty_score":0.5552242},"labels":[],"label_agreement":null},{"id":"W3045101785","doi":"10.1109/lra.2020.3011394","title":"Cluster-based Penalty Scaling for Robust Pose Graph Optimization","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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","funders":"","keywords":"Spurious relationship; Computer science; Consistency (knowledge bases); Estimator; Graph; Scaling; Global optimization; Cluster (spacecraft); Penalty method; Data mining; Artificial intelligence; Mathematical optimization; Algorithm; Machine learning; Mathematics; Theoretical computer science; Statistics","score_opus":0.019178512419040252,"score_gpt":0.20481971293744103,"score_spread":0.18564120051840077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045101785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0126490565,0.000030082358,0.98030907,0.0059231063,0.00036741426,0.00032935006,0.000014234437,0.00035916126,0.00001855087],"genre_scores_gemma":[0.68849295,0.00002450537,0.30589494,0.0050171874,0.00027942247,0.00001806584,0.0002001253,0.00007022888,0.0000025809518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999154,0.000022562017,0.0002913986,0.0001936084,0.00014233764,0.00019610066],"domain_scores_gemma":[0.99959165,0.00007296263,0.0000616977,0.00009901951,0.000060298196,0.00011435987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008694792,0.00016672691,0.0001670515,0.000095566545,0.00012266335,0.00012656218,0.00006777632,0.00007676205,0.0000066779094],"category_scores_gemma":[0.000028831639,0.00017895081,0.000063114945,0.00020106239,0.00002594793,0.0001426146,0.000005681246,0.0000773817,0.0000031934849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008595046,0.0000088758725,0.00004249739,0.00017556427,0.00001700498,9.742236e-7,0.0000992139,0.99413294,0.0029465652,0.00012431384,0.001996224,0.0004472273],"study_design_scores_gemma":[0.0006875103,0.000030514919,0.00006396836,0.000035461144,0.000037020938,0.0000010040318,0.000014571764,0.9975732,0.0012454182,0.000016913378,0.00007925529,0.00021519348],"about_ca_topic_score_codex":0.0000018327074,"about_ca_topic_score_gemma":7.8308346e-7,"teacher_disagreement_score":0.6758439,"about_ca_system_score_codex":0.000030196537,"about_ca_system_score_gemma":0.000010444858,"threshold_uncertainty_score":0.7297402},"labels":[],"label_agreement":null},{"id":"W3045452617","doi":"10.1109/lra.2020.3010444","title":"Incorporating Object Intrinsic Features Within Deep Grasp Affordance Prediction","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Affordance; GRASP; Object (grammar); Artificial intelligence; Robot; Computer science; Task (project management); Context (archaeology); Process (computing); Set (abstract data type); Computer vision; Human–computer interaction; Engineering","score_opus":0.012131109613602354,"score_gpt":0.19863796728431765,"score_spread":0.1865068576707153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045452617","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":[0.31905755,0.00010662521,0.6751336,0.0035115771,0.000757058,0.00019548683,0.0000010992002,0.0010545166,0.00018247354],"genre_scores_gemma":[0.98742604,0.000009446122,0.0110617215,0.0011874541,0.00025755813,0.0000052558967,0.000019595276,0.000025742294,0.0000071651725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993305,0.000026467289,0.00023146746,0.00014833103,0.00013898013,0.0001242396],"domain_scores_gemma":[0.9997315,0.000026202688,0.000075902186,0.00007200789,0.000018482664,0.0000759175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007567945,0.0001296181,0.00012439488,0.000064282845,0.00011012702,0.000105065206,0.00005317278,0.000053167987,0.0000050732983],"category_scores_gemma":[0.000028192992,0.0001356095,0.000027295575,0.00019606335,0.000022532427,0.00023959867,0.0000099198605,0.0001988761,0.0000137485895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021577246,0.0000020530342,0.0011460619,0.000060765266,0.000014544994,0.0000030123178,0.0008860498,0.9804662,0.014304633,0.0004757021,0.000764677,0.0018741285],"study_design_scores_gemma":[0.00020886443,0.000018785237,0.020868678,0.000029489487,0.000012361242,0.0000068713393,0.00007274787,0.9775327,0.0010197663,0.000038549984,0.00005062814,0.00014056564],"about_ca_topic_score_codex":0.0000035108656,"about_ca_topic_score_gemma":0.000004954297,"teacher_disagreement_score":0.6683685,"about_ca_system_score_codex":0.000028780327,"about_ca_system_score_gemma":0.000005453297,"threshold_uncertainty_score":0.5529995},"labels":[],"label_agreement":null},{"id":"W3048709413","doi":"10.1109/lra.2020.3015464","title":"Target Search on Road Networks With Range-Constrained UAVs and Ground-Based Mobile Recharging Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"AUG Signals (Canada); University of Toronto","funders":"","keywords":"Computer science; Rendezvous; Range (aeronautics); Integer programming; Linear programming; Routing (electronic design automation); Real-time computing; Engineering; Computer network; Algorithm","score_opus":0.009896165271067327,"score_gpt":0.19822880587559222,"score_spread":0.1883326406045249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048709413","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":[0.29242176,0.00005897084,0.7031456,0.0038287526,0.000035375713,0.00023606095,0.000004679274,0.0002310243,0.000037774233],"genre_scores_gemma":[0.9647547,0.00003999621,0.03303212,0.0020227062,0.0000622921,0.00003054285,0.000030261346,0.000025430572,0.0000019361755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946123,0.000016834963,0.00012708531,0.0001563033,0.00010066992,0.00013785073],"domain_scores_gemma":[0.999762,0.00003807359,0.000025352092,0.00007988706,0.000020191097,0.0000745067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068736445,0.00011437145,0.0001094529,0.000050497816,0.000095744785,0.00009324123,0.00004001359,0.000041173193,0.000006011928],"category_scores_gemma":[0.0000021704518,0.00010746621,0.000013855973,0.00015874482,0.0000400299,0.00009317773,0.000004688745,0.00011238128,0.000002991606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008614685,0.0000062239415,0.00018889889,0.000033087075,0.0000121921585,0.0000014808243,0.00014132774,0.99189574,0.002027763,0.0000811867,0.0003113978,0.0052920873],"study_design_scores_gemma":[0.00041339337,0.000057155547,0.001319797,0.000030026815,0.000010122791,0.0000016068635,0.000039581737,0.9973763,0.0004912702,0.000002910394,0.00012541392,0.00013241384],"about_ca_topic_score_codex":0.000004658328,"about_ca_topic_score_gemma":6.165443e-7,"teacher_disagreement_score":0.67233294,"about_ca_system_score_codex":0.000019642903,"about_ca_system_score_gemma":0.000006431348,"threshold_uncertainty_score":0.43823448},"labels":[],"label_agreement":null},{"id":"W3083023308","doi":"10.1109/lra.2020.3021382","title":"Path Planning Under Malicious Injections and Removals of Perceived Obstacles: A Probabilistic Programming Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Aeronautics and Space Administration","keywords":"Probabilistic logic; Path (computing); Computer science; Computer security; Motion planning; Artificial intelligence; Computer network","score_opus":0.03390529083658966,"score_gpt":0.25031610608732957,"score_spread":0.2164108152507399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083023308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13645947,0.00006177871,0.8598371,0.0030432257,0.00010526932,0.00026165543,0.0000021518858,0.00020852778,0.000020779078],"genre_scores_gemma":[0.54466397,0.000003189757,0.45455584,0.00071285735,0.000038454575,0.000008931332,0.0000038955227,0.000009683421,0.0000032050414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877846,0.00007833676,0.00031666475,0.0003745379,0.00023017748,0.00022180354],"domain_scores_gemma":[0.9993571,0.00010796893,0.00017553856,0.0001872283,0.000050299055,0.0001218538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017373169,0.00015807002,0.00023327829,0.000091139234,0.00014811654,0.0001687624,0.00019294799,0.000059459282,2.8442565e-7],"category_scores_gemma":[0.00006230383,0.00015243098,0.000033709686,0.0003078885,0.00009802241,0.00025349346,0.00006582935,0.00014119345,0.0000011874752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002695458,0.000040878153,0.00077522715,0.00024887305,0.00003538832,0.000014220062,0.008369741,0.9771964,0.008607945,0.0015528867,0.00016306793,0.0029926777],"study_design_scores_gemma":[0.00027484898,0.00007572529,0.012759129,0.00009302255,0.000022437533,0.000058602564,0.0002804809,0.98608565,0.000034359535,0.00012387302,0.000015317482,0.00017654726],"about_ca_topic_score_codex":0.000012827217,"about_ca_topic_score_gemma":8.287892e-8,"teacher_disagreement_score":0.40820447,"about_ca_system_score_codex":0.00002541158,"about_ca_system_score_gemma":0.00003069976,"threshold_uncertainty_score":0.6215955},"labels":[],"label_agreement":null},{"id":"W3088256830","doi":"10.1109/lra.2020.3026958","title":"RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model","year":2020,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Calibration; Lidar; Artificial intelligence; Deep learning; Focus (optics); Scalability; Computer vision; Feature (linguistics); Generative model; Camera resectioning; Generative grammar; Remote sensing; Mathematics; Database","score_opus":0.015759381426075223,"score_gpt":0.24545430928655376,"score_spread":0.22969492786047854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088256830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04604563,0.000119599776,0.93313974,0.020421699,0.00003571935,0.000091036796,0.000001554701,0.00013898923,0.0000060375173],"genre_scores_gemma":[0.6232233,0.00006140534,0.36933696,0.0072842166,0.000057790872,0.0000026545147,0.00000811292,0.000009986421,0.000015590764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918807,0.00003395545,0.00014797114,0.0003054822,0.00017785857,0.00014668932],"domain_scores_gemma":[0.9996256,0.00003702378,0.00010561953,0.00008334757,0.000046222736,0.00010217466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004334066,0.00012878921,0.00013771084,0.00009687736,0.00018433957,0.00022975534,0.000099743054,0.000024721494,0.0000012542381],"category_scores_gemma":[0.000016392012,0.00011240628,0.000014824467,0.0004081758,0.00003747158,0.00076878536,0.000044075783,0.00014552208,0.0000014440877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002918572,0.000008529952,0.0003758576,0.000015797634,0.0000057503057,0.000004242258,0.00076655333,0.962986,0.00509501,0.00026842256,0.00009751874,0.030373404],"study_design_scores_gemma":[0.0003179658,0.00005897147,0.0006276998,0.00002166032,0.000005090575,0.0000065580057,0.000038749422,0.9978515,0.000766297,0.000032614935,0.000116624804,0.00015628061],"about_ca_topic_score_codex":0.0000014338696,"about_ca_topic_score_gemma":0.0000010308343,"teacher_disagreement_score":0.57717764,"about_ca_system_score_codex":0.0000140152415,"about_ca_system_score_gemma":0.000015249108,"threshold_uncertainty_score":0.4583795},"labels":[],"label_agreement":null},{"id":"W3107693114","doi":"10.1109/lra.2021.3062338","title":"Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Computer science; Visibility; Probabilistic roadmap; Artificial intelligence; Motion planning; Nonholonomic system; Feature (linguistics); Mathematical optimization; Modular design; Trajectory; Robot; Mobile robot; Computer vision; Mathematics","score_opus":0.012754305684298407,"score_gpt":0.2675171638974879,"score_spread":0.2547628582131895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107693114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029729811,0.0000627974,0.95453924,0.012574926,0.0018941974,0.0008933766,0.000013074986,0.00028872959,0.000003846756],"genre_scores_gemma":[0.2631767,0.0000074453096,0.73593986,0.00043727222,0.00008088604,0.000102747705,0.00021136343,0.000029413864,0.000014279033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973456,0.00015587526,0.00059801136,0.0009708798,0.00040786143,0.0005217702],"domain_scores_gemma":[0.99847287,0.00028302064,0.0003897518,0.0006165371,0.00011042931,0.00012736738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039379153,0.00043091466,0.00055016036,0.00038833037,0.00015508481,0.00091009535,0.0005753283,0.0002965819,0.0000011962283],"category_scores_gemma":[0.00007231485,0.00045752144,0.00016585822,0.0002826383,0.00008531447,0.00023843722,0.0002769973,0.00048711526,0.0000014237789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043138525,0.000086668806,0.000072786184,0.00029960962,0.0000293563,0.000041993175,0.0003750678,0.977564,0.002513965,0.00020548384,0.00008291426,0.018723866],"study_design_scores_gemma":[0.00091390085,0.000056604003,0.0026926438,0.00084341306,0.000031475378,0.000009956967,0.00002068708,0.99464107,0.00012976548,0.0001678875,0.000017112223,0.00047546296],"about_ca_topic_score_codex":0.00005586894,"about_ca_topic_score_gemma":0.000014925854,"teacher_disagreement_score":0.23344691,"about_ca_system_score_codex":0.00020032258,"about_ca_system_score_gemma":0.0002516194,"threshold_uncertainty_score":0.9997876},"labels":[],"label_agreement":null},{"id":"W3111920287","doi":"10.1109/lra.2021.3052439","title":"Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Radar; Computer science; Odometry; Distortion (music); Computer vision; Spinning; Artificial intelligence; Doppler radar; Radar imaging; Radar engineering details; Lidar; Remote sensing; Motion (physics); Geology; Engineering; Telecommunications","score_opus":0.011470060587621154,"score_gpt":0.23267567595832794,"score_spread":0.2212056153707068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111920287","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":[0.41138214,0.0001887473,0.5854735,0.0015923089,0.0007163646,0.00053708284,0.000007922508,0.00009986993,0.0000021212788],"genre_scores_gemma":[0.9631501,0.00012341751,0.035880636,0.00024996643,0.0001307053,0.00006151224,0.00034689592,0.00005375846,0.0000029966418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881023,0.000048565696,0.00041039573,0.00034829235,0.00017208453,0.0002104563],"domain_scores_gemma":[0.999488,0.00008101399,0.00009903316,0.000181871,0.0000636174,0.00008648159],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017107696,0.00026336024,0.00033078386,0.00024261583,0.000081538165,0.0002722451,0.00005802995,0.00018285016,0.0000011571343],"category_scores_gemma":[0.00002305451,0.0003073588,0.000052606487,0.00016112857,0.000021731657,0.00012128054,0.00003701021,0.00021086505,0.0000011942424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004116736,0.00001050722,0.0003471579,0.0011276592,0.000026125987,0.0000054291672,0.0007051763,0.9791611,0.0141187515,0.0002483876,0.00022352685,0.004022091],"study_design_scores_gemma":[0.0005213764,0.000019540883,0.0075398427,0.0010698012,0.000048042864,0.0000047143885,0.000060814105,0.9883447,0.0017493429,0.00024824,0.00004047355,0.0003530738],"about_ca_topic_score_codex":0.000025452258,"about_ca_topic_score_gemma":0.000010041694,"teacher_disagreement_score":0.551768,"about_ca_system_score_codex":0.00015733123,"about_ca_system_score_gemma":0.000012448208,"threshold_uncertainty_score":0.99993783},"labels":[],"label_agreement":null},{"id":"W3118456481","doi":"10.1109/lra.2020.3048657","title":"Learning Goal Conditioned Socially Compliant Navigation From Demonstration Using Risk-Based Features","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Evacuation and Crowd Dynamics","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung","keywords":"Reinforcement learning; Computer science; Representation (politics); Feature (linguistics); Artificial intelligence; Feature learning; Machine learning; Mobile robot; Robot; Space (punctuation); Human–computer interaction; Simple (philosophy); Quality (philosophy)","score_opus":0.009660835417818158,"score_gpt":0.22868832106137266,"score_spread":0.2190274856435545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118456481","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":[0.5456895,0.000027693624,0.45324114,0.00053268444,0.00024819095,0.00004869386,0.000017783144,0.00016997848,0.000024348763],"genre_scores_gemma":[0.9708431,0.000013220953,0.028012404,0.00031515153,0.00010637043,0.0000030359286,0.0006789786,0.000020143465,0.0000076254805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930465,0.000066854926,0.00021050868,0.00013874701,0.00016096313,0.0001182647],"domain_scores_gemma":[0.9996633,0.00006387932,0.000091802176,0.00007351128,0.00006367761,0.000043863478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078246456,0.00011679092,0.000117636686,0.000047658854,0.00023422892,0.00015900434,0.000031572345,0.000082270635,0.00001438215],"category_scores_gemma":[0.000018629393,0.00013962948,0.000040958083,0.000117581185,0.000035832018,0.00020282362,0.000004439114,0.00018403924,0.0000070759456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000125841,0.000007045935,0.0010003605,0.00001852918,0.000024403722,0.000004608818,0.0001648445,0.87384707,0.123535976,0.00029447043,0.0000981764,0.0010032769],"study_design_scores_gemma":[0.00034848283,0.0000060081607,0.023815783,0.000051969582,0.00004653573,0.0000044563153,0.000095032745,0.9705208,0.0046208054,0.00030994794,0.000022500084,0.00015766831],"about_ca_topic_score_codex":0.000020663017,"about_ca_topic_score_gemma":0.00003750653,"teacher_disagreement_score":0.42522871,"about_ca_system_score_codex":0.00007661324,"about_ca_system_score_gemma":0.000039114737,"threshold_uncertainty_score":0.5693925},"labels":[],"label_agreement":null},{"id":"W3127391403","doi":"10.1109/lra.2021.3060397","title":"UAV Localization Using Autoencoded Satellite Images","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Defence Research and Development Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Autoencoder; Satellite; Kernel (algebra); Computation; Representation (politics); Pattern recognition (psychology); Remote sensing; Deep learning; Geography; Algorithm; Mathematics; Engineering","score_opus":0.016209838819008053,"score_gpt":0.22643897871130506,"score_spread":0.210229139892297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127391403","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":[0.08199385,0.0006336832,0.91444284,0.0003866408,0.0017211139,0.0002529794,0.000016509988,0.0004919839,0.000060391405],"genre_scores_gemma":[0.93159723,0.0012114766,0.06533438,0.00075670413,0.00034054593,0.000010702021,0.000591923,0.00013903184,0.000018022885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839646,0.0000735384,0.00054325204,0.00039709517,0.00029674082,0.0002928927],"domain_scores_gemma":[0.9992336,0.00004008872,0.00015071948,0.00034852937,0.00012397254,0.00010307943],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013461345,0.0003813485,0.00039229778,0.00022848937,0.00013140749,0.0004921314,0.00011958313,0.00030230824,0.000012171529],"category_scores_gemma":[0.000018026854,0.0004402364,0.0001032017,0.00021524164,0.000060208247,0.00018566032,0.00006386754,0.000323565,0.000005185148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010552507,0.000013123366,0.00022133677,0.00050003733,0.00006534902,0.000016924345,0.00023018388,0.9801944,0.017140547,0.0000875128,0.0003515283,0.0011780357],"study_design_scores_gemma":[0.00018312862,0.0000054254733,0.00064949115,0.00033828797,0.00010106449,0.000010114674,0.00003415955,0.9931781,0.004862183,0.00009211442,0.00008223076,0.0004636441],"about_ca_topic_score_codex":0.000033420714,"about_ca_topic_score_gemma":0.0000059993786,"teacher_disagreement_score":0.84960335,"about_ca_system_score_codex":0.00016236358,"about_ca_system_score_gemma":0.00004391193,"threshold_uncertainty_score":0.9998049},"labels":[],"label_agreement":null},{"id":"W3127566536","doi":"10.1109/lra.2021.3056346","title":"Optimal Cooperative Maneuver Planning for Multiple Nonholonomic Robots in a Tiny Environment via Adaptive-Scaling Constrained Optimization","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Nonholonomic system; Convexity; Mathematical optimization; Computer science; Trajectory; Robot; Computation; Kinematics; Convergence (economics); Control theory (sociology); Range (aeronautics); Constraint (computer-aided design); Trajectory optimization; Motion planning; Mobile robot; Optimal control; Mathematics; Artificial intelligence; Engineering; Algorithm; Control (management)","score_opus":0.02128553484344437,"score_gpt":0.23518339533228216,"score_spread":0.2138978604888378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127566536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029064432,0.00004505318,0.96859616,0.0015912752,0.00029083865,0.0003085347,0.000007280583,0.000086135326,0.000010262579],"genre_scores_gemma":[0.2606603,0.0000040970444,0.7387404,0.0004697967,0.00004341774,0.000028465458,0.000030493853,0.000012922304,0.000010132078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986505,0.0000844778,0.00035567975,0.00046378892,0.00015332033,0.000292252],"domain_scores_gemma":[0.999293,0.00024457293,0.00014859995,0.00019899364,0.00004143154,0.000073358875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022612777,0.00018759415,0.00023455534,0.00012054546,0.00015390602,0.00015968623,0.00016135498,0.0000787115,0.0000027408926],"category_scores_gemma":[0.00003476478,0.00020892626,0.00004196865,0.00014499157,0.000056349883,0.0003640134,0.000061717816,0.00012894851,0.0000049146997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069002053,0.000035109395,0.00023871586,0.000012344245,0.00002374832,0.000053093514,0.00089830067,0.99303246,0.004627882,0.00013880288,0.00005106501,0.0008815635],"study_design_scores_gemma":[0.0010325614,0.000044360764,0.0014186074,0.00007509014,0.000011305397,0.000043146745,0.000068339825,0.9956917,0.0013578751,0.000011483269,0.000007994427,0.00023752278],"about_ca_topic_score_codex":0.000004942605,"about_ca_topic_score_gemma":4.5186323e-7,"teacher_disagreement_score":0.23159586,"about_ca_system_score_codex":0.00010572017,"about_ca_system_score_gemma":0.00004735013,"threshold_uncertainty_score":0.8519766},"labels":[],"label_agreement":null},{"id":"W3128128049","doi":"10.1109/lra.2021.3057557","title":"Design of a Reconfigurable Parallel Continuum Robot With Tendon-Actuated Kinematic Chains","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kinematics; Parallel manipulator; Robot; Modular design; Control reconfiguration; Orientation (vector space); Computer science; Position (finance); Repeatability; Robot end effector; Simulation; Artificial intelligence; Control theory (sociology); Physics; Geometry; Mathematics; Classical mechanics","score_opus":0.014902815629334931,"score_gpt":0.20313540641719488,"score_spread":0.18823259078785995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128128049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033430196,0.00007921423,0.9643442,0.0015225429,0.0001018117,0.00022931665,0.0000043230666,0.00015727505,0.00013113157],"genre_scores_gemma":[0.8355835,0.000082984334,0.16382404,0.000266679,0.000035965077,0.000040736846,0.000024972858,0.00003743935,0.000103668004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924195,0.000022881644,0.00028261286,0.00015207018,0.000120145254,0.00018034948],"domain_scores_gemma":[0.99948,0.000104560524,0.00008063595,0.00020129813,0.000074414966,0.00005907906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007618148,0.00014693438,0.00023592796,0.000064685795,0.00005791491,0.000053995613,0.000065528955,0.000048078287,0.000022824022],"category_scores_gemma":[0.000011583969,0.00013608801,0.000028579801,0.00022488892,0.000042108448,0.000081629114,0.0000050009726,0.00008619697,0.000008919901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018622062,0.000019951633,0.000039023722,0.000085507214,0.00005407134,0.0000050988056,0.00012310271,0.8425815,0.15570807,0.00030808468,0.0007872354,0.00028649427],"study_design_scores_gemma":[0.00056267984,0.000026886217,0.001423832,0.00012563942,0.00005825483,0.000030362515,0.000039812945,0.9741055,0.02323771,0.00010004506,0.00006220032,0.0002270968],"about_ca_topic_score_codex":0.0000036987713,"about_ca_topic_score_gemma":0.000004700762,"teacher_disagreement_score":0.8021533,"about_ca_system_score_codex":0.000019479521,"about_ca_system_score_gemma":0.000020941821,"threshold_uncertainty_score":0.55495083},"labels":[],"label_agreement":null},{"id":"W3128639218","doi":"10.1109/lra.2021.3058909","title":"Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Vector Institute; University of Toronto","funders":"Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Feature (linguistics); Pipeline (software); Computer vision; Convolutional neural network; Visual odometry; Matching (statistics); Process (computing); Simultaneous localization and mapping; Odometry; Pattern recognition (psychology); Robot; Mathematics; Mobile robot","score_opus":0.006078282824255606,"score_gpt":0.21180983428276243,"score_spread":0.20573155145850683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128639218","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":[0.2985874,0.00018727993,0.69871175,0.0020581032,0.00020236892,0.0001503749,0.0000064384394,0.00009305288,0.0000031971176],"genre_scores_gemma":[0.99215835,0.00009604903,0.006817676,0.00074575853,0.00010292594,0.000009021924,0.000023461742,0.000027957929,0.00001877182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994509,0.000026597514,0.00014677721,0.00016938125,0.000060972852,0.00014532277],"domain_scores_gemma":[0.9997254,0.00007355994,0.00003778926,0.000063475716,0.000042860214,0.000056897858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008445647,0.00012850306,0.00015421474,0.000055344524,0.00012742533,0.00015636001,0.000016144446,0.000091136535,8.9798596e-7],"category_scores_gemma":[0.000018318495,0.00013508504,0.000024576857,0.00006637836,0.000021488813,0.00011206469,0.0000052529326,0.00009693615,2.9638974e-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":[0.0000073020333,0.0000053006393,0.000057017718,0.00011309709,0.00003148412,0.0000020455632,0.00014839157,0.5965751,0.39323094,0.000099800796,0.00008504416,0.009644501],"study_design_scores_gemma":[0.0009381853,0.000037727026,0.0011548384,0.000027957385,0.000040914398,0.000016432428,0.00006624098,0.9898571,0.007488514,0.00010448308,0.00010632327,0.00016128964],"about_ca_topic_score_codex":0.000004216239,"about_ca_topic_score_gemma":0.000012701897,"teacher_disagreement_score":0.693571,"about_ca_system_score_codex":0.000021075519,"about_ca_system_score_gemma":0.00000617396,"threshold_uncertainty_score":0.55086076},"labels":[],"label_agreement":null},{"id":"W3129528580","doi":"10.1109/lra.2021.3060402","title":"Parallelism in Autonomous Robotic Surgery","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Intuitive Surgical","keywords":"Computer science; Parallelism (grammar); Task (project management); Automation; Robot; Robotic surgery; Artificial intelligence; State (computer science); Data parallelism; Motion (physics); Distributed computing; Human–computer interaction; Parallel computing; Programming language","score_opus":0.013564451743108108,"score_gpt":0.21054864401494763,"score_spread":0.19698419227183953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129528580","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":[0.67088944,0.0005297006,0.31774893,0.0087717995,0.001005725,0.00017949546,0.000004261571,0.00053314876,0.00033748738],"genre_scores_gemma":[0.989889,0.00016346472,0.00910349,0.00065666717,0.000079424586,0.000019778692,0.0000257681,0.000026197546,0.000036160745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925846,0.000016952667,0.00026858822,0.00016110057,0.00009164949,0.00020326764],"domain_scores_gemma":[0.9995998,0.00012402057,0.000028070996,0.00016793255,0.00002061836,0.000059524875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008004546,0.00012081193,0.0001837797,0.00010306228,0.000047781457,0.000076678065,0.00004826596,0.000056798803,0.000013106603],"category_scores_gemma":[0.000014286354,0.00013934565,0.00004375517,0.00023743347,0.000023414657,0.00009378426,0.000011115597,0.000109758425,0.000025666986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0189253e-7,0.000015214385,0.0011473491,0.00003635388,0.000011562245,0.000019808382,0.00007054679,0.9882494,0.0056476053,0.0010948133,0.0022326855,0.0014744147],"study_design_scores_gemma":[0.00018413244,0.0000021364401,0.041750625,0.000050863116,0.00001643772,0.000028501281,0.00002265429,0.9552128,0.0012824817,0.00038596895,0.00078575645,0.00027764853],"about_ca_topic_score_codex":0.000006239206,"about_ca_topic_score_gemma":0.000013148911,"teacher_disagreement_score":0.31899962,"about_ca_system_score_codex":0.000045905035,"about_ca_system_score_gemma":0.000020725483,"threshold_uncertainty_score":0.56823504},"labels":[],"label_agreement":null},{"id":"W3130085563","doi":"10.1109/lra.2021.3060369","title":"Robots Asking for Favors: The Effects of Directness and Familiarity on Persuasive HRI","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL","keywords":"Persuasion; Robot; Trustworthiness; Psychology; Human–computer interaction; Social psychology; Human–robot interaction; Social robot; Internet privacy; Computer science; Applied psychology; Cognitive psychology; Artificial intelligence; Mobile robot; Robot control","score_opus":0.018762195748651803,"score_gpt":0.3145300129258214,"score_spread":0.2957678171771696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130085563","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":[0.9279573,0.0002781464,0.052591242,0.01414097,0.00372712,0.00050532597,0.000010560587,0.000070999915,0.0007183473],"genre_scores_gemma":[0.99499667,0.000027920738,0.0006471753,0.003908501,0.00015292432,0.000033658984,0.000007890741,0.000014071238,0.00021117216],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99929506,0.00011028237,0.00016600268,0.0001923787,0.00010159664,0.00013468928],"domain_scores_gemma":[0.998723,0.0009259397,0.00011670435,0.00013097125,0.000069212605,0.000034184715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010930799,0.00010255589,0.00016478286,0.0000450473,0.00016726789,0.000048205806,0.000048366874,0.000067787245,0.000012918617],"category_scores_gemma":[0.00013618608,0.00008477725,0.00006263696,0.00009490541,0.00007014294,0.00005424557,0.000012743316,0.00010734528,0.0000040163486],"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":[0.000555377,0.0020118821,0.013821098,0.002578063,0.0024583105,0.00016787722,0.11292252,0.04528234,0.27089116,0.24785732,0.09848149,0.20297258],"study_design_scores_gemma":[0.014611374,0.0015406894,0.763381,0.0020714712,0.0012436537,0.00021406358,0.023525355,0.09124637,0.08537847,0.004098941,0.01023133,0.002457292],"about_ca_topic_score_codex":0.000038299622,"about_ca_topic_score_gemma":0.000013491986,"teacher_disagreement_score":0.7495599,"about_ca_system_score_codex":0.000020266512,"about_ca_system_score_gemma":0.000011668884,"threshold_uncertainty_score":0.34571162},"labels":[],"label_agreement":null},{"id":"W3130946423","doi":"10.1109/lra.2021.3060407","title":"Unsupervised Learning of Lidar Features for Use ina Probabilistic Trajectory Estimator","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Lidar; Artificial intelligence; Computer science; Trajectory; Deep learning; Estimator; Unsupervised learning; Feature (linguistics); Inference; Machine learning; Mobile robot; Robot; Remote sensing; Mathematics; Geography; Statistics","score_opus":0.010972416070390078,"score_gpt":0.2102501805823955,"score_spread":0.19927776451200543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130946423","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":[0.7800866,0.00014199439,0.21850616,0.0005913505,0.00016208511,0.00014851673,0.000008164787,0.00033387836,0.000021274653],"genre_scores_gemma":[0.9653669,0.00002149774,0.034420054,0.00009217392,0.000022551932,0.000013270554,0.000017440892,0.000020208014,0.00002591599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994835,0.000018732671,0.00018419998,0.000121961995,0.000057751662,0.0001338558],"domain_scores_gemma":[0.99965894,0.00012794332,0.00003773434,0.000108100496,0.000040091483,0.00002720975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006876189,0.00010193241,0.00016290895,0.000057861416,0.000076862896,0.00002423574,0.000046673293,0.000098663026,0.0000038813228],"category_scores_gemma":[0.00006115324,0.00010875627,0.00004235674,0.00009063584,0.00005392538,0.00010096473,0.000008335485,0.00013776399,0.000001258234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004716877,0.000016713522,0.0008582215,0.0002996973,0.00004786089,0.000005374668,0.00020728834,0.92634505,0.066002905,0.0019370473,0.0003631085,0.0039120265],"study_design_scores_gemma":[0.0004959563,0.00003461033,0.023990849,0.00007173716,0.000051932348,0.000017826698,0.000052292216,0.95300734,0.021548998,0.00020305405,0.00031523587,0.00021014376],"about_ca_topic_score_codex":0.0000016649262,"about_ca_topic_score_gemma":0.0000023108653,"teacher_disagreement_score":0.18528031,"about_ca_system_score_codex":0.000025913585,"about_ca_system_score_gemma":0.000017847482,"threshold_uncertainty_score":0.4434952},"labels":[],"label_agreement":null},{"id":"W3133564702","doi":"10.1109/lra.2021.3063992","title":"Real-Time Path Planning With Virtual Magnetic Fields","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Computer science; Robot; Path (computing); Magnetic field; Virtual reality; Field (mathematics); Artificial intelligence; Real-time computing; Simulation; Control engineering; Mathematics; Engineering; Physics","score_opus":0.009880572350459716,"score_gpt":0.22026016579683558,"score_spread":0.21037959344637586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133564702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10616952,0.000030137286,0.88835126,0.004549643,0.00030439466,0.000067212975,0.0000014897197,0.0002524426,0.00027389222],"genre_scores_gemma":[0.22112896,0.000016675436,0.7760604,0.0021834527,0.00014476608,0.000007994553,0.000015018774,0.000021234791,0.0004214978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988871,0.00006456604,0.00019308653,0.00033661508,0.00027056574,0.0002480498],"domain_scores_gemma":[0.99933094,0.00012011638,0.00008426737,0.0003207846,0.000052788717,0.00009110516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012562599,0.00014682405,0.00016725772,0.00007190084,0.00013338013,0.00025125153,0.00020370142,0.000059718473,0.0000062394593],"category_scores_gemma":[0.000016483868,0.00013461482,0.000025370586,0.00024110866,0.000039246122,0.00027209052,0.000056537527,0.00012587025,0.000028546452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034315901,0.000040577746,0.0007675203,0.000027648717,0.00003113554,0.00076536846,0.0015680586,0.9587984,0.022240022,0.002120722,0.004961954,0.008675163],"study_design_scores_gemma":[0.00035841018,0.00015544686,0.0070679947,0.0001115281,0.0000140592965,0.00017758993,0.000027030863,0.99087965,0.000837731,0.000076280456,0.000052949705,0.00024134215],"about_ca_topic_score_codex":0.000007498347,"about_ca_topic_score_gemma":1.6930852e-7,"teacher_disagreement_score":0.11495943,"about_ca_system_score_codex":0.000022264065,"about_ca_system_score_gemma":0.000055801913,"threshold_uncertainty_score":0.5489433},"labels":[],"label_agreement":null},{"id":"W3133744940","doi":"10.1109/lra.2021.3061874","title":"Force-Controlled Mechanical Stimulation and Single-Neuron Fluorescence Imaging of <i>Drosophila</i> Larvae","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuron; SIGNAL (programming language); Stimulation; Biological system; Mechanotransduction; Neuroscience; Biophysics; Sensory system; Biomedical engineering; Physics; Computer science; Biology; Engineering","score_opus":0.02309396271256471,"score_gpt":0.2610817584883044,"score_spread":0.2379877957757397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133744940","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":[0.9917555,0.00003060473,0.0035108437,0.004210641,0.00022310411,0.00018036478,0.0000049482346,0.000044343935,0.000039673323],"genre_scores_gemma":[0.9969973,0.00004641003,0.00056011166,0.0023137038,0.000041666874,0.0000051773286,0.0000039374336,0.000009972966,0.000021748578],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988399,0.00025301194,0.00025352938,0.00032948022,0.00012976406,0.00019429145],"domain_scores_gemma":[0.9991823,0.00047464768,0.00011104746,0.0001351314,0.000046211208,0.000050694078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013200083,0.00011079347,0.00023022335,0.00006811545,0.00014195664,0.000040787265,0.00007643228,0.00004871191,0.000006270311],"category_scores_gemma":[0.0003130582,0.00010218726,0.000042784643,0.00011010004,0.00019830855,0.00018493205,0.000054372013,0.00015234086,0.0000025072004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053536834,0.00004341017,0.00028441194,0.000025760071,0.0000040639816,0.000018953386,0.000035760808,0.004426503,0.9937162,0.000955774,0.000054568805,0.0003810718],"study_design_scores_gemma":[0.0015107559,0.00007368989,0.0037393305,0.000027894182,0.000017645334,0.00006244224,0.0000044350845,0.21549486,0.7780316,0.0009205464,0.000005732665,0.00011109283],"about_ca_topic_score_codex":0.000001340414,"about_ca_topic_score_gemma":4.5424997e-7,"teacher_disagreement_score":0.21568461,"about_ca_system_score_codex":0.000008696509,"about_ca_system_score_gemma":0.000018115206,"threshold_uncertainty_score":0.41670755},"labels":[],"label_agreement":null},{"id":"W3134199806","doi":"10.1109/lra.2021.3062592","title":"Analysis of the Effect of Common Disturbances on the Safety of a Wearable Tremor Suppression Device","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Neurological disorders and treatments","field":"Medicine","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":"Western University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Wearable computer; Computer science; Kalman filter; Mechatronics; Simulation; Artificial intelligence; Embedded system","score_opus":0.011859004617021976,"score_gpt":0.25631472829258367,"score_spread":0.2444557236755617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134199806","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":[0.99103457,0.000112458445,0.00011458072,0.008423486,0.00004579807,0.0001415586,0.000010446405,0.000004376894,0.0001127281],"genre_scores_gemma":[0.9991178,0.00003443005,0.000035643945,0.0007760944,0.0000040487166,0.0000022992147,0.000006697732,0.0000027002645,0.00002026669],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994072,0.00012332197,0.00017674519,0.00008695997,0.00015235315,0.00005343139],"domain_scores_gemma":[0.9993327,0.00031707776,0.0001388872,0.00017552596,0.000021986456,0.000013803255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008008655,0.00006171965,0.00026298588,0.000030211007,0.000042829128,0.0000037337743,0.000039779687,0.000022757917,0.000013447674],"category_scores_gemma":[0.00003984528,0.000027440621,0.00011805667,0.00029016891,0.000065694796,0.000013759551,0.000012958402,0.000049572434,2.4330222e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054325507,0.00063905056,0.7036945,0.0005450837,0.002032973,0.000017499955,0.0003732402,0.11462267,0.1691807,0.00037710345,0.0010343568,0.006939567],"study_design_scores_gemma":[0.00095384155,0.0003116518,0.86640954,0.00020392721,0.001446368,9.978824e-7,0.00002042943,0.028931206,0.10162532,0.000032530064,0.000021899816,0.000042303684],"about_ca_topic_score_codex":0.000015507678,"about_ca_topic_score_gemma":0.000005630913,"teacher_disagreement_score":0.16271502,"about_ca_system_score_codex":0.0000051574166,"about_ca_system_score_gemma":0.0000052244773,"threshold_uncertainty_score":0.1118996},"labels":[],"label_agreement":null},{"id":"W3134244741","doi":"10.1109/lra.2021.3062795","title":"A Switchable Unmanned Aerial Manipulator System for Window-Cleaning Robot Installation","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Window (computing); Installation; Robot; Task (project management); Computer science; Manipulator (device); Control system; Simulation; Real-time computing; Embedded system; Engineering; Artificial intelligence; Electrical engineering; Operating system; Systems engineering","score_opus":0.011901918599771745,"score_gpt":0.20015158235627337,"score_spread":0.18824966375650162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134244741","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":[0.13865224,0.00004943608,0.85861325,0.00046826998,0.001403397,0.00026410076,0.000010088142,0.00038821695,0.0001509945],"genre_scores_gemma":[0.97996694,0.00001236752,0.019278517,0.00021715456,0.00030532118,0.000014489944,0.000105744155,0.000052790005,0.00004664963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904615,0.000024504732,0.00033264433,0.00020913182,0.00015563359,0.00023193975],"domain_scores_gemma":[0.99957573,0.000048411774,0.00006840691,0.000153196,0.0000836635,0.0000706105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010330118,0.0001688826,0.00020829002,0.000091901995,0.00015287082,0.00017352446,0.000047723042,0.00009612379,0.000006490917],"category_scores_gemma":[0.00001623472,0.000191947,0.000058505822,0.00016671224,0.000014219294,0.00018388768,0.000009743579,0.00006848968,0.000010610028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052497016,0.0000072705197,0.00004710245,0.0002535011,0.000032447857,0.0000056100707,0.00009677302,0.8803506,0.11562157,0.0026801715,0.00051817833,0.00038154665],"study_design_scores_gemma":[0.0007229554,0.000016358932,0.00038421783,0.00010972614,0.000042451065,0.000014807801,0.00009204682,0.9814706,0.016560787,0.000034995024,0.00032275118,0.00022833103],"about_ca_topic_score_codex":0.000004824496,"about_ca_topic_score_gemma":0.000006655605,"teacher_disagreement_score":0.84131473,"about_ca_system_score_codex":0.000109254586,"about_ca_system_score_gemma":0.000018440398,"threshold_uncertainty_score":0.7827371},"labels":[],"label_agreement":null},{"id":"W3134792577","doi":"10.1109/lra.2021.3062003","title":"Foot Placement Prediction for Assistive Walking by Fusing Sequential 3D Gaze and Environmental Context","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Southern University of Science and Technology; National Natural Science Foundation of China","keywords":"Gaze; Terrain; Context (archaeology); Foot (prosody); Computer science; Computer vision; Intersection (aeronautics); Artificial intelligence; Gait; Human–computer interaction; Physical medicine and rehabilitation; Geography; Cartography; Medicine","score_opus":0.0071288583203745325,"score_gpt":0.19848655605860305,"score_spread":0.19135769773822853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134792577","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":[0.3712662,0.00037566575,0.62654084,0.0008327369,0.0005576486,0.0002167996,0.000109468645,0.000083261926,0.000017374326],"genre_scores_gemma":[0.9862643,0.00009404874,0.013089858,0.0002789494,0.00006528733,0.000016651731,0.00014433617,0.00002180422,0.00002479632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936664,0.000019575898,0.00020356079,0.0001601733,0.00010971663,0.00014033321],"domain_scores_gemma":[0.9997572,0.000067200504,0.000041049385,0.00006843258,0.000015594493,0.00005053696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083771796,0.00011516566,0.00011400707,0.000036965987,0.00012679203,0.00008588155,0.000020858111,0.00005442887,0.0000045695747],"category_scores_gemma":[0.0000089585,0.00012501098,0.000029337607,0.00003328306,0.000048993646,0.000092172035,0.000012572632,0.000064271764,8.5595224e-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":[0.0000064677592,0.00003747136,0.00069991534,0.00016853702,0.00009269967,0.0000025204795,0.0007834667,0.6640364,0.3187936,0.0002068054,0.0021308372,0.0130413035],"study_design_scores_gemma":[0.00075579336,0.000045005236,0.0018660515,0.00006438048,0.00005685822,0.00001209569,0.0003147019,0.9860313,0.00938171,0.000037813887,0.001244099,0.0001901743],"about_ca_topic_score_codex":0.0000010788507,"about_ca_topic_score_gemma":0.0000015266879,"teacher_disagreement_score":0.6149981,"about_ca_system_score_codex":0.00007673179,"about_ca_system_score_gemma":0.000006492902,"threshold_uncertainty_score":0.50978},"labels":[],"label_agreement":null},{"id":"W3134820413","doi":"10.1109/lra.2021.3061976","title":"Delay-Robust Nonlinear Control of Bounded-Input Telerobotic Systems With Synchronization Enhancement","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Control theory (sociology); Teleoperation; Robustness (evolution); Settling time; Bounded function; Lyapunov function; Nonlinear system; Computer science; Synchronization (alternating current); Tracking error; Controller (irrigation); Robot; Control engineering; Mathematics; Engineering; Control (management); Artificial intelligence; Step response; Physics","score_opus":0.008164473045547321,"score_gpt":0.1879783367697442,"score_spread":0.1798138637241969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134820413","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":[0.108855166,0.00027805156,0.88952434,0.00034745934,0.00055693946,0.00021091859,0.0000064351125,0.00012880252,0.000091871174],"genre_scores_gemma":[0.99027354,0.000043056974,0.009279728,0.00017889339,0.000119017604,0.000014596639,0.000027808897,0.000025216448,0.00003815199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909747,0.0000360449,0.0003578427,0.00014708863,0.0002103177,0.00015121148],"domain_scores_gemma":[0.9995445,0.000038144717,0.000081825,0.00015641688,0.00012544286,0.000053698364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009045299,0.00013915235,0.0002352125,0.00006869592,0.000062825755,0.00011351513,0.000044682456,0.000054360822,0.000012942348],"category_scores_gemma":[0.0000074842264,0.00012915877,0.000024893045,0.00015364906,0.000032828277,0.00012800965,0.0000044263584,0.000063923195,0.000009766452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021452229,0.000018864559,0.00021600955,0.00024307065,0.00008094847,0.000008414258,0.00013523977,0.9819962,0.016167818,0.0005015362,0.0002253948,0.00040438815],"study_design_scores_gemma":[0.0006684142,0.000030332947,0.0003028913,0.000165311,0.000045843455,0.00003744514,0.000069995935,0.9961306,0.002198789,0.0000011358287,0.00019424625,0.00015499388],"about_ca_topic_score_codex":0.000008117835,"about_ca_topic_score_gemma":0.0000087111885,"teacher_disagreement_score":0.88141835,"about_ca_system_score_codex":0.000062474275,"about_ca_system_score_gemma":0.00003331275,"threshold_uncertainty_score":0.5266942},"labels":[],"label_agreement":null},{"id":"W3134957701","doi":"10.1109/lra.2021.3063972","title":"Low-Level Force-Control of MR-Hydrostatic Actuators","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Actuator; Control theory (sociology); Magnetorheological fluid; Robot; Torque; Computer science; Control engineering; Haptic technology; Engineering; Simulation; Control (management); Artificial intelligence; Physics","score_opus":0.007146102316861018,"score_gpt":0.19951586822570086,"score_spread":0.19236976590883983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134957701","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":[0.37518242,0.00007142634,0.62280214,0.0013127052,0.00039103252,0.000099328994,0.00002119791,0.00007981386,0.000039915332],"genre_scores_gemma":[0.9832324,0.000041544023,0.016206125,0.00041463226,0.000032591295,0.000005257798,0.00001239643,0.000024141602,0.000030920954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922484,0.000023154653,0.00031196343,0.00012245456,0.00015581597,0.0001617884],"domain_scores_gemma":[0.999537,0.000115227514,0.00005864636,0.0001587223,0.00006758963,0.0000628153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008775191,0.00012518845,0.00019443354,0.00007557049,0.000046326517,0.000037184967,0.00005403643,0.000058008845,0.000008523268],"category_scores_gemma":[0.000030957814,0.00012480197,0.00006153247,0.00013733453,0.000055882116,0.000089186564,0.000008433883,0.00008198051,0.000007356491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011741193,0.00002178506,0.00026794776,0.00020715586,0.000045476518,0.000004188403,0.00041451125,0.9031426,0.09206409,0.001138484,0.00029855222,0.0023940841],"study_design_scores_gemma":[0.0007554994,0.00002670652,0.0034856042,0.000097870194,0.000048250913,0.000009908057,0.00006587478,0.9625472,0.03184835,0.0008022587,0.00007808844,0.00023442152],"about_ca_topic_score_codex":0.0000013059916,"about_ca_topic_score_gemma":0.0000020964085,"teacher_disagreement_score":0.60805,"about_ca_system_score_codex":0.000026965656,"about_ca_system_score_gemma":0.00001911959,"threshold_uncertainty_score":0.5089277},"labels":[],"label_agreement":null},{"id":"W3135089179","doi":"10.1109/lra.2021.3067281","title":"Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"California Institute of Technology; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Terrain; Mars Exploration Program; Robustness (evolution); Computer science; Sample (material); Context (archaeology); Matching (statistics); Artificial intelligence; Computer vision; Set (abstract data type); Geography; Cartography; Mathematics; Astrobiology; Statistics","score_opus":0.009827105674851604,"score_gpt":0.20860748995981598,"score_spread":0.1987803842849644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135089179","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":[0.17909437,0.00017163804,0.8184289,0.00082348444,0.00082062016,0.00030772464,0.00013771468,0.00020872297,0.0000068349336],"genre_scores_gemma":[0.9587565,0.0006880235,0.03840644,0.00071470713,0.00016827887,0.00004125665,0.001066096,0.00013878342,0.000019924124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986929,0.0000493342,0.00044100767,0.00038079335,0.00019748625,0.00023846298],"domain_scores_gemma":[0.9991031,0.00038257017,0.00013481773,0.00022479646,0.00006650054,0.00008816253],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017263713,0.0003142799,0.00035788302,0.00012667586,0.00014449235,0.00041390758,0.00008217026,0.00028810947,0.0000065707827],"category_scores_gemma":[0.00009211582,0.00035709474,0.000073162504,0.00008823929,0.0000390232,0.00015234151,0.00004880073,0.00021336228,6.991471e-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":[0.0000034629888,0.000009253386,0.0000849235,0.0004869089,0.00007934874,0.0000019956683,0.0002625691,0.9846891,0.0090512615,0.00020812558,0.0031332509,0.0019897788],"study_design_scores_gemma":[0.00021816813,0.00001191641,0.0001435448,0.0003117064,0.0001081299,0.000003757175,0.000054983968,0.99517,0.0029461638,0.00028750225,0.0003303352,0.00041381852],"about_ca_topic_score_codex":0.00004663401,"about_ca_topic_score_gemma":0.000008522729,"teacher_disagreement_score":0.78002244,"about_ca_system_score_codex":0.00009336481,"about_ca_system_score_gemma":0.000018722765,"threshold_uncertainty_score":0.9998881},"labels":[],"label_agreement":null},{"id":"W3136410377","doi":"10.1109/lra.2021.3066978","title":"Optimal Design of Continuum Robots With Reachability Constraints","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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":"Autodesk (Canada)","funders":"Office of Naval Research; Advanced Research Projects Agency - Energy; National Aeronautics and Space Administration","keywords":"Reachability; Robot; Workspace; Kinematics; Leverage (statistics); Actuator; Inverse kinematics; Computer science; Torque; Genetic algorithm; Control theory (sociology); Computation; Mathematical optimization; Robot kinematics; Mathematics; Algorithm; Mobile robot; Artificial intelligence; Physics","score_opus":0.012170755109106838,"score_gpt":0.2067340302858943,"score_spread":0.19456327517678745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136410377","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":[0.18194498,0.000035047346,0.8169685,0.00065243203,0.00007834053,0.00012060283,0.0000062542335,0.000107286105,0.00008657568],"genre_scores_gemma":[0.81903684,0.000015370722,0.1807836,0.00009257471,0.00002456164,0.000009913689,0.000010001578,0.000014928353,0.000012177765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994065,0.000020714187,0.00020350273,0.00013709147,0.00010297393,0.00012923083],"domain_scores_gemma":[0.99957544,0.00009435491,0.00004541874,0.00016796954,0.00006646753,0.000050356375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000867937,0.00010469665,0.00015867953,0.000033728134,0.000041247233,0.000033083612,0.00004711835,0.00004138714,0.000011534374],"category_scores_gemma":[0.000011749337,0.00010171586,0.000023745442,0.00012897025,0.00011673081,0.00006272459,0.000007730751,0.00007367185,0.0000028645272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013928374,0.000018414848,0.00027073923,0.00004146469,0.000031342915,0.000003662544,0.00009154114,0.9251153,0.07256619,0.00048592503,0.00042290593,0.0009511571],"study_design_scores_gemma":[0.000641669,0.000035391568,0.009371522,0.000088421766,0.00006762704,0.000048095193,0.00007933153,0.9424042,0.04674767,0.000118290794,0.000089950416,0.00030779946],"about_ca_topic_score_codex":0.0000019044196,"about_ca_topic_score_gemma":0.0000019389172,"teacher_disagreement_score":0.6370919,"about_ca_system_score_codex":0.000017575348,"about_ca_system_score_gemma":0.000019799196,"threshold_uncertainty_score":0.41478524},"labels":[],"label_agreement":null},{"id":"W3136955234","doi":"10.1109/lra.2021.3067253","title":"Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Range (aeronautics); Position (finance); Estimation; Computer science; Control theory (sociology); Artificial intelligence; Engineering; Aerospace engineering; Economics; Control (management); Systems engineering","score_opus":0.04588793386219,"score_gpt":0.2584813938968959,"score_spread":0.2125934600347059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136955234","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":[0.27978027,0.00014303587,0.7191535,0.00019086126,0.00046324375,0.00016998517,0.0000024891115,0.00008621697,0.000010377494],"genre_scores_gemma":[0.96937114,0.000026300211,0.03035133,0.00012273037,0.000028802242,0.0000073113797,0.00005973041,0.000025951007,0.0000067174224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990533,0.00006658738,0.0003326652,0.00016785986,0.00021790118,0.00016166946],"domain_scores_gemma":[0.999672,0.00002814566,0.000065198765,0.00011061121,0.00007751006,0.000046557823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015618806,0.00014120838,0.00016790297,0.00013432682,0.000077691846,0.000111203604,0.000031663778,0.00007757398,0.0000028960576],"category_scores_gemma":[0.000020965123,0.0001606814,0.000030009402,0.00022120695,0.000015577785,0.0002673852,0.0000065325735,0.00009117433,0.0000066550942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015112851,0.00002401922,0.0012020109,0.00010210117,0.00002975577,0.000012298114,0.00019473549,0.9334973,0.0646822,0.00013546093,0.000023683093,0.00009494196],"study_design_scores_gemma":[0.0006409098,0.000005966528,0.0069092405,0.00018772246,0.000034569384,0.00001004341,0.000024133482,0.99067193,0.0013327257,0.000011170017,0.0000030061094,0.00016858948],"about_ca_topic_score_codex":0.00002954616,"about_ca_topic_score_gemma":0.000014228008,"teacher_disagreement_score":0.6895909,"about_ca_system_score_codex":0.00023627357,"about_ca_system_score_gemma":0.000014733097,"threshold_uncertainty_score":0.65523976},"labels":[],"label_agreement":null},{"id":"W3137573274","doi":"10.1109/lra.2021.3065197","title":"Wheel-Legged