{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":14,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":14,"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":"ff2a971a3fec","filters":{"venue":"Forecasting"}},"results":[{"id":"W4206915428","doi":"10.3390/forecast4010007","title":"Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow","year":2022,"lang":"en","type":"article","venue":"Forecasting","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"United Nations University Institute for Water, Environment, and Health; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flash flood; Flood myth; Computer science; Flood forecasting; Replicate; Warning system; Environmental science; Statistics; Geography","authors":[{"name":"André D. L. Zanchetta","is_ca":true},{"name":"Paulin Coulibaly","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02503053438892784,"gpt":0.2136167318107759,"spread":0.188586197421848,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005106946,0.0003025583,0.0004031476,0.0002246754,0.0001697422,0.0005264598,0.0003643323,0.0004465586,0.0006817183],"category_scores_gemma":[0.00145399,0.0001619659,0.0003035245,0.0002235883,0.0001950323,0.0003483586,0.0004104839,0.0003810851,0.00008781446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003328584,"about_ca_system_score_gemma":0.0005133486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005394674,"about_ca_topic_score_gemma":0.003454695,"domain_scores_codex":[0.9998431,0.00006477579,0.00001094908,0.00002656199,0.00003177735,0.00002278301],"domain_scores_gemma":[0.9995183,0.0002578751,0.0000619898,0.00003842758,0.00009920207,0.0000242703],"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":[0.00005696204,0.00002347722,0.001092729,0.000009847796,0.000009399136,0.00001963386,0.000009380456,0.9935619,0.001017967,0.0003130481,0.00006640543,0.003819219],"study_design_scores_gemma":[0.000001374207,0.00001126701,0.0001447756,4.539952e-7,7.830849e-7,0.000001222248,0.000001190035,0.999624,0.0001431838,0.00005278655,0.00001790113,9.719688e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.705757,0.000177129,0.2893809,0.0001994172,0.00007015915,0.00005669834,0.0003519889,0.0006149249,0.003391766],"genre_scores_gemma":[0.9939315,0.00002411228,0.005488809,0.00000949601,0.00000343143,0.00002054402,0.00011275,0.000006030669,0.000403344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005394674,"threshold_uncertainty_score":0.01072651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3172587485","doi":"10.3390/forecast4010005","title":"SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting","year":2022,"lang":"en","type":"article","venue":"Forecasting","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Machine Intelligence Institute; Compute Canada","keywords":"Coronavirus disease 2019 (COVID-19); Government (linguistics); Computer science; Range (aeronautics); Econometrics; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Machine learning; Operations research; Economics; Engineering; Infectious disease (medical specialty); Medicine","authors":[{"name":"Roberto Vega","is_ca":true},{"name":"Leonardo Albitres Flores","is_ca":false},{"name":"Russell Greiner","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5438756906021927,"gpt":0.4238698281744385,"spread":0.1200058624277542,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004099654,0.000998686,0.001308534,0.0009953756,0.0004318787,0.001097591,0.001761388,0.00131633,0.003656363],"category_scores_gemma":[0.01119283,0.0005424553,0.001262302,0.0009816549,0.0004863822,0.001454214,0.001192427,0.002612496,0.00144188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008270146,"about_ca_system_score_gemma":0.001374831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01465078,"about_ca_topic_score_gemma":0.0110646,"domain_scores_codex":[0.9988196,0.0006515227,0.00007215005,0.0002047622,0.000181223,0.00007070044],"domain_scores_gemma":[0.9958632,0.002923581,0.0002935082,0.0002922257,0.0005121634,0.0001153658],"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":[0.00007