{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"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":"9096f131a294","filters":{"venue":"Encyclopedia of Machine Learning and Data Mining"}},"results":[{"id":"W1514644537","doi":"10.1007/978-1-4899-7687-1_110","title":"Class Imbalance Problem","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":42,"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":"Class (philosophy); Computer science; Artificial intelligence","authors":[{"name":"Charles X. Ling","is_ca":true},{"name":"Victor S. Sheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02749729938215088,"gpt":0.2897457171230958,"spread":0.2622484177409449,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002904014,0.001160716,0.001402294,0.001523765,0.00152955,0.003969512,0.002253928,0.002219334,0.019598],"category_scores_gemma":[0.01306469,0.000474981,0.0009440252,0.002160929,0.0009256052,0.004500118,0.002591383,0.003755331,0.007824343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136109,"about_ca_system_score_gemma":0.001411089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007845116,"about_ca_topic_score_gemma":0.0008872726,"domain_scores_codex":[0.996549,0.000658186,0.0001779234,0.001032514,0.001305714,0.000276585],"domain_scores_gemma":[0.9960569,0.001789335,0.0003511996,0.0007295532,0.0009011577,0.0001718437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001987346,0.0002006497,0.001953797,0.000438633,0.00009145067,0.0003136701,0.0001557623,0.009160281,0.002372424,0.1280353,0.2497552,0.6073239],"study_design_scores_gemma":[0.00009813842,0.0001121208,0.002406023,0.0003302549,0.0001061202,0.002434802,0.0002646805,0.1408715,0.009443772,0.4583398,0.3855317,0.00006119165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01379928,0.00925977,0.8120865,0.01394912,0.005662809,0.0005694003,0.003720056,0.002227141,0.138726],"genre_scores_gemma":[0.3669365,0.01403106,0.3713161,0.00928203,0.01322185,0.001694722,0.01404849,0.001363294,0.2081061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.019598,"threshold_uncertainty_score":0.06556189,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4245359331","doi":"10.1007/978-1-4899-7687-1_31","title":"Boltzmann Machines","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":10,"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":"","keywords":"Boltzmann machine; Computer science; Artificial intelligence; Artificial neural network","authors":[{"name":"Geoffrey E. Hinton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02501946106191617,"gpt":0.2715170864765714,"spread":0.2464976254146552,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003774196,0.001053228,0.00116514,0.0007716964,0.0004271493,0.002185754,0.001542319,0.001529079,0.04800499],"category_scores_gemma":[0.001828854,0.0005594011,0.0006993802,0.001097847,0.000699033,0.001855419,0.001436742,0.002094455,0.03406256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00053865,"about_ca_system_score_gemma":0.0006059168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008815155,"about_ca_topic_score_gemma":0.001311428,"domain_scores_codex":[0.9996486,0.00007396485,0.0000172746,0.0001013374,0.0001266693,0.00003221836],"domain_scores_gemma":[0.9996408,0.0001327146,0.00001883852,0.0001000204,0.00008224294,0.0000253493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006699782,0.00006883537,0.0003453783,0.0003827159,0.00007506734,0.00006098661,0.00005671462,0.06802852,0.003381243,0.4583038,0.08861982,0.38061],"study_design_scores_gemma":[0.00002833737,0.00002905356,0.0003247615,0.0001572047,0.00003234563,0.0002496986,0.00002299503,0.2209132,0.003674456,0.5080014,0.2665176,0.00004895036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002283698,0.01265487,0.8305553,0.001250309,0.00111801,0.00008574937,0.001481469,0.003515551,0.1470551],"genre_scores_gemma":[0.1680092,0.02265845,0.4657397,0.001535009,0.00161354,0.0006845834,0.007486092,0.002926137,0.3293471],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.04800499,"threshold_uncertainty_score":0.1605927,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2612181456","doi":"10.1007/978-1-4899-7502-7_35-1","title":"Categorical Data Clustering","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":5,"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":"","keywords":"Categorical variable; Cluster analysis; Computer science; Data mining; Domain (mathematical analysis); Cluster (spacecraft); Artificial intelligence; Machine learning; Mathematics","authors":[{"name":"Periklis Andritsos","is_ca":true},{"name":"Panayiotis