Robotic Limb to Assist Human With Load Carriage: An Application For Environmental Disinfection During COVID-19","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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 British Columbia","funders":"Southern University of Science and Technology; State Key Laboratory of Robotics; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Sprayer; Coronavirus disease 2019 (COVID-19); Simulation; Computer science; Environmental science; Engineering; Medicine; Mechanical engineering","score_opus":0.007252148087073238,"score_gpt":0.23080990775291063,"score_spread":0.22355775966583738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137573274","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":[0.48477513,0.000023101438,0.51285064,0.0016397794,0.00015271352,0.00036789873,0.000012861157,0.00016476118,0.000013131295],"genre_scores_gemma":[0.98614264,0.000008593928,0.0129253855,0.0005184171,0.00010460308,0.00009483117,0.000114229275,0.000046005083,0.000045282883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989927,0.000031706677,0.00026013143,0.0003011962,0.00019968307,0.00021460083],"domain_scores_gemma":[0.9993928,0.00004841337,0.000058560257,0.0002455204,0.00003597757,0.00021869187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011838385,0.00018227672,0.00017518704,0.000088022556,0.00035256115,0.00012703125,0.00006262447,0.000071448776,0.0000045700926],"category_scores_gemma":[0.000019629644,0.00018624873,0.000046572502,0.00013306564,0.000049439128,0.00016897493,0.0000141248465,0.00009176054,0.0000054482384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057604143,0.000041097806,0.0012272676,0.00014485065,0.000022490256,0.0000021276298,0.00032190612,0.7869478,0.21057826,0.00025352876,0.000056867266,0.00039806636],"study_design_scores_gemma":[0.002840324,0.00042079831,0.12982297,0.00008929945,0.00020644595,0.00008111152,0.0004616905,0.84077823,0.022574631,0.00030522497,0.0012582324,0.0011610364],"about_ca_topic_score_codex":0.000007857721,"about_ca_topic_score_gemma":0.00006575857,"teacher_disagreement_score":0.5013675,"about_ca_system_score_codex":0.00033293717,"about_ca_system_score_gemma":0.000030526324,"threshold_uncertainty_score":0.75950027},"labels":[],"label_agreement":null},{"id":"W3140646774","doi":"10.1109/lra.2021.3068708","title":"Hey Robot, Which Way Are You Going? Nonverbal Motion Legibility Cues for Human-Robot Spatial Interaction","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Legibility; Motion (physics); Robot; Nonverbal communication; Mobile robot; Path (computing)","score_opus":0.04278551884643927,"score_gpt":0.3499708932833331,"score_spread":0.3071853744368938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140646774","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":[0.3476138,0.000050686223,0.6217975,0.019246032,0.009330716,0.0005391831,0.000034307996,0.00032590044,0.0010618557],"genre_scores_gemma":[0.9933192,0.000008333423,0.0023439014,0.002317702,0.0009888803,0.000062453146,0.00017802349,0.000040207346,0.00074129616],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99814534,0.00020034808,0.00053017447,0.000565589,0.0002254078,0.00033311057],"domain_scores_gemma":[0.998699,0.00019047066,0.00036985174,0.00031657965,0.0003182277,0.00010585738],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002667954,0.00025021695,0.00032827942,0.00012792673,0.00040495873,0.00022449245,0.00010207364,0.000202648,0.00038111542],"category_scores_gemma":[0.00012244351,0.00027794347,0.00016381741,0.00020308797,0.000060204133,0.00035819612,0.000028790977,0.0002875314,0.000117514],"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":[0.0008886844,0.0036834334,0.07386016,0.0010855843,0.0017817524,0.00014465187,0.022688363,0.2412911,0.38079312,0.027189624,0.14144467,0.10514885],"study_design_scores_gemma":[0.008093171,0.00053784257,0.78801376,0.0005577025,0.0006198619,0.00024046455,0.0065422812,0.15438332,0.020846874,0.002248674,0.015548219,0.002367852],"about_ca_topic_score_codex":0.00028680507,"about_ca_topic_score_gemma":0.0008381415,"teacher_disagreement_score":0.7141536,"about_ca_system_score_codex":0.00017351039,"about_ca_system_score_gemma":0.000025515857,"threshold_uncertainty_score":0.9999673},"labels":[],"label_agreement":null},{"id":"W3146841301","doi":"10.1109/lra.2021.3068554","title":"A Gravity-Referenced Moving Frame for Vehicle Path Following Applications in 3D","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Reference frame; Frame (networking); Trajectory; Path (computing); Curvature; Moving frame; Gravitational field; Motion planning; Computer vision; Control theory (sociology); Artificial intelligence; Mathematics; Control (management); Physics; Geometry; Classical mechanics; Robot","score_opus":0.016375031968111898,"score_gpt":0.25795880835869783,"score_spread":0.24158377639058592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146841301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105618864,0.000039324856,0.8909405,0.0027893889,0.0002603203,0.00021141335,0.0000031397926,0.00012309475,0.000013970765],"genre_scores_gemma":[0.43625185,0.0000023270823,0.56279486,0.0008171478,0.0000387376,0.000058572725,0.000014098646,0.000008480444,0.000013933536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989482,0.00004607762,0.0002464784,0.00034492614,0.0001713973,0.00024289514],"domain_scores_gemma":[0.99930894,0.00021663481,0.00008527728,0.00028388738,0.00004373039,0.000061507344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020838894,0.00011835278,0.00017159787,0.000098074896,0.0001578293,0.00020901553,0.00021456428,0.000058695234,3.8085477e-7],"category_scores_gemma":[0.000048438014,0.00012608596,0.00005277657,0.00035391073,0.000016317279,0.00028688394,0.00005233211,0.00011418596,0.000003728117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002467939,0.00020084073,0.015802799,0.00016895482,0.00007077902,0.000088814966,0.0033235997,0.82243156,0.09971016,0.02177916,0.000407161,0.036013708],"study_design_scores_gemma":[0.00037507518,0.000012494402,0.012002578,0.00006696761,0.000009717574,0.000005062508,0.00003562978,0.9849838,0.0010285358,0.001227397,0.000076889315,0.00017584105],"about_ca_topic_score_codex":0.00001088269,"about_ca_topic_score_gemma":0.0000018300235,"teacher_disagreement_score":0.33063298,"about_ca_system_score_codex":0.00004493793,"about_ca_system_score_gemma":0.000049793834,"threshold_uncertainty_score":0.51416355},"labels":[],"label_agreement":null},{"id":"W3148946639","doi":"10.1109/lra.2021.3068976","title":"On the Optimal Design of Underactuated Fingers Using Rolling Contact Joints","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Underactuation; Revolute joint; Joint (building); Optimal design; Engineering; Computer science; Robot; Structural engineering; Artificial intelligence","score_opus":0.04480899754397073,"score_gpt":0.23850167296341293,"score_spread":0.19369267541944218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148946639","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":[0.3550416,0.000018477294,0.6440112,0.00064803165,0.00014791965,0.000054907734,2.1728661e-7,0.00005709949,0.000020567079],"genre_scores_gemma":[0.98812044,0.000009259563,0.011422842,0.00039223436,0.00002633343,0.0000010509033,0.0000049052733,0.000016954473,0.00000597853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944496,0.000059849368,0.0001842118,0.00008710568,0.00011537199,0.00010849853],"domain_scores_gemma":[0.999576,0.00022289566,0.000057278503,0.00009069486,0.000028840439,0.000024296132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012764332,0.00008958179,0.00011192253,0.000054225227,0.00008895712,0.000060400518,0.000035186047,0.000034988407,0.00004134463],"category_scores_gemma":[0.000039694933,0.00007785829,0.000029551631,0.0001154918,0.000013445939,0.00007944045,0.0000050349317,0.000115235234,0.00000739687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019557206,0.0000039304923,0.000025614441,0.00001449604,0.000026550695,0.0000033407305,0.00017415559,0.8838353,0.11520336,0.0004071833,0.0001995236,0.00010455273],"study_design_scores_gemma":[0.00015608006,0.000007698406,0.00051358214,0.00006933102,0.000015162912,0.000004959778,0.00005144385,0.987447,0.011617955,0.000022900154,0.000008242747,0.000085623564],"about_ca_topic_score_codex":0.0000019756155,"about_ca_topic_score_gemma":2.2985043e-7,"teacher_disagreement_score":0.6330788,"about_ca_system_score_codex":0.000031449028,"about_ca_system_score_gemma":0.000011147324,"threshold_uncertainty_score":0.3174969},"labels":[],"label_agreement":null},{"id":"W3150360606","doi":"10.1109/lra.2021.3068669","title":"Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Specular reflection; Artificial intelligence; Polarimetry; Computer vision; Azimuth; Computer science; Polarization (electrochemistry); Monocular; Remote sensing; Optics; Geology; Physics","score_opus":0.018115661353274207,"score_gpt":0.21843935015500987,"score_spread":0.20032368880173568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150360606","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":[0.2564696,0.0003489308,0.7423346,0.00027456996,0.00030164103,0.0000812454,0.0000016073128,0.00013799268,0.000049824957],"genre_scores_gemma":[0.8663879,0.00006510413,0.13294218,0.00043015883,0.000086616375,0.0000014839146,0.00003173253,0.000041804342,0.000012991545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991095,0.000046965215,0.00027301518,0.00018840437,0.00016903106,0.00021310094],"domain_scores_gemma":[0.9995817,0.000060362374,0.00005717375,0.00015401478,0.00007105251,0.00007568858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089322006,0.00015782844,0.00017376604,0.00020847833,0.00014171896,0.00012407082,0.00004479544,0.00009031963,0.00000535008],"category_scores_gemma":[0.000039405673,0.00017898082,0.0000538633,0.0005131819,0.000024368152,0.00021320692,0.000013357762,0.00013249552,0.000010755706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.168562e-7,0.000008449005,0.0007258181,0.000050650655,0.000050001658,0.000029969533,0.0001860392,0.941011,0.05600207,0.00035289145,0.000048209717,0.0015342706],"study_design_scores_gemma":[0.00024978665,0.000005180071,0.004945517,0.000048749564,0.000045151555,0.000026801828,0.00004574827,0.98879755,0.0054307175,0.00008104024,0.00010736165,0.00021638651],"about_ca_topic_score_codex":0.000011105244,"about_ca_topic_score_gemma":0.000005029644,"teacher_disagreement_score":0.6099183,"about_ca_system_score_codex":0.00010100499,"about_ca_system_score_gemma":0.00001766375,"threshold_uncertainty_score":0.7298626},"labels":[],"label_agreement":null},{"id":"W3172982719","doi":"10.1109/lra.2021.3083465","title":"Expectations Vs. Reality: Unreliability and Transparency in a Treasure Hunt Game With Icub","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"European Commission","keywords":"iCub; Transparency (behavior); Affect (linguistics); Robot; Treasure; Humanoid robot; Computer science; Human–computer interaction; Psychology; Quality (philosophy); Simulation; Artificial intelligence; Computer security; Communication; Geography","score_opus":0.024699854357770064,"score_gpt":0.261043406057689,"score_spread":0.23634355169991894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172982719","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":[0.41454014,0.000061392595,0.56393814,0.021151507,0.00009032093,0.00010091643,0.0000022215236,0.00007190164,0.000043452856],"genre_scores_gemma":[0.974653,0.000025552887,0.024650784,0.00061691366,0.000020155901,0.0000132728765,0.0000036708439,0.000005663588,0.000010985655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895406,0.00009164974,0.00026814046,0.0003384197,0.00018367749,0.00016405275],"domain_scores_gemma":[0.9994242,0.00012205861,0.0000653793,0.0002579176,0.00006816911,0.00006224539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014526008,0.000110133304,0.00015252008,0.00007300936,0.00007206161,0.00016866837,0.00011383016,0.000041540923,0.0000028827476],"category_scores_gemma":[0.00003589801,0.00010078322,0.000020789677,0.00033132563,0.00009354203,0.0003558925,0.00001889002,0.00009868304,0.0000027387796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004934375,0.0007359775,0.084512345,0.00039474553,0.00011336589,0.00037777683,0.06574573,0.6506008,0.030319264,0.06578865,0.0010294992,0.10033253],"study_design_scores_gemma":[0.00040134887,0.00011149028,0.1552327,0.00018834756,0.000027422155,0.0000593656,0.0007534396,0.8280767,0.008941802,0.005561378,0.00017266684,0.00047334083],"about_ca_topic_score_codex":0.00008344138,"about_ca_topic_score_gemma":0.0003652719,"teacher_disagreement_score":0.56011283,"about_ca_system_score_codex":0.000035101,"about_ca_system_score_gemma":0.000048518657,"threshold_uncertainty_score":0.41098204},"labels":[],"label_agreement":null},{"id":"W3176880374","doi":"10.1109/lra.2021.3093551","title":"A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Traverse; Terrain; Reinforcement learning; Robot; Pipeline (software); Artificial intelligence; Computer science; Computer vision; Point (geometry); Mobile robot; Trajectory; Geography; Mathematics; Cartography","score_opus":0.01694845159004387,"score_gpt":0.26733563832348006,"score_spread":0.2503871867334362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176880374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007852049,0.000011474693,0.9766116,0.013914068,0.00053377234,0.0008387378,0.0000010649644,0.00018769012,0.00004952416],"genre_scores_gemma":[0.7057514,0.000010474908,0.29053578,0.0027777755,0.0001558136,0.00015257754,0.00017119013,0.00003171467,0.00041322544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997997,0.00007629301,0.000659862,0.0005080188,0.00028278772,0.00047606946],"domain_scores_gemma":[0.9988467,0.00027593222,0.00026087242,0.0003244483,0.0001777475,0.00011428802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048340816,0.00023871631,0.00029237714,0.000201134,0.0002359925,0.00037306343,0.00029101473,0.00009665285,0.0000026166024],"category_scores_gemma":[0.00016710868,0.0002680089,0.00009426251,0.00036329866,0.000026644517,0.00046242133,0.00010353127,0.00017124185,0.000007440198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010556152,0.00001819746,0.00014115545,0.00009421139,0.000016793172,0.000006045904,0.0013961623,0.9822825,0.007529847,0.0022750439,0.00038062254,0.0058488403],"study_design_scores_gemma":[0.0012266351,0.00012497578,0.00061590195,0.000107947824,0.000016873033,0.000009100382,0.000051754912,0.9950338,0.0013759525,0.00017259578,0.0009547368,0.000309726],"about_ca_topic_score_codex":0.000027357803,"about_ca_topic_score_gemma":0.000016115706,"teacher_disagreement_score":0.6978994,"about_ca_system_score_codex":0.00020153735,"about_ca_system_score_gemma":0.00007190674,"threshold_uncertainty_score":0.99997723},"labels":[],"label_agreement":null},{"id":"W3183869404","doi":"10.1109/lra.2021.3098915","title":"Intelligent Locomotion Planning With Enhanced Postural Stability for Lower-Limb Exoskeletons","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Exoskeleton; Trajectory; Inverted pendulum; Controller (irrigation); Gait; Control theory (sociology); Computer science; Humanoid robot; Robot; Simulation; Physical medicine and rehabilitation; Artificial intelligence; Control (management)","score_opus":0.013723283788007568,"score_gpt":0.23108158624919975,"score_spread":0.21735830246119217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183869404","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":[0.44476056,0.000051457308,0.55312467,0.0013879177,0.0003445902,0.00018422955,0.000007436302,0.00011520742,0.000023954166],"genre_scores_gemma":[0.95971674,0.00001982194,0.039884284,0.00022195323,0.000056877638,0.00002339256,0.000039341026,0.00002529289,0.000012281501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999224,0.000021377495,0.00024225313,0.00018819777,0.00012589198,0.00019825813],"domain_scores_gemma":[0.999496,0.00011990012,0.000044508302,0.00015867426,0.00011374597,0.00006719278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009793206,0.00014591817,0.00015677784,0.000051743755,0.000105351464,0.0000814695,0.000047769005,0.00005590665,0.0000043559044],"category_scores_gemma":[0.000022761564,0.00013237704,0.000051744686,0.0001209056,0.00005312677,0.00010646867,0.000009971349,0.000097329066,0.0000027252481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001087513,0.000035267938,0.00017134905,0.00018806102,0.00003572057,0.0000028376048,0.00057570706,0.88212657,0.11480203,0.0004516069,0.0001870086,0.001412965],"study_design_scores_gemma":[0.0007705632,0.00028710868,0.006439425,0.00018187815,0.00006426451,0.000017533242,0.00043719626,0.9084147,0.0821189,0.00041149167,0.00035399702,0.00050293474],"about_ca_topic_score_codex":8.662989e-7,"about_ca_topic_score_gemma":0.0000048418115,"teacher_disagreement_score":0.5149562,"about_ca_system_score_codex":0.0000621404,"about_ca_system_score_gemma":0.000018759622,"threshold_uncertainty_score":0.5398179},"labels":[],"label_agreement":null},{"id":"W3188272423","doi":"10.1109/lra.2021.3102300","title":"Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Heading (navigation); RSS; Gyroscope; Orientation (vector space); Compass; Robot; Signal strength; Gaussian; Position (finance)","score_opus":0.012184573701099504,"score_gpt":0.2228633227354646,"score_spread":0.2106787490343651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188272423","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":[0.3607329,0.00020150091,0.6379476,0.000529828,0.0001171379,0.00006462183,0.0000039714632,0.00034681353,0.000055615397],"genre_scores_gemma":[0.9825096,0.00012825556,0.017127529,0.00015694664,0.000033252036,0.0000029319945,0.000018011493,0.000017416085,0.000006057184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994266,0.000013925447,0.00018190131,0.0001382985,0.00009596751,0.0001433093],"domain_scores_gemma":[0.9997563,0.0000535525,0.000038500773,0.000072256094,0.00004400175,0.000035441815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047845802,0.000118239244,0.00012466258,0.0000911117,0.00012455258,0.00013733443,0.00003225585,0.000068783,0.0000067670517],"category_scores_gemma":[0.00004493699,0.00012248437,0.000013507402,0.00021117939,0.0000424211,0.00023148843,0.000006081172,0.00007982834,0.0000012460328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.5302653e-7,0.0000062445783,0.0004621323,0.0002578934,0.000019095014,0.0000068391423,0.00028464303,0.89667094,0.09753746,0.0002491208,0.00012843283,0.0043762657],"study_design_scores_gemma":[0.00020866896,0.0000070076603,0.00037379743,0.000121159836,0.000023803215,0.000028541524,0.00012120734,0.82558894,0.17309926,0.0002171525,0.000040931216,0.00016953799],"about_ca_topic_score_codex":0.0000032568973,"about_ca_topic_score_gemma":0.0000048545157,"teacher_disagreement_score":0.6217767,"about_ca_system_score_codex":0.000033266428,"about_ca_system_score_gemma":0.000018206232,"threshold_uncertainty_score":0.49947676},"labels":[],"label_agreement":null},{"id":"W3195146316","doi":"10.1109/lra.2021.3105996","title":"Adaptive CPG-Based Gait Planning With Learning-Based Torque Estimation and Control for Exoskeletons","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":53,"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 Institutes of Health Research; Canada Foundation for Innovation","keywords":"Exoskeleton; Gait; Torque; Central pattern generator; Control theory (sociology); Controller (irrigation); Trajectory; Computer science; Autoregressive model; Engineering; Simulation; Artificial intelligence; Control (management); Mathematics; Physical medicine and rehabilitation","score_opus":0.00736667913045379,"score_gpt":0.2112536715423875,"score_spread":0.2038869924119337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195146316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06613635,0.00008793018,0.93057233,0.0026527513,0.00011317445,0.0002459525,0.0000128465745,0.00016640432,0.00001228394],"genre_scores_gemma":[0.91121423,0.0000023274717,0.08810391,0.00053030846,0.000027947612,0.000049645194,0.00003564996,0.000028470908,0.0000074935433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993352,0.000032173597,0.00018490365,0.00016775625,0.000114086266,0.00016586758],"domain_scores_gemma":[0.99939966,0.00029609603,0.000057546004,0.00008903737,0.00009045243,0.000067189336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102835926,0.00014503146,0.00016643587,0.00007847094,0.00014160178,0.000091147274,0.000028546401,0.00006252689,0.0000017835212],"category_scores_gemma":[0.00003800304,0.0001381508,0.00003255257,0.000094647425,0.00006188043,0.00008334951,0.0000030944054,0.00010937447,9.569341e-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":[0.000011515843,0.000012852658,0.0004384757,0.00013572264,0.00002651027,0.0000036631843,0.000114378134,0.9942611,0.00374676,0.00037676917,0.00011644452,0.0007558166],"study_design_scores_gemma":[0.0012697942,0.00011539249,0.0020678206,0.00012054036,0.00004847885,0.0000039315773,0.000049694725,0.9944772,0.0014630962,0.00008150776,0.0001138199,0.0001887167],"about_ca_topic_score_codex":0.0000014840514,"about_ca_topic_score_gemma":0.0000028411118,"teacher_disagreement_score":0.8450779,"about_ca_system_score_codex":0.000036251535,"about_ca_system_score_gemma":0.000036444933,"threshold_uncertainty_score":0.56336266},"labels":[],"label_agreement":null},{"id":"W3196552464","doi":"10.1109/lra.2021.3102946","title":"Multiobjective Trajectory Tracking of a Flexible Tool During Robotic Percutaneous Nephrolithotomy","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","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":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot end effector; Percutaneous nephrolithotomy; Path (computing); Artificial intelligence; Trajectory; Computer science; Motion planning; Sorting; Computer vision; Robot; Medicine; Percutaneous; Surgery; Algorithm","score_opus":0.014585437986857346,"score_gpt":0.2568056130092255,"score_spread":0.24222017502236814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196552464","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":[0.99299544,0.0001921669,0.0047851615,0.0013302948,0.00022241805,0.00026946104,0.0000063953403,0.0000883081,0.00011034618],"genre_scores_gemma":[0.9889319,0.000034378692,0.009818861,0.00083180465,0.00008454785,0.000009572116,0.000016242966,0.000025195424,0.0002474858],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989432,0.000042424923,0.0003092618,0.00026621413,0.00021916999,0.00021970537],"domain_scores_gemma":[0.99942297,0.00005320143,0.000112888796,0.00020034116,0.000110332934,0.000100238874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005518217,0.00016392246,0.00032868693,0.00011060545,0.00010143335,0.000032113676,0.000031016763,0.00007070116,0.00003412322],"category_scores_gemma":[0.0000377762,0.00015582156,0.0001124614,0.00016945311,0.000057188936,0.000114456016,0.000013913311,0.00011643875,0.000006066718],"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":[0.00014999836,0.0005802678,0.03836329,0.0006967112,0.000657835,0.0011883235,0.0037038743,0.059392653,0.8910737,0.0002591891,0.00011526872,0.0038188642],"study_design_scores_gemma":[0.0031101785,0.00016054114,0.89846265,0.000395776,0.0005977702,0.003868357,0.0005131089,0.026517505,0.065934174,0.000026296313,0.00005377284,0.00035989002],"about_ca_topic_score_codex":0.000022147073,"about_ca_topic_score_gemma":9.69179e-7,"teacher_disagreement_score":0.8600993,"about_ca_system_score_codex":0.00008809376,"about_ca_system_score_gemma":0.00007434568,"threshold_uncertainty_score":0.6354219},"labels":[],"label_agreement":null},{"id":"W3198835173","doi":"10.1109/lra.2021.3136867","title":"Keeping an Eye on Things: Deep Learned Features for Long-Term Visual Localization","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pipeline (software); Generalizability theory; Computer vision; Robot; Estimator; Ground truth; Feature (linguistics); Term (time); Artificial neural network; Path (computing); Mathematics","score_opus":0.012969542933268555,"score_gpt":0.259535263609319,"score_spread":0.24656572067605045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198835173","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":[0.11826004,0.00007270887,0.87967575,0.0009838898,0.00051854044,0.00017472246,0.0000026131463,0.00027815512,0.00003357075],"genre_scores_gemma":[0.98840547,0.000073105664,0.007581928,0.0032523698,0.00024354461,0.00001177022,0.00033959773,0.00006604283,0.000026154412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990696,0.000041085066,0.00024620345,0.0002460673,0.00017706098,0.0002200273],"domain_scores_gemma":[0.9995689,0.000059186867,0.00005611633,0.00014972452,0.000084993386,0.00008112357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009873584,0.00017749767,0.00017131882,0.00010939738,0.00018012994,0.00022568031,0.00005642593,0.000112126916,0.0000060537436],"category_scores_gemma":[0.00003284831,0.0001945012,0.00005075523,0.00015743946,0.000024229275,0.0002744071,0.0000072512007,0.0001051186,0.0000043959603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061862984,0.000029966617,0.00037491636,0.00014213938,0.00002854593,0.000007460365,0.00026820155,0.9776803,0.015151466,0.0009667019,0.00019417249,0.005149889],"study_design_scores_gemma":[0.00042003347,0.000050657607,0.0043773763,0.00008509504,0.00003356882,0.000004843862,0.000033709643,0.9849162,0.009703547,0.00007530312,0.00005730457,0.00024235298],"about_ca_topic_score_codex":0.000001845668,"about_ca_topic_score_gemma":0.000008525044,"teacher_disagreement_score":0.87209386,"about_ca_system_score_codex":0.00005839707,"about_ca_system_score_gemma":0.000012747299,"threshold_uncertainty_score":0.79315287},"labels":[],"label_agreement":null},{"id":"W3199710032","doi":"10.1109/lra.2021.3111078","title":"Precision Grasp Using an Arm-Hand System as a Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thumb; GRASP; Revolute joint; Robotic arm; Computer science; Inverse kinematics; Workspace; Redundancy (engineering); Kinematics; Wrench; Projection (relational algebra); Artificial intelligence; Computer vision; Control theory (sociology); Robot; Algorithm; Engineering; Control (management)","score_opus":0.029807096850118415,"score_gpt":0.23925394445730008,"score_spread":0.20944684760718166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199710032","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":[0.3924364,0.000022737584,0.6060638,0.00009994039,0.00086988456,0.0001400848,0.0000014771238,0.00031597193,0.000049743318],"genre_scores_gemma":[0.9558913,0.00000539686,0.043609943,0.00010409561,0.0002740737,0.000006931447,0.000052263622,0.000043955857,0.000012059115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872154,0.00008225825,0.00044857315,0.0002404554,0.0002701577,0.0002370242],"domain_scores_gemma":[0.9994129,0.000041366657,0.00012951925,0.00021930746,0.00007909761,0.00011780915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020613124,0.00020627215,0.00026305954,0.00013859237,0.00024399003,0.00031901634,0.00007083321,0.000085282416,0.000008410695],"category_scores_gemma":[0.000026506803,0.0002248302,0.00005825043,0.00017830306,0.00002844185,0.0003788134,0.000020796128,0.00014474873,0.00002138732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004920844,0.000020275955,0.000024529969,0.0004259993,0.000028442659,0.000021917174,0.00028211865,0.88329333,0.11496556,0.0006694719,0.00013442828,0.00012901258],"study_design_scores_gemma":[0.0006810462,0.000019834604,0.00029807186,0.00039848743,0.00006498278,0.00020600256,0.00030736905,0.9957243,0.0020080607,0.0000074524755,0.000034309727,0.00025008444],"about_ca_topic_score_codex":0.00002882768,"about_ca_topic_score_gemma":0.000009532274,"teacher_disagreement_score":0.56345487,"about_ca_system_score_codex":0.0001851212,"about_ca_system_score_gemma":0.00002788338,"threshold_uncertainty_score":0.91683096},"labels":[],"label_agreement":null},{"id":"W3200142631","doi":"10.1109/lra.2022.3191939","title":"Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristics; Computer science; Path (computing); Set (abstract data type); Mathematical optimization; Regular polygon; Line (geometry); Time complexity; Line segment; Algorithm; Mathematics; Artificial intelligence","score_opus":0.015312047606541755,"score_gpt":0.24194091139813234,"score_spread":0.22662886379159058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200142631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14389953,0.000021626016,0.85441613,0.0009488894,0.00047549178,0.00016932239,0.000012774106,0.000041624957,0.00001458036],"genre_scores_gemma":[0.7037604,0.0000015187766,0.2953753,0.00071547314,0.000045128392,0.00004181381,0.000019572517,0.000010358291,0.00003045862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989574,0.000042928157,0.00026991352,0.00024997705,0.00026797142,0.00021182495],"domain_scores_gemma":[0.999403,0.0001292301,0.00021534156,0.0001923024,0.000010085311,0.000050028182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026373265,0.00011083192,0.00016699218,0.00010111868,0.0002764505,0.000067202,0.00025239872,0.000025115794,0.0000032873156],"category_scores_gemma":[0.0000120231825,0.00012647484,0.00004559482,0.000113378796,0.0000343566,0.00023577479,0.000098082295,0.000101504294,0.0000019221436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002946156,0.000024649948,0.0005170528,0.000018694247,0.000018477154,0.000009111374,0.0009430122,0.98264414,0.014064261,0.00014304083,0.0009074388,0.0007071881],"study_design_scores_gemma":[0.00048692952,0.00009475092,0.0046679215,0.000023487037,0.0000112259695,0.00001682958,0.00003816292,0.9925877,0.0016605553,0.000056737324,0.00021314091,0.00014259265],"about_ca_topic_score_codex":0.000002675046,"about_ca_topic_score_gemma":1.4848002e-8,"teacher_disagreement_score":0.5598609,"about_ca_system_score_codex":0.00004510902,"about_ca_system_score_gemma":0.000019069737,"threshold_uncertainty_score":0.51574945},"labels":[],"label_agreement":null},{"id":"W3201099806","doi":"10.1109/lra.2021.3139145","title":"Learning Selective Communication for Multi-Agent Path Finding","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":75,"is_retracted":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; Simon Fraser University","funders":"","keywords":"Computer science; Overhead (engineering); Imitation; Reinforcement learning; Artificial intelligence; Path (computing); Focus (optics); Simple (philosophy); Scheme (mathematics); Machine learning; Telecommunications network; Distributed computing; Computer network; Mathematics","score_opus":0.03388270364324859,"score_gpt":0.28015295791465555,"score_spread":0.24627025427140697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201099806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0100318445,0.000043485157,0.98623323,0.0030477496,0.00025175785,0.00017277352,5.389846e-7,0.00017232096,0.000046282745],"genre_scores_gemma":[0.5175215,0.000040801417,0.48131567,0.00082992116,0.000028911854,0.000016643478,0.000021067175,0.0000118260305,0.000213666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990408,0.000110443005,0.00023241244,0.00024229991,0.0001674819,0.00020657151],"domain_scores_gemma":[0.9991868,0.00019136579,0.00017443245,0.00026708446,0.00013137513,0.000048933765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026664807,0.00011444107,0.00012782753,0.00007345909,0.00038248408,0.00030634715,0.00022170972,0.000048792055,0.0000015345986],"category_scores_gemma":[0.00012230285,0.00012538613,0.000048151094,0.00021287095,0.000027017473,0.00034379584,0.000089797344,0.0001693894,0.000008038716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.404927e-7,0.000012907325,0.000603686,0.000026747868,0.000021878881,0.0000021120738,0.0011028436,0.9854792,0.0060035563,0.004177393,0.0003360714,0.0022328652],"study_design_scores_gemma":[0.00039356964,0.000034267257,0.0025912074,0.000047273395,0.000011771623,0.000007954215,0.000061326886,0.9941434,0.0019781617,0.000051234005,0.0005295276,0.00015030448],"about_ca_topic_score_codex":0.0000028573438,"about_ca_topic_score_gemma":0.0000012993012,"teacher_disagreement_score":0.5074896,"about_ca_system_score_codex":0.00007351528,"about_ca_system_score_gemma":0.00003764358,"threshold_uncertainty_score":0.51130974},"labels":[],"label_agreement":null},{"id":"W3201490607","doi":"10.1109/lra.2021.3133587","title":"Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"Canada Foundation for Innovation; Canadian Institute for Advanced Research","keywords":"Affine transformation; Linearization; State (computer science); Control theory (sociology); Estimation; Control (management); Mathematics; Computer science; Applied mathematics; Nonlinear system; Algorithm; Engineering; Artificial intelligence; Pure mathematics","score_opus":0.012966138432338535,"score_gpt":0.22965940671824134,"score_spread":0.2166932682859028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201490607","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":[0.054372754,0.000110502566,0.9434907,0.0005559822,0.0008956919,0.00029469925,0.00008919567,0.00017139447,0.000019083755],"genre_scores_gemma":[0.99540895,0.000020760328,0.004075405,0.00009240179,0.00008929726,0.000021731981,0.00022708478,0.000021425114,0.000042919575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992234,0.000033070595,0.00035673214,0.00014729594,0.00012531083,0.000114181195],"domain_scores_gemma":[0.9994989,0.00007569383,0.000094548304,0.00021359506,0.00008108051,0.00003620472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013824237,0.00010145743,0.00020647321,0.00006551899,0.00004945024,0.00008544871,0.00006699548,0.000046521174,0.0000021490714],"category_scores_gemma":[0.00003254638,0.00010679518,0.000026686801,0.00011385261,0.000013646292,0.00019951093,0.000007476806,0.000045412817,0.0000031391282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003999327,0.000007518984,0.000032348282,0.00024144768,0.000054637618,8.576062e-7,0.000056161465,0.95612764,0.039704703,0.00011457055,0.0008518454,0.0028042605],"study_design_scores_gemma":[0.00084318186,0.0000148623085,0.00023999161,0.00005724663,0.00003864579,0.0000065477298,0.000017517212,0.9967804,0.0012481128,0.000009368847,0.0006373703,0.000106749016],"about_ca_topic_score_codex":0.000014446713,"about_ca_topic_score_gemma":0.000009190819,"teacher_disagreement_score":0.9410362,"about_ca_system_score_codex":0.000026360789,"about_ca_system_score_gemma":0.000013116533,"threshold_uncertainty_score":0.4354981},"labels":[],"label_agreement":null},{"id":"W3201588370","doi":"10.1109/lra.2022.3189165","title":"Robust Visual Teach and Repeat for UGVs Using 3D Semantic Maps","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Robot; Path (computing); Orientation (vector space); Point cloud; Pose; Orb (optics); Simultaneous localization and mapping; Mobile robot; Image (mathematics); Mathematics","score_opus":0.019529474180043714,"score_gpt":0.22357746318095742,"score_spread":0.2040479890009137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201588370","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":[0.33029994,0.00006293529,0.6683887,0.00049944955,0.00039347803,0.00019598228,0.00001142153,0.00013620415,0.0000118581],"genre_scores_gemma":[0.96971965,0.000019938274,0.029445948,0.0005405597,0.00011499485,0.000015126955,0.00007365995,0.000048320842,0.00002181607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925065,0.00002934491,0.00021807833,0.00017535583,0.00014283866,0.0001837115],"domain_scores_gemma":[0.99974316,0.000047273905,0.000046832534,0.0000930329,0.000020407671,0.000049290426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014623751,0.00013001225,0.00014529735,0.00012095564,0.00029121432,0.00008742686,0.00004214801,0.00003615394,0.0000056754784],"category_scores_gemma":[0.000008763416,0.00014773339,0.000029825376,0.00011296049,0.000024584877,0.00009544993,0.000020271362,0.00009631591,6.3551346e-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":[0.0000028844147,0.000010849606,0.00022089024,0.000099987294,0.000020047459,0.0000031621316,0.00013809289,0.98169404,0.015874557,0.00019852817,0.0009360272,0.00080095034],"study_design_scores_gemma":[0.000349134,0.00003215522,0.00027155146,0.000016787651,0.000040729203,0.00002184257,0.00005655834,0.9982953,0.00034616343,0.000037843933,0.00034839672,0.00018351973],"about_ca_topic_score_codex":0.000008772298,"about_ca_topic_score_gemma":0.0000015950916,"teacher_disagreement_score":0.63941973,"about_ca_system_score_codex":0.00006797274,"about_ca_system_score_gemma":0.000007391009,"threshold_uncertainty_score":0.6024393},"labels":[],"label_agreement":null},{"id":"W3202938653","doi":"10.1109/lra.2022.3224667","title":"Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinforcement learning; Computer science; Robot; Robustness (evolution); Scalability; Artificial intelligence; Benchmark (surveying); Action selection; Macro; Computation; Machine learning; Distributed computing","score_opus":0.03577697130002772,"score_gpt":0.26641158264475134,"score_spread":0.23063461134472363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202938653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014885179,0.000013722965,0.9924181,0.0047376663,0.00047067585,0.00056147954,3.68764e-7,0.00028743563,0.000022006194],"genre_scores_gemma":[0.7050973,0.000025600695,0.29304078,0.0011759979,0.000042206928,0.00023563065,0.00005988253,0.000027333857,0.0002952317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853027,0.0000930653,0.00032513603,0.00032302397,0.0003983619,0.00033012015],"domain_scores_gemma":[0.99919766,0.00010887201,0.0002838777,0.0002554156,0.00007300006,0.000081197795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000259682,0.00017725988,0.00016255451,0.00016963496,0.0009911851,0.0002962599,0.0002846015,0.000029648972,0.000014214636],"category_scores_gemma":[0.000027540562,0.00017869785,0.000052732354,0.00030462898,0.000037216436,0.0007348805,0.00010171265,0.00021814855,0.000005468016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012398139,0.000019148176,0.00014987802,0.000022341288,0.000034402085,0.0000026090481,0.0009966803,0.99158317,0.0027533239,0.002978636,0.0002120618,0.0012353237],"study_design_scores_gemma":[0.0011922945,0.00023394744,0.00027678927,0.000012694718,0.00002460875,0.000014198287,0.00014888558,0.9957434,0.000338297,0.000023528177,0.0017540478,0.0002373348],"about_ca_topic_score_codex":0.000008203789,"about_ca_topic_score_gemma":0.0000029123562,"teacher_disagreement_score":0.7036088,"about_ca_system_score_codex":0.00015584375,"about_ca_system_score_gemma":0.000039035447,"threshold_uncertainty_score":0.7623495},"labels":[],"label_agreement":null},{"id":"W3206454137","doi":"10.1109/lra.2022.3189164","title":"Lifelong Topological Visual Navigation","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Spurious relationship; Computer science; Graph; Topological graph; Topological map; Topology (electrical circuits); Artificial intelligence; Real-time computing; Computer vision; Theoretical computer science; Machine learning; Mathematics; Robot; Mobile robot","score_opus":0.008427013100457658,"score_gpt":0.21513806640068125,"score_spread":0.2067110533002236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206454137","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":[0.66818464,0.000037660735,0.32904467,0.0016038748,0.0006658663,0.00010400411,0.000005301453,0.00028913407,0.0000648395],"genre_scores_gemma":[0.9966333,0.000008960811,0.0022840004,0.00084888807,0.000100784964,0.00001224607,0.00007947019,0.000018456516,0.000013929464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993241,0.00003941144,0.00018593608,0.00012208492,0.00018403625,0.00014445091],"domain_scores_gemma":[0.99980134,0.000032190623,0.000032778673,0.000072547984,0.000014280709,0.000046885027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009994597,0.000098494966,0.00009833486,0.00007228554,0.00021278749,0.00005447742,0.0000514085,0.000032908065,0.000033059994],"category_scores_gemma":[0.0000050260014,0.00010665317,0.000028205322,0.00014796681,0.000025933754,0.000082565704,0.00001653118,0.00012897376,0.00000708663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015210722,0.000012779257,0.00023114104,0.000019119527,0.000007994657,0.0000070603346,0.00010326223,0.985758,0.010335015,0.0012700928,0.0013329724,0.0009210603],"study_design_scores_gemma":[0.00017748756,0.000033533037,0.0013762929,0.000005046694,0.000010529697,0.000014318209,0.000045052366,0.9967675,0.00072747114,0.00011725031,0.000581544,0.00014397867],"about_ca_topic_score_codex":0.0000037446566,"about_ca_topic_score_gemma":4.4003218e-7,"teacher_disagreement_score":0.3284486,"about_ca_system_score_codex":0.00006590873,"about_ca_system_score_gemma":0.000005158079,"threshold_uncertainty_score":0.434919},"labels":[],"label_agreement":null},{"id":"W3213354518","doi":"10.1109/lra.2021.3129136","title":"OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Benchmark (surveying); Upload; Computer science; Robot; Competition (biology); Cloud computing; Table (database); Artificial intelligence; Workflow; Set (abstract data type); Field (mathematics); Data mining; Database; Operating system; Programming language","score_opus":0.016250725308444625,"score_gpt":0.22201195693813652,"score_spread":0.2057612316296919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213354518","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":[0.16570832,0.00014582025,0.8318641,0.0016303093,0.00031339092,0.00016504744,3.8642682e-7,0.00014083208,0.000031751046],"genre_scores_gemma":[0.98588383,0.000028450297,0.013328733,0.00053303625,0.0001054632,0.000011683565,0.00007591935,0.00002241812,0.000010482016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993733,0.000027591708,0.00019880229,0.00017115052,0.00008850684,0.00014060654],"domain_scores_gemma":[0.9996952,0.000089297246,0.0000466091,0.00007636133,0.000036165227,0.000056370016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089216905,0.00012341524,0.00014266052,0.00008778361,0.00015078638,0.00014330898,0.000019493198,0.000055717283,0.0000071467744],"category_scores_gemma":[0.000019364548,0.00014337931,0.00002515622,0.00009275722,0.000025769166,0.00015209956,0.0000072795383,0.00007828192,0.0000012826716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019852657,0.0000047197805,0.00092475733,0.00019553138,0.000013504632,0.0000020919317,0.00009966916,0.9821409,0.01344563,0.0022227142,0.00012942127,0.0008190696],"study_design_scores_gemma":[0.00047501244,0.000013750941,0.026401775,0.00008752763,0.0000305429,0.000012103246,0.000030127512,0.9720101,0.0005192132,0.00015512537,0.000100161844,0.00016457493],"about_ca_topic_score_codex":0.0000025961206,"about_ca_topic_score_gemma":0.0000060359416,"teacher_disagreement_score":0.82017547,"about_ca_system_score_codex":0.000026510956,"about_ca_system_score_gemma":0.0000068760723,"threshold_uncertainty_score":0.58468384},"labels":[],"label_agreement":null},{"id":"W3215761265","doi":"10.1109/lra.2023.3242201","title":"Learning to Search in Task and Motion Planning With Streams","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"STREAMS; Task (project management); Motion (physics); Computer science; Artificial intelligence; Economics; Computer network; Management","score_opus":0.018195057553560334,"score_gpt":0.2579148893678889,"score_spread":0.2397198318143286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215761265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49524522,0.0000048029906,0.50119543,0.0032222003,0.00006961998,0.00008076238,3.4739764e-7,0.00017023197,0.000011408003],"genre_scores_gemma":[0.89967215,0.0000036275508,0.09988112,0.000358564,0.000031940173,0.0000070069227,0.0000056594085,0.000010087377,0.000029839095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990402,0.000060815135,0.00014002972,0.00028627162,0.00022206763,0.0002506028],"domain_scores_gemma":[0.999639,0.00009688133,0.00003889401,0.00012326923,0.000019952797,0.000082023784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032864936,0.00010171543,0.00011519706,0.0003186224,0.00011648867,0.00019078543,0.0001216897,0.000033325046,2.1445524e-7],"category_scores_gemma":[0.000019995729,0.00009499249,0.000007914496,0.0006026227,0.000023776005,0.0002672338,0.000060505674,0.00014952348,0.00001496499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013972025,0.000004513573,0.011136292,0.000015295092,0.0000043257187,0.000055413628,0.003144931,0.96664375,0.0019753322,0.00010833333,0.00009916678,0.016811272],"study_design_scores_gemma":[0.00021287015,0.0000636807,0.099394046,0.00009912067,0.000001974451,0.000017211149,0.00013496194,0.8998095,0.00011978191,0.000020519723,0.000011301904,0.000115041454],"about_ca_topic_score_codex":0.00002399985,"about_ca_topic_score_gemma":9.4392954e-7,"teacher_disagreement_score":0.40442693,"about_ca_system_score_codex":0.000027594342,"about_ca_system_score_gemma":0.000012715121,"threshold_uncertainty_score":0.3873681},"labels":[],"label_agreement":null},{"id":"W3217751284","doi":"10.1109/lra.2021.3130648","title":"Direct Sparse Odometry With Planes","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"","keywords":"Odometry; Artificial intelligence; Computer science; Plane (geometry); Pose; Computer vision; Visual odometry; Artificial neural network; Segmentation; Algorithm; Mathematics; Robot; Geometry; Mobile robot","score_opus":0.008284449927900232,"score_gpt":0.1849438401600627,"score_spread":0.17665939023216248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217751284","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":[0.40629295,0.00018636261,0.59034526,0.0013189288,0.0005130702,0.000093734714,0.00000861528,0.00039787698,0.0008432272],"genre_scores_gemma":[0.98881334,0.00007942127,0.010343178,0.0005503126,0.00008624126,0.0000024274846,0.00004846398,0.00002853106,0.000048073573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944973,0.000017050352,0.0001364614,0.00012785214,0.0001293573,0.0001395195],"domain_scores_gemma":[0.9997285,0.000042599466,0.00002384132,0.00012065095,0.00003141149,0.000053008967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004064888,0.000113110065,0.00012891817,0.00007298665,0.000057043486,0.00008659682,0.00003082009,0.00004253078,0.000011994034],"category_scores_gemma":[0.0000074489512,0.00010488368,0.000019253273,0.00019444592,0.000023565592,0.0000860806,0.000004553222,0.000060259707,0.000010989058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014234616,0.000008362014,0.0005807768,0.000055208016,0.000028548151,0.000033522378,0.000057422356,0.9841591,0.012971699,0.0002443323,0.0013187348,0.0005408791],"study_design_scores_gemma":[0.00035548065,0.000017347633,0.0034089515,0.000063714884,0.000035793695,0.000038357055,0.000030145016,0.9765342,0.01824351,0.000015658068,0.0010049692,0.00025186074],"about_ca_topic_score_codex":0.0000028558989,"about_ca_topic_score_gemma":0.0000061588853,"teacher_disagreement_score":0.5825204,"about_ca_system_score_codex":0.00002057567,"about_ca_system_score_gemma":0.000008498191,"threshold_uncertainty_score":0.42770323},"labels":[],"label_agreement":null},{"id":"W4205250459","doi":"10.1109/lra.2021.3137535","title":"Dynamic Modeling of Tendon-Driven Co-Manipulative Continuum Robots","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Robot; Computer science; Object (grammar); Equations of motion; Dynamic equation; Motion (physics); Bending; Simulation; Classical mechanics; Control theory (sociology); Physics; Structural engineering; Engineering; Artificial intelligence; Mathematics","score_opus":0.016782430667277134,"score_gpt":0.24325592769341092,"score_spread":0.22647349702613379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205250459","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":[0.24715313,0.00009205158,0.7513958,0.0007189503,0.00017185036,0.00010183973,0.000011976442,0.0001405343,0.00021382896],"genre_scores_gemma":[0.9708613,0.00006257421,0.028827105,0.00012329686,0.000027241873,0.00000884933,0.0000450192,0.000024766385,0.000019803432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993022,0.0000131995575,0.00027222122,0.00014784702,0.000116049705,0.00014849518],"domain_scores_gemma":[0.9996224,0.000049280123,0.00005146244,0.00016240677,0.00006591089,0.000048513466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004167757,0.0001216435,0.00018561175,0.00006387518,0.000059882415,0.00003850313,0.00006174339,0.000051260256,0.0000068388904],"category_scores_gemma":[0.000008788403,0.00013417305,0.000048357742,0.00013904793,0.000030875366,0.00008886216,0.000012071849,0.000091665184,0.0000067483343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.9616774e-7,0.000011682451,0.00015193406,0.000041914296,0.00003387395,0.000002433522,0.00012368531,0.8950571,0.10354324,0.0005756534,0.00021296715,0.00024515443],"study_design_scores_gemma":[0.00020628197,0.0000048301245,0.0012574104,0.000044142253,0.000027546983,0.0000067445985,0.00004063956,0.99439627,0.0036501947,0.00019809959,0.00002640148,0.00014141374],"about_ca_topic_score_codex":0.0000034267184,"about_ca_topic_score_gemma":0.000008835902,"teacher_disagreement_score":0.7237082,"about_ca_system_score_codex":0.0000307358,"about_ca_system_score_gemma":0.000011105677,"threshold_uncertainty_score":0.54714185},"labels":[],"label_agreement":null},{"id":"W4205344192","doi":"10.1109/lra.2021.3133610","title":"Contact Sequence Planning for Hexapod Robots in Sparse Foothold Environment Based on Monte-Carlo Tree","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","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":"Toronto Metropolitan University","funders":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Hexapod; Monte Carlo method; Sequence (biology); Monte Carlo tree search; Tree (set theory); Computer science; Robot; Artificial intelligence; Mathematics; Statistics; Combinatorics; Chemistry","score_opus":0.02492905328424697,"score_gpt":0.22670257123906037,"score_spread":0.2017735179548134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205344192","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":[0.16569293,0.0001134324,0.82707405,0.0057558753,0.0005067006,0.00046770804,0.000017177596,0.0002060248,0.00016608325],"genre_scores_gemma":[0.98739195,0.000012713202,0.01090759,0.00151206,0.00005638373,0.000047228816,0.000017677303,0.000026892005,0.000027487706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913174,0.000032600514,0.00025672815,0.00020733227,0.0001466624,0.00022491631],"domain_scores_gemma":[0.9996027,0.00010967012,0.00004228254,0.00016915513,0.000010958124,0.00006523766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010598145,0.00016550359,0.0002029657,0.00009581395,0.00005079045,0.00006883855,0.000058885034,0.00006410211,0.000009218681],"category_scores_gemma":[0.000014031063,0.00017738299,0.000057728987,0.000071944814,0.000015148968,0.00009717829,0.000005696945,0.00011353192,0.000006374548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067075316,0.000027185593,0.00045937233,0.000042481122,0.000014581075,0.000028863005,0.000096442054,0.9726014,0.02461044,0.00014713209,0.00036001584,0.00160539],"study_design_scores_gemma":[0.0013417054,0.00003472436,0.0070597674,0.00011374959,0.000016038986,0.0000040213595,0.00002653878,0.98936754,0.0016300895,0.00001977588,0.00019398624,0.00019205007],"about_ca_topic_score_codex":0.0000033330875,"about_ca_topic_score_gemma":0.0000043800774,"teacher_disagreement_score":0.821699,"about_ca_system_score_codex":0.00011043892,"about_ca_system_score_gemma":0.000012948307,"threshold_uncertainty_score":0.7233468},"labels":[],"label_agreement":null},{"id":"W4205863870","doi":"10.1109/lra.2022.3140813","title":"Hybrid Hierarchical Learning for Adaptive Persuasion in Human-Robot Interaction","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Persuasion; Robot; Artificial intelligence; Human–computer interaction; Robustness (evolution); Benchmark (surveying); Architecture; Machine learning; Psychology","score_opus":0.09853142058525839,"score_gpt":0.4002143626322813,"score_spread":0.3016829420470229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205863870","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":[0.31437424,0.000014605968,0.6806785,0.004165967,0.0003780014,0.00031300835,0.000006640903,0.000042230935,0.000026840278],"genre_scores_gemma":[0.9920699,0.000003297887,0.0071570547,0.00032481877,0.00009653172,0.00007955681,0.00001977483,0.000015686735,0.0002333772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976413,0.0003316676,0.00043494682,0.0004204961,0.00090552855,0.00026603855],"domain_scores_gemma":[0.9984812,0.001029395,0.00018073063,0.00015562859,0.00008785235,0.00006519152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014530588,0.00011413359,0.00019760933,0.0005506259,0.0006547672,0.0001627981,0.00023819106,0.000023458055,0.00007495341],"category_scores_gemma":[0.00041355524,0.0001073788,0.000068274676,0.0003507277,0.00007671664,0.00037300523,0.00012753738,0.00048031894,0.000009266347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004897959,0.000037970283,0.00022664212,0.0000035780017,0.000005379326,0.000010740826,0.0004741579,0.96737516,0.009630842,0.00027809292,0.0009120476,0.0209964],"study_design_scores_gemma":[0.0007075059,0.00023205606,0.0027475243,0.000010960168,0.000003686606,0.000019914885,0.0008176317,0.98869044,0.00063201826,0.003948492,0.0020404307,0.00014935897],"about_ca_topic_score_codex":0.000014052161,"about_ca_topic_score_gemma":0.000004621309,"teacher_disagreement_score":0.6776957,"about_ca_system_score_codex":0.00020148592,"about_ca_system_score_gemma":0.000026269829,"threshold_uncertainty_score":0.5036006},"labels":[],"label_agreement":null},{"id":"W4205991048","doi":"10.1109/lra.2021.3135940","title":"Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Toyota Research Institute","keywords":"Computer science; Artificial intelligence; Component (thermodynamics); Leverage (statistics); Automaton; Theoretical computer science; Generator (circuit theory); Pipeline (software); Machine learning; Key (lock); Representation (politics); Finite-state machine; Generative model; Artificial neural network; Generative grammar; Programming language","score_opus":0.01624430306944624,"score_gpt":0.2481310575836374,"score_spread":0.23188675451419116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205991048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27715334,0.00014133267,0.7186513,0.003153738,0.0005945746,0.0000638158,3.8737087e-7,0.00021620913,0.000025308998],"genre_scores_gemma":[0.52622855,0.000029382061,0.46757984,0.0047453553,0.0011378407,0.000011664393,0.000014612791,0.000031894037,0.00022087297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844944,0.0004269446,0.00021614297,0.0003619017,0.00023377097,0.00031176576],"domain_scores_gemma":[0.9993231,0.00008719736,0.00013072847,0.00029973945,0.000072276685,0.00008693531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003806764,0.0001651468,0.00016280395,0.00004678015,0.0008364969,0.0005856444,0.00023650836,0.00005257879,0.00000736128],"category_scores_gemma":[0.000023289369,0.00013452183,0.000048799597,0.0003350245,0.000045627272,0.00045313424,0.00007059104,0.00027348075,0.000004134327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.81249e-7,0.000013857776,0.0003075738,0.000009105,0.000017146469,0.000028453918,0.001016049,0.9762396,0.013985035,0.0022559636,0.0004577287,0.0056690047],"study_design_scores_gemma":[0.00015840266,0.00003210384,0.0007567615,0.000021194273,0.000011742457,0.00006143491,0.00007824722,0.9956763,0.0021480953,0.00012097934,0.00074650475,0.00018824334],"about_ca_topic_score_codex":0.000019948993,"about_ca_topic_score_gemma":0.0000045732263,"teacher_disagreement_score":0.25107145,"about_ca_system_score_codex":0.00003909944,"about_ca_system_score_gemma":0.000075091055,"threshold_uncertainty_score":0.64337426},"labels":[],"label_agreement":null},{"id":"W4206422650","doi":"10.1109/lra.2021.3135928","title":"Learning