667232,0.00004932721,0.00238152,0.00004843389,0.00008450307,0.00005120641,0.00003637431,0.9393141,0.0003346093,0.006427543,0.004241812,0.04695383],"study_design_scores_gemma":[0.000003350694,0.000008570026,0.00007171537,0.000002862504,0.000002421671,0.000004037772,0.000002622186,0.9969067,0.00007806502,0.002571838,0.0003443499,0.000003418401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02116424,0.0007080254,0.9685147,0.001247235,0.0001844581,0.00005980785,0.000700278,0.004902135,0.002519134],"genre_scores_gemma":[0.5251952,0.0008740489,0.4633146,0.0008146309,0.0004853235,0.0002726227,0.002745033,0.0005941194,0.005704425],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01465078,"threshold_uncertainty_score":0.02913105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384929685","doi":"10.3390/forecast5030028","title":"A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes","year":2023,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity price forecasting; Volatility (finance); Econometrics; Electricity market; Computer science; Electricity price; Electricity; Artificial neural network; Dimension (graph theory); Time horizon; Market price; Economics; Artificial intelligence; Microeconomics; Finance; Engineering","authors":[{"name":"Daniel Manfre Jaimes","is_ca":false},{"name":"Manuel Zamudio López","is_ca":true},{"name":"Hamidreza Zareipour","is_ca":true},{"name":"Mike Quashie","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08294379196822219,"gpt":0.2577073454779408,"spread":0.1747635535097186,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004761073,0.0006050031,0.000709041,0.0005227166,0.0003026922,0.0009327902,0.001484998,0.0009211558,0.001530004],"category_scores_gemma":[0.0007237731,0.0004095841,0.0008111502,0.000577848,0.0002545022,0.0009339699,0.0006008149,0.0009073539,0.0003459684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004225067,"about_ca_system_score_gemma":0.0007254235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127078,"about_ca_topic_score_gemma":0.01200214,"domain_scores_codex":[0.999799,0.00003348089,0.00001330605,0.00005924764,0.00005837411,0.00003654024],"domain_scores_gemma":[0.9997718,0.0001061312,0.000028899,0.00001368176,0.00006552023,0.00001399372],"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":[0.00006806827,0.00004613796,0.001182461,0.00003434822,0.00007190031,0.00007286604,0.00003219791,0.9616879,0.001905953,0.002853958,0.0008613417,0.03118278],"study_design_scores_gemma":[0.000001990809,0.000006890325,0.0001167368,0.000001164845,0.000004465135,0.000004439658,0.000001403262,0.9992679,0.0001091125,0.0003445904,0.0001387314,0.000002572049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07189589,0.0005839467,0.92006,0.0003431136,0.0002103924,0.00005423401,0.0004011784,0.001014604,0.005436627],"genre_scores_gemma":[0.9271638,0.0003173648,0.0652953,0.0001186596,0.0001021395,0.000129643,0.0005275134,0.00006314903,0.0062824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0127078,"threshold_uncertainty_score":0.02526766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4307948995","doi":"10.3390/forecast4040048","title":"Precision and Reliability of Forecasts Performance Metrics","year":2022,"lang":"en","type":"article","venue":"Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Mitacs","keywords":"Variance (accounting); Reliability (semiconductor); Metric (unit); Computer science; Sensitivity (control systems); Noise (video); Series (stratigraphy); Selection (genetic algorithm); Econometrics; Quality (philosophy); Model selection; Performance metric; Statistics; Data mining; Reliability engineering; Machine learning; Artificial intelligence; Mathematics; Engineering","authors":[{"name":"Philippe St-Aubin","is_ca":true},{"name":"Bruno