{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":7,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":7,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"1f7f600ec48f","filters":{"venue":"Data Science for Transportation"}},"results":[{"id":"W4385259995","doi":"10.1007/s42421-023-00074-x","title":"Deep Reinforcement Learning-Based Short-Term Traffic Signal Optimizing Using Disaggregated Vehicle Data","year":2023,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Reinforcement learning; Computer science; Benchmark (surveying); SIGNAL (programming language); Real-time computing; Intersection (aeronautics); Traffic signal; Traffic flow (computer networking); Artificial intelligence; Simulation; Engineering; Computer network; Transport engineering","authors":[{"name":"Kazi Redwan Shabab","is_ca":false},{"name":"Syed Mostaquim Ali","is_ca":true},{"name":"Mohamed H. Zaki","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05388485786037995,"gpt":0.2836464597080622,"spread":0.2297616018476822,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009516752,0.0008466871,0.001304292,0.0004598704,0.0002937372,0.0006945592,0.001451782,0.001184715,0.001594544],"category_scores_gemma":[0.003293761,0.0006438124,0.0005804554,0.0007043707,0.0005724322,0.001027604,0.0008883241,0.001902938,0.0004648336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000980785,"about_ca_system_score_gemma":0.001455899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0207834,"about_ca_topic_score_gemma":0.01820308,"domain_scores_codex":[0.9996588,0.00007935434,0.00001660166,0.0001111028,0.0000569509,0.00007715612],"domain_scores_gemma":[0.9986925,0.0007152312,0.0001216208,0.0001056292,0.0002638931,0.000101142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000114236,0.00008301226,0.001309955,0.00001941697,0.00003352748,0.00002848,0.00001414211,0.9699548,0.000875614,0.001092341,0.00111261,0.02536193],"study_design_scores_gemma":[0.00000176188,0.000004461624,0.00005515603,6.416425e-7,0.000001161124,9.516564e-7,6.575701e-7,0.9996101,0.00006760829,0.0002332274,0.00002348416,8.442674e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3072676,0.0008921353,0.6856008,0.0008664862,0.0002267181,0.00005869681,0.0008343111,0.001814888,0.002438301],"genre_scores_gemma":[0.9758334,0.0001002376,0.02106251,0.00009749848,0.00005181162,0.00003178377,0.0008054718,0.00005530494,0.001962112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0207834,"threshold_uncertainty_score":0.04132485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415816809","doi":"10.1007/s42421-025-00139-z","title":"A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Baidu","keywords":"Reinforcement learning; Construct (python library); Control (management); SIGNAL (programming language); State (computer science); Inference; Traffic flow (computer networking)","authors":[{"name":"Jianxiong Li","is_ca":false},{"name":"Shichao Lin","is_ca":false},{"name":"Tianyu Shi","is_ca":true},{"name":"Chujie Tian","is_ca":false},{"name":"Yu Mei","is_ca":false},{"name":"Jian Song","is_ca":false},{"name":"Xianyuan Zhan","is_ca":false},{"name":"Ruimin Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04114882550449673,"gpt":0.2831969920624237,"spread":0.2420481665579269,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008913029,0.0007043435,0.001146406,0.0002957918,0.0003962294,0.0008809016,0.001457886,0.001322825,0.003729716],"category_scores_gemma":[0.003471072,0.0007270295,0.0006259603,0.0002874402,0.0008419341,0.0009821539,0.001396383,0.001927191,0.0004533907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000856806,"about_ca_system_score_gemma":0.001677379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009034621,"about_ca_topic_score_gemma":0.007334649,"domain_scores_codex":[0.9996243,0.0001179173,0.00001501114,0.00007465335,0.0001125894,0.00005551719],"domain_scores_gemma":[0.9984919,0.0009463384,0.00009415847,0.0001170709,0.0002516051,0.00009890806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002803782,0.00002500859,0.0001012148,0.00001772116,0.000009796062,0.00002333597,0.00001450988,0.9891391,0.0004030279,0.004053886,0.0002700636,0.005914248],"study_design_scores_gemma":[0.000002182843,0.000003095565,0.000007525908,7.483656e-7,6.453228e-7,0.000001249125,5.535796e-7,0.9990146,0.00004546189,0.0008817259,0.0000413925,8.3366e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01194536,0.00007242214,0.9851047,0.0001615081,0.00004038129,0.00003557586,0.00005167324,0.0002730684,0.002315311],"genre_scores_gemma":[0.8680462,0.00007040197,0.1267418,0.0001468133,0.00006633412,0.0001747228,0.0001459022,0.0001565844,0.004451152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009034621,"threshold_uncertainty_score":0.01796407,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413233205","doi":"10.1007/s42421-025-00130-8","title":"Fusing Unstructured Text and Time Series Demand and Economic Data for Demand Prediction in Air Cargo Transportation","year":2025,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Time series; Series (stratigraphy); Demand forecasting; Computer science; Unstructured data; Operations research; Data mining; Engineering; Big data; Machine learning; Geology","authors":[{"name":"Hongting