Submodular Objectives for Team Environmental Monitoring","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regret; Ranking (information retrieval); Computer science; Submodular set function; Set (abstract data type); Robot; Machine learning; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.008081190280587158,"score_gpt":0.2125070409888544,"score_spread":0.20442585070826724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206422650","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":[0.45734188,0.000059843034,0.5414166,0.000710001,0.00032037127,0.00005081627,4.3531645e-7,0.00008435677,0.000015708292],"genre_scores_gemma":[0.93798774,0.000015867054,0.061597455,0.00017081988,0.00013589977,0.000006389182,0.0000031020109,0.000009552004,0.000073171585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926716,0.000042973592,0.00013107671,0.00025953772,0.0001245781,0.00017465423],"domain_scores_gemma":[0.99964356,0.000080850565,0.00006076605,0.00015275783,0.000015405543,0.000046627294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012934375,0.00009397708,0.000101921876,0.000041915173,0.00026802934,0.00023356176,0.00008295616,0.000033818043,6.7075314e-7],"category_scores_gemma":[0.000022348846,0.00010245334,0.000045051296,0.00007277582,0.000025655761,0.0002220579,0.000038335667,0.00008846095,0.000003147743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015984347,0.00003800902,0.0046247155,0.000040867435,0.00003350225,0.00001498337,0.0019298336,0.34247726,0.6078542,0.00067150034,0.00016663955,0.042146884],"study_design_scores_gemma":[0.00040965428,0.000047942383,0.0135553805,0.000067225585,0.000015867607,0.00005040705,0.00033058014,0.8639529,0.120415024,0.00017638654,0.00070599123,0.00027267012],"about_ca_topic_score_codex":0.0000015283664,"about_ca_topic_score_gemma":1.5430669e-7,"teacher_disagreement_score":0.5214756,"about_ca_system_score_codex":0.000037529953,"about_ca_system_score_gemma":0.0000126466975,"threshold_uncertainty_score":0.4177926},"labels":[],"label_agreement":null},{"id":"W4206474659","doi":"10.1109/lra.2021.3133934","title":"A Reinforcement Learning Approach in Assignment of Task Priorities in Kinematic Control of Redundant Robots","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Kinematics; Task (project management); Robot; Computer science; Control (management); Artificial intelligence; Engineering; Physics; Systems engineering","score_opus":0.01096546022137327,"score_gpt":0.20858787270766657,"score_spread":0.1976224124862933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206474659","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":[0.24493432,0.00012124445,0.7541952,0.0002546685,0.000088709035,0.0001608665,2.0130354e-7,0.000036644324,0.00020813197],"genre_scores_gemma":[0.9946446,0.000028935248,0.005216846,0.000050270344,0.000012007649,0.00000879137,0.000008080566,0.000011999258,0.000018462344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906385,0.000064158165,0.00047869195,0.00009939823,0.0001647534,0.00012914487],"domain_scores_gemma":[0.99971116,0.00005622151,0.00010430881,0.00008317451,0.000022553186,0.000022589405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019688431,0.00009593908,0.000251979,0.00016901728,0.000017305556,0.000019111207,0.000036646677,0.00004055116,0.000007650321],"category_scores_gemma":[0.000025800482,0.000104289706,0.00003006268,0.00018127421,0.000022001657,0.00008995814,0.000008667713,0.00013591723,6.224601e-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":[0.0000024162116,0.000015328456,0.0026574868,0.00037422727,0.000016176473,0.000003549152,0.0009958774,0.9524549,0.04278591,0.00041101358,0.000010987527,0.00027212888],"study_design_scores_gemma":[0.0007217145,0.000013854631,0.018100154,0.00021790605,0.000009695156,0.0000029401751,0.00021727142,0.979578,0.0010255853,0.000011120929,0.0000069302228,0.000094788134],"about_ca_topic_score_codex":0.000011169338,"about_ca_topic_score_gemma":0.0000031225343,"teacher_disagreement_score":0.7497103,"about_ca_system_score_codex":0.000053462994,"about_ca_system_score_gemma":0.000013633251,"threshold_uncertainty_score":0.42528108},"labels":[],"label_agreement":null},{"id":"W4213421957","doi":"10.1109/lra.2022.3154021","title":"An MPC Formulation on $SO(3)$ for a Quadrotor With Bidirectional Thrust and Nonlinear Thrust Constraints","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thrust; Control theory (sociology); Nonlinear system; Model predictive control; Controller (irrigation); Trajectory; Linearization; Hessian matrix; Computer science; Control engineering; Mathematics; Engineering; Applied mathematics; Physics; Control (management); Artificial intelligence; Aerospace engineering","score_opus":0.012405891357893155,"score_gpt":0.2264872568562803,"score_spread":0.21408136549838713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213421957","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":[0.58881205,0.000045383378,0.4080452,0.0010828809,0.00052036013,0.00091696955,0.00016538946,0.00031938747,0.0000923868],"genre_scores_gemma":[0.9854744,0.0000025416073,0.013742524,0.00036898,0.00021744873,0.000090914225,0.000061932435,0.000032849584,0.000008399938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993074,0.000027393748,0.000177459,0.00016840365,0.00017630703,0.00014302848],"domain_scores_gemma":[0.99967086,0.00008402016,0.00006155777,0.00009350035,0.000033751825,0.000056303295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014297036,0.0001332971,0.00014675056,0.00010441648,0.00025298158,0.00007259208,0.000044449847,0.0000315566,0.000017285965],"category_scores_gemma":[0.00000623816,0.0001282952,0.00002374484,0.00007343715,0.000047553323,0.0001613943,0.0000062070853,0.00010346644,0.0000014755107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004428212,0.00003316224,0.0003684255,0.000051713145,0.00005849073,0.0000030937413,0.00023068691,0.980069,0.012598817,0.0006353792,0.00036797777,0.005538996],"study_design_scores_gemma":[0.0009424965,0.00023232742,0.0037900666,0.000021201984,0.000020941867,0.000025461115,0.00009101142,0.9932563,0.00021197952,0.000011433256,0.0012178795,0.0001789155],"about_ca_topic_score_codex":0.0000048673123,"about_ca_topic_score_gemma":0.000005951758,"teacher_disagreement_score":0.39666238,"about_ca_system_score_codex":0.00007240222,"about_ca_system_score_gemma":0.000016590713,"threshold_uncertainty_score":0.5231727},"labels":[],"label_agreement":null},{"id":"W4220783120","doi":"10.1109/lra.2022.3160833","title":"Pressing and Rubbing: Physics-Informed Features Facilitate Haptic Terrain Classification for Legged Robots","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","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":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Terrain; Artificial intelligence; Robot; Computer science; Slipping; Feature extraction; Computer vision; Rubbing; Pattern recognition (psychology); Feature (linguistics); Haptic technology; Machine learning; Engineering; Geography; Cartography; Mechanical engineering","score_opus":0.02333862690166939,"score_gpt":0.2298319599979744,"score_spread":0.20649333309630502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220783120","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":[0.06453037,0.00013762762,0.9281203,0.0053770076,0.0006205868,0.0006360979,0.000024163413,0.00039276725,0.00016107237],"genre_scores_gemma":[0.991511,0.000013170756,0.0074438425,0.0006201044,0.00008144589,0.00012689152,0.00004725905,0.000025813883,0.00013045878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992242,0.0000319635,0.00022440156,0.00016852678,0.00015110293,0.00019983342],"domain_scores_gemma":[0.99964654,0.00008861214,0.00006600034,0.000121841935,0.000019055833,0.000057915564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012957113,0.00014717468,0.00015868849,0.00007562514,0.0003281292,0.00013528441,0.00006865878,0.00003311633,0.000005040993],"category_scores_gemma":[0.0000144519,0.00015599352,0.000044081662,0.00008200691,0.000038040434,0.00020791318,0.000016904234,0.00012894919,0.0000014322932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006091604,0.00001130103,0.000037779533,0.000105520645,0.000036682344,5.997888e-7,0.0011970848,0.9353752,0.046501797,0.0010485807,0.002392255,0.01328708],"study_design_scores_gemma":[0.0007979295,0.000027021524,0.0042478936,0.00001908809,0.000031224197,0.000008350262,0.00022011493,0.9932513,0.00024360596,0.00019626386,0.0007631508,0.00019405314],"about_ca_topic_score_codex":0.0000040213868,"about_ca_topic_score_gemma":0.0000013055204,"teacher_disagreement_score":0.9269806,"about_ca_system_score_codex":0.00007998567,"about_ca_system_score_gemma":0.000013067611,"threshold_uncertainty_score":0.6361231},"labels":[],"label_agreement":null},{"id":"W4221152040","doi":"10.1109/lra.2022.3174971","title":"Intensity Image-Based LiDAR Fiducial Marker System","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Fiducial marker; Lidar; Point cloud; Coordinate system; Computer vision; Artificial intelligence; Intensity (physics); Computer science; Remote sensing; Physics; Optics; Geology","score_opus":0.006510953459521566,"score_gpt":0.18238573747363487,"score_spread":0.1758747840141133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221152040","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":[0.28602248,0.000030739906,0.7089957,0.0019840985,0.0017670196,0.00022872358,0.000024229636,0.0006590746,0.00028794137],"genre_scores_gemma":[0.9926898,0.0000023083303,0.0062364237,0.0008502649,0.00010993329,0.000013121003,0.000052109255,0.000034049855,0.000011987877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991493,0.000049225444,0.00023361118,0.00016019908,0.00022464799,0.0001829695],"domain_scores_gemma":[0.99966574,0.000032267646,0.000048959468,0.00015495038,0.000040578445,0.000057513163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015919734,0.00013814411,0.00015954189,0.00012462834,0.00024179847,0.00008449645,0.00007628456,0.000035152443,0.00002375089],"category_scores_gemma":[0.0000071305194,0.00015573359,0.00004870345,0.00017043651,0.000029286155,0.00007981266,0.000018360499,0.00014670535,0.00001126555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006825545,0.0000105655345,0.00018471673,0.00010022148,0.000016594813,0.000016038814,0.00006465026,0.98122543,0.013543961,0.00032662606,0.004255625,0.00024871744],"study_design_scores_gemma":[0.00030174298,0.00001852837,0.001710973,0.000019550147,0.000023165569,0.000012361761,0.00006445591,0.9960981,0.001032199,0.000011083002,0.0005221631,0.00018566505],"about_ca_topic_score_codex":0.00001003119,"about_ca_topic_score_gemma":0.0000010602353,"teacher_disagreement_score":0.7066673,"about_ca_system_score_codex":0.00015462017,"about_ca_system_score_gemma":0.0000132060295,"threshold_uncertainty_score":0.6350632},"labels":[],"label_agreement":null},{"id":"W4226194912","doi":"10.1109/lra.2022.3167065","title":"A Discrete Non-Linear Series Elastic Actuator for Active Ankle-Foot Orthoses","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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":"Carleton University; Ontario Tech University","funders":"","keywords":"Actuator; Crank; Stiffness; Control theory (sociology); Ankle; Linear actuator; Torque; Exoskeleton; Power (physics); Joint stiffness; Linear model; Displacement (psychology); Computer science; Engineering; Simulation; Structural engineering; Mechanical engineering; Physics; Cylinder","score_opus":0.007978825634381865,"score_gpt":0.22202363421957858,"score_spread":0.2140448085851967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226194912","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":[0.5914041,0.00004272555,0.40322796,0.0032288365,0.0010873051,0.0005593878,0.00016802346,0.00025010965,0.00003156745],"genre_scores_gemma":[0.9593413,0.000019100078,0.03988442,0.00034473432,0.00010393608,0.0001468125,0.000065468776,0.000048859652,0.000045370798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921674,0.000020435002,0.00022884292,0.00016465687,0.00016343243,0.00020591325],"domain_scores_gemma":[0.9995913,0.00012184719,0.000058931473,0.00012882605,0.000036177622,0.00006291738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000954488,0.00015371457,0.00016632136,0.00009923923,0.00027691753,0.00004815843,0.000084467494,0.000034888086,0.000009871562],"category_scores_gemma":[0.000022099743,0.00015354772,0.0000650075,0.00013021252,0.000056418514,0.00014604209,0.000029391636,0.00012680111,0.000003973499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013712013,0.000016675422,0.00006330147,0.00011052237,0.000044298158,0.0000016678036,0.0007503068,0.9862344,0.009794298,0.00032447441,0.0006601036,0.0019862347],"study_design_scores_gemma":[0.0005522153,0.0001793946,0.0015613616,0.000021601141,0.00004900496,0.000012597362,0.00031168872,0.9921946,0.0017165963,0.00046267337,0.002612733,0.00032553135],"about_ca_topic_score_codex":0.0000021404373,"about_ca_topic_score_gemma":0.0000022926881,"teacher_disagreement_score":0.3679372,"about_ca_system_score_codex":0.0000644372,"about_ca_system_score_gemma":0.000017361675,"threshold_uncertainty_score":0.6261494},"labels":[],"label_agreement":null},{"id":"W4283327156","doi":"10.1109/lra.2022.3179424","title":"Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Weighting; Residual; Gaussian; Computer science; Mathematical optimization; Mathematics; Norm (philosophy); Mode (computer interface); Convergence (economics); Multivariate statistics; Econometrics; Algorithm; Statistics","score_opus":0.0380937386487361,"score_gpt":0.23969678695688698,"score_spread":0.20160304830815087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283327156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010181777,0.00005176934,0.97522175,0.012858393,0.0010679857,0.00042662732,0.00006132712,0.000116799245,0.000013547265],"genre_scores_gemma":[0.92001057,0.000008385156,0.07725272,0.0022396473,0.00020085691,0.00011628155,0.000047629506,0.000020078855,0.00010383921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986632,0.000118843964,0.00026375763,0.00036783074,0.0003019738,0.0002843945],"domain_scores_gemma":[0.9990555,0.00028974543,0.00017886731,0.0003568023,0.00006197209,0.000057107063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035525046,0.00015567434,0.00014221297,0.00007533801,0.00089291204,0.00025322192,0.0005189834,0.00003598365,0.0000120170325],"category_scores_gemma":[0.000013976482,0.00012327985,0.00006466141,0.0002338476,0.000065772205,0.00027540448,0.00017885702,0.00020500875,0.000005968709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075817657,0.000032757496,0.00007031888,0.000013522914,0.000019669558,0.0000049929895,0.0010233661,0.9782194,0.000424766,0.003201829,0.0074940887,0.009487704],"study_design_scores_gemma":[0.0004124833,0.00007228516,0.0006672941,0.00001991585,0.000013701629,0.000025966005,0.000111419264,0.9924796,0.00007640901,0.0002980376,0.0056328718,0.00018999988],"about_ca_topic_score_codex":0.00003253058,"about_ca_topic_score_gemma":0.000006903242,"teacher_disagreement_score":0.9098288,"about_ca_system_score_codex":0.00004113574,"about_ca_system_score_gemma":0.000025263347,"threshold_uncertainty_score":0.6867648},"labels":[],"label_agreement":null},{"id":"W4285048301","doi":"10.1109/lra.2022.3190096","title":"A High-Fidelity Simulation Platform for Industrial Manufacturing by Incorporating Robotic Dynamics Into an Industrial Simulation Tool","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Flexible and Reconfigurable Manufacturing Systems","field":"Engineering","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 Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modular design; Robot; Automation; Computer science; Software; Simulation software; Fidelity; Task (project management); Industrial robot; Control engineering; High fidelity; Control system; Simulation; Embedded system; Engineering; Systems engineering; Artificial intelligence","score_opus":0.027858371546887428,"score_gpt":0.23784881094469604,"score_spread":0.20999043939780862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285048301","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":[0.674835,0.000009500244,0.322279,0.00019836977,0.0016846175,0.00062218425,0.000043988082,0.00032106746,0.000006222265],"genre_scores_gemma":[0.9968659,0.000001054206,0.001728292,0.00013447009,0.00052983826,0.00007220156,0.00059105025,0.00005821635,0.000018974028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835557,0.000068539026,0.00066156354,0.00031082795,0.00031015367,0.00029335244],"domain_scores_gemma":[0.99918395,0.0002585303,0.00023760677,0.0002080066,0.000029521389,0.00008237785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004619613,0.00026753766,0.0003158672,0.00020531677,0.000548688,0.00022296765,0.00014050322,0.00017944301,0.000011901915],"category_scores_gemma":[0.000028933826,0.00029872113,0.00006473518,0.00013354138,0.000027119862,0.0005790805,0.000029904933,0.00038024594,0.0000017567945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025348461,0.000019924924,0.0000704421,0.00008296202,0.000043347587,0.0000011867916,0.00016840476,0.98269856,0.001948835,0.00011090158,0.0003267787,0.0145033365],"study_design_scores_gemma":[0.0012462904,0.00009400819,0.00008808539,0.00003181658,0.000039569237,0.0000024743545,0.000105054285,0.9947831,0.0026693016,0.00035962692,0.0002152375,0.0003654152],"about_ca_topic_score_codex":0.000121456396,"about_ca_topic_score_gemma":0.000017538052,"teacher_disagreement_score":0.32203084,"about_ca_system_score_codex":0.0004916286,"about_ca_system_score_gemma":0.000026331112,"threshold_uncertainty_score":0.9999465},"labels":[],"label_agreement":null},{"id":"W4285163324","doi":"10.1109/lra.2022.3176102","title":"Adaptative Friction Shock Absorbers and Reverse Thrust for Fast Multirotor Landing on Inclined Surfaces","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"","keywords":"Thrust; Multirotor; Shock (circulatory); Aerospace engineering; Drone; Landing gear; Marine engineering; Envelope (radar); Kinetic energy; Environmental science; Geology; Engineering; Physics","score_opus":0.012267614168802142,"score_gpt":0.20589258609444924,"score_spread":0.1936249719256471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285163324","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":[0.8210389,0.00005955275,0.17626107,0.0007317849,0.0011706125,0.00028766642,0.000035352718,0.00036336732,0.000051685525],"genre_scores_gemma":[0.995923,0.00001918029,0.0036447095,0.00010856125,0.000087670385,0.00006865179,0.000021188815,0.00002904984,0.000098002354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994585,0.000018672443,0.00014128204,0.00013354253,0.000108716056,0.00013926916],"domain_scores_gemma":[0.9997603,0.000081190774,0.000039308463,0.00006697454,0.000009914163,0.000042279116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001185401,0.00012214975,0.00012883276,0.00007842925,0.00021228724,0.000036901,0.000035025434,0.0000314987,0.0000038270546],"category_scores_gemma":[0.000008268373,0.00012788588,0.000025146415,0.00007895944,0.000013111026,0.00008429176,0.000010995692,0.000100695055,0.000001305472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006410672,0.0000050981866,0.00009556965,0.00005389336,0.000027561357,0.0000010016072,0.0009981497,0.98199046,0.014401964,0.00013854788,0.001964947,0.00031639225],"study_design_scores_gemma":[0.00041824742,0.00005091201,0.0010600766,0.000027645581,0.000013254967,0.0000029454468,0.0005701376,0.99619234,0.00035256098,0.000005136111,0.0011473168,0.0001594407],"about_ca_topic_score_codex":0.000026992811,"about_ca_topic_score_gemma":0.0000036577646,"teacher_disagreement_score":0.17488408,"about_ca_system_score_codex":0.00008146562,"about_ca_system_score_gemma":0.0000029211387,"threshold_uncertainty_score":0.52150345},"labels":[],"label_agreement":null},{"id":"W4285186326","doi":"10.1109/lra.2022.3187262","title":"Sample-Efficient Policy Adaptation for Exoskeletons Under Variations in the Users and the Environment","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","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":"Exoskeleton; Computer science; Robot; Sample (material); Controller (irrigation); Adaptation (eye); Set (abstract data type); Traverse; Simulation; Artificial intelligence","score_opus":0.011507091152488855,"score_gpt":0.21346742014617648,"score_spread":0.20196032899368763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285186326","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":[0.06498757,0.000058846945,0.8935416,0.0406333,0.00014890406,0.00055646175,0.000029412979,0.000032310156,0.00001162036],"genre_scores_gemma":[0.9892314,0.000029903507,0.009119751,0.0013710882,0.000029367846,0.00018191569,0.00001949936,0.000012632546,0.0000044623557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993656,0.00007903807,0.00018518858,0.00009787115,0.00014748432,0.00012481172],"domain_scores_gemma":[0.99925435,0.00056160334,0.000039645005,0.000117548574,0.0000073163287,0.000019544212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037921147,0.00008117889,0.00007918498,0.00009297726,0.0003145553,0.000057541234,0.0000754349,0.000018247823,0.0000023772373],"category_scores_gemma":[0.000026219765,0.000057917776,0.000031312746,0.00013175,0.00007834915,0.00003058493,0.000017234508,0.000096132724,5.316213e-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":[0.000002044378,0.000014442469,0.000026445068,0.000011623164,0.0000090271105,7.970066e-8,0.0028816576,0.8961704,0.00017650708,0.10011802,0.00009797494,0.0004917806],"study_design_scores_gemma":[0.0006296866,0.000017654158,0.0038068965,0.0000029484024,0.000017315215,0.0000021472144,0.00090949977,0.991194,0.000004171071,0.0027569234,0.00058272976,0.00007602242],"about_ca_topic_score_codex":0.000034976783,"about_ca_topic_score_gemma":0.000004841267,"teacher_disagreement_score":0.9242438,"about_ca_system_score_codex":0.00007738831,"about_ca_system_score_gemma":0.000011988992,"threshold_uncertainty_score":0.24193369},"labels":[],"label_agreement":null},{"id":"W4285187007","doi":"10.1109/lra.2022.3186769","title":"A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Government of Alberta","keywords":"Artificial intelligence; Domain (mathematical analysis); Computer science; Notation; Machine learning; Set (abstract data type); Smoothness; Algorithm; Mathematics; Arithmetic; Programming language","score_opus":0.027469276895766018,"score_gpt":0.29891436594571463,"score_spread":0.27144508904994863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285187007","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":[0.72553724,0.00007111819,0.27278998,0.0008770958,0.00010504288,0.00048122628,0.000004970105,0.00005351582,0.00007978465],"genre_scores_gemma":[0.98732716,0.0000047623266,0.011746414,0.00047295252,0.000032194126,0.000052372303,0.0003358447,0.0000151285785,0.000013169007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988984,0.00015762042,0.00032838495,0.00018348501,0.000320403,0.000111723704],"domain_scores_gemma":[0.9992306,0.00037507352,0.00020635901,0.000055676195,0.00007870736,0.000053542328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008169934,0.00009738657,0.00024781213,0.00019054729,0.00017630002,0.000021616519,0.000018273142,0.00003300828,0.000023087861],"category_scores_gemma":[0.00010043628,0.00009500004,0.000067758265,0.00018088597,0.000037080645,0.00006275869,0.000015714539,0.00011511265,1.7555963e-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":[0.00028358708,0.00021437586,0.008769519,0.00019360334,0.00012318017,0.0000023681891,0.0013940993,0.8449867,0.009252449,0.00028532973,0.000050157432,0.13444465],"study_design_scores_gemma":[0.0021364451,0.000107505126,0.032630287,0.000032017746,0.00010523709,0.00001967238,0.00020456518,0.9643589,0.000110876,0.000028106226,0.00017240565,0.00009394576],"about_ca_topic_score_codex":0.0000053717577,"about_ca_topic_score_gemma":4.824379e-7,"teacher_disagreement_score":0.26178992,"about_ca_system_score_codex":0.000051683233,"about_ca_system_score_gemma":0.000028524022,"threshold_uncertainty_score":0.38739893},"labels":[],"label_agreement":null},{"id":"W4285191739","doi":"10.1109/lra.2022.3185377","title":"Shape Representation and Modeling of Tendon-Driven Continuum Robots Using Euler Arc Splines","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Euler's formula; Representation (politics); Robot; Curvature; Notation; Mathematics; Algorithm; Computer science; Topology (electrical circuits); Applied mathematics; Mathematical analysis; Artificial intelligence; Combinatorics; Geometry; Arithmetic","score_opus":0.03237939778497945,"score_gpt":0.2561930547901232,"score_spread":0.22381365700514377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285191739","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":[0.7035408,0.00006924808,0.29546002,0.00048752857,0.00020182878,0.00012966672,0.000008934698,0.00008518145,0.000016769864],"genre_scores_gemma":[0.9735886,0.000032311287,0.026159996,0.000096024465,0.000059530557,0.000016773281,0.000019079886,0.000023184692,0.0000045093066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992957,0.000017541364,0.0002657892,0.00015446632,0.0001413064,0.0001251797],"domain_scores_gemma":[0.9996834,0.000053125077,0.000069327005,0.00012351148,0.000031454863,0.000039208884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007345005,0.0001041083,0.00015057677,0.0001079354,0.00015057005,0.000037797858,0.00006132654,0.000024591325,0.000008682966],"category_scores_gemma":[0.000007442564,0.000118712276,0.000030595937,0.00015803687,0.0000291496,0.00010216634,0.00003746379,0.00009452496,5.7756307e-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":[0.0000010358935,0.00000919288,0.00069260993,0.000031177544,0.000019589805,9.327305e-7,0.00016293806,0.9013429,0.09700034,0.00022342961,0.00012112891,0.0003947225],"study_design_scores_gemma":[0.0002242424,0.000007745334,0.0010939895,0.000016074177,0.00003248344,0.000013514707,0.00007838824,0.9974727,0.0007930631,0.00012880845,0.000015161116,0.00012380861],"about_ca_topic_score_codex":0.000018970175,"about_ca_topic_score_gemma":0.0000023741602,"teacher_disagreement_score":0.27004778,"about_ca_system_score_codex":0.0000266846,"about_ca_system_score_gemma":0.000006127671,"threshold_uncertainty_score":0.48409462},"labels":[],"label_agreement":null},{"id":"W4285210748","doi":"10.1109/lra.2022.3176716","title":"External Wrench Estimation for UAVs Based on Variational Bayesian Unscented Kalman Filter","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","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":"National Natural Science Foundation of China","keywords":"Kalman filter; Unscented transform; Moving horizon estimation; Extended Kalman filter; Wrench; Computer science; Bayesian probability; Estimation; Fast Kalman filter; Ensemble Kalman filter; Control theory (sociology); Artificial intelligence; Engineering","score_opus":0.013098858161789415,"score_gpt":0.23410491337626738,"score_spread":0.22100605521447797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285210748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023484833,0.000006919277,0.98584604,0.010273245,0.001079381,0.00021086498,0.000039096587,0.00016796772,0.000027988697],"genre_scores_gemma":[0.60091275,0.0000011733229,0.39171442,0.0069830003,0.00013121039,0.00005299848,0.00016552671,0.000012435121,0.000026457408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876416,0.00008653607,0.0002490951,0.00032078236,0.0003841383,0.00019527915],"domain_scores_gemma":[0.99926245,0.00022403063,0.0001450584,0.00026874428,0.00003906856,0.000060651957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022706788,0.00012595557,0.000108895845,0.0001567278,0.0004955962,0.00019681703,0.00027776882,0.00003171316,0.000038260274],"category_scores_gemma":[0.00001958606,0.00013112783,0.00005129719,0.0001906072,0.000019093932,0.000222437,0.000047353373,0.00013484749,0.000004614284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009196614,0.000053436648,0.000112028196,0.000011455966,0.0000055617315,0.0000030518975,0.000089388035,0.9719995,0.0006193481,0.010209745,0.010254639,0.006632595],"study_design_scores_gemma":[0.00055115356,0.000076966666,0.0027674146,0.00002198828,0.000007745004,0.000007529122,0.000003160498,0.9943244,0.000097625525,0.0009126842,0.0010748284,0.00015453146],"about_ca_topic_score_codex":0.0000055410615,"about_ca_topic_score_gemma":5.2518965e-7,"teacher_disagreement_score":0.59856427,"about_ca_system_score_codex":0.0000579484,"about_ca_system_score_gemma":0.000027862,"threshold_uncertainty_score":0.53472376},"labels":[],"label_agreement":null},{"id":"W4285228733","doi":"10.1109/lra.2022.3189190","title":"Reduced Interface Models for Haptic Interfacing With Virtual Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interfacing; Haptic technology; Computer science; Interface (matter); Virtual machine; Rendering (computer graphics); Simulation; Computation; Virtual reality; Human–computer interaction; Computer graphics (images); Computer hardware; Algorithm","score_opus":0.013093049698882116,"score_gpt":0.20197181641073175,"score_spread":0.18887876671184964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285228733","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":[0.37655032,0.000027424032,0.62230605,0.000439799,0.00032858737,0.0001924947,0.000007272179,0.00010364997,0.00004437435],"genre_scores_gemma":[0.99679124,0.0000043812956,0.0027093892,0.0002233868,0.00003722369,0.00007129881,0.00000827719,0.000026404176,0.00012837566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994372,0.00001707199,0.00017024507,0.00012608348,0.00012426538,0.00012510447],"domain_scores_gemma":[0.99980885,0.00002507667,0.000032612472,0.00009221586,0.0000049403475,0.000036281224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007936368,0.0001030006,0.00010983637,0.00006424937,0.00013241418,0.00005437912,0.000058504338,0.000017006589,0.000013091397],"category_scores_gemma":[0.0000016245473,0.00010227575,0.000019226534,0.00004427739,0.000016500688,0.00010101976,0.000019044672,0.00008302588,0.0000037055715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052781984,0.0000065124245,0.0000038350663,0.00001723875,0.000028059007,6.259122e-7,0.0008113991,0.9144564,0.08237047,0.00051769504,0.0006571936,0.0011252675],"study_design_scores_gemma":[0.00041053805,0.00008696842,0.000025561265,0.00001735345,0.00001379315,0.000014644041,0.00030022967,0.99570966,0.0027372544,0.000021631628,0.00052325317,0.00013911299],"about_ca_topic_score_codex":0.0000022307042,"about_ca_topic_score_gemma":9.290393e-7,"teacher_disagreement_score":0.6202409,"about_ca_system_score_codex":0.00008042461,"about_ca_system_score_gemma":0.00000464076,"threshold_uncertainty_score":0.4170684},"labels":[],"label_agreement":null},{"id":"W4285504848","doi":"10.1109/lra.2022.3190830","title":"Planar Magnetic Actuation for Soft and Rigid Robots Using a Scalable Electromagnet Array","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","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":"Toronto Metropolitan University","funders":"Tsinghua Shenzhen International Graduate School","keywords":"Electromagnet; Electromagnetic coil; Magnetic field; Robot; Workspace; Actuator; Magnet; Mechanical engineering; Planar; Computer science; Magnetism; Materials science; Acoustics; Physics; Electrical engineering; Engineering; Artificial intelligence; Condensed matter physics","score_opus":0.01075398871704897,"score_gpt":0.22417990123784212,"score_spread":0.21342591252079315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285504848","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":[0.42340794,0.00009534848,0.574461,0.0012948337,0.00022081204,0.00038093448,0.000049234182,0.00004107899,0.000048859485],"genre_scores_gemma":[0.96287495,0.0000042477973,0.0358832,0.00066749717,0.00021069859,0.000044119566,0.00012317911,0.000032224274,0.00015990545],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908394,0.000037272606,0.00022459679,0.00024570434,0.00013995123,0.0002685513],"domain_scores_gemma":[0.9995906,0.00007342238,0.0001299019,0.000114959956,0.000026951811,0.00006416916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014586153,0.00015631641,0.00017638748,0.00009945669,0.0004894516,0.00010659062,0.000071443916,0.000024560046,0.00006811857],"category_scores_gemma":[0.0000028855789,0.00017007373,0.000045807403,0.00012373368,0.00003651036,0.00013322753,0.000018679097,0.00011057012,0.0000018722365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026995276,0.000090076006,0.002212762,0.000059451075,0.00005581822,0.0000012437368,0.0004026902,0.366856,0.61708,0.0036541433,0.0035006667,0.0060601775],"study_design_scores_gemma":[0.0029320067,0.0005234021,0.0025105164,0.000043114567,0.00030205373,0.000033020264,0.00027261282,0.97309196,0.0102829505,0.0063799354,0.002731223,0.0008971916],"about_ca_topic_score_codex":0.00004241359,"about_ca_topic_score_gemma":0.0000013484537,"teacher_disagreement_score":0.60679704,"about_ca_system_score_codex":0.000041206466,"about_ca_system_score_gemma":0.000037740054,"threshold_uncertainty_score":0.6935406},"labels":[],"label_agreement":null},{"id":"W4285549009","doi":"10.1109/lra.2021.3131698","title":"Bridging the Model-Reality Gap With Lipschitz Network Adaptation","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","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":"Dynamic Systems Analysis (Canada); Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Inverted pendulum; Lipschitz continuity; Computer science; Control theory (sociology); Robot; Controller (irrigation); Leverage (statistics); Nonlinear system; Control engineering; Artificial neural network; Affine transformation; Artificial intelligence; Engineering; Control (management); Mathematics","score_opus":0.029680731423424034,"score_gpt":0.2405365223277584,"score_spread":0.21085579090433437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285549009","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":[0.10680461,0.000023884017,0.8799183,0.012520902,0.00018959386,0.00009139185,0.000003140983,0.000041300296,0.00040692854],"genre_scores_gemma":[0.99213356,0.0000071279514,0.0056357966,0.0014120942,0.00062332745,0.000009711202,0.000032948912,0.00001242194,0.00013298694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929315,0.000057394973,0.00015649958,0.00018132954,0.00013763644,0.00017396268],"domain_scores_gemma":[0.99962574,0.000031865606,0.00009409836,0.00015296666,0.000046166584,0.000049168444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009647945,0.00011039698,0.00010625751,0.0000137624675,0.00026926908,0.00012550082,0.000047271304,0.000019763627,0.000018706247],"category_scores_gemma":[0.0000010667776,0.00008008702,0.00004005074,0.0001353401,0.000042559892,0.00013078275,0.000013936592,0.00013678161,0.0000031632546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040213226,0.000011680349,0.00028918195,0.0000044896533,0.000025204923,9.257163e-7,0.00013459833,0.9762035,0.0003416339,0.0094219055,0.0034079792,0.010154868],"study_design_scores_gemma":[0.00018780734,0.0000055625737,0.0005606357,0.000025285866,0.000028755298,0.0000045994325,0.00005524425,0.9976634,0.00019955939,0.0009741405,0.00018479124,0.000110222485],"about_ca_topic_score_codex":0.000020336065,"about_ca_topic_score_gemma":0.0000057419097,"teacher_disagreement_score":0.885329,"about_ca_system_score_codex":0.00000939703,"about_ca_system_score_gemma":0.000030147477,"threshold_uncertainty_score":0.32658538},"labels":[],"label_agreement":null},{"id":"W4285743256","doi":"10.1109/lra.2022.3191788","title":"Real-Time Intraoperative Surgical Guidance System in the da Vinci Surgical Robot Based on Transrectal Ultrasound/Photoacoustic Imaging With Photoacoustic Markers: An <i>Ex Vivo</i> Demonstration","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Johns Hopkins University; National Cancer Institute; National Institutes of Health; Intuitive Surgical; National Science Foundation","keywords":"Photoacoustic imaging in biomedicine; Ultrasound; Medicine; Transducer; Endoscope; Medical imaging; Surgical robot; Photoacoustic effect; Surgical instrument; Biomedical engineering; Radiology; Nuclear medicine; Computer science; Artificial intelligence; Robot; Acoustics; Physics; Optics","score_opus":0.004745358393175111,"score_gpt":0.19866150687239512,"score_spread":0.19391614847922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285743256","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":[0.85573727,0.000042283387,0.14055924,0.0005312628,0.00038198452,0.00091609097,0.00013083422,0.0005534585,0.0011475735],"genre_scores_gemma":[0.99704105,0.000010573117,0.0021932132,0.0003633958,0.000105368,0.00013471303,0.00008307332,0.000061415034,0.000007189299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755484,0.00038673086,0.000492463,0.00044283783,0.00062355294,0.0004995744],"domain_scores_gemma":[0.99868566,0.0007709411,0.0000967482,0.0002987981,0.000037001944,0.000110868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081262144,0.00037980688,0.00035246272,0.00019788998,0.00041046317,0.00027056847,0.00025162764,0.000054235963,0.000050264312],"category_scores_gemma":[0.000013848263,0.0003173501,0.000067201014,0.0004652089,0.00013779824,0.00030194406,0.000009056717,0.00050119625,0.0000033821586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008888949,0.00007731022,0.00016118599,0.000098608165,0.00002250819,0.0005000327,0.00060048,0.9138849,0.08413409,0.00004842099,0.00017283393,0.000210781],"study_design_scores_gemma":[0.0013859967,0.00012270211,0.0005417362,0.00015233332,0.00008267766,0.0010000061,0.0011270244,0.9942035,0.0009244321,0.0000031039885,0.000036260873,0.00042022855],"about_ca_topic_score_codex":0.000079741454,"about_ca_topic_score_gemma":0.000009660423,"teacher_disagreement_score":0.1413038,"about_ca_system_score_codex":0.00040746614,"about_ca_system_score_gemma":0.000077005956,"threshold_uncertainty_score":0.9999279},"labels":[],"label_agreement":null},{"id":"W4286543002","doi":"10.1109/lra.2022.3192885","title":"Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":69,"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":"Lidar; Radar; Backup; Remote sensing; Weather radar; Meteorology; 3D radar; Computer science; Environmental science; Radar imaging; Radar engineering details; Geography; Telecommunications","score_opus":0.031193633414856647,"score_gpt":0.24649662786805898,"score_spread":0.21530299445320233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286543002","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":[0.09687433,0.000106527325,0.89582545,0.00628915,0.00029513234,0.00042187533,0.000020542639,0.00014484306,0.000022132961],"genre_scores_gemma":[0.97754973,0.00007556647,0.017816732,0.004186176,0.00008514928,0.00007664928,0.00007157427,0.00006821879,0.00007022829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923533,0.00003210885,0.00024171079,0.00018737488,0.00013246294,0.00017103861],"domain_scores_gemma":[0.9996958,0.00005012119,0.000059377267,0.000117757125,0.000021221014,0.00005576282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016379514,0.000120395,0.00014879997,0.00019113334,0.00012589716,0.00006297901,0.00005078313,0.000035042747,0.0000060039665],"category_scores_gemma":[0.000014953439,0.0001398925,0.000018057242,0.00020912207,0.000012503338,0.00007575028,0.000018013858,0.000076427816,0.0000012586262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046848013,0.000010333415,0.00077367516,0.00008137025,0.000013782848,0.0000040923,0.00054398767,0.9903458,0.0033567734,0.00041834603,0.0039790813,0.00046808695],"study_design_scores_gemma":[0.00040689728,0.000023265417,0.0010439623,0.000040399602,0.000011671232,0.000005463305,0.00023778506,0.9865959,0.00022040874,0.00008693101,0.011135207,0.00019206002],"about_ca_topic_score_codex":0.000007924945,"about_ca_topic_score_gemma":0.000008735098,"teacher_disagreement_score":0.8806754,"about_ca_system_score_codex":0.000086339474,"about_ca_system_score_gemma":0.000005208674,"threshold_uncertainty_score":0.570465},"labels":[],"label_agreement":null},{"id":"W4286580165","doi":"10.1109/lra.2022.3193242","title":"Min-Max Vertex Cycle Covers With Connectivity Constraints for Multi-Robot Patrolling","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Optimization and Search Problems","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":"Vector Institute; University of Toronto","funders":"","keywords":"Patrolling; Robot; Vertex (graph theory); Vertex cover; Computer science; Greedy algorithm; Mathematical optimization; Disjoint sets; Time complexity; Mathematics; Algorithm; Theoretical computer science; Combinatorics; Artificial intelligence; Graph","score_opus":0.024789209281276106,"score_gpt":0.25371809794594163,"score_spread":0.22892888866466554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286580165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02071722,0.000008336201,0.972748,0.005644651,0.00029145903,0.00040989651,0.000015402717,0.00013539512,0.000029678784],"genre_scores_gemma":[0.76851004,0.0000031837305,0.22941807,0.0019591192,0.000013262661,0.00005164769,0.000010653388,0.000010245263,0.000023795996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898094,0.0000834506,0.00016789394,0.00029107535,0.00024393976,0.00023267882],"domain_scores_gemma":[0.99942905,0.00015748793,0.00010735411,0.0001708226,0.000056405624,0.0000789062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029438612,0.00011358139,0.00014082274,0.000096529504,0.00046750112,0.00017773572,0.00020457864,0.000022098639,0.000012075257],"category_scores_gemma":[0.000021453401,0.0001109354,0.000038659153,0.00019300326,0.00007859404,0.0003059236,0.000059102782,0.00011247021,0.000002442978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008183504,0.000058154368,0.00018399867,0.000016668482,0.00002294542,0.0000049086257,0.00045379467,0.98796487,0.0024102738,0.0045549427,0.0003681761,0.003953108],"study_design_scores_gemma":[0.001365743,0.000121203455,0.0003092863,0.000010268119,0.00000774608,0.000012127298,0.00005437,0.99739635,0.00025248036,0.00012684232,0.00018147139,0.0001621135],"about_ca_topic_score_codex":0.000009387894,"about_ca_topic_score_gemma":0.0000039434913,"teacher_disagreement_score":0.7477928,"about_ca_system_score_codex":0.0000614592,"about_ca_system_score_gemma":0.00005640096,"threshold_uncertainty_score":0.45238143},"labels":[],"label_agreement":null},{"id":"W4286580841","doi":"10.1109/lra.2022.3193246","title":"Reliable, Robust, Accurate and Real-Time 2D LiDAR Human Tracking in Cluttered Environment: A Social Dynamic Filtering Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","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 Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lidar; Computer vision; Artificial intelligence; Robustness (evolution); Video tracking; Tracking (education); Usability; Object (grammar); Human–computer interaction; Geography","score_opus":0.0260939825446983,"score_gpt":0.2631230884860992,"score_spread":0.23702910594140092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286580841","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":[0.47365925,0.00002651357,0.5237846,0.0020463828,0.000120244964,0.00016564406,0.0000053498507,0.00012032502,0.000071728784],"genre_scores_gemma":[0.8918441,0.00002481208,0.107497014,0.0005043406,0.00003950149,0.000026496493,0.000018785744,0.000018309645,0.000026657937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985573,0.00021577792,0.00030422932,0.00041209854,0.0002332434,0.0002772928],"domain_scores_gemma":[0.99953026,0.00006162503,0.00016089078,0.0001988058,0.000007167241,0.000041258056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007117638,0.00015707647,0.0002186555,0.00017178248,0.00050246477,0.0002392009,0.00024757866,0.000041507712,0.0000066050256],"category_scores_gemma":[0.000005189428,0.00017862559,0.00003651518,0.0002138508,0.000043917815,0.00039257293,0.0001770509,0.00020498993,0.0000017591007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072457824,0.000088228764,0.001716356,0.00008977845,0.000030230762,0.000046119258,0.0034817213,0.88038146,0.100742966,0.00077332975,0.00022411591,0.01241844],"study_design_scores_gemma":[0.00055175176,0.000036182722,0.057966422,0.0000176018,0.000008027337,0.000030288516,0.00004448853,0.9405425,0.00011807749,0.00032168734,0.00008216045,0.0002807933],"about_ca_topic_score_codex":0.000035351844,"about_ca_topic_score_gemma":0.000002273795,"teacher_disagreement_score":0.41818485,"about_ca_system_score_codex":0.00009231211,"about_ca_system_score_gemma":0.000010888422,"threshold_uncertainty_score":0.728414},"labels":[],"label_agreement":null},{"id":"W4288064674","doi":"10.1109/lra.2022.3194308","title":"Adaptive Autonomous Navigation of Multiple Optoelectronic Microrobots in Dynamic Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Economic and Social Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Visual servoing; Artificial intelligence; Computer vision; Simulation; Robot","score_opus":0.006932407722161662,"score_gpt":0.19734415329969618,"score_spread":0.1904117455775345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288064674","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":[0.7496638,0.00014387582,0.24963406,0.00015366543,0.00017877448,0.00016257525,0.000009127933,0.00004243795,0.000011702855],"genre_scores_gemma":[0.99662536,0.000035697252,0.0031870792,0.000061087805,0.0000071057534,0.000023215589,0.000027511505,0.000018163704,0.000014801058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992913,0.00003224799,0.00024349184,0.00013618016,0.0001320326,0.00016472842],"domain_scores_gemma":[0.99978423,0.000026198752,0.000056925688,0.000104652376,0.0000038548874,0.000024126835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009832136,0.000103455124,0.00012891371,0.00010170438,0.00005954322,0.000010671751,0.00008321832,0.000026042853,0.00001354512],"category_scores_gemma":[0.000001970854,0.00012292623,0.000026699921,0.00011154941,0.000026573503,0.000082586856,0.000021679638,0.00014929398,0.0000030602866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026108564,0.000018335008,0.00023020348,0.000016881488,0.00001195862,0.000002932441,0.0002815119,0.86729,0.12863135,0.000064461296,0.000024056828,0.003425696],"study_design_scores_gemma":[0.00020253829,0.000036914556,0.004281941,0.000015417274,0.0000066033367,0.0000073474434,0.00005003653,0.986359,0.008791513,0.000050267136,0.00006957285,0.00012885017],"about_ca_topic_score_codex":0.00002087063,"about_ca_topic_score_gemma":0.0000073310825,"teacher_disagreement_score":0.24696155,"about_ca_system_score_codex":0.00020368438,"about_ca_system_score_gemma":0.000007839941,"threshold_uncertainty_score":0.50127864},"labels":[],"label_agreement":null},{"id":"W4288391271","doi":"10.1109/lra.2022.3194691","title":"TAPE: Tether-Aware Path Planning for Autonomous Exploration of Unknown 3D Cavities Using a Tangle-Compatible Tethered Aerial Robot","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Université de Sherbrooke","keywords":"Travelling salesman problem; Motion planning; Path (computing); Computer science; Path length; Robot; Robotics; Function (biology); Mathematical optimization; Simulation; Algorithm; Artificial intelligence; Mathematics","score_opus":0.054080465320992206,"score_gpt":0.27731925043374267,"score_spread":0.22323878511275047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288391271","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059913035,0.000060854883,0.9370274,0.0010491649,0.0012733188,0.00043102165,0.000030673025,0.00020398642,0.000010575402],"genre_scores_gemma":[0.4625124,0.000001525854,0.5367474,0.0003991591,0.00015965506,0.00007846467,0.000042045853,0.000030302657,0.0000290689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828684,0.00015416445,0.00048103466,0.00037784255,0.00038005738,0.00032005247],"domain_scores_gemma":[0.9989115,0.00019525882,0.0004362442,0.00031202086,0.000080480124,0.000064522756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042073175,0.00021280235,0.00032118426,0.00022551235,0.0005046569,0.00016139509,0.00037293878,0.000054635282,0.0000040198174],"category_scores_gemma":[0.000026330139,0.00023206325,0.000069854716,0.00031767687,0.00005929298,0.00068172335,0.0001157437,0.00015207987,8.400979e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009531953,0.00004178885,0.00010498607,0.00005357814,0.000027111582,0.000009216068,0.0040779067,0.97573787,0.017046444,0.0005726034,0.00032789586,0.001991092],"study_design_scores_gemma":[0.0006834354,0.0001548558,0.00015911677,0.00006746357,0.00002560143,0.000025848907,0.00031970095,0.99662685,0.0012321599,0.0002749278,0.00016333115,0.0002666879],"about_ca_topic_score_codex":0.000052697727,"about_ca_topic_score_gemma":6.1598433e-7,"teacher_disagreement_score":0.40259936,"about_ca_system_score_codex":0.00014546244,"about_ca_system_score_gemma":0.00011577404,"threshold_uncertainty_score":0.94632643},"labels":[],"label_agreement":null},{"id":"W4289950755","doi":"10.1109/lra.2022.3186491","title":"MapLite 2.0: Online HD Map Inference Using a Prior SD Map","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Toyota Research Institute","keywords":"Road map; Computer science; Scope (computer science); Scale (ratio); Inference; Electronic map; Artificial intelligence; Computer vision; Data mining; Cartography; Real-time computing; Geography","score_opus":0.017879890378056595,"score_gpt":0.23510329242864034,"score_spread":0.21722340205058374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289950755","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":[0.38757467,0.00010388729,0.6089199,0.0018858808,0.0009972191,0.00018249777,0.00003694602,0.0002854404,0.000013577081],"genre_scores_gemma":[0.9728873,0.00001850751,0.025551323,0.0011767872,0.00014531112,0.000008110592,0.00012333394,0.000046533358,0.000042800173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990222,0.000040249965,0.00029367267,0.00017832282,0.00024342859,0.00022212644],"domain_scores_gemma":[0.99962723,0.000044798784,0.00006565253,0.00016758224,0.0000283148,0.00006641518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104725485,0.00015984804,0.00016461936,0.00014919636,0.00022872252,0.00009150293,0.000095122494,0.00004094681,0.000035619978],"category_scores_gemma":[0.000007475748,0.00018241252,0.000040094015,0.00017674269,0.00002777608,0.00011936383,0.000034314195,0.00017491194,0.000008361434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019822448,0.000020189458,0.0002640087,0.000087051485,0.000018280858,0.000008498019,0.0001737628,0.9827189,0.014562293,0.0004085008,0.00080631464,0.0009302193],"study_design_scores_gemma":[0.00026208043,0.000018704623,0.00082800665,0.000028442284,0.000024069688,0.000007401989,0.000049282673,0.99677736,0.00026594131,0.000086413966,0.0014308941,0.00022142331],"about_ca_topic_score_codex":0.000016157097,"about_ca_topic_score_gemma":0.0000040905375,"teacher_disagreement_score":0.5853126,"about_ca_system_score_codex":0.00011741074,"about_ca_system_score_gemma":0.00001653664,"threshold_uncertainty_score":0.74385667},"labels":[],"label_agreement":null},{"id":"W4293370597","doi":"10.1109/lra.2022.3196132","title":"Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","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":"Dynamic Systems Analysis (Canada); Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Suite; Benchmark (surveying); Machine learning","score_opus":0.009230860443678211,"score_gpt":0.22130848297750758,"score_spread":0.21207762253382936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293370597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006357235,0.000059659164,0.9805166,0.0113608,0.00046868427,0.0009226509,0.0000026679706,0.00020757006,0.00010410149],"genre_scores_gemma":[0.9711221,0.000015913529,0.024365302,0.0038949717,0.00006247828,0.00011565884,0.0000467332,0.00003474267,0.0003420866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971209,0.00035532887,0.0007372752,0.0005660666,0.0005966036,0.0006238142],"domain_scores_gemma":[0.99814206,0.00078672497,0.000499915,0.00033738127,0.000089223875,0.0001446706],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012693545,0.0003365871,0.00044657534,0.00044672488,0.00079884665,0.00037808117,0.00044006683,0.000086750006,0.000014673877],"category_scores_gemma":[0.00018286853,0.00037684862,0.00009875868,0.00043899115,0.000089863985,0.00039786275,0.000115323186,0.0006451203,0.000004103025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049495942,0.000024409856,0.0031993156,0.00007543294,0.00004018018,0.000012237522,0.00039852908,0.9892525,0.0014961001,0.004204102,0.00028439375,0.00096327404],"study_design_scores_gemma":[0.004472965,0.00047512102,0.0027374423,0.000041545696,0.000043595206,0.000009637048,0.00005664865,0.98931646,0.000042510226,0.00007070688,0.0023164677,0.000416904],"about_ca_topic_score_codex":0.000025745263,"about_ca_topic_score_gemma":0.0000043453892,"teacher_disagreement_score":0.9647649,"about_ca_system_score_codex":0.00022912427,"about_ca_system_score_gemma":0.000110097855,"threshold_uncertainty_score":0.99986833},"labels":[],"label_agreement":null},{"id":"W4294982655","doi":"10.1109/lra.2022.3205122","title":"Experimental Performance Comparison of Bidirectional Actuator Configurations for Suspended Aerial Platforms","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Thrust; Payload (computing); Actuator; Bandwidth (computing); Marine engineering; Computer science; Propeller; Sampling (signal processing); Simulation; Aerospace engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.031582876850239805,"score_gpt":0.283864397842166,"score_spread":0.2522815209919262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294982655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42972258,0.000017558226,0.5679451,0.0007310721,0.0012262014,0.00022641521,0.000014855262,0.00008809636,0.000028097345],"genre_scores_gemma":[0.87014,5.3189615e-7,0.12950183,0.00017893325,0.00006876852,0.000058611193,0.00002800676,0.000006628847,0.000016665479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990531,0.00001995208,0.00029102826,0.00019646257,0.00028401802,0.00015541038],"domain_scores_gemma":[0.9994817,0.00009783115,0.00019327007,0.00014789519,0.000035732934,0.00004359745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016690066,0.00010019807,0.00016382361,0.00011318822,0.00039969798,0.000059625854,0.00022156621,0.000023628483,0.000013723185],"category_scores_gemma":[0.000010042195,0.000104231796,0.000041205276,0.00016278728,0.00004077589,0.00029174122,0.000053487,0.00008466225,0.0000017112379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002512573,0.00016521591,0.00040268924,0.000027918773,0.00004105238,0.0000012623846,0.0024498708,0.83361757,0.14751582,0.0093761915,0.0033779808,0.0029992736],"study_design_scores_gemma":[0.00059278763,0.00016659741,0.0010283286,0.000006815851,0.0000063675875,0.000015354677,0.000109551926,0.9552779,0.042437725,0.000057660993,0.00017359683,0.00012728378],"about_ca_topic_score_codex":0.0000040493637,"about_ca_topic_score_gemma":1.2279376e-7,"teacher_disagreement_score":0.44041744,"about_ca_system_score_codex":0.000066092856,"about_ca_system_score_gemma":0.000048285638,"threshold_uncertainty_score":0.42504492},"labels":[],"label_agreement":null},{"id":"W4312674867","doi":"10.1109/lra.2022.3227866","title":"Self-Supervised