Agard","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1467905736224284,"gpt":0.3568156231551683,"spread":0.2100250495327398,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03374534,0.001828279,0.001868589,0.00852739,0.0006637075,0.003569803,0.001061572,0.001805434,0.001518258],"category_scores_gemma":[0.1601459,0.0004935236,0.001302557,0.004187729,0.001122564,0.003451384,0.001884259,0.001139826,0.0007944987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288042,"about_ca_system_score_gemma":0.00109484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003998267,"about_ca_topic_score_gemma":0.001621644,"domain_scores_codex":[0.9598054,0.01069565,0.004700711,0.005186008,0.0181879,0.001424332],"domain_scores_gemma":[0.8387008,0.09796711,0.01676376,0.01760744,0.02795425,0.001006595],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001600994,0.000206138,0.1490568,0.001202515,0.001441865,0.0004269974,0.001004292,0.5305982,0.01050724,0.01078721,0.005742875,0.287425],"study_design_scores_gemma":[0.0001146868,0.001509187,0.1618456,0.0006000072,0.0005861589,0.0009966897,0.0009256139,0.762314,0.03136682,0.02507513,0.01408793,0.0005781996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5151539,0.00656492,0.4472659,0.001457737,0.0007997042,0.0005286289,0.005799403,0.00425736,0.01817238],"genre_scores_gemma":[0.9671377,0.000569067,0.02918586,0.00007027223,0.0002122125,0.0001107398,0.001754193,0.0001952587,0.0007645713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9662547,"threshold_uncertainty_score":0.1784646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389624199","doi":"10.3390/forecast5040037","title":"Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess","year":2023,"lang":"en","type":"article","venue":"Forecasting","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Decomposition; Divide and conquer algorithms; Computer science; Preprocessor; Margin (machine learning); Series (stratigraphy); Task (project management); Term (time); Time series; Machine learning; Simple (philosophy); Data mining; Artificial intelligence; Algorithm; Engineering","authors":[{"name":"Amirhossein Sohrabbeig","is_ca":true},{"name":"Omid Ardakanian","is_ca":true},{"name":"Petr Musı́lek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03342874522067381,"gpt":0.2500170008863459,"spread":0.2165882556656721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001772926,0.001397217,0.001212517,0.001854031,0.000495102,0.001530462,0.001145829,0.001176531,0.003657203],"category_scores_gemma":[0.00460752,0.0004040156,0.001179881,0.001777566,0.0004864058,0.002225743,0.001075153,0.001675966,0.001554392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006583118,"about_ca_system_score_gemma":0.001051339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00872611,"about_ca_topic_score_gemma":0.009470318,"domain_scores_codex":[0.9996204,0.0000869492,0.00002745793,0.0001080286,0.0001005339,0.00005662075],"domain_scores_gemma":[0.998965,0.0004751653,0.00009744716,0.000182587,0.0002208249,0.00005900534],"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":[0.000597306,0.0002026864,0.006720246,0.0001746974,0.0001880116,0.0002814403,0.0001820794,0.4349096,0.00691514,0.007031889,0.009192162,0.5336047],"study_design_scores_gemma":[0.00001622278,0.00003547371,0.0003559421,0.000006524119,0.00001075602,0.00002085633,0.00002009737,0.9942116,0.001203363,0.002824717,0.001287552,0.000006907496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0713599,0.001530357,0.9123625,0.0009430009,0.0002340412,0.0001398204,0.0007555429,0.008908994,0.003765933],"genre_scores_gemma":[0.4194484,0.0007244169,0.5694692,0.0003470182,0.0001859083,0.000158142,0.00321605,0.0007439574,0.005706891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00872611,"threshold_uncertainty_score":0.01735067,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389046557","doi":"10.3390/forecast5040036","title":"Macroeconomic Predictions Using Payments Data and Machine Learning","year":2023,"lang":"en","type":"article","venue":"Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Bank of Canada","funders":"","keywords":"Overfitting; Nowcasting; Interpretability; Payment; Computer science; Econometrics; Value (mathematics); Machine learning; Economics; Artificial neural network","authors":[{"name":"James Chapman","is_ca":true},{"name":"Ajit