Zhou","is_ca":true},{"name":"Saiedeh Razavi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07400768503955048,"gpt":0.3745208420552631,"spread":0.3005131570157127,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007250853,0.0007652201,0.000569954,0.002574319,0.0002708849,0.0007818662,0.000483228,0.0008156383,0.001046622],"category_scores_gemma":[0.004705442,0.0002218111,0.0008047651,0.002582326,0.0002369032,0.001901439,0.0007876573,0.000757315,0.001066725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003225176,"about_ca_system_score_gemma":0.0004666258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004956581,"about_ca_topic_score_gemma":0.006747089,"domain_scores_codex":[0.999467,0.0001219776,0.00007621336,0.000116984,0.000150975,0.00006686684],"domain_scores_gemma":[0.9970893,0.00182595,0.0002556305,0.0003135481,0.0004286066,0.00008690033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002285703,0.002592424,0.1104853,0.00096703,0.0004754741,0.002364652,0.0006973651,0.2927158,0.03616154,0.004675554,0.02535434,0.5212249],"study_design_scores_gemma":[0.00002099707,0.0001659324,0.02408605,0.00004009132,0.00009733243,0.0001391399,0.0004560533,0.9587903,0.005855961,0.00649528,0.003813811,0.00003915656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944878,0.001506716,0.1696372,0.001845729,0.0006458046,0.0002109879,0.02564094,0.001929771,0.004094851],"genre_scores_gemma":[0.9294788,0.0006693203,0.03949152,0.0001535072,0.0003413485,0.0001253143,0.0277881,0.00007819608,0.001873775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004956581,"threshold_uncertainty_score":0.009855449,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411170169","doi":"10.1007/s42421-025-00125-5","title":"At the Heart of Intersections: Analyzing Their Influence on Driver Heart Behaviour","year":2025,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Internal medicine; Cardiology; Medicine","authors":[{"name":"Joel Miller","is_ca":true},{"name":"Mohamed H. Zaki","is_ca":true},{"name":"Soodeh Nikan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04732636716925957,"gpt":0.4066154902198491,"spread":0.3592891230505895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000388526,0.0002421299,0.0002657768,0.0007184884,0.0002624627,0.0008261132,0.0001868996,0.0004984169,0.002049444],"category_scores_gemma":[0.003472629,0.0001587102,0.0002419547,0.0005416689,0.0001507349,0.0002851202,0.0002866881,0.0004669838,0.0004654637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734495,"about_ca_system_score_gemma":0.0002436439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076967,"about_ca_topic_score_gemma":0.00377925,"domain_scores_codex":[0.9995072,0.0001627179,0.00001838209,0.0001113596,0.00009708344,0.0001033276],"domain_scores_gemma":[0.9965752,0.00212989,0.0004367023,0.0001343301,0.0003119625,0.0004118954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002167003,0.0004638826,0.9504943,0.00005887305,0.0002006493,0.0001850394,0.0007880906,0.001190343,0.01419008,0.0002061992,0.0007574983,0.02929816],"study_design_scores_gemma":[0.00001332473,0.0003124538,0.9945226,0.000008064766,0.0001068811,0.0001179355,0.0003985122,0.002910874,0.001035544,0.0001186175,0.0004442093,0.00001092239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975593,0.0000933203,0.000870957,0.00005402026,0.00001444524,0.00001108773,0.0002521538,0.00002054202,0.001124247],"genre_scores_gemma":[0.9988952,0.00004779286,0.0003921227,0.0000152262,0.00001855628,0.0000117275,0.000268475,0.00001113281,0.0003397048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002076967,"threshold_uncertainty_score":0.006856084,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402372095","doi":"10.1007/s42421-024-00107-z","title":"A Network-Based, Data-Driven Methodology for Identifying and Ranking Freight Bottlenecks","year":2024,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Geotab (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Computer science; Data mining; Data science; Artificial intelligence","authors":[{"name":"Yunfei Ma","is_ca":true},{"name":"Chien An Liu","is_ca":true},{"name":"Elkafi Hassini","is_ca":true},{"name":"Saiedeh Razavi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.199271848917433,"gpt":0.3707327468527745,"spread":0.1714608979353415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001952137,0.001542324,0.00131972,0.005805121,0.00076858,0.002355429,0.00226193,0.001467806,0.001810306],"category_scores_gemma":[0.00689063,0.0006753867,0.001189257,0.003872886,0.0006179031,0.002115625,0.001545322,0.001370635,0.0006886196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543299,"about_ca_system_score_gemma":0.002774249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01328378,"about_ca_topic_score_gemma":0.02081261,"domain_scores_codex":[0.9984741,0.000291989,0.0001094505,0.000406295,0.0005767885,0.0001413774],"domain_scores_gemma":[0.9967682,0.001398728,0.0003592169,0.0003372658,0.0009552228,0.0001813651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000204936,0.0004144694,0.01006164,0.0002129695,0.0002447977,0.0001651822,0.0001112306,0.7382075,0.008607561,0.01303799,0.005348013,0.2233837],"study_design_scores_gemma":[0.000008253443,0.00003167648,0.0005326225,0.00001057984,0.00001811886,0.00003258068,0.00001832413,0.9912871,0.001431827,0.005776415,0.0008412942,0.00001131254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009261669,0.00009412917,0.9875978,0.0001140141,0.00003124326,0.0001483776,0.001037723,0.001157708,0.0005573643],"genre_scores_gemma":[0.2628038,0.0001632537,0.7307324,0.0001120241,0.0000694097,0.0004679702,0.00377263,0.0001428768,0.00173567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01328378,"threshold_uncertainty_score":0.02641296,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4365140904","doi":"10.1007/s42421-023-00069-8","title":"Driver