Feature Learning for Long-Term Metric Visual Localization","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Ground truth; Computer science; Computer vision; Feature (linguistics); Metric (unit); Pattern recognition (psychology); Visualization; Pipeline (software); Artificial neural network; Matching (statistics); Estimator; Mathematics","score_opus":0.00783108971355151,"score_gpt":0.21908763933700232,"score_spread":0.2112565496234508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312674867","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":[0.08873319,0.00013191845,0.9090659,0.000641772,0.0005729879,0.00034015323,0.000004859201,0.0004896342,0.000019603185],"genre_scores_gemma":[0.9902368,0.00005709442,0.008539481,0.000580948,0.00013804452,0.00004566269,0.00029772354,0.00006317069,0.000041049647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990811,0.00005516617,0.00021709934,0.00019241813,0.00023479552,0.00021945518],"domain_scores_gemma":[0.999669,0.00007088111,0.00006093486,0.000091338006,0.000046249435,0.00006158006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014726604,0.0001633952,0.00016280482,0.00026046397,0.000394565,0.00011033418,0.00007437401,0.000060774553,0.000013649419],"category_scores_gemma":[0.000015461139,0.00018662971,0.000056723366,0.00044350358,0.00001345115,0.00012892824,0.000017924152,0.000171203,0.0000027222018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004380537,0.000024111696,0.001917754,0.0001753086,0.000039286086,0.0000031225104,0.00019401316,0.99183893,0.002226408,0.000164979,0.0011405622,0.0022711493],"study_design_scores_gemma":[0.00053334155,0.00006420474,0.001694907,0.000011031106,0.000051104926,0.0000075452167,0.000030798303,0.9961861,0.0004747405,0.000010686464,0.00070875976,0.00022682363],"about_ca_topic_score_codex":0.0000012739657,"about_ca_topic_score_gemma":8.98307e-7,"teacher_disagreement_score":0.9015036,"about_ca_system_score_codex":0.00012449396,"about_ca_system_score_gemma":0.0000112182315,"threshold_uncertainty_score":0.76105386},"labels":[],"label_agreement":null},{"id":"W4312751607","doi":"10.1109/lra.2022.3232033","title":"Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Leverage (statistics); Computer science; Artificial intelligence; Calibration; Domain (mathematical analysis); Code (set theory); Set (abstract data type); Machine learning; Global Positioning System; Robust optimization; Graph; Robustness (evolution); Pose; Data mining; Pattern recognition (psychology); Mathematics; Mathematical optimization; Theoretical computer science","score_opus":0.01025261152693278,"score_gpt":0.1994360423107283,"score_spread":0.18918343078379554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312751607","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":[0.23596922,0.000031322525,0.76154506,0.0014679651,0.00028614877,0.0003796011,0.000034954646,0.00027125998,0.000014474452],"genre_scores_gemma":[0.87183934,0.00003942212,0.1264901,0.00077681255,0.00008771611,0.00011179999,0.00060396496,0.000043546836,0.0000072846105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992763,0.000048891634,0.00022623298,0.00016144,0.00014518324,0.00014196929],"domain_scores_gemma":[0.9997091,0.000082937324,0.000052068583,0.00008047313,0.000026634518,0.000048727474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017782405,0.0001239161,0.00011737784,0.00011028667,0.00028299823,0.000093509676,0.000035974586,0.000042080108,0.000008002136],"category_scores_gemma":[0.000009998932,0.00014353602,0.000025574858,0.00012770348,0.000016735137,0.00018749874,0.0000101136275,0.000078159326,8.336171e-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":[0.000007970519,0.000012856508,0.0000208731,0.00012451227,0.000017434546,7.379858e-7,0.00041409163,0.9896923,0.004463729,0.0005251114,0.00092526525,0.003795135],"study_design_scores_gemma":[0.0006168966,0.000045806883,0.00006893221,0.00001155119,0.000029021141,0.000007717132,0.00008931419,0.9976218,0.00034101933,0.0007045115,0.00028181478,0.0001816257],"about_ca_topic_score_codex":0.000004992312,"about_ca_topic_score_gemma":0.000002789754,"teacher_disagreement_score":0.6358701,"about_ca_system_score_codex":0.00009166118,"about_ca_system_score_gemma":0.000010438538,"threshold_uncertainty_score":0.58532286},"labels":[],"label_agreement":null},{"id":"W4312930837","doi":"10.1109/lra.2022.3230593","title":"A Safety Planning and Control Architecture Applied to a Quadrotor Autopilot","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autopilot; Control theory (sociology); Quadratic programming; Control engineering; Actuator; Computer science; Controller (irrigation); Engineering; Mathematical optimization; Control (management); Mathematics","score_opus":0.010289775218993292,"score_gpt":0.22510635203074517,"score_spread":0.21481657681175187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312930837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015371759,0.000030566745,0.96757936,0.015916862,0.00036958,0.000411263,0.000009721762,0.00025753796,0.00005331914],"genre_scores_gemma":[0.6849755,5.6287155e-7,0.30499697,0.009801267,0.00008802564,0.00008998744,0.0000051956445,0.00001544811,0.000027060516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987297,0.00008354408,0.00023249415,0.0003783908,0.00030657792,0.00026928648],"domain_scores_gemma":[0.99936736,0.0001435001,0.00009899781,0.00024628235,0.000013130944,0.00013070513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032155795,0.00015541624,0.00020219889,0.00017850149,0.0004234798,0.00016386667,0.00028998297,0.000026464755,0.0000025932914],"category_scores_gemma":[0.000015163382,0.00015907286,0.000023493772,0.00024309033,0.000028862158,0.00009230943,0.0001448061,0.00021092517,0.000004407384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011697526,0.000014494528,0.00020875975,0.000014312877,0.000019258841,0.000025691339,0.0019415433,0.98152363,0.007212023,0.0023625018,0.0005501856,0.006115906],"study_design_scores_gemma":[0.00096880697,0.00011830093,0.011749479,0.000023935341,0.000013300234,0.0001027408,0.000042786116,0.98503196,0.00009516923,0.00029446426,0.0012521232,0.00030691267],"about_ca_topic_score_codex":0.000007873277,"about_ca_topic_score_gemma":1.684684e-7,"teacher_disagreement_score":0.6696037,"about_ca_system_score_codex":0.000054803888,"about_ca_system_score_gemma":0.000025944279,"threshold_uncertainty_score":0.64868027},"labels":[],"label_agreement":null},{"id":"W4313051640","doi":"10.1109/lra.2022.3226068","title":"Picking up Speed: Continuous-Time Lidar-Only Odometry Using Doppler Velocity Measurements","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":30,"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":"Lidar; Odometry; Continuous wave; Doppler effect; Computer science; Distortion (music); Remote sensing; Trajectory; Artificial intelligence; Computer vision; Geology; Laser; Physics; Optics; Robot; Telecommunications; Mobile robot","score_opus":0.02948866818609483,"score_gpt":0.26048451066454076,"score_spread":0.23099584247844593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313051640","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":[0.8306771,0.000012400542,0.16732273,0.0011551891,0.000374565,0.00015147544,0.000012370123,0.00018632232,0.00010785907],"genre_scores_gemma":[0.9598587,3.508261e-7,0.03957834,0.00036930744,0.00009693976,0.0000022959398,0.00001550319,0.00002302324,0.00005554308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887234,0.000049296188,0.0002433264,0.0002520833,0.00030710245,0.00027586403],"domain_scores_gemma":[0.99950314,0.000057521116,0.00016791763,0.00018380072,0.000041543,0.00004605712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001709845,0.00016182919,0.00021140483,0.00011043092,0.00043333982,0.0000942152,0.00013140727,0.000028425738,0.00008728951],"category_scores_gemma":[0.0000159326,0.00017424082,0.00005672161,0.00022555164,0.00008160222,0.00015254645,0.00012556804,0.0002328768,0.000011757541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017724919,0.00016887442,0.08333859,0.000027661012,0.00025995332,0.00001148785,0.00035997783,0.6273142,0.25728607,0.003920315,0.0023599928,0.02493514],"study_design_scores_gemma":[0.0047376063,0.00025325568,0.035415035,0.00020506862,0.0004112002,0.00009065011,0.0014270215,0.89644957,0.047128,0.008130184,0.0029716946,0.0027806931],"about_ca_topic_score_codex":0.00003095379,"about_ca_topic_score_gemma":2.2302378e-7,"teacher_disagreement_score":0.26913536,"about_ca_system_score_codex":0.00010557786,"about_ca_system_score_gemma":0.000022434626,"threshold_uncertainty_score":0.7105335},"labels":[],"label_agreement":null},{"id":"W4313316246","doi":"10.1109/lra.2022.3233232","title":"Safe and Smooth: Certified Continuous-Time Range-Only Localization","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Solver; Certificate; Mathematical optimization; Range (aeronautics); Smoothness; Computer science; Mathematics; Algorithm","score_opus":0.006363341950708757,"score_gpt":0.18448280808311682,"score_spread":0.17811946613240806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313316246","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":[0.31478983,0.0002930454,0.67854863,0.0033017506,0.000678225,0.00034227365,0.000026257889,0.0015024724,0.00051753636],"genre_scores_gemma":[0.99733186,0.000051868425,0.0015152064,0.00089630776,0.000037589663,0.000017746988,0.000037398077,0.000029275005,0.000082762686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999323,0.000028401497,0.00020305837,0.00014090925,0.00014800449,0.00015663586],"domain_scores_gemma":[0.9997607,0.0000335714,0.000042602976,0.00011203485,0.000019022973,0.000032051143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010044737,0.000121480254,0.00014299454,0.00013755374,0.00022631549,0.000068813824,0.00007052976,0.000049161117,0.00005299346],"category_scores_gemma":[0.0000127154235,0.00013325115,0.000021804315,0.0001692448,0.000053415948,0.000111532005,0.00003126714,0.00010771184,0.000009629356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004692702,0.000010138968,0.0011897439,0.000053257394,0.000022882903,0.0000055542446,0.00031641545,0.9710889,0.00909749,0.0011873519,0.008069081,0.008954519],"study_design_scores_gemma":[0.0004480751,0.000026031023,0.0014854769,0.000011532728,0.00002208652,0.000019587154,0.00010371383,0.9892068,0.0016307168,0.0001667393,0.0066501587,0.00022910489],"about_ca_topic_score_codex":0.000006071714,"about_ca_topic_score_gemma":0.0000013917154,"teacher_disagreement_score":0.682542,"about_ca_system_score_codex":0.000048342943,"about_ca_system_score_gemma":0.0000064450664,"threshold_uncertainty_score":0.5433824},"labels":[],"label_agreement":null},{"id":"W4313639556","doi":"10.1109/lra.2023.3234778","title":"A Simple Self-Supervised IMU Denoising Method for Inertial Aided Navigation","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inertial measurement unit; Artificial intelligence; Computer science; Leverage (statistics); Noise reduction; Inference; Deep learning; Machine learning; Generalization; Computer vision; Mathematics","score_opus":0.013460251554702499,"score_gpt":0.25411382274139305,"score_spread":0.24065357118669056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313639556","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":[0.26140603,0.000010326883,0.73656577,0.00070804724,0.0003269179,0.00023905393,0.000008771052,0.0007267656,0.000008312563],"genre_scores_gemma":[0.8605624,0.000025725556,0.13803457,0.0006147879,0.00028467408,0.000033601566,0.0003576862,0.00007694763,0.000009607471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991077,0.00003240834,0.00029434837,0.00017749544,0.00015079274,0.00023723235],"domain_scores_gemma":[0.9995776,0.00013181698,0.000047283287,0.00012845079,0.000051716706,0.000063132604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021530395,0.00014999667,0.00016407146,0.00015731472,0.00014346847,0.00011715134,0.00005810544,0.00008137794,0.0000023845116],"category_scores_gemma":[0.00002270209,0.00016112381,0.00005410267,0.00031386546,0.000011846253,0.00016039102,0.000008529695,0.000069185255,0.000011456663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002598494,0.000005515521,0.000052067786,0.0001410054,0.000026930897,0.0000020219584,0.00030806792,0.882213,0.11254088,0.00034356906,0.001890778,0.0024735688],"study_design_scores_gemma":[0.00050912995,0.00001790352,0.0004766603,0.00003171026,0.000038667957,0.0000028982722,0.0000328465,0.991056,0.007142439,0.00024357125,0.0002532401,0.00019489398],"about_ca_topic_score_codex":0.0000098606815,"about_ca_topic_score_gemma":0.0000025130241,"teacher_disagreement_score":0.5991564,"about_ca_system_score_codex":0.00005361426,"about_ca_system_score_gemma":0.000009600537,"threshold_uncertainty_score":0.6570439},"labels":[],"label_agreement":null},{"id":"W4317553727","doi":"10.1109/lra.2023.3238173","title":"An Efficient Global Optimality Certificate for Landmark-Based SLAM","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Computer science; Simultaneous localization and mapping; Solver; Mathematical optimization; Graph; Certificate; Robustness (evolution); Theoretical computer science; Artificial intelligence; Mathematics; Robot; Mobile robot","score_opus":0.03365948370194767,"score_gpt":0.2627700697857473,"score_spread":0.22911058608379964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317553727","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":[0.32631615,0.0000066584325,0.67141014,0.0011206379,0.0003795724,0.00018415213,0.00003631886,0.00052750204,0.000018894983],"genre_scores_gemma":[0.98837984,0.000004956194,0.010961824,0.00033065563,0.0000775375,0.00001728811,0.00019918676,0.000024464089,0.000004234745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921423,0.000022487156,0.00021722128,0.0001805303,0.00013626496,0.00022924472],"domain_scores_gemma":[0.9996304,0.00004505635,0.000034780664,0.00016888526,0.000040457337,0.00008044684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016743617,0.00013162388,0.00012868193,0.00007277175,0.000107356456,0.00010659111,0.000070225535,0.000061998886,0.000002565535],"category_scores_gemma":[0.000010959559,0.00013404174,0.0000440252,0.00024034742,0.000027986332,0.000055808538,0.0000038803264,0.000041435946,0.000013907136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000505419,0.000015257808,0.00028277366,0.00008433388,0.000011285428,0.0000018352029,0.00003158794,0.98970246,0.0071377875,0.0006177472,0.0013818647,0.00072799023],"study_design_scores_gemma":[0.00035853992,0.00002264184,0.005528404,0.000015955622,0.000018784289,7.5873623e-7,0.000008265766,0.9921962,0.001489989,0.000041296385,0.00015214387,0.00016700865],"about_ca_topic_score_codex":0.0000052685446,"about_ca_topic_score_gemma":0.0000027714293,"teacher_disagreement_score":0.6620637,"about_ca_system_score_codex":0.00005536748,"about_ca_system_score_gemma":0.0000095134155,"threshold_uncertainty_score":0.5466063},"labels":[],"label_agreement":null},{"id":"W4321021200","doi":"10.1109/lra.2023.3244415","title":"A Hybrid Approach to 3D Shape Estimation of Catheters Using Ultrasound Images","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Medical Image Segmentation Techniques","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; 3D ultrasound; Artificial intelligence; Robustness (evolution); Imaging phantom; Kalman filter; Visualization; Ultrasound; Catheter; Radiology; Medicine","score_opus":0.024018009657285948,"score_gpt":0.2792205530442587,"score_spread":0.25520254338697274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321021200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09837081,0.0000035807043,0.900124,0.00083567254,0.000120216195,0.0001949795,0.0000036674935,0.00032179718,0.000025283998],"genre_scores_gemma":[0.32023028,0.000004467149,0.6786362,0.0010788164,0.000016464355,0.00000943707,0.000010426905,0.0000076487395,0.0000063101893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990081,0.00004729361,0.00026287668,0.00023041054,0.00029129122,0.00015998681],"domain_scores_gemma":[0.999456,0.000099726676,0.00011878397,0.00020677333,0.000040952553,0.00007776828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002832951,0.00010000811,0.00013087959,0.00025303624,0.00007428827,0.00012717536,0.00021938537,0.00002249159,0.000001956317],"category_scores_gemma":[0.000067296634,0.0000990714,0.000030008496,0.00038015892,0.000056213645,0.0004115754,0.000049448267,0.000053987864,0.000009899529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013793698,0.000044150336,0.000052765772,0.0001141882,0.000019447369,0.0000049446044,0.0010646236,0.5299864,0.42038575,0.00039411025,0.0048818393,0.04305043],"study_design_scores_gemma":[0.0000955785,0.000017402544,0.0008097589,0.000033350356,0.000007620241,0.000014245538,0.000013686932,0.9352214,0.0635538,0.00012389789,0.0000031776124,0.00010612834],"about_ca_topic_score_codex":0.000011133615,"about_ca_topic_score_gemma":4.866143e-8,"teacher_disagreement_score":0.405235,"about_ca_system_score_codex":0.000032377142,"about_ca_system_score_gemma":0.000017423568,"threshold_uncertainty_score":0.40400144},"labels":[],"label_agreement":null},{"id":"W4321194910","doi":"10.1109/lra.2023.3245405","title":"Towards More Efficient EfficientDets and Real-Time Marine Debris Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":54,"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 Science and Technology Council; Queen's University; Natural Environment Research Council; Queen's University Belfast; Sight Research UK","keywords":"Underwater; Computer science; Object detection; Environmental science; Debris; Detector; Latency (audio); Real-time computing; Remote sensing; Artificial intelligence; Computer vision; Marine engineering; Geology; Pattern recognition (psychology); Engineering; Oceanography; Telecommunications","score_opus":0.0057511590980584785,"score_gpt":0.20109899077472068,"score_spread":0.1953478316766622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321194910","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":[0.96841425,0.000002276635,0.029760709,0.0010886713,0.00024926392,0.00012226023,0.000009017103,0.00013284718,0.00022069787],"genre_scores_gemma":[0.9982162,0.00003544593,0.0013977323,0.00018796459,0.00003672853,0.0000042977495,0.000015068129,0.000011714736,0.00009481677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999169,0.000024227922,0.00015982985,0.00023092757,0.0002049873,0.00021105277],"domain_scores_gemma":[0.9996973,0.000053939264,0.00006324616,0.00009413232,0.0000054735387,0.00008585357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017478359,0.00011620748,0.00010078011,0.00007872042,0.00017961855,0.000058887246,0.00004617024,0.000048646187,0.000079550155],"category_scores_gemma":[0.000025576583,0.00010850398,0.000021144446,0.0002513573,0.00010193783,0.000049069513,0.000082232975,0.00005641456,0.00023644103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000076227675,0.00002026244,0.0010681474,0.000018316241,0.000008550016,0.000008855931,0.0002680472,0.51760423,0.4602655,0.000032460204,0.0014404218,0.019257609],"study_design_scores_gemma":[0.00020311768,0.000025015244,0.18498744,0.000010470794,0.000016737684,0.000014241783,0.000010366068,0.8110449,0.0033485158,0.000023204464,0.00018821153,0.00012780917],"about_ca_topic_score_codex":0.00017887699,"about_ca_topic_score_gemma":0.000013340566,"teacher_disagreement_score":0.45691696,"about_ca_system_score_codex":0.00006001926,"about_ca_system_score_gemma":0.000003774847,"threshold_uncertainty_score":0.44246638},"labels":[],"label_agreement":null},{"id":"W4321380867","doi":"10.1109/lra.2023.3246845","title":"Learning Optimal Topology for Ad-Hoc Robot Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Energy Efficient Wireless Sensor Networks","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 Alberta","funders":"","keywords":"Network topology; Computer science; Boosting (machine learning); Robot; Estimator; Artificial intelligence; Class (philosophy); Topology (electrical circuits); Wireless ad hoc network; Task (project management); Machine learning; Mathematics; Engineering; Computer network","score_opus":0.01298452757621391,"score_gpt":0.2385511438415847,"score_spread":0.2255666162653708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321380867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06152267,0.000066644694,0.92794293,0.00848473,0.001157577,0.00013535871,4.2986417e-7,0.0006722896,0.00001734631],"genre_scores_gemma":[0.80859065,0.00014766493,0.18871044,0.0018716602,0.0002946797,0.00003379993,0.000028884831,0.000033690772,0.0002885239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878454,0.0000654603,0.0002330486,0.00035266604,0.00014623548,0.00041803435],"domain_scores_gemma":[0.9992603,0.00028972168,0.00011589457,0.00021504732,0.000044008902,0.00007500171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002633281,0.00014578427,0.00017167332,0.00015175373,0.00028245407,0.00017666844,0.00028036482,0.00009737486,0.0000014768882],"category_scores_gemma":[0.000023015753,0.00015031855,0.00006019721,0.0004068407,0.00005965139,0.00019207799,0.00008407958,0.00015904632,0.000014249186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002495966,0.000007170145,0.0000445386,0.000008922816,0.000012407359,0.00000686891,0.00017109083,0.97943276,0.00096304703,0.0038563537,0.0019994597,0.013494892],"study_design_scores_gemma":[0.000273838,0.000069153204,0.0006774096,0.000018278404,0.0000070108654,0.000008477496,0.000017859935,0.9972617,0.000119533725,0.000036613506,0.001335466,0.00017467349],"about_ca_topic_score_codex":0.000001620728,"about_ca_topic_score_gemma":0.0000017181994,"teacher_disagreement_score":0.747068,"about_ca_system_score_codex":0.00002672604,"about_ca_system_score_gemma":0.000011451435,"threshold_uncertainty_score":0.61298126},"labels":[],"label_agreement":null},{"id":"W4323914534","doi":"10.1109/lra.2023.3254860","title":"Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation Using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Reinforcement learning; Task (project management); Markov decision process; Computer science; Workload; Artificial intelligence; Process (computing); Robot; Point (geometry); Robotics; Motion planning; Simulation; Computer vision; Markov process; Engineering; Mathematics","score_opus":0.025447112907124526,"score_gpt":0.28983652868083304,"score_spread":0.2643894157737085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323914534","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":[0.45488885,0.000011553903,0.54381925,0.0004668919,0.0001608344,0.00021519714,0.0000010291091,0.00037817843,0.000058212947],"genre_scores_gemma":[0.9857511,0.000022595585,0.013793626,0.000067848516,0.00016244457,0.00002738275,0.000050398354,0.00004292096,0.0000816734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991083,0.00001664274,0.00028520983,0.00020101025,0.00012592292,0.0002628709],"domain_scores_gemma":[0.9996196,0.00013091102,0.00006388628,0.0000665594,0.000026903554,0.000092138915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017891517,0.0001565729,0.00016433308,0.00018913679,0.0002507148,0.00014231281,0.00005136758,0.00006272615,0.0000033633792],"category_scores_gemma":[0.000017719569,0.00017780448,0.00002877868,0.00017937498,0.000019311834,0.00011271154,0.00003133059,0.0001479338,0.000011215706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002801752,0.0000024972223,0.0018664764,0.000052211857,0.000021096976,0.0000049549044,0.0003657349,0.9780527,0.017259771,0.0003944193,0.00009793695,0.0018794248],"study_design_scores_gemma":[0.0003212233,0.00003172777,0.0038036064,0.00006998015,0.000023301283,0.00001507363,0.00006225988,0.9934516,0.0006117482,0.000041479125,0.0013655095,0.00020250064],"about_ca_topic_score_codex":0.000018064971,"about_ca_topic_score_gemma":0.0000010871026,"teacher_disagreement_score":0.5308623,"about_ca_system_score_codex":0.000066352695,"about_ca_system_score_gemma":0.0000060852694,"threshold_uncertainty_score":0.7250656},"labels":[],"label_agreement":null},{"id":"W4328007707","doi":"10.1109/lra.2023.3259731","title":"Ev-Conv: Fast CNN Inference on Event Camera Inputs for High-Speed Robot Perception","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Artificial intelligence; Computer science; Event (particle physics); Computer vision; Leverage (statistics); Frame rate; Convolutional neural network","score_opus":0.02031087332978778,"score_gpt":0.26294062515756156,"score_spread":0.24262975182777377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328007707","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":[0.66048026,0.0000034501031,0.33699784,0.001311639,0.0006011096,0.0001628367,0.000004683244,0.00042945836,0.000008736842],"genre_scores_gemma":[0.99644566,0.000022220851,0.0025316814,0.0007036152,0.00018755086,0.000010320449,0.000037090427,0.000024780189,0.00003708263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993014,0.000014689169,0.0001961785,0.00016885855,0.00010847702,0.00021043066],"domain_scores_gemma":[0.99965334,0.00011900669,0.0000552203,0.0001030352,0.00002114743,0.00004825411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081516504,0.00014068586,0.00013524924,0.000110862034,0.0001238513,0.00004449855,0.000058027723,0.000046608508,0.000004299895],"category_scores_gemma":[0.000022234552,0.00014357133,0.000035608817,0.0001496848,0.000018192046,0.00014166244,0.000009073455,0.00010896612,0.000042426083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030634376,0.0000037828474,0.000021751359,0.000051098417,0.0000071109644,0.0000017562292,0.00016069225,0.773933,0.21776225,0.00011234212,0.0003693011,0.00757385],"study_design_scores_gemma":[0.00038276546,0.00005746891,0.008412852,0.00009411696,0.000012442865,0.0000023030027,0.000028304676,0.97049123,0.020046603,0.00016535642,0.0000772524,0.00022929527],"about_ca_topic_score_codex":0.0000013780357,"about_ca_topic_score_gemma":8.9078145e-7,"teacher_disagreement_score":0.3359654,"about_ca_system_score_codex":0.000047179747,"about_ca_system_score_gemma":0.0000040703476,"threshold_uncertainty_score":0.5854669},"labels":[],"label_agreement":null},{"id":"W4362496313","doi":"10.1109/lra.2023.3264199","title":"Multidirectional Human-in-the-Loop Balance Robotic System","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Balance (ability); Robot; Computer science; Tracking (education); Trajectory; Simulation; Control theory (sociology); Artificial intelligence; Computer vision; Physical medicine and rehabilitation; Control (management); Psychology; Physics; Medicine","score_opus":0.03295116870902059,"score_gpt":0.3359980158478225,"score_spread":0.3030468471388019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362496313","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":[0.97721493,0.000048355687,0.008956438,0.00911441,0.0023834133,0.00080809946,0.000009550353,0.0005767429,0.0008880538],"genre_scores_gemma":[0.9968733,0.000030514273,0.000463215,0.0014264677,0.00036649208,0.0001054788,0.000065345586,0.00001830856,0.00065088796],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983968,0.0003576363,0.00042789505,0.00022647511,0.000248167,0.00034301358],"domain_scores_gemma":[0.9992935,0.00026101345,0.00017541405,0.00018480244,0.000040765226,0.00004449461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078861514,0.00012844874,0.00019083386,0.0001571113,0.0006552388,0.000027929982,0.00012103942,0.00010655054,0.0000036030192],"category_scores_gemma":[0.000022591257,0.00010697446,0.00004832531,0.00038785502,0.000041123763,0.00013414446,0.000021112697,0.00030690708,0.000294544],"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":[0.000060798513,0.0007843392,0.20458277,0.006260817,0.00023411028,0.00021339399,0.013546235,0.11706752,0.3227464,0.07401153,0.25690955,0.003582565],"study_design_scores_gemma":[0.0010701204,0.000017528893,0.78443235,0.00045365628,0.000022965052,0.000007562112,0.00096237846,0.21221097,0.000004697898,0.00014571803,0.000489282,0.00018276961],"about_ca_topic_score_codex":0.00006004112,"about_ca_topic_score_gemma":0.000046823934,"teacher_disagreement_score":0.5798496,"about_ca_system_score_codex":0.000096901575,"about_ca_system_score_gemma":0.000024371511,"threshold_uncertainty_score":0.50396335},"labels":[],"label_agreement":null},{"id":"W4362654044","doi":"10.1109/lra.2023.3264869","title":"Modeling and Analysis of Tendon-Driven Continuum Robots for Rod-Based Locking","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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 Toronto","funders":"","keywords":"Workspace; Robot; Curvature; Rod; Stiffness; Control theory (sociology); Twist; Computer science; Simulation; Compliant mechanism; Engineering; Structural engineering; Artificial intelligence; Mathematics; Geometry; Finite element method; Control (management)","score_opus":0.018158106511166314,"score_gpt":0.2415163871136158,"score_spread":0.2233582806024495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362654044","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":[0.37470895,0.00002077504,0.6243219,0.0005895959,0.00006182832,0.000119651384,0.000017616125,0.00015486417,0.000004836486],"genre_scores_gemma":[0.9794843,0.000023668965,0.02022974,0.00010451163,0.000030222724,0.000027427104,0.00007563299,0.000020194546,0.0000042755337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938583,0.0000061415276,0.00023412067,0.00013971557,0.00008556708,0.0001486344],"domain_scores_gemma":[0.9996453,0.00011115627,0.00004174457,0.00011900598,0.00004080046,0.000042018444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009006739,0.00010080148,0.00020859971,0.00030486676,0.00007330222,0.000042111733,0.000050365594,0.00004188982,9.309704e-7],"category_scores_gemma":[0.000011334683,0.00010715256,0.00006484536,0.0004847777,0.00002301261,0.00005285561,0.0000085877655,0.000042729545,9.047781e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.8009356e-7,0.0000044928015,0.0007544733,0.00006259655,0.00013807937,2.734535e-7,0.00007261546,0.97238964,0.025608666,0.00023596763,0.00018839269,0.00054400926],"study_design_scores_gemma":[0.0002237182,0.000006082996,0.0033425945,0.000023477545,0.00026176372,2.3093372e-7,0.00001615575,0.9949635,0.00097251066,0.000059460494,0.000015599042,0.000114894356],"about_ca_topic_score_codex":0.000006066726,"about_ca_topic_score_gemma":0.0000101755295,"teacher_disagreement_score":0.60477537,"about_ca_system_score_codex":0.0000133851445,"about_ca_system_score_gemma":0.000004830398,"threshold_uncertainty_score":0.43695548},"labels":[],"label_agreement":null},{"id":"W4366148847","doi":"10.1109/lra.2023.3268043","title":"Know What You Don't Know: Consistency in Sliding Window Filtering With Unobservable States Applied to Visual-Inertial SLAM","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Unobservable; Context (archaeology); Sliding window protocol; Computer science; Consistency (knowledge bases); Inertial measurement unit; Filter (signal processing); Inertial frame of reference; Computer vision; Artificial intelligence; Control theory (sociology); Window (computing); Mathematics; Econometrics; Control (management); Geography","score_opus":0.009439724350857848,"score_gpt":0.2124531057050452,"score_spread":0.20301338135418734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366148847","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":[0.8323744,0.000070086644,0.16413486,0.0019443961,0.0005174002,0.0003643571,0.00000408473,0.00053762656,0.00005282331],"genre_scores_gemma":[0.99412704,0.00028304022,0.0047295545,0.0005607496,0.00009217217,0.000029242497,0.00008018557,0.00006454352,0.000033462846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988223,0.000020991945,0.00033715734,0.00025595597,0.00020055642,0.0003630725],"domain_scores_gemma":[0.9996035,0.00006915607,0.000046370675,0.00015017518,0.000034993405,0.00009582347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014928525,0.00021921942,0.00024382459,0.00030757466,0.0001047884,0.00030439824,0.00007563874,0.00006781715,0.0000057713332],"category_scores_gemma":[0.000009549097,0.00021835868,0.000026353215,0.0006082054,0.000022285276,0.00031116308,0.000021363125,0.00011290069,0.000027884915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008330238,0.000010066233,0.00036306673,0.00011217507,0.000020024889,0.000014891717,0.0005067502,0.9073977,0.088386156,0.00019127592,0.00036335815,0.0026262081],"study_design_scores_gemma":[0.00052930455,0.00003738014,0.0021609801,0.00029694795,0.000016915023,0.0000031337763,0.00035595405,0.98507243,0.0108400835,0.000020504003,0.00033938393,0.00032699434],"about_ca_topic_score_codex":0.000022153792,"about_ca_topic_score_gemma":0.000040017563,"teacher_disagreement_score":0.16175267,"about_ca_system_score_codex":0.00007658478,"about_ca_system_score_gemma":0.000013159804,"threshold_uncertainty_score":0.8904408},"labels":[],"label_agreement":null},{"id":"W4366668996","doi":"10.1109/lra.2023.3269306","title":"Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Discriminative model; Deep learning; Computer vision; Robot; Pattern recognition (psychology); Invariant (physics); Machine learning; Mathematics","score_opus":0.018503124107269998,"score_gpt":0.2823260928955713,"score_spread":0.2638229687883013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366668996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034605984,0.00000811577,0.9541116,0.009408772,0.00070748926,0.00024470122,0.0000015851244,0.00087313924,0.000038621303],"genre_scores_gemma":[0.41698158,0.0000043218242,0.58217335,0.0006993739,0.000078093915,0.000024413275,0.0000050742783,0.000016574291,0.000017218606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804026,0.00038747324,0.00036575578,0.00050347054,0.0002641168,0.00043891193],"domain_scores_gemma":[0.9982337,0.0010816928,0.00017862287,0.0002487041,0.000110835455,0.00014647671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013756214,0.00020568309,0.00028328117,0.00034412777,0.00047641073,0.00043282483,0.0002934094,0.00008322758,0.0000027481547],"category_scores_gemma":[0.00043426699,0.00021589232,0.00006604371,0.0011561944,0.000020038498,0.00046398214,0.00011172365,0.00030540625,0.00005389627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019755566,0.000010786855,0.0007892447,0.0000146909215,0.00003095622,0.000010422695,0.002971608,0.90266883,0.006676113,0.0015112951,0.000054885473,0.0852592],"study_design_scores_gemma":[0.00029180464,0.000038943504,0.06662555,0.000027460797,0.000013292837,0.000018491159,0.00008095905,0.9318607,0.00046388246,0.00027129604,0.00006060811,0.0002470003],"about_ca_topic_score_codex":0.000069685884,"about_ca_topic_score_gemma":0.00008025466,"teacher_disagreement_score":0.3823756,"about_ca_system_score_codex":0.000073366915,"about_ca_system_score_gemma":0.000043698205,"threshold_uncertainty_score":0.8803833},"labels":[],"label_agreement":null},{"id":"W4367663209","doi":"10.1109/lra.2023.3272272","title":"Double-Deck Multi-Agent Pickup and Delivery: Multi-Robot Rearrangement in Large-Scale Warehouses","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pickup; Deck; Artificial intelligence; Geology","score_opus":0.02886979877994927,"score_gpt":0.24748731768164717,"score_spread":0.2186175189016979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367663209","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":[0.30837768,0.00007295046,0.6901249,0.00042661923,0.0003051445,0.00018425057,0.000008657748,0.0004932013,0.000006559269],"genre_scores_gemma":[0.92567027,0.0005640008,0.07339621,0.00018391719,0.000038501094,0.000022223945,0.000040342416,0.000036760113,0.00004775444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992741,0.000013219285,0.00020193578,0.00018138082,0.00009818374,0.0002312263],"domain_scores_gemma":[0.99975646,0.000030605777,0.00003315346,0.00011014357,0.0000150759515,0.00005455202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010285216,0.00014074086,0.0001378045,0.00016429,0.0000789978,0.000053312495,0.000045819583,0.000054763444,0.0000026725934],"category_scores_gemma":[0.0000066194993,0.00014732803,0.00001828091,0.00013512002,0.000025396763,0.00012094079,0.00002428368,0.00009527821,0.000014137586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033862136,0.00001830559,0.0009244835,0.00007789982,0.000011327609,0.0000075529424,0.00064502994,0.9946154,0.002711107,0.000018107827,0.00025410455,0.000713344],"study_design_scores_gemma":[0.0013310942,0.000009080121,0.032704893,0.000043878823,0.000011910862,0.0000015293284,0.00009404311,0.9645945,0.00077319663,0.000008214293,0.00024365357,0.00018403443],"about_ca_topic_score_codex":0.000017913377,"about_ca_topic_score_gemma":0.00007993827,"teacher_disagreement_score":0.6172926,"about_ca_system_score_codex":0.000052210373,"about_ca_system_score_gemma":0.0000034648094,"threshold_uncertainty_score":0.60078627},"labels":[],"label_agreement":null},{"id":"W4375798913","doi":"10.1109/lra.2023.3273421","title":"Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","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":"Toyota Research Institute","keywords":"Interpretability; Controller (irrigation); Computer science; Artificial neural network; Artificial intelligence; Set (abstract data type); Machine learning; Programming language","score_opus":0.014628632478758305,"score_gpt":0.2679701900467087,"score_spread":0.2533415575679504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375798913","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":[0.069010675,0.0000034344787,0.92161196,0.0075188135,0.0007980746,0.00060327275,0.000005365678,0.00044183538,0.000006554151],"genre_scores_gemma":[0.92805547,0.0000014034716,0.07024477,0.0014488386,0.00014353426,0.00004605089,0.0000138144605,0.00002058715,0.000025526306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839425,0.00012764843,0.00033988905,0.00043351372,0.00029039785,0.00041428042],"domain_scores_gemma":[0.998525,0.0007811995,0.00021594405,0.00025204144,0.000086088854,0.0001397151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003422221,0.00021545454,0.0002588879,0.00025948748,0.00029057328,0.0003690407,0.00036155566,0.000071827635,0.0000010103965],"category_scores_gemma":[0.00014390613,0.00019730712,0.00009598145,0.00024180925,0.00008042701,0.00042068274,0.00006243196,0.000294901,0.000012860376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017075155,0.000027810192,0.00009888705,0.0000154112,0.00004410351,0.000006956865,0.0011164809,0.9843815,0.0102124065,0.0008695204,0.00012536804,0.0030844936],"study_design_scores_gemma":[0.0017751347,0.00012828053,0.00950422,0.000027937558,0.000018541616,0.000009747995,0.000041746483,0.9879948,0.00013803813,0.00007026967,0.000071781484,0.00021952827],"about_ca_topic_score_codex":0.0000040649325,"about_ca_topic_score_gemma":0.0000017892144,"teacher_disagreement_score":0.8590448,"about_ca_system_score_codex":0.000055312477,"about_ca_system_score_gemma":0.000023112669,"threshold_uncertainty_score":0.8045951},"labels":[],"label_agreement":null},{"id":"W4379116572","doi":"10.1109/lra.2023.3282381","title":"Use Your Imagination: A Detector-Independent Approach for LiDAR Quality Booster","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","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":"Point cloud; Lidar; Computer science; Inference; Artificial intelligence; RGB color model; Point (geometry); Block (permutation group theory); Detector; Process (computing); Computer vision; Remote sensing; Mathematics; Geography; Telecommunications","score_opus":0.06530455764265676,"score_gpt":0.31174152298987323,"score_spread":0.24643696534721649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379116572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036108594,0.000009956007,0.9527117,0.01004819,0.00019599935,0.00043420523,0.000007924695,0.00046909612,0.000014352963],"genre_scores_gemma":[0.6462201,0.000011668574,0.35106152,0.002223265,0.00013574964,0.00017071255,0.000034579287,0.000018822486,0.00012357725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988728,0.00004433355,0.0002533105,0.00037165036,0.00022248404,0.00023541652],"domain_scores_gemma":[0.99918723,0.00018457879,0.0001329227,0.0003531471,0.000072447074,0.000069674126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002247531,0.0001259302,0.00012640143,0.000114126444,0.00020997666,0.00028821215,0.000261619,0.000040556017,5.3170737e-7],"category_scores_gemma":[0.000027508158,0.00012514315,0.00005141141,0.0004027547,0.00003596372,0.00071152684,0.000072656156,0.000080794984,0.000014324917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012234992,0.00010690987,0.0012817294,0.00016908463,0.00006026098,0.000007114373,0.0011304917,0.7897727,0.07721618,0.06824747,0.009332055,0.052663762],"study_design_scores_gemma":[0.00029612993,0.000014943895,0.013679338,0.000006808252,0.000007636221,0.000006937879,0.000012142304,0.9821417,0.0014487751,0.0013694242,0.0008113409,0.00020483666],"about_ca_topic_score_codex":0.0000032737644,"about_ca_topic_score_gemma":9.4128643e-7,"teacher_disagreement_score":0.61011153,"about_ca_system_score_codex":0.000039103743,"about_ca_system_score_gemma":0.000012560078,"threshold_uncertainty_score":0.510319},"labels":[],"label_agreement":null},{"id":"W4380075577","doi":"10.1109/lra.2023.3281261","title":"Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Obstacle avoidance; Software deployment; Terrain; Computer science; Artificial intelligence; End-to-end principle; Emulation; Simulation; Human–computer interaction; Real-time computing; Mobile robot; Robot; Computer vision","score_opus":0.017619467290041567,"score_gpt":0.2505866685451731,"score_spread":0.2329672012551315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380075577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078202724,0.00000757256,0.91357106,0.0064671705,0.0008936602,0.000308945,0.000010421753,0.00052533986,0.000013081772],"genre_scores_gemma":[0.68686444,0.000002548182,0.3110643,0.0017117276,0.000120099365,0.000034059667,0.00013553585,0.000023182296,0.00004409806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853283,0.00004732197,0.00028618792,0.000403082,0.0003589065,0.00037164806],"domain_scores_gemma":[0.99890584,0.00052445754,0.00014293427,0.00023587573,0.000057773097,0.00013309809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048421143,0.00017907233,0.00020418175,0.0006210422,0.0002615403,0.00025118946,0.00024731123,0.00006941075,0.0000017006344],"category_scores_gemma":[0.00011735739,0.00019463118,0.00005407535,0.0010678462,0.00003536794,0.000287511,0.000045960147,0.0001477415,0.000060411294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002229999,0.000006879582,0.0005327302,0.00005241392,0.000016693724,0.000023591738,0.00026862722,0.9838391,0.002746835,0.000044790402,0.0029082461,0.00955789],"study_design_scores_gemma":[0.00045905155,0.00010331014,0.008965917,0.000076820215,0.000013417088,0.00001124132,0.000019170668,0.9883471,0.0014002596,0.000042125543,0.00032711102,0.00023443607],"about_ca_topic_score_codex":0.000019656725,"about_ca_topic_score_gemma":5.844072e-7,"teacher_disagreement_score":0.6086617,"about_ca_system_score_codex":0.000060323353,"about_ca_system_score_gemma":0.00006246641,"threshold_uncertainty_score":0.7936829},"labels":[],"label_agreement":null},{"id":"W4383112356","doi":"10.1109/lra.2023.3292004","title":"SACHA: Soft Actor-Critic With Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Path (computing); Observable; Computer science; Mathematical optimization; Artificial intelligence; Mathematics; Physics","score_opus":0.0428821579873367,"score_gpt":0.2687953178799828,"score_spread":0.2259131598926461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383112356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031739555,0.0000064173114,0.9619867,0.004795012,0.0005736906,0.00040340563,0.0000038355597,0.0004876241,0.000003739416],"genre_scores_gemma":[0.7800946,0.0000055417863,0.21821764,0.00127649,0.00007913647,0.00005726359,0.000057426623,0.000030820796,0.00018106302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850965,0.000051764222,0.0003155372,0.00036578538,0.00034229574,0.00041498308],"domain_scores_gemma":[0.9990896,0.0002389,0.00017919898,0.00030459862,0.00009178499,0.00009591697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033941946,0.00019256858,0.00018681343,0.00016085742,0.00033131312,0.00039220566,0.0002695439,0.000058386293,0.0000018021244],"category_scores_gemma":[0.00007239057,0.00018001032,0.00006170283,0.0003493013,0.00004785861,0.00038706162,0.0000447926,0.000107807486,0.000030991712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042699708,0.000018717503,0.0016662767,0.00013074797,0.000022295117,0.000011082712,0.00013991185,0.9902044,0.0054433476,0.0008630463,0.0009151591,0.00058073126],"study_design_scores_gemma":[0.0008383132,0.000110782865,0.009727848,0.00011678848,0.00002932468,0.0000026305754,0.000016210257,0.98824996,0.0003812626,0.000023545159,0.00026747293,0.00023585632],"about_ca_topic_score_codex":0.0000052029723,"about_ca_topic_score_gemma":0.0000025964493,"teacher_disagreement_score":0.7483551,"about_ca_system_score_codex":0.00006704871,"about_ca_system_score_gemma":0.000052929678,"threshold_uncertainty_score":0.73406076},"labels":[],"label_agreement":null},{"id":"W4385212075","doi":"10.1109/lra.2023.3295251","title":"Optimizing Task Waiting Times in Dynamic Vehicle Routing","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Bounded function; Robot; Queue; Set (abstract data type); Euclidean geometry; Mathematical optimization; Artificial intelligence; Mathematics; Computer network; Programming language","score_opus":0.009246013254352873,"score_gpt":0.22177385451958626,"score_spread":0.21252784126523339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385212075","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":[0.94702864,0.000009889869,0.04998958,0.001915951,0.0001977981,0.000092914444,0.0000051533198,0.0006445508,0.000115532675],"genre_scores_gemma":[0.9967023,0.000012368974,0.002976622,0.00020847484,0.000016679536,0.000008847327,0.000041585514,0.000016106555,0.000017015573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994304,0.00000813211,0.0002277322,0.000098063036,0.00007621655,0.00015945321],"domain_scores_gemma":[0.999834,0.000040894305,0.000024192717,0.00006601655,0.0000122006395,0.000022693026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212698,0.00007659784,0.0000823222,0.00017382049,0.00006155842,0.000042294727,0.000035661084,0.000032945136,0.000004453583],"category_scores_gemma":[0.000007620311,0.00008970138,0.000017490484,0.00040472805,0.000015270307,0.00014047249,0.0000035115352,0.000090908296,0.000018697303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.6628697e-7,0.0000029800467,0.0007019272,0.000028927325,0.000006537004,0.0000032860833,0.00061702536,0.95074105,0.045334466,0.00073689554,0.00010904318,0.0017175734],"study_design_scores_gemma":[0.00017056792,0.0000021036617,0.02930695,0.000037665784,0.0000045423467,7.169557e-7,0.0001358275,0.9697959,0.0003674855,0.00003715158,0.00004384184,0.00009727041],"about_ca_topic_score_codex":0.000006906066,"about_ca_topic_score_gemma":0.000020584428,"teacher_disagreement_score":0.049673673,"about_ca_system_score_codex":0.000032507232,"about_ca_system_score_gemma":0.000005384424,"threshold_uncertainty_score":0.36579162},"labels":[],"label_agreement":null},{"id":"W4385413751","doi":"10.1109/lra.2023.3300253","title":"Uncoupled Stability Dynamic Range for Hunt-Crossley Modeled Virtual Environments","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","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":"Queen's University","funders":"","keywords":"Viscoelasticity; Haptic technology; Elasticity (physics); Stability (learning theory); Virtual machine; Simulation; Virtual reality; Computer science; Viscosity; Range (aeronautics); Parameter space; Statistical physics; Mechanics; Control theory (sociology); Physics; Mathematics; Engineering; Thermodynamics; Human–computer interaction; Artificial intelligence; Aerospace engineering; Geometry","score_opus":0.015549958102799402,"score_gpt":0.2213900333757785,"score_spread":0.2058400752729791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385413751","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":[0.61935455,0.000011829171,0.37900686,0.0004726669,0.0005064134,0.000288826,0.000025117579,0.00032112634,0.000012613596],"genre_scores_gemma":[0.9985184,0.000021046573,0.0009885931,0.00016659562,0.00005476463,0.000057545407,0.00006110228,0.000027543483,0.00010440094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927384,0.000015290672,0.0002389753,0.00015314344,0.00013302997,0.00018573535],"domain_scores_gemma":[0.99971133,0.00006186492,0.000028893635,0.00013543338,0.000009523212,0.000052948715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016383326,0.00012181678,0.00014851966,0.00006984083,0.0000964406,0.000074751384,0.00005444081,0.000055653443,0.000010785084],"category_scores_gemma":[0.000010584565,0.00012491303,0.00004121487,0.00009015651,0.000027441658,0.0001234348,0.0000075322278,0.000049356426,0.000047095975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003330642,0.000007632733,0.00015381648,0.000054857617,0.000025129511,7.1661026e-7,0.00039807596,0.92952013,0.06762536,0.00017699362,0.00075917365,0.0012747769],"study_design_scores_gemma":[0.00060805114,0.000015111236,0.0031901628,0.00001121945,0.000011605358,9.773815e-7,0.00006606164,0.9949286,0.0005965689,0.000019338207,0.00041105482,0.00014122123],"about_ca_topic_score_codex":0.0000037164946,"about_ca_topic_score_gemma":0.0000114399,"teacher_disagreement_score":0.37916386,"about_ca_system_score_codex":0.000069918824,"about_ca_system_score_gemma":0.000005175685,"threshold_uncertainty_score":0.5093805},"labels":[],"label_agreement":null},{"id":"W4385696128","doi":"10.1109/lra.2023.3303696","title":"Multi-Modal Streaming 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Computer science; Artificial intelligence; Computer vision; Context (archaeology); Object detection; Perception; Field of view; Pattern recognition (psychology); Remote sensing; Geography","score_opus":0.019717748110599294,"score_gpt":0.26056861909984685,"score_spread":0.24085087098924757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385696128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092668355,0.0000070918986,0.9035448,0.0027254785,0.00025607878,0.00012767623,9.908965e-7,0.00065985974,0.000009679329],"genre_scores_gemma":[0.8132926,0.00001769891,0.1859353,0.00059976504,0.00008015234,0.000023749146,0.000004373444,0.000010150316,0.000036173264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992489,0.000025092873,0.00015107983,0.0002545122,0.00013114921,0.00018923203],"domain_scores_gemma":[0.99955416,0.00008409453,0.000075171076,0.00021564073,0.00002212969,0.000048835132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008328979,0.000094512776,0.000079314355,0.000120039054,0.00021744172,0.00010464491,0.00016401426,0.000032214586,5.3482927e-7],"category_scores_gemma":[0.000008837604,0.00009699897,0.00002455315,0.0005440946,0.000026508518,0.00034966366,0.00005230939,0.000082328464,0.000054738808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.67386e-7,0.000013221654,0.00010878247,0.000011644992,0.000008305265,0.0000059915046,0.00021259076,0.7173938,0.17868988,0.00081509654,0.0002872172,0.10245277],"study_design_scores_gemma":[0.00014452745,0.0000100018315,0.009545321,0.000008576822,0.0000034763395,0.000007473876,0.000006105969,0.9865518,0.003236798,0.00016815367,0.00020579038,0.00011193397],"about_ca_topic_score_codex":0.0000047965023,"about_ca_topic_score_gemma":0.00000655557,"teacher_disagreement_score":0.72062427,"about_ca_system_score_codex":0.000029283665,"about_ca_system_score_gemma":0.0000067437068,"threshold_uncertainty_score":0.3955503},"labels":[],"label_agreement":null},{"id":"W4385756663","doi":"10.1109/lra.2023.3304561","title":"Torque-Based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","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":"Université de Montréal","funders":"National Research Foundation of Korea","keywords":"Reinforcement learning; Torque; Robot; Task (project management); Computer science; Artificial intelligence; Action (physics); Control (management); Position (finance); Process (computing); Space (punctuation); Control theory (sociology); Control engineering; Engineering","score_opus":0.015529989236336451,"score_gpt":0.23542849836200966,"score_spread":0.2198985091256732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385756663","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":[0.14018945,0.000010320879,0.8565979,0.0017347941,0.00038918978,0.00045079185,0.0000012061887,0.0005880857,0.00003828536],"genre_scores_gemma":[0.99574804,0.000026688936,0.0031190047,0.0007491584,0.00014124475,0.00005015296,0.00004500894,0.000060901144,0.00005977804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987483,0.0000477789,0.00033378837,0.00025607712,0.00022184364,0.00039221835],"domain_scores_gemma":[0.99940926,0.00026036048,0.00003713146,0.00010780107,0.00003659448,0.00014887135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021116437,0.00024362569,0.00025275466,0.00028592092,0.0002679053,0.0001335551,0.00006970058,0.000088385765,0.0000083626355],"category_scores_gemma":[0.00004983954,0.00025638108,0.00007534433,0.0002387212,0.000027697937,0.00011024129,0.000009608204,0.00020407473,0.000024739385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013313789,0.0000064623723,0.00017523835,0.000104806895,0.000037413934,0.0000045116763,0.0003053889,0.9596726,0.036532167,0.00018828221,0.000094553696,0.0028652803],"study_design_scores_gemma":[0.0010994475,0.00012863353,0.0012448367,0.00010551696,0.000055433604,0.0000021255594,0.000043346467,0.99617887,0.0007108176,0.000009814987,0.00012822547,0.00029296044],"about_ca_topic_score_codex":0.000015699692,"about_ca_topic_score_gemma":0.000005871518,"teacher_disagreement_score":0.85555863,"about_ca_system_score_codex":0.000091028414,"about_ca_system_score_gemma":0.000014478802,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4385819898","doi":"10.1109/lra.2023.3304845","title":"Stability of Human Balance During Quiet Stance With Physiological and Exoskeleton Time Delays","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Control theory (sociology); Torque; Angular velocity; Ankle; Stability (learning theory); Controller (irrigation); Inverted pendulum; Ordinary differential equation; Exponential