Tsaparas","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05026195322463737,"gpt":0.3275556204352275,"spread":0.2772936672105901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025443,0.001004287,0.001108012,0.003636122,0.001184596,0.002652238,0.002135462,0.0008306275,0.03074577],"category_scores_gemma":[0.003389126,0.0005975033,0.001263751,0.007035077,0.0005996338,0.001807377,0.002015249,0.001636737,0.03405502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012899,"about_ca_system_score_gemma":0.001489957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659734,"about_ca_topic_score_gemma":0.002521211,"domain_scores_codex":[0.9986207,0.0001592823,0.00009331089,0.0003550104,0.0007098349,0.00006170809],"domain_scores_gemma":[0.9985606,0.0002422471,0.00004460322,0.0004559379,0.000631267,0.00006529984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004035785,0.00004695279,0.0004771482,0.0003436088,0.00004593951,0.0000486726,0.0001248388,0.003715056,0.003381341,0.0517556,0.1509543,0.7890661],"study_design_scores_gemma":[0.00001479949,0.00006364826,0.001880671,0.0002216886,0.00005167303,0.0009043564,0.0001912992,0.05505541,0.009964566,0.1741544,0.7574114,0.00008610731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001621298,0.004119958,0.9251781,0.000792303,0.0009177334,0.0002614647,0.003672235,0.007820287,0.05561672],"genre_scores_gemma":[0.02261514,0.004992927,0.8799335,0.0006020788,0.0005653283,0.0004184896,0.01586464,0.001457106,0.07355078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03074577,"threshold_uncertainty_score":0.1028548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4232120185","doi":"10.1007/978-1-4899-7687-1_67","title":"Deep Belief Nets","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","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":"","keywords":"Psychology; Computer science; Artificial intelligence","authors":[{"name":"Geoffrey E. Hinton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02275188405138471,"gpt":0.2647078751643677,"spread":0.241955991112983,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003303341,0.00105846,0.0007701101,0.0007266018,0.0002794317,0.001845652,0.001135541,0.001177779,0.03176652],"category_scores_gemma":[0.001371825,0.0005418176,0.0004777639,0.001157627,0.0004827563,0.001448107,0.001253169,0.001940742,0.01943703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005817639,"about_ca_system_score_gemma":0.0007603111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002021277,"about_ca_topic_score_gemma":0.003660599,"domain_scores_codex":[0.9997544,0.00003470731,0.00001329517,0.00005857108,0.0001193424,0.00001967775],"domain_scores_gemma":[0.99967,0.0001360481,0.0000161688,0.00006545387,0.00009317414,0.00001910602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004673896,0.00006220446,0.0002237594,0.0003068932,0.00005139408,0.00004980744,0.00003368444,0.06123911,0.002147657,0.1042899,0.08914331,0.7424056],"study_design_scores_gemma":[0.00002115004,0.00003800018,0.0003845596,0.0002767662,0.00003884042,0.0002097484,0.00002359575,0.3652303,0.005746842,0.291939,0.3360485,0.00004260423],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001900974,0.01759673,0.8873129,0.001080576,0.000766337,0.00004758942,0.001639152,0.004098479,0.0855573],"genre_scores_gemma":[0.1270246,0.03209672,0.5483361,0.001016032,0.0009605621,0.0003291918,0.01005689,0.002397343,0.2777826],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03176652,"threshold_uncertainty_score":0.1062695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4230558268","doi":"10.1007/978-1-4899-7502-7_109-1","title":"Gaussian Process Reinforcement Learning","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Process (computing); Gaussian process; Computer science; Artificial intelligence; Gaussian; Machine learning; Physics","authors":[{"name":"Yaakov Engel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01665900846311922,"gpt":0.2669173883033986,"spread":0.2502583798402794,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005479961,0.0007649238,0.0008763583,0.0003648011,0.0002802967,0.001345659,0.001028352,0.001057048,0.01679357],"category_scores_gemma":[0.002172852,0.0002796273,0.0003641111,0.0007112856,0.0007669667,0.0008733452,0.0009133997,0.001595828,0.004750254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007224762,"about_ca_system_score_gemma":0.0008740582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862737,"about_ca_topic_score_gemma":0.002683198,"domain_scores_codex":[0.9997411,0.00007208724,0.00001020021,0.0000641556,0.00009180037,0.00002055317],"domain_scores_gemma":[0.9995455,0.0002517642,0.00002571171,0.00005976247,0.00008791596,0.00002924715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006385325,0.0001282436,0.0004836604,0.0002275218,0.00006608758,0.00008264636,0.00007814122,0.1684417,0.001604067,0.3407079,0.04777882,0.4403374],"study_design_scores_gemma":[0.00002673291,0.00005726319,0.0003497364,0.00009456261,0.00002506078,0.0001153239,0.00001792281,0.584456,0.001102157,0.3566315,0.05709218,0.00003149796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003000325,0.00689579,0.9157613,0.001036462,0.0004401018,0.0000471406,0.0001904025,0.0009976226,0.07163079],"genre_scores_gemma":[0.4063084,0.01439018,0.3829026,0.001184689,0.0007733925,0.0003910146,0.001090847,0.000620038,0.1923388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01679357,"threshold_uncertainty_score":0.05618012,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4251839393","doi":"10.1007/978-1-4899-7502-7_77-1","title":"Dynamic Programming","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Reinforcement Learning in Robotics","field":"Computer Science","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 Ottawa; University of British Columbia","funders":"","keywords":"Computer science; Programming language","authors":[{"name":"Martin L. Puterman","is_ca":true},{"name":"Jonathan