Classification Using Self-reported, Psychophysiological, and Performance Metrics Within a Simulated Environment","year":2023,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Mid-America Transportation Center, University of Nebraska-Lincoln; Drexel University","keywords":"Cognition; Task (project management); Cluster analysis; Computer science; Dynamic time warping; Interpersonal Reactivity Index; Affect (linguistics); Psychology; Applied psychology; Artificial intelligence; Machine learning; Cognitive psychology; Empathy; Social psychology; Engineering","authors":[{"name":"Vishal C. Kummetha","is_ca":false},{"name":"Umair Durrani","is_ca":true},{"name":"Justin Mason","is_ca":false},{"name":"Sisinnio Concas","is_ca":false},{"name":"Alexandra Kondyli","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2821296379746054,"gpt":0.4441502196432079,"spread":0.1620205816686025,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001350543,0.0005174813,0.0003773668,0.001099007,0.0002713646,0.001142038,0.0004143918,0.000779888,0.00124301],"category_scores_gemma":[0.006142821,0.0001588443,0.0005836068,0.0004663688,0.0002130411,0.0008611115,0.000453014,0.0003982167,0.000490529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005609668,"about_ca_system_score_gemma":0.0006101296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008123091,"about_ca_topic_score_gemma":0.01048446,"domain_scores_codex":[0.999086,0.0002664521,0.00009095808,0.0002411399,0.0001915606,0.0001238236],"domain_scores_gemma":[0.9967471,0.001187669,0.0006291967,0.0003395482,0.0007413785,0.000355147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001168299,0.001062635,0.96659,0.00004077752,0.0002869056,0.00005843929,0.0002310182,0.01246483,0.00275011,0.0001335018,0.0005561076,0.01465743],"study_design_scores_gemma":[0.00006164566,0.002027439,0.8492974,0.0000184901,0.0001216298,0.0001456429,0.0004892062,0.1434113,0.003557159,0.000317251,0.0004867641,0.00006599502],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972883,0.00001013384,0.001759486,0.000018517,0.000008184085,0.00003947826,0.0005451083,0.00005333112,0.0002773869],"genre_scores_gemma":[0.9978979,0.00001254397,0.0008666864,0.000006097755,0.000002848964,0.00003070977,0.0009340884,0.000006447525,0.0002426498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008123091,"threshold_uncertainty_score":0.01615161,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4415400457","doi":"10.1007/s42421-025-00137-1","title":"Dynamic Campus Origin–Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs","year":2025,"lang":"en","type":"article","venue":"Data Science for Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Transport Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Estimator; Graph; Perceptron; Convolutional neural network; Set (abstract data type); Artificial neural network; Convolution (computer science); Occupancy","authors":[{"name":"Godwin Badu-Marfo","is_ca":true},{"name":"Bilal Farooq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04650248749507278,"gpt":0.3707933728451095,"spread":0.3242908853500368,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002318524,0.0007535137,0.000409709,0.001329214,0.0002477196,0.0004057621,0.0008446673,0.000567795,0.0009909261],"category_scores_gemma":[0.001207012,0.0002477423,0.0004961733,0.0011806,0.000183035,0.0007951512,0.0005090231,0.0008452338,0.0006663387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000692126,"about_ca_system_score_gemma":0.0004989982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04922357,"about_ca_topic_score_gemma":0.06980513,"domain_scores_codex":[0.9998394,0.00001791522,0.000007197105,0.00006689481,0.00002267705,0.00004592841],"domain_scores_gemma":[0.9997004,0.0001122055,0.00003892107,0.00004030239,0.00007587446,0.00003225661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006189581,0.0006973543,0.1489558,0.0001291279,0.0002745701,0.0005358508,0.0001023919,0.5972294,0.004268718,0.001992146,0.02082304,0.2243726],"study_design_scores_gemma":[0.000003140006,0.00001225358,0.005532112,0.00000395316,0.00001222319,0.00001763238,0.00001616553,0.9933105,0.0003208519,0.000510445,0.0002568127,0.000004033001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9024572,0.0008567158,0.08150459,0.0008164271,0.0003156798,0.00005597085,0.009040136,0.002296241,0.002656997],"genre_scores_gemma":[0.9883605,0.0001485069,0.004995133,0.00003760423,0.0000411819,0.00001541661,0.004841236,0.00002313777,0.001537185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04922357,"threshold_uncertainty_score":0.0978741,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}