stability; Mathematics; Computer science; Physics; Differential equation; Simulation; Mathematical analysis; Nonlinear system; Classical mechanics; Control (management)","score_opus":0.027041352406039783,"score_gpt":0.3122358614315603,"score_spread":0.2851945090255205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385819898","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":[0.9973605,0.000019061057,0.0011608013,0.00086913194,0.00008662424,0.00028849827,0.000016880245,0.00014544284,0.000053029864],"genre_scores_gemma":[0.9987883,0.00005600123,0.0007300914,0.00021924237,0.000071471135,0.000020266767,0.00003432932,0.000011079911,0.000069227106],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989392,0.00016579225,0.0002979099,0.00022138434,0.00013938257,0.00023630481],"domain_scores_gemma":[0.99946207,0.00008828928,0.00020007258,0.00015046944,0.00004680377,0.000052288702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030559336,0.00010819049,0.00022028289,0.00004603544,0.0003185938,0.00000836532,0.00005378362,0.00008010296,0.000007876718],"category_scores_gemma":[0.000013810798,0.00008781745,0.000022209431,0.00015318101,0.00010561094,0.000120325014,0.000031419586,0.00017144151,0.00001668924],"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":[0.000028229462,0.00005801844,0.030593015,0.00071109546,0.000021165244,0.0000029483472,0.00077303045,0.00033373537,0.9659778,0.00033155121,0.0010415176,0.00012789294],"study_design_scores_gemma":[0.0008197791,0.000075402815,0.9861591,0.0002365738,0.0000137912275,8.30886e-7,0.00015432689,0.011997711,0.0002465127,0.00015273385,0.00001699927,0.00012623912],"about_ca_topic_score_codex":0.000014310729,"about_ca_topic_score_gemma":0.0000107763735,"teacher_disagreement_score":0.96573126,"about_ca_system_score_codex":0.000030398822,"about_ca_system_score_gemma":0.000014809808,"threshold_uncertainty_score":0.35810915},"labels":[],"label_agreement":null},{"id":"W4386038368","doi":"10.1109/lra.2023.3306996","title":"Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning With Efficient Heuristics","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Mathematical optimization; Integer programming; Computer science; Path (computing); Reduction (mathematics); Planner; Mathematics; Artificial intelligence","score_opus":0.026652384698934235,"score_gpt":0.2562367640614579,"score_spread":0.22958437936252366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386038368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031637624,0.00001541478,0.9651714,0.0014251835,0.00055302813,0.0004573864,0.000009419079,0.0007256528,0.0000048779266],"genre_scores_gemma":[0.1276989,0.0000016390741,0.87159204,0.000398772,0.0000932283,0.000053724314,0.000045208246,0.000033701614,0.00008278875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983314,0.00005182128,0.00031616635,0.00047131762,0.00033463124,0.00049462594],"domain_scores_gemma":[0.9990143,0.0002903411,0.00018941176,0.00029744513,0.00008692332,0.00012155041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041433927,0.00024276148,0.00025349468,0.00021331475,0.00028702398,0.00036701915,0.00031097193,0.000071395436,4.532857e-7],"category_scores_gemma":[0.00006340838,0.0002142681,0.00005276354,0.0004578313,0.00006654707,0.00018188315,0.00007967236,0.00015329996,0.0000337811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005817686,0.000038196955,0.00019776616,0.00005223662,0.00002965664,0.000066294495,0.0008366759,0.99163556,0.0012431811,0.00019815394,0.001368117,0.0043283696],"study_design_scores_gemma":[0.0007663175,0.00011598686,0.0020038332,0.00015614033,0.000021270604,0.000031358162,0.000030887863,0.99619,0.00021517879,0.00000929168,0.00016155596,0.00029815215],"about_ca_topic_score_codex":0.000003737845,"about_ca_topic_score_gemma":1.07078506e-7,"teacher_disagreement_score":0.09606128,"about_ca_system_score_codex":0.000051882304,"about_ca_system_score_gemma":0.000041899915,"threshold_uncertainty_score":0.8737599},"labels":[],"label_agreement":null},{"id":"W4386453715","doi":"10.1109/lra.2023.3312038","title":"Learning Nonprehensile Dynamic Manipulation: Sim2real Vision-Based Policy With a Surgical Robot","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Reinforcement learning; Computer science; Task (project management); Artificial intelligence; Robotics; Block (permutation group theory); Transfer of learning; Robot; Human–computer interaction; Computer vision; Engineering","score_opus":0.009245334821474762,"score_gpt":0.2426046624547575,"score_spread":0.23335932763328274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386453715","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":[0.5783636,0.000026562895,0.41194868,0.0059008095,0.00032371577,0.00028027038,0.0000011634847,0.0026717766,0.00048343468],"genre_scores_gemma":[0.9969503,0.000013384717,0.0024189358,0.00020448407,0.00012768227,0.000009888575,0.000089592635,0.000054602253,0.00013108419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989564,0.000051441533,0.00024404503,0.00020917722,0.0002529661,0.00028596175],"domain_scores_gemma":[0.99956304,0.00011958531,0.00005917805,0.00013183914,0.00003171309,0.00009464883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012370967,0.000190808,0.00018163702,0.0003560878,0.00020346824,0.00013929812,0.00006290169,0.00007635222,0.0000242035],"category_scores_gemma":[0.0000125961205,0.00018357967,0.00004444506,0.00060400495,0.00004043406,0.00017707939,0.0000115283765,0.00022628243,0.00009605864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058607643,0.000005531391,0.0012460527,0.000055423163,0.000017451644,0.000024600042,0.00014337095,0.9937301,0.0020340062,0.00014942994,0.0002136839,0.0023744733],"study_design_scores_gemma":[0.00054574583,0.00004202563,0.041723263,0.00005883966,0.000012701499,0.000016279198,0.000030751464,0.956738,0.000039291943,0.000009684124,0.00055757316,0.00022585069],"about_ca_topic_score_codex":0.000014101216,"about_ca_topic_score_gemma":0.000007599624,"teacher_disagreement_score":0.41858676,"about_ca_system_score_codex":0.00007066981,"about_ca_system_score_gemma":0.000020579997,"threshold_uncertainty_score":0.74861616},"labels":[],"label_agreement":null},{"id":"W4386736799","doi":"10.1109/lra.2023.3315545","title":"Fast Motion Performance of a Bionic Ray Robot With Serial Pectoral Fins","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Biomimetic flight and propulsion mechanisms","field":"Engineering","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 Guelph","funders":"National Natural Science Foundation of China","keywords":"Fin; Propulsion; Fish fin; Robot; Motion (physics); Simulation; Jet propulsion; Vortex; Position (finance); Perspective (graphical); Computer science; Physics; Acoustics; Optics; Mechanics; Artificial intelligence; Mechanical engineering; Fish <Actinopterygii>; Engineering; Aerospace engineering; Biology","score_opus":0.009558872205064248,"score_gpt":0.19006026614372948,"score_spread":0.18050139393866524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386736799","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":[0.9316677,0.00001008409,0.06674725,0.0006851851,0.0005110725,0.000103538136,0.0000067508795,0.0002549129,0.000013533024],"genre_scores_gemma":[0.9946312,0.000031976124,0.00514633,0.00004327934,0.00007357235,0.000005428652,0.000024203508,0.000017913168,0.000026097478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994638,0.000011531483,0.00015753381,0.00009764563,0.00012863141,0.00014084103],"domain_scores_gemma":[0.9998042,0.000011989991,0.00003857174,0.00009278504,0.00001819665,0.00003428571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007630732,0.00010130424,0.00011824454,0.00012245447,0.000052266205,0.000026697418,0.000048745496,0.000049368904,0.000016277594],"category_scores_gemma":[0.0000016476496,0.000084783016,0.000021694637,0.000251094,0.000026714039,0.00009774648,0.000007777555,0.000060181803,0.000032367538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008304356,0.0000054833763,0.00018452742,0.00012020559,0.000015714304,0.0000014628704,0.00013100107,0.72791076,0.2680806,0.000062397274,0.00046508206,0.0030145058],"study_design_scores_gemma":[0.0004550046,0.00009182481,0.007846845,0.00006754723,0.000020244623,0.0000044388576,0.000017080501,0.91564524,0.07562594,0.00001573552,0.000050292587,0.000159791],"about_ca_topic_score_codex":0.0000030365245,"about_ca_topic_score_gemma":0.0000017276225,"teacher_disagreement_score":0.19245465,"about_ca_system_score_codex":0.000015603602,"about_ca_system_score_gemma":0.0000054956913,"threshold_uncertainty_score":0.3457351},"labels":[],"label_agreement":null},{"id":"W4386824853","doi":"10.1109/lra.2023.3316607","title":"Multimodal Tremor Suppression of the Wrist Using FES and Electric Motors–A Simulation Study","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Neurological disorders and treatments","field":"Medicine","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":"Western University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Functional electrical stimulation; Computer science; Torque; Physical medicine and rehabilitation; Wrist; Muscle fatigue; Electromyography; Stimulation; Medicine; Psychology; Neuroscience","score_opus":0.030227859715677718,"score_gpt":0.30168260263943464,"score_spread":0.27145474292375693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386824853","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":[0.9975639,0.000016674434,0.0005219118,0.0013790441,0.000083246705,0.00039350128,0.0000015177305,0.00003643432,0.000003774201],"genre_scores_gemma":[0.99948096,0.000008803287,0.00016152543,0.00030728022,0.000017929173,0.0000026359376,0.0000026106336,0.0000067470514,0.0000115091225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994892,0.00004086727,0.00013326656,0.000121345,0.0001303324,0.00008499601],"domain_scores_gemma":[0.99974,0.00006728531,0.000065904555,0.00008367141,0.000017194237,0.000025947222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049328588,0.00007099169,0.000118254444,0.00006472024,0.00009718531,0.000013265589,0.000020097987,0.000026777245,0.0000017511743],"category_scores_gemma":[0.000025321877,0.000043943026,0.000025507275,0.00018545284,0.000020930896,0.000037954862,0.000016069711,0.00004837981,8.2893314e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008466326,0.0005188943,0.41747317,0.000093246534,0.00008548457,0.0000251784,0.0005762257,0.3503167,0.22231025,0.000012486756,0.00017320838,0.008330517],"study_design_scores_gemma":[0.0006883793,0.000092245675,0.50988644,0.00001305506,0.00005229227,0.0000010483474,0.000020838921,0.48867995,0.00051249645,0.00002470847,0.0000015037717,0.000027060378],"about_ca_topic_score_codex":0.000018531533,"about_ca_topic_score_gemma":7.97214e-7,"teacher_disagreement_score":0.22179775,"about_ca_system_score_codex":0.000008992874,"about_ca_system_score_gemma":0.000005244706,"threshold_uncertainty_score":0.17919447},"labels":[],"label_agreement":null},{"id":"W4387757603","doi":"10.1109/lra.2023.3325688","title":"Multi-Scale Visual Servoing Framework for Optical Microscopy Based on SIFT Matching","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Vision and Imaging","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":"Visual servoing; Magnification; Computer vision; Artificial intelligence; Robustness (evolution); Computer science; Microscope; Calibration; Scale-invariant feature transform; Field of view; Optics; Mathematics; Feature extraction; Image (mathematics); Physics","score_opus":0.019897467119751815,"score_gpt":0.3260284979201893,"score_spread":0.30613103080043746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387757603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03407271,0.0000034018085,0.9523649,0.012460628,0.0005534838,0.00015651184,0.000001909291,0.0003816314,0.0000048241127],"genre_scores_gemma":[0.13114315,0.000002129455,0.8621041,0.006644457,0.000063559724,0.000010185708,0.0000054390416,0.000015151366,0.000011809889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903953,0.000020343803,0.00019111934,0.00030737594,0.00017340166,0.00026823496],"domain_scores_gemma":[0.99929667,0.00034349167,0.00006797304,0.00018606267,0.000027427848,0.00007837849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018874265,0.00012680684,0.00012923256,0.00014026671,0.00024232295,0.00026530222,0.00018158647,0.000047154652,0.0000010534458],"category_scores_gemma":[0.000039844363,0.0001230974,0.000051205792,0.00025776,0.00002858281,0.00028749052,0.000045571625,0.0001295103,0.0000296682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073362266,0.000057480527,0.00014950636,0.00007388085,0.0000071044915,0.000008238136,0.0005021921,0.7780038,0.19794607,0.0035522291,0.0005699183,0.01912225],"study_design_scores_gemma":[0.00035057045,0.00003247235,0.0012777373,0.00013046792,0.0000033974272,0.0000013495933,0.000023761073,0.99049854,0.006683621,0.0007650171,0.000079688405,0.00015336603],"about_ca_topic_score_codex":9.846663e-7,"about_ca_topic_score_gemma":3.5838258e-7,"teacher_disagreement_score":0.21249476,"about_ca_system_score_codex":0.000024611467,"about_ca_system_score_gemma":0.00001399588,"threshold_uncertainty_score":0.50197667},"labels":[],"label_agreement":null},{"id":"W4387757734","doi":"10.1109/lra.2023.3325776","title":"CoNi-MPC: Cooperative Non-inertial Frame Based Model Predictive Control","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Distributed Control Multi-Agent Systems","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","funders":"National Natural Science Foundation of China","keywords":"Computer science; Inertial measurement unit; Inertial frame of reference; Frame (networking); Trajectory; Control theory (sociology); Model predictive control; Reference frame; Motion (physics); Controller (irrigation); Artificial intelligence; Robot; Computer vision; Simulation; Control (management)","score_opus":0.012176917279572886,"score_gpt":0.23071561507544971,"score_spread":0.21853869779587684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387757734","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":[0.018517867,0.0000075288162,0.9672423,0.012362195,0.00066470297,0.0005110592,0.00007967778,0.00056498294,0.000049670813],"genre_scores_gemma":[0.9911578,0.0000018468162,0.005032515,0.0035277344,0.0001079371,0.000073254705,0.00003838606,0.000016188906,0.00004431486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838686,0.00010353532,0.00035627582,0.00042193543,0.0003747849,0.00035662227],"domain_scores_gemma":[0.99903536,0.0001892523,0.00016094837,0.00033976673,0.00014458102,0.00013011575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028688897,0.00021601052,0.00028656973,0.00016749007,0.00019440278,0.00033394317,0.00035434242,0.00009309746,0.0000019829483],"category_scores_gemma":[0.000050494706,0.00020331994,0.00006977355,0.0003966793,0.00006972854,0.0004727698,0.000040538205,0.00014620883,0.00008275122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008852782,0.000022832017,0.00012047664,0.000015341418,0.00004649757,0.0000135686905,0.00025415918,0.96702427,0.02612001,0.0015328074,0.0046289293,0.00021223137],"study_design_scores_gemma":[0.0019832607,0.00005103596,0.0016102057,0.000040424817,0.000022069311,0.0000021897272,0.000014488112,0.9952828,0.00067998306,0.000048817805,0.000046178873,0.00021852809],"about_ca_topic_score_codex":0.000013132062,"about_ca_topic_score_gemma":0.0000028534255,"teacher_disagreement_score":0.97264,"about_ca_system_score_codex":0.000070263,"about_ca_system_score_gemma":0.00009073427,"threshold_uncertainty_score":0.8291147},"labels":[],"label_agreement":null},{"id":"W4387886001","doi":"10.1109/lra.2023.3326700","title":"Bayesian Filtering for Homography Estimation","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Homography; Estimation; Bayesian probability; Artificial intelligence; Bayes estimator; Computer science; Computer vision; Mathematics; Pattern recognition (psychology); Statistics; Engineering","score_opus":0.01048168704102067,"score_gpt":0.2171679599217537,"score_spread":0.20668627288073302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387886001","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":[0.053800583,0.000012851084,0.9438785,0.0009554619,0.0004724314,0.00018394184,0.000009517161,0.0006570917,0.000029663437],"genre_scores_gemma":[0.960072,0.00003688933,0.039350756,0.00023785607,0.00009440346,0.000026883872,0.0001277689,0.000039671362,0.000013781237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942267,0.000008183795,0.00018178152,0.00011760416,0.000093768766,0.00017601313],"domain_scores_gemma":[0.9997545,0.000054119806,0.000027967644,0.00009680789,0.00002074332,0.000045847264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008560361,0.00010804399,0.00010245837,0.00021114142,0.00010205932,0.00008968413,0.000041662242,0.000047309957,0.0000022682677],"category_scores_gemma":[0.000010332424,0.00011849668,0.000042350595,0.00027589555,0.000017859555,0.00012267096,0.0000046269442,0.000040875922,0.000009426817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.7862344e-7,0.0000026039077,0.000057389792,0.00010744067,0.000014097084,0.00000108776,0.00008539864,0.9792322,0.012841992,0.0004770817,0.0031783148,0.0040015504],"study_design_scores_gemma":[0.00019442807,0.000011798288,0.0010299945,0.000030877338,0.000014347151,0.0000015527233,0.000010959957,0.9964305,0.0016225976,0.00028885616,0.00022223161,0.00014186153],"about_ca_topic_score_codex":0.0000015175142,"about_ca_topic_score_gemma":0.0000011701205,"teacher_disagreement_score":0.9062714,"about_ca_system_score_codex":0.000017668566,"about_ca_system_score_gemma":0.0000029024525,"threshold_uncertainty_score":0.48321545},"labels":[],"label_agreement":null},{"id":"W4388430275","doi":"10.1109/lra.2023.3330678","title":"EMG-Based Intention Detection Using Deep Learning for Shared Control in Upper-Limb Assistive Exoskeletons","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":54,"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 Institutes of Health Research; Canada Foundation for Innovation; Government of Alberta","keywords":"Exoskeleton; Computer science; Payload (computing); Task (project management); Electromyography; Artificial intelligence; Convolutional neural network; Robot; Trajectory; Simulation; Engineering; Physical medicine and rehabilitation","score_opus":0.014719225644479864,"score_gpt":0.2328867005310322,"score_spread":0.21816747488655233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388430275","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":[0.4183218,0.00001525747,0.5805241,0.00048226977,0.00018615318,0.00018130081,0.0000022732368,0.00028002058,0.0000068573554],"genre_scores_gemma":[0.9983043,0.000013145341,0.0013596764,0.00017414373,0.00003923164,0.000060181435,0.00002443959,0.00002137896,0.0000034699053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993527,0.000028009094,0.00019861333,0.00013290075,0.000083960535,0.00020384193],"domain_scores_gemma":[0.9997143,0.000120391436,0.000051075018,0.000049573664,0.00003747426,0.000027191374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012537939,0.0001115675,0.00013328326,0.0003996227,0.0001542575,0.000055028657,0.000028854243,0.000049258753,0.0000022671197],"category_scores_gemma":[0.000042134372,0.00012495069,0.00006661039,0.00038996336,0.000018125666,0.0001386019,0.0000034347827,0.00010557045,0.0000011025338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008266318,0.0000060214375,0.0021187307,0.000061647996,0.0000343181,6.1738007e-7,0.00009120734,0.8568859,0.13030064,0.000012250185,0.000064741595,0.01041565],"study_design_scores_gemma":[0.00057904056,0.00002596279,0.14350753,0.000036994777,0.000017947796,5.1946176e-7,0.00006910775,0.8543626,0.0011949162,0.000015232384,0.000074216274,0.0001158982],"about_ca_topic_score_codex":0.000010127012,"about_ca_topic_score_gemma":0.000029092627,"teacher_disagreement_score":0.5799826,"about_ca_system_score_codex":0.00007918413,"about_ca_system_score_gemma":0.0000040229293,"threshold_uncertainty_score":0.5095341},"labels":[],"label_agreement":null},{"id":"W4388642425","doi":"10.1109/lra.2023.3332501","title":"Model-Based Co-Simulation of Flexible Mechanical Systems With Contacts Using Reduced Interface Models","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interface (matter); Flexibility (engineering); Computer science; Co-simulation; Scheme (mathematics); Mechanical system; Information exchange; Rigid body; Data exchange; Work (physics); Simulation; Control engineering; Distributed computing; Mechanical engineering; Engineering; Artificial intelligence","score_opus":0.03206652738523336,"score_gpt":0.2578764986554985,"score_spread":0.22580997127026514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388642425","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":[0.34500706,0.000016542135,0.6542943,0.00007733045,0.00020121178,0.00016997209,0.0000072309704,0.00020597465,0.000020392854],"genre_scores_gemma":[0.99806774,0.0000022043776,0.0018025953,0.00003667332,0.000029718636,0.000009826649,0.000009775885,0.00003144333,0.000010004386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913716,0.000024224017,0.00031114597,0.00014130784,0.00020902745,0.00017710925],"domain_scores_gemma":[0.9996027,0.00006077716,0.000088354565,0.00013878438,0.00004656328,0.0000628254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016952018,0.00013189214,0.00024191891,0.00011276564,0.00004082926,0.00005735538,0.0000674706,0.00007253002,6.20544e-7],"category_scores_gemma":[0.0000035403991,0.000120915494,0.000034341978,0.00015822363,0.0000130890985,0.0001431721,0.000006196913,0.00007234723,0.0000025454835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010580299,0.0000060787534,0.0000020220523,0.00009932534,0.00003391334,0.000001416266,0.00004439036,0.88802326,0.110040486,0.0016206135,0.000023242175,0.000094657255],"study_design_scores_gemma":[0.0005139082,0.000031137846,0.000009009208,0.00017231167,0.000029182635,0.0000015522379,0.000023044426,0.9971165,0.0018647253,0.00009390714,0.0000020777293,0.00014263966],"about_ca_topic_score_codex":0.000014362458,"about_ca_topic_score_gemma":0.0000011163579,"teacher_disagreement_score":0.65306073,"about_ca_system_score_codex":0.00006602983,"about_ca_system_score_gemma":0.000016248721,"threshold_uncertainty_score":0.49307907},"labels":[],"label_agreement":null},{"id":"W4388740180","doi":"10.1109/lra.2023.3333741","title":"Improved Generalization of Probabilistic Movement Primitives for Manipulation Trajectories","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic logic; Artificial intelligence; Generalization; Task (project management); Movement (music); Machine learning; Trajectory; Object (grammar); Parameterized complexity; GRASP; Point (geometry); Algorithm; Mathematics; Engineering","score_opus":0.022348722846051355,"score_gpt":0.23797574604526772,"score_spread":0.21562702319921637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388740180","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":[0.2855026,0.000011220896,0.7132951,0.00029874113,0.0002951737,0.00030035962,0.0000018824431,0.0002779882,0.000016921971],"genre_scores_gemma":[0.9933653,0.000014343123,0.006292393,0.00007993792,0.00007136481,0.0000326147,0.00009120411,0.000021613021,0.000031263953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947435,0.000013607573,0.00022368328,0.00009696809,0.00008264295,0.00010875744],"domain_scores_gemma":[0.99977005,0.000046402176,0.000059736547,0.00006365802,0.000038621703,0.000021548673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009583068,0.00008508744,0.00010286053,0.000115661605,0.000057618046,0.000030651583,0.000028155853,0.000034849723,0.000002973243],"category_scores_gemma":[0.000022446235,0.000090070374,0.00002910581,0.00015967857,0.000016639415,0.0001158304,0.0000041981225,0.000029488468,0.0000013260858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018123969,0.0000035532314,0.00030281334,0.0001776833,0.000014438746,8.839594e-8,0.0003381018,0.8986475,0.096937135,0.0026563455,0.00020601225,0.0007145509],"study_design_scores_gemma":[0.00022355492,0.00001663646,0.024535902,0.00002208846,0.000014525033,2.0964926e-7,0.000029691197,0.9710969,0.003668588,0.00026248928,0.00003366179,0.000095771284],"about_ca_topic_score_codex":0.0000031326404,"about_ca_topic_score_gemma":0.00000277952,"teacher_disagreement_score":0.7078627,"about_ca_system_score_codex":0.0000270437,"about_ca_system_score_gemma":0.000004364133,"threshold_uncertainty_score":0.36729634},"labels":[],"label_agreement":null},{"id":"W4388740431","doi":"10.1109/lra.2023.3333702","title":"Bag of Views: An Appearance-Based Approach to Next-Best-View Planning for 3D Reconstruction","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"National Research Council Canada; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Victoria","funders":"National Research Council","keywords":"Computer science; Computer vision; Artificial intelligence","score_opus":0.04896819076127711,"score_gpt":0.26039546679772074,"score_spread":0.21142727603644362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388740431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114671774,0.00006580926,0.883926,0.00028642986,0.00038631243,0.00035781137,0.0000082298075,0.00024332691,0.00005433577],"genre_scores_gemma":[0.82856786,0.000046498742,0.17042834,0.000544202,0.00016581365,0.000057018424,0.00011978069,0.00006111075,0.000009402144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917346,0.000025396941,0.00031038016,0.00017946248,0.00012500041,0.00018627907],"domain_scores_gemma":[0.9996396,0.00003546785,0.00006111171,0.00014647299,0.0000459906,0.0000713895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018153315,0.0001349401,0.00020333772,0.00021334799,0.000077159966,0.00006175963,0.00006249752,0.00006505121,0.000001074437],"category_scores_gemma":[0.0000134072725,0.00014288431,0.000041754884,0.00033210428,0.000024115474,0.00016243514,0.0000041242465,0.000055424363,0.0000061342653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000443543,0.0000126385985,0.00006283989,0.00039682814,0.000012641311,3.5656316e-7,0.00017492293,0.9684587,0.014930088,0.00017757835,0.00031510353,0.01545387],"study_design_scores_gemma":[0.00029587478,0.000043116474,0.0003171272,0.00019444172,0.000022343022,0.0000022720822,0.00005606592,0.99714905,0.0013990455,0.000025853196,0.00032969096,0.0001651129],"about_ca_topic_score_codex":0.000003052041,"about_ca_topic_score_gemma":7.4386895e-7,"teacher_disagreement_score":0.71389604,"about_ca_system_score_codex":0.00002770542,"about_ca_system_score_gemma":0.000009624623,"threshold_uncertainty_score":0.5826653},"labels":[],"label_agreement":null},{"id":"W4388755261","doi":"10.1109/lra.2023.3333742","title":"Swarm-SLAM: Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":146,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Canadian Space Agency","keywords":"Simultaneous localization and mapping; Swarm behaviour; Robot; Computer science; Artificial intelligence; Computer vision; Mobile robot","score_opus":0.026014591820443578,"score_gpt":0.25477920194150705,"score_spread":0.22876461012106347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388755261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030626412,0.0002248148,0.96644187,0.00050407706,0.00090917456,0.00067984086,0.000030850213,0.00057940505,0.0000035788728],"genre_scores_gemma":[0.9273598,0.00078138476,0.07097177,0.00038914708,0.00014261392,0.00005520693,0.00018835845,0.00008616517,0.000025506843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988477,0.000046338653,0.0003760072,0.00025006811,0.00016730692,0.00031254577],"domain_scores_gemma":[0.9992545,0.00029492564,0.00009251289,0.00013489737,0.00012068582,0.00010245493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015054517,0.00022278378,0.00026266548,0.00019837673,0.0002060973,0.00022685113,0.00005796123,0.00015116867,0.0000011994897],"category_scores_gemma":[0.00011958432,0.00023698121,0.000032551132,0.0005247681,0.00005094943,0.00013486818,0.000011058963,0.00009246427,0.000008542945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005622627,0.000009692637,0.00013247957,0.00028251336,0.000052512976,0.00000787194,0.0006786133,0.99173975,0.002850004,0.00294368,0.00068481686,0.0006124216],"study_design_scores_gemma":[0.0006510807,0.000020363477,0.00022112476,0.00016445578,0.000040988063,0.000003887841,0.0002444679,0.9970431,0.00050007855,0.00014115969,0.00069022,0.00027909622],"about_ca_topic_score_codex":0.000008266512,"about_ca_topic_score_gemma":0.0000045092092,"teacher_disagreement_score":0.8967334,"about_ca_system_score_codex":0.000056687604,"about_ca_system_score_gemma":0.000013230512,"threshold_uncertainty_score":0.9663814},"labels":[],"label_agreement":null},{"id":"W4389664997","doi":"10.1109/lra.2023.3342549","title":"Contact Representation in Robotic Mechanical Systems Employing Reduced Models","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Interface (matter); Computer science; Representation (politics); Constraint (computer-aided design); Task (project management); Robot; Mechanical system; Contact force; Perspective (graphical); Simulation; Control engineering; Distributed computing; Engineering; Artificial intelligence; Mechanical engineering; Systems engineering","score_opus":0.023076686846784145,"score_gpt":0.23654762242542504,"score_spread":0.21347093557864089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389664997","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":[0.59605336,0.000067034984,0.39980882,0.0012164102,0.0016497532,0.00040966627,0.0000035141506,0.0006925734,0.00009887237],"genre_scores_gemma":[0.99935627,0.00004573107,0.00032272752,0.00007355404,0.00008995731,0.000040618583,0.000016407574,0.00003264306,0.000022094302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886316,0.00005275624,0.00041357602,0.0002016499,0.00020883657,0.00026002617],"domain_scores_gemma":[0.99960536,0.00009328996,0.00005459052,0.00015518947,0.000019130353,0.000072470015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002734253,0.00014042285,0.00026591404,0.00017987388,0.000047108315,0.00012743018,0.00008774886,0.000087408356,9.1815514e-7],"category_scores_gemma":[0.00001410196,0.00014506085,0.000045579596,0.00030379873,0.000006668778,0.00022043822,0.000014363797,0.00012519672,0.000021691616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002145495,0.000004864761,0.00002972455,0.000056975707,0.000020195219,0.000012923024,0.00007302266,0.9242639,0.06776004,0.007099881,0.00023882826,0.00043748718],"study_design_scores_gemma":[0.00043060005,0.000012762428,0.0007528862,0.00011084214,0.000012424886,0.000006231093,0.000056522036,0.99772394,0.00012502995,0.00059815205,0.000008381488,0.00016225039],"about_ca_topic_score_codex":0.00005917749,"about_ca_topic_score_gemma":0.000010434638,"teacher_disagreement_score":0.4033029,"about_ca_system_score_codex":0.00009533112,"about_ca_system_score_gemma":0.0000066,"threshold_uncertainty_score":0.591541},"labels":[],"label_agreement":null},{"id":"W4389692243","doi":"10.1109/lra.2023.3342671","title":"Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Task (project management); Workspace; Robot; Artificial intelligence; Inverse kinematics; Engineering","score_opus":0.02463957206024244,"score_gpt":0.2684519002067864,"score_spread":0.24381232814654394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389692243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013812799,0.0000072789653,0.9809109,0.003529009,0.0006103201,0.0005360956,0.00003406426,0.0005469522,0.000012575103],"genre_scores_gemma":[0.7677254,0.0000035338364,0.23089531,0.0009480752,0.00016950081,0.00013726729,0.000059307316,0.000017437107,0.000044172786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998753,0.000052890708,0.00026578445,0.0003576654,0.0002714246,0.00029924655],"domain_scores_gemma":[0.9993368,0.00017679122,0.00011192645,0.00022843732,0.000057875874,0.00008821279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030534397,0.00014389138,0.00017165703,0.00017576413,0.00019949273,0.00016689222,0.00023207531,0.000077938006,4.0560946e-7],"category_scores_gemma":[0.000027563776,0.00014475599,0.000058715472,0.00023336007,0.0000484463,0.00036733676,0.000041183568,0.00010964945,0.00001570546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051796364,0.000012068481,0.000049776347,0.000018609777,0.00002246451,0.00000577468,0.00039335995,0.9870218,0.0060852356,0.0027638432,0.0019235158,0.0016983893],"study_design_scores_gemma":[0.0006944457,0.00006724155,0.0013858709,0.00002074637,0.000019542991,0.000007824608,0.0000066075295,0.9954743,0.00013406132,0.002005545,0.000026452033,0.00015738928],"about_ca_topic_score_codex":0.000002880746,"about_ca_topic_score_gemma":1.633327e-7,"teacher_disagreement_score":0.75391257,"about_ca_system_score_codex":0.00004695972,"about_ca_system_score_gemma":0.00003700917,"threshold_uncertainty_score":0.5902978},"labels":[],"label_agreement":null},{"id":"W4389692377","doi":"10.1109/lra.2023.3342669","title":"Multibeam Forward-Looking Sonar Video Object Tracking Using Truncated - Sparsity and Aberrances Repression Regularization","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced SAR Imaging Techniques","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Regularization (linguistics); Notation; Clutter; Mathematics; Algorithm; Artificial intelligence; Computer science","score_opus":0.018964889305718036,"score_gpt":0.25463763914603055,"score_spread":0.2356727498403125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389692377","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":[0.48474836,0.00006878129,0.5133748,0.0002564253,0.00015937358,0.00013162954,0.0000029642536,0.0012473613,0.0000102872655],"genre_scores_gemma":[0.9244706,0.00011241193,0.07518517,0.00010154858,0.000058601898,0.000004897086,0.00002113847,0.000038087925,0.00000756632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919075,0.00002669847,0.00021761202,0.00021818813,0.0001454242,0.00020132017],"domain_scores_gemma":[0.999672,0.00005125556,0.000075038595,0.00013383685,0.000026932219,0.00004092915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017490833,0.00014841928,0.00016011804,0.00017666705,0.00018791275,0.000096534764,0.000055942226,0.00006629225,9.130532e-7],"category_scores_gemma":[0.000025811703,0.0001614961,0.000024809176,0.0002571718,0.00004595154,0.00047479526,0.000024758283,0.00011338575,0.0000014775743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017123359,0.0000026022221,0.0009081121,0.000078878016,0.000012430146,0.0000051848942,0.0003405094,0.69205195,0.29720998,0.00002491998,0.00020913094,0.009154576],"study_design_scores_gemma":[0.00018403259,0.0000050435874,0.0076626204,0.00021022781,0.000021219194,0.000011528809,0.000025344867,0.95765877,0.033475596,0.00048100404,0.00007353858,0.00019108919],"about_ca_topic_score_codex":0.000009894263,"about_ca_topic_score_gemma":0.0000017987744,"teacher_disagreement_score":0.4397222,"about_ca_system_score_codex":0.000051739255,"about_ca_system_score_gemma":0.0000044828644,"threshold_uncertainty_score":0.65856194},"labels":[],"label_agreement":null},{"id":"W4390120220","doi":"10.1109/lra.2023.3346271","title":"MoSS: Monocular Shape Sensing for Continuum Robots","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Robot; Monocular; Segmentation; Encoder; RGB color model","score_opus":0.016548307633796094,"score_gpt":0.23344464109101887,"score_spread":0.21689633345722278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390120220","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":[0.2521872,0.000044687404,0.7423163,0.0035231402,0.00050457905,0.00034072355,0.000012612024,0.0010181543,0.000052576688],"genre_scores_gemma":[0.9306077,0.000069188565,0.06801261,0.00071986974,0.00030294748,0.000043025266,0.00008624551,0.000078839745,0.000079579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993289,0.0000062344325,0.00019670604,0.0001531025,0.00008852552,0.00022651622],"domain_scores_gemma":[0.9996439,0.00009362229,0.00003273238,0.00013974762,0.00003136938,0.000058589165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009737795,0.00012530519,0.00013645874,0.00009829414,0.00012618212,0.00009771774,0.00005822389,0.000054924756,0.0000022190309],"category_scores_gemma":[0.000012815029,0.0001374858,0.00004965318,0.0002145509,0.000024597079,0.00008180813,0.000010444292,0.000061257226,0.000033454933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.467154e-7,0.0000032528828,0.000046969482,0.000052977874,0.00002768898,0.0000017432216,0.00010040682,0.928244,0.052244768,0.0003925201,0.012438744,0.0064463886],"study_design_scores_gemma":[0.00022044248,0.0000055178425,0.0016225389,0.000023524113,0.000022509174,0.0000030579026,0.000020167334,0.9941301,0.0020318981,0.00036225992,0.0013894186,0.0001685631],"about_ca_topic_score_codex":0.0000019742304,"about_ca_topic_score_gemma":0.0000020766768,"teacher_disagreement_score":0.6784205,"about_ca_system_score_codex":0.000021878386,"about_ca_system_score_gemma":0.000004386617,"threshold_uncertainty_score":0.5606508},"labels":[],"label_agreement":null},{"id":"W4390204308","doi":"10.1109/lra.2023.3346751","title":"Toward Certifying Maps for Safe Registration-Based Localization Under Adverse Conditions","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Université Laval; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noise (video); Context (archaeology); Metric (unit); Iterative closest point; Computer science; Robot; Resilience (materials science); Algorithm; Point (geometry); Artificial intelligence; Gaussian; Mathematics; Point cloud; Engineering; Geography","score_opus":0.03344067715853862,"score_gpt":0.2487749932713397,"score_spread":0.21533431611280107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390204308","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":[0.014049912,0.000011085451,0.98026824,0.00414074,0.0004618553,0.0003308583,0.000046264297,0.00061973615,0.000071323935],"genre_scores_gemma":[0.991573,0.000024778983,0.0057401755,0.0012873078,0.00011508515,0.000042498807,0.0011192098,0.000048954713,0.00004900828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990971,0.000021544278,0.00031432902,0.00018077488,0.00016697902,0.00021923664],"domain_scores_gemma":[0.9995383,0.000118469885,0.00006545101,0.00013820802,0.00007030195,0.0000692873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011936448,0.00015377719,0.00013700538,0.00020991861,0.00020090454,0.000081133054,0.000054377135,0.000088839784,0.00000944719],"category_scores_gemma":[0.000025616371,0.0001730624,0.000056945988,0.00033509746,0.000042630294,0.00015718189,0.000004079178,0.00006063078,0.000026262169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027185815,0.0000075990392,0.0000893252,0.00015883484,0.000019420939,0.0000018500832,0.00008198189,0.97599775,0.006248849,0.0036605175,0.013593938,0.0001372369],"study_design_scores_gemma":[0.000491438,0.00001548033,0.0008344662,0.000051255316,0.00003392275,0.0000013669032,0.00006639998,0.99540335,0.0014497715,0.0006178512,0.00082661485,0.00020809492],"about_ca_topic_score_codex":0.0000055664163,"about_ca_topic_score_gemma":0.000007011842,"teacher_disagreement_score":0.9775231,"about_ca_system_score_codex":0.00007257169,"about_ca_system_score_gemma":0.000021347125,"threshold_uncertainty_score":0.70572805},"labels":[],"label_agreement":null},{"id":"W4390591099","doi":"10.1109/lra.2024.3349812","title":"Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","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":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Semantics (computer science); Scheme (mathematics); Computation; Artificial intelligence; Image resolution; Pixel; Image segmentation; Computer vision; Computer engineering; Algorithm","score_opus":0.014213060752968172,"score_gpt":0.2581731518465156,"score_spread":0.24396009109354747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390591099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011162857,0.000074602525,0.9689734,0.018223576,0.0006447243,0.00043517686,0.000008100189,0.00045211887,0.000025449974],"genre_scores_gemma":[0.71503603,0.000030182624,0.28320464,0.001334446,0.00018898393,0.00009405199,0.00003512776,0.000015943238,0.00006058899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990595,0.000027186949,0.0002361111,0.0003403506,0.00014150115,0.00019533964],"domain_scores_gemma":[0.9994235,0.00021619492,0.0000711304,0.00020131521,0.000037013444,0.000050829134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001417067,0.000121912,0.00010388763,0.00011859652,0.00018005283,0.0002712243,0.00014466935,0.000037179496,0.0000016181815],"category_scores_gemma":[0.000012388897,0.00012139837,0.000045888315,0.0002965763,0.000053643515,0.0006264102,0.000015029248,0.00007589911,0.00001536884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003835024,0.000026005502,0.000007280121,0.00018109789,0.000036788835,0.000009270758,0.00043584348,0.4525373,0.408593,0.058761083,0.007874589,0.07153392],"study_design_scores_gemma":[0.00021104851,0.000023022352,0.00020862707,0.000053731434,0.000021441661,0.000024689933,0.000012070021,0.9788913,0.015672999,0.0044693644,0.00024155258,0.00017015323],"about_ca_topic_score_codex":0.0000033064994,"about_ca_topic_score_gemma":0.0000012850345,"teacher_disagreement_score":0.7038732,"about_ca_system_score_codex":0.00004942373,"about_ca_system_score_gemma":0.000021111624,"threshold_uncertainty_score":0.4950482},"labels":[],"label_agreement":null},{"id":"W4390905236","doi":"10.1109/lra.2024.3354617","title":"Whole-Body Intuitive Physical Human-Robot Interaction With Flexible Robots Using Non-Collocated Proprioceptive Sensing","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Robot; Computer science; Jacobian matrix and determinant; Robot end effector; Encoder; Deflection (physics); Control theory (sociology); Simulation; Rotary encoder; Serial manipulator; Control engineering; Parallel manipulator; Engineering; Control (management); Artificial intelligence; Physics; Mathematics","score_opus":0.016904629887060183,"score_gpt":0.26186699484830483,"score_spread":0.24496236496124466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390905236","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":[0.49709103,0.000015498297,0.5013205,0.00029682025,0.00050918903,0.00022881204,0.000002650665,0.00041062725,0.00012491287],"genre_scores_gemma":[0.9958007,0.0000018963339,0.00363625,0.00010927013,0.00032326372,0.0000067139713,0.000020670164,0.00004957264,0.000051680712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991434,0.000027136924,0.00022647844,0.00023262105,0.00017760486,0.00019273565],"domain_scores_gemma":[0.99970126,0.00003553633,0.0000398536,0.0001034502,0.00006011894,0.000059807688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007241596,0.00020577478,0.00020875805,0.00019111954,0.0001428342,0.00035420727,0.00003855868,0.00005757928,0.0000038015835],"category_scores_gemma":[0.000003098266,0.00018164738,0.000039734005,0.00027002877,0.00005074958,0.0004139138,0.000009341907,0.0001930604,0.000033813805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022799754,0.000007881154,0.000010567607,0.000088522815,0.00006228869,0.000011091023,0.0013435623,0.59989977,0.3974482,0.00014137893,0.00013758925,0.0008468411],"study_design_scores_gemma":[0.00023065908,0.00004340312,0.00052530965,0.00047363166,0.00005410501,0.000054997912,0.00032257556,0.97104245,0.026946023,0.000011797368,0.000054709602,0.00024031827],"about_ca_topic_score_codex":0.000028702658,"about_ca_topic_score_gemma":0.000005893625,"teacher_disagreement_score":0.49870965,"about_ca_system_score_codex":0.00016534484,"about_ca_system_score_gemma":0.000021604903,"threshold_uncertainty_score":0.74073654},"labels":[],"label_agreement":null},{"id":"W4390933810","doi":"10.1109/lra.2024.3354623","title":"Optimal Initialization Strategies for Range-Only Trajectory Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Maxima and minima; Initialization; Trajectory; Range (aeronautics); Computer science; Mathematical optimization; Pose; Regular polygon; Iterative method; Algorithm; Mathematics; Artificial intelligence; Physics","score_opus":0.012531601051405293,"score_gpt":0.23574126517420715,"score_spread":0.22320966412280185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390933810","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":[0.06845325,0.00017534234,0.9290007,0.0005590495,0.0008857544,0.00024268177,0.000014966531,0.00059119076,0.000077062774],"genre_scores_gemma":[0.9625412,0.00005201485,0.03677909,0.00019983517,0.0001979247,0.0000213101,0.0001464944,0.000048464542,0.000013653683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999311,0.000016283984,0.0002393189,0.00015577102,0.00012262183,0.00015501732],"domain_scores_gemma":[0.99974597,0.0000736289,0.000024065837,0.00007931803,0.00003564775,0.00004140254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010054146,0.00014438223,0.00011976164,0.00016328135,0.00008276166,0.00036495534,0.000040009414,0.00007607717,0.000006030649],"category_scores_gemma":[0.000008457342,0.00015130322,0.00004466137,0.00015285538,0.00002940315,0.00040244852,0.0000028442196,0.00006526817,0.0000073634064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024761077,0.0000050068215,0.000007820459,0.00033402958,0.000025528203,0.0000024107655,0.00031839043,0.9797391,0.0053434535,0.008317129,0.001960725,0.0039438824],"study_design_scores_gemma":[0.00019942776,0.000023853552,0.00016068145,0.00008159674,0.000042261174,0.000008117862,0.0000492878,0.9980226,0.0005644768,0.00031188846,0.00035672012,0.00017906966],"about_ca_topic_score_codex":0.0000027832536,"about_ca_topic_score_gemma":0.0000029436733,"teacher_disagreement_score":0.894088,"about_ca_system_score_codex":0.000048730562,"about_ca_system_score_gemma":0.00003104899,"threshold_uncertainty_score":0.6169966},"labels":[],"label_agreement":null},{"id":"W4390969193","doi":"10.1109/lra.2024.3355729","title":"Multi-Axis Force Sensing in Laparoscopic Surgery","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Surgical Simulation and Training","field":"Medicine","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transducer; Deflection (physics); Calibration; Stiffness; Pressure sensor; Acoustics; Surgical instrument; Cannula; Torque; Engineering; Mechanical engineering; Simulation; Structural engineering; Optics; Electrical engineering; Physics; Surgery","score_opus":0.037148985176369595,"score_gpt":0.2962235190940434,"score_spread":0.25907453391767377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390969193","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":[0.9689062,0.00014941822,0.024895797,0.0052325423,0.00044792614,0.00010533339,8.683359e-7,0.00016117546,0.000100697136],"genre_scores_gemma":[0.9945378,0.000019252435,0.0034933449,0.0017708774,0.000077339,9.874075e-7,0.00001078337,0.000011908302,0.00007775668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994023,0.000021480148,0.00019769918,0.0001453515,0.00010546028,0.00012772276],"domain_scores_gemma":[0.99961853,0.00023505394,0.0000205518,0.00006019176,0.000011962928,0.000053723106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015676364,0.00007553342,0.0001569933,0.00016304402,0.00003171427,0.000055158023,0.0000098061755,0.000042027357,0.0000140760385],"category_scores_gemma":[0.00003653678,0.00006724414,0.00004340372,0.00018937919,0.000025652593,0.00007731352,0.0000045715196,0.000099860146,0.000015966372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009788471,0.00023050068,0.10957017,0.0021906795,0.0002891351,0.0020362984,0.004520256,0.41241148,0.08989079,0.001576147,0.0027286443,0.37445801],"study_design_scores_gemma":[0.0007120584,0.000007743514,0.044877786,0.0004199751,0.000019183246,0.000023442622,0.0000326257,0.952633,0.0004095841,0.000019076697,0.00075185683,0.00009363072],"about_ca_topic_score_codex":0.000009428764,"about_ca_topic_score_gemma":0.000003976647,"teacher_disagreement_score":0.5402216,"about_ca_system_score_codex":0.000031289776,"about_ca_system_score_gemma":0.000018294591,"threshold_uncertainty_score":0.27421364},"labels":[],"label_agreement":null},{"id":"W4391128583","doi":"10.1109/lra.2024.3357313","title":"Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristic; Maximization; Planner; Mathematical optimization; Motion planning; Computer science; Function (biology); Correctness; Range (aeronautics); Pareto principle; Path (computing); Simple (philosophy); Robot; Artificial intelligence; Mathematics; Algorithm; Engineering","score_opus":0.034383656741541695,"score_gpt":0.27343320200726107,"score_spread":0.23904954526571937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391128583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013100847,0.00028735117,0.9825581,0.0016287684,0.0013829448,0.00023687088,0.0000022155027,0.00078165764,0.000021288357],"genre_scores_gemma":[0.23428488,0.000005516823,0.7651598,0.00037722828,0.0001056054,0.000008309983,0.000009845587,0.000026629963,0.000022211472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984204,0.00008705299,0.0003396861,0.0005186881,0.0002984017,0.00033578114],"domain_scores_gemma":[0.9993803,0.00011469474,0.000114677074,0.00023859086,0.00005907602,0.00009269832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031113005,0.00022269477,0.00019780715,0.00034946424,0.00026614542,0.00079036335,0.0002492416,0.00009560972,0.0000010845348],"category_scores_gemma":[0.000020791684,0.00021740772,0.000050843817,0.00060714356,0.000048691163,0.0009788367,0.00007549726,0.00022799258,0.000015435833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.808367e-7,0.000015878364,0.00056625094,0.00007687654,0.000037877933,0.000053034975,0.0016090297,0.9761522,0.016977137,0.0007919265,0.00008251937,0.0036368],"study_design_scores_gemma":[0.00021422535,0.000018872377,0.0026893134,0.0004583238,0.000024237119,0.00006962106,0.000025032681,0.99542874,0.0005864725,0.00019220499,0.000021953585,0.00027099418],"about_ca_topic_score_codex":0.000021116639,"about_ca_topic_score_gemma":2.1679428e-7,"teacher_disagreement_score":0.22118403,"about_ca_system_score_codex":0.00011943424,"about_ca_system_score_gemma":0.00005426269,"threshold_uncertainty_score":0.88656294},"labels":[],"label_agreement":null},{"id":"W4391462717","doi":"10.1109/lra.2024.3414180","title":"Force Push: Robust Single-Point Pushing With Force Feedback","year":2024,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Technische Universität München; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Single point; Point (geometry); Haptic technology; Computer science; Physics; Mathematics; Simulation; Geometry","score_opus":0.021522495001964993,"score_gpt":0.209132363070022,"score_spread":0.18760986806805702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391462717","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":[0.08502542,0.00035112177,0.90385985,0.004891587,0.0023437182,0.00047248867,0.0000048296038,0.0016789354,0.0013720148],"genre_scores_gemma":[0.97687304,0.00003186656,0.021240316,0.00054772384,0.0004200246,0.000028361417,0.000085199994,0.00016675559,0.0006066942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845,0.00003674879,0.00043573976,0.00043488469,0.000296343,0.0003463275],"domain_scores_gemma":[0.9993254,0.000071589086,0.00013125372,0.00031391438,0.000044775916,0.000113056776],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016998437,0.00042789953,0.00036108374,0.00027195853,0.00011812777,0.00071257807,0.00015835716,0.00023072252,0.000020812882],"category_scores_gemma":[0.000013220743,0.0004128915,0.00009903491,0.00014326238,0.000047526584,0.0001897963,0.00011945402,0.0008939017,0.00005218352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000298324,0.000006887327,0.000045446854,0.00089918746,0.00010356924,0.000018479785,0.0006374399,0.98966265,0.0051539373,0.00029927248,0.0023770544,0.00079312],"study_design_scores_gemma":[0.0002226003,0.000020300498,0.0003790236,0.0009033189,0.00009133651,0.000029268322,0.00006166184,0.9966926,0.00045100827,0.00034752276,0.0002683088,0.0005330301],"about_ca_topic_score_codex":0.000011300671,"about_ca_topic_score_gemma":0.000022821005,"teacher_disagreement_score":0.8918476,"about_ca_system_score_codex":0.00017315778,"about_ca_system_score_gemma":0.00002074781,"threshold_uncertainty_score":0.9998323},"labels":[],"label_agreement":null},{"id":"W4391800510","doi":"10.1109/lra.2024.3412638","title":"NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments","year":2024,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Automated Systems","field":"Engineering","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Architecture; Transformer; Robot; Computer science; Artificial intelligence; Human–computer interaction; Engineering; Geography; Electrical engineering","score_opus":0.006516000442612268,"score_gpt":0.21739907641920944,"score_spread":0.21088307597659717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391800510","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":[0.31437775,0.001445217,0.6761274,0.003618775,0.0020821537,0.0016946603,0.00016782826,0.00043419158,0.000052015876],"genre_scores_gemma":[0.97876936,0.00028874306,0.020063838,0.00010580145,0.0001033018,0.0001617587,0.00035501114,0.00011162534,0.000040571722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846023,0.000026741343,0.0005531545,0.00042746903,0.00019533295,0.00033708703],"domain_scores_gemma":[0.9995822,0.000047258167,0.000088015215,0.00018764588,0.000010602566,0.00008430216],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015013266,0.0004111256,0.0004333938,0.00028493255,0.00006601808,0.00020432458,0.0001115943,0.00031894573,0.0000018633152],"category_scores_gemma":[0.0000030535691,0.0004100664,0.00010163195,0.00010051686,0.00005413007,0.00007799758,0.000037763864,0.0005372418,0.000006057682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028058396,0.000011362545,0.000025240895,0.0019511504,0.00009538811,0.000006828509,0.00072049105,0.96270907,0.030667374,0.00011977176,0.0001537262,0.0035367925],"study_design_scores_gemma":[0.00040225888,0.000020613666,0.0005168599,0.0009786773,0.00007352005,0.000014885182,0.000020798732,0.99481857,0.0005893246,0.0014776249,0.0006440221,0.0004428513],"about_ca_topic_score_codex":0.000010437876,"about_ca_topic_score_gemma":0.000014437915,"teacher_disagreement_score":0.6643916,"about_ca_system_score_codex":0.00015688049,"about_ca_system_score_gemma":0.000017179502,"threshold_uncertainty_score":0.99983513},"labels":[],"label_agreement":null},{"id":"W4391936084","doi":"10.1109/lra.2024.3367270","title":"Laser-to-Vehicle Extrinsic Calibration in Low-Observability Scenarios for Subsea Mapping","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subsea; Observability; Calibration; Laser; Environmental science; Computer science; Remote sensing; Marine