Patrick","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01693753404202373,"gpt":0.2689480489715373,"spread":0.2520105149295136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205506,0.00104363,0.0008091932,0.0004804275,0.0004059991,0.002021414,0.0008745077,0.0007234483,0.04383065],"category_scores_gemma":[0.001200647,0.0003782293,0.000514314,0.0007665181,0.0006992275,0.001104991,0.001096607,0.001926571,0.01569711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000734653,"about_ca_system_score_gemma":0.0007690847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181833,"about_ca_topic_score_gemma":0.00148679,"domain_scores_codex":[0.9997292,0.00005378321,0.00001178838,0.00008775703,0.00009218872,0.00002518817],"domain_scores_gemma":[0.9997386,0.0001192841,0.00001492315,0.0000405286,0.00006454125,0.00002203449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000368505,0.0000724247,0.0001849782,0.0002221429,0.00003348563,0.00005330854,0.0000616892,0.03129702,0.000980109,0.6248428,0.07756718,0.264648],"study_design_scores_gemma":[0.00002729553,0.00002967045,0.0001977122,0.000118433,0.00001831026,0.0001199862,0.00003289642,0.09274334,0.0008765605,0.6953681,0.2104465,0.00002126675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.002003351,0.005746179,0.6692113,0.001691681,0.0006127212,0.0000677681,0.0008044293,0.001042451,0.3188201],"genre_scores_gemma":[0.1786075,0.01370369,0.3362813,0.001573472,0.001068098,0.0006752574,0.003451149,0.001552842,0.4630868],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04383065,"threshold_uncertainty_score":0.1466282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4235900849","doi":"10.1007/978-1-4899-7502-7_33-1","title":"Cascade-Correlation","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Cascade; Engineering","authors":[{"name":"Thomas R. Shultz","is_ca":true},{"name":"Scott E. Fahlman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0191806981138645,"gpt":0.2622898245828162,"spread":0.2431091264689517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009967573,0.001452651,0.001637345,0.001529805,0.001187221,0.002269107,0.002010861,0.001574817,0.06326868],"category_scores_gemma":[0.003496173,0.0008335667,0.001189988,0.002111492,0.0007984872,0.002556671,0.002771128,0.002148534,0.0323737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008590536,"about_ca_system_score_gemma":0.001635151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001888821,"about_ca_topic_score_gemma":0.003996327,"domain_scores_codex":[0.9988394,0.0001953429,0.00004928653,0.0003719074,0.0004118034,0.0001323384],"domain_scores_gemma":[0.9988245,0.0002245341,0.00006592953,0.0005214743,0.0002910127,0.00007246366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003921148,0.0001893813,0.001138622,0.0005276492,0.0001900248,0.0004100917,0.0001265731,0.03097973,0.01052599,0.2836191,0.1585397,0.513361],"study_design_scores_gemma":[0.000101044,0.0002446632,0.00162559,0.0002078013,0.0001584296,0.001429567,0.0000588746,0.4770986,0.03091294,0.273479,0.2145289,0.0001545775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007148454,0.003293018,0.8595363,0.0006726349,0.001467398,0.0003499062,0.002108613,0.008201556,0.1172221],"genre_scores_gemma":[0.23145,0.003821346,0.5384637,0.001330073,0.001277822,0.0005987161,0.008712959,0.003332341,0.2110131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06326868,"threshold_uncertainty_score":0.2116548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1527359826","doi":"10.1007/978-1-4899-7687-1_668","title":"Privacy-Related Aspects and Techniques","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","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 Ottawa","funders":"","keywords":"Computer science; Internet privacy; Psychology","authors":[{"name":"Stan Matwin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02735825691540373,"gpt":0.2887079047757312,"spread":0.2613496478603274,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003612802,0.001187117,0.001032795,0.002146087,0.0015578,0.007608342,0.002499059,0.002779144,0.01097146],"category_scores_gemma":[0.009244046,0.0008978453,0.001773984,0.004171331,0.003655768,0.01096295,0.003354183,0.008160709,0.005235346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823912,"about_ca_system_score_gemma":0.001517972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005679571,"about_ca_topic_score_gemma":0.0003837175,"domain_scores_codex":[0.9957904,0.001286362,0.0003284339,0.0006754685,0.00161858,0.000300725],"domain_scores_gemma":[0.9943432,0.002898585,0.0001941615,0.001958136,0.0005024298,0.0001034846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004196645,0.00008647487,0.000214898,0.0006290032,0.00003925373,0.0001795528,0.000267693,0.002797276,0.0009656261,0.8021842,0.0220683,0.1705258],"study_design_scores_gemma":[0.000008323764,0.00003688489,0.0001540048,0.0003984744,0.00004005482,0.001306337,0.00009545559,0.01023607,0.002735251,0.848493,0.1364642,0.00003182835],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003761852,0.09995378,0.7576018,0.0126845,0.002646816,0.0002358186,0.0006570689,0.0006134283,0.121845],"genre_scores_gemma":[0.1969265,0.2108204,0.4849024,0.007950589,0.009916837,0.0005285807,0.002222489,0.0006989559,0.08603331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01097146,"threshold_uncertainty_score":0.03670317,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}