Desai","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3317320262824558,"gpt":0.2771811568458795,"spread":0.05455086943657628,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001971162,0.0007551954,0.000480816,0.002180949,0.0006695199,0.001961452,0.000619899,0.0006714819,0.002140129],"category_scores_gemma":[0.01401753,0.0002335388,0.0004384099,0.003502557,0.0005038153,0.001200416,0.0007546879,0.000861306,0.0005481121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004642441,"about_ca_system_score_gemma":0.005972702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6983125,"about_ca_topic_score_gemma":0.698608,"domain_scores_codex":[0.9990024,0.0002901541,0.00006143848,0.0001393025,0.0003533104,0.000153382],"domain_scores_gemma":[0.9940122,0.002244882,0.00077923,0.0003987706,0.00222681,0.0003381464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003650886,0.0001697511,0.4559418,0.0001560598,0.0002204701,0.0003068533,0.0001895423,0.4474734,0.0005402545,0.007467802,0.01363623,0.07353282],"study_design_scores_gemma":[0.00004517865,0.00008038298,0.2498451,0.0001567763,0.00006714327,0.00005397697,0.000508782,0.7270646,0.001477103,0.008304418,0.01231486,0.00008169882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325697,0.001106812,0.01652684,0.003725218,0.0001488992,0.0001079281,0.02659375,0.0006983161,0.01852259],"genre_scores_gemma":[0.9815607,0.0003460138,0.004201695,0.00008752688,0.00004615396,0.00001463314,0.01200192,0.00001815022,0.001723378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6983125,"threshold_uncertainty_score":0.6069283,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2891479923","doi":"10.3390/forecast1010005","title":"Improved Brain Tumor Segmentation via Registration-Based Brain Extraction","year":2018,"lang":"en","type":"article","venue":"Forecasting","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University of Edmonton; University of Alberta","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Skull; Fluid-attenuated inversion recovery; False positive paradox; Computer vision; Volume (thermodynamics); Pattern recognition (psychology); Process (computing); Image segmentation; Nuclear medicine; Medicine; Magnetic resonance imaging; Radiology; Anatomy","authors":[{"name":"Maxwell Uhlich","is_ca":true},{"name":"Russell Greiner","is_ca":true},{"name":"Bret Hoehn","is_ca":true},{"name":"Melissa Woghiren","is_ca":true},{"name":"Idanis Díaz","is_ca":false},{"name":"Tatiana S. Ivanova","is_ca":true},{"name":"Albert Murtha","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0603655812904097,"gpt":0.3001008746147609,"spread":0.2397352933243512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001293355,0.001317436,0.001653315,0.003431612,0.0005185372,0.001481698,0.001631379,0.001175957,0.004399163],"category_scores_gemma":[0.003460957,0.000892034,0.001441648,0.002203866,0.0004071429,0.001611385,0.001186968,0.0008630801,0.003877065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007022269,"about_ca_system_score_gemma":0.001189126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004670165,"about_ca_topic_score_gemma":0.009409962,"domain_scores_codex":[0.9985098,0.0002850243,0.0001346496,0.0004111622,0.0005426274,0.0001166461],"domain_scores_gemma":[0.9981942,0.0005352487,0.0002136875,0.000542272,0.0004768102,0.00003786513],"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":[0.0003392241,0.0001490737,0.003517032,0.000244132,0.0002800902,0.0002712136,0.0002036402,0.03269409,0.1941727,0.00187271,0.00862818,0.7576279],"study_design_scores_gemma":[0.0001361264,0.0003134907,0.01831901,0.00003639205,0.000366896,0.00271149,0.0001035073,0.6785199,0.2730747,0.004741184,0.02148711,0.0001902411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06563366,0.0006742444,0.9113505,0.0003080321,0.00009537626,0.0001680571,0.0004911472,0.01897111,0.002307777],"genre_scores_gemma":[0.1775699,0.0004593002,0.8142605,0.0001976945,0.0001256855,0.0001364527,0.00129815,0.001548696,0.004403567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004670165,"threshold_uncertainty_score":0.01471663,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2981554396","doi":"10.3390/forecast1010012","title":"Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study","year":2019,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prediction