engineering; Geology; Engineering; Physics; Optics; Mathematics; Statistics","score_opus":0.021759421133933306,"score_gpt":0.24465310018525208,"score_spread":0.22289367905131877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391936084","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":[0.3000014,0.000032924316,0.68932486,0.009473085,0.0007041029,0.0002286189,0.000006660227,0.00022491228,0.0000034295035],"genre_scores_gemma":[0.91335887,0.00000855536,0.08441795,0.0020013286,0.00013900688,0.000025829433,0.000018739585,0.000011833639,0.000017906445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988779,0.000049349735,0.00028975797,0.0003969462,0.00015774304,0.00022829689],"domain_scores_gemma":[0.9993815,0.00021542776,0.000037115624,0.00026006915,0.000029249308,0.00007668365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033144583,0.00011646199,0.00012687882,0.00016492579,0.00010605862,0.00047469066,0.00020122633,0.000059113197,0.0000022934248],"category_scores_gemma":[0.000026898782,0.00011475341,0.000040584495,0.00046436884,0.000020531299,0.0005723115,0.000050657032,0.00011337093,0.000010489314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043712857,0.00004377322,0.0012052793,0.00021885011,0.000009934201,0.000017592103,0.0010434934,0.92815536,0.009217848,0.0045165615,0.004999152,0.050567772],"study_design_scores_gemma":[0.00016738109,0.000015764605,0.0052083866,0.00016418572,0.0000028206139,0.000002804027,0.000011249722,0.992657,0.0005718728,0.00034300218,0.0007111825,0.00014432192],"about_ca_topic_score_codex":0.00001892146,"about_ca_topic_score_gemma":0.000015474798,"teacher_disagreement_score":0.6133574,"about_ca_system_score_codex":0.00004820561,"about_ca_system_score_gemma":0.000025808897,"threshold_uncertainty_score":0.46795085},"labels":[],"label_agreement":null},{"id":"W4392502297","doi":"10.1109/lra.2024.3374170","title":"A Parallel SCARA Robot With Low-Impedance Backdrivability and a Remotely Operated Gripper With Unlimited Rotation","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SCARA; Electrical impedance; Rotation (mathematics); Robot; Computer science; Electrical engineering; Engineering; Artificial intelligence","score_opus":0.007979017555959353,"score_gpt":0.20550159722226272,"score_spread":0.19752257966630338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392502297","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":[0.51516056,0.00015689715,0.48314926,0.00096371496,0.00008526415,0.00019495001,0.0000029054424,0.0002623224,0.000024126528],"genre_scores_gemma":[0.97186285,0.000098266,0.027638946,0.00026417192,0.00004526504,0.000014502987,0.000016150414,0.00003868193,0.000021156893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990731,0.000027789922,0.00021511443,0.00030157613,0.00017437105,0.00020808371],"domain_scores_gemma":[0.99963677,0.000043350927,0.000025105934,0.00016551104,0.000049357008,0.00007991543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000910352,0.00021086715,0.00017769336,0.00009285069,0.000081912076,0.0002732632,0.000058010068,0.0000605589,0.000007198763],"category_scores_gemma":[0.000005598438,0.00015886145,0.000019113288,0.0002672034,0.00008589223,0.0003149868,0.0000086077525,0.00015974813,0.000008377342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013612941,0.000008704382,0.00027627486,0.00027101865,0.00005372175,0.000022663704,0.00045172565,0.9782752,0.014668065,0.00021127121,0.00013548983,0.005612279],"study_design_scores_gemma":[0.00023070919,0.000059494036,0.008627177,0.00036531896,0.000031120817,0.00005567292,0.000031986896,0.9887981,0.0014624529,0.000027561508,0.00004892055,0.00026151762],"about_ca_topic_score_codex":0.000019484474,"about_ca_topic_score_gemma":0.00002272116,"teacher_disagreement_score":0.4567023,"about_ca_system_score_codex":0.000046736623,"about_ca_system_score_gemma":0.000017252949,"threshold_uncertainty_score":0.6478182},"labels":[],"label_agreement":null},{"id":"W4393372065","doi":"10.1109/lra.2024.3384037","title":"Learning to Communicate Functional States With Nonverbal Expressions for Improved Human-Robot Collaboration","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Initialization; Robot; Nonverbal communication; Computer science; Human–robot interaction; Process (computing); Speech recognition; Artificial intelligence; Human–computer interaction; Communication; Psychology","score_opus":0.03451899184064556,"score_gpt":0.29993908712336353,"score_spread":0.265420095282718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393372065","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":[0.6683065,0.0000090795365,0.3024538,0.027280161,0.00084656425,0.0005438693,0.000058646154,0.0004045732,0.00009678215],"genre_scores_gemma":[0.99383956,0.000007118489,0.0031600103,0.0023525583,0.00009723877,0.00006630735,0.000043428157,0.000021406302,0.0004123729],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992945,0.000052283835,0.00016683512,0.0002375638,0.00010579796,0.00014299255],"domain_scores_gemma":[0.99938124,0.00033198454,0.00005407558,0.00012092682,0.00004894199,0.000062804735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042462365,0.00010712142,0.00009085645,0.00012215602,0.0005079608,0.00035254232,0.00006417388,0.000031265652,0.000018911825],"category_scores_gemma":[0.000043308868,0.00009277633,0.000028761046,0.00020801985,0.000042647727,0.00035056722,0.000016598402,0.00015134025,0.000018714145],"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":[0.000018782699,0.000015454512,0.0000054842117,0.000018217019,0.000007922235,0.0000019151157,0.0005655152,0.21276341,0.7829289,0.0005929851,0.0027943635,0.00028707107],"study_design_scores_gemma":[0.00055594306,0.0003582909,0.00034104806,0.00017704026,0.000053461004,0.000037251684,0.00069907773,0.7186691,0.2638379,0.00021062802,0.014709255,0.00035097645],"about_ca_topic_score_codex":0.000008989633,"about_ca_topic_score_gemma":0.000012353981,"teacher_disagreement_score":0.51909095,"about_ca_system_score_codex":0.000039461283,"about_ca_system_score_gemma":0.000023669303,"threshold_uncertainty_score":0.39068753},"labels":[],"label_agreement":null},{"id":"W4393372097","doi":"10.1109/lra.2024.3384053","title":"Design, Control, and Validation of a Brake-by-Wire Actuator for Scaled Electric Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Brake; Actuator; Automotive engineering; Control (management); Engineering; Computer science; Control engineering; Electrical engineering; Artificial intelligence","score_opus":0.006003887985975055,"score_gpt":0.1964598029156141,"score_spread":0.19045591492963904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393372097","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":[0.39906546,0.00048447112,0.59928215,0.00066410686,0.00012436934,0.0002510472,0.000014408967,0.00011064361,0.0000033715735],"genre_scores_gemma":[0.9984779,0.000046195793,0.0012565731,0.00009124465,0.000054393422,0.000032368345,0.000009490393,0.000022892174,0.000008949006],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944335,0.000020705875,0.00021024515,0.00011743322,0.00008388656,0.00012438811],"domain_scores_gemma":[0.9996988,0.00015034046,0.00003355543,0.000058890186,0.000023087876,0.000035309393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016184612,0.000101702906,0.0001604603,0.00008628316,0.00003903934,0.00010481278,0.000034398592,0.000053665397,6.172793e-7],"category_scores_gemma":[0.0000068406825,0.0000975558,0.000031600226,0.00009305545,0.0000138475125,0.000119421136,0.000002311837,0.000046581114,9.1085184e-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":[0.0000074858976,0.000005474697,0.000035701632,0.00028464056,0.00008360904,7.6801e-7,0.00009477608,0.14722455,0.84067184,0.00033988137,0.0012472738,0.010003981],"study_design_scores_gemma":[0.0005093929,0.00003303762,0.00021172363,0.000057455014,0.000048169062,0.000003868231,0.000004537297,0.99035937,0.008470982,0.00010596195,0.000090345005,0.00010515817],"about_ca_topic_score_codex":0.000003315111,"about_ca_topic_score_gemma":3.9768156e-7,"teacher_disagreement_score":0.8431348,"about_ca_system_score_codex":0.000023889246,"about_ca_system_score_gemma":0.0000077726445,"threshold_uncertainty_score":0.397821},"labels":[],"label_agreement":null},{"id":"W4394782045","doi":"10.1109/lra.2024.3396056","title":"Estimating Visibility From Alternate Perspectives for Motion Planning With Occlusions","year":2024,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Perspective (graphical); Planner; Computer vision; Computer science; Artificial intelligence; Motion planning; Geography; Robot","score_opus":0.014632931856835759,"score_gpt":0.25247320064851886,"score_spread":0.2378402687916831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394782045","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":[0.2777002,0.00021614057,0.71921736,0.00092835736,0.0010205258,0.00036326796,0.000089284775,0.00043787254,0.000027029564],"genre_scores_gemma":[0.85921395,0.000014739564,0.13992482,0.00009075316,0.00039589338,0.000030433142,0.00025091262,0.000072404066,0.000006110018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988012,0.000025238272,0.0003341742,0.0004454552,0.00018964906,0.00020428534],"domain_scores_gemma":[0.99941427,0.00009972286,0.00010188125,0.00024290469,0.00007348276,0.000067736815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012540404,0.00029798414,0.00028126573,0.00015748883,0.00013646661,0.00031526637,0.00009455794,0.00015389177,0.0000036034646],"category_scores_gemma":[0.000021905553,0.00028190872,0.000074918724,0.00008440369,0.000052113435,0.00007955216,0.00006534533,0.00034471997,0.0000046454643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005217286,0.000011343068,0.00011313914,0.0004673973,0.00011207551,0.000006361849,0.0014431534,0.9940319,0.0023583223,0.00032894386,0.00032243476,0.00079968787],"study_design_scores_gemma":[0.00022155352,0.000017638215,0.00055114337,0.0006749049,0.00013350978,0.0000028199945,0.000110030145,0.99456596,0.00035713566,0.0030369158,0.000007820506,0.0003205485],"about_ca_topic_score_codex":0.000040280942,"about_ca_topic_score_gemma":0.000007502701,"teacher_disagreement_score":0.58151376,"about_ca_system_score_codex":0.00014965172,"about_ca_system_score_gemma":0.000020404386,"threshold_uncertainty_score":0.9999633},"labels":[],"label_agreement":null},{"id":"W4396542517","doi":"10.1109/lra.2024.3396091","title":"Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Notation; Computer science; Mathematics; Discrete mathematics; Algorithm; Combinatorics; Arithmetic","score_opus":0.026578821108556628,"score_gpt":0.2746432342162779,"score_spread":0.24806441310772126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396542517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021218352,0.00012628654,0.9699753,0.0070817326,0.0009040587,0.0001416078,0.0000027907645,0.0004691757,0.00008072612],"genre_scores_gemma":[0.5591255,0.000005443295,0.43876663,0.0017917944,0.0001950418,0.00000942636,0.000033321583,0.00001739645,0.000055475273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858594,0.00008108475,0.0002708389,0.00044612054,0.00037400663,0.00024198489],"domain_scores_gemma":[0.99937457,0.00018368178,0.00006445728,0.00021671262,0.000060433293,0.000100118275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017970758,0.00017685119,0.00019487906,0.00021107409,0.00014435129,0.00069614593,0.00018254499,0.000070142705,0.0000026495145],"category_scores_gemma":[0.000013979045,0.00016133595,0.000049053604,0.0003585612,0.00007214199,0.0005056458,0.000048976177,0.00016949295,0.000047443235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001907814,0.00003578929,0.000025898129,0.00012059194,0.00008005503,0.000093872,0.0007687616,0.8771295,0.009028763,0.10241581,0.005267463,0.0050315824],"study_design_scores_gemma":[0.00016232708,0.00002760732,0.001000438,0.00009245033,0.00002129432,0.00007990965,0.000017283333,0.9977148,0.00024536037,0.00026575438,0.00017519311,0.00019754506],"about_ca_topic_score_codex":0.00001539951,"about_ca_topic_score_gemma":8.192896e-8,"teacher_disagreement_score":0.5379071,"about_ca_system_score_codex":0.000051578212,"about_ca_system_score_gemma":0.000044718345,"threshold_uncertainty_score":0.67129517},"labels":[],"label_agreement":null},{"id":"W4399039571","doi":"10.1109/lra.2024.3405795","title":"Consistent Fusion of Correlated Pose Estimates on Matrix Lie Groups","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Algebraic and Geometric Analysis","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lie group; Fusion; Matrix (chemical analysis); Mathematics; Artificial intelligence; Psychology; Computer science; Pure mathematics; Materials science; Philosophy; Linguistics","score_opus":0.021246623520825972,"score_gpt":0.28720260921392204,"score_spread":0.2659559856930961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399039571","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":[0.7936682,0.0004317529,0.20044625,0.004158496,0.00061183254,0.00019890955,0.000013032905,0.000266414,0.00020508355],"genre_scores_gemma":[0.9892259,0.0000633528,0.010120887,0.00027120506,0.00005919299,0.0000035164612,0.000017325918,0.000021444792,0.00021716989],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989652,0.000029529263,0.00038729009,0.00020354554,0.00026759313,0.00014683335],"domain_scores_gemma":[0.9989787,0.0006214289,0.00013245475,0.00016591625,0.000046617235,0.000054883112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021980972,0.00015167911,0.0002652149,0.00035606395,0.00007520926,0.00006603894,0.00006708198,0.00008118479,0.000059643866],"category_scores_gemma":[0.00011692038,0.00012349161,0.000108665976,0.0005109402,0.00006972333,0.0000826966,0.000021425047,0.000120027114,0.000044366854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547164,0.0008152671,0.0025781111,0.0036929282,0.0023294822,0.00028819032,0.0042807395,0.123471744,0.035256233,0.6775987,0.118726596,0.030862417],"study_design_scores_gemma":[0.0006123724,0.00019004544,0.0018389609,0.000749525,0.0008479494,0.000045302793,0.00018414632,0.95753896,0.001957048,0.035320908,0.00025860438,0.0004561889],"about_ca_topic_score_codex":0.000008263763,"about_ca_topic_score_gemma":7.9355925e-7,"teacher_disagreement_score":0.8340672,"about_ca_system_score_codex":0.00003782812,"about_ca_system_score_gemma":0.000016331183,"threshold_uncertainty_score":0.5035842},"labels":[],"label_agreement":null},{"id":"W4399207457","doi":"10.1109/lra.2024.3408087","title":"Cooperative Motion Mechanism of a Bionic Sailfish Robot With High Motion Performance","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Mechanism (biology); Motion (physics); Computer science; Artificial intelligence; Physics","score_opus":0.009479826033380752,"score_gpt":0.1886055603956284,"score_spread":0.17912573436224766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399207457","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":[0.42183927,0.00006680369,0.57700324,0.0006561754,0.000116555784,0.00010417284,0.0000032852965,0.00018517021,0.0000253222],"genre_scores_gemma":[0.9952211,0.00010697311,0.0045010345,0.00006200635,0.00003911982,0.000013911247,0.000018651504,0.00002152518,0.000015725851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994115,0.000026474723,0.00021424021,0.00011573373,0.00012813031,0.00010390793],"domain_scores_gemma":[0.99974006,0.000019529303,0.000034893546,0.00013683477,0.000040341474,0.000028354234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098605145,0.00011097763,0.00012482144,0.00010560518,0.000059706035,0.000095466254,0.00006585243,0.000044477383,0.0000061093],"category_scores_gemma":[4.2457435e-7,0.00009347632,0.000020641639,0.00017961953,0.00002895004,0.00026406263,0.000009334436,0.000088787274,0.000011389195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026514601,0.000012765731,0.000066478045,0.0003275744,0.00006665858,0.0000013764846,0.0006633696,0.824017,0.16361263,0.0033015532,0.00006219531,0.00786575],"study_design_scores_gemma":[0.00018732864,0.000049252012,0.0012880161,0.00023850547,0.000024932011,0.000015160037,0.000048667294,0.92873156,0.069133714,0.00006823911,0.00007481932,0.00013981125],"about_ca_topic_score_codex":0.000011635534,"about_ca_topic_score_gemma":0.000004679147,"teacher_disagreement_score":0.5733818,"about_ca_system_score_codex":0.000046479927,"about_ca_system_score_gemma":0.0000070272436,"threshold_uncertainty_score":0.38118538},"labels":[],"label_agreement":null},{"id":"W4400187964","doi":"10.1109/lra.2024.3421192","title":"NeRF-VO: Real-Time Sparse Visual Odometry With Neural Radiance Fields","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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 Toronto","funders":"","keywords":"Radiance; Artificial intelligence; Visual odometry; Computer vision; Computer science; Remote sensing; Geology; Robot","score_opus":0.0065525841568722255,"score_gpt":0.2082950660955234,"score_spread":0.20174248193865116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400187964","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":[0.50056136,0.00015214246,0.49477813,0.0026696662,0.0007390599,0.00014526158,0.0000056571344,0.0007072017,0.00024154272],"genre_scores_gemma":[0.9946265,0.00010476768,0.004356033,0.0005392076,0.00020524171,0.0000042025804,0.000029844527,0.000046266046,0.00008791006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992361,0.000018168357,0.00018824059,0.00017980422,0.0001578625,0.00021980566],"domain_scores_gemma":[0.99967974,0.0000642546,0.000021160116,0.00011241962,0.000018783789,0.00010366141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007077809,0.00016011481,0.00014478345,0.0001536387,0.00006122619,0.000201527,0.000050941693,0.00007413735,0.0000183094],"category_scores_gemma":[0.0000073432257,0.00014516686,0.00003264407,0.00028412856,0.000036841757,0.00018906624,0.0000055621103,0.00012708546,0.000038179332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026046382,0.000005950199,0.000071412636,0.00010922371,0.00002936228,0.000033692148,0.00010318408,0.9784511,0.01398475,0.0002674856,0.004842706,0.0020985338],"study_design_scores_gemma":[0.00015631763,0.00003789735,0.0005200319,0.00008049282,0.000028089014,0.000018163257,0.0000071507616,0.9975621,0.0011213043,0.000015577472,0.00025382743,0.00019901988],"about_ca_topic_score_codex":0.0000112488215,"about_ca_topic_score_gemma":0.000002901295,"teacher_disagreement_score":0.4940652,"about_ca_system_score_codex":0.000040980573,"about_ca_system_score_gemma":0.0000138087025,"threshold_uncertainty_score":0.59197325},"labels":[],"label_agreement":null},{"id":"W4400187978","doi":"10.1109/lra.2024.3421191","title":"Autonomous Blood Suction for Robot-Assisted Surgery: A Sim-to-Real Reinforcement Learning Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates; China Scholarship Council; Canada Foundation for Innovation","keywords":"Reinforcement learning; Suction; Robot; Robotic surgery; Autonomous robot; Computer science; Artificial intelligence; Human–computer interaction; Engineering; Mobile robot; Mechanical engineering","score_opus":0.01590985089928028,"score_gpt":0.238277447491607,"score_spread":0.22236759659232674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400187978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032650672,0.00004250455,0.9515989,0.014006813,0.00030746285,0.00038667957,0.0000013093119,0.0009360622,0.00006962285],"genre_scores_gemma":[0.8979619,0.000022252954,0.101174496,0.00044693693,0.00007711151,0.00017505784,0.000022987748,0.000013961923,0.00010526828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988841,0.000030157604,0.0002856463,0.00041704945,0.00013844985,0.00024463062],"domain_scores_gemma":[0.9994111,0.00013920634,0.000077256394,0.00025757667,0.000045962952,0.00006888951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036902065,0.00014395095,0.0001718221,0.0002818857,0.00026718448,0.0002898653,0.00020096714,0.0000989614,0.0000011580255],"category_scores_gemma":[0.000017214219,0.00014472163,0.000072772775,0.00046777062,0.000038394883,0.00019526653,0.00005455229,0.0001533618,0.000007662594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027214508,0.000061469276,0.00007702996,0.00015612604,0.00008077289,0.0000048249967,0.00055881595,0.81876165,0.007971703,0.117918216,0.0026640543,0.051742602],"study_design_scores_gemma":[0.000116206,0.000038820817,0.0008322271,0.000028359884,0.000029521327,0.0000312022,0.000009259557,0.9958828,0.00072482286,0.00035756978,0.0017689563,0.00018024055],"about_ca_topic_score_codex":0.000022407385,"about_ca_topic_score_gemma":0.0000019777494,"teacher_disagreement_score":0.86531126,"about_ca_system_score_codex":0.000051872197,"about_ca_system_score_gemma":0.00004156792,"threshold_uncertainty_score":0.5901577},"labels":[],"label_agreement":null},{"id":"W4400488043","doi":"10.1109/lra.2024.3426385","title":"Breathing Compensation in Magnetic Robotic Bronchoscopy via Shape Forming","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","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":"McMaster University","funders":"Engineering and Physical Sciences Research Council","keywords":"Compensation (psychology); Bronchoscopy; Breathing; Medicine; Computer science; Computer vision; Artificial intelligence; Psychology; Anatomy; Radiology; Psychoanalysis","score_opus":0.009066472236671534,"score_gpt":0.2218442229547782,"score_spread":0.21277775071810667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400488043","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":[0.2789765,0.00067227887,0.71623766,0.0023574766,0.00069123675,0.00026998264,0.000002449936,0.0006288786,0.00016355961],"genre_scores_gemma":[0.98069584,0.00005869101,0.018825492,0.0002288827,0.00009930278,0.000023269864,0.000017178803,0.00003714652,0.000014213584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992032,0.000011121051,0.0002728873,0.00018512245,0.000120339835,0.00020733652],"domain_scores_gemma":[0.99970967,0.000087656845,0.000019264351,0.000120154626,0.000011668235,0.00005160903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000099113284,0.00014715662,0.00013845807,0.00020089337,0.000057225552,0.00018770303,0.00006956162,0.000060255057,0.000017149605],"category_scores_gemma":[0.0000042756797,0.00015941623,0.000032662654,0.00027361672,0.000025661619,0.00023024742,0.00001101547,0.00015922125,0.0000435061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.502128e-7,0.0000074061973,0.00017115695,0.0001536708,0.000009753742,0.0000085297515,0.00035479767,0.94965124,0.02996138,0.0020595808,0.00025138166,0.017370746],"study_design_scores_gemma":[0.00012026061,0.000009614388,0.0035588325,0.00015095992,0.000020020278,0.000020341702,0.000016407224,0.9946699,0.00040634506,0.00071791396,0.00013207858,0.00017730452],"about_ca_topic_score_codex":0.000014710877,"about_ca_topic_score_gemma":0.000008996612,"teacher_disagreement_score":0.70171934,"about_ca_system_score_codex":0.000086943815,"about_ca_system_score_gemma":0.000009900181,"threshold_uncertainty_score":0.6500805},"labels":[],"label_agreement":null},{"id":"W4400905217","doi":"10.1109/lra.2024.3432350","title":"A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Least-squares function approximation; Nonlinear system; Non-linear least squares; Mathematics; Applied mathematics; Gaussian; Mixture model; Mathematical optimization; Statistics; Explained sum of squares; Chemistry; Physics; Computational chemistry","score_opus":0.012656731739097682,"score_gpt":0.2723217609700466,"score_spread":0.2596650292309489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400905217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002058443,0.00035452438,0.9679385,0.028396187,0.0007590862,0.00022143932,0.0000070408887,0.0001844034,0.000080396974],"genre_scores_gemma":[0.13672736,0.000024966941,0.860722,0.0021825356,0.00023865057,0.000025405072,0.000006503782,0.000017971843,0.000054584096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989645,0.000057345784,0.00023478948,0.0003650192,0.00013322117,0.00024511485],"domain_scores_gemma":[0.9995615,0.00009385729,0.000040484585,0.00021051802,0.000018924602,0.00007473833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030221546,0.0001519622,0.0001670868,0.00020411228,0.00007641945,0.00041401444,0.00021364745,0.00008479924,0.0000013867825],"category_scores_gemma":[0.000011587841,0.00013037633,0.00006653259,0.00029502212,0.000029526644,0.0003750086,0.000029520572,0.00014644043,0.000004963746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011297976,0.00011047042,0.00013005301,0.0008672164,0.00006850065,0.0001258015,0.0042578233,0.011077109,0.019339304,0.35362402,0.013934486,0.5964539],"study_design_scores_gemma":[0.00022706273,0.000034457415,0.00034899364,0.0001862012,0.000009719141,0.000019517804,0.000006194204,0.9849164,0.00054658594,0.010272037,0.0032329704,0.00019987748],"about_ca_topic_score_codex":0.0000071270274,"about_ca_topic_score_gemma":0.000008920324,"teacher_disagreement_score":0.9738393,"about_ca_system_score_codex":0.00002842798,"about_ca_system_score_gemma":0.00003988708,"threshold_uncertainty_score":0.53165925},"labels":[],"label_agreement":null},{"id":"W4401386967","doi":"10.1109/lra.2024.3440097","title":"DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":316,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Natural Science Foundation of China","keywords":"End-to-end principle; Computer science; Language model; End of history; Artificial intelligence; Political science","score_opus":0.008262373931495188,"score_gpt":0.2518810611154174,"score_spread":0.24361868718392224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401386967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023653755,0.00008693798,0.96486735,0.0100214705,0.00037086292,0.0001973293,0.000005719584,0.00067755,0.00011903348],"genre_scores_gemma":[0.8169503,0.000007579738,0.18011692,0.0026305125,0.00008303758,0.000029332643,0.0000062404465,0.000018481122,0.00015759256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988326,0.000024322395,0.00022756388,0.00042906083,0.0001753979,0.00031105138],"domain_scores_gemma":[0.9993523,0.000097184544,0.000049312257,0.00036594787,0.00002060712,0.000114683135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012914054,0.00015743249,0.00013669583,0.000156074,0.0001597401,0.00035071452,0.00033855517,0.0000405293,0.0000068809372],"category_scores_gemma":[0.0000073952365,0.00015634696,0.000047139973,0.0003779884,0.000026269881,0.0006154322,0.00014926655,0.00015592786,0.000082158425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.3456464e-7,0.000016550423,0.000018107077,0.00002385089,0.000015745167,0.000016586408,0.001581237,0.88140714,0.0603378,0.026470631,0.001981657,0.028130263],"study_design_scores_gemma":[0.000064363354,0.000010674755,0.00012198727,0.00004975606,0.000009062791,0.000016643093,0.000007797491,0.9962739,0.0013330466,0.0010417005,0.0008903808,0.00018073808],"about_ca_topic_score_codex":0.0000040086356,"about_ca_topic_score_gemma":0.000007716859,"teacher_disagreement_score":0.7932966,"about_ca_system_score_codex":0.00007136637,"about_ca_system_score_gemma":0.000024785024,"threshold_uncertainty_score":0.6375644},"labels":[],"label_agreement":null},{"id":"W4401748249","doi":"10.1109/lra.2024.3447470","title":"Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematic (MASK)","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Persona Design and Applications","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":"Embedding; Persona; Kinematics; Human–computer interaction; Robot; Computer science; Computer graphics (images); Multimedia; Computer vision; Artificial intelligence; Art; Physics","score_opus":0.014719575283039103,"score_gpt":0.2901604733920122,"score_spread":0.2754408981089731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401748249","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":[0.08865881,0.000083698964,0.89420444,0.016054662,0.00029670243,0.00018849988,0.0000036211081,0.00036888826,0.00014069764],"genre_scores_gemma":[0.9472923,0.000012034067,0.05184651,0.0007133411,0.00005260379,0.000031448115,0.0000073752576,0.000014884732,0.000029485404],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989114,0.000055457753,0.0002570981,0.0003532906,0.0001874765,0.00023529679],"domain_scores_gemma":[0.9996513,0.0000904729,0.00006344754,0.00012425602,0.00002134715,0.00004916232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018811604,0.0001590913,0.00017201788,0.00028719133,0.00014740461,0.0005433777,0.00022668851,0.000055472447,0.0000049661576],"category_scores_gemma":[0.000011434135,0.00015832763,0.00005517682,0.0005446578,0.000038104634,0.00068099645,0.000045325673,0.00018559927,0.000025015026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001153155,0.00022361883,0.000084174375,0.0009487318,0.00021536963,0.0002602119,0.06761935,0.19766103,0.4495119,0.20861013,0.0049108556,0.06994311],"study_design_scores_gemma":[0.00014697635,0.000010836326,0.0012435981,0.00016544017,0.000010619911,0.0000254696,0.00017629757,0.996679,0.00016957593,0.0011380184,0.000050918712,0.0001832678],"about_ca_topic_score_codex":0.000015630827,"about_ca_topic_score_gemma":0.0000045991924,"teacher_disagreement_score":0.8586335,"about_ca_system_score_codex":0.00019280435,"about_ca_system_score_gemma":0.000038010963,"threshold_uncertainty_score":0.6456413},"labels":[],"label_agreement":null},{"id":"W4402300119","doi":"10.1109/lra.2024.3455797","title":"Accounting for Hysteresis in the Forward Kinematics of Nonlinearly-Routed Tendon-Driven Continuum Robots via a Learned Deep Decoder Network","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":8,"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":"Kinematics; Robot; Hysteresis; Computer science; Physics; Artificial intelligence; Classical mechanics; Quantum mechanics","score_opus":0.012904318054172375,"score_gpt":0.2458392152702475,"score_spread":0.2329348972160751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402300119","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":[0.11350901,0.0001774688,0.8829741,0.002360194,0.0002989785,0.00048991496,0.000009860025,0.00015248236,0.000027986314],"genre_scores_gemma":[0.92127305,0.000035224442,0.0779653,0.00032078673,0.00023297017,0.0000859402,0.00003506265,0.00004320451,0.000008490033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990175,0.00002052248,0.00042403728,0.0001604411,0.00013670194,0.00024084121],"domain_scores_gemma":[0.99926364,0.00041861067,0.00006990514,0.00017803512,0.000041287018,0.000028523398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023043975,0.00015487986,0.00022245043,0.00010450036,0.000071617476,0.0001539835,0.0001440538,0.00007129311,0.000002144048],"category_scores_gemma":[0.000021576754,0.00012842509,0.00007897474,0.00032180693,0.000032829605,0.000121706355,0.000014015488,0.00012859431,0.0000037396414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014952806,0.000012923554,0.00027105643,0.0003079438,0.000050699677,0.0000014417184,0.00047369828,0.9880341,0.0066984445,0.0005446528,0.0011705364,0.00243295],"study_design_scores_gemma":[0.00023318785,0.000011765181,0.001761815,0.00015629169,0.00006651843,0.0000048485945,0.00005025852,0.9965882,0.0002682386,0.00050293776,0.00021108385,0.00014488446],"about_ca_topic_score_codex":0.0000064588544,"about_ca_topic_score_gemma":0.00004183915,"teacher_disagreement_score":0.807764,"about_ca_system_score_codex":0.000025246003,"about_ca_system_score_gemma":0.000008619335,"threshold_uncertainty_score":0.52370226},"labels":[],"label_agreement":null},{"id":"W4402350775","doi":"10.1109/lra.2024.3455907","title":"Tailoring Solution Accuracy for Fast Whole-Body Model Predictive Control of Legged Robots","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":40,"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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Robot; Computer science; Control (management); Artificial intelligence","score_opus":0.011263018311853883,"score_gpt":0.22625164533101422,"score_spread":0.21498862701916033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402350775","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":[0.0085449135,0.00017990898,0.9868921,0.0026860274,0.00072848314,0.00045533056,0.000039076283,0.0004133737,0.000060779643],"genre_scores_gemma":[0.9935423,0.000021060026,0.0059458665,0.00017925218,0.0001550475,0.00006477713,0.000020079975,0.00003395412,0.000037654492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911827,0.000017469913,0.00032703672,0.00017723707,0.00014808691,0.0002119017],"domain_scores_gemma":[0.99959457,0.00012711331,0.000050916595,0.00011733811,0.00005113739,0.000058943664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013773417,0.00016139023,0.00021644901,0.00013858016,0.0000713882,0.00009400533,0.000071591916,0.00007077249,0.0000024371896],"category_scores_gemma":[0.000020447822,0.00016068689,0.00008979904,0.000104752085,0.00003566381,0.00034116558,0.000006949039,0.00011124851,0.000005974246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008114051,0.000010129272,0.000009073105,0.00019405488,0.000089988185,8.94243e-7,0.00020259692,0.87285626,0.12119847,0.0015575119,0.0009285055,0.0029443954],"study_design_scores_gemma":[0.000844654,0.000030657524,0.00023318651,0.00012761506,0.00008972435,0.000003060188,0.000021206464,0.9964742,0.0017548216,0.00019434321,0.00007312837,0.00015341322],"about_ca_topic_score_codex":0.0000020335308,"about_ca_topic_score_gemma":7.808603e-7,"teacher_disagreement_score":0.9849974,"about_ca_system_score_codex":0.00006408319,"about_ca_system_score_gemma":0.000018967032,"threshold_uncertainty_score":0.6552621},"labels":[],"label_agreement":null},{"id":"W4402742690","doi":"10.1109/lra.2024.3466068","title":"CoFiI2P: Coarse-to-Fine Correspondences-Based Image to Point Cloud Registration","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":21,"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; Ministry of Natural Resources","keywords":"Point cloud; Image registration; Artificial intelligence; Computer vision; Computer science; Cloud computing; Point (geometry); Image (mathematics); Computer graphics (images); Mathematics; Geometry","score_opus":0.009536624883138965,"score_gpt":0.22985121297076966,"score_spread":0.2203145880876307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402742690","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":[0.074763454,0.000042532058,0.9121177,0.010478496,0.001621795,0.00028830676,0.00001651092,0.0005681569,0.00010302303],"genre_scores_gemma":[0.95875704,0.000008452139,0.038214587,0.0024789865,0.0003027807,0.000018177472,0.000067912864,0.000058536338,0.00009355109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893296,0.000030685464,0.00031933727,0.00024932928,0.00024531985,0.0002223785],"domain_scores_gemma":[0.9995031,0.00008456092,0.000027028054,0.00018778844,0.000050270028,0.00014729025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019826127,0.00018743699,0.0001588102,0.00026870312,0.00007755324,0.0003475637,0.00008276813,0.00006607127,0.000020736647],"category_scores_gemma":[0.000032418196,0.00019291767,0.000045717887,0.00041234613,0.000027365308,0.00016204444,0.000008154045,0.00010973611,0.00012784624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000688438,0.000007265768,0.000010511927,0.000105887615,0.000011917749,0.000020719244,0.00015054247,0.8784614,0.09802454,0.001012242,0.02097843,0.0012096248],"study_design_scores_gemma":[0.00015502376,0.0000557361,0.00021753421,0.00016180689,0.000023560018,0.0000061385836,0.000015315025,0.9867642,0.010501679,0.00006013826,0.0017927021,0.00024618895],"about_ca_topic_score_codex":0.0000117343425,"about_ca_topic_score_gemma":0.000016072605,"teacher_disagreement_score":0.88399357,"about_ca_system_score_codex":0.00009495919,"about_ca_system_score_gemma":0.000024851652,"threshold_uncertainty_score":0.7866954},"labels":[],"label_agreement":null},{"id":"W4403094418","doi":"10.1109/lra.2024.3474551","title":"Di-NeRF: Distributed NeRF for Collaborative Learning With Relative Pose Refinement","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.011743074229562528,"score_gpt":0.22331771761786418,"score_spread":0.21157464338830165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403094418","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":[0.037804157,0.00017826933,0.95752454,0.003020531,0.0003820936,0.0002733155,0.000007332888,0.0006064173,0.00020336553],"genre_scores_gemma":[0.9916547,0.000022334438,0.007722772,0.000114222246,0.00012391724,0.00003378231,0.00014153386,0.000039274582,0.0001474475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993214,0.000029728511,0.0001874586,0.00017241208,0.000121902456,0.0001670868],"domain_scores_gemma":[0.9996765,0.00011889706,0.000039227718,0.00006624101,0.000049737428,0.000049354745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010226504,0.00014972754,0.00013467386,0.00010048871,0.00014504543,0.0001866674,0.00003441523,0.000047328715,0.000010026292],"category_scores_gemma":[0.000018049257,0.00013094404,0.000028235987,0.0002764866,0.000027043317,0.00024878004,0.0000062665913,0.00017933834,0.000010141118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063357843,0.000003852075,0.00014933619,0.00009675073,0.000077491175,0.0000050737426,0.00070631586,0.989575,0.0035157313,0.0026121347,0.0019499853,0.0013019703],"study_design_scores_gemma":[0.0003115545,0.00006179937,0.0018048793,0.00013385316,0.000044093063,0.0000037845462,0.00013744742,0.9900076,0.0003062338,0.000032163043,0.0069630253,0.00019358445],"about_ca_topic_score_codex":0.00000228399,"about_ca_topic_score_gemma":0.000002968619,"teacher_disagreement_score":0.95385057,"about_ca_system_score_codex":0.00007478756,"about_ca_system_score_gemma":0.000013904017,"threshold_uncertainty_score":0.53397435},"labels":[],"label_agreement":null},{"id":"W4403918455","doi":"10.1109/lra.2024.3488400","title":"Learning Based Estimation of Tool-Tissue Interaction Forces for Stationary and Moving Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Estimation; Computer science; Human–computer interaction; Artificial intelligence; Engineering; Systems engineering","score_opus":0.010598424427242951,"score_gpt":0.24150307175244085,"score_spread":0.2309046473251979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403918455","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":[0.2326557,0.000059189784,0.7665883,0.00028462298,0.00018566939,0.000109285014,8.177172e-7,0.00010531268,0.0000111349855],"genre_scores_gemma":[0.9821916,0.000016953036,0.01760627,0.000048541326,0.00003240455,0.000011109501,0.000044763976,0.0000181285,0.000030204585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995563,0.000015721562,0.00017799881,0.00009834786,0.00007904137,0.00007259401],"domain_scores_gemma":[0.9997639,0.00014059753,0.00003759197,0.000034210614,0.0000060297716,0.000017660894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000949175,0.00007686636,0.00007769175,0.000121281795,0.00006242952,0.00007394486,0.000016083564,0.000030696825,0.0000073915176],"category_scores_gemma":[0.00001751707,0.00008389499,0.000016681443,0.000052152336,0.000014682847,0.00030278368,0.0000036489055,0.00006954714,0.0000021337166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019916133,0.0000018110901,0.000080037855,0.00025382193,0.000012964491,4.139271e-7,0.0003680573,0.9503475,0.026416535,0.00017795077,0.000072920564,0.022266015],"study_design_scores_gemma":[0.00013505356,0.000023985234,0.0022019327,0.0001029451,0.00001795528,0.0000019002903,0.000057289773,0.99472225,0.002187152,0.000039461316,0.00042445274,0.00008562513],"about_ca_topic_score_codex":0.000002018449,"about_ca_topic_score_gemma":3.1582138e-7,"teacher_disagreement_score":0.7495359,"about_ca_system_score_codex":0.000026826501,"about_ca_system_score_gemma":0.000003327281,"threshold_uncertainty_score":0.34211385},"labels":[],"label_agreement":null},{"id":"W4404238413","doi":"10.1109/lra.2024.3495453","title":"Spinning-Base Space Robot for Seamless Capture and Stabilization of Rotating Objects","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Space Satellite Systems and Control","field":"Engineering","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":"Canadian Space Agency","funders":"","keywords":"Spinning; Base (topology); Robot; Space (punctuation); Computer science; Robotic spacecraft; Computer vision; Artificial intelligence; Engineering; Mechanical engineering; Mathematics; Operating system","score_opus":0.007482219407693893,"score_gpt":0.2118862875175257,"score_spread":0.2044040681098318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404238413","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":[0.33142364,0.0014288685,0.6649051,0.0011402651,0.00050576514,0.00034566608,0.000012850356,0.0002139234,0.000023980805],"genre_scores_gemma":[0.9923733,0.000031109514,0.007368141,0.00006686181,0.00008847934,0.000017370523,0.000009080137,0.000027413194,0.00001828136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994744,0.000012246321,0.00017709946,0.00013096145,0.00008330413,0.00012196433],"domain_scores_gemma":[0.9997531,0.00007599916,0.000034210145,0.00007055951,0.000030314668,0.000035798166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011503147,0.000109095614,0.00014478706,0.00008531526,0.000038520968,0.00008669839,0.000024185869,0.00004984815,0.0000011980328],"category_scores_gemma":[0.000011968593,0.00010431059,0.000029449075,0.00010453488,0.000019320072,0.00012048525,0.0000041572343,0.000054818,6.527514e-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":[0.0000018488851,0.0000027193707,0.00028692523,0.0011711533,0.000034836426,0.000001702623,0.0012430106,0.8854398,0.107485145,0.00066551386,0.00021913869,0.0034482183],"study_design_scores_gemma":[0.00021791717,0.000017217952,0.0009415352,0.0002483638,0.00003068183,0.000004237832,0.00018777869,0.9946985,0.0033856845,0.000031381955,0.000114549875,0.00012212528],"about_ca_topic_score_codex":0.000014466714,"about_ca_topic_score_gemma":0.000010172159,"teacher_disagreement_score":0.66094965,"about_ca_system_score_codex":0.00001817207,"about_ca_system_score_gemma":0.0000087838325,"threshold_uncertainty_score":0.42536625},"labels":[],"label_agreement":null},{"id":"W4404307335","doi":"10.1109/lra.2024.3497717","title":"Differentiable-Optimization Based Neural Policy for Occlusion-Aware Target Tracking","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Eesti Teadusagentuur","keywords":"Differentiable function; Computer science; Occlusion; Tracking (education); Artificial neural network; Artificial intelligence; Medicine; Psychology; Mathematics; Internal medicine","score_opus":0.014249281692056437,"score_gpt":0.2518760457864968,"score_spread":0.23762676409444036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404307335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021936425,0.00008604614,0.9792705,0.01633971,0.0012638645,0.0001979874,0.000026492999,0.00060822576,0.000013511547],"genre_scores_gemma":[0.74507475,0.000016000024,0.25126943,0.0029947734,0.00044246283,0.000012950558,0.000128672,0.000026405978,0.000034554785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882567,0.000045373476,0.00026468007,0.00038489897,0.00020857305,0.0002707893],"domain_scores_gemma":[0.99930733,0.00023387313,0.00006822544,0.00025252148,0.00005644446,0.00008158574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015588233,0.00016686486,0.0001436112,0.000248368,0.0002834666,0.0008196349,0.00024745375,0.00007392014,0.000009295508],"category_scores_gemma":[0.000026284148,0.00015215762,0.00007451925,0.00036698944,0.000030380965,0.00055167027,0.000048187703,0.0001172525,0.00000427148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002065608,0.000013913761,0.000046862762,0.00007823029,0.0000091117245,0.0000067076908,0.000102350365,0.98462224,0.0004877241,0.0038262205,0.006357968,0.004446603],"study_design_scores_gemma":[0.00021290741,0.000022349373,0.00018612981,0.00009282549,0.000011714767,0.000010697289,0.0000036145925,0.99783444,0.00024754633,0.0003190347,0.00087399833,0.00018471834],"about_ca_topic_score_codex":0.000007975982,"about_ca_topic_score_gemma":6.4699503e-7,"teacher_disagreement_score":0.7428811,"about_ca_system_score_codex":0.00003885096,"about_ca_system_score_gemma":0.000041478055,"threshold_uncertainty_score":0.79037577},"labels":[],"label_agreement":null},{"id":"W4404520551","doi":"10.1109/lra.2024.3502066","title":"GraspAgent 1.0: Adversarial Continual Dexterous Grasp Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"GRASP; Adversarial system; Computer science; Artificial intelligence; Human–computer interaction","score_opus":0.00833948835200547,"score_gpt":0.23967357420486957,"score_spread":0.2313340858528641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404520551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040654384,0.00012300676,0.94894224,0.006426914,0.0027417357,0.00012486853,6.946734e-7,0.00085153244,0.00013462159],"genre_scores_gemma":[0.9426614,0.000016703758,0.056074962,0.00068269746,0.00040238694,0.0000067751616,0.0000062557665,0.000023983986,0.00012481153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851185,0.00013705401,0.000279127,0.00044722427,0.00032081694,0.0003039063],"domain_scores_gemma":[0.9993865,0.00019636934,0.00008692598,0.00020716382,0.00003192356,0.00009112922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003651455,0.00019545459,0.00018083668,0.00019406075,0.0002520219,0.0006804995,0.0003120353,0.00007481617,0.000008948231],"category_scores_gemma":[0.00006297454,0.00019086347,0.0000733502,0.00031039887,0.00006341414,0.0006983033,0.00012687429,0.00039202414,0.00006138207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003740801,0.000013651891,0.0004612602,0.00006126136,0.000056444962,0.00012854763,0.0014747584,0.9158947,0.0054153036,0.037929982,0.0014774543,0.03708292],"study_design_scores_gemma":[0.00027316707,0.000040397674,0.001231189,0.000074084506,0.000024187268,0.000043508142,0.000026251197,0.99449825,0.000185725,0.0004454407,0.0029093903,0.00024839683],"about_ca_topic_score_codex":0.00002263795,"about_ca_topic_score_gemma":0.0000021236206,"teacher_disagreement_score":0.90200704,"about_ca_system_score_codex":0.00006373026,"about_ca_system_score_gemma":0.000035139372,"threshold_uncertainty_score":0.77831864},"labels":[],"label_agreement":null},{"id":"W4404627598","doi":"10.1109/lra.2024.3505821","title":"Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"GNSS positioning and interference","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Doppler effect; Remote sensing; Spinning; Odometry; Doppler radar; Radar; Geodesy; Geology; Physics; Computer science; Artificial intelligence; Materials science; Astronomy; Telecommunications","score_opus":0.04148929861756736,"score_gpt":0.25287219305631914,"score_spread":0.21138289443875177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404627598","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":[0.3036266,0.00026399508,0.6925184,0.0013775121,0.0013572408,0.00014053355,0.0000149388,0.0005565416,0.00014422117],"genre_scores_gemma":[0.9897927,0.0000085366455,0.009673704,0.00034602362,0.00010940333,0.000013656654,0.000010396545,0.000022997332,0.000022567127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994522,0.000008305137,0.00014777601,0.00013169934,0.000114452116,0.00014556454],"domain_scores_gemma":[0.99980414,0.00003346008,0.000025324567,0.00007303736,0.000026758868,0.000037297723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010539996,0.00010466121,0.00009956491,0.00010397059,0.00007424069,0.00018119623,0.000049542938,0.000040359784,0.000005171483],"category_scores_gemma":[0.000014248326,0.00010535074,0.00003728967,0.00010760622,0.000017187065,0.00015472686,0.0000053400595,0.00008340326,0.000015820619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006260364,0.000027476128,0.0014915122,0.0014158709,0.00030565844,0.000008731723,0.00082093704,0.7238781,0.16870184,0.00089368154,0.094544806,0.0079051675],"study_design_scores_gemma":[0.00024652304,0.000020646206,0.011701216,0.000536768,0.00004514206,0.000011787451,0.0000231715,0.9731287,0.012291326,0.00012392289,0.0016037326,0.00026706795],"about_ca_topic_score_codex":0.0000025132476,"about_ca_topic_score_gemma":9.3714107e-7,"teacher_disagreement_score":0.6861661,"about_ca_system_score_codex":0.00005552498,"about_ca_system_score_gemma":0.000004311408,"threshold_uncertainty_score":0.42960784},"labels":[],"label_agreement":null},{"id":"W4405055855","doi":"10.1109/lra.2024.3512374","title":"Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Occupational Health and Safety Research","field":"Health Professions","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 Toronto","funders":"","keywords":"Training (meteorology); Sample (material); Reinforcement learning; Reinforcement; Computer science; Artificial intelligence; Psychology; Chromatography; Social psychology; Geography; Chemistry","score_opus":0.07189234968498567,"score_gpt":0.3722205461360454,"score_spread":0.3003281964510597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405055855","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":[0.9064565,0.000100241006,0.08614743,0.005819718,0.00053431035,0.00055577763,0.00000858207,0.00007106428,0.0003064083],"genre_scores_gemma":[0.9981514,0.00014570336,0.0010151407,0.00046930806,0.00010572034,0.000025577434,0.000012326083,0.000010170457,0.00006464173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988576,0.00011115828,0.00037858656,0.0001446467,0.00023238838,0.00027561482],"domain_scores_gemma":[0.9987086,0.0009972949,0.00009342436,0.0000868599,0.00004712295,0.000066687506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086858467,0.00007977698,0.00011437851,0.00008413121,0.0007711107,0.000024624916,0.00006110449,0.00004438552,0.000045990364],"category_scores_gemma":[0.00012767629,0.00005817445,0.000020022451,0.00014467568,0.00005858499,0.00012824181,0.000045675624,0.00033197177,0.00000801847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106943815,0.000024029838,0.038892996,0.007482022,0.00006471199,0.000004062694,0.032305103,0.7511612,0.011824306,0.004391509,0.0015076458,0.15223548],"study_design_scores_gemma":[0.00022067905,0.000068918656,0.054032113,0.0003384652,0.0000084953435,0.0000014719415,0.00059448293,0.94353765,0.000049754013,0.000014822434,0.0010635933,0.00006954305],"about_ca_topic_score_codex":0.00008379698,"about_ca_topic_score_gemma":0.000003579992,"teacher_disagreement_score":0.19237646,"about_ca_system_score_codex":0.000053890828,"about_ca_system_score_gemma":0.0001215528,"threshold_uncertainty_score":0.5930838},"labels":[],"label_agreement":null},{"id":"W4405179831","doi":"10.1109/lra.2024.3514513","title":"H-Net: A Multitask Architecture for Simultaneous 3D Force Estimation and Stereo Semantic Segmentation in Intracardiac Catheters","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Intracardiac injection; Segmentation; Computer science; Artificial intelligence; Architecture; Estimation; Computer vision; Engineering; Medicine; Geography; Surgery; Systems engineering","score_opus":0.007631856924360475,"score_gpt":0.29777071320999904,"score_spread":0.29013885628563857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405179831","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":[0.86232555,0.00039620127,0.13368781,0.0024166366,0.00022291768,0.0008240832,0.000046463123,0.00007517371,0.000005181732],"genre_scores_gemma":[0.9870282,0.00005042361,0.012010967,0.00060579303,0.0000615136,0.00005205802,0.00014916986,0.000021901076,0.000019966958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993108,0.00002016868,0.0001834898,0.000226992,0.0001167953,0.00014176253],"domain_scores_gemma":[0.99961734,0.0001786073,0.000036847458,0.00008191121,0.000018975998,0.00006634166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006758289,0.00013381358,0.00018118533,0.00015514236,0.000047522135,0.00008966077,0.000013667605,0.000045305747,0.0000012675226],"category_scores_gemma":[0.00002687428,0.00011808698,0.00013060737,0.00008835256,0.00002946568,0.00009202642,0.0000067725823,0.00006487134,0.0000027634646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017621148,0.00013926026,0.010071628,0.002368627,0.00082091097,0.000108048764,0.0030177517,0.66755146,0.020982735,0.000119755736,0.00034982676,0.2942938],"study_design_scores_gemma":[0.0013567284,0.000109804685,0.015442873,0.0003328061,0.00053326605,0.000034468896,0.00006752304,0.98158574,0.00024411194,0.00010594286,0.000042174048,0.00014456421],"about_ca_topic_score_codex":0.000015984622,"about_ca_topic_score_gemma":0.000003102804,"teacher_disagreement_score":0.31403428,"about_ca_system_score_codex":0.00006772059,"about_ca_system_score_gemma":0.000016082542,"threshold_uncertainty_score":0.48154473},"labels":[],"label_agreement":null},{"id":"W4405429256","doi":"10.1109/lra.2024.3518111","title":"DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mobile robot; Human–computer interaction; Artificial intelligence; Robot; Computer vision","score_opus":0.009981586368624056,"score_gpt":0.2349829968031261,"score_spread":0.22500141043450203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405429256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061716467,0.00026140964,0.9343782,0.001001322,0.0016294769,0.00029434127,0.00012115668,0.00059018336,0.000007447955],"genre_scores_gemma":[0.84497255,0.00022567078,0.1533138,0.0002934102,0.0004857786,0.00005815166,0.00056050986,0.00007526333,0.000014847153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922234,0.00001360497,0.00026980476,0.00019653395,0.00010462931,0.00019308562],"domain_scores_gemma":[0.9996322,0.00012284495,0.000029534307,0.00012216532,0.000032569376,0.00006073136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006630809,0.00015921265,0.00015340126,0.000113837144,0.00008635303,0.00017943405,0.00004776936,0.00011234491,0.000008400001],"category_scores_gemma":[0.00001892069,0.00016010366,0.000058644106,0.00021172939,0.000034292127,0.00016281292,0.0000054608645,0.0001298602,0.0000069375333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020919517,0.000011424332,0.00003452137,0.00021509845,0.000038053266,0.000002119617,0.000073854215,0.9750458,0.003559031,0.0024717818,0.0010034657,0.017542772],"study_design_scores_gemma":[0.00013829599,0.000026647676,0.00022270401,0.00018907832,0.000043400054,0.000012298294,0.000024934034,0.9968919,0.00045885216,0.000948996,0.0008453844,0.00019750428],"about_ca_topic_score_codex":0.000002368908,"about_ca_topic_score_gemma":0.0000026718992,"teacher_disagreement_score":0.7832561,"about_ca_system_score_codex":0.000065725115,"about_ca_system_score_gemma":0.000009568473,"threshold_uncertainty_score":0.65288377},"labels":[],"label_agreement":null},{"id":"W4405429414","doi":"10.1109/lra.2024.3518102","title":"Visual-Tactile Inference of 2.5D Object Shape From Marker Texture","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Artificial intelligence; Texture (cosmology); Object (grammar); Inference; Computer vision; Computer science; Pattern recognition (psychology); Image (mathematics)","score_opus":0.013930661616736752,"score_gpt":0.24429209424746293,"score_spread":0.23036143263072617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405429414","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":[0.87031114,0.00026107676,0.12607905,0.00019740452,0.0022915744,0.0001438534,0.000022756874,0.000402257,0.00029086482],"genre_scores_gemma":[0.9992855,0.000022466143,0.00032049388,0.000065475695,0.0002502239,0.0000049039554,0.000009338042,0.0000193375,0.000022237668],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993713,0.000022912625,0.00023398163,0.000129032,0.0001393757,0.000103411956],"domain_scores_gemma":[0.99971324,0.00011913005,0.00003262165,0.00008160673,0.00001929121,0.000034081088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000906754,0.00011105792,0.0001482001,0.000117487594,0.00003430572,0.00010564473,0.000035162313,0.000105112355,0.000062170206],"category_scores_gemma":[0.000013561377,0.00010007735,0.0000473047,0.0001708249,0.000014946164,0.00016038124,0.0000072567336,0.00013898555,0.000029862342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013816521,0.00001999989,0.0005633852,0.00046232957,0.00021878682,0.000028813609,0.00085281045,0.3430682,0.53534144,0.00032383154,0.016342819,0.10276375],"study_design_scores_gemma":[0.00013517877,0.00002125235,0.0038282922,0.00022521675,0.000023919392,0.000005005283,0.000027711152,0.98617494,0.007722918,0.00003509219,0.0016606393,0.00013984935],"about_ca_topic_score_codex":0.00004163618,"about_ca_topic_score_gemma":0.0000028041975,"teacher_disagreement_score":0.6431067,"about_ca_system_score_codex":0.0000281245,"about_ca_system_score_gemma":0.000009710048,"threshold_uncertainty_score":0.4081036},"labels":[],"label_agreement":null},{"id":"W4405520745","doi":"10.1109/lra.2024.3519875","title":"Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test With Handover Task","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Embodied cognition; Task (project management); Handover; Human–computer interaction; Test (biology); Computer science; Robot; Human–robot interaction; Artificial intelligence; Engineering; Systems engineering; Computer network","score_opus":0.040283720148507576,"score_gpt":0.37518473164647625,"score_spread":0.3349010114979687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405520745","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":[0.2432384,0.0011622686,0.70018387,0.023319036,0.0046996935,0.0011329065,0.000026510945,0.0017798496,0.024457483],"genre_scores_gemma":[0.99123365,0.000029964942,0.0037399947,0.0022133866,0.00031437303,0.000024536785,0.000027946888,0.000044194752,0.0023719375],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989016,0.00010472202,0.0002489444,0.00034324155,0.0001741758,0.00022729953],"domain_scores_gemma":[0.9992852,0.00038934106,0.000095928095,0.00011424078,0.00003467427,0.00008062155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026696685,0.0001726185,0.0001926792,0.00017170829,0.00027442566,0.00032921505,0.000053540116,0.00008683259,0.00021784291],"category_scores_gemma":[0.000034556328,0.00015263578,0.000040215822,0.00014757323,0.00008070062,0.00020203237,0.000013188051,0.00027056885,0.00007791145],"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":[0.00023754025,0.0005888653,0.0317776,0.00086947816,0.0022653711,0.0005909271,0.041414168,0.057379197,0.5079752,0.15671335,0.12852044,0.071667865],"study_design_scores_gemma":[0.020453734,0.003698959,0.22601482,0.003432368,0.002594326,0.00141136,0.012915782,0.20017919,0.011314951,0.0034483538,0.50761384,0.0069223293],"about_ca_topic_score_codex":0.0000559006,"about_ca_topic_score_gemma":0.000007950498,"teacher_disagreement_score":0.74799526,"about_ca_system_score_codex":0.000043341788,"about_ca_system_score_gemma":0.000012478828,"threshold_uncertainty_score":0.6224307},"labels":[],"label_agreement":null},{"id":"W4405710329","doi":"10.1109/lra.2024.3520921","title":"Complete Coverage Path Planning Algorithm Based on Improved Biologically Inspired Neural Networks in Spray Painting","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Plant Surface Properties and Treatments","field":"Agricultural and Biological Sciences","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":"Path (computing); Artificial neural network; Computer science; Artificial intelligence; Algorithm; Computer network","score_opus":0.019904276570539937,"score_gpt":0.21171594315284956,"score_spread":0.1918116665823096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405710329","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":[0.9883477,0.00012405332,0.0071670185,0.0036724228,0.00028015627,0.00018857079,0.000049829505,0.00014821257,0.000022010709],"genre_scores_gemma":[0.99674934,0.000012926949,0.0009749713,0.0020251523,0.000081574326,0.000008126936,0.00014046942,0.0000016217055,0.0000058436203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915034,0.000070155766,0.00019432258,0.00025739134,0.000092522976,0.00023527135],"domain_scores_gemma":[0.999646,0.00021994938,0.00004853379,0.00003006236,0.000008858176,0.00004657692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015591622,0.00014520483,0.0001463424,0.000021573383,0.00012675449,0.00018487709,0.00006614728,0.000064140855,0.000008942175],"category_scores_gemma":[0.000009530453,0.000058126894,0.000044895613,0.00015274894,0.000025410563,0.000089572895,0.00001676015,0.0001321982,0.0000036083125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025603335,0.000066287204,0.010060731,0.00002117457,0.000021919108,0.00014069662,0.000056364705,0.66378075,0.14238073,0.000021325046,0.00017225306,0.18325214],"study_design_scores_gemma":[0.00015469162,0.00013801812,0.032534502,0.00010423174,0.0000057975817,0.000003004891,0.000010600055,0.96658736,0.000060880207,0.000009949229,0.00025619956,0.0001347791],"about_ca_topic_score_codex":0.00006477724,"about_ca_topic_score_gemma":0.000008936523,"teacher_disagreement_score":0.3028066,"about_ca_system_score_codex":0.000026998792,"about_ca_system_score_gemma":0.0000024815597,"threshold_uncertainty_score":0.2370346},"labels":[],"label_agreement":null},{"id":"W4405907349","doi":"10.1109/lra.2024.3520436","title":"Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"","keywords":"Pose; Estimation; Artificial intelligence; Sample (material); Object (grammar); Computer science; Computer vision; 3D pose estimation; Pattern recognition (psychology); Engineering; Chemistry; Chromatography","score_opus":0.019749246927318855,"score_gpt":0.2520500083984356,"score_spread":0.23230076147111675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405907349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076181434,0.00007348696,0.9188798,0.0029208343,0.0006855315,0.00037013975,0.00001995665,0.00085281377,0.000016001413],"genre_scores_gemma":[0.9161009,0.000008829384,0.0832162,0.0004043618,0.00007508404,0.000025558153,0.000109882465,0.000049104492,0.000010071952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992285,0.000029226148,0.00023396671,0.00022251395,0.000105562285,0.00018023547],"domain_scores_gemma":[0.9993226,0.00043707408,0.000038865837,0.00011065661,0.00002546035,0.00006537272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016985749,0.0001815032,0.00016242011,0.00023298526,0.00015493234,0.00022371659,0.000040405055,0.000079549674,0.000017178818],"category_scores_gemma":[0.00014248613,0.00017844047,0.000046087665,0.00015329225,0.00002075403,0.000199477,0.000007169277,0.00010958934,0.000007581791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010515653,0.0000071309346,0.000032180917,0.00022629916,0.000020691661,0.0000018969536,0.00012202225,0.94037753,0.049928714,0.000093493574,0.0010083233,0.008171199],"study_design_scores_gemma":[0.0002563723,0.000035814497,0.0035184165,0.00020016416,0.00004805039,0.0000035837068,0.000008858021,0.993105,0.0024418598,0.000059651746,0.00011461145,0.00020760423],"about_ca_topic_score_codex":0.00001015958,"about_ca_topic_score_gemma":0.0000015892873,"teacher_disagreement_score":0.83991945,"about_ca_system_score_codex":0.000059908078,"about_ca_system_score_gemma":0.000012185585,"threshold_uncertainty_score":0.7276591},"labels":[],"label_agreement":null},{"id":"W4406022307","doi":"10.1109/lra.2025.3535184","title":"From Decision to Action in Surgical Autonomy: Multi-Modal Large Language Models for Robot-Assisted Blood Suction","year":2025,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates; Canada Foundation for Innovation","keywords":"Robot; Artificial intelligence; Robotics; Autonomy; Computer science; Modal; Agency (philosophy); Automation; Human–computer interaction; Engineering; Political science; Sociology","score_opus":0.12245814160493286,"score_gpt":0.42462435308098506,"score_spread":0.3021662114760522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406022307","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":[0.57311094,0.00009847402,0.4177969,0.006375678,0.0015418438,0.0009216543,0.00006585907,0.00008292594,0.0000057017814],"genre_scores_gemma":[0.9129944,0.00007501784,0.08412687,0.0011430887,0.00070891494,0.00019753518,0.00062521186,0.0000235809,0.000105397114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982345,0.00006663783,0.0006704151,0.00054671365,0.00020065735,0.00028108337],"domain_scores_gemma":[0.99893355,0.00029722333,0.00018179961,0.00030422545,0.00014606939,0.0001371165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003412434,0.00023302765,0.00041800557,0.00040966165,0.000113345224,0.00008481126,0.000084270694,0.0003811312,0.000009718192],"category_scores_gemma":[0.00009849297,0.00024242651,0.00011449411,0.00019364638,0.000019914414,0.00013014063,0.000057544497,0.00038505497,0.0000052810497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030126912,0.0005581478,0.0011838621,0.00057737547,0.00009580455,0.000024204648,0.0062444676,0.7972208,0.005666442,0.00015017668,0.00057403377,0.18740337],"study_design_scores_gemma":[0.00054297136,0.00006477341,0.009284885,0.0010418545,0.0001797952,0.000009133611,0.0005469021,0.9837446,0.003402584,0.00068905257,0.00022342095,0.0002700298],"about_ca_topic_score_codex":0.0017190468,"about_ca_topic_score_gemma":0.00046462784,"teacher_disagreement_score":0.33988345,"about_ca_system_score_codex":0.00037689973,"about_ca_system_score_gemma":0.0003016566,"threshold_uncertainty_score":0.9885866},"labels":[],"label_agreement":null},{"id":"W4406158092","doi":"10.1109/lra.2025.3527280","title":"Real-Time Sit-to-Stand Phase Classification With a Mobile Assistive Robot From Close Proximity Utilizing 3D Visual Skeleton Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Japan Society for the Promotion of Science","keywords":"Artificial intelligence; Computer science; Kinematics; Classifier (UML); Pose; Computer vision; Robotics; Motion capture; Context (archaeology); Robot; Motion (physics)","score_opus":0.010377451868657022,"score_gpt":0.25773942596750626,"score_spread":0.24736197409884925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406158092","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":[0.6404287,0.00003011055,0.35742003,0.0008082898,0.00020965538,0.0005791501,0.000046402765,0.00029525385,0.00018235069],"genre_scores_gemma":[0.9313857,0.00006456535,0.06786584,0.0002335361,0.000072213734,0.00011057991,0.00017803784,0.00004046915,0.0000490534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987886,0.000059304683,0.00037808562,0.00033843308,0.0002026862,0.00023287992],"domain_scores_gemma":[0.9993289,0.00015273299,0.00009417266,0.00019691742,0.00012443823,0.00010282801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001602227,0.00023280435,0.00025264156,0.00021911293,0.00016463772,0.00014448901,0.00007595931,0.0001122881,0.000008313159],"category_scores_gemma":[0.000025053743,0.00022086555,0.000043534008,0.00034329057,0.0000719927,0.00022212694,0.000016551612,0.00015480815,0.000025984098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007619495,0.0002441395,0.00042711766,0.00022381167,0.00014432753,0.0000056323743,0.00078956515,0.57042634,0.3785801,0.00016229694,0.0008775136,0.04804292],"study_design_scores_gemma":[0.001317271,0.00021085533,0.009623865,0.00034146517,0.000117664495,0.0000018646691,0.00016185433,0.9805973,0.0068723834,0.00012706906,0.00024809394,0.00038027106],"about_ca_topic_score_codex":0.000019068342,"about_ca_topic_score_gemma":0.0000066229077,"teacher_disagreement_score":0.41017097,"about_ca_system_score_codex":0.00016173822,"about_ca_system_score_gemma":0.000038945953,"threshold_uncertainty_score":0.90066355},"labels":[],"label_agreement":null},{"id":"W4406610381","doi":"10.1109/lra.2025.3532159","title":"A Non-Linear Model Predictive Task-Space Controller Satisfying Shape Constraints for Tendon-Driven Continuum Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robot; Model predictive control; Control theory (sociology); Task (project management); Computer science; Mathematics; Artificial intelligence; Engineering; Control (management)","score_opus":0.0072228340832387,"score_gpt":0.21659634484376947,"score_spread":0.20937351076053076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406610381","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":[0.027038246,0.00005785237,0.9687945,0.0021768555,0.000605491,0.0008073197,0.00005809487,0.00021619342,0.00024542853],"genre_scores_gemma":[0.98534614,0.000016517322,0.013567021,0.0007445743,0.00009817317,0.00011053432,0.000014375037,0.000028829698,0.000073826355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989449,0.0000156151,0.00037439162,0.00023589918,0.00013694537,0.0002922886],"domain_scores_gemma":[0.9995084,0.00013249567,0.00008434874,0.00012196971,0.00007338593,0.000079436075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015653198,0.00021193895,0.00037944948,0.000105840554,0.000108832326,0.00009267746,0.00010866824,0.00011511148,0.0000015774627],"category_scores_gemma":[0.000023775667,0.00020832059,0.000101599435,0.000092299124,0.000049376245,0.00011962788,0.000016504937,0.00013014465,0.000003119125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016322403,0.000011623264,0.000053728934,0.000103440485,0.00016660268,0.0000014181645,0.000072007184,0.9377286,0.05505024,0.0029054224,0.0017131978,0.0021774317],"study_design_scores_gemma":[0.0022203808,0.000027894637,0.00025706537,0.00016460477,0.000079920675,0.0000018633804,0.000025930698,0.99638194,0.00016876882,0.0004111256,0.00006246555,0.00019801363],"about_ca_topic_score_codex":0.0000040793693,"about_ca_topic_score_gemma":0.000007625318,"teacher_disagreement_score":0.9583079,"about_ca_system_score_codex":0.00008136463,"about_ca_system_score_gemma":0.00002363623,"threshold_uncertainty_score":0.8495067},"labels":[],"label_agreement":null},{"id":"W4406610554","doi":"10.1109/lra.2025.3532158","title":"A Multi-Robot Exploration Planner for Space Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Planner; Computer science; Robot; Space (punctuation); Human–computer interaction; Artificial intelligence","score_opus":0.012640214260648383,"score_gpt":0.2340162962521309,"score_spread":0.22137608199148254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406610554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023366474,0.00021650098,0.9921802,0.004006142,0.00035754993,0.0005660262,0.000008274502,0.0002480965,0.000080510115],"genre_scores_gemma":[0.9377365,0.0000761842,0.059812546,0.00083511247,0.00021556963,0.00072384597,0.00004498682,0.000033090284,0.0005221419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955505,0.000008079371,0.00015410614,0.00011441919,0.00004911885,0.000119250704],"domain_scores_gemma":[0.99976456,0.00004159753,0.000027555398,0.00011137298,0.000027921056,0.000026971602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000061986415,0.0000956491,0.00010887058,0.00009259979,0.00007957946,0.00006981258,0.000042552278,0.00004757162,9.2385704e-7],"category_scores_gemma":[0.000004137137,0.000096348835,0.000032005282,0.000106641564,0.000012740657,0.00012648881,0.0000035047892,0.00004209846,0.000007780926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025540337,0.000011657783,0.00006901582,0.00016797481,0.00004738325,2.2869483e-7,0.0001816508,0.8936572,0.090693206,0.0037565036,0.0036979355,0.007714689],"study_design_scores_gemma":[0.0005483678,0.0000061142564,0.00072682305,0.000039624072,0.000027042384,6.926167e-7,0.00007059845,0.9842783,0.0013705618,0.0001317786,0.012668215,0.00013183481],"about_ca_topic_score_codex":0.0000049637983,"about_ca_topic_score_gemma":0.000008656967,"teacher_disagreement_score":0.9353999,"about_ca_system_score_codex":0.00002803411,"about_ca_system_score_gemma":0.0000066014914,"threshold_uncertainty_score":0.39289913},"labels":[],"label_agreement":null},{"id":"W4406812170","doi":"10.1109/lra.2025.3533457","title":"3DGS-CD: 3D Gaussian Splatting-Based Change Detection for Physical Object Rearrangement","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","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":"Reach Technologies (Canada)","funders":"MIT Portugal","keywords":"Computer science; Object (grammar); Gaussian; Artificial intelligence; Computer vision; Physics","score_opus":0.027433489322985227,"score_gpt":0.2986896547707165,"score_spread":0.2712561654477313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406812170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042166952,0.000023131068,0.94734216,0.009119398,0.0008006067,0.00030947998,0.0000020056877,0.00019114837,0.000045095003],"genre_scores_gemma":[0.8875275,0.0000026206021,0.10910913,0.0030857758,0.00018472764,0.0000656931,0.0000038343746,0.000007709565,0.000012992525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991343,0.00007445557,0.00016314504,0.00028657232,0.00013889425,0.00020261836],"domain_scores_gemma":[0.9994261,0.00017745838,0.00008888825,0.00023078253,0.00004163165,0.000035110425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003750189,0.0001232352,0.00015477769,0.00015314404,0.00019103303,0.0001696429,0.00015076886,0.000041661944,4.3275384e-7],"category_scores_gemma":[0.000027626822,0.00011737277,0.00006352108,0.00029464552,0.000026060576,0.00022339087,0.000022159345,0.000074500356,0.0000029755918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003759712,0.00017944592,0.0019149039,0.0005230062,0.00011064399,0.00000956662,0.0011826182,0.051357064,0.09659241,0.0066572996,0.0007274653,0.84070796],"study_design_scores_gemma":[0.0005817679,0.00007897978,0.023399862,0.000085560845,0.00002018891,0.0000013883351,0.000004971146,0.9612862,0.012936944,0.00056446774,0.0008671905,0.00017251764],"about_ca_topic_score_codex":0.000013726488,"about_ca_topic_score_gemma":0.000012842084,"teacher_disagreement_score":0.9099291,"about_ca_system_score_codex":0.000040103718,"about_ca_system_score_gemma":0.00001908325,"threshold_uncertainty_score":0.47863227},"labels":[],"label_agreement":null},{"id":"W4406856912","doi":"10.1109/lra.2025.3534570","title":"Multiagent Trajectory Prediction With Difficulty-Guided Feature Enhancement Network","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Feature (linguistics); Computer science; Artificial intelligence; Machine learning; Physics","score_opus":0.005175245754432134,"score_gpt":0.19423553429482004,"score_spread":0.18906028854038792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406856912","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":[0.4251982,0.00017433401,0.56962717,0.003189727,0.00054452627,0.00023779132,0.0000040089453,0.00078364194,0.00024059515],"genre_scores_gemma":[0.99059325,0.00006835489,0.0085166795,0.00056905474,0.000065007756,0.000025640757,0.000018739558,0.000012575625,0.00013066852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945074,0.000013059235,0.00014938877,0.00014040103,0.000072955394,0.00017346736],"domain_scores_gemma":[0.99978495,0.000019606592,0.00002954824,0.00012552323,0.00001498959,0.000025377562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006067249,0.00012831493,0.00012227445,0.00006207785,0.00011943996,0.000024154824,0.000054854434,0.000104520965,0.000004032835],"category_scores_gemma":[0.0000018891616,0.00011287646,0.00002199861,0.00014066939,0.000043284363,0.00007236391,0.000008106353,0.0001632798,0.0000037100397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043937634,0.000013257282,0.00071164704,0.000050889932,0.00008066227,0.0000029562868,0.00008980109,0.9640417,0.012995337,0.00034793015,0.018386362,0.0032750748],"study_design_scores_gemma":[0.0007342467,0.000035862162,0.050866343,0.000136003,0.00006962081,0.0000074084296,0.00002537675,0.9379144,0.007160713,0.00004261384,0.0027886038,0.00021884755],"about_ca_topic_score_codex":0.0000014332732,"about_ca_topic_score_gemma":0.000005071503,"teacher_disagreement_score":0.56539506,"about_ca_system_score_codex":0.00007169329,"about_ca_system_score_gemma":0.0000085434895,"threshold_uncertainty_score":0.46029687},"labels":[],"label_agreement":null},{"id":"W4406857045","doi":"10.1109/lra.2025.3533967","title":"Deep Koopman Approach for Nonlinear Dynamics and Control of Tendon-Driven Continuum Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","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":"Western University; London Health Sciences Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Nonlinear system; Dynamics (music); Robot; Control theory (sociology); Tendon; Control (management); Classical mechanics; Computer science; Control engineering; Physics; Engineering; Artificial intelligence; Medicine; Acoustics; Anatomy","score_opus":0.004084732443389113,"score_gpt":0.19282101388029785,"score_spread":0.18873628143690874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023695892,0.00013447105,0.97437936,0.00087380776,0.00029983872,0.00040919642,0.000024381383,0.00008219641,0.00010084905],"genre_scores_gemma":[0.97320044,0.000021046277,0.026364332,0.00025610512,0.00005196759,0.000030085219,0.00002963691,0.000017361766,0.000029036239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992933,0.000015481675,0.0003060612,0.00014382694,0.000080304984,0.00016101766],"domain_scores_gemma":[0.9996474,0.000089528745,0.00006717485,0.000108600085,0.000042705487,0.000044589902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011927683,0.00013091587,0.00029710846,0.00007998735,0.000046608762,0.000048425372,0.00007514526,0.000078544654,3.361977e-7],"category_scores_gemma":[0.000012558385,0.0001262025,0.000055369033,0.000070746595,0.000030357278,0.000060332088,0.00000980243,0.00006906067,2.2985714e-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":[0.00001176794,0.000024768697,0.00030457447,0.00040664166,0.00015430296,6.0964214e-7,0.000033687462,0.9634308,0.020454245,0.009450043,0.0001505452,0.005578006],"study_design_scores_gemma":[0.0012894417,0.000023971987,0.00046511187,0.00004391421,0.0000660081,0.0000016261165,0.000019600544,0.9976557,0.00015319273,0.00011758112,0.000044817072,0.000119037264],"about_ca_topic_score_codex":0.0000049341234,"about_ca_topic_score_gemma":0.00001605553,"teacher_disagreement_score":0.94950455,"about_ca_system_score_codex":0.00003515557,"about_ca_system_score_gemma":0.000006102866,"threshold_uncertainty_score":0.5146389},"labels":[],"label_agreement":null},{"id":"W4407025032","doi":"10.1109/lra.2025.3537860","title":"Adapting to Frequent Human Direction Changes in Autonomous Frontal Following Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Artificial intelligence; Computer science; Psychology; Computer vision; Physical medicine and rehabilitation; Medicine","score_opus":0.007277385109792957,"score_gpt":0.22550985012409655,"score_spread":0.2182324650143036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407025032","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":[0.89142746,0.0000953032,0.10204047,0.004214185,0.0006992603,0.0002120317,0.0000014198156,0.0007763153,0.0005335759],"genre_scores_gemma":[0.9948109,0.0000074994696,0.0044980356,0.00055717316,0.000034060497,0.000026826478,0.000005029217,0.000015374417,0.000045142253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993097,0.00001763767,0.00021580119,0.00017641352,0.00006252081,0.00021791265],"domain_scores_gemma":[0.9997935,0.000024724017,0.000026153886,0.00011689871,0.000006631595,0.00003205633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001397588,0.00013478893,0.0001741905,0.00029757168,0.00012650833,0.000034366665,0.00007697569,0.000107305284,0.0000032695166],"category_scores_gemma":[0.000007768318,0.00015667826,0.000032718825,0.00020940712,0.000017141301,0.00009432428,0.000021098507,0.00016323336,0.000006218648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016372453,0.000013868195,0.003626818,0.000039655006,0.000042515327,0.000012657038,0.0003564994,0.87859535,0.09924531,0.0012436921,0.0004127088,0.016409302],"study_design_scores_gemma":[0.0009044697,0.000053130694,0.121801116,0.0003735721,0.000053205,0.0000070884744,0.0002000025,0.8536604,0.021192739,0.00045509412,0.0006864204,0.0006127489],"about_ca_topic_score_codex":0.00004064246,"about_ca_topic_score_gemma":0.00034201692,"teacher_disagreement_score":0.11817429,"about_ca_system_score_codex":0.00016297029,"about_ca_system_score_gemma":0.000007201912,"threshold_uncertainty_score":0.6389154},"labels":[],"label_agreement":null},{"id":"W4407168549","doi":"10.1109/lra.2025.3539103","title":"Quantifying Human Mental State in Interactive pHRI: Maintaining Balancing","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council","keywords":"State (computer science); Mental state; Computer science; Human–computer interaction; Psychology; Process management; Business; Applied psychology","score_opus":0.02923871463433123,"score_gpt":0.3111385279413175,"score_spread":0.28189981330698627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407168549","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":[0.96155506,0.000008839548,0.034771807,0.0026601444,0.000593568,0.0001311817,0.0000029309458,0.0000809505,0.00019549648],"genre_scores_gemma":[0.99534816,0.0000047848944,0.0009288936,0.0036417535,0.000022823784,0.000004405897,0.0000019897477,0.000007841084,0.000039332386],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990764,0.00008627188,0.0002506281,0.00027234704,0.000110773944,0.00020357684],"domain_scores_gemma":[0.99960953,0.00017838557,0.00008828646,0.00008815232,0.000009241147,0.000026410466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014965385,0.0001171372,0.000136269,0.00022128396,0.00015364686,0.00017903165,0.000111649424,0.000023602672,0.0000037980801],"category_scores_gemma":[0.00003097996,0.00011588242,0.000032400447,0.00018098029,0.000051031653,0.00027420823,0.000052091505,0.00016164419,0.0000046576342],"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":[0.000007728522,0.00003051798,0.003545115,0.000034317723,0.00000951982,0.000018348255,0.0029221985,0.04384814,0.94520473,0.0013503737,0.00045149477,0.0025775144],"study_design_scores_gemma":[0.001154611,0.000059403206,0.019345311,0.0007901517,0.000011788154,0.000019228053,0.0005492425,0.56277066,0.41377553,0.0009306427,0.0002123417,0.00038107394],"about_ca_topic_score_codex":0.00003497243,"about_ca_topic_score_gemma":0.000026605654,"teacher_disagreement_score":0.53142923,"about_ca_system_score_codex":0.00008009389,"about_ca_system_score_gemma":0.00001031246,"threshold_uncertainty_score":0.4725548},"labels":[],"label_agreement":null},{"id":"W4407690624","doi":"10.1109/lra.2025.3543137","title":"Image-Based Visual Servoing for Enhanced Cooperation of Dual-Arm Manipulation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Visual servoing; Dual (grammatical number); Artificial intelligence; Image manipulation; Computer vision; Computer science; Image (mathematics); Art","score_opus":0.024759405793162825,"score_gpt":0.30454813560580335,"score_spread":0.2797887298126405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407690624","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":[0.49443343,6.7490345e-7,0.502591,0.0023261155,0.00032961654,0.00018604363,0.0000049488854,0.000042492225,0.00008569909],"genre_scores_gemma":[0.9926684,0.0000023190648,0.0051757586,0.0019927637,0.00003547402,0.000017078331,0.000012806597,0.0000073199312,0.000088070716],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937207,0.000038350066,0.0002352382,0.00017008676,0.000086345986,0.000097914366],"domain_scores_gemma":[0.99946654,0.00025358328,0.0001148405,0.00008014957,0.00006676233,0.000018131554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038540387,0.000080634985,0.00010328857,0.00013769399,0.00015594535,0.000070193695,0.00003239153,0.000034763918,0.0000075685452],"category_scores_gemma":[0.00011249773,0.00008349391,0.00003923689,0.00015292328,0.000033213033,0.00024622987,0.000005084288,0.000050658455,0.0000033722586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016924829,0.000038852013,0.000011945534,0.000053151332,0.0000035223438,3.8245992e-7,0.000060423903,0.10967888,0.88832474,0.0010768074,0.00022990596,0.0005044728],"study_design_scores_gemma":[0.00025876163,0.00002464077,0.00019499997,0.000030149566,0.000012004486,6.60787e-7,0.0000125093275,0.41903067,0.5803015,0.00003065689,0.00005616469,0.000047233494],"about_ca_topic_score_codex":0.000005172551,"about_ca_topic_score_gemma":0.0000039623046,"teacher_disagreement_score":0.498235,"about_ca_system_score_codex":0.000027355483,"about_ca_system_score_gemma":0.000022125685,"threshold_uncertainty_score":0.3404783},"labels":[],"label_agreement":null},{"id":"W4407781742","doi":"10.1109/lra.2025.3544033","title":"Rotational Impedance Formulation in a Unified Viewpoint of Lie Algebra","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Geophysics and Sensor Technology","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Algebra over a field; Mathematics; Lie algebra; Pure mathematics","score_opus":0.005751164728554683,"score_gpt":0.2065991050431855,"score_spread":0.2008479403146308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407781742","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":[0.90990984,0.000038578728,0.08784675,0.001788892,0.00013490644,0.0000928801,0.000001792311,0.00007335402,0.00011300398],"genre_scores_gemma":[0.99662775,0.000021730502,0.003119486,0.00019925128,0.00000857702,0.000005401675,0.0000061706332,0.000004932961,0.0000067224205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996073,0.000006280836,0.00018793663,0.00006759631,0.00005200021,0.00007887409],"domain_scores_gemma":[0.9998442,0.000029692479,0.00003043506,0.000069269816,0.000017808256,0.000008605283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003971379,0.000059834932,0.00010042493,0.00014620676,0.000015700103,0.000009223734,0.00003588479,0.00004146417,0.0000013473044],"category_scores_gemma":[0.000006572034,0.00006512015,0.000017539673,0.00018207917,0.000014892539,0.00006128004,0.0000053305057,0.00005957618,0.0000015725152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017180602,0.000010462763,0.00054748566,0.0001009782,0.00001416999,8.0579304e-7,0.000111996356,0.8039286,0.112159595,0.07577145,0.00015105272,0.007201646],"study_design_scores_gemma":[0.0003016509,0.000007316585,0.044677623,0.000058206067,0.0000066076814,6.3308124e-7,0.0000134467355,0.93525636,0.010747079,0.008805008,0.000046950106,0.00007912112],"about_ca_topic_score_codex":0.0000059405725,"about_ca_topic_score_gemma":0.0000059588456,"teacher_disagreement_score":0.13132772,"about_ca_system_score_codex":0.000021504744,"about_ca_system_score_gemma":0.0000053379895,"threshold_uncertainty_score":0.26555225},"labels":[],"label_agreement":null},{"id":"W4407826006","doi":"10.1109/lra.2025.3544491","title":"Adaptive Trajectory Learning With Obstacle Awareness for Motion Planning","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Beijing Nova Program; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Obstacle; Trajectory; Motion (physics); Computer science; Artificial intelligence; Computer vision; Physics; Geography","score_opus":0.021674991594780254,"score_gpt":0.26228597712077634,"score_spread":0.24061098552599608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407826006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031874236,0.00004650779,0.9655218,0.0015990488,0.0004245837,0.00020842509,0.0000012518661,0.00027560611,0.000048519032],"genre_scores_gemma":[0.5306388,0.0000012646251,0.46861938,0.00056883844,0.0000474144,0.000028565823,0.000007462414,0.000010311541,0.000077955316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907964,0.000053700052,0.00017709791,0.00031377852,0.00015379064,0.00022197932],"domain_scores_gemma":[0.9994137,0.00020757833,0.0001112064,0.0001592772,0.00006527993,0.000042941636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019843085,0.00013798974,0.00015798317,0.00016893086,0.00029481735,0.00016459744,0.00018392073,0.000050195413,2.2420764e-7],"category_scores_gemma":[0.000025492944,0.00012890548,0.000028903603,0.00025204,0.00003865248,0.0003416182,0.000025057243,0.00012459101,0.0000014118082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059976433,0.0000125127735,0.00157514,0.000034593137,0.00002722387,0.0000064534797,0.00060593686,0.98534083,0.00086284796,0.0020687168,0.00021649979,0.009243272],"study_design_scores_gemma":[0.0004722576,0.00006898199,0.012784909,0.00015780672,0.00001716848,0.000008318629,0.000072801515,0.98542565,0.0006109375,0.0001648042,0.00005839087,0.00015798754],"about_ca_topic_score_codex":0.000008207313,"about_ca_topic_score_gemma":4.144768e-7,"teacher_disagreement_score":0.49876457,"about_ca_system_score_codex":0.000052839943,"about_ca_system_score_gemma":0.000051028575,"threshold_uncertainty_score":0.5256613},"labels":[],"label_agreement":null},{"id":"W4407948601","doi":"10.1109/lra.2025.3546106","title":"DR-MPC: Deep Residual Model Predictive Control for Real-World Social Navigation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Social Robot Interaction and HRI","field":"Psychology","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":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Model predictive control; Residual; Control (management); Computer science; Artificial intelligence; Algorithm","score_opus":0.024310910207965275,"score_gpt":0.3480323701422947,"score_spread":0.32372145993432944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948601","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":[0.03285604,0.000016204604,0.92970824,0.02985721,0.0018562883,0.0006068537,0.000047517067,0.0002116357,0.0048400373],"genre_scores_gemma":[0.9889604,0.0000037717782,0.001846016,0.006279353,0.0004549181,0.00014919929,0.00006830476,0.000020358397,0.002217716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990396,0.00007737158,0.00030467685,0.00024539576,0.00012346452,0.00020950576],"domain_scores_gemma":[0.99938536,0.00021819063,0.00015639102,0.00009864745,0.000102669925,0.00003876505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016559714,0.00013318351,0.00019507992,0.00016278232,0.0003220464,0.000076584525,0.000073103736,0.000113053815,0.000024351802],"category_scores_gemma":[0.000021304146,0.00014524441,0.00008078504,0.00015002265,0.00007141502,0.00013090325,0.00000802922,0.0001403591,0.000014385222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006318489,0.00023625107,0.0016742276,0.00011429291,0.0006738798,0.0000051665943,0.009386731,0.37826285,0.0075256205,0.33794484,0.2534212,0.010123135],"study_design_scores_gemma":[0.003202532,0.000061642146,0.029269967,0.000059561113,0.00022564763,0.000001637047,0.0006871701,0.9589957,0.0001946287,0.0058169295,0.0011993732,0.00028519923],"about_ca_topic_score_codex":0.00004962648,"about_ca_topic_score_gemma":0.000040218194,"teacher_disagreement_score":0.95610434,"about_ca_system_score_codex":0.00010162119,"about_ca_system_score_gemma":0.000031405707,"threshold_uncertainty_score":0.5922895},"labels":[],"label_agreement":null},{"id":"W4408100255","doi":"10.1109/lra.2025.3547631","title":"GPT-Driven Gestures: Leveraging Large Language Models to Generate Expressive Robot Motion for Enhanced Human-Robot Interaction","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Multimodal Machine Learning Applications","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 Calgary; University of Victoria","funders":"","keywords":"Gesture; Robot; Computer science; Motion (physics); Human–computer interaction; Human–robot interaction; Artificial intelligence; Communication; Psychology","score_opus":0.02232718226525392,"score_gpt":0.3102131524758498,"score_spread":0.28788597021059587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408100255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17805159,0.0000133866215,0.8136369,0.00720894,0.0003084607,0.00047454092,0.0000046345226,0.00024354835,0.000057983776],"genre_scores_gemma":[0.81236285,0.0000022030601,0.18485892,0.0024245298,0.000083422914,0.00015201031,0.000029165361,0.0000127905805,0.00007410864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987704,0.000070696995,0.00028640495,0.00046469248,0.0001492362,0.0002585828],"domain_scores_gemma":[0.9992108,0.00010715279,0.0001567343,0.0003628426,0.000095078736,0.00006739103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016514082,0.00017644756,0.00017598164,0.00025334297,0.0003941834,0.00029933572,0.00031160808,0.00006224907,0.0000017367532],"category_scores_gemma":[0.000025818992,0.00018578417,0.0000576012,0.00025701718,0.000014765781,0.0004754294,0.00009628625,0.0001565562,0.000008185952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019829567,0.000024476189,0.000018291219,0.000026614245,0.000014511158,4.6537124e-7,0.0019719014,0.67867833,0.3105779,0.005145337,0.000324207,0.003215971],"study_design_scores_gemma":[0.00044139658,0.000018743925,0.0021576255,0.000081334416,0.000015577345,0.000001266132,0.00006907151,0.9714866,0.024846353,0.00062782195,0.0000693143,0.00018486813],"about_ca_topic_score_codex":0.00007592809,"about_ca_topic_score_gemma":0.0000097939865,"teacher_disagreement_score":0.63431126,"about_ca_system_score_codex":0.00009215227,"about_ca_system_score_gemma":0.000017538094,"threshold_uncertainty_score":0.75760585},"labels":[],"label_agreement":null},{"id":"W4408222809","doi":"10.1109/lra.2025.3548866","title":"AeroHaptix: A Wearable Vibrotactile Feedback System for Enhancing Collision Avoidance in UAV Teleoperation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Teleoperation; Collision avoidance; Wearable computer; Haptic technology; Computer science; Human–computer interaction; Simulation; Collision; Embedded system; Robot; Artificial intelligence; Computer security","score_opus":0.01013348647966138,"score_gpt":0.24987150793107954,"score_spread":0.23973802145141815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408222809","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":[0.11590711,0.00003718619,0.8779707,0.005133833,0.00023328647,0.0004673465,0.000002653412,0.00009826141,0.00014962631],"genre_scores_gemma":[0.9588031,0.000017222192,0.040148858,0.0008184465,0.000032211818,0.000086148895,0.0000038261865,0.000005591594,0.00008457647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990874,0.000038005564,0.00029807107,0.0002661991,0.00012080567,0.00018951623],"domain_scores_gemma":[0.9994372,0.0001251849,0.00009145712,0.00024397147,0.000060022212,0.00004215387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028662695,0.00010481061,0.00015161908,0.00015427076,0.0001970952,0.0002400917,0.00017835795,0.00005590938,3.4830157e-7],"category_scores_gemma":[0.000036464797,0.00010434822,0.000028402705,0.0004250741,0.00001694015,0.00053857506,0.000030530624,0.0000707475,0.0000069048783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001667798,0.00009768544,0.00018444995,0.00048684678,0.000023294559,0.000002048462,0.0008548669,0.59291,0.28631148,0.09871217,0.0026611136,0.017739352],"study_design_scores_gemma":[0.000474558,0.00003079222,0.0024812545,0.00033091364,0.0000056682134,0.0000030376248,0.00006597212,0.97136045,0.02445962,0.00021303436,0.0004457251,0.00012900609],"about_ca_topic_score_codex":0.00005000864,"about_ca_topic_score_gemma":0.000057922785,"teacher_disagreement_score":0.842896,"about_ca_system_score_codex":0.00013755984,"about_ca_system_score_gemma":0.00005618718,"threshold_uncertainty_score":0.4255197},"labels":[],"label_agreement":null},{"id":"W4408254145","doi":"10.1109/lra.2025.3548501","title":"Decentralized Density Control of Multi-Robot Systems Using PDE-Constrained Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Robot; Control (management); Computer science; Control theory (sociology); Control engineering; Mathematical optimization; Mathematics; Artificial intelligence; Engineering","score_opus":0.009252038360749327,"score_gpt":0.22428596893494476,"score_spread":0.21503393057419543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408254145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02127222,0.00023091878,0.9768716,0.00014620488,0.000765708,0.0004644689,0.000008599303,0.00022666443,0.00001358885],"genre_scores_gemma":[0.9116012,0.000024787783,0.08821879,0.000087846856,0.000023586252,0.000009125049,0.000010592864,0.00001852964,0.000005532943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905276,0.000056787474,0.00045502995,0.00014674592,0.00011277319,0.00017590236],"domain_scores_gemma":[0.9995128,0.00006716125,0.0001374568,0.00014210251,0.000099515906,0.00004098914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012105069,0.00015663455,0.000307477,0.00016445089,0.00007073739,0.00005413308,0.00005994288,0.00008566954,0.0000013998227],"category_scores_gemma":[0.000031604424,0.00017143533,0.000041589323,0.00020227529,0.000043433203,0.00019407138,0.0000059607064,0.000065006345,5.7685463e-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":[0.000005988469,0.0000091339225,0.00032883842,0.00015673024,0.000073777985,0.0000010078654,0.00003376911,0.9304754,0.06830049,0.00040899095,0.000017356555,0.00018847731],"study_design_scores_gemma":[0.0016792511,0.0000044303797,0.0003175321,0.00015226119,0.00007474689,0.000004388898,0.000017381928,0.99660254,0.0009999372,0.000004770652,0.0000064294504,0.0001363576],"about_ca_topic_score_codex":0.000022128264,"about_ca_topic_score_gemma":0.0000021616322,"teacher_disagreement_score":0.890329,"about_ca_system_score_codex":0.000097117765,"about_ca_system_score_gemma":0.00001679502,"threshold_uncertainty_score":0.699093},"labels":[],"label_agreement":null},{"id":"W4408399703","doi":"10.1109/lra.2025.3551067","title":"Robust Nonprehensile Object Transportation With Uncertain Inertial Parameters","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Inertial frame of reference; Object (grammar); Computer science; Artificial intelligence; Physics; Classical mechanics","score_opus":0.018583426637891882,"score_gpt":0.2311739636981385,"score_spread":0.2125905370602466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408399703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07554798,0.000017695276,0.91603786,0.007456293,0.00046222037,0.00017460818,0.000002715299,0.0002444782,0.000056182478],"genre_scores_gemma":[0.40557858,0.000004417407,0.5916349,0.002667786,0.000028226874,0.000015732605,0.000016691725,0.000009469458,0.000044151602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887305,0.000051863328,0.00025209395,0.00035525532,0.00023143974,0.00023627157],"domain_scores_gemma":[0.99939704,0.00011455146,0.00010500521,0.00027233927,0.000052328967,0.000058735106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013492355,0.00016977698,0.00018227252,0.0001858526,0.00014421929,0.00018765769,0.00023549634,0.00005795906,6.0796066e-7],"category_scores_gemma":[0.000011292387,0.00014953851,0.00003424828,0.00044723254,0.00006441679,0.00035945178,0.000010956104,0.00012370486,0.000004756882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005624643,0.000018542729,0.00082384393,0.00003228689,0.000032943866,0.00002679558,0.00041749454,0.9902998,0.0020670516,0.0013293654,0.0013930526,0.0035531768],"study_design_scores_gemma":[0.0005761758,0.000054854896,0.020307558,0.00011924158,0.0000309376,0.000009643664,0.000024658382,0.97721684,0.001288097,0.00009553801,0.000057577516,0.00021890669],"about_ca_topic_score_codex":0.00006275877,"about_ca_topic_score_gemma":0.00000723195,"teacher_disagreement_score":0.33003062,"about_ca_system_score_codex":0.00004316904,"about_ca_system_score_gemma":0.000059623246,"threshold_uncertainty_score":0.60980034},"labels":[],"label_agreement":null},{"id":"W4409129143","doi":"10.1109/lra.2025.3557751","title":"Multi-Robot Reliable Navigation in Uncertain Topological Environments With Graph Attention Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Agency for Science, Technology and Research","keywords":"Computer science; Robot; Topology (electrical circuits); Human–computer interaction; Distributed computing; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.01341694836921523,"score_gpt":0.24448333265965158,"score_spread":0.23106638429043636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409129143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05151936,0.000039582737,0.9440681,0.003711235,0.00035397295,0.00019643056,4.4337548e-7,0.00009595494,0.000014972107],"genre_scores_gemma":[0.51144683,0.000017396218,0.4873654,0.001019199,0.000020812871,0.000020560543,0.000016375043,0.000006322421,0.00008714101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886066,0.000074514865,0.0002659972,0.00036586556,0.00018058946,0.0002523717],"domain_scores_gemma":[0.99953336,0.000060772767,0.000108894616,0.00023712435,0.000014766314,0.00004508415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025236647,0.00014747569,0.00016048121,0.00016405915,0.0001281431,0.00012507445,0.00021507418,0.00008761667,5.8290937e-7],"category_scores_gemma":[0.000009326081,0.00013013897,0.000025305171,0.000429609,0.000073175484,0.00031808243,0.000046946217,0.00017026851,0.0000042489087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002841871,0.000045458084,0.010209708,0.000012005172,0.000011029109,0.000023752697,0.00007232097,0.9851133,0.0022059386,0.0008252652,0.00010016288,0.0013781759],"study_design_scores_gemma":[0.0005534687,0.000030918036,0.11366999,0.00014766598,0.000007947932,0.000008282445,0.000011840946,0.8851838,0.00007522108,0.00016350705,0.000014346397,0.00013297777],"about_ca_topic_score_codex":0.000036673497,"about_ca_topic_score_gemma":0.000001795856,"teacher_disagreement_score":0.45992744,"about_ca_system_score_codex":0.000088649715,"about_ca_system_score_gemma":0.000014561874,"threshold_uncertainty_score":0.5306913},"labels":[],"label_agreement":null},{"id":"W4409426179","doi":"10.1109/lra.2025.3560841","title":"Dual Agent Learning Based Aerial Trajectory Tracking","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Tracking (education); Artificial intelligence; Dual (grammatical number); Computer science; Computer vision; Psychology; Art; Physics","score_opus":0.014006100916398687,"score_gpt":0.23775193750210952,"score_spread":0.22374583658571084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409426179","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":[0.061884873,0.000042667652,0.93115604,0.0043901363,0.0019772744,0.00008245909,0.0000013458529,0.00035413363,0.00011105414],"genre_scores_gemma":[0.94956493,0.000010748024,0.047324575,0.002836494,0.00018116109,0.000004719477,0.000012140809,0.000008231236,0.000057021443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896896,0.000098203556,0.00023958606,0.0002935771,0.00018508753,0.00021456482],"domain_scores_gemma":[0.99945027,0.00015922073,0.00008569033,0.00021857809,0.00003367469,0.000052560234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023205744,0.00012694305,0.0001355285,0.00015027582,0.00027469956,0.00034189704,0.00019187058,0.000063517036,0.0000065697423],"category_scores_gemma":[0.000030536306,0.00012736666,0.000048370683,0.00024488714,0.000039447565,0.0002481847,0.00004138297,0.00019058421,0.0000084481035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003638617,0.000023535502,0.00032363035,0.000025137175,0.0000128856755,0.000014065657,0.0001669082,0.967117,0.0064780745,0.0028980367,0.005534484,0.017402582],"study_design_scores_gemma":[0.00041519266,0.000017437245,0.004797699,0.0000722543,0.000011385374,0.000004133107,0.000010718389,0.9893064,0.0011894186,0.000069712674,0.003945349,0.00016029137],"about_ca_topic_score_codex":0.000007499295,"about_ca_topic_score_gemma":0.0000015689437,"teacher_disagreement_score":0.88768005,"about_ca_system_score_codex":0.000029410436,"about_ca_system_score_gemma":0.000032689903,"threshold_uncertainty_score":0.5193862},"labels":[],"label_agreement":null},{"id":"W4409427391","doi":"10.1109/lra.2025.3560887","title":"A Predictor-Corrector Algorithm for the Fast Determination of the Wrench-Feasible Workspace of Cable-Driven Parallel Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wrench; Workspace; Robot; Predictor–corrector method; Computer science; Algorithm; Parallel manipulator; Engineering; Artificial intelligence; Structural engineering","score_opus":0.0072122565147406505,"score_gpt":0.21174704593576751,"score_spread":0.20453478942102687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409427391","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015943032,0.00007380041,0.995848,0.0009468512,0.0010085779,0.0004304023,0.000016333923,0.00004986119,0.000031841384],"genre_scores_gemma":[0.09291859,0.0000870438,0.90637064,0.0002222995,0.000077042416,0.00006586334,0.000010721462,0.000027576578,0.00022021026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993459,0.000019970747,0.00026198587,0.000104618455,0.0001328623,0.00013467677],"domain_scores_gemma":[0.9994265,0.00018936896,0.00010630395,0.00020145283,0.000055722583,0.000020661622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001096364,0.000114111805,0.00016536909,0.00006547793,0.00008677021,0.000028921517,0.0001530863,0.00006463766,0.0000021846313],"category_scores_gemma":[0.000019514466,0.00007885778,0.00007204315,0.00018595275,0.00005047552,0.00007013125,0.000021188807,0.00007741721,2.4087765e-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":[0.0000017067994,0.000009611689,0.000084980165,0.00010310216,0.00003172475,1.1032359e-7,0.00013904052,0.9807888,0.0022070368,0.0017808583,0.00062368024,0.0142293405],"study_design_scores_gemma":[0.00030336517,0.000013939185,0.0019115292,0.00013108058,0.00006625135,0.0000011607633,0.000042375126,0.99671006,0.00038910934,0.0003365704,0.000020855154,0.000073682815],"about_ca_topic_score_codex":0.000014426821,"about_ca_topic_score_gemma":0.000013223963,"teacher_disagreement_score":0.09132429,"about_ca_system_score_codex":0.00003359054,"about_ca_system_score_gemma":0.000022428978,"threshold_uncertainty_score":0.32157272},"labels":[],"label_agreement":null},{"id":"W4409882786","doi":"10.1109/lra.2025.3565124","title":"Closed-Loop Shape-Forming Control of a Magnetic Soft Continuum Robot","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"European Research Council; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research","keywords":"Loop (graph theory); Control theory (sociology); Physics; Closed loop; Computer science; Control (management); Mathematics; Control engineering; Artificial intelligence; Engineering; Combinatorics","score_opus":0.0057431670927617475,"score_gpt":0.2081402776858209,"score_spread":0.20239711059305918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409882786","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":[0.19003578,0.00037573362,0.80575806,0.0025370424,0.0003796906,0.00030551615,0.000011556885,0.00025903436,0.0003375637],"genre_scores_gemma":[0.9929029,0.00003572901,0.0061552655,0.00072188204,0.000044218144,0.000026365033,0.0000072023104,0.000018860124,0.00008759427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921995,0.000010916778,0.00033858325,0.00014016678,0.000102583595,0.00018779469],"domain_scores_gemma":[0.99956423,0.000116051364,0.00005851656,0.00017293026,0.000043550695,0.00004474082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083746876,0.00014228106,0.00021744007,0.00013497514,0.00007088617,0.000049909107,0.00010284,0.00006501567,0.000012523523],"category_scores_gemma":[0.000015067931,0.00015050852,0.00005371882,0.00020529843,0.000056804816,0.00007489164,0.000011977245,0.00009850678,0.0000072960947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032502774,0.000030259913,0.0011252266,0.00020673215,0.00008741672,0.0000015802812,0.00010745405,0.7826486,0.18835968,0.0036554374,0.0029864518,0.020787913],"study_design_scores_gemma":[0.00069928396,0.000015465517,0.009382461,0.00007998228,0.000088468034,0.0000019425804,0.0000203833,0.98505604,0.0036546502,0.00025930288,0.0005686273,0.0001733833],"about_ca_topic_score_codex":0.000005649338,"about_ca_topic_score_gemma":0.000004051998,"teacher_disagreement_score":0.8028671,"about_ca_system_score_codex":0.000024230172,"about_ca_system_score_gemma":0.000012378084,"threshold_uncertainty_score":0.613756},"labels":[],"label_agreement":null},{"id":"W4410027533","doi":"10.1109/lra.2025.3566610","title":"GNN-Based Decentralized Perception in Multi-Robot Systems for Predicting Worker Actions","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"Perception; Robot; Computer science; Artificial intelligence; Human–computer interaction; Psychology; Neuroscience","score_opus":0.02954542765657481,"score_gpt":0.26676377746963953,"score_spread":0.23721834981306472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410027533","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":[0.10377516,0.00003518504,0.89328223,0.00088063476,0.0010270642,0.0004360746,0.000015453936,0.00032048416,0.0002276963],"genre_scores_gemma":[0.9912441,0.000017029908,0.008327141,0.0001930249,0.000024298492,0.00010402478,0.00003557039,0.000016533755,0.000038313905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930495,0.000015530164,0.00031303216,0.0001082703,0.0000809343,0.00017728955],"domain_scores_gemma":[0.99973273,0.00008921219,0.000033110984,0.00008433033,0.000026913862,0.00003367556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101557045,0.00011303518,0.00012302616,0.00019962879,0.000071412556,0.00014174874,0.000049811708,0.00009355065,0.0000026323019],"category_scores_gemma":[0.000015055036,0.00012586861,0.000036087156,0.00020187705,0.00002043331,0.0002806949,0.0000024490237,0.00011078771,0.0000032139344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032121081,0.000015024835,0.001603202,0.00019344923,0.000015693164,2.6063913e-7,0.00010923202,0.9926345,0.0032784867,0.00013886162,0.00077582133,0.0012322797],"study_design_scores_gemma":[0.000867732,0.000003608935,0.008972632,0.0002748167,0.000016045604,8.449688e-7,0.00018622141,0.9888857,0.00021011409,0.0000056391928,0.0004651729,0.00011147203],"about_ca_topic_score_codex":0.000009410707,"about_ca_topic_score_gemma":0.0000061043015,"teacher_disagreement_score":0.8874689,"about_ca_system_score_codex":0.00013360217,"about_ca_system_score_gemma":0.000013890445,"threshold_uncertainty_score":0.5132773},"labels":[],"label_agreement":null},{"id":"W4410428188","doi":"10.1109/lra.2025.3570948","title":"Sensor Query Schedule and Sensor Noise Covariances for Accuracy-Constrained Trajectory Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Noise (video); Schedule; Computer science; Estimation; Algorithm; Real-time computing; Artificial