interval; Quantile regression; Probabilistic forecasting; Quantile; Cluster analysis; Probabilistic logic; Computer science; Model output statistics; Numerical weather prediction; Weather forecasting; Consensus forecast; Econometrics; Statistics; Machine learning; Mathematics; Artificial intelligence; Meteorology; Geography","authors":[{"name":"Ashkan Zarnani","is_ca":true},{"name":"Soheila Karimi","is_ca":true},{"name":"Petr Musı́lek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05801115352355336,"gpt":0.2715022503263511,"spread":0.2134910968027977,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009091272,0.0007150201,0.001009522,0.001548345,0.0002935827,0.001157948,0.001668237,0.0007337113,0.001366843],"category_scores_gemma":[0.02256231,0.0003275362,0.00104635,0.001774619,0.0004361967,0.001811974,0.0005563319,0.001040433,0.000331068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416285,"about_ca_system_score_gemma":0.0007539586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0179964,"about_ca_topic_score_gemma":0.005713528,"domain_scores_codex":[0.9978983,0.001250144,0.00008354768,0.0003098093,0.0003320179,0.0001261993],"domain_scores_gemma":[0.9803092,0.01550722,0.001087412,0.0008722995,0.00204951,0.0001743252],"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":[0.00009278374,0.00003790023,0.002998032,0.00005685799,0.00007684637,0.00001603504,0.00006983934,0.9573953,0.000178718,0.006243057,0.0004119207,0.03242261],"study_design_scores_gemma":[0.000001891361,0.00001156612,0.0008110124,0.000004091981,0.000006957124,0.000003468688,0.000007214614,0.9980026,0.00006110098,0.001015107,0.00007027639,0.000004658416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2222096,0.003568671,0.7690986,0.0005791715,0.00007627982,0.00009530169,0.0003264885,0.0007473574,0.003298511],"genre_scores_gemma":[0.9466214,0.001274752,0.05025403,0.00005587447,0.00007839911,0.0000613517,0.0004745352,0.0001072049,0.001072332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0179964,"threshold_uncertainty_score":0.04807985,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3155141822","doi":"10.3390/forecast3020017","title":"Tobacco Endgame Simulation Modelling: Assessing the Impact of Policy Changes on Smoking Prevalence in 2035","year":2021,"lang":"en","type":"article","venue":"Forecasting","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Impact; McMaster University; Public Health Ontario; University of Toronto; Centre for Addiction and Mental Health","funders":"Health Canada","keywords":"Excise; Chess endgame; Revenue; Tax revenue; Smoking prevalence; Environmental health; Tobacco industry; Smoking cessation; Tax policy; Medicine; Business; Economics; Public economics; Tax reform; Population; Finance","authors":[{"name":"Michael Chaiton","is_ca":true},{"name":"Jolene Dubray","is_ca":true},{"name":"G. Emmanuel Guindon","is_ca":true},{"name":"Robert Schwartz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1604776423926424,"gpt":0.4082746709757248,"spread":0.2477970285830824,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169411,0.0005978115,0.0005667688,0.0006008458,0.0006467803,0.0007346828,0.001107582,0.0008136491,0.004287601],"category_scores_gemma":[0.003517285,0.0003480155,0.00110273,0.0008264092,0.0004689347,0.0004095532,0.0004769653,0.0006256704,0.0002391463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01120915,"about_ca_system_score_gemma":0.009369517,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8064108,"about_ca_topic_score_gemma":0.778396,"domain_scores_codex":[0.9995409,0.0001848073,0.00001507283,0.00004626969,0.0000836402,0.0001293539],"domain_scores_gemma":[0.997835,0.001085457,0.0002054023,0.00007116658,0.0006041094,0.0001987909],"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":[0.0001708518,0.00006992946,0.01997629,0.00006769301,0.00007