intelligence; Engineering; Physics","score_opus":0.008049005393917142,"score_gpt":0.22863694309732335,"score_spread":0.2205879377034062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410428188","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":[0.39262345,0.000099295896,0.6024949,0.003757275,0.0002571305,0.00024005114,0.000013630073,0.00044965753,0.00006461964],"genre_scores_gemma":[0.92118907,0.000038158305,0.07816989,0.0004945068,0.000030457342,0.000025377645,0.00001279675,0.000012808202,0.00002690811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994075,0.000014772352,0.00021730996,0.00015654985,0.000045294444,0.0001585787],"domain_scores_gemma":[0.9996046,0.00019216361,0.00004521183,0.00010770189,0.00002177301,0.000028542221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011922092,0.00013158191,0.00016270307,0.00012298876,0.00013702642,0.00004391801,0.000047064048,0.00012390323,0.0000022745614],"category_scores_gemma":[0.000046633748,0.00013898252,0.000028946428,0.00009489386,0.00010068579,0.00015473616,0.000007700391,0.00010889834,0.0000023818538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014405123,0.00001738253,0.0004026825,0.0003894001,0.00010918988,0.0000033279632,0.0001818467,0.8738177,0.08590794,0.0071772565,0.00078209187,0.031196821],"study_design_scores_gemma":[0.00053457625,0.0000125796505,0.00441069,0.000055165026,0.000041581785,0.000008040164,0.000053381336,0.9866631,0.0074406997,0.0004036804,0.00021703223,0.00015949496],"about_ca_topic_score_codex":0.000002095732,"about_ca_topic_score_gemma":0.000002361083,"teacher_disagreement_score":0.52856565,"about_ca_system_score_codex":0.0000316802,"about_ca_system_score_gemma":0.000018013005,"threshold_uncertainty_score":0.5667543},"labels":[],"label_agreement":null},{"id":"W4411086218","doi":"10.1109/lra.2025.3577456","title":"Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computer vision; Artificial intelligence; Mobile robot; Mobile robot navigation; Robot; Human–computer interaction; Robot control","score_opus":0.014094567093177877,"score_gpt":0.27743394928512494,"score_spread":0.2633393821919471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411086218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049120978,0.000117206946,0.9492437,0.0005589426,0.00037646422,0.00025671927,0.0000035205644,0.0002297906,0.000092633185],"genre_scores_gemma":[0.29868495,0.0000027661463,0.7004181,0.0007256648,0.0000382394,0.000014835505,0.000022159653,0.000009898822,0.00008339246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869436,0.00006761067,0.00029950603,0.00041412291,0.00026645206,0.0002579544],"domain_scores_gemma":[0.9993304,0.000039913488,0.00013348785,0.00038033066,0.000051564955,0.00006427291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027125707,0.0001820628,0.00019940868,0.00021888228,0.00037159122,0.00063331163,0.00031596445,0.00008998953,3.0724874e-7],"category_scores_gemma":[0.000011817766,0.00018021461,0.00004900473,0.00042137067,0.00008860799,0.000546322,0.00009573938,0.00014990752,0.000005112528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012055632,0.000033575943,0.000030599123,0.000054856268,0.0000134197835,0.0000065224967,0.0016793513,0.9560839,0.03677041,0.0012183866,0.0004511935,0.0036565778],"study_design_scores_gemma":[0.0003273432,0.000020935224,0.00014697958,0.00014711094,0.00001815922,0.000017744562,0.00004718974,0.9970015,0.0017707163,0.00030431736,0.000013622281,0.00018435236],"about_ca_topic_score_codex":0.000023117696,"about_ca_topic_score_gemma":1.6963824e-7,"teacher_disagreement_score":0.24956398,"about_ca_system_score_codex":0.00008568169,"about_ca_system_score_gemma":0.00007230394,"threshold_uncertainty_score":0.7348939},"labels":[],"label_agreement":null},{"id":"W4412076485","doi":"10.1109/lra.2025.3585713","title":"SICNav-Diffusion: Safe and Interactive Crowd Navigation With Diffusion Trajectory Predictions","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Evacuation and Crowd Dynamics","field":"Engineering","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":"Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Diffusion; Trajectory; Computer science; Statistical physics; Physics","score_opus":0.003921955880888896,"score_gpt":0.2071716388555527,"score_spread":0.2032496829746638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412076485","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":[0.5992289,0.000041449734,0.3985309,0.0011856445,0.0003040349,0.00013464513,0.0000061818328,0.00020356498,0.00036467263],"genre_scores_gemma":[0.9960425,0.00006816638,0.0031601803,0.00047578922,0.00004341153,0.000015896734,0.000039226827,0.000015580865,0.00013923776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942183,0.000020293664,0.00018219123,0.00014960558,0.00011291381,0.000113165814],"domain_scores_gemma":[0.9997132,0.0000617619,0.00003977065,0.00010082014,0.00003692649,0.000047508678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005425278,0.00013301283,0.00011597857,0.00013264178,0.00015075822,0.00008901107,0.000037766444,0.000060710427,0.0000073064766],"category_scores_gemma":[0.000005990437,0.00012405502,0.000019756018,0.00016440461,0.000054040447,0.00022308156,0.000011811858,0.0001403149,0.0000028310496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025719983,0.000074290794,0.0029322081,0.00025600562,0.000116952135,0.000006153623,0.0015485361,0.9095974,0.06746106,0.0021805307,0.002364228,0.013436898],"study_design_scores_gemma":[0.0005360689,0.000028590577,0.036047548,0.0001942545,0.00004567557,0.000009782422,0.00014474556,0.9617958,0.0005104187,0.000068158115,0.00046854568,0.0001503871],"about_ca_topic_score_codex":0.000006157097,"about_ca_topic_score_gemma":0.00001365314,"teacher_disagreement_score":0.3968136,"about_ca_system_score_codex":0.000064246975,"about_ca_system_score_gemma":0.000011602788,"threshold_uncertainty_score":0.50588167},"labels":[],"label_agreement":null},{"id":"W4412079944","doi":"10.1109/lra.2025.3585384","title":"HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","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":"Huawei Technologies (Canada); York University","funders":"","keywords":"Prior probability; Image (mathematics); Shot (pellet); Artificial intelligence; Computer vision; Zero (linguistics); Computer science; Mathematics; Bayesian probability; Chemistry; Philosophy","score_opus":0.012855399171569384,"score_gpt":0.2682594775499806,"score_spread":0.2554040783784112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412079944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013621851,0.00003323554,0.9788689,0.005977365,0.00016140947,0.0004022967,0.000014317294,0.0007311855,0.00018944436],"genre_scores_gemma":[0.30255246,0.0000063303273,0.6961267,0.0010612956,0.00003027714,0.00011468694,0.00002285571,0.0000266626,0.00005870952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928623,0.000006258879,0.0002464672,0.00018267453,0.000088661836,0.00018967771],"domain_scores_gemma":[0.99957085,0.000038667888,0.000044862823,0.00023764235,0.000062130464,0.000045853365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114649825,0.00014727814,0.00013787395,0.00016769978,0.00018764625,0.00022131512,0.00014780299,0.000061319595,0.000001079747],"category_scores_gemma":[0.000031809916,0.00016111784,0.000035592417,0.00020499891,0.00003083883,0.00025397155,0.000026577676,0.00008335365,0.000003313826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002145082,0.000010821931,0.000004025789,0.00021388319,0.000010852118,3.2658411e-7,0.00012042622,0.884842,0.0799417,0.001650827,0.015074936,0.018128091],"study_design_scores_gemma":[0.00020172058,0.0000058170945,0.0000720092,0.00011817704,0.000033650398,0.0000016049466,0.0000051433835,0.9819464,0.012111164,0.005087036,0.0002505885,0.00016666035],"about_ca_topic_score_codex":0.000002408083,"about_ca_topic_score_gemma":6.899078e-7,"teacher_disagreement_score":0.28893062,"about_ca_system_score_codex":0.000064224696,"about_ca_system_score_gemma":0.00002051586,"threshold_uncertainty_score":0.6570195},"labels":[],"label_agreement":null},{"id":"W4412605582","doi":"10.1109/lra.2025.3592140","title":"Towards Fast Correspondence-Free Odometry Using Multiple FMCW Lidars","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","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":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Odometry; Lidar; Artificial intelligence; Computer science; Remote sensing; Computer vision; Environmental science; Geology; Robot","score_opus":0.013633178606470365,"score_gpt":0.2648733642323391,"score_spread":0.25124018562586875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412605582","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":[0.37263063,0.00001515794,0.62422365,0.002334213,0.00031366321,0.000089105495,0.000008668523,0.00014245705,0.00024247495],"genre_scores_gemma":[0.8525463,0.0000015963476,0.14693753,0.0003686559,0.000054094577,0.00000253571,0.000006156038,0.000010850211,0.00007231932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991881,0.000021138265,0.00021151995,0.0002232592,0.000129915,0.00022601655],"domain_scores_gemma":[0.9994303,0.00012648123,0.00008561389,0.00028880886,0.0000315839,0.000037205995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006741587,0.00014947866,0.00017714483,0.00016884215,0.00016654952,0.000102315214,0.00017369402,0.000054676246,0.0000086543605],"category_scores_gemma":[0.00006949319,0.00014568851,0.00005069409,0.00033912002,0.00013824139,0.0001562885,0.00010443535,0.00015952869,0.0000065952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048084927,0.00019224313,0.05232532,0.0000833132,0.0002570925,0.000016911841,0.00028237957,0.43538886,0.15617687,0.069862686,0.0056692013,0.27969703],"study_design_scores_gemma":[0.0011986274,0.000029975252,0.01200787,0.00020808908,0.00007962239,0.000002797942,0.0002926891,0.9403693,0.024121122,0.02043242,0.00077075174,0.00048670673],"about_ca_topic_score_codex":0.000032627806,"about_ca_topic_score_gemma":9.130199e-7,"teacher_disagreement_score":0.50498044,"about_ca_system_score_codex":0.00004521563,"about_ca_system_score_gemma":0.00002501741,"threshold_uncertainty_score":0.59410053},"labels":[],"label_agreement":null},{"id":"W4413104756","doi":"10.1109/lra.2025.3597866","title":"Globally Optimal Data-Association-Free Landmark-Based Localization Using Semidefinite Relaxations","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Association (psychology); Data association; Computer science; Mathematics; Artificial intelligence; Psychology","score_opus":0.018585317525305182,"score_gpt":0.24701985287310607,"score_spread":0.2284345353478009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413104756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040740613,0.000073063056,0.95623344,0.001567378,0.00036706435,0.000112931986,0.000034401422,0.00060621795,0.0002649013],"genre_scores_gemma":[0.8916166,0.000041758514,0.10568012,0.002321979,0.00008088299,0.0000046956684,0.00020145485,0.000025711104,0.00002679504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992954,0.000030423931,0.00022196634,0.00015634706,0.00015399425,0.00014184904],"domain_scores_gemma":[0.9993766,0.00009153971,0.00007794547,0.00036586294,0.00006203449,0.000026051799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014501518,0.00011452696,0.00012462324,0.00012899116,0.00012569767,0.00013592074,0.00018092932,0.000081519065,0.000002749892],"category_scores_gemma":[0.00007298866,0.0001277049,0.000023065642,0.00024341344,0.000017835635,0.00018527645,0.000041913387,0.00008529318,0.000002391191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013095467,0.000007028093,0.0016355242,0.000019555022,0.000039044833,0.0000013464952,0.00001659663,0.9631397,0.0037179403,0.00041381273,0.03072374,0.00028440024],"study_design_scores_gemma":[0.00023826225,0.0000035032865,0.0011670671,0.00010394234,0.00006419033,9.961793e-7,0.0000046645187,0.99540126,0.0013981903,0.00019991171,0.0012914637,0.0001265702],"about_ca_topic_score_codex":0.000024204362,"about_ca_topic_score_gemma":0.00001054056,"teacher_disagreement_score":0.850876,"about_ca_system_score_codex":0.00012560647,"about_ca_system_score_gemma":0.000025475465,"threshold_uncertainty_score":0.5207655},"labels":[],"label_agreement":null},{"id":"W4413344155","doi":"10.1109/lra.2025.3600145","title":"Clarke Coordinates are Generalized Improved State Parametrization for Continuum Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parametrization (atmospheric modeling); Generalized coordinates; Robot; Mathematics; Action-angle coordinates; State (computer science); Computer science; Classical mechanics; Applied mathematics; Physics; Artificial intelligence; Mathematical analysis; Algorithm; Quantum mechanics","score_opus":0.0052208887605070965,"score_gpt":0.20525325313245987,"score_spread":0.20003236437195276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413344155","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":[0.11991633,0.00014016307,0.87599987,0.00229997,0.00096633687,0.0004212273,0.000018293495,0.00021326353,0.000024551973],"genre_scores_gemma":[0.9931932,0.00003560132,0.0056398096,0.0007573313,0.0000589711,0.00006469386,0.000028475344,0.000022673254,0.00019920582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928844,0.00002024427,0.000289323,0.00014856893,0.00006799352,0.00018542736],"domain_scores_gemma":[0.99963135,0.00008564756,0.00007810975,0.000106594365,0.000056421126,0.00004185841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001488794,0.00013307919,0.00022832693,0.000104537,0.00007009067,0.0001361674,0.0000701587,0.00006823784,9.2493025e-7],"category_scores_gemma":[0.000028729582,0.00013125468,0.00005670827,0.00014435478,0.000012300866,0.00008852198,0.000008052403,0.00006109208,0.0000012811425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013672692,0.000013939122,0.00018740226,0.00021202546,0.00012299768,8.647187e-7,0.000023853074,0.8558843,0.1307475,0.002567813,0.003521395,0.0067042536],"study_design_scores_gemma":[0.0010442265,0.000013570274,0.0009413939,0.000058597772,0.00003292425,4.485531e-7,0.0000064927326,0.99478096,0.0015139183,0.000773076,0.00068846345,0.00014593487],"about_ca_topic_score_codex":0.00000807575,"about_ca_topic_score_gemma":0.000018805595,"teacher_disagreement_score":0.8732769,"about_ca_system_score_codex":0.000049235994,"about_ca_system_score_gemma":0.0000065775603,"threshold_uncertainty_score":0.53524107},"labels":[],"label_agreement":null},{"id":"W4413925460","doi":"10.1109/lra.2025.3604732","title":"Minimum-Length Coverage Path Planning for Grid Environments With Approximation Guarantees","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Grid; Path length; Path (computing); Computer science; Motion planning; Mathematical optimization; Mathematics; Computer network; Artificial intelligence; Geometry","score_opus":0.010804377827114938,"score_gpt":0.23438241739813143,"score_spread":0.2235780395710165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413925460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025113383,0.000044515502,0.97109896,0.0026647279,0.00051994936,0.00035603627,0.000006993664,0.00013478192,0.000060634557],"genre_scores_gemma":[0.34019932,0.000011127473,0.6571668,0.0022812863,0.00010112352,0.00006368575,0.000039174367,0.000016646442,0.000120885314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891573,0.00003752011,0.0002452279,0.00034794115,0.00021467856,0.00023888743],"domain_scores_gemma":[0.9993763,0.00015892384,0.00014659198,0.000257773,0.000017997998,0.000042414744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019832345,0.00016912217,0.00017908675,0.00014378467,0.00020421889,0.00020307829,0.00024085997,0.000054106775,3.2924208e-7],"category_scores_gemma":[0.000017953786,0.00015133958,0.000031579395,0.00016625701,0.000042306336,0.00039965467,0.00003565241,0.000086325934,0.000003234354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014698871,0.000059209906,0.0012466139,0.00011994742,0.000074589094,0.000016514923,0.0011100181,0.97698617,0.0055015306,0.0032016563,0.0033046503,0.008364407],"study_design_scores_gemma":[0.000817321,0.000065212975,0.0052347835,0.00017403757,0.00002217664,0.000009183806,0.000021363798,0.99197817,0.00083832437,0.00029509253,0.00035359644,0.00019076133],"about_ca_topic_score_codex":0.0000027640283,"about_ca_topic_score_gemma":7.805342e-8,"teacher_disagreement_score":0.31508595,"about_ca_system_score_codex":0.000053154818,"about_ca_system_score_gemma":0.000025358846,"threshold_uncertainty_score":0.6171449},"labels":[],"label_agreement":null},{"id":"W4414110606","doi":"10.1109/lra.2025.3608649","title":"Development of a Stick-Slip Dielectric Elastomer Actuator for Robotic Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Dielectric materials and actuators","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Actuator; Elastomer; Dielectric; Dielectric elastomers; Interface (matter); Rotation (mathematics); Face (sociological concept); Transmission (telecommunications)","score_opus":0.0067025212026494115,"score_gpt":0.21850400213086413,"score_spread":0.2118014809282147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414110606","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":[0.10409213,0.0000740659,0.89475155,0.00021584656,0.00025351188,0.00036445426,0.0000027687825,0.00012268405,0.00012298339],"genre_scores_gemma":[0.9480501,0.000025365882,0.051499058,0.0001703459,0.000041606338,0.0001486957,0.000012128545,0.000016572709,0.000036136338],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993398,0.0000054717784,0.00029739857,0.00011510905,0.0000753373,0.00016688318],"domain_scores_gemma":[0.99971694,0.000072890536,0.000051598116,0.000094573996,0.00003132715,0.000032649576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079811776,0.00011489942,0.00017495263,0.00017570205,0.00008496571,0.000035730765,0.000071081005,0.000044318112,0.0000046271007],"category_scores_gemma":[0.000010937784,0.00011370853,0.000030408917,0.0002371608,0.000016178818,0.00005456346,0.0000075529597,0.00003850234,0.000003532186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011273179,0.000084540494,0.000038296563,0.0014156849,0.0002837788,3.9866666e-7,0.00047310282,0.33590704,0.5367443,0.009537713,0.004449407,0.11105447],"study_design_scores_gemma":[0.0010564796,0.00003590457,0.0025167658,0.00016305539,0.00018677757,0.0000020152454,0.000042863947,0.79834807,0.17855076,0.0005876425,0.01792795,0.00058171025],"about_ca_topic_score_codex":6.8463453e-7,"about_ca_topic_score_gemma":9.696912e-7,"teacher_disagreement_score":0.84395796,"about_ca_system_score_codex":0.000042013253,"about_ca_system_score_gemma":0.00003060187,"threshold_uncertainty_score":0.46368992},"labels":[],"label_agreement":null},{"id":"W4414603437","doi":"10.1109/lra.2025.3615522","title":"AORRTC: Almost-Surely Asymptotically Optimal Planning With RRT-Connect","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"AI-based Problem Solving and Planning","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satisficing; Asymptotically optimal algorithm; Convergence (economics); Probabilistic logic; Motion planning; Selection (genetic algorithm); Stability theory","score_opus":0.008647812944369643,"score_gpt":0.23274600963690012,"score_spread":0.22409819669253048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414603437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07995319,0.000082852996,0.90508527,0.013693104,0.00030568644,0.000114560855,0.0000014485104,0.0002957754,0.0004681245],"genre_scores_gemma":[0.7673452,0.000002604291,0.22774611,0.0047256383,0.000048904585,0.000007443377,0.0000066006446,0.000010810231,0.000106702784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882716,0.00006287296,0.00023750105,0.00035691744,0.00021431698,0.00030123978],"domain_scores_gemma":[0.9992287,0.00026970237,0.00009879372,0.00026790856,0.00005550731,0.00007936287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002924677,0.0001804516,0.00018514902,0.00016104497,0.00027321393,0.00037229728,0.0003020578,0.00007185078,0.0000023062573],"category_scores_gemma":[0.00002774397,0.00015760034,0.000033745782,0.00028373883,0.000053858384,0.00033753723,0.000053946205,0.00020023939,0.000009424702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012413465,0.000022120978,0.0039039808,0.00006297796,0.00006351382,0.00005135585,0.0007024407,0.9678552,0.0018351701,0.016683266,0.004235899,0.0045716283],"study_design_scores_gemma":[0.00051080517,0.0001002895,0.006716248,0.0003618499,0.00002870155,0.000031524985,0.000020351765,0.9902535,0.00077280094,0.00028723184,0.00061308726,0.0003035818],"about_ca_topic_score_codex":0.000009884928,"about_ca_topic_score_gemma":6.9595876e-7,"teacher_disagreement_score":0.687392,"about_ca_system_score_codex":0.000028763878,"about_ca_system_score_gemma":0.000076625314,"threshold_uncertainty_score":0.6426756},"labels":[],"label_agreement":null},{"id":"W4415353261","doi":"10.1109/lra.2025.3623436","title":"Breaking the Static Assumption: A Dynamic-Aware LIO Framework via Spatio-Temporal Normal Analysis","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Simulation Techniques and Applications","field":"Decision Sciences","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":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Consistency (knowledge bases); Point cloud; Point (geometry); Face (sociological concept); Perspective (graphical); Identification (biology); Dynamic data; Dependency (UML)","score_opus":0.024011100046561006,"score_gpt":0.3512024607141185,"score_spread":0.3271913606675575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415353261","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":[0.056333806,0.00011677518,0.8844308,0.057555165,0.0005827721,0.0006820288,0.000050238144,0.00016897665,0.00007938699],"genre_scores_gemma":[0.9673268,0.00007158297,0.025899515,0.006022806,0.000102601116,0.000069315385,0.00008088347,0.000020679478,0.00040581464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99568367,0.0003686797,0.0015761899,0.0008236579,0.0011024546,0.00044532717],"domain_scores_gemma":[0.9954795,0.0019556158,0.0008957451,0.0011222169,0.0004111618,0.00013573277],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001578332,0.00040338622,0.00059194595,0.0010804045,0.0014981295,0.0016064944,0.000747819,0.00026613212,0.0003471631],"category_scores_gemma":[0.00019248825,0.00032123236,0.000380447,0.00508586,0.00034615688,0.000570591,0.00015675179,0.00048019862,0.00006828379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020857053,0.000116775955,0.01520546,0.00006004304,0.0006039009,0.000004375185,0.001110895,0.9113417,0.00016431088,0.008599715,0.0039859256,0.058786],"study_design_scores_gemma":[0.00022724127,0.000023780438,0.040451355,0.0001266535,0.0008050788,0.0000032008845,0.00024336443,0.945468,0.00006134438,0.009892152,0.0023708828,0.0003269541],"about_ca_topic_score_codex":0.00012758018,"about_ca_topic_score_gemma":0.00011919241,"teacher_disagreement_score":0.910993,"about_ca_system_score_codex":0.00016410598,"about_ca_system_score_gemma":0.000118116346,"threshold_uncertainty_score":0.999924},"labels":[],"label_agreement":null},{"id":"W4415593722","doi":"10.1109/lra.2025.3626250","title":"Accuracy/Stability Trade-Off and Hybrid Impedance and Admittance Control for Haptic Devices","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Teleoperation and Haptic Systems","field":"Engineering","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":"Admittance; Electrical impedance; Haptic technology; Parametric statistics; Control theory (sociology); Stability (learning theory); Impedance control; Sensitivity (control systems)","score_opus":0.01136786530971479,"score_gpt":0.2380991601726061,"score_spread":0.2267312948628913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415593722","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":[0.49604696,0.007367305,0.47869158,0.015131613,0.0012574818,0.0012352194,0.00007370044,0.00015797913,0.000038166418],"genre_scores_gemma":[0.99424994,0.0006698152,0.0026058878,0.0021956563,0.00015575164,0.00006745029,0.0000067652973,0.000026764954,0.000021972646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982824,0.000076563265,0.00068983366,0.0004628408,0.0001474891,0.00034089174],"domain_scores_gemma":[0.9986674,0.00073895144,0.00016374802,0.00023608058,0.000055920482,0.00013789612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039403743,0.00033902688,0.00050141,0.00012117574,0.00030813902,0.00046262683,0.00009248618,0.00010891117,0.0000063248294],"category_scores_gemma":[0.00012859883,0.00035763776,0.00006390762,0.000120151795,0.00014440888,0.00040496088,0.000015973079,0.00015751013,0.000001651613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002576794,0.000252955,0.019649712,0.017628036,0.0016487977,0.00002324233,0.006643446,0.32729927,0.1764593,0.025992261,0.0076546944,0.4164906],"study_design_scores_gemma":[0.001991068,0.00004625326,0.016574234,0.0003504512,0.00017479049,0.00001807087,0.00014372656,0.97691154,0.00094768096,0.000087419845,0.002396305,0.0003584344],"about_ca_topic_score_codex":0.000010236217,"about_ca_topic_score_gemma":0.00002852676,"teacher_disagreement_score":0.6496123,"about_ca_system_score_codex":0.00006740269,"about_ca_system_score_gemma":0.000045601457,"threshold_uncertainty_score":0.9998876},"labels":[],"label_agreement":null},{"id":"W4415748036","doi":"10.1109/lra.2025.3627071","title":"Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Quantum machine learning; Kinematics; Oracle; Robotics; Parameterized complexity; Quantum; Quantum algorithm; Robot; Dimensionality reduction","score_opus":0.009307229253889743,"score_gpt":0.23742717849078998,"score_spread":0.22811994923690024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415748036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014688356,0.001185312,0.9755684,0.005682056,0.002119445,0.0005877995,0.000015020013,0.00014769153,0.0000059239087],"genre_scores_gemma":[0.53023595,0.0004237072,0.46814734,0.0008360084,0.0001859784,0.000013396093,0.000034089127,0.000048719605,0.00007483176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972655,0.00019291483,0.00089786644,0.0007900631,0.00033869492,0.000514918],"domain_scores_gemma":[0.99832636,0.0004465233,0.0005941045,0.0003152736,0.00017843833,0.00013927053],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057085196,0.00046220585,0.00065473374,0.0005175879,0.000723194,0.0004384705,0.00033477004,0.0001901569,0.000002758827],"category_scores_gemma":[0.000080120604,0.00047397972,0.00015333631,0.0006601736,0.00021806956,0.00033151297,0.00021378527,0.0003764462,7.503375e-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":[0.0000071122386,0.000062793326,0.00022440404,0.00040069493,0.00010652998,0.0000037070997,0.0004307171,0.8976154,0.0004827488,0.008871829,0.00009469593,0.09169939],"study_design_scores_gemma":[0.0010817844,0.00024967355,0.00074092177,0.0005340135,0.00014592944,0.000021884982,0.000035336307,0.99542314,0.00020093625,0.0010262382,0.00010344771,0.00043667454],"about_ca_topic_score_codex":0.00005916217,"about_ca_topic_score_gemma":0.0000018426401,"teacher_disagreement_score":0.5155476,"about_ca_system_score_codex":0.000053358435,"about_ca_system_score_gemma":0.00008540885,"threshold_uncertainty_score":0.9997712},"labels":[],"label_agreement":null},{"id":"W4416214734","doi":"10.1109/lra.2025.3632604","title":"VOCALoco: Viability-Optimized Cost-Aware Adaptive Locomotion","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Tree traversal; Robustness (evolution); Robot; Modular design; Set (abstract data type); Quadrupedalism; Reinforcement learning; Mobile robot","score_opus":0.007599420582272312,"score_gpt":0.21359146463824577,"score_spread":0.20599204405597346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214734","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":[0.008826254,0.000037351212,0.98352474,0.005141314,0.0008009996,0.00055482535,0.000004749392,0.000523939,0.0005858143],"genre_scores_gemma":[0.9894602,0.000023404697,0.008737687,0.0015036105,0.00006370449,0.00006519634,0.000015667727,0.000020162886,0.000110370005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910384,0.00005654728,0.00030121105,0.00019540667,0.00012960639,0.0002133909],"domain_scores_gemma":[0.9995685,0.000081814884,0.000041081414,0.00019003234,0.000050629398,0.00006791874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014519258,0.00018029929,0.00022565442,0.00014522056,0.0001053341,0.000086337415,0.00008825238,0.000090402646,0.000025083527],"category_scores_gemma":[0.00001284369,0.00018282662,0.00006612342,0.00020351824,0.00005770057,0.0001641504,0.000013161664,0.00015519898,0.00003003602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069059174,0.000022887805,0.000080066224,0.000057053527,0.000056821023,0.0000019783008,0.000074010524,0.9826747,0.0017935149,0.0014863333,0.0035011817,0.010244558],"study_design_scores_gemma":[0.0010716317,0.000013294246,0.00207438,0.000058195852,0.00004069358,0.0000021666938,0.00003466445,0.99512887,0.00060072285,0.00027453445,0.0005233635,0.00017748846],"about_ca_topic_score_codex":0.0000057687994,"about_ca_topic_score_gemma":0.0000029488392,"teacher_disagreement_score":0.9806339,"about_ca_system_score_codex":0.000107767104,"about_ca_system_score_gemma":0.000014857689,"threshold_uncertainty_score":0.7455453},"labels":[],"label_agreement":null},{"id":"W4416214740","doi":"10.1109/lra.2025.3632747","title":"Fine-Grained Classification for Depth Estimation From Monocular Microscopy for Robotic Micromanipulation of Motile Cells","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Image Processing Techniques and Applications","field":"Engineering","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":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Monocular; Focus (optics); Feature (linguistics); Pipette; Discriminative model; Generalization; Pattern recognition (psychology); Sperm cell","score_opus":0.019198290124920945,"score_gpt":0.27909751299870006,"score_spread":0.2598992228737791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022029836,0.00032512547,0.970666,0.004183424,0.0005234095,0.001913078,0.00011993613,0.0002252243,0.000013968277],"genre_scores_gemma":[0.49968034,0.0000301604,0.4993855,0.00015611043,0.000060094593,0.00022558687,0.00039135336,0.000039506875,0.00003135518],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982157,0.000028050317,0.00088924577,0.00044826276,0.00012747468,0.0002913119],"domain_scores_gemma":[0.9986807,0.00024727106,0.00042182804,0.00036467216,0.00023251672,0.00005300988],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021174301,0.00032878312,0.00042244923,0.00028447734,0.00027713977,0.00020886707,0.0001897612,0.00023477044,0.000002691792],"category_scores_gemma":[0.00005000265,0.00039787806,0.00015896416,0.00032558807,0.00008917366,0.0002815046,0.00002071469,0.00011511339,0.0000020151424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128093025,0.000048716094,0.000028697272,0.0007823348,0.00006154841,5.380358e-8,0.00012442768,0.49320775,0.49353054,0.0013486142,0.0021724727,0.008682034],"study_design_scores_gemma":[0.0006608302,0.000031062504,0.00093975494,0.00034997906,0.00030031046,3.0477787e-7,0.00001908715,0.76105887,0.23308101,0.0031565833,0.00016531297,0.00023687357],"about_ca_topic_score_codex":0.000028464501,"about_ca_topic_score_gemma":0.0000040513146,"teacher_disagreement_score":0.4776505,"about_ca_system_score_codex":0.0001387266,"about_ca_system_score_gemma":0.00005452651,"threshold_uncertainty_score":0.9998473},"labels":[],"label_agreement":null},{"id":"W4416214752","doi":"10.1109/lra.2025.3632610","title":"Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Solver; Robot; Model predictive control; Dimension (graph theory); Controller (irrigation); Code (set theory); Control (management); Legged robot; State (computer science)","score_opus":0.008644447005847508,"score_gpt":0.2360870953339175,"score_spread":0.22744264832806999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416214752","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":[0.057121933,0.00076834677,0.92028433,0.018514687,0.0013412401,0.0014854718,0.000012147431,0.00025672175,0.0002151107],"genre_scores_gemma":[0.99126595,0.00019974478,0.0055374987,0.0024291137,0.00013674247,0.00008887298,0.000020708254,0.000048575694,0.00027279486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977941,0.0001515626,0.00082556874,0.00048238624,0.00018233353,0.0005640524],"domain_scores_gemma":[0.99910086,0.00034768585,0.00016474602,0.000156435,0.00010181427,0.00012845077],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043836588,0.00042680226,0.0006452834,0.00042409124,0.0002874799,0.00041726537,0.00009541724,0.00024667446,0.00001880204],"category_scores_gemma":[0.000091508635,0.00048243397,0.00009598103,0.00038006093,0.000102172475,0.00030772123,0.00002535382,0.0004408281,0.0000055792507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005548685,0.00005555884,0.0012901445,0.00042255508,0.00018098362,0.000008189795,0.00036384596,0.9442821,0.040052045,0.0028943797,0.00045087872,0.009943845],"study_design_scores_gemma":[0.0074263243,0.00007463883,0.0095697455,0.00034733355,0.00016039604,0.000006945062,0.000115237504,0.98073024,0.0004403925,0.00013235485,0.00055059936,0.00044581844],"about_ca_topic_score_codex":0.000020487927,"about_ca_topic_score_gemma":0.000013694538,"teacher_disagreement_score":0.934144,"about_ca_system_score_codex":0.0001492501,"about_ca_system_score_gemma":0.00005987768,"threshold_uncertainty_score":0.9997627},"labels":[],"label_agreement":null},{"id":"W4416366450","doi":"10.1109/lra.2025.3634882","title":"Decentralized Swarm Control Via SO(3) Embeddings for 3D Trajectories","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exploit; Position (finance); Stability (learning theory); Simple (philosophy); Adaptability; Controller (irrigation); Control theory (sociology); Range (aeronautics); Class (philosophy); Trajectory","score_opus":0.009679145705331389,"score_gpt":0.25095134876824954,"score_spread":0.24127220306291816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416366450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076133865,0.00071930984,0.9561935,0.026122648,0.006904573,0.0019848587,0.000115791154,0.00031999793,0.000025910875],"genre_scores_gemma":[0.9567818,0.00006341126,0.0360879,0.0063714487,0.00027859106,0.00016945602,0.000035827234,0.00003525118,0.00017633733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962133,0.00022924082,0.0012102023,0.0009271886,0.0004813933,0.0009386524],"domain_scores_gemma":[0.99760383,0.0006111109,0.0006038363,0.0006214482,0.00033089504,0.00022889876],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00070488575,0.0005749241,0.00086229655,0.0003214891,0.0007593773,0.0017814173,0.0007108281,0.0002540433,0.0000067190085],"category_scores_gemma":[0.000149053,0.00061070087,0.0002958434,0.0006128347,0.00023698261,0.0007808191,0.00006631592,0.00022422432,0.000018815508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042009473,0.0007131611,0.001984571,0.0024046106,0.0026177643,0.000049418824,0.0047451067,0.6552967,0.092173174,0.06276868,0.03704823,0.13977851],"study_design_scores_gemma":[0.0066884253,0.00008087564,0.0013240398,0.0003132368,0.00032791065,0.000008658912,0.000031729476,0.98135036,0.0012873429,0.00034930534,0.0076813423,0.00055676827],"about_ca_topic_score_codex":0.00006351875,"about_ca_topic_score_gemma":0.000010376691,"teacher_disagreement_score":0.9491684,"about_ca_system_score_codex":0.00025540259,"about_ca_system_score_gemma":0.00021826402,"threshold_uncertainty_score":0.99963444},"labels":[],"label_agreement":null},{"id":"W4416714573","doi":"10.1109/lra.2025.3632066","title":"Design of an Active Morphable Pneumatic Bilayer Planar Actuator Inspired by Starfish","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Materials and Mechanics","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Actuator; Bilayer; Planar; Rod; Deformation (meteorology); Bistability; Suction; Pneumatic actuator; Suspension (topology)","score_opus":0.010655479960959271,"score_gpt":0.2243338950872473,"score_spread":0.21367841512628802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416714573","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":[0.28289136,0.00014104962,0.71468824,0.0005644724,0.0010113291,0.00047884393,0.00009601852,0.000114137736,0.000014578562],"genre_scores_gemma":[0.92651564,0.00045056466,0.07198571,0.0007768374,0.00006141081,0.000033740238,0.000047353762,0.00006990494,0.00005883452],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839383,0.00009233775,0.00063093076,0.00031949775,0.00020324967,0.0003601215],"domain_scores_gemma":[0.9991486,0.00012250805,0.00024768242,0.0003039361,0.000064550695,0.00011269739],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019711744,0.00034168223,0.0005090348,0.00019065641,0.00013160004,0.00010838719,0.00016211014,0.00017488544,0.00002633835],"category_scores_gemma":[0.0000266306,0.00037093967,0.000046296052,0.00024233431,0.00005146012,0.00054623705,0.000021668055,0.00014572557,0.0000048523143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025402202,0.000048477203,6.148966e-7,0.00037752674,0.00009221684,0.0000024958547,0.00027444397,0.4369047,0.5588463,0.00023312034,0.0006526422,0.0025420636],"study_design_scores_gemma":[0.00068738795,0.00009671358,0.000031078314,0.00027758584,0.00012006022,0.000001986192,0.000094894145,0.617897,0.37983674,0.0005190438,0.00014972975,0.00028780484],"about_ca_topic_score_codex":0.000016938018,"about_ca_topic_score_gemma":0.0000014305566,"teacher_disagreement_score":0.6436243,"about_ca_system_score_codex":0.00012617334,"about_ca_system_score_gemma":0.000056690224,"threshold_uncertainty_score":0.99987423},"labels":[],"label_agreement":null},{"id":"W4416756242","doi":"10.1109/lra.2025.3632119","title":"X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mobile robot; Robot; Reinforcement learning; Robot learning; Robotics; Generalizability theory; Mobile robot navigation","score_opus":0.010608148763294734,"score_gpt":0.2849909912877431,"score_spread":0.27438284252444833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416756242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037026238,0.000026902644,0.95692444,0.0038993426,0.0010108695,0.00064289477,0.0000013520562,0.00029692266,0.00017103819],"genre_scores_gemma":[0.824947,0.000008968121,0.17085338,0.0029016421,0.00009620824,0.0001397702,0.000035969948,0.000020207835,0.0009968398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984191,0.00005896256,0.00042472055,0.00043338933,0.00031123593,0.0003526017],"domain_scores_gemma":[0.9990526,0.00022462734,0.00018359433,0.00032458492,0.00012185569,0.00009274443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004205156,0.00020096805,0.00020448097,0.00023017607,0.0004231341,0.00066277356,0.000373037,0.00007995609,0.000005197604],"category_scores_gemma":[0.000058466765,0.00021285299,0.000072980794,0.0003772176,0.00005450021,0.00047974294,0.00012082222,0.00017877122,0.000026795731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003671609,0.000013259952,0.00069777423,0.00007005905,0.000027953089,0.0000015118311,0.00035905457,0.97485065,0.0068519632,0.008689193,0.0008151844,0.0076197484],"study_design_scores_gemma":[0.0004877783,0.00010890493,0.002542723,0.00011387357,0.000018660054,0.0000028690833,0.000017745157,0.98923385,0.0035946677,0.00018015451,0.0034664155,0.00023233423],"about_ca_topic_score_codex":0.000008735646,"about_ca_topic_score_gemma":5.0666324e-7,"teacher_disagreement_score":0.7879208,"about_ca_system_score_codex":0.00012452871,"about_ca_system_score_gemma":0.00004819098,"threshold_uncertainty_score":0.8679893},"labels":[],"label_agreement":null},{"id":"W4417131509","doi":"10.1109/lra.2025.3641153","title":"A Piezoelectrically-Actuated Mesoscale Compliant Parallel Robot via Additive Manufacture","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Piezoelectric Actuators and Control","field":"Engineering","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":"Workspace; Compliant mechanism; Robot; Actuator; Planar; Parallel manipulator; Mechanism (biology); Hinge; Transmission (telecommunications); Robotics","score_opus":0.005192353314977035,"score_gpt":0.20701376273107158,"score_spread":0.20182140941609455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417131509","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":[0.0070928046,0.0012412212,0.97734153,0.011252622,0.0012248349,0.0008538647,0.000050353883,0.00042864884,0.0005141018],"genre_scores_gemma":[0.98583543,0.0006397617,0.00776465,0.004957007,0.00024383074,0.00006771985,0.00007497012,0.000073251605,0.00034339496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968144,0.00012947511,0.0009430861,0.0007077172,0.0004205823,0.0009846922],"domain_scores_gemma":[0.9985784,0.00032315118,0.00025458916,0.00044480737,0.00012125157,0.00027777025],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020450789,0.00076573726,0.00089677,0.00048658575,0.0004277419,0.00037165015,0.00035972527,0.00042056598,0.0001081388],"category_scores_gemma":[0.00004368415,0.000781983,0.00024375792,0.0008505802,0.00013382304,0.00031698457,0.000056952078,0.0008015651,0.0000705041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008209714,0.00017448337,0.000069174064,0.00024526194,0.0010217086,0.00006136087,0.0003659217,0.5293574,0.014896392,0.0012701962,0.029556219,0.42289978],"study_design_scores_gemma":[0.0018075879,0.00010713207,0.0026049188,0.0002147776,0.00038489583,0.000023991322,0.000020234818,0.9898831,0.0010674375,0.00056487496,0.0025379825,0.0007830302],"about_ca_topic_score_codex":0.000060779203,"about_ca_topic_score_gemma":0.000008608849,"teacher_disagreement_score":0.9787426,"about_ca_system_score_codex":0.0003680225,"about_ca_system_score_gemma":0.000100479025,"threshold_uncertainty_score":0.9994631},"labels":[],"label_agreement":null},{"id":"W4417251916","doi":"10.1109/lra.2025.3643300","title":"OCT Imaging for Pose Estimation and Feedback Control of an Articulated Magnetic Surgical Tool","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Pose; Visualization; Robustness (evolution); Optical coherence tomography; Surgical planning; Visual feedback; Robot; Medical imaging","score_opus":0.004792694148153347,"score_gpt":0.2303154035573698,"score_spread":0.22552270940921645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251916","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":[0.47961769,0.00034011615,0.51540834,0.003451937,0.0001816776,0.00082913094,0.000036791225,0.000101457095,0.00003288301],"genre_scores_gemma":[0.9663132,0.000035193734,0.033284914,0.00020694594,0.000038946324,0.00006552015,0.000025445835,0.000022044054,0.000007791789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855465,0.000052717907,0.0006363325,0.000317559,0.00014580761,0.00029295543],"domain_scores_gemma":[0.99907076,0.00034291463,0.000118111326,0.00024666972,0.00012009132,0.00010146533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023580615,0.00024403607,0.00033989598,0.00024463522,0.0001450436,0.00019297273,0.00009114833,0.00010532405,0.000011310023],"category_scores_gemma":[0.000041443036,0.00027295094,0.00007856485,0.00038749864,0.00020005646,0.000319617,0.000014826205,0.00013360595,0.000002075386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045846293,0.000115713265,0.00065786875,0.0007026179,0.000106692074,0.0000034594843,0.00015817606,0.8632544,0.050546136,0.0100331465,0.000123077,0.07425282],"study_design_scores_gemma":[0.0019227171,0.000069469585,0.013447189,0.00017518761,0.00030548422,0.000008463647,0.000024253834,0.9812443,0.0014082167,0.0011057389,0.000042352367,0.00024657755],"about_ca_topic_score_codex":0.000008594899,"about_ca_topic_score_gemma":0.000001284032,"teacher_disagreement_score":0.48669553,"about_ca_system_score_codex":0.00003560195,"about_ca_system_score_gemma":0.000023310324,"threshold_uncertainty_score":0.9999723},"labels":[],"label_agreement":null},{"id":"W4417251965","doi":"10.1109/lra.2025.3643269","title":"Self-Supervised Learning for Object Pose Estimation Through Active Real Sample Capture","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Dynamic Systems Analysis (Canada); University of Toronto","funders":"","keywords":"Pose; Leverage (statistics); 3D pose estimation; Object (grammar); Robotics; Articulated body pose estimation; Process (computing); Object detection","score_opus":0.014020296396866761,"score_gpt":0.25602351515369154,"score_spread":0.2420032187568248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025118979,0.0001446696,0.9666546,0.004178845,0.0016230661,0.000903402,0.0000072157904,0.00067279604,0.000696436],"genre_scores_gemma":[0.8223117,0.00024067897,0.1758702,0.00084522157,0.00024550216,0.00005019096,0.00020425503,0.00007320038,0.00015908123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805605,0.00014583892,0.0006203534,0.0004694767,0.00024200096,0.00046625722],"domain_scores_gemma":[0.9987041,0.0006401796,0.00022210072,0.00022393995,0.00012614798,0.000083568346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024538633,0.00042533086,0.0004399879,0.00027141089,0.0006494406,0.00037193438,0.00013132747,0.00028654485,0.000028012002],"category_scores_gemma":[0.00018734235,0.00049715914,0.00015666221,0.00044567575,0.000047393878,0.00067999726,0.000030816165,0.0005114771,0.000012606111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027550828,0.0000327086,0.0002484208,0.00078060525,0.00022414612,0.0000016724089,0.0057913265,0.9760798,0.0029789386,0.0036790057,0.0007959613,0.009359839],"study_design_scores_gemma":[0.0012788235,0.00004974133,0.0033393619,0.0002661635,0.00024974652,0.0000030468984,0.00053763576,0.99184763,0.00051798444,0.00035591275,0.0010996533,0.0004543229],"about_ca_topic_score_codex":0.0001334537,"about_ca_topic_score_gemma":0.000011667468,"teacher_disagreement_score":0.7971927,"about_ca_system_score_codex":0.00028188215,"about_ca_system_score_gemma":0.000069599795,"threshold_uncertainty_score":0.999748},"labels":[],"label_agreement":null},{"id":"W4417251971","doi":"10.1109/lra.2025.3643292","title":"A Universal Framework for Extrinsic Calibration of Camera, Radar, and LiDAR","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Research Manitoba","funders":"","keywords":"Calibration; Lidar; Scalability; Limiting; Sensor fusion; Iterative method","score_opus":0.00887081303824346,"score_gpt":0.22386396171868503,"score_spread":0.21499314868044156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07173087,0.00051651994,0.9212087,0.0049649873,0.00093538815,0.00050859345,0.000031691874,0.00007380985,0.000029471666],"genre_scores_gemma":[0.9105981,0.00046790022,0.08801341,0.0006677127,0.00011472251,0.0000070766555,0.000039378385,0.000038153055,0.000053536372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867845,0.00005346978,0.00054985506,0.0003015993,0.00016347623,0.00025314424],"domain_scores_gemma":[0.99911535,0.00032263997,0.00017688463,0.00020737419,0.00009559876,0.00008216826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014434756,0.0002650345,0.0003742701,0.00032185586,0.00017712063,0.00014901409,0.00007847415,0.0002394902,0.0000056539875],"category_scores_gemma":[0.00006189361,0.00031132062,0.0000776574,0.00036537834,0.0001234599,0.00023491542,0.000021443457,0.00016577357,4.6999693e-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":[0.000027759843,0.0000342283,0.0002640187,0.0009416907,0.00013580969,0.00000209762,0.0004672675,0.886902,0.011867388,0.09276712,0.00070083234,0.005889843],"study_design_scores_gemma":[0.0008679834,0.00006839557,0.00081775273,0.0005131675,0.00020536098,0.0000017009903,0.00013159377,0.99092066,0.003995908,0.0019546982,0.00024911296,0.00027368093],"about_ca_topic_score_codex":0.000029395793,"about_ca_topic_score_gemma":0.0000044287476,"teacher_disagreement_score":0.83886725,"about_ca_system_score_codex":0.00007625248,"about_ca_system_score_gemma":0.000056354816,"threshold_uncertainty_score":0.9999339},"labels":[],"label_agreement":null},{"id":"W4417251985","doi":"10.1109/lra.2025.3643304","title":"An Intention-Guided Reinforcement Learning Approach With Dirichlet Energy Constraint for Heterogeneous Multi-Robot Cooperation","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","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":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Observability; Constraint (computer-aided design); Adaptability; Process (computing); Robot; Variety (cybernetics); Convergence (economics); Software deployment","score_opus":0.022887647404219565,"score_gpt":0.26651502927352133,"score_spread":0.24362738186930177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417251985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017738162,0.00008328824,0.9931938,0.0023403568,0.0010243682,0.0011621013,0.0000030877611,0.00030884266,0.00011035586],"genre_scores_gemma":[0.7805316,0.00008799412,0.2154958,0.0027324688,0.000091625785,0.00012525068,0.00018569075,0.000044723445,0.000704894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960561,0.000297859,0.0012502288,0.0010885517,0.0005433047,0.00076399185],"domain_scores_gemma":[0.99755746,0.0001692713,0.0007370053,0.00072257465,0.0005824842,0.00023122413],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00068011513,0.0006469145,0.00059307384,0.0005244253,0.0010880915,0.0015956692,0.0006197892,0.00024055717,0.0000083338155],"category_scores_gemma":[0.00008615583,0.00063662406,0.00015151114,0.0006543679,0.00038367513,0.00095804647,0.00013043426,0.00037933612,0.0000041668595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035890986,0.00012839546,0.00018395488,0.00028075455,0.00027573196,0.000006242256,0.00072765024,0.9746766,0.0054431665,0.012637107,0.0002777412,0.005326747],"study_design_scores_gemma":[0.0025116669,0.0006510589,0.00017227975,0.00033215844,0.00019041734,0.000042410564,0.000094654446,0.99296325,0.0019360404,0.000014902432,0.00040108783,0.0006900642],"about_ca_topic_score_codex":0.00005920162,"about_ca_topic_score_gemma":0.000008002096,"teacher_disagreement_score":0.77875775,"about_ca_system_score_codex":0.00027319067,"about_ca_system_score_gemma":0.00023476683,"threshold_uncertainty_score":0.9996085},"labels":[],"label_agreement":null},{"id":"W4417284254","doi":"10.1109/lra.2025.3643272","title":"Low-Light Amodal Objects Tracking: A Benchmark","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Amodal perception; Benchmark (surveying); Object detection; Bounding overwatch; Video tracking; Object (grammar); Minimum bounding box; Metric (unit)","score_opus":0.013562384411396703,"score_gpt":0.2691983522466626,"score_spread":0.25563596783526593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417284254","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":[0.06268785,0.00048386445,0.8969351,0.032436874,0.0057015526,0.00038813223,0.0000051163897,0.00025022175,0.0011112634],"genre_scores_gemma":[0.9504219,0.00014132797,0.0418213,0.0070940987,0.00028838977,0.000012972096,0.0000057001703,0.000022986382,0.0001913049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966073,0.00043871143,0.00081995374,0.0009381484,0.00048859883,0.0007072788],"domain_scores_gemma":[0.99812835,0.0004073564,0.00034794633,0.000764342,0.00017705951,0.00017494604],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011321895,0.0004762164,0.0005722046,0.00048431824,0.0005750039,0.0012839132,0.00065356167,0.0002353089,0.000012960618],"category_scores_gemma":[0.00013134904,0.00049706124,0.000197764,0.0011978792,0.00016815454,0.0008810102,0.00015954817,0.00043292614,0.000030475472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005421126,0.00069739425,0.008491652,0.001833907,0.0006916287,0.000281503,0.007003393,0.24953665,0.08534805,0.05157999,0.016119387,0.5783622],"study_design_scores_gemma":[0.0016829167,0.00012725285,0.074618965,0.0012955773,0.00013284854,0.00003975678,0.000056796234,0.89299995,0.0230938,0.0030566768,0.0017889664,0.0011065233],"about_ca_topic_score_codex":0.000021744223,"about_ca_topic_score_gemma":0.000015525598,"teacher_disagreement_score":0.88773406,"about_ca_system_score_codex":0.000109603796,"about_ca_system_score_gemma":0.00022117658,"threshold_uncertainty_score":0.9997529},"labels":[],"label_agreement":null},{"id":"W7104562205","doi":"10.1109/lra.2025.3630870","title":"Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Robot; Scalability; Block (permutation group theory); Computation; Decentralised system; Mobile robot; State (computer science); Control (management)","score_opus":0.010155790497617527,"score_gpt":0.24134783019429762,"score_spread":0.2311920396966801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104562205","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":[0.3535956,0.00049620564,0.6317797,0.011523161,0.0015787008,0.0008686601,0.000038772654,0.000104676954,0.000014514378],"genre_scores_gemma":[0.9922407,0.0003011176,0.0038777778,0.003271455,0.00013450196,0.000029955389,0.00007212651,0.00002026062,0.00005206126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972175,0.0004444157,0.0007675356,0.00078364223,0.00036765673,0.00041930182],"domain_scores_gemma":[0.9986675,0.00024230487,0.00037150807,0.00032438236,0.00022551573,0.00016882653],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00043693915,0.0004231138,0.00051447394,0.00024259674,0.00056500407,0.0014253644,0.00021416273,0.00021159247,0.0000030268668],"category_scores_gemma":[0.00008566871,0.0004381907,0.00005866275,0.00048714512,0.00025592634,0.00066016335,0.00008896415,0.00019506268,0.000008220823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000652516,0.00048549852,0.0056183035,0.0014298372,0.00085498375,0.00007867798,0.007157774,0.51426154,0.33918852,0.044176314,0.010337935,0.0757581],"study_design_scores_gemma":[0.005206422,0.00012346261,0.006782263,0.00085834047,0.00014072152,0.000014034766,0.00007740045,0.97915184,0.00649593,0.00015411316,0.0005500883,0.0004454067],"about_ca_topic_score_codex":0.000060810526,"about_ca_topic_score_gemma":0.000008504948,"teacher_disagreement_score":0.6386451,"about_ca_system_score_codex":0.00014805427,"about_ca_system_score_gemma":0.000096330805,"threshold_uncertainty_score":0.999807},"labels":[],"label_agreement":null},{"id":"W7105987019","doi":"10.1109/lra.2025.3634905","title":"Hybrid Contact Dynamics and Residual-RL Framework for Multi-Point Object Pushing","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Trajectory; Object (grammar); Grippers; Controller (irrigation); Control theory (sociology); Robot; Point (geometry); Contact dynamics; Contact force","score_opus":0.01890955365903763,"score_gpt":0.26912760517038087,"score_spread":0.25021805151134324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105987019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04543715,0.00055405765,0.94058377,0.010702762,0.0017364768,0.00065905426,0.000010802182,0.0002642868,0.000051617008],"genre_scores_gemma":[0.89550877,0.00018617255,0.10173831,0.0021249312,0.0001858165,0.0000245886,0.000056898996,0.0000645125,0.000109995875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818325,0.0000944821,0.00069705787,0.00040798355,0.0001623856,0.00045481964],"domain_scores_gemma":[0.9987319,0.00056906027,0.00021132619,0.0002631481,0.00007220404,0.00015236113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041483587,0.00039786706,0.0004719189,0.00034861965,0.00047701984,0.00063264405,0.00011977197,0.00021222909,0.0000111802765],"category_scores_gemma":[0.0003663277,0.00046768563,0.000104081664,0.00019658273,0.00006327777,0.0003794544,0.000047751295,0.0005223464,0.000007052129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018281324,0.000023691024,0.00077551865,0.00074675016,0.00018736703,0.0000046080017,0.00080817397,0.95083344,0.00158863,0.0393731,0.0008832652,0.004757191],"study_design_scores_gemma":[0.0011143868,0.00003649615,0.011429949,0.0007637665,0.0001575776,0.0000065873414,0.00028112036,0.98473996,0.00021213197,0.00068339164,0.00012824849,0.00044641196],"about_ca_topic_score_codex":0.000025256104,"about_ca_topic_score_gemma":0.000018007167,"teacher_disagreement_score":0.8500716,"about_ca_system_score_codex":0.00019216897,"about_ca_system_score_gemma":0.00004507351,"threshold_uncertainty_score":0.9997775},"labels":[],"label_agreement":null}]}