764788,0.00005555624,0.0001073077,0.9711079,0.0002247628,0.003129921,0.001710944,0.003301159],"study_design_scores_gemma":[0.00008384966,0.0001401407,0.008469716,0.00002457315,0.00007341772,0.00001155961,0.0002012759,0.9867759,0.0001847743,0.001160469,0.00285334,0.00002099851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377939,0.0004390238,0.01164999,0.001562396,0.00007685194,0.0003223821,0.008377438,0.0002223127,0.03955572],"genre_scores_gemma":[0.989589,0.0002944853,0.00368793,0.00009165651,0.00001156419,0.0001303413,0.00243706,0.00001787324,0.003740015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8064108,"threshold_uncertainty_score":0.3894584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391451243","doi":"10.3390/forecast6010007","title":"Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study","year":2024,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Decision tree; Machine learning; Hyperparameter; Econometrics; Artificial intelligence; Electricity; Electricity price forecasting; Statistical model; Binary classification; Random forest; Electricity market; Data mining; Support vector machine; Economics; Engineering","authors":[{"name":"Manuel Zamudio López","is_ca":true},{"name":"Hamidreza Zareipour","is_ca":true},{"name":"Mike Quashie","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03612643993758135,"gpt":0.2410028268789844,"spread":0.204876386941403,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01076395,0.0005244136,0.0005416116,0.001219134,0.0005754338,0.001745349,0.0009117359,0.001131408,0.0008458227],"category_scores_gemma":[0.05314022,0.0002469473,0.000555206,0.001774505,0.001149146,0.001890955,0.0005943793,0.00111917,0.0001655512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194846,"about_ca_system_score_gemma":0.001590172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02123976,"about_ca_topic_score_gemma":0.01604199,"domain_scores_codex":[0.9963301,0.001644715,0.0002363727,0.000352157,0.00124409,0.0001926256],"domain_scores_gemma":[0.9327081,0.05926638,0.002873663,0.002138255,0.002728537,0.0002851061],"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":[0.003204718,0.00330211,0.3296233,0.0003908323,0.0005251527,0.000905587,0.001075332,0.5066088,0.01540433,0.02547747,0.002812593,0.1106697],"study_design_scores_gemma":[0.0001071423,0.001114995,0.07856174,0.00002181883,0.00007915798,0.0001278285,0.0007938228,0.9049607,0.007245835,0.006138243,0.0007821227,0.00006661411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812541,0.0001161742,0.01469577,0.0006409701,0.00002555953,0.0001865306,0.0003305532,0.0001009449,0.002649369],"genre_scores_gemma":[0.9926907,0.00004875253,0.006659317,0.00004882447,0.00001635741,0.00005587321,0.0002298292,0.000008656729,0.0002416033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02123976,"threshold_uncertainty_score":0.05692595,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205864191","doi":"10.3390/forecast4010006","title":"Analysing Historical and Modelling Future Soil Temperature at Kuujjuaq, Quebec (Canada): Implications on Aviation Infrastructure","year":2022,"lang":"en","type":"article","venue":"Forecasting","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"The Scarborough Hospital; University of Toronto; Environment and Climate Change Canada","funders":"University of Toronto Scarborough; Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; University of Toronto","keywords":"Downscaling; Environmental science; Climate change; Climatology; Soil water; Climate model; Baseline (sea); Representative Concentration Pathways; Soil science; Geology; Oceanography","authors":[{"name":"Andrew C. W. Leung","is_ca":true},{"name":"William A. Gough","is_ca":true},{"name":"Tanzina Mohsin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03032195433305802,"gpt":0.1975200713937575,"spread":0.1671981170606995,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003084322,0.0002749111,0.0001313738,0.0006552543,0.0008642726,0.001001879,0.0007865937,0.0003425094,0.001577786],"category_scores_gemma":[0.001166997,0.0001276385,0.0003012297,0.001626328,0.0003294833,0.0003583335,0.0002489756,0.000354782,0.000186743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02698341,"about_ca_system_score_gemma":0.01886522,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9969429,"about_ca_topic_score_gemma":0.9979961,"domain_scores_codex":[0.9998265,0.00002415821,0.00000608716,0.00003509262,0.00004312298,0.00006510679],"domain_scores_gemma":[0.9994195,0.00008109877,0.0000566368,0.000016479,0.0003514069,0.00007490945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001585063,0.00009120219,0.7982239,0.0001897595,0.000163943,0.0004064877,0.0009074752,0.1450512,0.002219675,0.001830289,0.008410405,0.04234718],"study_design_scores_gemma":[0.00002439575,0.00003503099,0.8315329,0.00005613547,0.00006449069,0.00004121342,0.002249654,0.1577369,0.0008990773,0.0002510909,0.007063567,0.00004549129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834889,0.0003879822,0.00143513,0.0005376191,0.00001480934,0.0000495591,0.008585584,0.00006091735,0.005439621],"genre_scores_gemma":[0.993715,0.000290433,0.001275122,0.00004049634,0.000003870537,0.0000244555,0.002851933,0.000009868861,0.001788818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02698341,"threshold_uncertainty_score":0.1957793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4292068976","doi":"10.3390/forecast4030039","title":"Nowcasting GDP: An Application to Portugal","year":2022,"lang":"en","type":"article","venue":"Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Nowcasting; Quarter (Canadian coin); Bridge (graph theory); Econometrics; Computer science; Real gross domestic product; Business cycle; Economics; Macroeconomics; Economy; Geography; Meteorology","authors":[{"name":"João L. Assunção","is_ca":false},{"name":"Pedro Afonso Fernandes","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1361182833545793,"gpt":0.2387683145645577,"spread":0.1026500312099783,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001978304,0.000516753,0.0004981155,0.001168782,0.000582534,0.001338902,0.0006469967,0.001068311,0.003010912],"category_scores_gemma":[0.009379506,0.00016129,0.0006049584,0.002419302,0.000341926,0.0007391266,0.0008219829,0.0009299846,0.0005375246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102141,"about_ca_system_score_gemma":0.000818699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06136264,"about_ca_topic_score_gemma":0.04592032,"domain_scores_codex":[0.9996542,0.0001501151,0.00002844092,0.00005223164,0.00008004411,0.00003492729],"domain_scores_gemma":[0.9975304,0.00170462,0.0001547816,0.0002438024,0.0002743186,0.00009198324],"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":[0.0007347819,0.0003579414,0.04052855,0.0006579423,0.0001495691,0.002962209,0.001259522,0.6700428,0.001914916,0.02807612,0.02363083,0.2296848],"study_design_scores_gemma":[0.000104576,0.00004824764,0.0176507,0.0000869006,0.00002234701,0.0002095822,0.0004447778,0.9523344,0.001542154,0.01412543,0.01339353,0.00003734619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7282773,0.00325825,0.1699775,0.007536353,0.001177881,0.0002245366,0.01088182,0.006993598,0.07167289],"genre_scores_gemma":[0.9385916,0.001012333,0.05237088,0.0001586002,0.0001611311,0.00004858836,0.002211423,0.0004544863,0.004991008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06136264,"threshold_uncertainty_score":0.1220109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413127595","doi":"10.3390/forecast7030043","title":"Enhancing Neural Architecture Search Using Transfer Learning and Dynamic Search Spaces for Global Horizontal Irradiance Prediction","year":2025,"lang":"en","type":"article","venue":"Forecasting","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Irradiance; Architecture; Computer science; Transfer of learning; Artificial intelligence; Machine learning; Geography; Optics; Physics","authors":[{"name":"Inoussa Legrene","is_ca":true},{"name":"Tony Wong","is_ca":true},{"name":"Louis‐A. Dessaint","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02347973145871257,"gpt":0.2840208873720174,"spread":0.2605411559133048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005530018,0.0004442052,0.0003895106,0.0004693137,0.0001953984,0.0004292082,0.0005875973,0.0005556521,0.0008779203],"category_scores_gemma":[0.001570056,0.0002395356,0.0004381005,0.0004459588,0.0003035516,0.0009141955,0.000537014,0.0005391028,0.0001489522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004444164,"about_ca_system_score_gemma":0.0004868938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003771497,"about_ca_topic_score_gemma":0.003599479,"domain_scores_codex":[0.9998779,0.00003507491,0.000008391826,0.00002538766,0.00003725617,0.00001596075],"domain_scores_gemma":[0.9996779,0.0001784438,0.00003704014,0.00003391437,0.00006246219,0.00001023819],"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":[0.00003031559,0.00005779582,0.0009205645,0.0000205528,0.00003432018,0.00003384472,0.00003499918,0.9109311,0.003744006,0.001264812,0.0001847217,0.08274293],"study_design_scores_gemma":[0.000001405009,0.00001526786,0.00009769413,9.060589e-7,0.000002071359,0.000003495306,0.000002150186,0.9991738,0.0004290866,0.0002166627,0.00005610391,0.000001205529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2026727,0.0005202222,0.7926005,0.0001387333,0.00003922825,0.00004713023,0.00002284124,0.0005939,0.003364726],"genre_scores_gemma":[0.9379675,0.0001247651,0.06068974,0.00003694168,0.00001037307,0.00004932569,0.00003283344,0.00002306871,0.001065313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003771497,"threshold_uncertainty_score":0.007499039,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413836895","doi":"10.3390/forecast7030046","title":"Improving Dry-Bulb Air Temperature Prediction Using a Hybrid Model Integrating Genetic Algorithms with a Fourier–Bessel Series Expansion-Based LSTM Model","year":2025,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Prince Edward Island","funders":"King Faisal University","keywords":"Bessel function; Fourier series; Series (stratigraphy); Algorithm; Genetic algorithm; Fourier transform; Computer science; Mathematics; Machine learning; Mathematical analysis; Geology","authors":[{"name":"Hussein Alabdally","is_ca":false},{"name":"Mumtaz Ali","is_ca":false},{"name":"Mohammed Diykh","is_ca":true},{"name":"Anwar Ali Aldhafeeri","is_ca":false},{"name":"Shahab Abdulla","is_ca":false},{"name":"Aitazaz A. Farooque","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0109789550203682,"gpt":0.198034323575894,"spread":0.1870553685555258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004032562,0.0006998595,0.0004978314,0.0003730969,0.0002265717,0.0005795338,0.0006914644,0.0006915818,0.0009041232],"category_scores_gemma":[0.0007947831,0.0002373744,0.0006653817,0.0003960216,0.0002116955,0.0007102313,0.0003337258,0.0007603238,0.0003308039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004824442,"about_ca_system_score_gemma":0.0006879541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01564891,"about_ca_topic_score_gemma":0.01732097,"domain_scores_codex":[0.999886,0.00001874036,0.000007756845,0.00004245687,0.00002706553,0.00001793883],"domain_scores_gemma":[0.9998353,0.00006942213,0.00001816546,0.00001079859,0.00005903743,0.000007396726],"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":[0.00007008877,0.00007134428,0.001698988,0.00004385169,0.00008018768,0.00007692946,0.00004519854,0.9107781,0.006427069,0.001145146,0.0008839643,0.07867911],"study_design_scores_gemma":[0.000001800809,0.000009128959,0.0001572452,0.000001701908,0.000005742794,0.000005307905,0.000002059265,0.9990863,0.0004839385,0.0001457043,0.00009854369,0.000002420782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2374153,0.001340225,0.7513227,0.0005303322,0.0001985987,0.00005393781,0.0002708438,0.002433627,0.006434372],"genre_scores_gemma":[0.9240064,0.0004015423,0.0709651,0.0001708409,0.00004127492,0.00006861918,0.0003040745,0.00007736339,0.003964751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01564891,"threshold_uncertainty_score":0.03111565,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}